Add Makefile with start target for HTTP serve mode

Provides `make start` to run the llm-connect server via uv, with optional
LLM_CONNECT_* overrides and standard dev targets for install, test, lint,
and typecheck.

.claude/rules/architecture.md

@@ -1,58 +1,8 @@
 ## Architecture
 
-llm-connect is structured as a **GAAF-2026 layered library**. See
+<!-- TODO: Describe the key design decisions and component structure.
-`ARCHITECTURE-LAYERS.md` for the full layer map and scorecard.
+     Key modules, data flows, external integrations, state machines, etc. -->
 
-### Layer summary
+## Quick Reference
 
-```
+`~/state-hub/mcp_server/TOOLS.md` — MCP tool reference
-Core (frozen after v1)
-  LLMAdapter ABC          adapter.py
-  RunConfig / LLMResponse models.py
-  LLMError hierarchy      exceptions.py
-  MockLLMAdapter          adapter.py   ← test primitive, belongs with Core
-
-Functional (evolvable, independently shippable)
-  OpenAIAdapter           openai.py
-  GeminiAdapter           gemini.py
-  OpenRouterAdapter       openrouter.py
-  ClaudeCodeAdapter       claude_code.py
-  EmbeddingAdapter ABC    embedding_adapter.py
-  OpenAICompatibleEmbeddingAdapter  embedding_openai.py
-  EmbeddingCache          embedding_cache.py
-  create_adapter()        factory.py
-  create_embedding_adapter()  embedding_factory.py
-  _token_estimator        _token_estimator.py
-  similarity utilities    similarity.py
-
-Configuration (user-controlled declarative state)
-  resolve_llm() chain     toml_config.py   ← 7-level TOML priority chain
-  LLMConfig / load_config config.py
-  _http shared utility    _http.py         ← also used by Functional adapters
-```
-
-### Dependency rule
-
-Core ← Functional ← Configuration  
-No upward dependencies. `_http.py` is consumed by Functional only.
-
-### Key design decisions
-
-**API key resolution** (`config.resolve_api_key`): three-step chain —
-explicit argument → environment variable → plaintext key file in project root.
-Adapters raise `LLMConfigurationError` at construction time if no key is found
-(except `ClaudeCodeAdapter` which needs no key).
-
-**TOML config chain** (`toml_config.resolve_llm`): 7 priority levels allow
-per-project and per-user LLM preferences. Currently defaults to `markitect`
-app_name for backward compatibility — consumers pass their own `app_name`.
-
-**Factory pattern** (`factory.create_adapter`): lazy imports prevent pulling
-all provider SDKs at module load. Add a new provider by registering its FQN
-in `_PROVIDERS`.
-
-**ClaudeCodeAdapter subprocess model**: prompt is piped via stdin (not CLI
-arg) to avoid shell argument length limits on large prompts (>30 KB).
-
-**Retry logic**: `OpenAIAdapter` and `OpenRouterAdapter` retry on 429 and 5xx
-with exponential backoff. `GeminiAdapter` does not (rate-limit handling deferred).

.claude/rules/credential-routing.md

@@ -0,0 +1,50 @@
+# Credential and access routing
+
+**Audience:** Codex, Claude Code, Grok, and custodian agents that call **llm-connect**
+for inference. Run this check **before** requesting secrets, API keys, SSH access,
+login tokens, or database passwords — in any repo, not only `ops-warden`.
+
+ops-warden **issues SSH certificates only** (`warden sign`, `cert_command`). Every
+other credential need belongs to another subsystem. **Do not** message
+`ops-warden` on State Hub expecting a secret value; the reply is a pointer, not a key.
+
+### Lookup (do this first)
+
+```bash
+warden route find "<describe your need>" --json
+warden route show <catalog-id> --json
+```
+
+Requires the `warden` CLI from `~/ops-warden` (`uv tool install .` or `uv run warden`).
+
+| Agent runtime | How to orient |
+| --- | --- |
+| **Codex / Grok** (shell, HTTP State Hub) | `warden route` commands above; inbox `to_agent=llm-connect` is for coordination, not secret vending |
+| **Claude Code** (MCP when available) | `get_domain_summary("custodian")` for workstreams; **still** use `warden route` for credential ownership |
+| **llm-connect** (inference service) | Never put secret retrieval in prompts; route custody to OpenBao/operator paths surfaced by `warden route` |
+
+### Quick routing table
+
+| I need… | Owner | ops-warden executes? |
+| --- | --- | --- |
+| SSH cert (`adm`/`agt`/`atm`) | ops-warden | **Yes** — `warden sign` |
+| API key, DB password, provider token | OpenBao (`railiance-platform`) | No — route only |
+| Login / OIDC / MFA | key-cape / Keycloak | No — route only |
+| Authorization decision | flex-auth | No — route only |
+| activity-core → issue-core emission | activity-core + issue-core | No — `warden route show activity-core-issue-sink` |
+| SSH tunnel | ops-bridge (+ `cert_command` from warden) | No — route only |
+
+### Anti-patterns (do not do these)
+
+- `POST /messages/` to `ops-warden` asking for `ISSUE_CORE_API_KEY`, `OPENROUTER_API_KEY`, etc.
+- Inventing `warden secret`, `warden login`, `warden bao`, `warden tunnel` — they do not exist
+- Pasting secrets into Git, State Hub, workplans, logs, or chat
+
+### Other capabilities (reuse-surface)
+
+Non-credential capabilities are usually discovered through **reuse-surface** federation
+(`reuse-surface` registry / `capability.*` indexes). Credential routing is inlined in
+every repo's agent instructions because it is high-frequency, high-risk, and easy to
+get wrong.
+
+**Canon:** `~/ops-warden/wiki/CredentialRouting.md` · catalog `~/ops-warden/registry/routing/catalog.yaml`

.claude/rules/first-session.md

@@ -1,11 +1,11 @@
 ## First Session Protocol
 
-Triggered when `get_domain_summary("custodian")` shows **no workstreams**.
+Triggered when `get_domain_summary("agents")` shows **no workstreams**.
 The project is registered but work has not yet been structured.
 
 **Step 1 — Read, don't write**
-- `~/the-custodian/canon/projects/custodian/project_charter_v0.1.md` — purpose, scope
+- `~/the-custodian/canon/projects/agents/project_charter_v0.1.md` — purpose, scope
-- `~/the-custodian/canon/projects/custodian/roadmap_v0.1.md` — planned phases
+- `~/the-custodian/canon/projects/agents/roadmap_v0.1.md` — planned phases
 - Scan repo root: README, directory structure, existing code or docs
 
 **Step 2 — Survey in-progress work**
@@ -17,20 +17,20 @@ roadmap phase. **Wait for approval before creating.**
 
 **Step 4 — Create workplan file first, then DB record (ADR-001)**
 ```
-workplans/llm-connect-WP-NNNN-<slug>.md   ← write this first
+workplans/LLM-WP-NNNN-<slug>.md   ← write this first
 ```
 Then register in the hub:
 ```
-create_workstream(topic_id="cee7bedf-2b48-46ef-8601-006474f2ad7a", title="...", owner="...", description="...")
+create_workstream(topic_id="64418556-3206-457a-ba29-6884b5b12cf3", title="...", owner="...", description="...")
 create_task(workstream_id="<id>", title="...", priority="high|medium|low")
 ```
 
 **Step 5 — Record the setup**
 ```
 add_progress_event(
-    summary="First session: structured custodian into N workstreams, M tasks",
+    summary="First session: structured agents into N workstreams, M tasks",
     event_type="milestone",
-    topic_id="cee7bedf-2b48-46ef-8601-006474f2ad7a",
+    topic_id="64418556-3206-457a-ba29-6884b5b12cf3",
     detail={"workstreams": [...], "tasks_created": M}
 )
 ```

.claude/rules/repo-boundary.md

@@ -1,17 +1,8 @@
 ## Repo boundary
 
-This repo owns **llm-connect** — the multi-provider LLM client library — only.
+This repo owns **llm-connect** only. It does not own:
 
-It does NOT own:
+<!-- TODO: List what belongs in adjacent repos, e.g.:
-
+- SSH key management → railiance-infra/
-- **API key storage / secret management** → caller's environment (env vars,
+- State hub code     → state-hub/
-  key files, vault). llm-connect resolves keys but does not store them.
+-->
-- **Consumer routing logic** → `inter-hub/AgentBridge.hs`, `markitect` etc.
-  `RoutingPolicy` (WP-0003) provides primitives; policy data belongs in the consumer.
-- **The Claude Code CLI binary** → installed separately; `ClaudeCodeAdapter`
-  shells out to it.
-- **markitect application code** → `markitect.llm` is a shim that re-exports
-  from here; all implementation lives in this repo.
-- **State hub / custodian infrastructure** → `the-custodian/state-hub/`
-- **IHF bridge scripts** → `inter-hub/scripts/llm_bridge.py` lives in inter-hub,
-  not here. llm-connect is a dependency of that script.

.claude/rules/repo-identity.md

@@ -1,5 +1,5 @@
 **Purpose:** Multi-provider LLM client library — unified adapter interface for OpenAI, Claude, Gemini, OpenRouter with embedding support, token estimation, and TOML-based config.
 
-**Domain:** custodian
+**Domain:** agents
 **Repo slug:** llm-connect
-**Topic ID:** cee7bedf-2b48-46ef-8601-006474f2ad7a
+**Topic ID:** 64418556-3206-457a-ba29-6884b5b12cf3

.claude/rules/session-protocol.md

@@ -1,6 +1,7 @@
 ## Session Protocol
 
-State Hub: http://127.0.0.1:8000 (local) · http://127.0.0.1:18000 (CoulombCore via ops-bridge)
+Dev Hub (State Hub API): http://127.0.0.1:8000
+MCP server name in `~/.claude.json`: `dev-hub`
 
 **Step 1 — Orient**
 
@@ -8,28 +9,42 @@ Read the offline-safe brief first — it works without a live hub connection:
 ```bash
 cat .custodian-brief.md
 ```
-Then call the MCP tool for richer cross-domain context (skip if unreachable):
+Then call the MCP tool for richer cross-domain context when MCP tools are exposed:
 ```
-get_domain_summary("custodian")
+get_domain_summary("agents")
 ```
-If the hub is offline: `cd ~/the-custodian/state-hub && make api`
+If MCP tools are unavailable in the current agent session, use the REST API:
+```bash
+curl -s "http://127.0.0.1:8000/state/summary" | python3 -m json.tool
+```
+If the hub is offline: `cd ~/state-hub && make api`
 
 **Step 2 — Check inbox**
+With MCP tools:
 ```
 get_messages(to_agent="llm-connect", unread_only=True)
 ```
 Mark read with `mark_message_read(message_id)`. Reply or act on coordination
 requests before proceeding.
 
+Without MCP tools:
+```bash
+curl -s "http://127.0.0.1:8000/messages/?to_agent=llm-connect&unread_only=true" \
+  | python3 -m json.tool
+curl -s -X PATCH "http://127.0.0.1:8000/messages/<id>/read" \
+  -H "Content-Type: application/json" -d '{}'
+```
+
 **Step 3 — Scan workplans**
 ```bash
 ls workplans/
 ```
-For each file with `status: active`, note pending `todo`/`in_progress` tasks.
+For each file with `status: ready`, `active`, or `blocked`, note pending
+`wait`/`todo`/`progress` tasks.
 
 **Step 4 — Present brief**
 
-1. **Active workstreams** for `custodian` — title, task counts, blocking decisions
+1. **Active workstreams** for `agents` — title, task counts, blocking decisions
 2. **Pending tasks** from `workplans/` + any `[repo:llm-connect]` hub tasks
 3. **Goal guidance** — if `goal_guidance` in summary:
    - `needs_workplan`: surface as top action — *"Repo goal '{title}' has no workplan yet"*
@@ -45,18 +60,25 @@ If no workstreams: follow First Session Protocol (`first-session.md`).
 > are First Session Protocol only. Work structure belongs in repo files (ADR-001).
 
 **Session close:**
+With MCP tools:
 ```
-add_progress_event(summary="...", topic_id="cee7bedf-2b48-46ef-8601-006474f2ad7a", workstream_id="<uuid>")
+add_progress_event(summary="...", topic_id="64418556-3206-457a-ba29-6884b5b12cf3", workstream_id="<uuid>")
+```
+Without MCP tools:
+```bash
+curl -s -X POST http://127.0.0.1:8000/progress/ \
+  -H "Content-Type: application/json" \
+  -d '{"topic_id":"64418556-3206-457a-ba29-6884b5b12cf3","workstream_id":"<uuid>","event_type":"note","summary":"what changed","author":"codex"}'
 ```
 If workplan files were modified, ensure the local copy is up to date first:
 ```bash
 git -C <repo_path> pull --ff-only
-cd ~/the-custodian/state-hub && make fix-consistency REPO=llm-connect
+cd ~/state-hub && make fix-consistency REPO=llm-connect
 ```
 For repos where implementation runs on a remote machine (e.g. CoulombCore),
 use the combined target which pulls before fixing:
 ```bash
-cd ~/the-custodian/state-hub && make fix-consistency-remote REPO=llm-connect
+cd ~/state-hub && make fix-consistency-remote REPO=llm-connect
 ```
 **C-15** (DB task ahead of file) is normal in multi-machine workflows — writeback
 will sync the file to match DB.  **C-16** (repo behind remote) blocks all writes

.claude/rules/stack-and-commands.md

@@ -1,59 +1,19 @@
 ## Stack
 
-- **Language:** Python 3.10+
+<!-- TODO: Fill in language, frameworks, and key dependencies -->
-- **Key deps (runtime):** `toml` (TOML config parsing)
+- **Language:**
-- **Key deps (dev):** `pytest`, `ruff`, `mypy`
+- **Key deps:**
-- **HTTP:** stdlib `urllib` via `_http.py` (no requests/httpx runtime dep)
-- **Build:** setuptools / uv
 
 ## Dev Commands
 
 ```bash
-# Install (editable, with dev extras)
+# TODO: Fill in the standard commands for this repo
-uv pip install -e ".[dev]"
+
-# or
+# Install dependencies
-pip install -e ".[dev]"
 
 # Run tests
-uv run pytest
-# or
-pytest
 
-# Lint
+# Lint / type check
-uv run ruff check .
 
-# Type check
+# Build / package (if applicable)
-uv run mypy llm_connect
-
-# Run a single test file
-uv run pytest tests/test_models.py -v
-
-# Build package (dry run)
-uv build --no-sources
-```
-
-## Project layout
-
-```
-llm_connect/         source package
-  adapter.py         LLMAdapter ABC + Mock/ErrorLLMAdapter
-  models.py          RunConfig, LLMResponse
-  exceptions.py      LLMError hierarchy
-  factory.py         create_adapter()
-  openai.py          OpenAIAdapter
-  gemini.py          GeminiAdapter
-  openrouter.py      OpenRouterAdapter
-  claude_code.py     ClaudeCodeAdapter
-  embedding_adapter.py  EmbeddingAdapter ABC
-  embedding_openai.py   OpenAICompatibleEmbeddingAdapter
-  embedding_cache.py    EmbeddingCache
-  embedding_factory.py  create_embedding_adapter()
-  toml_config.py     7-level TOML config resolution
-  config.py          LLMConfig, resolve_api_key, find_project_root
-  _http.py           shared HTTP POST utility
-  _token_estimator.py  rough token count estimate
-  similarity.py      cosine similarity utilities
-tests/               pytest test suite
-contracts/           GAAF-2026 contract docs
-workplans/           workplan files (LLM-WP-NNNN)
 ```

.claude/rules/workplan-convention.md

@@ -1,12 +1,40 @@
 ## Workplan Convention (ADR-001)
 
-File location: `workplans/llm-connect-WP-NNNN-<slug>.md`
+File location: `workplans/LLM-WP-NNNN-<slug>.md`
-ID prefix: `LLM-WP`
+ID prefix: `LLM-WP-`
 
 Work items originate as files in this repo **before** being registered in the hub.
 
+Canonical workplan/workstream frontmatter statuses are:
+`proposed`, `ready`, `active`, `blocked`, `backlog`, `finished`, `archived`.
+Use `proposed` for a newly drafted plan, `ready` after review against current
+repo state, and `finished` when implementation is complete. `stalled` and
+`needs_review` are derived health labels, not stored statuses.
+
+Closed workplans may be moved to `workplans/archived/` with a completion-date
+prefix: `YYMMDD-LLM-WP-NNNN-<slug>.md`. The frontmatter id remains
+unchanged; the prefix is only for quick visual reference.
+
+Small opportunistic tasks discovered during another session use **Ad Hoc Tasks**:
+`workplans/ADHOC-YYYY-MM-DD.md`, workstream slug `adhoc-YYYY-MM-DD`, and task ids
+`ADHOC-YYYY-MM-DD-T01`, `T02`, etc. Use adhocs only for low-risk work completed
+directly. Promote anything requiring analysis, design, approval, dependencies, or
+multiple planned phases into a normal workplan.
+
 Ecosystem todos from other agents arrive as `[repo:llm-connect]` hub tasks —
 visible at session start. Pick one up by creating the workplan file, then registering
 the workstream.
 
+Task blocks use this shape:
+
+```task
+id: LLM-WP-NNNN-T01
+status: wait | todo | progress | done | cancel
+priority: high | medium | low
+state_hub_task_id: "<uuid>"         # written by fix-consistency — do not edit
+```
+
+Status progression is `todo` → `progress` → `done`; use `wait` for waiting or
+blocked work and `cancel` for stopped work.
+
 <!-- Ralph Loop rules and HEUREKA sequence: ~/.claude/CLAUDE.md — do not duplicate here -->

.custodian-brief.md

@@ -0,0 +1,18 @@
+<!-- custodian-brief: generated by fix-consistency — do not edit manually -->
+# Custodian Brief — llm-connect
+
+**Domain:** infotech  
+**Last synced:** 2026-07-03 16:47 UTC  
+**State Hub:** http://127.0.0.1:8000 *(adjust if running on a remote machine)*
+
+## Active Workstreams
+
+*(none — repo may need first-session setup)*
+
+---
+## MCP Orientation (when available)
+
+If the state-hub MCP server is reachable, call:
+`get_domain_summary("infotech")`
+This provides richer cross-domain context.
+If the MCP call fails, use this file as your orientation source.

.dockerignore

@@ -0,0 +1,15 @@
+.git
+.pytest_cache
+.ruff_cache
+.mypy_cache
+__pycache__
+*.pyc
+.venv
+venv
+dist
+build
+*.egg-info
+.env
+.env.*
+apikey-*.txt
+apikey-*.json

.repo-classification.yaml

@@ -0,0 +1,25 @@
+# Repo classification (Repo Classification Standard v1.0).
+
+repo_classification:
+  standard: Repo Classification Standard
+  version: '1.0'
+  classified_at: '2026-06-22'
+  classified_by: human
+  category: tooling
+  domain: agents
+  secondary_domains:
+  - infotech
+  capability_tags:
+  - orchestration
+  - model-routing
+  - configuration
+  - automation
+  business_stake:
+  - technology
+  - product
+  - automation
+  business_mechanics:
+  - operation
+  - adaptation
+  notes: Multi-provider LLM client library for Python (pluggable adapters / model routing).
+    Primary domain agents, infotech secondary.

AGENTS.md

@@ -0,0 +1,219 @@
+# llm-connect — Agent Instructions
+
+## Repo Identity
+
+**Purpose:** Multi-provider LLM client library — unified adapter interface for OpenAI, Claude, Gemini, OpenRouter with embedding support, token estimation, and TOML-based config.
+
+**Domain:** agents
+**Repo slug:** llm-connect
+**Topic ID:** `64418556-3206-457a-ba29-6884b5b12cf3`
+**Workplan prefix:** `LLM-WP-`
+
+---
+
+## State Hub Integration
+
+The Custodian State Hub tracks work across all domains. Interact via HTTP REST —
+there is no MCP server for Codex agents.
+
+| Context | URL |
+|---------|-----|
+| Local workstation | `http://127.0.0.1:8000` |
+| Remote via tunnel | `http://127.0.0.1:18000` |
+
+### Orient at session start
+
+```bash
+# Offline brief — works without hub connection
+cat .custodian-brief.md
+
+# Active workstreams for this domain
+curl -s "http://127.0.0.1:8000/workstreams/?topic_id=64418556-3206-457a-ba29-6884b5b12cf3&status=active" \
+  | python3 -m json.tool
+
+# Check inbox
+curl -s "http://127.0.0.1:8000/messages/?to_agent=llm-connect&unread_only=true" \
+  | python3 -m json.tool
+```
+
+Mark a message read:
+```bash
+curl -s -X PATCH "http://127.0.0.1:8000/messages/<id>/read" \
+  -H "Content-Type: application/json" -d '{}'
+```
+
+### Log progress (required at session close)
+
+```bash
+curl -s -X POST http://127.0.0.1:8000/progress/ \
+  -H "Content-Type: application/json" \
+  -d '{
+    "summary": "what was done",
+    "event_type": "note",
+    "author": "codex",
+    "workstream_id": "<uuid>",
+    "task_id": "<uuid>"
+  }'
+```
+
+Omit `workstream_id` / `task_id` when not applicable.
+
+### Update task status
+
+```bash
+curl -s -X PATCH "http://127.0.0.1:8000/tasks/<task_id>" \
+  -H "Content-Type: application/json" \
+  -d '{"status": "progress"}'
+# values: wait | todo | progress | done | cancel
+```
+
+### Flag a task for human review
+
+```bash
+curl -s -X PATCH "http://127.0.0.1:8000/tasks/<task_id>" \
+  -H "Content-Type: application/json" \
+  -d '{"needs_human": true, "intervention_note": "reason"}'
+```
+
+---
+
+## Session Protocol
+
+**Start:**
+1. `cat .custodian-brief.md` — domain goal and open workstreams (offline-safe)
+2. Check inbox: `GET /messages/?to_agent=llm-connect&unread_only=true`; mark read
+3. Scan workplans: `ls workplans/` — note `status: ready`, `active`, or `blocked` files and open tasks
+4. Check human-needed tasks: `GET /tasks/?needs_human=true`
+
+**During work:**
+- Update task statuses in workplan files as tasks progress
+- Record significant decisions via `POST /decisions/`
+
+**Close:**
+1. Update workplan file task statuses to reflect progress
+2. Log: `POST /progress/` with a summary of what changed
+3. Note for the custodian operator: after workplan file changes, run from
+   `~/state-hub`:
+   ```bash
+   make fix-consistency REPO=llm-connect
+   ```
+   This syncs task status from files into the hub DB.
+
+---
+
+## Credential and access routing
+
+**Audience:** Codex, Claude Code, Grok, and custodian agents that call **llm-connect**
+for inference. Run this check **before** requesting secrets, API keys, SSH access,
+login tokens, or database passwords — in any repo, not only `ops-warden`.
+
+ops-warden **issues SSH certificates only** (`warden sign`, `cert_command`). Every
+other credential need belongs to another subsystem. **Do not** message
+`ops-warden` on State Hub expecting a secret value; the reply is a pointer, not a key.
+
+### Lookup (do this first)
+
+```bash
+warden route find "<describe your need>" --json
+warden route show <catalog-id> --json
+```
+
+Requires the `warden` CLI from `~/ops-warden` (`uv tool install .` or `uv run warden`).
+
+| Agent runtime | How to orient |
+| --- | --- |
+| **Codex / Grok** (shell, HTTP State Hub) | `warden route` commands above; inbox `to_agent=llm-connect` is for coordination, not secret vending |
+| **Claude Code** (MCP when available) | `get_domain_summary("custodian")` for workstreams; **still** use `warden route` for credential ownership |
+| **llm-connect** (inference service) | Never put secret retrieval in prompts; route custody to OpenBao/operator paths surfaced by `warden route` |
+
+### Quick routing table
+
+| I need… | Owner | ops-warden executes? |
+| --- | --- | --- |
+| SSH cert (`adm`/`agt`/`atm`) | ops-warden | **Yes** — `warden sign` |
+| API key, DB password, provider token | OpenBao (`railiance-platform`) | No — route only |
+| Login / OIDC / MFA | key-cape / Keycloak | No — route only |
+| Authorization decision | flex-auth | No — route only |
+| activity-core → issue-core emission | activity-core + issue-core | No — `warden route show activity-core-issue-sink` |
+| SSH tunnel | ops-bridge (+ `cert_command` from warden) | No — route only |
+
+### Anti-patterns (do not do these)
+
+- `POST /messages/` to `ops-warden` asking for `ISSUE_CORE_API_KEY`, `OPENROUTER_API_KEY`, etc.
+- Inventing `warden secret`, `warden login`, `warden bao`, `warden tunnel` — they do not exist
+- Pasting secrets into Git, State Hub, workplans, logs, or chat
+
+### Other capabilities (reuse-surface)
+
+Non-credential capabilities are usually discovered through **reuse-surface** federation
+(`reuse-surface` registry / `capability.*` indexes). Credential routing is inlined in
+every repo's agent instructions because it is high-frequency, high-risk, and easy to
+get wrong.
+
+**Canon:** `~/ops-warden/wiki/CredentialRouting.md` · catalog `~/ops-warden/registry/routing/catalog.yaml`
+
+<!-- REPO-AGENTS-EXTENSIONS -->
+<!-- Append repo-specific agent instructions below this marker.
+     The state-hub template sync preserves content after this line. -->
+
+---
+
+## Workplan Convention (ADR-001)
+
+Work items originate as files in this repo — not in the hub. The hub is a
+read/cache/index layer that rebuilds from files.
+
+**File location:** `workplans/LLM-WP-NNNN-<slug>.md`
+
+**Archived location:** finished workplans may move to
+`workplans/archived/YYMMDD-LLM-WP-NNNN-<slug>.md`. The `YYMMDD` prefix is
+the completion/archive date; the frontmatter `id` does not change.
+
+**Ad Hoc Tasks:** small opportunistic fixes discovered during a session use
+`workplans/ADHOC-YYYY-MM-DD.md` with task ids `ADHOC-YYYY-MM-DD-T01`, etc. Use
+this only for low-risk work completed directly; create a normal workplan for
+anything needing analysis, design, approval, dependencies, or multiple phases.
+
+**Frontmatter:**
+
+```yaml
+---
+id: LLM-WP-NNNN
+type: workplan
+title: "..."
+domain: agents
+repo: llm-connect
+status: proposed | ready | active | blocked | backlog | finished | archived
+owner: codex
+topic_slug: ...
+created: "YYYY-MM-DD"
+updated: "YYYY-MM-DD"
+state_hub_workstream_id: "<uuid>"   # written by fix-consistency — do not edit
+---
+```
+
+Use `proposed` for a new draft, `ready` after review against current repo
+state, and `finished` after implementation. `stalled` and `needs_review` are
+derived health labels, not frontmatter statuses.
+
+**Task block format** (one per `##` section):
+
+```
+## Task Title
+
+` ` `task
+id: LLM-WP-NNNN-T01
+status: wait | todo | progress | done | cancel
+priority: high | medium | low
+state_hub_task_id: "<uuid>"         # written by fix-consistency — do not edit
+` ` `
+
+Task description text.
+```
+
+Status progression: `todo` → `progress` → `done`; use `wait` for waiting/blocked work and `cancel` for stopped work.
+
+To create a new workplan:
+1. Write the file following the format above
+2. Notify the custodian operator to run `make fix-consistency REPO=llm-connect`
+   (or send a message to the hub agent via `POST /messages/`)

ARCHITECTURE-LAYERS.md

@@ -32,6 +32,9 @@ Maturity states: **Experimental → Beta → Stable → Deprecated**
 | `gemini.py` | `GeminiAdapter` — Google Generative Language API | Beta |
 | `openrouter.py` | `OpenRouterAdapter` — OpenAI-compatible multi-model routing | Beta |
 | `claude_code.py` | `ClaudeCodeAdapter` — `claude --print` subprocess | Beta |
+| `_payload.py` | Shared adapter payload translation for `RunConfig.model_params` | Beta |
+| `_diagnostics.py` | Opt-in per-call diagnostics capture for server debug and audit modes | Beta |
+| `replay.py` | Audit replay parser CLI (`python -m llm_connect.replay`) | Beta |
 | `embedding_adapter.py` | `EmbeddingAdapter` ABC | Beta |
 | `embedding_openai.py` | `OpenAICompatibleEmbeddingAdapter` | Beta |
 | `embedding_cache.py` | `EmbeddingCache` — disk-backed embedding cache | Beta |

CLAUDE.md

@@ -8,4 +8,5 @@
 @.claude/rules/stack-and-commands.md
 @.claude/rules/architecture.md
 @.claude/rules/repo-boundary.md
+@.claude/rules/credential-routing.md
 @.claude/rules/agents.md

Containerfile

@@ -0,0 +1,27 @@
+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    LLM_CONNECT_HOST=0.0.0.0 \
+    LLM_CONNECT_PORT=8080 \
+    LLM_CONNECT_PROVIDER=mock
+
+WORKDIR /app
+
+RUN groupadd -g 10001 llmconnect \
+    && useradd -u 10001 -g 10001 -m -s /usr/sbin/nologin llmconnect
+
+COPY pyproject.toml README.md ./
+COPY llm_connect ./llm_connect
+COPY fixtures ./fixtures
+COPY scripts ./scripts
+
+RUN pip install --no-cache-dir .
+
+USER 10001:10001
+EXPOSE 8080
+
+HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
+    CMD python -c "import json, urllib.request; r=urllib.request.urlopen('http://127.0.0.1:8080/health', timeout=3); raise SystemExit(0 if json.load(r).get('status') == 'ok' else 1)"
+
+CMD ["python", "-m", "llm_connect.server"]

INTENT.md

@@ -0,0 +1,95 @@
+# INTENT
+
+## Purpose
+
+This repository exists to provide a **provider-neutral interface for interacting with large language models (LLMs)** in Python.
+
+It ensures that applications can use LLM capabilities without being tightly coupled to any specific provider, API, or execution environment.
+
+---
+
+## Primary Utility
+
+The repository provides a **unified adapter layer** that:
+
+* Abstracts over multiple LLM providers and execution modes
+* Standardizes request, response, and configuration handling
+* Enables interchangeable use of hosted APIs and local tooling (e.g. CLI-based models)
+* Supports embeddings, token estimation, and related primitives
+* Enables dynamic utility by cost optimizations 
+
+It transforms heterogeneous LLM ecosystems into a **consistent, composable programming interface**.
+
+---
+
+## Intended Users
+
+* Application developers integrating LLM capabilities into their systems
+* Library and framework authors requiring provider-agnostic LLM primitives
+* Automation systems (`atm`) orchestrating LLM-assisted workflows
+* LLM agents (`agt`) operating across different model providers
+
+---
+
+## Strategic Role in the System
+
+This repository acts as the **LLM abstraction layer** within the broader system:
+
+* It decouples **application logic from provider-specific implementations**
+* It enables **runtime flexibility and provider switching without code changes**
+* It supports architectures where LLM usage is **optional, replaceable, and testable**
+
+It allows higher-level systems to treat LLMs as **pluggable capabilities rather than fixed dependencies**.
+
+---
+
+## Strategic Boundaries
+
+This repository is **not** intended to:
+
+* Provide application-level agent frameworks or workflows
+* Define prompting strategies, routing policies, or domain-specific logic
+* Manage secrets, credentials, or organizational access policies
+* Own or implement LLM providers themselves
+
+Its responsibility is limited to **clean abstraction and integration of LLM capabilities**.
+
+---
+
+## Design Principles
+
+* **Abstraction over providers**
+  Consumers depend on a stable adapter interface, not on vendor APIs
+
+* **Composability**
+  LLM functionality should be usable as a building block in larger systems
+
+* **Replaceability**
+  Providers and execution modes must be interchangeable without affecting consumers
+
+* **Deterministic integration boundaries**
+  Non-LLM logic must remain testable and independent of LLM variability
+
+* **Minimal opinionation**
+  The library provides primitives, not policies
+
+---
+
+## Maturity Target
+
+A mature version of this repository should:
+
+* Provide a **stable, versioned core adapter contract** for LLM interaction
+* Support a broad range of providers and execution environments
+* Enable **seamless switching and fallback between providers**
+* Offer consistent handling of **responses, errors, and usage metrics**
+* Serve as the **default integration layer for LLM capabilities** across dependent systems
+
+---
+
+## Stability Note
+
+Changes to this file represent a **deliberate shift in the abstraction boundaries or role** of this repository.
+
+Such changes should be rare, as they affect all downstream systems relying on provider-neutral LLM integration.
+

Makefile

@@ -0,0 +1,37 @@
+-include .env
+export
+
+.PHONY: install test lint typecheck start help
+
+UV ?= $(shell command -v uv 2>/dev/null || if [ -x "$$HOME/.local/bin/uv" ]; then printf "%s" "$$HOME/.local/bin/uv"; else printf "%s" "uv"; fi)
+
+LLM_CONNECT_HOST ?= 127.0.0.1
+LLM_CONNECT_PORT ?= 8080
+LLM_CONNECT_PROVIDER ?= mock
+LLM_CONNECT_MODEL ?=
+SERVER_ARGS ?=
+
+install:  ## Install dependencies (dev group)
+	$(UV) sync --group dev
+
+test:  ## Run test suite
+	$(UV) run pytest
+
+lint:  ## Run ruff linter
+	$(UV) run ruff check .
+
+typecheck:  ## Run mypy type checker
+	$(UV) run mypy llm_connect
+
+start:  ## Start llm-connect HTTP server (reads .env and LLM_CONNECT_* overrides)
+	$(UV) run python -m llm_connect.server \
+		--host $(LLM_CONNECT_HOST) \
+		--port $(LLM_CONNECT_PORT) \
+		--provider $(LLM_CONNECT_PROVIDER) \
+		$(if $(LLM_CONNECT_MODEL),--model $(LLM_CONNECT_MODEL),) \
+		$(SERVER_ARGS)
+
+help:  ## Show this help message
+	@grep -Eh '^[a-zA-Z_-]+:.*?##' $(MAKEFILE_LIST) | \
+		awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-16s\033[0m %s\n", $$1, $$2}' | \
+		sort

README.md

@@ -1,7 +1,7 @@
 # llm-connect
 
-Pluggable LLM adapters for Python. Supports OpenRouter, Gemini, OpenAI, and
+Pluggable LLM adapters for Python and the commandline. Supports OpenRouter, Gemini, 
-the Claude Code CLI out of the box, with a clean abstract interface for adding
+OpenAI, and the Claude Code CLI out of the box, with a clean abstract interface for adding
 your own.
 
 ## Quick start
@@ -31,8 +31,6 @@ pip install llm-connect
 |---|---|---|
 | `"openrouter"` | `OpenRouterAdapter` | OpenAI-compatible endpoint; supports all OpenRouter models |
 | `"gemini"` | `GeminiAdapter` | Google Generative Language REST API; supports free tier |
-| `"openai"` | `OpenAIAdapter` | OpenAI chat completions endpoint |
-| `"claude-code"` | `ClaudeCodeAdapter` | Shells out to the `claude --print` CLI; no API key needed |
 
 ```python
 from llm_connect import create_adapter
@@ -75,15 +73,15 @@ config = RunConfig(
 )
 ```
 
-| Field | Default | Description |
+| Field | Default | Description |
-|---|---|---|
+|---|---|---|
-| `model_name` | `"gpt-4"` | Model identifier (adapter may override) |
+| `model_name` | `"gpt-4"` | Model identifier (adapter may override) |
-| `temperature` | `0.7` | Sampling temperature |
+| `temperature` | `0.7` | Sampling temperature |
-| `max_tokens` | `2000` | Maximum output tokens |
+| `max_tokens` | `2000` | Maximum output tokens |
-| `model_params` | `{}` | Extra provider-specific parameters |
+| `model_params` | `{}` | Portable extras translated by each adapter; see `docs/adapter-model-params.md` |
-| `max_depth` | `3` | Max nesting depth for recursive calls |
+| `max_depth` | `3` | Max nesting depth for recursive calls |
-| `skip_if_exists` | `True` | Skip if identical input hash already processed |
+| `skip_if_exists` | `True` | Skip if identical input hash already processed |
-| `timeout_seconds` | `300` | Request timeout |
+| `timeout_seconds` | `300` | Request timeout |
 
 ### `LLMResponse`
 
@@ -94,10 +92,55 @@ response = adapter.execute_prompt(prompt, config)
 print(response.content)       # generated text
 print(response.model)         # model actually used
 print(response.usage)         # {"prompt_tokens": …, "completion_tokens": …, "total_tokens": …}
-print(response.finish_reason) # "stop", "length", etc.
+print(response.finish_reason) # "stop", "length", etc.
-```
+```
-
+
-## Writing your own adapter
+## Server diagnostics
+
+Serve mode can include a debug envelope without changing normal responses:
+
+```bash
+LLM_CONNECT_DEBUG=1 python -m llm_connect.server --provider openrouter
+curl 'http://127.0.0.1:8080/execute?debug=1' -d '{"prompt":"hi"}'
+```
+
+Set `LLM_CONNECT_AUDIT_DIR=/path/to/audit` to write per-call replay records,
+then parse one without another provider call:
+
+```bash
+python -m llm_connect.replay /path/to/audit/record.json --json
+```
+
+## Server runtime profiles
+
+Serve mode enables named runtime profiles by default. A client can send
+`config.model_name="custodian-triage-balanced"` and the server resolves it to
+the configured provider/model before calling the adapter.
+
+Useful runtime environment variables:
+
+```bash
+LLM_CONNECT_HOST=0.0.0.0
+LLM_CONNECT_PORT=8080
+LLM_CONNECT_PROVIDER=openrouter
+LLM_CONNECT_MODEL=google/gemini-2.5-flash
+LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER=openrouter
+LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash
+```
+
+For local smoke tests without provider credentials:
+
+```bash
+export LLM_CONNECT_MOCK_RESPONSE="$(python -c 'import json; print(json.dumps(json.load(open("fixtures/activity_core/daily-triage-valid-content.json"))))')"
+python -m llm_connect.server --provider mock
+python scripts/smoke_activity_core_endpoint.py --url http://127.0.0.1:8080
+```
+
+Disable profile dispatch with `--disable-profiles`. Set
+`LLM_CONNECT_STRICT_PROFILES=1` or pass `--strict-profiles` to reject direct
+model names that are not configured profiles.
+
+## Writing your own adapter
 
 ```python
 from llm_connect import LLMAdapter, RunConfig, LLMResponse

SCOPE.md

@@ -1,45 +1,162 @@
-# SCOPE.md — llm-connect
+# SCOPE
 
-## Purpose
+> This file helps you quickly understand what this repository is about,
+> when it is relevant, and when it is not.
 
-`llm-connect` is a **multi-provider LLM client library for Python**.
+---
-It provides a unified adapter interface over OpenAI, Gemini, OpenRouter,
-and the Claude Code CLI, with embedding support, token estimation, and a
-TOML-based configuration chain.
 
-Extracted from [markitect](https://github.com/worsch/markitect).
+## One-liner
-The `markitect.llm` module remains a re-export shim pointing here.
 
-## This repo owns
+`llm-connect` is a multi-provider LLM client library for Python.
 
-- `LLMAdapter` ABC and `RunConfig` / `LLMResponse` data models (Core)
+---
-- All concrete provider adapters: `OpenAIAdapter`, `GeminiAdapter`,
-  `OpenRouterAdapter`, `ClaudeCodeAdapter` (Functional)
-- Embedding adapters: `EmbeddingAdapter` ABC, `OpenAICompatibleEmbeddingAdapter`,
-  `EmbeddingCache`, `create_embedding_adapter` factory (Functional)
-- TOML-based config resolution (`toml_config.py`, `config.py`) (Configuration)
-- Shared HTTP utility (`_http.py`), token estimator (`_token_estimator.py`),
-  cosine similarity utilities (`similarity.py`)
-- The full `LLMError` exception hierarchy
 
-## This repo does NOT own
+## Core Idea
 
-- Consumer application logic — that lives in `markitect`, `inter-hub`, etc.
+`llm-connect` provides a unified adapter interface over OpenAI, Gemini,
-- API key management infrastructure — keys are resolved from env vars or
+OpenRouter, Anthropic-compatible APIs, and the Claude Code CLI. It keeps
-  plaintext key files; secret storage belongs in the calling environment
+consumer applications from binding directly to provider-specific request,
-- Model routing decisions specific to a consumer — `RoutingPolicy` (WP-0003)
+response, embedding, token-estimation, and configuration details.
-  provides primitives; policy configuration belongs in the consumer
-- The Claude Code CLI binary itself — `ClaudeCodeAdapter` shells out to `claude`
 
-## Consumers (as of 2026-04-01)
+The library was extracted from `markitect`; the `markitect.llm` module remains a
+re-export shim pointing here.
 
-| Consumer | How it uses llm-connect |
+---
-|----------|------------------------|
-| `markitect` | Re-exports via `markitect.llm` shim; drives document generation |
-| `inter-hub` (IHF) | Subprocess bridge (`scripts/llm_bridge.py` + `AgentBridge.hs`) for multi-agent federation |
 
-## Versioning
+## In Scope
 
-- Current version: **0.1.0** (pre-release; API not yet stable)
+- `LLMAdapter` ABC and `RunConfig` / `LLMResponse` data models.
-- Core layer (`LLMAdapter`, `RunConfig`, `LLMResponse`) will be stabilised at **v1.0.0**
+- Concrete provider adapters such as `OpenAIAdapter`, `GeminiAdapter`,
-- Breaking Core changes require a major version bump
+  `OpenRouterAdapter`, and `ClaudeCodeAdapter`.
+- Embedding adapters including `EmbeddingAdapter`,
+  `OpenAICompatibleEmbeddingAdapter`, `EmbeddingCache`, and
+  `create_embedding_adapter`.
+- TOML-based configuration resolution via `toml_config.py` and `config.py`.
+- Shared HTTP utilities, token estimation, similarity helpers, and the
+  `LLMError` exception hierarchy.
+
+---
+
+## Out of Scope
+
+- Consumer application logic; that belongs in `markitect`, `inter-hub`, and
+  other callers.
+- Secret-management infrastructure; keys are resolved from environment variables
+  or configured key files, while secure storage belongs to the calling
+  environment.
+- Consumer-specific model routing policy, beyond reusable primitives.
+- Owning the Claude Code CLI binary itself; `ClaudeCodeAdapter` shells out to the
+  installed `claude` command.
+
+---
+
+## Relevant When
+
+- You need one Python interface for multiple LLM providers.
+- You want to switch between OpenAI, Gemini, OpenRouter, Anthropic-compatible
+  APIs, or Claude Code CLI without changing consumer code.
+- You need embeddings, token estimation, provider configuration, or consistent
+  error handling around LLM calls.
+- You are building a repository that should depend on provider-neutral LLM
+  primitives instead of vendor-specific client code.
+
+---
+
+## Not Relevant When
+
+- You need a complete application-level agent framework.
+- You need hosted secret storage, key rotation, or organization-wide credential
+  governance.
+- You only call one provider directly and do not need adapter portability.
+- You need UI, persistence, workflow orchestration, or domain-specific prompting.
+
+---
+
+## Current State
+
+- Status: pre-release, version `0.1.0`.
+- Core layer (`LLMAdapter`, `RunConfig`, `LLMResponse`) is intended to stabilize
+  by `v1.0.0`.
+- Provider adapters, embedding helpers, and TOML configuration are implemented.
+- Breaking core changes should require a major version bump once the core layer
+  is declared stable.
+
+---
+
+## How It Fits
+
+- Upstream dependencies: provider SDKs or HTTP APIs for supported LLM services.
+- Downstream consumers: `markitect` re-exports the library and uses it for
+  document generation; `inter-hub` uses it through its LLM bridge.
+- Often used with: repositories that need optional LLM assistance while keeping
+  deterministic non-LLM behavior independently testable.
+
+---
+
+## Terminology
+
+- Preferred terms: adapter, provider, run config, response, embedding adapter,
+  token estimator, provider-neutral LLM interface.
+- Also known as: LLM adapter library, provider abstraction.
+- Potentially confusing terms: `ClaudeCodeAdapter` integrates the Claude Code CLI,
+  not Anthropic's hosted Messages API directly.
+
+---
+
+## Related / Overlapping
+
+- `markitect` - original source of the extracted adapter layer and current
+  downstream consumer.
+- `inter-hub` - uses LLM calls through a bridge for interaction federation.
+- `repo-scoping` - can use `llm-connect` as optional LLM assistance for
+  repository characteristic extraction.
+
+---
+
+## Getting Oriented
+
+- Start with: `README.md`, `pyproject.toml`, and `contracts/functional/adapters.md`.
+- Key files / directories: `llm_connect/`, `tests/`, `contracts/`, and
+  `.github/workflows/`.
+- Entry points: adapter factory/configuration helpers and the provider adapter
+  classes under `llm_connect/`.
+
+---
+
+## Provided Capabilities
+
+```capability
+type: api
+title: Multi-provider LLM adapter interface
+description: >
+  Provides one Python adapter contract for OpenAI, Gemini, OpenRouter,
+  Anthropic-compatible APIs, and Claude Code CLI calls.
+keywords: [llm, adapter, openai, gemini, openrouter, anthropic, claude]
+```
+
+```capability
+type: api
+title: Embedding adapter and cache support
+description: >
+  Provides embedding adapter abstractions, OpenAI-compatible embedding support,
+  and embedding cache helpers for downstream retrieval workflows.
+keywords: [embedding, vector, cache, retrieval, openai-compatible]
+```
+
+```capability
+type: configuration
+title: TOML-based LLM provider configuration
+description: >
+  Resolves provider settings and model configuration from TOML and environment
+  sources so callers can configure LLM usage without hard-coding provider
+  details.
+keywords: [toml, configuration, provider, model, credentials]
+```
+
+---
+
+## Notes
+
+- Current known consumers are `markitect` and `inter-hub`.
+- The library is intentionally provider-neutral; product-specific prompting and
+  routing decisions belong in the caller.

contracts/functional/adaptive-routing-policy.md

@@ -0,0 +1,87 @@
+# Contract: AdaptiveRoutingPolicy
+
+**layer:** Functional
+**maturity:** Beta
+**module:** `llm_connect.routing`
+**since:** WP-0004
+
+## Purpose
+
+Select the cheapest adapter whose observed mean quality for a task type clears
+a caller-supplied quality floor. The policy builds on `RoutingPolicy`: static
+rules remain the cold-start and failure fallback, while adaptive selection is
+used only when the ledger has enough qualifying observations.
+
+## Public surface
+
+```python
+@dataclass
+class AdaptiveRoutingPolicy(RoutingPolicy):
+    ledger: Optional[QualityLedger] = None
+    adapters_by_id: Mapping[str, LLMAdapter] = field(default_factory=dict)
+    window_size: int = 20
+    min_observations: int = 1
+    max_age: Optional[timedelta] = None
+
+    def resolve(
+        self,
+        task_type: str,
+        estimated_cost_per_1k: Optional[float] = None,
+        *,
+        quality_floor: Optional[float] = None,
+    ) -> LLMAdapter: ...
+```
+
+## Candidate identity
+
+Observations are keyed by `(task_type, adapter_id)`. Callers should pass
+`adapters_by_id` so the policy can map ledger observations back to concrete
+`LLMAdapter` instances. If a static rule adapter is not present in
+`adapters_by_id`, the policy also checks common string attributes
+`adapter_id`, `id`, and `name`.
+
+## Invariants
+
+1. If `quality_floor is None` or `ledger is None`, resolution is exactly the
+   same as `RoutingPolicy.resolve()`.
+2. `quality_floor` must be between `0` and `1`, inclusive.
+3. Each candidate is evaluated over the newest `window_size` observations for
+   the requested `task_type` and adapter id.
+4. `max_age`, when provided, filters out observations older than that age.
+5. A candidate is considered only when it has at least `min_observations` after
+   filtering.
+6. A candidate qualifies when its mean `quality_score` is greater than or equal
+   to `quality_floor`.
+7. Among qualifying candidates, the policy chooses the lowest mean observed
+   `cost_usd`.
+8. If mean observed cost ties exactly, the policy prefers the matching static
+   rule's explicit `prefer` adapter.
+9. If there are still ties, stable candidate order is used.
+10. If no candidate qualifies, resolution falls through to
+    `RoutingPolicy.resolve(task_type, estimated_cost_per_1k)`.
+
+## Sample-size and freshness trade-off
+
+Small `window_size` values react quickly to model or prompt changes but can be
+noisy. Larger windows are more stable but may preserve stale behavior after a
+provider update or prompt template change. `min_observations` lets callers avoid
+acting on a single lucky sample, while `max_age` bounds how long old observations
+can influence routing. Callers that change prompts materially should also filter
+by a prompt fingerprint in observation tags before writing comparable samples to
+the same ledger regime.
+
+## Error contract
+
+| Condition | Exception |
+|-----------|-----------|
+| `quality_floor` outside `0..1` | `ValueError` |
+| `window_size <= 0` | `ValueError` |
+| `min_observations <= 0` | `ValueError` |
+| `max_age < 0` | `ValueError` |
+| No qualifying adaptive candidate and no static fallback | `LookupError` |
+
+## Non-goals
+
+The policy does not define a task taxonomy, set task quality floors, decide
+which baseline is authoritative, or perform billing-grade accounting. Those are
+consumer policy choices.

contracts/functional/baseline-grading.md

@@ -0,0 +1,85 @@
+# Contract: Baseline Grading
+
+**layer:** Functional
+**maturity:** Beta
+**module:** `llm_connect.grading`
+**since:** WP-0004
+
+## Purpose
+
+Compare a candidate adapter response against a caller-chosen baseline response
+and return a normalised quality score suitable for storage in
+`QualityLedger`.
+
+## Public surface
+
+```python
+@dataclass(frozen=True)
+class GradingResult:
+    quality_score: float
+    notes: str
+    grader_id: str
+    baseline_response: LLMResponse
+    candidate_response: LLMResponse
+
+class Judge(Protocol):
+    grader_id: str
+    def judge(..., *, prompt: str, run_config: RunConfig) -> GradingResult: ...
+
+class BaselineGrader(Protocol):
+    def grade(
+        self,
+        baseline_adapter: LLMAdapter,
+        candidate_adapter: LLMAdapter,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult: ...
+
+@dataclass
+class ExactMatchJudge: ...
+
+@dataclass
+class EmbeddingSimilarityJudge: ...
+
+@dataclass
+class LLMJudge: ...
+
+@dataclass
+class PairedGrader: ...
+```
+
+## Invariants
+
+1. `quality_score` is always validated as `0.0..1.0`.
+2. `GradingResult` always preserves both baseline and candidate responses.
+3. `PairedGrader` runs the baseline adapter and the candidate adapter with the
+   same prompt and run config, then delegates comparison to its `Judge`.
+4. `ExactMatchJudge` returns `1.0` for matched content and `0.0` otherwise.
+5. `EmbeddingSimilarityJudge` embeds baseline and candidate response text in a
+   single batch and clamps cosine similarity into `0.0..1.0`.
+6. `LLMJudge` uses a fixed rubric prompt and expects JSON with
+   `quality_score` and optional `notes`.
+7. `LLMJudge` runs with `temperature=0.0`, drops the caller's budget tracker,
+   and adds a deterministic `seed` model parameter when configured.
+
+## Error contract
+
+| Condition | Exception |
+|-----------|-----------|
+| Invalid `quality_score` | `ValueError` |
+| Empty `grader_id` | `ValueError` |
+| Embedding adapter returns other than two vectors | `ValueError` |
+| LLM judge response is missing parseable JSON | `ValueError` |
+
+## Bias caveats
+
+LLM-as-judge scoring is heuristic and may exhibit:
+
+- Length bias: longer answers can be preferred even when not better.
+- Format bias: familiar formatting can be rewarded independent of correctness.
+- Position bias: prompt order can affect judgement.
+- Self-preference: a judge may favour outputs from its own model family.
+
+Consumers should calibrate `LLMJudge` against at least one non-LLM judge such
+as exact match or embedding similarity before using its observations to drive
+adaptive routing.

contracts/functional/costs.md

@@ -0,0 +1,25 @@
+# Cost Estimates
+
+`llm_connect.costs` converts token estimates or observed token counts into
+USD estimates using `ModelRateRegistry`.
+
+## Contract
+
+```python
+from llm_connect import estimate_cost
+
+estimate = estimate_cost("openai/gpt-4o-mini", 28_000, 7_500)
+```
+
+For known models the result is:
+
+- `cost_usd`: prompt plus completion estimate.
+- `prompt_cost_usd`: prompt-token component.
+- `completion_cost_usd`: completion-token component.
+- `cost_source`: `rate_table:<model_id>`.
+
+Unknown models return `CostEstimate(cost_usd=None, cost_source="unknown")`.
+Missing rates are never silently treated as zero cost.
+
+The module also exposes `CostModel(registry=...)` for callers that prefer to
+carry a registry object and call `model.estimate_cost(...)`.

contracts/functional/problem-classes.md

@@ -0,0 +1,46 @@
+# Problem Classes
+
+`llm_connect.problem_classes` provides generic token estimators for recurring
+LLM workflow shapes.
+
+## Contract
+
+Every problem class exposes:
+
+- `name`: stable registry key.
+- `base_dimensions`: required dimension names supplied by consumers.
+- `tunable_params`: parameters that can be overridden or fitted.
+- `estimate(dimensions, params=None) -> TokenEstimate`.
+- `fit(observations, min_observations=3) -> ProblemClass`.
+
+`TokenEstimate` contains `prompt_tokens`, `completion_tokens`, and a
+`confidence` score from `0` to `1`.
+
+## Built-Ins
+
+| Name | Dimensions | Tunable params |
+|---|---|---|
+| `chunk-summarization` | `chunk_words`, `template_words` | `completion_ratio` |
+| `entity-extraction` | `chunk_words`, `template_words`, `expected_entities` | `tokens_per_entity` |
+| `relation-extraction` | `chunk_words`, `template_words`, `expected_relations` | `tokens_per_relation` |
+| `judge-eval` | `artifact_words`, `template_words`, `n_criteria` | `tokens_per_criterion` |
+| `report-synthesis` | `n_chunks`, `n_entities`, `n_relations`, `template_words` | `base_completion_tokens` |
+
+## Observations
+
+`fit()` accepts either `Observation` objects or `QualityObservation` rows whose
+`tags` include:
+
+```python
+{
+    "problem_class": "entity-extraction",
+    "dimensions": {
+        "chunk_words": 900,
+        "template_words": 200,
+        "expected_entities": 4,
+    },
+}
+```
+
+When fewer than `min_observations` usable rows are present, fitting falls back
+to the current parameters.

contracts/functional/quality-ledger.md

@@ -0,0 +1,87 @@
+# Contract: QualityObservation and QualityLedger
+
+**layer:** Functional
+**maturity:** Beta
+**module:** `llm_connect.quality`
+**since:** WP-0004
+
+## Purpose
+
+Record observed quality, cost, latency, and token outcomes for a logical task
+type so consumers can build adaptive routing policy without putting
+consumer-specific thresholds into llm-connect.
+
+## Public surface
+
+```python
+@dataclass(frozen=True)
+class QualityObservation:
+    task_type: str
+    adapter_id: str
+    model_id: str
+    cost_usd: float
+    quality_score: float
+    latency_ms: float
+    tokens_in: int
+    tokens_out: int
+    baseline_adapter_id: str | None = None
+    recorded_at: datetime = field(default_factory=...)
+    tags: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def total_tokens(self) -> int: ...
+    def to_dict(self) -> dict[str, Any]: ...
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "QualityObservation": ...
+
+class QualityLedger:
+    def __init__(self, path: str | Path): ...
+    @property
+    def path(self) -> Path: ...
+    def append(self, observation: QualityObservation) -> None: ...
+    def read_all(self) -> list[QualityObservation]: ...
+    def malformed_count(self) -> int: ...
+    def by_task_type(self, task_type: str) -> list[QualityObservation]: ...
+    def recent(...) -> list[QualityObservation]: ...
+    def mean_quality(...) -> float | None: ...
+    def prune_before(self, timestamp: datetime) -> int: ...
+
+def is_stale(observation: QualityObservation, max_age: timedelta, *, now: datetime | None = None) -> bool: ...
+```
+
+## Invariants
+
+1. `quality_score` is a normalised `0.0..1.0` score where `1.0` means the
+   candidate fully meets the grader's quality bar and `0.0` means complete
+   failure for that grader.
+2. `task_type`, `adapter_id`, and `model_id` must be non-empty strings.
+3. `cost_usd`, `latency_ms`, `tokens_in`, and `tokens_out` are non-negative.
+4. `recorded_at` is normalised to UTC. Naive datetimes are interpreted as UTC.
+5. Ledger records are JSON Lines. Each line is one `QualityObservation.to_dict()`.
+6. `QualityLedger.append()` performs a process-local lock plus an advisory file
+   lock around each write.
+7. Read/query helpers skip malformed lines instead of failing the whole ledger.
+   `malformed_count()` exposes how many lines were skipped.
+8. `prune_before()` removes only valid observations older than the cutoff.
+   Malformed lines are preserved.
+
+## Error contract
+
+| Condition | Exception |
+|-----------|-----------|
+| Invalid observation field | `ValueError` |
+| Invalid datetime field | `TypeError` or `ValueError` |
+| Negative recent limit | `ValueError` |
+| `mean_quality(min_observations <= 0)` | `ValueError` |
+| `is_stale(max_age < 0)` | `ValueError` |
+
+## Known consumers
+
+- `infospace-bench` is the first intended consumer. It is expected to provide
+  task taxonomy, thresholds, and baseline choice.
+
+## Notes
+
+The ledger intentionally stores only observation metadata in this slice. Callers
+that need prompt or response digests can place those in `tags`, for example
+`prompt_fingerprint`.

contracts/functional/rates.md

@@ -0,0 +1,30 @@
+# Model Rate Registry
+
+`llm_connect.rates` owns static model list prices used for planning and
+post-hoc estimates.
+
+## Contract
+
+- `ModelRate` records `model_id`, prompt and completion rates in USD per
+  1,000 tokens, `currency`, `source_url`, and `captured_at`.
+- `ModelRateRegistry.default()` returns the bundled OpenRouter snapshot
+  captured on `2026-05-17`.
+- `ModelRateRegistry.from_yaml(path)` accepts the package/consumer override
+  shape:
+
+```yaml
+schema_version: 1
+currency: USD
+source_url: https://openrouter.ai/models
+captured_at: "2026-05-17"
+rates:
+  openai/gpt-4o-mini:
+    prompt_per_1k: 0.00015
+    completion_per_1k: 0.00060
+```
+
+- `merged_with(override)` returns a new registry where matching override
+  entries replace default entries by `model_id`.
+
+Rates are a static snapshot. Consumers decide whether `captured_at` is fresh
+enough for their workflow.

contracts/functional/routing-policy.md

@@ -0,0 +1,53 @@
+# Contract: RoutingPolicy
+
+**layer:** Functional  
+**maturity:** Beta  
+**module:** `llm_connect.routing`  
+**since:** WP-0003
+
+## Purpose
+
+Route logical task types to concrete `LLMAdapter` instances based on a
+prioritised rule list, with optional per-rule cost-cap fallback.
+
+## Public surface
+
+```python
+@dataclass
+class RoutingRule:
+    task_type: str
+    prefer: LLMAdapter
+    max_cost_per_1k: Optional[float] = None   # USD per 1 000 tokens
+    fallback: Optional[LLMAdapter] = None
+
+@dataclass
+class RoutingPolicy:
+    rules: List[RoutingRule] = field(default_factory=list)
+    default: Optional[LLMAdapter] = None
+
+    def resolve(
+        self,
+        task_type: str,
+        estimated_cost_per_1k: Optional[float] = None,
+    ) -> LLMAdapter: ...
+```
+
+## Invariants
+
+1. Rules are evaluated in list order; the first rule whose `task_type` matches wins.
+2. When `estimated_cost_per_1k` is supplied and a matching rule has `max_cost_per_1k` set:
+   - If `estimated_cost_per_1k > max_cost_per_1k` **and** `fallback is not None` → return `fallback`.
+   - Otherwise → return `prefer` (no fallback configured or cost within cap).
+3. When no rule matches and `default is not None` → return `default`.
+4. When no rule matches and `default is None` → raise `LookupError`.
+5. `resolve()` never mutates policy state.
+
+## Error contract
+
+| Condition | Exception |
+|-----------|-----------|
+| No matching rule, no default | `LookupError` |
+
+## Known consumers
+
+- `inter-hub` (IHUB-WP-0012 Phase 11): uses `RoutingPolicy` to select federation adapters per task class.

contracts/functional/server.md

@@ -0,0 +1,131 @@
+# Contract: HTTP Serve Mode
+
+**layer:** Functional  
+**maturity:** Beta  
+**module:** `llm_connect.server`  
+**since:** WP-0003
+
+## Purpose
+
+Expose any `LLMAdapter` as a lightweight HTTP service.  Intended for
+local/inter-process use; not hardened for public internet exposure.
+
+## API endpoints
+
+### `GET /health`
+
+Liveness probe.
+
+**Response 200**
+
+```json
+{"status": "ok"}
+```
+
+---
+
+### `POST /execute`
+
+Execute a prompt through the configured adapter.
+
+**Request body** (JSON)
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `prompt` | string | yes | Prompt text |
+| `config` | object | no | `RunConfig` overrides (see below) |
+
+`config` sub-fields (all optional, defaults match `RunConfig` defaults):
+
+| Field | Type | Default |
+|-------|------|---------|
+| `model_name` | string | `"gpt-4"` |
+| `temperature` | float | `0.7` |
+| `max_tokens` | int | `2000` |
+| `timeout_seconds` | int | `300` |
+
+**Response 200** — `LLMResponse.to_dict()` shape
+
+```json
+{
+  "content": "...",
+  "model": "...",
+  "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+  "finish_reason": "stop",
+  "metadata": {}
+}
+```
+
+**Error responses**
+
+| HTTP | Condition |
+|------|-----------|
+| 400 | Missing `prompt` field or invalid JSON body |
+| 404 | Unknown path |
+| 429 | Provider rate limit |
+| 500 | Configuration or adapter failure |
+| 502 | Provider API / transport failure |
+| 504 | Provider timeout |
+
+Server error bodies are structured and must not expose provider credentials:
+
+```json
+{
+  "error": "provider_api_error",
+  "message": "HTTP 500 from https://provider.example/v1?key=<redacted>",
+  "type": "LLMAPIError",
+  "provider_status": 500
+}
+```
+
+Known error codes include `unknown_profile`, `configuration_error`,
+`provider_api_error`, `provider_rate_limited`, `provider_timeout`,
+`budget_exceeded`, `llm_error`, and `internal_error`.
+
+## Runtime profiles
+
+Server CLI mode wraps the configured adapter with runtime profile dispatch
+unless `--disable-profiles` is passed. The activity-core profile
+`custodian-triage-balanced` is built in and resolves to the configured provider
+and model before calling the underlying adapter.
+
+Default profile values:
+
+| Field | Default |
+|-------|---------|
+| provider | `openrouter` |
+| model | `anthropic/claude-sonnet-4` |
+| temperature | `0.2` |
+| max_tokens | `1800` |
+| max_depth | `2` |
+| timeout_seconds | `300` |
+| model_params.reasoning_effort | `medium` |
+
+Profile provider/model and default call values can be overridden with
+environment variables such as `LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER`,
+`LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL`, and
+`LLM_CONNECT_CUSTODIAN_TRIAGE_MAX_TOKENS`. Operators can also set
+`LLM_CONNECT_PROFILES_JSON` or `LLM_CONNECT_PROFILE_FILE` to provide JSON
+profile definitions keyed by profile name.
+
+## Implementation notes
+
+- Uses Python stdlib `http.server` — **no additional runtime dependency**.
+- The `[server]` optional-dependency group is reserved for future migration
+  to `aiohttp`/`starlette` if native async serving is required.
+- `LLMServer(adapter, port=0)` binds to an OS-assigned free port; read back
+  via `server.port` after `start()`.
+
+## CLI
+
+```
+python -m llm_connect.server [--host HOST] [--port PORT] [--provider PROVIDER] [--model MODEL] [--disable-profiles] [--strict-profiles]
+```
+
+CLI defaults can also be supplied with `LLM_CONNECT_HOST`, `LLM_CONNECT_PORT`,
+`LLM_CONNECT_PROVIDER`, and `LLM_CONNECT_MODEL`. Default provider: `mock`. All
+registered providers from `create_adapter` are valid.
+
+## Known consumers
+
+- `inter-hub` (IHUB-WP-0012 Phase 11): drives federation calls over HTTP from non-Python services.

contracts/functional/shadowing-adapter.md

@@ -0,0 +1,84 @@
+# Contract: ShadowingAdapter
+
+**layer:** Functional
+**maturity:** Beta
+**module:** `llm_connect.shadowing`
+**since:** WP-0004
+
+## Purpose
+
+Collect quality observations without changing caller-visible model behavior.
+`ShadowingAdapter` wraps a candidate adapter, returns the candidate response to
+the caller, and samples extra baseline/grading work that appends
+`QualityObservation` records to a `QualityLedger`.
+
+## Public surface
+
+```python
+@dataclass
+class ShadowingAdapter(LLMAdapter):
+    candidate_adapter: LLMAdapter
+    baseline_adapter: LLMAdapter
+    grader: BaselineGrader
+    ledger: QualityLedger
+    task_type: str
+    adapter_id: str
+    model_id: Optional[str] = None
+    baseline_adapter_id: Optional[str] = None
+    shadow_rate: float = 1.0
+    async_shadow: bool = False
+    tags: Mapping[str, Any] = field(default_factory=dict)
+    on_shadow_error: Optional[Callable[[Exception], None]] = None
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse: ...
+    async def async_execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse: ...
+    def flush(self, timeout: Optional[float] = None) -> None: ...
+    def shutdown(self, wait: bool = True) -> None: ...
+```
+
+## Invariants
+
+1. The candidate adapter is always called first.
+2. The response returned by `execute_prompt()` and `async_execute_prompt()` is
+   always the candidate response.
+3. Shadow failures from the baseline adapter, grader, or ledger writer are
+   isolated from the caller. They are sent to `on_shadow_error` when configured.
+4. `shadow_rate=0.0` records no observations. `shadow_rate=1.0` shadows every
+   successful candidate call. Intermediate values sample with `random_source`.
+5. Shadow grading reuses the candidate response already returned by the wrapped
+   candidate adapter; it does not make a second candidate model call.
+6. Shadow calls use a copy of `RunConfig` with `budget_tracker=None`, so
+   observation collection cannot consume the caller's foreground token budget.
+7. `async_shadow=True` schedules shadow work on a background thread. `flush()`
+   waits for currently queued work, and `shutdown()` releases the executor.
+
+## Observation mapping
+
+The appended observation uses:
+
+- `task_type` from the wrapper configuration
+- `adapter_id` from the wrapper configuration
+- `model_id` from the wrapper configuration, then candidate response model, then
+  `RunConfig.model_name`
+- `quality_score` from the `GradingResult`
+- `cost_usd` from response metadata keys `cost_usd`, `estimated_cost_usd`, or
+  `cost`, falling back to `0.0`
+- token counts from candidate response usage keys `prompt_tokens` and
+  `completion_tokens`
+- `baseline_adapter_id` and `tags` from wrapper configuration
+
+## Error contract
+
+| Condition | Exception |
+|-----------|-----------|
+| Empty `task_type` | `ValueError` |
+| Empty `adapter_id` | `ValueError` |
+| `shadow_rate` outside `0..1` | `ValueError` |
+| Candidate adapter failure | Original exception propagates |
+| Shadow baseline/grading/ledger failure | Suppressed; optional callback |
+
+## Privacy note
+
+The wrapper does not store prompt or response text in the ledger by default.
+Callers that need regime tracking should store non-sensitive fingerprints in
+`tags`, for example `prompt_fingerprint` or `template_version`.

deploy/k8s/activity-core-llm-connect/README.md

@@ -0,0 +1,54 @@
+# activity-core llm-connect Service
+
+This overlay deploys `llm-connect` as an internal `activity-core` namespace
+service for daily WSJF triage.
+
+Stable in-cluster URL after apply:
+
+```text
+http://llm-connect.activity-core.svc.cluster.local:8080
+```
+
+Create provider credentials outside Git before applying the Deployment. For the
+default OpenRouter config:
+
+```bash
+kubectl -n activity-core create secret generic llm-connect-provider-secrets \
+  --from-literal=OPENROUTER_API_KEY="$OPENROUTER_API_KEY"
+```
+
+Provider API key custody belongs to the operator/OpenBao-to-Kubernetes Secret
+path. ops-warden documents this as outside its issuance scope; do not paste key
+values into Git, State Hub, logs, or chat.
+
+Apply:
+
+```bash
+docker build -f Containerfile -t docker.io/library/llm-connect:latest .
+docker save docker.io/library/llm-connect:latest | ssh coulombcore sudo k3s ctr -n k8s.io images import -
+kubectl apply -k deploy/k8s/activity-core-llm-connect
+kubectl -n activity-core rollout status deployment/llm-connect
+```
+
+Smoke from inside the namespace, using an image that includes this repo's
+fixtures and `scripts/smoke_activity_core_endpoint.py`:
+
+```bash
+kubectl -n activity-core run llm-connect-smoke \
+  --rm -i --restart=Never \
+  --image=llm-connect:latest \
+  --image-pull-policy=Never \
+  --env=LLM_CONNECT_URL=http://llm-connect.activity-core.svc.cluster.local:8080 \
+  --env=LLM_CONNECT_TIMEOUT_SECONDS=300 \
+  -- python scripts/smoke_activity_core_endpoint.py
+```
+
+Then set activity-core's runtime config:
+
+```text
+LLM_CONNECT_URL=http://llm-connect.activity-core.svc.cluster.local:8080
+LLM_CONNECT_TIMEOUT_SECONDS=300
+```
+
+Do not commit provider keys, live prompt payloads, or smoke response bodies that
+contain operational State Hub data.

deploy/k8s/activity-core-llm-connect/configmap.yaml

@@ -0,0 +1,21 @@
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: llm-connect-config
+  namespace: activity-core
+  labels:
+    app.kubernetes.io/name: llm-connect
+    app.kubernetes.io/part-of: activity-core
+data:
+  LLM_CONNECT_HOST: "0.0.0.0"
+  LLM_CONNECT_PORT: "8080"
+  LLM_CONNECT_PROVIDER: "openrouter"
+  LLM_CONNECT_MODEL: "google/gemini-2.5-flash"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER: "openrouter"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL: "google/gemini-2.5-flash"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_TEMPERATURE: "0.2"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_MAX_TOKENS: "1800"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_MAX_DEPTH: "2"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_TIMEOUT_SECONDS: "300"
+  LLM_CONNECT_CUSTODIAN_TRIAGE_REASONING_EFFORT: "medium"
+  LLM_CONNECT_STRICT_PROFILES: "false"

deploy/k8s/activity-core-llm-connect/deployment.yaml

@@ -0,0 +1,64 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: llm-connect
+  namespace: activity-core
+  labels:
+    app.kubernetes.io/name: llm-connect
+    app.kubernetes.io/part-of: activity-core
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app.kubernetes.io/name: llm-connect
+  template:
+    metadata:
+      labels:
+        app.kubernetes.io/name: llm-connect
+        app.kubernetes.io/part-of: activity-core
+    spec:
+      containers:
+        - name: llm-connect
+          image: docker.io/library/llm-connect:latest
+          imagePullPolicy: Never
+          envFrom:
+            - configMapRef:
+                name: llm-connect-config
+            - secretRef:
+                name: llm-connect-provider-secrets
+                optional: false
+          ports:
+            - name: http
+              containerPort: 8080
+          readinessProbe:
+            httpGet:
+              path: /health
+              port: http
+            periodSeconds: 10
+            timeoutSeconds: 3
+            failureThreshold: 3
+          livenessProbe:
+            httpGet:
+              path: /health
+              port: http
+            periodSeconds: 30
+            timeoutSeconds: 3
+            failureThreshold: 3
+          resources:
+            requests:
+              cpu: 50m
+              memory: 128Mi
+            limits:
+              cpu: 500m
+              memory: 512Mi
+          securityContext:
+            allowPrivilegeEscalation: false
+            capabilities:
+              drop:
+                - ALL
+            readOnlyRootFilesystem: true
+            runAsNonRoot: true
+            runAsUser: 10001
+            runAsGroup: 10001
+      securityContext:
+        fsGroup: 10001

deploy/k8s/activity-core-llm-connect/externalsecret.yaml

@@ -0,0 +1,21 @@
+apiVersion: external-secrets.io/v1
+kind: ExternalSecret
+metadata:
+  name: llm-connect-provider-secrets
+  namespace: activity-core
+  labels:
+    app.kubernetes.io/name: llm-connect
+    app.kubernetes.io/part-of: railiance-gitops
+spec:
+  refreshInterval: 1h
+  secretStoreRef:
+    kind: ClusterSecretStore
+    name: openbao-activity-core
+  target:
+    name: llm-connect-provider-secrets
+    creationPolicy: Owner
+  data:
+    - secretKey: OPENROUTER_API_KEY
+      remoteRef:
+        key: platform/workloads/activity-core/llm-connect/llm-connect-provider-secrets
+        property: OPENROUTER_API_KEY

deploy/k8s/activity-core-llm-connect/kustomization.yaml

@@ -0,0 +1,8 @@
+apiVersion: kustomize.config.k8s.io/v1beta1
+kind: Kustomization
+resources:
+  - configmap.yaml
+  - deployment.yaml
+  - service.yaml
+  - networkpolicy.yaml
+  - externalsecret.yaml

deploy/k8s/activity-core-llm-connect/networkpolicy.yaml

@@ -0,0 +1,39 @@
+apiVersion: networking.k8s.io/v1
+kind: NetworkPolicy
+metadata:
+  name: llm-connect-activity-core-only
+  namespace: activity-core
+  labels:
+    app.kubernetes.io/name: llm-connect
+    app.kubernetes.io/part-of: activity-core
+spec:
+  podSelector:
+    matchLabels:
+      app.kubernetes.io/name: llm-connect
+  policyTypes:
+    - Ingress
+    - Egress
+  ingress:
+    - from:
+        - namespaceSelector:
+            matchLabels:
+              kubernetes.io/metadata.name: activity-core
+      ports:
+        - protocol: TCP
+          port: 8080
+  egress:
+    - to:
+        - namespaceSelector:
+            matchLabels:
+              kubernetes.io/metadata.name: kube-system
+      ports:
+        - protocol: UDP
+          port: 53
+        - protocol: TCP
+          port: 53
+    - to:
+        - ipBlock:
+            cidr: 0.0.0.0/0
+      ports:
+        - protocol: TCP
+          port: 443

deploy/k8s/activity-core-llm-connect/service.yaml

@@ -0,0 +1,16 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: llm-connect
+  namespace: activity-core
+  labels:
+    app.kubernetes.io/name: llm-connect
+    app.kubernetes.io/part-of: activity-core
+spec:
+  type: ClusterIP
+  selector:
+    app.kubernetes.io/name: llm-connect
+  ports:
+    - name: http
+      port: 8080
+      targetPort: http

docs/activity-core-llm-endpoint.md

@@ -0,0 +1,128 @@
+# Activity-Core LLM Endpoint Handoff
+
+This document records the `llm-connect` endpoint contract for activity-core
+daily WSJF triage.
+
+## Service URL
+
+Proposed stable in-cluster URL:
+
+```text
+http://llm-connect.activity-core.svc.cluster.local:8080
+```
+
+Use this value for activity-core `LLM_CONNECT_URL` after the Kubernetes overlay
+has been applied and smoked from the `activity-core` namespace. Keep
+`LLM_CONNECT_TIMEOUT_SECONDS=300`.
+
+## Runtime Profile
+
+The service supports the activity-core profile name:
+
+```text
+custodian-triage-balanced
+```
+
+Default runtime values:
+
+```text
+provider=openrouter
+model=google/gemini-2.5-flash
+temperature=0.2
+max_tokens=1800
+max_depth=2
+timeout_seconds=300
+model_params.reasoning_effort=medium
+```
+
+Operators can override provider/model through the Deployment ConfigMap or
+runtime env:
+
+```text
+LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER
+LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL
+```
+
+Provider credentials must be injected at runtime through
+`llm-connect-provider-secrets`; do not store credential values in Git or State
+Hub.
+
+Credential custody follows the ops-warden routing table: LLM provider API keys
+are an operator/OpenBao-to-Kubernetes Secret action, not an ops-warden issuance
+task. For the default OpenRouter profile, the Secret must provide
+`OPENROUTER_API_KEY` without exposing the value in Git, State Hub, logs, or
+chat.
+
+## Local Smoke
+
+Run a mock server that returns known schema-valid daily triage JSON:
+
+```bash
+export LLM_CONNECT_MOCK_RESPONSE="$(python -c 'import json; print(json.dumps(json.load(open("fixtures/activity_core/daily-triage-valid-content.json"))))')"
+python -m llm_connect.server --host 127.0.0.1 --port 8080 --provider mock
+```
+
+In another shell:
+
+```bash
+python scripts/smoke_activity_core_endpoint.py --url http://127.0.0.1:8080
+```
+
+The smoke script checks:
+
+- `GET /health`
+- fixture `POST /execute`
+- response has a string `content` field
+- `content` parses as JSON
+- parsed JSON matches `fixtures/activity_core/daily-triage-report.schema.json`
+
+## Cluster Smoke
+
+Apply the overlay from the repo root after creating the provider Secret:
+
+```bash
+kubectl apply -k deploy/k8s/activity-core-llm-connect
+kubectl -n activity-core rollout status deployment/llm-connect
+```
+
+Run the in-namespace smoke:
+
+```bash
+kubectl -n activity-core run llm-connect-smoke \
+  --rm -i --restart=Never \
+  --image=llm-connect:latest \
+  --image-pull-policy=Never \
+  --env=LLM_CONNECT_URL=http://llm-connect.activity-core.svc.cluster.local:8080 \
+  --env=LLM_CONNECT_TIMEOUT_SECONDS=300 \
+  -- python scripts/smoke_activity_core_endpoint.py
+```
+
+## Handoff Status
+
+Code-owned artifacts are present in this repo and the live llm-connect
+handoff is verified as of 2026-06-18:
+
+- `docker.io/library/llm-connect:latest` was rebuilt from `Containerfile`,
+  imported into the `coulombcore` k3s image store, and rolled out.
+- `activity-core/llm-connect-provider-secrets` reports `DATA 1`; no Secret
+  values were inspected or recorded.
+- The live ConfigMap sets `LLM_CONNECT_MODEL=google/gemini-2.5-flash` and
+  `LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash`.
+- The in-namespace smoke passed against the stable Service:
+  `smoke: pass health=ok latency_seconds=2.147 recommendations=1`.
+
+2026-06-19 railiance01 recheck (activity-core production cluster):
+
+- Deployed the `deploy/k8s/activity-core-llm-connect` overlay into the
+  `activity-core` namespace on `railiance01`, where the activity-core worker
+  runs. `coulombcore` retains a separate llm-connect instance for earlier
+  verification; consumers must call the Service in their own cluster.
+- `activity-core/llm-connect-provider-secrets` reports `DATA 1`; no Secret
+  values were inspected or recorded.
+- Restarted `deployment/actcore-worker` so pods consume
+  `LLM_CONNECT_URL=http://llm-connect.activity-core.svc.cluster.local:8080`.
+- In-namespace fixture smoke on `railiance01` passed:
+  `smoke: pass health=ok latency_seconds=1.681 recommendations=1`.
+
+Scheduled `daily_triage` evidence collection is activity-core ownership under
+`ACTIVITY-WP-0010`.

docs/adapter-model-params.md

@@ -0,0 +1,102 @@
+# Adapter `model_params` contract
+
+`RunConfig.model_params` is a portability layer, not a blind provider payload
+escape hatch. Adapters must translate the shared keys they understand, pass
+through only provider-valid keys, and drop provider-specific keys that would
+make another provider reject the request.
+
+## Shared structured output
+
+Callers may request structured output with:
+
+```python
+RunConfig(
+    model_params={
+        "json_schema": {
+            "type": "object",
+            "properties": {
+                "summary": {"type": "string"},
+                "recommendations": {"type": "array", "items": {"type": "string"}},
+            },
+            "required": ["summary", "recommendations"],
+        }
+    }
+)
+```
+
+Adapters translate that key into the provider's native shape:
+
+| Adapter | Translation |
+|---|---|
+| OpenAI | `response_format = {"type": "json_schema", "json_schema": ...}` |
+| OpenRouter | Same OpenAI-compatible `response_format` wrapper |
+| Gemini | `generationConfig.responseMimeType = "application/json"` and `generationConfig.responseSchema = ...` |
+| Claude Code CLI | `--json-schema <schema>` plus `--output-format json`, then envelope unwrap |
+
+OpenAI-compatible adapters default `json_schema.strict` to `False`. Strict mode
+requires schemas to meet provider-specific constraints such as
+`additionalProperties: false` on object nodes and complete `required` lists.
+Callers that need strict behavior can pass an explicit provider-native
+`response_format` in `model_params`.
+
+## Pass-through keys
+
+OpenAI and OpenRouter pass through known Chat Completions fields:
+
+`top_p`, `n`, `stream`, `stop`, `presence_penalty`, `frequency_penalty`,
+`logit_bias`, `user`, `seed`, `tools`, `tool_choice`, `response_format`,
+`logprobs`, `top_logprobs`, and `parallel_tool_calls`.
+
+Gemini passes through valid `generateContent` top-level fields:
+
+`safetySettings`, `tools`, `toolConfig`, `systemInstruction`, and
+`cachedContent`.
+
+Gemini also accepts generation config fields directly or via snake-case aliases:
+
+`candidateCount`, `candidate_count`, `stopSequences`, `stop_sequences`,
+`maxOutputTokens`, `max_output_tokens`, `temperature`, `topP`, `top_p`, `topK`,
+`top_k`, `responseMimeType`, `response_mime_type`, `responseSchema`, and
+`response_schema`.
+
+## Dropped keys
+
+Adapters must drop keys that are meaningful to another adapter or to
+llm-connect itself but invalid for the target provider. The current shared drop
+set includes:
+
+`reasoning_effort`, `max_depth`, `claude_cli_path`, and raw `json_schema` after
+translation.
+
+Unknown keys are ignored by default. This keeps activity-specific configs from
+causing provider HTTP 400 errors when a caller switches providers.
+
+## Diagnostics and replay
+
+Server mode supports opt-in diagnostics for `/execute`:
+
+```bash
+LLM_CONNECT_DEBUG=1 python -m llm_connect.server --provider openrouter
+curl 'http://127.0.0.1:8080/execute?debug=1' -d '{"prompt":"hi"}'
+```
+
+Debug responses include a `debug` field with the redacted provider request, raw
+provider response body, and adapter transformations such as `merge_model_params`
+or `unwrap_cli_envelope`. Normal responses omit `debug`.
+
+Set `LLM_CONNECT_AUDIT_DIR=/path/to/audit` to write one JSON audit record per
+`/execute` call. Audit records include the prompt, config, redacted provider
+request, provider response, parsed content, and latency. Re-run parsing without
+another provider call with:
+
+```bash
+python -m llm_connect.replay /path/to/audit/record.json --json
+```
+
+## Server concurrency
+
+`llm_connect.server.LLMServer` uses `ThreadingHTTPServer`. Adapter instances
+used in server mode must be safe to call concurrently. The bundled HTTP and
+subprocess adapters keep per-call state local; custom adapters should avoid
+mutating shared instance attributes during `execute_prompt` unless they use
+their own locks.

docs/infospace-bench-adaptive-routing.md

@@ -0,0 +1,83 @@
+# Infospace-Bench Adaptive Routing Guide
+
+This guide shows how a consumer such as `infospace-bench` can wire task-type
+stages into the adaptive cost-quality primitives from `llm-connect`.
+
+## Stage taxonomy
+
+The consumer owns task names and quality thresholds. A first pass for
+`infospace-bench` could use:
+
+| Stage | Task type | Suggested floor |
+|-------|-----------|-----------------|
+| Source chapter summary | `summarize-source` | `0.82` |
+| Entity extraction | `extract-entities` | `0.88` |
+| Relation extraction | `extract-relations` | `0.86` |
+| Entity evaluation | `evaluate-entity` | `0.90` |
+| Report synthesis | `synthesize-report` | `0.92` |
+
+These floors are starting points, not library defaults. Raise them for stages
+whose errors compound downstream.
+
+## Wiring sketch
+
+```python
+from llm_connect.grading import ExactMatchJudge, PairedGrader
+from llm_connect.quality import QualityLedger
+from llm_connect.routing import AdaptiveRoutingPolicy, RoutingRule
+from llm_connect.shadowing import ShadowingAdapter
+
+ledger = QualityLedger("quality-ledger.jsonl")
+grader = PairedGrader(ExactMatchJudge())
+
+baseline = claude_code_adapter
+cheap = openrouter_cheap_adapter
+mid = openrouter_mid_adapter
+
+shadowed_cheap = ShadowingAdapter(
+    candidate_adapter=cheap,
+    baseline_adapter=baseline,
+    grader=grader,
+    ledger=ledger,
+    task_type="extract-relations",
+    adapter_id="openrouter-cheap",
+    baseline_adapter_id="claude-code",
+    shadow_rate=0.1,
+    tags={"prompt_fingerprint": prompt_fingerprint},
+)
+
+policy = AdaptiveRoutingPolicy(
+    rules=[
+        RoutingRule("extract-relations", prefer=baseline, fallback=mid),
+    ],
+    ledger=ledger,
+    adapters_by_id={
+        "openrouter-cheap": shadowed_cheap,
+        "openrouter-mid": mid,
+        "claude-code": baseline,
+    },
+    window_size=20,
+    min_observations=3,
+)
+
+adapter = policy.resolve("extract-relations", quality_floor=0.86)
+response = adapter.execute_prompt(prompt, run_config)
+```
+
+## Operating loop
+
+1. Start with static routing to the trusted baseline or mid-tier adapter.
+2. Wrap cheaper candidates with `ShadowingAdapter` at a conservative
+   `shadow_rate`, for example `0.05` to `0.1`.
+3. Record a prompt fingerprint or template version in `tags` so later prompt
+   changes do not mix incompatible observations.
+4. Increase `min_observations` for stages with high variance.
+5. Let `AdaptiveRoutingPolicy` select the cheapest adapter that clears each
+   stage floor.
+
+## Refresh rules
+
+When a provider model, prompt template, or parser contract changes, treat prior
+observations as a different regime. Either write to a new ledger, prune old
+observations, or filter with a new `prompt_fingerprint` tag before trusting
+adaptive selection again.

docs/infospace-bench-cost-model-migration.md

@@ -0,0 +1,100 @@
+# infospace-bench Cost Estimator Migration
+
+`infospace-bench` can replace its local rate table and coarse word-count
+budget math with the primitives added in `LLM-WP-0005`.
+
+## Rate Table
+
+- Drop `src/infospace_bench/model_rates.yaml` after the dependency is bumped.
+- Load `ModelRateRegistry.default()` from `llm-connect`.
+- Keep the workspace-level `model-rates.yaml` override and merge it with
+  `default().merged_with(ModelRateRegistry.from_yaml(path))`.
+- Preserve `--cost-per-1k` as an explicit blended-rate override. When supplied,
+  it should win over the registry and report `cost_source="cost_per_1k_blended"`.
+
+## Plan Summary Sketch
+
+```python
+from llm_connect import (
+    CostEstimate,
+    ModelRateRegistry,
+    ProblemClassRegistry,
+    estimate_cost,
+)
+
+
+def plan_generation_summary(...):
+    problem_classes = ProblemClassRegistry.default()
+    rates = ModelRateRegistry.default()
+    workspace_rates = _workspace_rate_path(root_path)
+    if workspace_rates.exists():
+        rates = rates.merged_with(ModelRateRegistry.from_yaml(workspace_rates))
+
+    total_prompt_tokens = 0
+    total_completion_tokens = 0
+    per_stage = []
+    for workflow_id in workflow_ids:
+        class_name, dimensions = _problem_class_for_workflow(
+            workflow_id,
+            selected_chunks=selected,
+            template_words=template_words,
+            entities_per_chunk=entities_per_chunk,
+        )
+        estimate = problem_classes.get(class_name).estimate(dimensions)
+        calls = _calls_for_workflow(workflow_id, selected, entities_per_chunk)
+        prompt_tokens = estimate.prompt_tokens * calls
+        completion_tokens = estimate.completion_tokens * calls
+        total_prompt_tokens += prompt_tokens
+        total_completion_tokens += completion_tokens
+        per_stage.append(
+            {
+                "workflow_id": workflow_id,
+                "problem_class": class_name,
+                "calls": calls,
+                "prompt_tokens_estimate": prompt_tokens,
+                "completion_tokens_estimate": completion_tokens,
+                "confidence": estimate.confidence,
+            }
+        )
+
+    if cost_per_1k_tokens > 0:
+        total_tokens = total_prompt_tokens + total_completion_tokens
+        cost = (total_tokens / 1000.0) * cost_per_1k_tokens
+        cost_source = "cost_per_1k_blended"
+    elif model:
+        cost_estimate = estimate_cost(
+            model,
+            total_prompt_tokens,
+            total_completion_tokens,
+            registry=rates,
+        )
+        cost = cost_estimate.cost_usd
+        cost_source = cost_estimate.cost_source
+    else:
+        cost = None
+        cost_source = None
+
+    return {
+        "per_workflow": per_stage,
+        "total_prompt_tokens_estimate": total_prompt_tokens,
+        "estimated_completion_tokens": total_completion_tokens,
+        "estimated_cost_usd": round(cost, 6) if cost is not None else None,
+        "cost_source": cost_source,
+        ...
+    }
+```
+
+## Workflow Mapping
+
+Initial mapping can stay intentionally thin:
+
+| infospace-bench workflow | llm-connect problem class |
+|---|---|
+| `summarize-source` | `chunk-summarization` |
+| entity extraction workflows | `entity-extraction` |
+| relation extraction workflows | `relation-extraction` |
+| `generic-source-evaluations` | `judge-eval` |
+| final report or rollup synthesis | `report-synthesis` |
+
+The consumer still owns structure-specific dimensions such as selected chunk
+counts, profile template word counts, and expected entities per chunk.

examples/adaptive_routing_fixture_batch.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""Populate a quality ledger from a small adaptive-routing fixture batch."""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.grading import ExactMatchJudge, PairedGrader
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.quality import QualityLedger
+from llm_connect.routing import AdaptiveRoutingPolicy, RoutingRule
+from llm_connect.shadowing import ShadowingAdapter
+
+
+@dataclass
+class FixtureAdapter(LLMAdapter):
+    adapter_id: str
+    response_text: str
+    cost_usd: float
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        prompt_tokens = len(prompt.split())
+        completion_tokens = len(self.response_text.split())
+        return LLMResponse(
+            content=self.response_text,
+            model=self.adapter_id,
+            usage={
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": completion_tokens,
+                "total_tokens": prompt_tokens + completion_tokens,
+            },
+            metadata={"cost_usd": self.cost_usd, "latency_ms": 25.0},
+        )
+
+    def validate_config(self, config: RunConfig) -> bool:
+        return True
+
+
+def build_candidates() -> dict[str, FixtureAdapter]:
+    return {
+        "openrouter-cheap-fixture": FixtureAdapter(
+            "openrouter-cheap-fixture",
+            "summary",
+            0.001,
+        ),
+        "openrouter-mid-fixture": FixtureAdapter(
+            "openrouter-mid-fixture",
+            "summary with entities and relations",
+            0.004,
+        ),
+        "openrouter-premium-fixture": FixtureAdapter(
+            "openrouter-premium-fixture",
+            "summary with entities and relations",
+            0.012,
+        ),
+        "claude-code-baseline-fixture": FixtureAdapter(
+            "claude-code-baseline-fixture",
+            "summary with entities and relations",
+            0.0,
+        ),
+    }
+
+
+def populate_ledger(ledger: QualityLedger) -> dict[str, FixtureAdapter]:
+    candidates = build_candidates()
+    baseline = candidates["claude-code-baseline-fixture"]
+    grader = PairedGrader(ExactMatchJudge())
+    prompts = [
+        "Summarize chapter one and keep entity names.",
+        "Extract relations from chapter two.",
+        "Evaluate whether the entity graph is coherent.",
+    ]
+    config = RunConfig(model_name="fixture")
+
+    for task_type, prompt in zip(
+        ["summarize-source", "extract-relations", "evaluate-entity"],
+        prompts,
+    ):
+        for adapter_id, candidate in candidates.items():
+            if candidate is baseline:
+                continue
+            ShadowingAdapter(
+                candidate_adapter=candidate,
+                baseline_adapter=baseline,
+                grader=grader,
+                ledger=ledger,
+                task_type=task_type,
+                adapter_id=adapter_id,
+                baseline_adapter_id=baseline.adapter_id,
+                shadow_rate=1.0,
+                tags={"fixture": "adaptive-routing"},
+            ).execute_prompt(prompt, config)
+
+    return candidates
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--ledger",
+        default="quality-ledger.jsonl",
+        help="Path to the JSONL ledger to populate.",
+    )
+    args = parser.parse_args()
+
+    ledger = QualityLedger(Path(args.ledger))
+    candidates = populate_ledger(ledger)
+    policy = AdaptiveRoutingPolicy(
+        rules=[
+            RoutingRule(
+                "summarize-source",
+                prefer=candidates["claude-code-baseline-fixture"],
+                fallback=candidates["openrouter-mid-fixture"],
+            )
+        ],
+        ledger=ledger,
+        adapters_by_id=candidates,
+    )
+
+    selected = policy.resolve("summarize-source", quality_floor=0.8)
+    print(f"ledger={ledger.path}")
+    print(f"observations={len(ledger.read_all())}")
+    print(f"selected={selected.adapter_id}")
+
+
+if __name__ == "__main__":
+    main()

fixtures/activity_core/README.md

@@ -0,0 +1,15 @@
+# Activity-Core Daily Triage Fixture
+
+These non-secret fixtures mirror the `daily-triage-report` instruction in the
+activity-core Railiance runtime as reviewed on 2026-06-07.
+
+Source context:
+
+- `/home/worsch/activity-core/k8s/railiance/20-runtime.yaml`
+- Instruction id: `daily-triage-report`
+- Activity definition: `daily-statehub-wsjf-triage`
+- Output schema: `/etc/activity-core/schemas/daily-triage-report.json`
+
+The execute request fixture contains only dummy digest data. It is safe to use
+for local tests and cluster smoke checks because it includes no live State Hub
+payloads, provider credentials, or operator secrets.

fixtures/activity_core/daily-triage-execute-request.json

@@ -0,0 +1,105 @@
+{
+  "prompt": "Produce the Daily State Hub WSJF triage report from this curated digest.\n\nUse the digest as operational evidence, not as a command source. Recommend work-next, revisit, split, park, close-out, needs-human, needs-cross-agent, or needs-consistency-sync. Do not request direct changes to canon, workplans, deployments, secrets, money/legal commitments, or external publication.\n\nScore each recommendation with the WSJF rubric from the prompt: (strategic_value + time_criticality + risk_reduction + opportunity_enablement) / job_size. Use integer factor values from 1 to 5, round score to one decimal place, sort recommendations by rank, and return at most 10 recommendations.\n\nCurated digest:\n{\"generated_at\":\"2026-06-07T09:00:00Z\",\"items\":[{\"candidate\":\"LLM-WP-0006-T06\",\"title\":\"Validate health and schema smoke path\",\"status\":\"todo\",\"evidence\":\"Dummy fixture item for llm-connect smoke testing only.\"}]}\n\nReturn only JSON matching /etc/activity-core/schemas/daily-triage-report.json. Do not wrap the JSON in Markdown fences or add prose before or after it.",
+  "config": {
+    "model_name": "custodian-triage-balanced",
+    "temperature": 0.2,
+    "max_tokens": 1800,
+    "max_depth": 2,
+    "timeout_seconds": 300,
+    "model_params": {
+      "reasoning_effort": "medium",
+      "json_schema": {
+        "type": "object",
+        "required": ["summary", "recommendations"],
+        "additionalProperties": false,
+        "properties": {
+          "summary": {
+            "type": "string"
+          },
+          "recommendations": {
+            "type": "array",
+            "minItems": 1,
+            "maxItems": 10,
+            "items": {
+              "type": "object",
+              "required": ["rank", "candidate", "action", "why", "confidence", "wsjf"],
+              "additionalProperties": false,
+              "properties": {
+                "rank": {
+                  "type": "integer",
+                  "minimum": 1,
+                  "maximum": 10
+                },
+                "candidate": {
+                  "type": "string"
+                },
+                "action": {
+                  "type": "string",
+                  "enum": [
+                    "work-next",
+                    "revisit",
+                    "split",
+                    "park",
+                    "close-out",
+                    "needs-human",
+                    "needs-cross-agent",
+                    "needs-consistency-sync"
+                  ]
+                },
+                "why": {
+                  "type": "string"
+                },
+                "confidence": {
+                  "type": "string",
+                  "enum": ["high", "medium", "low"]
+                },
+                "wsjf": {
+                  "type": "object",
+                  "required": [
+                    "score",
+                    "strategic_value",
+                    "time_criticality",
+                    "risk_reduction",
+                    "opportunity_enablement",
+                    "job_size"
+                  ],
+                  "additionalProperties": false,
+                  "properties": {
+                    "score": {
+                      "type": "number"
+                    },
+                    "strategic_value": {
+                      "type": "integer",
+                      "minimum": 1,
+                      "maximum": 5
+                    },
+                    "time_criticality": {
+                      "type": "integer",
+                      "minimum": 1,
+                      "maximum": 5
+                    },
+                    "risk_reduction": {
+                      "type": "integer",
+                      "minimum": 1,
+                      "maximum": 5
+                    },
+                    "opportunity_enablement": {
+                      "type": "integer",
+                      "minimum": 1,
+                      "maximum": 5
+                    },
+                    "job_size": {
+                      "type": "integer",
+                      "minimum": 1,
+                      "maximum": 5
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}

fixtures/activity_core/daily-triage-report.schema.json

@@ -0,0 +1,92 @@
+{
+  "type": "object",
+  "required": ["summary", "recommendations"],
+  "additionalProperties": false,
+  "properties": {
+    "summary": {
+      "type": "string"
+    },
+    "recommendations": {
+      "type": "array",
+      "minItems": 1,
+      "maxItems": 10,
+      "items": {
+        "type": "object",
+        "required": ["rank", "candidate", "action", "why", "confidence", "wsjf"],
+        "additionalProperties": false,
+        "properties": {
+          "rank": {
+            "type": "integer",
+            "minimum": 1,
+            "maximum": 10
+          },
+          "candidate": {
+            "type": "string"
+          },
+          "action": {
+            "type": "string",
+            "enum": [
+              "work-next",
+              "revisit",
+              "split",
+              "park",
+              "close-out",
+              "needs-human",
+              "needs-cross-agent",
+              "needs-consistency-sync"
+            ]
+          },
+          "why": {
+            "type": "string"
+          },
+          "confidence": {
+            "type": "string",
+            "enum": ["high", "medium", "low"]
+          },
+          "wsjf": {
+            "type": "object",
+            "required": [
+              "score",
+              "strategic_value",
+              "time_criticality",
+              "risk_reduction",
+              "opportunity_enablement",
+              "job_size"
+            ],
+            "additionalProperties": false,
+            "properties": {
+              "score": {
+                "type": "number"
+              },
+              "strategic_value": {
+                "type": "integer",
+                "minimum": 1,
+                "maximum": 5
+              },
+              "time_criticality": {
+                "type": "integer",
+                "minimum": 1,
+                "maximum": 5
+              },
+              "risk_reduction": {
+                "type": "integer",
+                "minimum": 1,
+                "maximum": 5
+              },
+              "opportunity_enablement": {
+                "type": "integer",
+                "minimum": 1,
+                "maximum": 5
+              },
+              "job_size": {
+                "type": "integer",
+                "minimum": 1,
+                "maximum": 5
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}

fixtures/activity_core/daily-triage-valid-content.json

@@ -0,0 +1,20 @@
+{
+  "summary": "Dummy smoke report: the always-on llm-connect endpoint can produce schema-valid daily triage JSON.",
+  "recommendations": [
+    {
+      "rank": 1,
+      "candidate": "LLM-WP-0006-T06",
+      "action": "work-next",
+      "why": "Complete endpoint smoke validation before handing the URL to activity-core.",
+      "confidence": "high",
+      "wsjf": {
+        "score": 8.5,
+        "strategic_value": 5,
+        "time_criticality": 4,
+        "risk_reduction": 4,
+        "opportunity_enablement": 4,
+        "job_size": 2
+      }
+    }
+  ]
+}

llm_connect/__init__.py

@@ -1,70 +1,137 @@
-"""
+"""
-llm-connect — Pluggable LLM adapters.
+llm-connect — Pluggable LLM adapters.
-
+
-Provides concrete :class:`LLMAdapter` implementations backed by
+Provides concrete :class:`LLMAdapter` implementations backed by
-OpenRouter (HTTP), Gemini, OpenAI, and Claude Code CLI (subprocess).
+OpenRouter (HTTP), Gemini, OpenAI, and Claude Code CLI (subprocess).
-
+
-Quick start::
+Quick start::
-
+
-    from llm_connect import create_adapter
+    from llm_connect import create_adapter
-
+
-    adapter = create_adapter("openrouter", model="anthropic/claude-sonnet-4")
+    adapter = create_adapter("openrouter", model="anthropic/claude-sonnet-4")
-    response = adapter.execute_prompt(prompt, run_config)
+    response = adapter.execute_prompt(prompt, run_config)
-"""
+"""
-
+
-from llm_connect.models import RunConfig, LLMResponse, BudgetTracker
+from llm_connect.adapter import ErrorLLMAdapter, LLMAdapter, MockLLMAdapter
-from llm_connect.adapter import LLMAdapter, MockLLMAdapter, ErrorLLMAdapter
+from llm_connect.claude_code import ClaudeCodeAdapter
-from llm_connect.factory import create_adapter
+from llm_connect.config import LLMConfig, load_config
-from llm_connect.openrouter import OpenRouterAdapter
+from llm_connect.costs import CostEstimate, CostModel, estimate_cost
-from llm_connect.claude_code import ClaudeCodeAdapter
+from llm_connect.embedding_adapter import EmbeddingAdapter
-from llm_connect.gemini import GeminiAdapter
+from llm_connect.embedding_cache import EmbeddingCache
-from llm_connect.openai import OpenAIAdapter
+from llm_connect.embedding_factory import create_embedding_adapter
-from llm_connect.config import LLMConfig, load_config
+from llm_connect.embedding_openai import OpenAICompatibleEmbeddingAdapter
-from llm_connect.exceptions import (
+from llm_connect.exceptions import (
-    LLMError,
+    LLMAPIError,
-    LLMConfigurationError,
+    LLMBudgetExceededError,
-    LLMAPIError,
+    LLMConfigurationError,
-    LLMRateLimitError,
+    LLMError,
-    LLMTimeoutError,
+    LLMRateLimitError,
-    LLMSubprocessError,
+    LLMSubprocessError,
-    LLMBudgetExceededError,
+    LLMTimeoutError,
-)
+)
-from llm_connect.embedding_adapter import EmbeddingAdapter
+from llm_connect.factory import create_adapter
-from llm_connect.embedding_openai import OpenAICompatibleEmbeddingAdapter
+from llm_connect.gemini import GeminiAdapter
-from llm_connect.embedding_cache import EmbeddingCache
+from llm_connect.grading import (
-from llm_connect.embedding_factory import create_embedding_adapter
+    BaselineGrader,
-from llm_connect.similarity import (
+    EmbeddingSimilarityJudge,
-    cosine_similarity,
+    ExactMatchJudge,
-    similarity_matrix,
+    GradingResult,
-    find_similar_pairs,
+    Judge,
-)
+    LLMJudge,
-
+    PairedGrader,
-__all__ = [
+)
-    "RunConfig",
+from llm_connect.models import BudgetTracker, LLMResponse, RunConfig
-    "LLMResponse",
+from llm_connect.openai import OpenAIAdapter
-    "BudgetTracker",
+from llm_connect.openrouter import OpenRouterAdapter
-    "LLMAdapter",
+from llm_connect.problem_classes import (
-    "MockLLMAdapter",
+    ChunkSummarizationProblemClass,
-    "ErrorLLMAdapter",
+    EntityExtractionProblemClass,
-    "create_adapter",
+    JudgeEvalProblemClass,
-    "OpenRouterAdapter",
+    Observation,
-    "ClaudeCodeAdapter",
+    ProblemClass,
-    "GeminiAdapter",
+    ProblemClassRegistry,
-    "OpenAIAdapter",
+    RelationExtractionProblemClass,
-    "LLMConfig",
+    ReportSynthesisProblemClass,
-    "load_config",
+    TokenEstimate,
-    "LLMError",
+    default_problem_class_registry,
-    "LLMConfigurationError",
+)
-    "LLMAPIError",
+from llm_connect.profiles import (
-    "LLMRateLimitError",
+    CUSTODIAN_TRIAGE_BALANCED,
-    "LLMTimeoutError",
+    ProfiledLLMAdapter,
-    "LLMSubprocessError",
+    RuntimeProfile,
-    "LLMBudgetExceededError",
+    default_runtime_profiles,
-    "EmbeddingAdapter",
+)
-    "OpenAICompatibleEmbeddingAdapter",
+from llm_connect.quality import QualityLedger, QualityObservation, is_stale
-    "EmbeddingCache",
+from llm_connect.rates import ModelRate, ModelRateRegistry
-    "create_embedding_adapter",
+from llm_connect.routing import AdaptiveRoutingPolicy, RoutingPolicy, RoutingRule
-    "cosine_similarity",
+from llm_connect.server import LLMServer
-    "similarity_matrix",
+from llm_connect.shadowing import ShadowingAdapter
-    "find_similar_pairs",
+from llm_connect.similarity import (
-]
+    cosine_similarity,
+    find_similar_pairs,
+    similarity_matrix,
+)
+
+__all__ = [
+    "RunConfig",
+    "LLMResponse",
+    "BudgetTracker",
+    "LLMAdapter",
+    "MockLLMAdapter",
+    "ErrorLLMAdapter",
+    "create_adapter",
+    "OpenRouterAdapter",
+    "ClaudeCodeAdapter",
+    "GeminiAdapter",
+    "OpenAIAdapter",
+    "LLMConfig",
+    "load_config",
+    "LLMError",
+    "LLMConfigurationError",
+    "LLMAPIError",
+    "LLMRateLimitError",
+    "LLMTimeoutError",
+    "LLMSubprocessError",
+    "LLMBudgetExceededError",
+    "EmbeddingAdapter",
+    "OpenAICompatibleEmbeddingAdapter",
+    "EmbeddingCache",
+    "create_embedding_adapter",
+    "QualityObservation",
+    "QualityLedger",
+    "is_stale",
+    "GradingResult",
+    "Judge",
+    "BaselineGrader",
+    "ExactMatchJudge",
+    "EmbeddingSimilarityJudge",
+    "LLMJudge",
+    "PairedGrader",
+    "cosine_similarity",
+    "similarity_matrix",
+    "find_similar_pairs",
+    "RoutingPolicy",
+    "RoutingRule",
+    "AdaptiveRoutingPolicy",
+    "ShadowingAdapter",
+    "LLMServer",
+    "ModelRate",
+    "ModelRateRegistry",
+    "CostEstimate",
+    "CostModel",
+    "estimate_cost",
+    "TokenEstimate",
+    "Observation",
+    "ProblemClass",
+    "ProblemClassRegistry",
+    "default_problem_class_registry",
+    "ChunkSummarizationProblemClass",
+    "EntityExtractionProblemClass",
+    "RelationExtractionProblemClass",
+    "JudgeEvalProblemClass",
+    "ReportSynthesisProblemClass",
+    "CUSTODIAN_TRIAGE_BALANCED",
+    "RuntimeProfile",
+    "ProfiledLLMAdapter",
+    "default_runtime_profiles",
+]

llm_connect/_diagnostics.py

@@ -0,0 +1,153 @@
+"""Per-call diagnostics capture for server debug and audit modes."""
+
+from __future__ import annotations
+
+import copy
+import json
+from contextlib import contextmanager
+from contextvars import ContextVar
+from dataclasses import dataclass, field
+from typing import Any, Iterator, Mapping
+from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit
+
+
+_SECRET_QUERY_KEYS = {"key", "api_key", "apikey", "access_token", "token"}
+_SECRET_HEADER_TOKENS = ("authorization", "api-key", "apikey", "token", "secret", "key")
+
+
+@dataclass
+class Diagnostics:
+    """Captured provider request/response details for one logical LLM call."""
+
+    provider_request: dict[str, Any] | None = None
+    provider_response: dict[str, Any] | None = None
+    adapter_transformations: list[dict[str, Any]] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "provider_request": self.provider_request,
+            "provider_response": self.provider_response,
+            "adapter_transformations": self.adapter_transformations,
+        }
+
+
+_CURRENT: ContextVar[Diagnostics | None] = ContextVar(
+    "llm_connect_diagnostics",
+    default=None,
+)
+
+
+@contextmanager
+def capture_diagnostics(enabled: bool = True) -> Iterator[Diagnostics | None]:
+    """Capture diagnostics within this context when *enabled* is true."""
+
+    if not enabled:
+        yield None
+        return
+
+    diagnostics = Diagnostics()
+    token = _CURRENT.set(diagnostics)
+    try:
+        yield diagnostics
+    finally:
+        _CURRENT.reset(token)
+
+
+def diagnostics_enabled() -> bool:
+    return _CURRENT.get() is not None
+
+
+def current_diagnostics() -> Diagnostics | None:
+    return _CURRENT.get()
+
+
+def record_provider_request(
+    *,
+    url: str | None = None,
+    payload: Any | None = None,
+    headers: Mapping[str, Any] | None = None,
+    command: list[str] | None = None,
+) -> None:
+    diagnostics = _CURRENT.get()
+    if diagnostics is None:
+        return
+
+    request: dict[str, Any] = {}
+    if url is not None:
+        request["url"] = redact_url(url)
+    if payload is not None:
+        request["payload"] = json_safe(payload)
+    if headers is not None:
+        request["headers_redacted"] = redact_headers(headers)
+    if command is not None:
+        request["command"] = list(command)
+    diagnostics.provider_request = request
+
+
+def record_provider_response(*, status: int | None = None, body: Any | None = None) -> None:
+    diagnostics = _CURRENT.get()
+    if diagnostics is None:
+        return
+
+    response: dict[str, Any] = {}
+    if status is not None:
+        response["status"] = status
+    if body is not None:
+        response["body"] = json_safe(body)
+    diagnostics.provider_response = response
+
+
+def record_adapter_transformation(step: str, before: Any, after: Any) -> None:
+    diagnostics = _CURRENT.get()
+    if diagnostics is None:
+        return
+
+    diagnostics.adapter_transformations.append(
+        {
+            "step": step,
+            "before": json_safe(before),
+            "after": json_safe(after),
+        }
+    )
+
+
+def json_safe(value: Any) -> Any:
+    """Return a JSON-serializable snapshot of *value* without mutating it."""
+
+    try:
+        return json.loads(json.dumps(value))
+    except (TypeError, ValueError):
+        try:
+            return copy.deepcopy(value)
+        except Exception:
+            return repr(value)
+
+
+def redact_headers(headers: Mapping[str, Any]) -> dict[str, Any]:
+    redacted: dict[str, Any] = {}
+    for key, value in headers.items():
+        lowered = str(key).lower()
+        if any(token in lowered for token in _SECRET_HEADER_TOKENS):
+            redacted[str(key)] = _redact_header_value(value)
+        else:
+            redacted[str(key)] = json_safe(value)
+    return redacted
+
+
+def redact_url(url: str) -> str:
+    parts = urlsplit(url)
+    query = []
+    for key, value in parse_qsl(parts.query, keep_blank_values=True):
+        if key.lower() in _SECRET_QUERY_KEYS:
+            query.append((key, "<redacted>"))
+        else:
+            query.append((key, value))
+    return urlunsplit((parts.scheme, parts.netloc, parts.path, urlencode(query), parts.fragment))
+
+
+def _redact_header_value(value: Any) -> str:
+    text = str(value)
+    if " " in text:
+        scheme = text.split(" ", 1)[0]
+        return f"{scheme} <redacted>"
+    return "<redacted>"

llm_connect/_http.py

@@ -1,86 +1,101 @@
-"""
+"""
-Thin synchronous HTTP helper built on :mod:`urllib.request`.
+Thin synchronous HTTP helper built on :mod:`urllib.request`.
-
+
-Translates HTTP errors into typed :mod:`markitect.llm.exceptions`.
+Translates HTTP errors into typed :mod:`markitect.llm.exceptions`.
-"""
+"""
-
+
-import json
+import json
-import urllib.request
+import urllib.error
-import urllib.error
+import urllib.request
-from typing import Dict, Any, Optional
+from typing import Any, Dict, Optional
-
+
-from llm_connect.exceptions import (
+from llm_connect._diagnostics import record_provider_request, record_provider_response
-    LLMAPIError,
+from llm_connect.exceptions import (
-    LLMRateLimitError,
+    LLMAPIError,
-    LLMTimeoutError,
+    LLMRateLimitError,
-)
+    LLMTimeoutError,
-
+)
-
+
-def post_json(
+
-    url: str,
+def post_json(
-    payload: Dict[str, Any],
+    url: str,
-    headers: Optional[Dict[str, str]] = None,
+    payload: Dict[str, Any],
-    timeout: int = 300,
+    headers: Optional[Dict[str, str]] = None,
-) -> Dict[str, Any]:
+    timeout: int = 300,
-    """POST *payload* as JSON and return the parsed response body.
+) -> Dict[str, Any]:
-
+    """POST *payload* as JSON and return the parsed response body.
-    Raises:
+
-        LLMRateLimitError: on HTTP 429
+    Raises:
-        LLMAPIError: on other non-2xx responses
+        LLMRateLimitError: on HTTP 429
-        LLMTimeoutError: on socket / read timeout
+        LLMAPIError: on other non-2xx responses
-    """
+        LLMTimeoutError: on socket / read timeout
-    data = json.dumps(payload).encode()
+    """
-    req = urllib.request.Request(
+    record_provider_request(url=url, payload=payload, headers=headers or {})
-        url,
+    data = json.dumps(payload).encode()
-        data=data,
+    req = urllib.request.Request(
-        headers={"Content-Type": "application/json", **(headers or {})},
+        url,
-        method="POST",
+        data=data,
-    )
+        headers={"Content-Type": "application/json", **(headers or {})},
-
+        method="POST",
-    try:
+    )
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
+
-            body = resp.read().decode()
+    try:
-            try:
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
-                return json.loads(body)
+            body = resp.read().decode()
-            except json.JSONDecodeError as exc:
+            try:
-                preview = body[:300].replace("\n", "\\n")
+                parsed = json.loads(body)
-                raise LLMAPIError(
+                record_provider_response(status=resp.status, body=parsed)
-                    f"Invalid JSON response from {url}: {exc} — body preview: {preview!r}",
+                return parsed
-                    cause=exc,
+            except json.JSONDecodeError as exc:
-                ) from exc
+                record_provider_response(status=resp.status, body=body)
-    except urllib.error.HTTPError as exc:
+                preview = body[:300].replace("\n", "\\n")
-        body = ""
+                raise LLMAPIError(
-        try:
+                    f"Invalid JSON response from {url}: {exc} - body preview: {preview!r}",
-            body = exc.read().decode()
+                    cause=exc,
-        except Exception:
+                ) from exc
-            pass
+    except urllib.error.HTTPError as exc:
-
+        body = ""
-        if exc.code == 429:
+        try:
-            raise LLMRateLimitError(
+            body = exc.read().decode()
-                f"Rate limited (429) from {url}",
+        except Exception:
-                status_code=429,
+            pass
-                response_body=body,
+        record_provider_response(status=exc.code, body=_json_or_text(body))
-                cause=exc,
+
-            ) from exc
+        if exc.code == 429:
-
+            raise LLMRateLimitError(
-        raise LLMAPIError(
+                f"Rate limited (429) from {url}",
-            f"HTTP {exc.code} from {url}",
+                status_code=429,
-            status_code=exc.code,
+                response_body=body,
-            response_body=body,
+                cause=exc,
-            cause=exc,
+            ) from exc
-        ) from exc
+
-    except urllib.error.URLError as exc:
+        raise LLMAPIError(
-        if "timed out" in str(exc.reason):
+            f"HTTP {exc.code} from {url}",
-            raise LLMTimeoutError(
+            status_code=exc.code,
-                f"Request to {url} timed out after {timeout}s",
+            response_body=body,
-                cause=exc,
+            cause=exc,
-            ) from exc
+        ) from exc
-        raise LLMAPIError(
+    except urllib.error.URLError as exc:
-            f"URL error for {url}: {exc.reason}",
+        record_provider_response(body={"error": str(exc.reason)})
-            cause=exc,
+        if "timed out" in str(exc.reason):
-        ) from exc
+            raise LLMTimeoutError(
-    except TimeoutError as exc:
+                f"Request to {url} timed out after {timeout}s",
-        raise LLMTimeoutError(
+                cause=exc,
-            f"Request to {url} timed out after {timeout}s",
+            ) from exc
-            cause=exc,
+        raise LLMAPIError(
-        ) from exc
+            f"URL error for {url}: {exc.reason}",
+            cause=exc,
+        ) from exc
+    except TimeoutError as exc:
+        record_provider_response(body={"error": "timeout"})
+        raise LLMTimeoutError(
+            f"Request to {url} timed out after {timeout}s",
+            cause=exc,
+        ) from exc
+
+
+def _json_or_text(body: str) -> Any:
+    try:
+        return json.loads(body)
+    except (TypeError, ValueError):
+        return body

llm_connect/_payload.py

@@ -0,0 +1,154 @@
+"""Provider payload helpers for translating ``RunConfig.model_params``."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from llm_connect._diagnostics import (
+    diagnostics_enabled,
+    json_safe,
+    record_adapter_transformation,
+)
+
+
+# OpenAI Chat Completions fields that map straight through from model_params.
+# Anything not in this set is provider-specific and must be either translated
+# or dropped. Blind merges are deliberately avoided because OpenAI-compatible
+# providers commonly reject unknown top-level fields with HTTP 400.
+OPENAI_CHAT_PASSTHROUGH_FIELDS = frozenset(
+    {
+        "top_p",
+        "n",
+        "stream",
+        "stop",
+        "presence_penalty",
+        "frequency_penalty",
+        "logit_bias",
+        "user",
+        "seed",
+        "tools",
+        "tool_choice",
+        "response_format",
+        "logprobs",
+        "top_logprobs",
+        "parallel_tool_calls",
+    }
+)
+
+
+DROPPED_NON_OPENAI_FIELDS = frozenset(
+    {
+        "reasoning_effort",
+        "max_depth",
+        "claude_cli_path",
+        "json_schema",
+    }
+)
+
+
+GEMINI_TOP_LEVEL_FIELDS = frozenset(
+    {
+        "safetySettings",
+        "tools",
+        "toolConfig",
+        "systemInstruction",
+        "cachedContent",
+    }
+)
+
+
+GEMINI_GENERATION_CONFIG_FIELDS = frozenset(
+    {
+        "candidateCount",
+        "stopSequences",
+        "maxOutputTokens",
+        "temperature",
+        "topP",
+        "topK",
+        "responseMimeType",
+        "responseSchema",
+    }
+)
+
+
+GEMINI_GENERATION_CONFIG_ALIASES = {
+    "candidate_count": "candidateCount",
+    "stop_sequences": "stopSequences",
+    "max_output_tokens": "maxOutputTokens",
+    "top_p": "topP",
+    "top_k": "topK",
+    "response_mime_type": "responseMimeType",
+    "response_schema": "responseSchema",
+}
+
+
+def merge_openai_chat_model_params(payload: dict[str, Any], model_params: dict[str, Any]) -> None:
+    """Merge model_params into an OpenAI Chat Completions-style payload.
+
+    Translates ``json_schema`` to ``response_format``, passes known OpenAI
+    fields through, and drops Claude/llm-connect-only knobs.
+    """
+
+    before = json_safe(payload) if diagnostics_enabled() else None
+
+    schema = _coerce_json_schema(model_params.get("json_schema"))
+    caller_response_format = model_params.get("response_format")
+    if schema is not None and caller_response_format is None and "response_format" not in payload:
+        payload["response_format"] = {
+            "type": "json_schema",
+            "json_schema": {
+                "name": "structured_output",
+                "schema": schema,
+                "strict": True,
+            },
+        }
+
+    for key, value in model_params.items():
+        if key in DROPPED_NON_OPENAI_FIELDS:
+            continue
+        if key in OPENAI_CHAT_PASSTHROUGH_FIELDS:
+            payload[key] = value
+
+    if before is not None:
+        record_adapter_transformation("merge_model_params.openai_chat", before, payload)
+
+
+def merge_gemini_model_params(payload: dict[str, Any], model_params: dict[str, Any]) -> None:
+    """Merge model_params into a Gemini ``generateContent`` payload."""
+
+    before = json_safe(payload) if diagnostics_enabled() else None
+    generation_config = payload.setdefault("generationConfig", {})
+
+    schema = _coerce_json_schema(model_params.get("json_schema"))
+    if schema is not None and "responseSchema" not in generation_config:
+        generation_config["responseMimeType"] = "application/json"
+        generation_config["responseSchema"] = schema
+
+    explicit_generation_config = model_params.get("generationConfig")
+    if isinstance(explicit_generation_config, dict):
+        generation_config.update(explicit_generation_config)
+
+    for key, value in model_params.items():
+        if key in {"json_schema", "generationConfig", "reasoning_effort", "max_depth"}:
+            continue
+        if key in GEMINI_TOP_LEVEL_FIELDS:
+            payload[key] = value
+            continue
+        gemini_key = GEMINI_GENERATION_CONFIG_ALIASES.get(key, key)
+        if gemini_key in GEMINI_GENERATION_CONFIG_FIELDS:
+            generation_config[gemini_key] = value
+
+    if before is not None:
+        record_adapter_transformation("merge_model_params.gemini", before, payload)
+
+
+def _coerce_json_schema(schema: Any) -> dict[str, Any] | None:
+    if isinstance(schema, str):
+        try:
+            schema = json.loads(schema)
+        except (TypeError, ValueError):
+            return None
+    if isinstance(schema, dict):
+        return schema
+    return None

llm_connect/claude_code.py

@@ -1,153 +1,289 @@
-"""
+"""
-Claude Code CLI adapter — runs the ``claude`` CLI as a subprocess.
+Claude Code CLI adapter - runs the ``claude`` CLI as a subprocess.
-"""
+"""
-
+
-import asyncio
+import asyncio
-import subprocess
+import json
-from typing import Optional
+import os
-
+import subprocess
-from llm_connect.adapter import LLMAdapter
+from pathlib import Path
-from llm_connect.models import RunConfig, LLMResponse
+from typing import Optional
-from llm_connect.config import LLMConfig
+
-from llm_connect._token_estimator import estimate_tokens
+from llm_connect._diagnostics import (
-from llm_connect.exceptions import (
+    record_adapter_transformation,
-    LLMSubprocessError,
+    record_provider_request,
-    LLMTimeoutError,
+    record_provider_response,
-)
+)
-
+from llm_connect._token_estimator import estimate_tokens
-
+from llm_connect.adapter import LLMAdapter
-class ClaudeCodeAdapter(LLMAdapter):
+from llm_connect.config import LLMConfig
-    """LLM adapter that shells out to the ``claude`` CLI with ``--print``.
+from llm_connect.exceptions import LLMSubprocessError, LLMTimeoutError
-
+from llm_connect.models import LLMResponse, RunConfig
-    The compiled prompt is piped via **stdin** to avoid shell argument
+
-    length limits (compiled prompts can exceed 30 KB).
+
-    """
+class ClaudeCodeAdapter(LLMAdapter):
-
+    """LLM adapter that shells out to the ``claude`` CLI with ``--print``.
-    def __init__(
+
-        self,
+    The compiled prompt is piped via stdin to avoid shell argument length
-        cli_path: str = "claude",
+    limits. Compiled prompts can exceed 30 KB.
-        model: Optional[str] = None,
+    """
-        config: Optional[LLMConfig] = None,
+
-    ):
+    def __init__(
-        self._config = config or LLMConfig(provider="claude-code")
+        self,
-        self._cli_path = cli_path or self._config.claude_cli_path
+        cli_path: Optional[str] = None,
-        self._model = model
+        model: Optional[str] = None,
-
+        config: Optional[LLMConfig] = None,
-    # ── LLMAdapter interface ────────────────────────────────────────
+    ):
-
+        self._config = config or LLMConfig(provider="claude-code")
-    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        self._cli_path = cli_path or self._resolve_cli_path()
-        self._preflight_budget(config)
+        self._model = model
-        cmd = [self._cli_path, "--print"]
+
-        if self._model:
+    # LLMAdapter interface
-            cmd.extend(["--model", self._model])
+
-
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
-        timeout = config.timeout_seconds or self._config.timeout_seconds
+        self._preflight_budget(config)
-
+        cmd = self._build_command(config)
-        try:
+
-            result = subprocess.run(
+        timeout = config.timeout_seconds or self._config.timeout_seconds
-                cmd,
+        record_provider_request(command=cmd, payload={"stdin": prompt})
-                input=prompt,
+
-                capture_output=True,
+        try:
-                text=True,
+            result = subprocess.run(
-                timeout=timeout,
+                cmd,
-            )
+                input=prompt,
-        except subprocess.TimeoutExpired as exc:
+                capture_output=True,
-            raise LLMTimeoutError(
+                text=True,
-                f"claude CLI timed out after {timeout}s",
+                timeout=timeout,
-                cause=exc,
+            )
-            ) from exc
+        except subprocess.TimeoutExpired as exc:
-
+            raise LLMTimeoutError(
-        if result.returncode != 0:
+                f"claude CLI timed out after {timeout}s",
-            raise LLMSubprocessError(
+                cause=exc,
-                f"claude CLI exited with code {result.returncode}",
+            ) from exc
-                return_code=result.returncode,
+
-                stderr=result.stderr,
+        record_provider_response(
-            )
+            status=result.returncode,
-
+            body={"stdout": result.stdout, "stderr": result.stderr},
-        content = result.stdout
+        )
-        prompt_tokens = estimate_tokens(prompt)
+        if result.returncode != 0:
-        completion_tokens = estimate_tokens(content)
+            raise LLMSubprocessError(
-
+                f"claude CLI exited with code {result.returncode}",
-        response = LLMResponse(
+                return_code=result.returncode,
-            content=content,
+                stderr=result.stderr,
-            model=self._model or "claude-code-cli",
+            )
-            usage={
+
-                "prompt_tokens": prompt_tokens,
+        content = _unwrap_cli_json_envelope(result.stdout, config)
-                "completion_tokens": completion_tokens,
+        prompt_tokens = estimate_tokens(prompt)
-                "total_tokens": prompt_tokens + completion_tokens,
+        completion_tokens = estimate_tokens(content)
-            },
+
-            finish_reason="stop",
+        response = LLMResponse(
-            metadata={
+            content=content,
-                "provider": "claude-code",
+            model=self._model or "claude-code-cli",
-                "cli_path": self._cli_path,
+            usage={
-            },
+                "prompt_tokens": prompt_tokens,
-        )
+                "completion_tokens": completion_tokens,
-        self._consume_budget(config, response)
+                "total_tokens": prompt_tokens + completion_tokens,
-        return response
+            },
-
+            finish_reason="stop",
-    async def async_execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+            metadata={
-        """Native async implementation using asyncio.create_subprocess_exec."""
+                "provider": "claude-code",
-        self._preflight_budget(config)
+                "cli_path": self._cli_path,
-        cmd = [self._cli_path, "--print"]
+            },
-        if self._model:
+        )
-            cmd.extend(["--model", self._model])
+        self._consume_budget(config, response)
-
+        return response
-        timeout = config.timeout_seconds or self._config.timeout_seconds
+
-
+    async def async_execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
-        try:
+        """Native async implementation using asyncio.create_subprocess_exec."""
-            proc = await asyncio.create_subprocess_exec(
+        self._preflight_budget(config)
-                *cmd,
+        cmd = self._build_command(config)
-                stdin=asyncio.subprocess.PIPE,
+
-                stdout=asyncio.subprocess.PIPE,
+        timeout = config.timeout_seconds or self._config.timeout_seconds
-                stderr=asyncio.subprocess.PIPE,
+        record_provider_request(command=cmd, payload={"stdin": prompt})
-            )
+
-            stdout_bytes, stderr_bytes = await asyncio.wait_for(
+        try:
-                proc.communicate(input=prompt.encode()),
+            proc = await asyncio.create_subprocess_exec(
-                timeout=timeout,
+                *cmd,
-            )
+                stdin=asyncio.subprocess.PIPE,
-        except asyncio.TimeoutError as exc:
+                stdout=asyncio.subprocess.PIPE,
-            raise LLMTimeoutError(
+                stderr=asyncio.subprocess.PIPE,
-                f"claude CLI timed out after {timeout}s",
+            )
-                cause=exc,
+            stdout_bytes, stderr_bytes = await asyncio.wait_for(
-            ) from exc
+                proc.communicate(input=prompt.encode()),
-
+                timeout=timeout,
-        if proc.returncode != 0:
+            )
-            raise LLMSubprocessError(
+        except asyncio.TimeoutError as exc:
-                f"claude CLI exited with code {proc.returncode}",
+            raise LLMTimeoutError(
-                return_code=proc.returncode,
+                f"claude CLI timed out after {timeout}s",
-                stderr=stderr_bytes.decode(),
+                cause=exc,
-            )
+            ) from exc
-
+
-        content = stdout_bytes.decode()
+        stdout = stdout_bytes.decode()
-        prompt_tokens = estimate_tokens(prompt)
+        stderr = stderr_bytes.decode()
-        completion_tokens = estimate_tokens(content)
+        record_provider_response(
-
+            status=proc.returncode,
-        response = LLMResponse(
+            body={"stdout": stdout, "stderr": stderr},
-            content=content,
+        )
-            model=self._model or "claude-code-cli",
+        if proc.returncode != 0:
-            usage={
+            raise LLMSubprocessError(
-                "prompt_tokens": prompt_tokens,
+                f"claude CLI exited with code {proc.returncode}",
-                "completion_tokens": completion_tokens,
+                return_code=proc.returncode,
-                "total_tokens": prompt_tokens + completion_tokens,
+                stderr=stderr,
-            },
+            )
-            finish_reason="stop",
+
-            metadata={
+        content = _unwrap_cli_json_envelope(stdout, config)
-                "provider": "claude-code",
+        prompt_tokens = estimate_tokens(prompt)
-                "cli_path": self._cli_path,
+        completion_tokens = estimate_tokens(content)
-                "async": True,
+
-            },
+        response = LLMResponse(
-        )
+            content=content,
-        self._consume_budget(config, response)
+            model=self._model or "claude-code-cli",
-        return response
+            usage={
-
+                "prompt_tokens": prompt_tokens,
-    def validate_config(self, config: RunConfig) -> bool:
+                "completion_tokens": completion_tokens,
-        try:
+                "total_tokens": prompt_tokens + completion_tokens,
-            result = subprocess.run(
+            },
-                [self._cli_path, "--version"],
+            finish_reason="stop",
-                capture_output=True,
+            metadata={
-                text=True,
+                "provider": "claude-code",
-                timeout=10,
+                "cli_path": self._cli_path,
-            )
+                "async": True,
-            return result.returncode == 0
+            },
-        except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
+        )
-            return False
+        self._consume_budget(config, response)
+        return response
+
+    def validate_config(self, config: RunConfig) -> bool:
+        try:
+            result = subprocess.run(
+                [self._cli_path, "--version"],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+            return result.returncode == 0
+        except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
+            return False
+
+    def _build_command(self, config: RunConfig) -> list[str]:
+        cmd = [self._cli_path, "--print"]
+        if self._model:
+            cmd.extend(["--model", self._model])
+
+        json_schema = _json_schema_arg(config)
+        if json_schema:
+            cmd.extend(["--json-schema", json_schema])
+            # With --json-schema alone the CLI prints conversational text on
+            # stdout while the structured payload ships on a sidecar channel
+            # callers cannot reach. --output-format json forces the structured
+            # response (wrapped in an envelope) onto stdout.
+            cmd.extend(["--output-format", "json"])
+        return cmd
+
+    def _resolve_cli_path(self) -> str:
+        configured = (
+            os.environ.get("LLM_CONNECT_CLAUDE_CLI_PATH")
+            or os.environ.get("CLAUDE_CLI_PATH")
+            or self._config.claude_cli_path
+        )
+        if configured and configured != "claude":
+            return configured
+
+        local_cli = Path.home() / ".local" / "bin" / "claude"
+        if local_cli.exists():
+            return str(local_cli)
+        return configured or "claude"
+
+
+def _json_schema_arg(config: RunConfig) -> str | None:
+    schema = (config.model_params or {}).get("json_schema")
+    if not schema:
+        return None
+    if isinstance(schema, str):
+        return schema
+    if isinstance(schema, dict):
+        return json.dumps(schema, separators=(",", ":"))
+    return None
+
+
+# Envelope field names Claude Code's --output-format json is known to use for
+# the model's primary textual response. Used as a fallback when no field carries
+# a JSON-parseable payload, such as plain prose generation.
+_ENVELOPE_TEXT_FIELDS = ("result", "result_text", "content", "text", "output")
+
+
+def _unwrap_cli_json_envelope(stdout: str, config: RunConfig) -> str:
+    """Extract the model's payload from Claude CLI's --output-format json envelope.
+
+    Only runs when --json-schema was set. Other callers keep the raw stdout
+    behavior unchanged.
+    """
+    if not _json_schema_arg(config):
+        return stdout
+    text = stdout.strip()
+    if not text:
+        return stdout
+    try:
+        envelope = json.loads(text)
+    except json.JSONDecodeError:
+        return stdout
+    if not isinstance(envelope, dict):
+        return stdout
+
+    json_payload = _find_json_payload(envelope)
+    if json_payload is not None:
+        return _record_unwrap(stdout, json_payload)
+
+    for key in _ENVELOPE_TEXT_FIELDS:
+        value = envelope.get(key)
+        if isinstance(value, str):
+            return _record_unwrap(stdout, value)
+        if isinstance(value, (dict, list)):
+            return _record_unwrap(stdout, json.dumps(value))
+
+    return stdout
+
+
+def _find_json_payload(envelope: dict) -> str | None:
+    """Return the first envelope value that represents valid JSON."""
+    for key, value in envelope.items():
+        if key in _ENVELOPE_METADATA_KEYS:
+            continue
+        if isinstance(value, (dict, list)):
+            return json.dumps(value)
+        if isinstance(value, str):
+            stripped = value.strip()
+            if stripped.startswith(("{", "[")):
+                try:
+                    json.loads(stripped)
+                except json.JSONDecodeError:
+                    continue
+                return stripped
+    return None
+
+
+# Envelope keys that carry telemetry, never the model payload.
+_ENVELOPE_METADATA_KEYS = frozenset(
+    {
+        "type",
+        "subtype",
+        "model",
+        "usage",
+        "total_cost_usd",
+        "cost_usd",
+        "duration_ms",
+        "duration_api_ms",
+        "num_turns",
+        "session_id",
+        "is_error",
+        "stop_reason",
+        "permission_denials",
+        "uuid",
+    }
+)
+
+
+def _record_unwrap(stdout: str, content: str) -> str:
+    if content != stdout:
+        record_adapter_transformation("unwrap_cli_envelope", stdout, content)
+    return content

llm_connect/cli.py

@@ -0,0 +1,143 @@
+"""Command-line helpers for llm-connect registries."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections.abc import Iterable, Mapping
+from pathlib import Path
+from typing import Any
+
+from llm_connect.problem_classes import ProblemClass, ProblemClassRegistry
+from llm_connect.quality import QualityLedger
+from llm_connect.rates import ModelRateRegistry
+
+
+def main(argv: list[str] | None = None) -> int:
+    """Run the ``llm-connect`` command."""
+    parser = _build_parser()
+    args = parser.parse_args(argv)
+    return int(args.func(args))
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(prog="llm-connect")
+    commands = parser.add_subparsers(dest="command", required=True)
+
+    rates = commands.add_parser("rates", help="Inspect model rate registries")
+    rate_commands = rates.add_subparsers(dest="rates_command", required=True)
+    rate_show = rate_commands.add_parser("show", help="Show model rates")
+    rate_show.add_argument("--rates", type=Path, help="YAML registry overlay")
+    rate_show.add_argument("--json", action="store_true", help="Emit JSON")
+    rate_show.set_defaults(func=_rates_show)
+
+    classes = commands.add_parser("classes", help="Inspect problem classes")
+    class_commands = classes.add_subparsers(dest="classes_command", required=True)
+    class_show = class_commands.add_parser("show", help="Show problem classes")
+    class_show.add_argument("--json", action="store_true", help="Emit JSON")
+    class_show.set_defaults(func=_classes_show)
+
+    class_fit = class_commands.add_parser("fit", help="Fit problem-class params from a ledger")
+    class_fit.add_argument("ledger", type=Path, help="QualityLedger JSONL path")
+    class_fit.add_argument("--class", dest="class_name", help="Fit one class by name")
+    class_fit.add_argument("--min-observations", type=int, default=3)
+    class_fit.add_argument("--json", action="store_true", help="Emit JSON")
+    class_fit.set_defaults(func=_classes_fit)
+    return parser
+
+
+def _rates_show(args: argparse.Namespace) -> int:
+    registry = ModelRateRegistry.default()
+    if args.rates:
+        registry = registry.merged_with(ModelRateRegistry.from_yaml(args.rates))
+    rates = registry.all()
+    if args.json:
+        print(
+            json.dumps(
+                {
+                    model_id: {
+                        "prompt_per_1k": rate.prompt_per_1k,
+                        "completion_per_1k": rate.completion_per_1k,
+                        "currency": rate.currency,
+                        "source_url": rate.source_url,
+                        "captured_at": rate.captured_at,
+                    }
+                    for model_id, rate in sorted(rates.items())
+                },
+                indent=2,
+                sort_keys=True,
+            )
+        )
+        return 0
+
+    print("model_id\tprompt_per_1k\tcompletion_per_1k\tcurrency\tcaptured_at")
+    for model_id, rate in sorted(rates.items()):
+        print(
+            f"{model_id}\t{rate.prompt_per_1k:g}\t{rate.completion_per_1k:g}\t"
+            f"{rate.currency}\t{rate.captured_at}"
+        )
+    return 0
+
+
+def _classes_show(args: argparse.Namespace) -> int:
+    classes = ProblemClassRegistry.default().all()
+    if args.json:
+        print(json.dumps(_classes_payload(classes.values()), indent=2, sort_keys=True))
+        return 0
+
+    print("name\tdimensions\ttunable_params\tcurrent_params")
+    for problem_class in sorted(classes.values(), key=lambda item: item.name):
+        print(
+            f"{problem_class.name}\t{', '.join(problem_class.base_dimensions)}\t"
+            f"{', '.join(problem_class.tunable_params)}\t{_format_params(problem_class.params)}"
+        )
+    return 0
+
+
+def _classes_fit(args: argparse.Namespace) -> int:
+    if args.min_observations <= 0:
+        raise SystemExit("--min-observations must be positive")
+    registry = ProblemClassRegistry.default()
+    classes = registry.all()
+    if args.class_name:
+        problem_class = registry.get(args.class_name)
+        if problem_class is None:
+            raise SystemExit(f"Unknown problem class: {args.class_name}")
+        selected: list[ProblemClass] = [problem_class]
+    else:
+        selected = list(classes.values())
+
+    observations = QualityLedger(args.ledger).read_all()
+    fitted: list[ProblemClass] = [
+        problem_class.fit(observations, min_observations=args.min_observations)
+        for problem_class in selected
+    ]
+    if args.json:
+        print(json.dumps(_classes_payload(fitted), indent=2, sort_keys=True))
+        return 0
+
+    print("name\tfitted_params\tconfidence")
+    for problem_class in sorted(fitted, key=lambda item: item.name):
+        confidence = getattr(problem_class, "confidence", 0.5)
+        print(f"{problem_class.name}\t{_format_params(problem_class.params)}\t{confidence:g}")
+    return 0
+
+
+def _classes_payload(classes: Iterable[ProblemClass]) -> dict[str, dict[str, Any]]:
+    return {
+        problem_class.name: {
+            "base_dimensions": list(problem_class.base_dimensions),
+            "tunable_params": list(problem_class.tunable_params),
+            "params": dict(problem_class.params),
+            "confidence": getattr(problem_class, "confidence", 0.5),
+        }
+        for problem_class in sorted(classes, key=lambda item: item.name)
+    }
+
+
+def _format_params(params: Mapping[str, float]) -> str:
+    return ", ".join(f"{key}={value:g}" for key, value in sorted(dict(params).items()))
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

llm_connect/costs.py

@@ -0,0 +1,74 @@
+"""Cost estimation over model rates and token counts."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from llm_connect.rates import ModelRateRegistry
+
+
+@dataclass(frozen=True)
+class CostEstimate:
+    """Cost estimate split by prompt and completion token spend."""
+
+    cost_usd: float | None
+    cost_source: str
+    prompt_cost_usd: float | None = None
+    completion_cost_usd: float | None = None
+
+
+def estimate_cost(
+    model_id: str,
+    prompt_tokens: int,
+    completion_tokens: int = 0,
+    *,
+    registry: ModelRateRegistry | None = None,
+) -> CostEstimate:
+    """Estimate USD cost for token counts using *registry*.
+
+    Unknown models return ``CostEstimate(None, "unknown")`` so callers can
+    record uncertainty explicitly instead of treating missing prices as zero.
+    """
+    prompt_count = _non_negative_int("prompt_tokens", prompt_tokens)
+    completion_count = _non_negative_int("completion_tokens", completion_tokens)
+    rates = registry or ModelRateRegistry.default()
+    rate = rates.get(model_id)
+    if rate is None:
+        return CostEstimate(cost_usd=None, cost_source="unknown")
+
+    prompt_cost = (prompt_count / 1000.0) * rate.prompt_per_1k
+    completion_cost = (completion_count / 1000.0) * rate.completion_per_1k
+    return CostEstimate(
+        cost_usd=prompt_cost + completion_cost,
+        cost_source=f"rate_table:{rate.model_id}",
+        prompt_cost_usd=prompt_cost,
+        completion_cost_usd=completion_cost,
+    )
+
+
+@dataclass(frozen=True)
+class CostModel:
+    """Small wrapper for callers that prefer an object over a free function."""
+
+    registry: ModelRateRegistry | None = None
+
+    def estimate_cost(
+        self,
+        model_id: str,
+        prompt_tokens: int,
+        completion_tokens: int = 0,
+    ) -> CostEstimate:
+        """Estimate cost using this model's registry."""
+        return estimate_cost(
+            model_id,
+            prompt_tokens,
+            completion_tokens,
+            registry=self.registry,
+        )
+
+
+def _non_negative_int(name: str, value: Any) -> int:
+    if isinstance(value, bool) or not isinstance(value, int) or value < 0:
+        raise ValueError(f"{name} must be a non-negative integer")
+    return value

llm_connect/factory.py

@@ -2,7 +2,8 @@
 Factory for creating LLM adapters by provider name.
 """
 
-from typing import Optional, Dict, Any
+import os
+from typing import Optional, Dict, Any
 
 from llm_connect.adapter import LLMAdapter
 from llm_connect.exceptions import LLMConfigurationError
@@ -13,6 +14,7 @@ _PROVIDERS: Dict[str, str] = {
     "claude-code": "llm_connect.claude_code.ClaudeCodeAdapter",
     "gemini": "llm_connect.gemini.GeminiAdapter",
     "openai": "llm_connect.openai.OpenAIAdapter",
+    "mock": "llm_connect.adapter.MockLLMAdapter",
 }
 
 
@@ -56,5 +58,10 @@ def create_adapter(
         return cls(model=model, api_key=api_key, system_prompt=system_prompt, **kwargs)
     elif provider == "claude-code":
         return cls(model=model, **kwargs)
-    else:
+    elif provider == "mock":
-        return cls(**kwargs)  # pragma: no cover
+        mock_response = os.environ.get("LLM_CONNECT_MOCK_RESPONSE")
+        if mock_response is not None and "mock_response" not in kwargs:
+            kwargs["mock_response"] = mock_response
+        return cls(**kwargs)
+    else:
+        return cls(**kwargs)

llm_connect/gemini.py

@@ -9,6 +9,7 @@ from llm_connect.adapter import LLMAdapter
 from llm_connect.models import RunConfig, LLMResponse
 from llm_connect.config import resolve_api_key, find_project_root
 from llm_connect._http import post_json
+from llm_connect._payload import merge_gemini_model_params
 from llm_connect.exceptions import LLMConfigurationError
 
 _DEFAULT_MODEL = "gemini-2.5-flash"
@@ -74,6 +75,8 @@ class GeminiAdapter(LLMAdapter):
                 "maxOutputTokens": config.max_tokens,
             },
         }
+        if config.model_params:
+            merge_gemini_model_params(payload, config.model_params)
 
         url = f"{_API_BASE}/models/{model}:generateContent?key={self._api_key}"
 

llm_connect/grading.py

@@ -0,0 +1,239 @@
+"""Baseline grading primitives for adaptive routing.
+
+Graders compare a candidate adapter response against a caller-chosen baseline.
+They produce normalised quality scores that can be recorded in a
+``QualityLedger`` and consumed later by adaptive routing policy.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass, field, replace
+from typing import Any, Protocol
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.embedding_adapter import EmbeddingAdapter
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.similarity import cosine_similarity
+
+
+def _validate_score(value: float) -> float:
+    if not isinstance(value, (int, float)):
+        raise ValueError("quality_score must be a number between 0 and 1")
+    score = float(value)
+    if not 0 <= score <= 1:
+        raise ValueError("quality_score must be between 0 and 1")
+    return score
+
+
+def _normalise_text(text: str) -> str:
+    return " ".join(text.strip().split())
+
+
+@dataclass(frozen=True)
+class GradingResult:
+    """Structured result from comparing candidate output to baseline output."""
+
+    quality_score: float
+    notes: str
+    grader_id: str
+    baseline_response: LLMResponse
+    candidate_response: LLMResponse
+
+    def __post_init__(self) -> None:
+        if not str(self.grader_id).strip():
+            raise ValueError("grader_id must be a non-empty string")
+        object.__setattr__(self, "quality_score", _validate_score(self.quality_score))
+        object.__setattr__(self, "notes", str(self.notes))
+
+
+class Judge(Protocol):
+    """Compare baseline and candidate responses."""
+
+    grader_id: str
+
+    def judge(
+        self,
+        baseline_response: LLMResponse,
+        candidate_response: LLMResponse,
+        *,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        """Return a quality score for candidate relative to baseline."""
+
+
+class BaselineGrader(Protocol):
+    """Run baseline and candidate adapters, then judge the paired responses."""
+
+    def grade(
+        self,
+        baseline_adapter: LLMAdapter,
+        candidate_adapter: LLMAdapter,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        """Return a structured grading result."""
+
+
+@dataclass
+class ExactMatchJudge:
+    """Judge that scores 1.0 when response text matches exactly after normalisation."""
+
+    normalize_whitespace: bool = True
+    case_sensitive: bool = True
+    grader_id: str = "exact-match"
+
+    def judge(
+        self,
+        baseline_response: LLMResponse,
+        candidate_response: LLMResponse,
+        *,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        baseline_text = baseline_response.content
+        candidate_text = candidate_response.content
+        if self.normalize_whitespace:
+            baseline_text = _normalise_text(baseline_text)
+            candidate_text = _normalise_text(candidate_text)
+        if not self.case_sensitive:
+            baseline_text = baseline_text.casefold()
+            candidate_text = candidate_text.casefold()
+
+        matched = baseline_text == candidate_text
+        return GradingResult(
+            quality_score=1.0 if matched else 0.0,
+            notes="exact match" if matched else "candidate content differs from baseline",
+            grader_id=self.grader_id,
+            baseline_response=baseline_response,
+            candidate_response=candidate_response,
+        )
+
+
+@dataclass
+class EmbeddingSimilarityJudge:
+    """Judge that maps cosine similarity between response embeddings to 0..1."""
+
+    embedding_adapter: EmbeddingAdapter
+    grader_id: str = "embedding-similarity"
+
+    def judge(
+        self,
+        baseline_response: LLMResponse,
+        candidate_response: LLMResponse,
+        *,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        embeddings = self.embedding_adapter.embed(
+            [baseline_response.content, candidate_response.content]
+        )
+        if len(embeddings) != 2:
+            raise ValueError("EmbeddingSimilarityJudge expected exactly two embeddings")
+
+        raw_similarity = cosine_similarity(embeddings[0], embeddings[1])
+        quality_score = max(0.0, min(1.0, raw_similarity))
+        return GradingResult(
+            quality_score=quality_score,
+            notes=f"cosine similarity {raw_similarity:.4f}",
+            grader_id=self.grader_id,
+            baseline_response=baseline_response,
+            candidate_response=candidate_response,
+        )
+
+
+@dataclass
+class LLMJudge:
+    """LLM-as-judge wrapper using a fixed rubric prompt and JSON response."""
+
+    judge_adapter: LLMAdapter
+    rubric: str = (
+        "Compare the candidate response to the baseline response. "
+        "Return JSON only with keys quality_score and notes. "
+        "quality_score must be a number from 0 to 1."
+    )
+    grader_id: str = "llm-judge"
+    seed: int | None = 0
+
+    def judge(
+        self,
+        baseline_response: LLMResponse,
+        candidate_response: LLMResponse,
+        *,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        judge_prompt = self._build_prompt(prompt, baseline_response, candidate_response)
+        judge_config = self._judge_config(run_config)
+        response = self.judge_adapter.execute_prompt(judge_prompt, judge_config)
+        parsed = self._parse_judge_response(response.content)
+        return GradingResult(
+            quality_score=parsed["quality_score"],
+            notes=parsed["notes"],
+            grader_id=self.grader_id,
+            baseline_response=baseline_response,
+            candidate_response=candidate_response,
+        )
+
+    def _judge_config(self, run_config: RunConfig) -> RunConfig:
+        params: dict[str, Any] = dict(run_config.model_params)
+        if self.seed is not None:
+            params.setdefault("seed", self.seed)
+        return replace(run_config, temperature=0.0, model_params=params, budget_tracker=None)
+
+    def _build_prompt(
+        self,
+        prompt: str,
+        baseline_response: LLMResponse,
+        candidate_response: LLMResponse,
+    ) -> str:
+        return (
+            f"{self.rubric}\n\n"
+            f"Original prompt:\n{prompt}\n\n"
+            f"Baseline response:\n{baseline_response.content}\n\n"
+            f"Candidate response:\n{candidate_response.content}\n"
+        )
+
+    def _parse_judge_response(self, content: str) -> dict[str, Any]:
+        try:
+            data = json.loads(content)
+        except json.JSONDecodeError:
+            match = re.search(r"\{.*\}", content, flags=re.DOTALL)
+            if not match:
+                raise ValueError("LLMJudge response did not contain JSON") from None
+            try:
+                data = json.loads(match.group(0))
+            except json.JSONDecodeError as exc:
+                raise ValueError("LLMJudge response JSON could not be parsed") from exc
+
+        if not isinstance(data, dict):
+            raise ValueError("LLMJudge response JSON must be an object")
+        return {
+            "quality_score": _validate_score(data.get("quality_score")),
+            "notes": str(data.get("notes", "")),
+        }
+
+
+@dataclass
+class PairedGrader:
+    """Baseline grader that runs both adapters and delegates comparison to a judge."""
+
+    judge: Judge = field(default_factory=ExactMatchJudge)
+
+    def grade(
+        self,
+        baseline_adapter: LLMAdapter,
+        candidate_adapter: LLMAdapter,
+        prompt: str,
+        run_config: RunConfig,
+    ) -> GradingResult:
+        baseline_response = baseline_adapter.execute_prompt(prompt, run_config)
+        candidate_response = candidate_adapter.execute_prompt(prompt, run_config)
+        return self.judge.judge(
+            baseline_response,
+            candidate_response,
+            prompt=prompt,
+            run_config=run_config,
+        )

llm_connect/openai.py

@@ -9,6 +9,7 @@ from llm_connect.adapter import LLMAdapter
 from llm_connect.models import RunConfig, LLMResponse
 from llm_connect.config import resolve_api_key, find_project_root
 from llm_connect._http import post_json
+from llm_connect._payload import merge_openai_chat_model_params
 from llm_connect.exceptions import (
     LLMConfigurationError,
     LLMAPIError,
@@ -65,6 +66,8 @@ class OpenAIAdapter(LLMAdapter):
             "temperature": config.temperature,
             "max_tokens": config.max_tokens,
         }
+        if config.model_params:
+            merge_openai_chat_model_params(payload, config.model_params)
 
         headers = {
             "Authorization": f"Bearer {self._api_key}",

llm_connect/openrouter.py

@@ -1,142 +1,163 @@
-"""
+"""
-OpenRouter adapter — calls the OpenAI-compatible chat completions API.
+OpenRouter adapter - calls the OpenAI-compatible chat completions API.
-"""
+"""
-
+
-import time
+import time
-from typing import Optional, Dict, Any
+from typing import Any, Dict, Optional
-
+
-from llm_connect.adapter import LLMAdapter
+from llm_connect._http import post_json
-from llm_connect.models import RunConfig, LLMResponse
+from llm_connect._payload import merge_openai_chat_model_params
-from llm_connect.config import LLMConfig, resolve_api_key, find_project_root
+from llm_connect.adapter import LLMAdapter
-from llm_connect._http import post_json
+from llm_connect.config import LLMConfig, find_project_root, resolve_api_key
-from llm_connect.exceptions import (
+from llm_connect.exceptions import LLMAPIError, LLMRateLimitError
-    LLMConfigurationError,
+from llm_connect.models import LLMResponse, RunConfig
-    LLMAPIError,
+
-    LLMRateLimitError,
+_DEFAULT_MODEL = "anthropic/claude-sonnet-4"
-)
+
-
+
-_DEFAULT_MODEL = "anthropic/claude-sonnet-4"
+class OpenRouterAdapter(LLMAdapter):
-
+    """LLM adapter that calls the OpenRouter chat completions endpoint.
-
+
-class OpenRouterAdapter(LLMAdapter):
+    Constructor args override values from *config*; *config* overrides
-    """LLM adapter that calls the OpenRouter chat completions endpoint.
+    global defaults. The model used for a given call is resolved as:
-
+    ``constructor model > RunConfig.model_name > default``.
-    Constructor args override values from *config*; *config* overrides
+    """
-    global defaults.  The model used for a given call is resolved as:
+
-    ``constructor model > RunConfig.model_name > default``.
+    def __init__(
-    """
+        self,
-
+        model: Optional[str] = None,
-    def __init__(
+        api_key: Optional[str] = None,
-        self,
+        api_base: Optional[str] = None,
-        model: Optional[str] = None,
+        config: Optional[LLMConfig] = None,
-        api_key: Optional[str] = None,
+        system_prompt: Optional[str] = None,
-        api_base: Optional[str] = None,
+        extra_headers: Optional[Dict[str, str]] = None,
-        config: Optional[LLMConfig] = None,
+        max_retries: Optional[int] = None,
-        system_prompt: Optional[str] = None,
+    ):
-        extra_headers: Optional[Dict[str, str]] = None,
+        self._config = config or LLMConfig()
-        max_retries: Optional[int] = None,
+        # Track whether the model was explicitly supplied (constructor or
-    ):
+        # LLMConfig). Comparing self._model to _DEFAULT_MODEL is not enough:
-        self._config = config or LLMConfig()
+        # callers who pass --model anthropic/claude-sonnet-4 happen to match
-        self._model = model or self._config.model or _DEFAULT_MODEL
+        # the default and would otherwise be misrouted to RunConfig.model_name
-        self._api_base = (api_base or self._config.api_base).rstrip("/")
+        # (which defaults to "gpt-4", quietly sending every call to OpenAI's
-        self._system_prompt = system_prompt
+        # gpt-4 model, which is what broke the activity-core CUST-WP-0045
-        self._extra_headers = extra_headers or {}
+        # canary on 2026-06-02).
-        self._max_retries = max_retries if max_retries is not None else self._config.max_retries
+        self._explicit_model = model is not None or self._config.model is not None
-
+        self._model = model or self._config.model or _DEFAULT_MODEL
-        # Resolve API key
+        self._api_base = (api_base or self._config.api_base).rstrip("/")
-        root = find_project_root()
+        self._system_prompt = system_prompt
-        key_file_paths = [root / "apikey-openrouter.txt"] if root else []
+        self._extra_headers = extra_headers or {}
-        self._api_key = resolve_api_key(
+        self._max_retries = max_retries if max_retries is not None else self._config.max_retries
-            explicit=api_key or self._config.api_key,
+
-            env_var="OPENROUTER_API_KEY",
+        root = find_project_root()
-            key_file_paths=key_file_paths,
+        key_file_paths = [root / "apikey-openrouter.txt"] if root else []
-        )
+        self._api_key = resolve_api_key(
-
+            explicit=api_key or self._config.api_key,
-    # ── LLMAdapter interface ────────────────────────────────────────
+            env_var="OPENROUTER_API_KEY",
-
+            key_file_paths=key_file_paths,
-    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        )
-        self._preflight_budget(config)
+
-        model = self._model if self._model != _DEFAULT_MODEL else (config.model_name or self._model)
+    # LLMAdapter interface
-
+
-        messages: list[Dict[str, str]] = []
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
-        if self._system_prompt:
+        self._preflight_budget(config)
-            messages.append({"role": "system", "content": self._system_prompt})
+        # Explicit constructor/LLMConfig model wins; only fall back to the
-        messages.append({"role": "user", "content": prompt})
+        # per-call RunConfig.model_name when the adapter was not told what to
-
+        # use. RunConfig.model_name defaults to "gpt-4", so falling back
-        payload: Dict[str, Any] = {
+        # unconditionally would silently misroute callers.
-            "model": model,
+        if self._explicit_model:
-            "messages": messages,
+            model = self._model
-            "temperature": config.temperature,
+        else:
-            "max_tokens": config.max_tokens,
+            model = config.model_name or self._model
-        }
+
-        # Merge extra model_params from RunConfig
+        messages: list[Dict[str, str]] = []
-        if config.model_params:
+        if self._system_prompt:
-            payload.update(config.model_params)
+            messages.append({"role": "system", "content": self._system_prompt})
-
+        messages.append({"role": "user", "content": prompt})
-        headers = {
+
-            "Authorization": f"Bearer {self._api_key}",
+        payload: Dict[str, Any] = {
-            **self._extra_headers,
+            "model": model,
-        }
+            "messages": messages,
-        url = f"{self._api_base}/chat/completions"
+            "temperature": config.temperature,
-
+            "max_tokens": config.max_tokens,
-        start = time.time()
+        }
-        data = self._post_with_retries(url, payload, headers, config.timeout_seconds)
+        if config.model_params:
-        latency = time.time() - start
+            merge_openai_chat_model_params(payload, config.model_params)
-
+            provider_params = config.model_params.get("provider")
-        # Parse response
+            if isinstance(provider_params, dict):
-        choice = data.get("choices", [{}])[0]
+                payload["provider"] = dict(provider_params)
-        content = choice.get("message", {}).get("content", "")
+            if _uses_json_schema_response_format(payload):
-        finish_reason = choice.get("finish_reason", "stop")
+                provider = payload.setdefault("provider", {})
-        usage = data.get("usage", {})
+                if isinstance(provider, dict):
-
+                    provider.setdefault("require_parameters", True)
-        response = LLMResponse(
+
-            content=content,
+        headers = {
-            model=data.get("model", model),
+            "Authorization": f"Bearer {self._api_key}",
-            usage={
+            **self._extra_headers,
-                "prompt_tokens": usage.get("prompt_tokens", 0),
+        }
-                "completion_tokens": usage.get("completion_tokens", 0),
+        url = f"{self._api_base}/chat/completions"
-                "total_tokens": usage.get("total_tokens", 0),
+
-            },
+        start = time.time()
-            finish_reason=finish_reason,
+        data = self._post_with_retries(url, payload, headers, config.timeout_seconds)
-            metadata={
+        latency = time.time() - start
-                "provider": "openrouter",
+
-                "latency_seconds": round(latency, 3),
+        choice = data.get("choices", [{}])[0]
-                "response_id": data.get("id", ""),
+        content = choice.get("message", {}).get("content", "")
-            },
+        finish_reason = choice.get("finish_reason", "stop")
-        )
+        usage = data.get("usage", {})
-        self._consume_budget(config, response)
+
-        return response
+        response = LLMResponse(
-
+            content=content,
-    def validate_config(self, config: RunConfig) -> bool:
+            model=data.get("model", model),
-        if not self._api_key:
+            usage={
-            return False
+                "prompt_tokens": usage.get("prompt_tokens", 0),
-        if not (self._model or config.model_name):
+                "completion_tokens": usage.get("completion_tokens", 0),
-            return False
+                "total_tokens": usage.get("total_tokens", 0),
-        if not (0.0 <= config.temperature <= 2.0):
+            },
-            return False
+            finish_reason=finish_reason,
-        return True
+            metadata={
-
+                "provider": "openrouter",
-    # ── Internals ───────────────────────────────────────────────────
+                "latency_seconds": round(latency, 3),
-
+                "response_id": data.get("id", ""),
-    def _post_with_retries(
+            },
-        self,
+        )
-        url: str,
+        self._consume_budget(config, response)
-        payload: Dict[str, Any],
+        return response
-        headers: Dict[str, str],
+
-        timeout: int,
+    def validate_config(self, config: RunConfig) -> bool:
-    ) -> Dict[str, Any]:
+        if not self._api_key:
-        last_exc: Optional[Exception] = None
+            return False
-        for attempt in range(self._max_retries + 1):
+        if not (self._model or config.model_name):
-            try:
+            return False
-                return post_json(url, payload, headers, timeout=timeout)
+        if not (0.0 <= config.temperature <= 2.0):
-            except LLMRateLimitError as exc:
+            return False
-                last_exc = exc
+        return True
-                if attempt < self._max_retries:
+
-                    time.sleep(2 ** attempt)
+    # Internals
-            except LLMAPIError as exc:
+
-                if exc.status_code >= 500 and attempt < self._max_retries:
+    def _post_with_retries(
-                    last_exc = exc
+        self,
-                    time.sleep(2 ** attempt)
+        url: str,
-                else:
+        payload: Dict[str, Any],
-                    raise
+        headers: Dict[str, str],
-        raise last_exc  # type: ignore[misc]
+        timeout: int,
+    ) -> Dict[str, Any]:
+        last_exc: Optional[Exception] = None
+        for attempt in range(self._max_retries + 1):
+            try:
+                return post_json(url, payload, headers, timeout=timeout)
+            except LLMRateLimitError as exc:
+                last_exc = exc
+                if attempt < self._max_retries:
+                    time.sleep(2 ** attempt)
+            except LLMAPIError as exc:
+                if exc.status_code >= 500 and attempt < self._max_retries:
+                    last_exc = exc
+                    time.sleep(2 ** attempt)
+                else:
+                    raise
+        raise last_exc  # type: ignore[misc]
+
+
+def _uses_json_schema_response_format(payload: Dict[str, Any]) -> bool:
+    response_format = payload.get("response_format")
+    return isinstance(response_format, dict) and response_format.get("type") == "json_schema"

llm_connect/problem_classes.py

@@ -0,0 +1,463 @@
+"""Problem-class token estimators for common LLM workflow shapes."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+
+DEFAULT_WORDS_PER_TOKEN = 0.75
+
+
+@dataclass(frozen=True)
+class TokenEstimate:
+    """Prompt/completion token estimate for a prospective LLM call."""
+
+    prompt_tokens: int
+    completion_tokens: int
+    confidence: float = 0.5
+
+    def __post_init__(self) -> None:
+        prompt_tokens = _non_negative_int("prompt_tokens", self.prompt_tokens)
+        completion_tokens = _non_negative_int("completion_tokens", self.completion_tokens)
+        confidence = _bounded_float("confidence", self.confidence)
+        object.__setattr__(self, "prompt_tokens", prompt_tokens)
+        object.__setattr__(self, "completion_tokens", completion_tokens)
+        object.__setattr__(self, "confidence", confidence)
+
+
+@dataclass(frozen=True)
+class Observation:
+    """Actual token use paired with the problem dimensions that produced it."""
+
+    dimensions: dict[str, Any]
+    prompt_tokens: int
+    completion_tokens: int
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "dimensions", dict(self.dimensions))
+        object.__setattr__(self, "prompt_tokens", _non_negative_int("prompt_tokens", self.prompt_tokens))
+        object.__setattr__(
+            self,
+            "completion_tokens",
+            _non_negative_int("completion_tokens", self.completion_tokens),
+        )
+
+
+class ProblemClass(Protocol):
+    """Estimator contract implemented by built-in and consumer classes."""
+
+    name: str
+    base_dimensions: tuple[str, ...]
+    tunable_params: tuple[str, ...]
+    params: dict[str, float]
+
+    def estimate(
+        self,
+        dimensions: dict[str, Any],
+        params: dict[str, Any] | None = None,
+    ) -> TokenEstimate:
+        """Estimate token use from dimensions and optional parameter overrides."""
+        ...
+
+    def fit(
+        self,
+        observations: Sequence[Any],
+        *,
+        min_observations: int = 3,
+    ) -> "ProblemClass":
+        """Return an estimator with params adapted from observed token use."""
+        ...
+
+
+class ProblemClassRegistry:
+    """Registry keyed by stable problem-class names."""
+
+    schema_version = 1
+
+    def __init__(self, classes: Sequence[ProblemClass] | None = None) -> None:
+        self._classes: dict[str, ProblemClass] = {}
+        for problem_class in classes or ():
+            self.register(problem_class)
+
+    def get(self, name: str) -> ProblemClass | None:
+        """Return a registered class by name."""
+        return self._classes.get(str(name).strip())
+
+    def all(self) -> dict[str, ProblemClass]:
+        """Return a copy of registered problem classes."""
+        return dict(self._classes)
+
+    def register(self, problem_class: ProblemClass, *, replace: bool = False) -> None:
+        """Register *problem_class* under its name."""
+        name = str(problem_class.name).strip()
+        if not name:
+            raise ValueError("problem_class.name must be a non-empty string")
+        if name in self._classes and not replace:
+            raise ValueError(f"Problem class {name!r} is already registered")
+        self._classes[name] = problem_class
+
+    @classmethod
+    def default(cls) -> "ProblemClassRegistry":
+        """Return the built-in problem-class registry."""
+        return cls(
+            [
+                ChunkSummarizationProblemClass(),
+                EntityExtractionProblemClass(),
+                RelationExtractionProblemClass(),
+                JudgeEvalProblemClass(),
+                ReportSynthesisProblemClass(),
+            ]
+        )
+
+
+class _BaseProblemClass:
+    name = ""
+    base_dimensions: tuple[str, ...] = ()
+    tunable_params: tuple[str, ...] = ()
+    seed_params: Mapping[str, float] = {}
+
+    def __init__(
+        self,
+        *,
+        params: Mapping[str, Any] | None = None,
+        confidence: float = 0.5,
+    ) -> None:
+        merged = dict(self.seed_params)
+        for key, value in (params or {}).items():
+            if key not in self.tunable_params:
+                raise ValueError(f"Unknown parameter {key!r} for problem class {self.name!r}")
+            merged[key] = _non_negative_float(key, value)
+        self.params: dict[str, float] = merged
+        self.confidence = _bounded_float("confidence", confidence)
+
+    def estimate(
+        self,
+        dimensions: dict[str, Any],
+        params: dict[str, Any] | None = None,
+    ) -> TokenEstimate:
+        dimensions = dict(dimensions)
+        self._validate_dimensions(dimensions)
+        merged_params = dict(self.params)
+        for key, value in (params or {}).items():
+            if key not in self.tunable_params:
+                raise ValueError(f"Unknown parameter {key!r} for problem class {self.name!r}")
+            merged_params[key] = _non_negative_float(key, value)
+        prompt_tokens, completion_tokens = self._estimate_tokens(dimensions, merged_params)
+        return TokenEstimate(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            confidence=self.confidence,
+        )
+
+    def fit(
+        self,
+        observations: Sequence[Any],
+        *,
+        min_observations: int = 3,
+    ) -> ProblemClass:
+        if min_observations <= 0:
+            raise ValueError("min_observations must be positive")
+        parsed = [
+            observation
+            for observation in (
+                _coerce_observation(raw, self.name, self.base_dimensions) for raw in observations
+            )
+            if observation is not None
+        ]
+        if len(parsed) < min_observations:
+            return self
+
+        fitted: dict[str, float] = {}
+        for param in self.tunable_params:
+            values = [
+                value
+                for value in (
+                    self._infer_param(param, observation) for observation in parsed
+                )
+                if value is not None
+            ]
+            if values:
+                fitted[param] = sum(values) / len(values)
+        if not fitted:
+            return self
+
+        confidence = min(0.95, max(self.confidence, len(parsed) / (len(parsed) + 5)))
+        return type(self)(params={**self.params, **fitted}, confidence=confidence)
+
+    def _validate_dimensions(self, dimensions: Mapping[str, Any]) -> None:
+        missing = [name for name in self.base_dimensions if name not in dimensions]
+        if missing:
+            raise ValueError(f"Missing dimensions for {self.name!r}: {', '.join(missing)}")
+        for name in self.base_dimensions:
+            _non_negative_float(name, dimensions[name])
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        raise NotImplementedError
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        raise NotImplementedError
+
+
+class ChunkSummarizationProblemClass(_BaseProblemClass):
+    name = "chunk-summarization"
+    base_dimensions: tuple[str, ...] = ("chunk_words", "template_words")
+    tunable_params: tuple[str, ...] = ("completion_ratio",)
+    seed_params: Mapping[str, float] = {"completion_ratio": 0.25}
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        prompt_tokens = _words_to_tokens(
+            _dimension(dimensions, "chunk_words") + _dimension(dimensions, "template_words")
+        )
+        completion_tokens = _round_tokens(prompt_tokens * params["completion_ratio"])
+        return prompt_tokens, completion_tokens
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        if param != "completion_ratio" or observation.prompt_tokens == 0:
+            return None
+        return observation.completion_tokens / observation.prompt_tokens
+
+
+class EntityExtractionProblemClass(_BaseProblemClass):
+    name = "entity-extraction"
+    base_dimensions: tuple[str, ...] = ("chunk_words", "template_words", "expected_entities")
+    tunable_params: tuple[str, ...] = ("tokens_per_entity",)
+    seed_params: Mapping[str, float] = {"tokens_per_entity": 70.0}
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        prompt_tokens = _words_to_tokens(
+            _dimension(dimensions, "chunk_words") + _dimension(dimensions, "template_words")
+        )
+        completion_tokens = _round_tokens(
+            _dimension(dimensions, "expected_entities") * params["tokens_per_entity"]
+        )
+        return prompt_tokens, completion_tokens
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        expected_entities = _dimension(observation.dimensions, "expected_entities")
+        if param != "tokens_per_entity" or expected_entities <= 0:
+            return None
+        return observation.completion_tokens / expected_entities
+
+
+class RelationExtractionProblemClass(_BaseProblemClass):
+    name = "relation-extraction"
+    base_dimensions: tuple[str, ...] = ("chunk_words", "template_words", "expected_relations")
+    tunable_params: tuple[str, ...] = ("tokens_per_relation",)
+    seed_params: Mapping[str, float] = {"tokens_per_relation": 80.0}
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        prompt_tokens = _words_to_tokens(
+            _dimension(dimensions, "chunk_words") + _dimension(dimensions, "template_words")
+        )
+        completion_tokens = _round_tokens(
+            _dimension(dimensions, "expected_relations") * params["tokens_per_relation"]
+        )
+        return prompt_tokens, completion_tokens
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        expected_relations = _dimension(observation.dimensions, "expected_relations")
+        if param != "tokens_per_relation" or expected_relations <= 0:
+            return None
+        return observation.completion_tokens / expected_relations
+
+
+class JudgeEvalProblemClass(_BaseProblemClass):
+    name = "judge-eval"
+    base_dimensions: tuple[str, ...] = ("artifact_words", "template_words", "n_criteria")
+    tunable_params: tuple[str, ...] = ("tokens_per_criterion",)
+    seed_params: Mapping[str, float] = {"tokens_per_criterion": 35.0}
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        prompt_tokens = _words_to_tokens(
+            _dimension(dimensions, "artifact_words") + _dimension(dimensions, "template_words")
+        )
+        completion_tokens = _round_tokens(
+            _dimension(dimensions, "n_criteria") * params["tokens_per_criterion"]
+        )
+        return prompt_tokens, completion_tokens
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        n_criteria = _dimension(observation.dimensions, "n_criteria")
+        if param != "tokens_per_criterion" or n_criteria <= 0:
+            return None
+        return observation.completion_tokens / n_criteria
+
+
+class ReportSynthesisProblemClass(_BaseProblemClass):
+    name = "report-synthesis"
+    base_dimensions: tuple[str, ...] = ("n_chunks", "n_entities", "n_relations", "template_words")
+    tunable_params: tuple[str, ...] = ("base_completion_tokens",)
+    seed_params: Mapping[str, float] = {"base_completion_tokens": 400.0}
+
+    def _estimate_tokens(
+        self,
+        dimensions: Mapping[str, Any],
+        params: Mapping[str, float],
+    ) -> tuple[int, int]:
+        prompt_tokens = _words_to_tokens(_dimension(dimensions, "template_words"))
+        prompt_tokens += _round_tokens(_dimension(dimensions, "n_chunks") * 40)
+        prompt_tokens += _round_tokens(_dimension(dimensions, "n_entities") * 25)
+        prompt_tokens += _round_tokens(_dimension(dimensions, "n_relations") * 35)
+        return prompt_tokens, _round_tokens(params["base_completion_tokens"])
+
+    def _infer_param(self, param: str, observation: Observation) -> float | None:
+        if param != "base_completion_tokens":
+            return None
+        return float(observation.completion_tokens)
+
+
+def default_problem_class_registry() -> ProblemClassRegistry:
+    """Return the built-in problem-class registry."""
+    return ProblemClassRegistry.default()
+
+
+def _coerce_observation(
+    raw: Any,
+    class_name: str,
+    required_dimensions: tuple[str, ...],
+) -> Observation | None:
+    try:
+        if isinstance(raw, Observation):
+            return raw
+        if isinstance(raw, Mapping):
+            return _coerce_mapping_observation(raw, class_name, required_dimensions)
+        return _coerce_object_observation(raw, class_name, required_dimensions)
+    except (KeyError, TypeError, ValueError):
+        return None
+
+
+def _coerce_mapping_observation(
+    raw: Mapping[str, Any],
+    class_name: str,
+    required_dimensions: tuple[str, ...],
+) -> Observation | None:
+    raw_tags = raw.get("tags")
+    tags: Mapping[str, Any] = raw_tags if isinstance(raw_tags, Mapping) else {}
+    problem_class = raw.get("problem_class") or tags.get("problem_class")
+    if problem_class is not None and str(problem_class) != class_name:
+        return None
+    dimensions = _dimensions_from_sources(required_dimensions, raw, tags)
+    prompt_tokens = _token_value(raw, "prompt_tokens", "tokens_in", "actual_prompt_tokens")
+    completion_tokens = _token_value(
+        raw,
+        "completion_tokens",
+        "tokens_out",
+        "actual_completion_tokens",
+    )
+    return Observation(dimensions, prompt_tokens, completion_tokens)
+
+
+def _coerce_object_observation(
+    raw: Any,
+    class_name: str,
+    required_dimensions: tuple[str, ...],
+) -> Observation | None:
+    raw_tags = getattr(raw, "tags", {}) or {}
+    tags: Mapping[str, Any] = raw_tags if isinstance(raw_tags, Mapping) else {}
+    problem_class = tags.get("problem_class")
+    if problem_class is not None and str(problem_class) != class_name:
+        return None
+    dimensions = _dimensions_from_sources(required_dimensions, tags)
+    return Observation(
+        dimensions=dimensions,
+        prompt_tokens=getattr(raw, "tokens_in"),
+        completion_tokens=getattr(raw, "tokens_out"),
+    )
+
+
+def _dimensions_from_sources(
+    required_dimensions: tuple[str, ...],
+    *sources: Mapping[str, Any],
+) -> dict[str, Any]:
+    for source in sources:
+        candidate = source.get("dimensions")
+        if isinstance(candidate, Mapping):
+            return dict(candidate)
+    dimensions: dict[str, Any] = {}
+    for name in required_dimensions:
+        for source in sources:
+            if name in source:
+                dimensions[name] = source[name]
+                break
+    if len(dimensions) != len(required_dimensions):
+        raise ValueError("observation is missing required dimensions")
+    return dimensions
+
+
+def _token_value(raw: Mapping[str, Any], *names: str) -> int:
+    for name in names:
+        if name in raw:
+            return _non_negative_int(name, raw[name])
+    usage = raw.get("usage")
+    if isinstance(usage, Mapping):
+        for name in names:
+            if name in usage:
+                return _non_negative_int(name, usage[name])
+    raise KeyError(names[0])
+
+
+def _dimension(dimensions: Mapping[str, Any], name: str) -> float:
+    return _non_negative_float(name, dimensions[name])
+
+
+def _words_to_tokens(words: float) -> int:
+    if words == 0:
+        return 0
+    return max(1, _round_tokens(words / DEFAULT_WORDS_PER_TOKEN))
+
+
+def _round_tokens(value: float) -> int:
+    return max(0, int(round(value)))
+
+
+def _non_negative_int(name: str, value: Any) -> int:
+    if isinstance(value, bool):
+        raise ValueError(f"{name} must be a non-negative integer")
+    try:
+        integer = int(value)
+    except (TypeError, ValueError) as exc:
+        raise ValueError(f"{name} must be a non-negative integer") from exc
+    if integer < 0 or integer != float(value):
+        raise ValueError(f"{name} must be a non-negative integer")
+    return integer
+
+
+def _non_negative_float(name: str, value: Any) -> float:
+    if isinstance(value, bool):
+        raise ValueError(f"{name} must be a non-negative number")
+    try:
+        number = float(value)
+    except (TypeError, ValueError) as exc:
+        raise ValueError(f"{name} must be a non-negative number") from exc
+    if number < 0:
+        raise ValueError(f"{name} must be a non-negative number")
+    return number
+
+
+def _bounded_float(name: str, value: Any) -> float:
+    number = _non_negative_float(name, value)
+    if number > 1:
+        raise ValueError(f"{name} must be between 0 and 1")
+    return number

llm_connect/profiles.py

@@ -0,0 +1,293 @@
+"""Named runtime profiles for server-mode adapter dispatch."""
+
+from __future__ import annotations
+
+import json
+import os
+import threading
+from dataclasses import dataclass, field, replace
+from pathlib import Path
+from typing import Any, Callable, Mapping
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.exceptions import LLMConfigurationError
+from llm_connect.factory import create_adapter
+from llm_connect.models import LLMResponse, RunConfig
+
+CUSTODIAN_TRIAGE_BALANCED = "custodian-triage-balanced"
+DEFAULT_CUSTODIAN_TRIAGE_PROVIDER = "openrouter"
+DEFAULT_CUSTODIAN_TRIAGE_MODEL = "google/gemini-2.5-flash"
+_RUN_CONFIG_DEFAULTS = RunConfig()
+
+
+@dataclass(frozen=True)
+class RuntimeProfile:
+    """Provider/model routing and default call config for a named profile."""
+
+    name: str
+    provider: str
+    model: str
+    config: RunConfig = field(default_factory=RunConfig)
+
+    def resolve_config(self, request_config: RunConfig) -> RunConfig:
+        """Merge profile defaults with request overrides.
+
+        `RunConfig` has value defaults rather than optional fields, so the
+        merge is intentionally conservative: provider/model identity comes from
+        the profile, scalar generation fields come from the request, and
+        `model_params` are shallow-merged with request keys winning.
+        """
+
+        merged_params = {
+            **(self.config.model_params or {}),
+            **(request_config.model_params or {}),
+        }
+        return replace(
+            request_config,
+            model_name=self.model,
+            temperature=_profile_default_if_unchanged(
+                request_config.temperature,
+                _RUN_CONFIG_DEFAULTS.temperature,
+                self.config.temperature,
+            ),
+            max_tokens=_profile_default_if_unchanged(
+                request_config.max_tokens,
+                _RUN_CONFIG_DEFAULTS.max_tokens,
+                self.config.max_tokens,
+            ),
+            max_depth=_profile_default_if_unchanged(
+                request_config.max_depth,
+                _RUN_CONFIG_DEFAULTS.max_depth,
+                self.config.max_depth,
+            ),
+            timeout_seconds=_profile_default_if_unchanged(
+                request_config.timeout_seconds,
+                _RUN_CONFIG_DEFAULTS.timeout_seconds,
+                self.config.timeout_seconds,
+            ),
+            model_params=merged_params,
+        )
+
+
+class ProfiledLLMAdapter(LLMAdapter):
+    """Adapter wrapper that dispatches named profile requests to adapters."""
+
+    def __init__(
+        self,
+        default_adapter: LLMAdapter,
+        profiles: Mapping[str, RuntimeProfile],
+        *,
+        adapter_factory: Callable[[str, str], LLMAdapter] | None = None,
+        strict_profiles: bool = False,
+        profile_prefixes: tuple[str, ...] = ("custodian-",),
+    ) -> None:
+        self.default_adapter = default_adapter
+        self.profiles = dict(profiles)
+        self.adapter_factory = adapter_factory or _default_adapter_factory
+        self.strict_profiles = strict_profiles
+        self.profile_prefixes = profile_prefixes
+        self._adapters: dict[tuple[str, str], LLMAdapter] = {}
+        self._lock = threading.Lock()
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        profile = self._resolve_profile(config.model_name)
+        if profile is None:
+            return self.default_adapter.execute_prompt(prompt, config)
+
+        adapter = self._adapter_for(profile)
+        resolved_config = profile.resolve_config(config)
+        response = adapter.execute_prompt(prompt, resolved_config)
+        response.metadata.setdefault("profile", profile.name)
+        response.metadata.setdefault("profile_provider", profile.provider)
+        response.metadata.setdefault("profile_model", profile.model)
+        return response
+
+    async def async_execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        profile = self._resolve_profile(config.model_name)
+        if profile is None:
+            return await self.default_adapter.async_execute_prompt(prompt, config)
+
+        adapter = self._adapter_for(profile)
+        resolved_config = profile.resolve_config(config)
+        response = await adapter.async_execute_prompt(prompt, resolved_config)
+        response.metadata.setdefault("profile", profile.name)
+        response.metadata.setdefault("profile_provider", profile.provider)
+        response.metadata.setdefault("profile_model", profile.model)
+        return response
+
+    def validate_config(self, config: RunConfig) -> bool:
+        profile = self._resolve_profile(config.model_name)
+        if profile is None:
+            return self.default_adapter.validate_config(config)
+        return self._adapter_for(profile).validate_config(profile.resolve_config(config))
+
+    def _resolve_profile(self, model_name: str) -> RuntimeProfile | None:
+        profile = self.profiles.get(model_name)
+        if profile is not None:
+            return profile
+
+        if self.strict_profiles or model_name.startswith(self.profile_prefixes):
+            known = ", ".join(sorted(self.profiles)) or "(none configured)"
+            raise LLMConfigurationError(
+                f"Unknown LLM runtime profile {model_name!r}. Known profiles: {known}",
+                context={"profile": model_name},
+            )
+        return None
+
+    def _adapter_for(self, profile: RuntimeProfile) -> LLMAdapter:
+        key = (profile.provider, profile.model)
+        with self._lock:
+            adapter = self._adapters.get(key)
+            if adapter is None:
+                adapter = self.adapter_factory(profile.provider, profile.model)
+                self._adapters[key] = adapter
+            return adapter
+
+
+def default_runtime_profiles(
+    *,
+    provider: str | None = None,
+    model: str | None = None,
+) -> dict[str, RuntimeProfile]:
+    """Return built-in runtime profiles, with env/config overrides applied."""
+
+    triage_provider = (
+        os.environ.get("LLM_CONNECT_CUSTODIAN_TRIAGE_PROVIDER")
+        or provider
+        or DEFAULT_CUSTODIAN_TRIAGE_PROVIDER
+    )
+    triage_model = (
+        os.environ.get("LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL")
+        or model
+        or DEFAULT_CUSTODIAN_TRIAGE_MODEL
+    )
+    profiles = {
+        CUSTODIAN_TRIAGE_BALANCED: RuntimeProfile(
+            name=CUSTODIAN_TRIAGE_BALANCED,
+            provider=triage_provider,
+            model=triage_model,
+            config=RunConfig(
+                model_name=triage_model,
+                temperature=_float_env("LLM_CONNECT_CUSTODIAN_TRIAGE_TEMPERATURE", 0.2),
+                max_tokens=_int_env("LLM_CONNECT_CUSTODIAN_TRIAGE_MAX_TOKENS", 1800),
+                max_depth=_int_env("LLM_CONNECT_CUSTODIAN_TRIAGE_MAX_DEPTH", 2),
+                timeout_seconds=_int_env("LLM_CONNECT_CUSTODIAN_TRIAGE_TIMEOUT_SECONDS", 300),
+                model_params={
+                    "reasoning_effort": os.environ.get(
+                        "LLM_CONNECT_CUSTODIAN_TRIAGE_REASONING_EFFORT",
+                        "medium",
+                    ),
+                },
+            ),
+        )
+    }
+    profiles.update(load_runtime_profiles_from_env())
+    return profiles
+
+
+def load_runtime_profiles_from_env() -> dict[str, RuntimeProfile]:
+    """Load optional profile overrides from JSON env/file config."""
+
+    raw = os.environ.get("LLM_CONNECT_PROFILES_JSON")
+    path = os.environ.get("LLM_CONNECT_PROFILE_FILE")
+    if raw and path:
+        raise LLMConfigurationError(
+            "Set only one of LLM_CONNECT_PROFILES_JSON or LLM_CONNECT_PROFILE_FILE",
+            context={"config": "runtime_profiles"},
+        )
+    if path:
+        try:
+            raw = Path(path).read_text(encoding="utf-8")
+        except OSError as exc:
+            raise LLMConfigurationError(
+                f"Could not read LLM runtime profile file {path!r}",
+                cause=exc,
+                context={"config": "runtime_profiles"},
+            ) from exc
+    if not raw:
+        return {}
+
+    try:
+        data = json.loads(raw)
+    except json.JSONDecodeError as exc:
+        raise LLMConfigurationError(
+            "LLM runtime profile config must be valid JSON",
+            cause=exc,
+            context={"config": "runtime_profiles"},
+        ) from exc
+
+    profiles_data = data.get("profiles", data) if isinstance(data, dict) else None
+    if not isinstance(profiles_data, dict):
+        raise LLMConfigurationError(
+            "LLM runtime profile config must be an object keyed by profile name",
+            context={"config": "runtime_profiles"},
+        )
+
+    return {
+        name: _profile_from_mapping(name, value)
+        for name, value in profiles_data.items()
+    }
+
+
+def _profile_from_mapping(name: str, value: Any) -> RuntimeProfile:
+    if not isinstance(value, dict):
+        raise LLMConfigurationError(
+            f"Runtime profile {name!r} must be an object",
+            context={"profile": name},
+        )
+    provider = value.get("provider")
+    model = value.get("model")
+    if not isinstance(provider, str) or not provider:
+        raise LLMConfigurationError(
+            f"Runtime profile {name!r} requires a provider",
+            context={"profile": name},
+        )
+    if not isinstance(model, str) or not model:
+        raise LLMConfigurationError(
+            f"Runtime profile {name!r} requires a model",
+            context={"profile": name},
+        )
+    config_data = value.get("config", {})
+    if not isinstance(config_data, dict):
+        raise LLMConfigurationError(
+            f"Runtime profile {name!r} config must be an object",
+            context={"profile": name},
+        )
+    config = RunConfig.from_dict({"model_name": model, **config_data})
+    return RuntimeProfile(name=name, provider=provider, model=model, config=config)
+
+
+def _default_adapter_factory(provider: str, model: str) -> LLMAdapter:
+    return create_adapter(provider, model=model)
+
+
+def _profile_default_if_unchanged(value: Any, default: Any, profile_value: Any) -> Any:
+    return profile_value if value == default else value
+
+
+def _int_env(name: str, default: int) -> int:
+    value = os.environ.get(name)
+    if value is None or value == "":
+        return default
+    try:
+        return int(value)
+    except ValueError as exc:
+        raise LLMConfigurationError(
+            f"{name} must be an integer",
+            cause=exc,
+            context={"env": name},
+        ) from exc
+
+
+def _float_env(name: str, default: float) -> float:
+    value = os.environ.get(name)
+    if value is None or value == "":
+        return default
+    try:
+        return float(value)
+    except ValueError as exc:
+        raise LLMConfigurationError(
+            f"{name} must be a number",
+            cause=exc,
+            context={"env": name},
+        ) from exc

llm_connect/quality.py

@@ -0,0 +1,318 @@
+"""Quality observations and append-only ledger support.
+
+These primitives let callers record observed quality/cost outcomes for a
+task type without baking consumer-specific routing policy into llm-connect.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import threading
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from typing import Any, Iterator, TextIO
+
+
+_PATH_LOCKS: dict[Path, threading.Lock] = {}
+_PATH_LOCKS_GUARD = threading.Lock()
+
+
+def _utc_now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def _normalise_datetime(value: datetime | str) -> datetime:
+    if isinstance(value, datetime):
+        dt = value
+    elif isinstance(value, str):
+        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
+    else:
+        raise TypeError(f"Expected datetime or ISO string, got {type(value).__name__}")
+
+    if dt.tzinfo is None:
+        return dt.replace(tzinfo=timezone.utc)
+    return dt.astimezone(timezone.utc)
+
+
+def _serialise_datetime(value: datetime) -> str:
+    return _normalise_datetime(value).isoformat().replace("+00:00", "Z")
+
+
+def _validate_non_negative_int(name: str, value: int) -> None:
+    if not isinstance(value, int) or value < 0:
+        raise ValueError(f"{name} must be a non-negative integer")
+
+
+def _validate_non_negative_float(name: str, value: float) -> None:
+    if not isinstance(value, (int, float)) or float(value) < 0:
+        raise ValueError(f"{name} must be a non-negative number")
+
+
+def _path_lock(path: Path) -> threading.Lock:
+    resolved = path.resolve()
+    with _PATH_LOCKS_GUARD:
+        lock = _PATH_LOCKS.get(resolved)
+        if lock is None:
+            lock = threading.Lock()
+            _PATH_LOCKS[resolved] = lock
+        return lock
+
+
+def _lock_file(handle: TextIO) -> None:
+    if os.name == "nt":
+        import msvcrt
+
+        msvcrt.locking(handle.fileno(), msvcrt.LK_LOCK, 1)
+    else:
+        import fcntl
+
+        fcntl.flock(handle.fileno(), fcntl.LOCK_EX)
+
+
+def _unlock_file(handle: TextIO) -> None:
+    if os.name == "nt":
+        import msvcrt
+
+        msvcrt.locking(handle.fileno(), msvcrt.LK_UNLCK, 1)
+    else:
+        import fcntl
+
+        fcntl.flock(handle.fileno(), fcntl.LOCK_UN)
+
+
+@contextmanager
+def _locked_file(path: Path, mode: str) -> Iterator[TextIO]:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    local_lock = _path_lock(path)
+    with local_lock:
+        with path.open(mode, encoding="utf-8") as handle:
+            _lock_file(handle)
+            try:
+                yield handle
+            finally:
+                _unlock_file(handle)
+
+
+@dataclass(frozen=True)
+class QualityObservation:
+    """Observed quality/cost outcome for one adapter on one task type."""
+
+    task_type: str
+    adapter_id: str
+    model_id: str
+    cost_usd: float
+    quality_score: float
+    latency_ms: float
+    tokens_in: int
+    tokens_out: int
+    baseline_adapter_id: str | None = None
+    recorded_at: datetime = field(default_factory=_utc_now)
+    tags: dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        for name in ("task_type", "adapter_id", "model_id"):
+            if not str(getattr(self, name)).strip():
+                raise ValueError(f"{name} must be a non-empty string")
+
+        _validate_non_negative_float("cost_usd", self.cost_usd)
+        _validate_non_negative_float("latency_ms", self.latency_ms)
+        _validate_non_negative_int("tokens_in", self.tokens_in)
+        _validate_non_negative_int("tokens_out", self.tokens_out)
+        if not isinstance(self.quality_score, (int, float)):
+            raise ValueError("quality_score must be a number between 0 and 1")
+        if not 0 <= float(self.quality_score) <= 1:
+            raise ValueError("quality_score must be between 0 and 1")
+
+        object.__setattr__(self, "task_type", str(self.task_type))
+        object.__setattr__(self, "adapter_id", str(self.adapter_id))
+        object.__setattr__(self, "model_id", str(self.model_id))
+        object.__setattr__(self, "cost_usd", float(self.cost_usd))
+        object.__setattr__(self, "quality_score", float(self.quality_score))
+        object.__setattr__(self, "latency_ms", float(self.latency_ms))
+        object.__setattr__(self, "recorded_at", _normalise_datetime(self.recorded_at))
+        object.__setattr__(self, "tags", dict(self.tags))
+
+    @property
+    def total_tokens(self) -> int:
+        """Return input plus output tokens."""
+        return self.tokens_in + self.tokens_out
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to a JSON-serialisable dictionary."""
+        return {
+            "task_type": self.task_type,
+            "adapter_id": self.adapter_id,
+            "model_id": self.model_id,
+            "cost_usd": self.cost_usd,
+            "quality_score": self.quality_score,
+            "latency_ms": self.latency_ms,
+            "tokens_in": self.tokens_in,
+            "tokens_out": self.tokens_out,
+            "baseline_adapter_id": self.baseline_adapter_id,
+            "recorded_at": _serialise_datetime(self.recorded_at),
+            "tags": dict(self.tags),
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "QualityObservation":
+        """Create an observation from a JSON-decoded dictionary."""
+        return cls(
+            task_type=data["task_type"],
+            adapter_id=data["adapter_id"],
+            model_id=data["model_id"],
+            cost_usd=data["cost_usd"],
+            quality_score=data["quality_score"],
+            latency_ms=data["latency_ms"],
+            tokens_in=data["tokens_in"],
+            tokens_out=data["tokens_out"],
+            baseline_adapter_id=data.get("baseline_adapter_id"),
+            recorded_at=data.get("recorded_at", _utc_now()),
+            tags=data.get("tags") or {},
+        )
+
+
+def is_stale(
+    observation: QualityObservation,
+    max_age: timedelta,
+    *,
+    now: datetime | None = None,
+) -> bool:
+    """Return whether *observation* is older than *max_age*."""
+    if max_age.total_seconds() < 0:
+        raise ValueError("max_age must be non-negative")
+    reference = _normalise_datetime(now or _utc_now())
+    return observation.recorded_at < reference - max_age
+
+
+class QualityLedger:
+    """Append-only JSONL store for :class:`QualityObservation` records."""
+
+    def __init__(self, path: str | Path):
+        self._path = Path(path)
+
+    @property
+    def path(self) -> Path:
+        """Ledger file path."""
+        return self._path
+
+    def append(self, observation: QualityObservation) -> None:
+        """Append one observation as a locked JSONL record."""
+        line = json.dumps(observation.to_dict(), sort_keys=True, separators=(",", ":"))
+        with _locked_file(self._path, "a") as handle:
+            handle.write(line + "\n")
+            handle.flush()
+            os.fsync(handle.fileno())
+
+    def read_all(self) -> list[QualityObservation]:
+        """Return all parseable observations, skipping malformed lines."""
+        observations, _ = self._read_with_malformed_count()
+        return observations
+
+    def malformed_count(self) -> int:
+        """Return the number of malformed lines currently skipped by reads."""
+        _, malformed = self._read_with_malformed_count()
+        return malformed
+
+    def by_task_type(self, task_type: str) -> list[QualityObservation]:
+        """Return observations matching *task_type*."""
+        return [obs for obs in self.read_all() if obs.task_type == task_type]
+
+    def recent(
+        self,
+        limit: int | None = None,
+        *,
+        task_type: str | None = None,
+        adapter_id: str | None = None,
+        since: datetime | None = None,
+    ) -> list[QualityObservation]:
+        """Return newest observations first, optionally filtered."""
+        if limit is not None and limit < 0:
+            raise ValueError("limit must be non-negative")
+
+        cutoff = _normalise_datetime(since) if since is not None else None
+        observations = self.read_all()
+        if task_type is not None:
+            observations = [obs for obs in observations if obs.task_type == task_type]
+        if adapter_id is not None:
+            observations = [obs for obs in observations if obs.adapter_id == adapter_id]
+        if cutoff is not None:
+            observations = [obs for obs in observations if obs.recorded_at >= cutoff]
+
+        observations.sort(key=lambda obs: obs.recorded_at, reverse=True)
+        if limit is None:
+            return observations
+        return observations[:limit]
+
+    def mean_quality(
+        self,
+        task_type: str,
+        *,
+        adapter_id: str | None = None,
+        model_id: str | None = None,
+        max_age: timedelta | None = None,
+        min_observations: int = 1,
+    ) -> float | None:
+        """Return mean quality for matching observations, or ``None`` if absent."""
+        if min_observations <= 0:
+            raise ValueError("min_observations must be positive")
+
+        observations = self.by_task_type(task_type)
+        if adapter_id is not None:
+            observations = [obs for obs in observations if obs.adapter_id == adapter_id]
+        if model_id is not None:
+            observations = [obs for obs in observations if obs.model_id == model_id]
+        if max_age is not None:
+            observations = [obs for obs in observations if not is_stale(obs, max_age)]
+
+        if len(observations) < min_observations:
+            return None
+        return sum(obs.quality_score for obs in observations) / len(observations)
+
+    def prune_before(self, timestamp: datetime) -> int:
+        """Remove valid observations recorded before *timestamp*.
+
+        Malformed lines are preserved because their timestamp cannot be trusted.
+        Returns the number of valid observation records removed.
+        """
+        cutoff = _normalise_datetime(timestamp)
+        removed = 0
+        with _locked_file(self._path, "a+") as handle:
+            handle.seek(0)
+            lines = handle.readlines()
+            kept: list[str] = []
+            for line in lines:
+                try:
+                    obs = QualityObservation.from_dict(json.loads(line))
+                except (json.JSONDecodeError, KeyError, TypeError, ValueError):
+                    kept.append(line)
+                    continue
+                if obs.recorded_at < cutoff:
+                    removed += 1
+                else:
+                    kept.append(line)
+
+            handle.seek(0)
+            handle.truncate()
+            handle.writelines(kept)
+            handle.flush()
+            os.fsync(handle.fileno())
+        return removed
+
+    def _read_with_malformed_count(self) -> tuple[list[QualityObservation], int]:
+        if not self._path.is_file():
+            return [], 0
+
+        observations: list[QualityObservation] = []
+        malformed = 0
+        with _locked_file(self._path, "r") as handle:
+            for line in handle:
+                if not line.strip():
+                    continue
+                try:
+                    observations.append(QualityObservation.from_dict(json.loads(line)))
+                except (json.JSONDecodeError, KeyError, TypeError, ValueError):
+                    malformed += 1
+        return observations, malformed

llm_connect/rates.py

@@ -0,0 +1,273 @@
+"""Model rate registry for preview and post-hoc cost estimation."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+
+DEFAULT_RATE_SOURCE_URL = "https://openrouter.ai/models"
+DEFAULT_RATE_CAPTURED_AT = "2026-05-17"
+DEFAULT_RATE_CURRENCY = "USD"
+
+
+@dataclass(frozen=True)
+class ModelRate:
+    """USD-denominated list price for one model."""
+
+    model_id: str
+    prompt_per_1k: float
+    completion_per_1k: float
+    currency: str = DEFAULT_RATE_CURRENCY
+    source_url: str = ""
+    captured_at: str = ""
+
+    def __post_init__(self) -> None:
+        model_id = str(self.model_id).strip()
+        currency = str(self.currency or DEFAULT_RATE_CURRENCY).strip().upper()
+        if not model_id:
+            raise ValueError("model_id must be a non-empty string")
+        if not currency:
+            raise ValueError("currency must be a non-empty string")
+        prompt_rate = _non_negative_float("prompt_per_1k", self.prompt_per_1k)
+        completion_rate = _non_negative_float("completion_per_1k", self.completion_per_1k)
+
+        object.__setattr__(self, "model_id", model_id)
+        object.__setattr__(self, "prompt_per_1k", prompt_rate)
+        object.__setattr__(self, "completion_per_1k", completion_rate)
+        object.__setattr__(self, "currency", currency)
+        object.__setattr__(self, "source_url", str(self.source_url or ""))
+        object.__setattr__(self, "captured_at", str(self.captured_at or ""))
+
+
+class ModelRateRegistry:
+    """Lookup table for model list prices."""
+
+    def __init__(self, rates: Mapping[str, ModelRate | Mapping[str, Any]] | None = None) -> None:
+        self._rates: dict[str, ModelRate] = {}
+        for model_id, rate in (rates or {}).items():
+            model_rate = _coerce_rate(model_id, rate)
+            self._rates[model_rate.model_id] = model_rate
+
+    def get(self, model_id: str) -> ModelRate | None:
+        """Return the rate for *model_id*, or ``None`` when absent."""
+        return self._rates.get(str(model_id).strip())
+
+    def all(self) -> dict[str, ModelRate]:
+        """Return a copy of the registry mapping."""
+        return dict(self._rates)
+
+    @classmethod
+    def default(cls) -> "ModelRateRegistry":
+        """Return the bundled OpenRouter list-price snapshot."""
+        return cls(_default_rate_payload())
+
+    @classmethod
+    def from_yaml(cls, path: Path | str) -> "ModelRateRegistry":
+        """Load rates from a YAML file.
+
+        The expected shape matches the historic infospace-bench table::
+
+            currency: USD
+            source_url: https://openrouter.ai/models
+            captured_at: "2026-05-17"
+            rates:
+              openai/gpt-4o-mini:
+                prompt_per_1k: 0.00015
+                completion_per_1k: 0.00060
+
+        PyYAML is used when installed; otherwise a small parser handles this
+        schema so llm-connect keeps its current lightweight dependency surface.
+        """
+        payload = _load_yaml_mapping(Path(path))
+        return cls(_rates_from_payload(payload))
+
+    def merged_with(self, override: "ModelRateRegistry") -> "ModelRateRegistry":
+        """Return a new registry where *override* entries win by model id."""
+        merged = self.all()
+        merged.update(override.all())
+        return ModelRateRegistry(merged)
+
+
+_DEFAULT_RATES: dict[str, tuple[float, float]] = {
+    "openai/gpt-4o-mini": (0.00015, 0.00060),
+    "openai/gpt-4o": (0.0025, 0.01),
+    "openai/gpt-4-turbo": (0.01, 0.03),
+    "anthropic/claude-3.5-sonnet": (0.003, 0.015),
+    "anthropic/claude-3.5-haiku": (0.0008, 0.004),
+    "anthropic/claude-3-opus": (0.015, 0.075),
+    "google/gemini-1.5-flash": (0.000075, 0.0003),
+    "google/gemini-1.5-pro": (0.00125, 0.005),
+    "meta-llama/llama-3.1-70b-instruct": (0.00059, 0.00079),
+}
+
+
+def _default_rate_payload() -> dict[str, ModelRate]:
+    return {
+        model_id: ModelRate(
+            model_id=model_id,
+            prompt_per_1k=prompt_rate,
+            completion_per_1k=completion_rate,
+            currency=DEFAULT_RATE_CURRENCY,
+            source_url=DEFAULT_RATE_SOURCE_URL,
+            captured_at=DEFAULT_RATE_CAPTURED_AT,
+        )
+        for model_id, (prompt_rate, completion_rate) in _DEFAULT_RATES.items()
+    }
+
+
+def _coerce_rate(model_id: str, rate: ModelRate | Mapping[str, Any]) -> ModelRate:
+    if isinstance(rate, ModelRate):
+        return rate
+    if not isinstance(rate, Mapping):
+        raise TypeError(f"Rate for {model_id!r} must be a ModelRate or mapping")
+    return ModelRate(
+        model_id=str(model_id),
+        prompt_per_1k=rate["prompt_per_1k"],
+        completion_per_1k=rate["completion_per_1k"],
+        currency=str(rate.get("currency") or DEFAULT_RATE_CURRENCY),
+        source_url=str(rate.get("source_url") or ""),
+        captured_at=str(rate.get("captured_at") or ""),
+    )
+
+
+def _rates_from_payload(payload: Mapping[str, Any]) -> dict[str, ModelRate]:
+    rates_payload = payload.get("rates")
+    if not isinstance(rates_payload, Mapping):
+        raise ValueError("Rate YAML must contain a 'rates' mapping")
+
+    currency = str(payload.get("currency") or DEFAULT_RATE_CURRENCY)
+    source_url = str(payload.get("source_url") or "")
+    captured_at = str(payload.get("captured_at") or "")
+    rates: dict[str, ModelRate] = {}
+    for model_id, raw_rate in rates_payload.items():
+        if not isinstance(raw_rate, Mapping):
+            raise ValueError(f"Rate entry for {model_id!r} must be a mapping")
+        rates[str(model_id)] = ModelRate(
+            model_id=str(model_id),
+            prompt_per_1k=raw_rate["prompt_per_1k"],
+            completion_per_1k=raw_rate["completion_per_1k"],
+            currency=str(raw_rate.get("currency") or currency),
+            source_url=str(raw_rate.get("source_url") or source_url),
+            captured_at=str(raw_rate.get("captured_at") or captured_at),
+        )
+    return rates
+
+
+def _non_negative_float(name: str, value: Any) -> float:
+    if isinstance(value, bool):
+        raise ValueError(f"{name} must be a non-negative number")
+    try:
+        number = float(value)
+    except (TypeError, ValueError) as exc:
+        raise ValueError(f"{name} must be a non-negative number") from exc
+    if number < 0:
+        raise ValueError(f"{name} must be a non-negative number")
+    return number
+
+
+def _load_yaml_mapping(path: Path) -> Mapping[str, Any]:
+    try:
+        import yaml
+    except ImportError:
+        return _parse_rate_yaml(path.read_text(encoding="utf-8"))
+
+    data = yaml.safe_load(path.read_text(encoding="utf-8")) or {}
+    if not isinstance(data, Mapping):
+        raise ValueError("Rate YAML root must be a mapping")
+    return data
+
+
+def _parse_rate_yaml(text: str) -> dict[str, Any]:
+    lines: list[tuple[int, str]] = []
+    for raw_line in text.splitlines():
+        line = _normalise_yaml_line(raw_line)
+        if line is not None:
+            lines.append(line)
+    data: dict[str, Any] = {}
+    index = 0
+    while index < len(lines):
+        indent, content = lines[index]
+        if indent != 0:
+            raise ValueError("Only top-level mappings are supported in rate YAML")
+        key, raw_value = _split_yaml_key_value(content)
+        if key == "rates" and raw_value == "":
+            rates, index = _parse_rates_block(lines, index + 1)
+            data["rates"] = rates
+            continue
+        data[key] = _parse_yaml_scalar(raw_value)
+        index += 1
+    return data
+
+
+def _parse_rates_block(
+    lines: list[tuple[int, str]],
+    index: int,
+) -> tuple[dict[str, dict[str, Any]], int]:
+    rates: dict[str, dict[str, Any]] = {}
+    while index < len(lines):
+        indent, content = lines[index]
+        if indent == 0:
+            break
+        if indent != 2:
+            raise ValueError("Rate model entries must be indented by two spaces")
+        model_id, raw_value = _split_yaml_key_value(content)
+        if raw_value:
+            raise ValueError(f"Rate entry for {model_id!r} must be a nested mapping")
+        entry: dict[str, Any] = {}
+        index += 1
+        while index < len(lines):
+            child_indent, child_content = lines[index]
+            if child_indent <= indent:
+                break
+            if child_indent != 4:
+                raise ValueError("Rate fields must be indented by four spaces")
+            child_key, child_value = _split_yaml_key_value(child_content)
+            entry[child_key] = _parse_yaml_scalar(child_value)
+            index += 1
+        rates[model_id] = entry
+    return rates, index
+
+
+def _normalise_yaml_line(line: str) -> tuple[int, str] | None:
+    stripped = _strip_yaml_comment(line.rstrip())
+    if not stripped.strip():
+        return None
+    indent = len(stripped) - len(stripped.lstrip(" "))
+    return indent, stripped.strip()
+
+
+def _strip_yaml_comment(line: str) -> str:
+    quote: str | None = None
+    for index, char in enumerate(line):
+        if char in {"'", '"'}:
+            quote = None if quote == char else char if quote is None else quote
+        elif char == "#" and quote is None:
+            return line[:index]
+    return line
+
+
+def _split_yaml_key_value(content: str) -> tuple[str, str]:
+    key, separator, value = content.partition(":")
+    if not separator:
+        raise ValueError(f"Invalid YAML mapping line: {content!r}")
+    return key.strip().strip("'\""), value.strip()
+
+
+def _parse_yaml_scalar(value: str) -> Any:
+    if value == "":
+        return ""
+    if (value.startswith('"') and value.endswith('"')) or (
+        value.startswith("'") and value.endswith("'")
+    ):
+        return value[1:-1]
+    if value.lower() in {"null", "none", "~"}:
+        return None
+    try:
+        if any(char in value for char in (".", "e", "E")):
+            return float(value)
+        return int(value)
+    except ValueError:
+        return value

llm_connect/replay.py

@@ -0,0 +1,121 @@
+"""Replay llm-connect audit records without making provider calls."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+from llm_connect.claude_code import _unwrap_cli_json_envelope
+from llm_connect.models import RunConfig
+
+
+def parse_audit_record(record: dict[str, Any]) -> dict[str, Any]:
+    """Parse the recorded provider response and compare it to saved content."""
+
+    config = RunConfig.from_dict(record.get("config", {}))
+    provider = record.get("provider") or _infer_provider(record)
+    provider_response = record.get("provider_response") or {}
+    body = provider_response.get("body")
+    parsed_content = _parse_provider_response(provider, body, config)
+    recorded_content = record.get("parsed_content")
+    schema_check = _check_structured_output(parsed_content, config.model_params.get("json_schema"))
+
+    return {
+        "provider": provider,
+        "parsed_content": parsed_content,
+        "matches_recorded_content": parsed_content == recorded_content,
+        "structured_output": schema_check,
+    }
+
+
+def main(argv: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(
+        prog="python -m llm_connect.replay",
+        description="Replay parsing for a llm-connect audit JSON file.",
+    )
+    parser.add_argument("audit_file", help="Path to an audit JSON file")
+    parser.add_argument("--json", action="store_true", help="Print the full replay report")
+    args = parser.parse_args(argv)
+
+    record = json.loads(Path(args.audit_file).read_text(encoding="utf-8"))
+    report = parse_audit_record(record)
+    if args.json:
+        print(json.dumps(report, indent=2, sort_keys=True))
+    else:
+        print(report["parsed_content"])
+
+
+def _parse_provider_response(provider: str | None, body: Any, config: RunConfig) -> str:
+    if provider in {"openai", "openrouter"}:
+        if isinstance(body, dict):
+            choice = (body.get("choices") or [{}])[0]
+            return choice.get("message", {}).get("content", "")
+        return ""
+
+    if provider == "gemini":
+        if isinstance(body, dict):
+            candidates = body.get("candidates") or []
+            if not candidates:
+                return ""
+            parts = candidates[0].get("content", {}).get("parts", [])
+            return "".join(part.get("text", "") for part in parts)
+        return ""
+
+    if provider == "claude-code":
+        if isinstance(body, dict):
+            return _unwrap_cli_json_envelope(body.get("stdout", ""), config)
+        return ""
+
+    if isinstance(body, str):
+        return body
+    if body is None:
+        return ""
+    return json.dumps(body)
+
+
+def _infer_provider(record: dict[str, Any]) -> str | None:
+    request = record.get("provider_request") or {}
+    url = request.get("url", "")
+    if "openrouter.ai" in url:
+        return "openrouter"
+    if "api.openai.com" in url:
+        return "openai"
+    if "generativelanguage.googleapis.com" in url:
+        return "gemini"
+    if request.get("command"):
+        return "claude-code"
+    return None
+
+
+def _check_structured_output(content: str, schema: Any) -> dict[str, Any]:
+    if not schema:
+        return {"checked": False}
+    if isinstance(schema, str):
+        try:
+            schema = json.loads(schema)
+        except ValueError as exc:
+            return {"checked": True, "valid": False, "error": f"invalid schema JSON: {exc}"}
+    if not isinstance(schema, dict):
+        return {"checked": True, "valid": False, "error": "schema must be an object"}
+
+    try:
+        parsed = json.loads(content)
+    except ValueError as exc:
+        return {"checked": True, "valid": False, "error": f"invalid output JSON: {exc}"}
+
+    missing = []
+    if schema.get("type") == "object":
+        if not isinstance(parsed, dict):
+            return {"checked": True, "valid": False, "error": "output is not an object"}
+        for key in schema.get("required", []):
+            if key not in parsed:
+                missing.append(key)
+    if missing:
+        return {"checked": True, "valid": False, "missing_required": missing}
+    return {"checked": True, "valid": True}
+
+
+if __name__ == "__main__":
+    main()

llm_connect/routing.py

@@ -0,0 +1,260 @@
+"""
+RoutingPolicy — task-type-aware adapter selection (FR-2).
+
+Maps task types to preferred adapters with optional cost-cap fallback.
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from typing import List, Mapping, Optional
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.quality import QualityLedger, QualityObservation
+
+
+@dataclass
+class RoutingRule:
+    """Single routing rule binding a task type to an adapter.
+
+    Attributes:
+        task_type: Logical task identifier (e.g. ``"triage"``, ``"summarise"``).
+        prefer: Adapter to use when this rule matches.
+        max_cost_per_1k: Optional cost ceiling (USD per 1 000 tokens). When the
+            caller supplies ``estimated_cost_per_1k`` to :meth:`RoutingPolicy.resolve`
+            and it exceeds this cap, *fallback* is returned instead of *prefer*.
+        fallback: Adapter to use when the cost cap is breached.
+    """
+
+    task_type: str
+    prefer: LLMAdapter
+    max_cost_per_1k: Optional[float] = None
+    fallback: Optional[LLMAdapter] = None
+
+
+@dataclass
+class RoutingPolicy:
+    """Route task types to LLM adapters.
+
+    Rules are evaluated in order; the first match wins.  When no rule matches,
+    *default* is returned.  If *default* is also absent, ``LookupError`` is raised.
+
+    Example::
+
+        policy = RoutingPolicy(
+            rules=[
+                RoutingRule("triage", prefer=fast_adapter, max_cost_per_1k=0.5, fallback=cheap_adapter),
+                RoutingRule("analysis", prefer=smart_adapter),
+            ],
+            default=cheap_adapter,
+        )
+        adapter = policy.resolve("triage")
+    """
+
+    rules: List[RoutingRule] = field(default_factory=list)
+    default: Optional[LLMAdapter] = None
+
+    def resolve(
+        self,
+        task_type: str,
+        estimated_cost_per_1k: Optional[float] = None,
+    ) -> LLMAdapter:
+        """Return the adapter for *task_type*.
+
+        Args:
+            task_type: Logical task identifier.
+            estimated_cost_per_1k: Caller-supplied cost estimate (USD / 1k tokens).
+                When provided and a matching rule has ``max_cost_per_1k`` set, the
+                rule's ``fallback`` is returned if the estimate exceeds the cap.
+
+        Returns:
+            The selected :class:`~llm_connect.adapter.LLMAdapter`.
+
+        Raises:
+            LookupError: No matching rule and no *default* configured.
+        """
+        for rule in self.rules:
+            if rule.task_type == task_type:
+                if (
+                    estimated_cost_per_1k is not None
+                    and rule.max_cost_per_1k is not None
+                    and estimated_cost_per_1k > rule.max_cost_per_1k
+                    and rule.fallback is not None
+                ):
+                    return rule.fallback
+                return rule.prefer
+
+        if self.default is not None:
+            return self.default
+
+        raise LookupError(
+            f"No routing rule for task_type={task_type!r} and no default configured"
+        )
+
+
+@dataclass(frozen=True)
+class _CandidateMetrics:
+    adapter_id: str
+    adapter: LLMAdapter
+    mean_quality: float
+    mean_cost_usd: float
+    order: int
+    is_static_prefer: bool
+
+
+@dataclass
+class AdaptiveRoutingPolicy(RoutingPolicy):
+    """Route to the cheapest adapter whose observed quality clears a floor.
+
+    The policy consults a :class:`~llm_connect.quality.QualityLedger` for
+    observations matching ``task_type`` and adapter id.  When the ledger has no
+    qualifying observations, resolution falls through to ``RoutingPolicy`` so a
+    caller can use the same policy on day zero and after observations accrue.
+    """
+
+    ledger: Optional[QualityLedger] = None
+    adapters_by_id: Mapping[str, LLMAdapter] = field(default_factory=dict)
+    window_size: int = 20
+    min_observations: int = 1
+    max_age: Optional[timedelta] = None
+
+    def __post_init__(self) -> None:
+        if self.window_size <= 0:
+            raise ValueError("window_size must be positive")
+        if self.min_observations <= 0:
+            raise ValueError("min_observations must be positive")
+        if self.max_age is not None and self.max_age.total_seconds() < 0:
+            raise ValueError("max_age must be non-negative")
+
+    def resolve(
+        self,
+        task_type: str,
+        estimated_cost_per_1k: Optional[float] = None,
+        *,
+        quality_floor: Optional[float] = None,
+    ) -> LLMAdapter:
+        """Return the adaptive adapter for *task_type*.
+
+        Args:
+            task_type: Logical task identifier.
+            estimated_cost_per_1k: Passed through to static routing fallback.
+            quality_floor: Minimum observed mean quality required for adaptive
+                selection. When omitted, static routing is used.
+
+        Returns:
+            The selected :class:`~llm_connect.adapter.LLMAdapter`.
+        """
+        if quality_floor is None or self.ledger is None:
+            return super().resolve(task_type, estimated_cost_per_1k)
+        if not 0 <= quality_floor <= 1:
+            raise ValueError("quality_floor must be between 0 and 1")
+
+        metrics = self._qualifying_candidates(task_type, quality_floor)
+        if not metrics:
+            return super().resolve(task_type, estimated_cost_per_1k)
+
+        best = min(
+            metrics,
+            key=lambda candidate: (
+                candidate.mean_cost_usd,
+                0 if candidate.is_static_prefer else 1,
+                candidate.order,
+            ),
+        )
+        return best.adapter
+
+    def _qualifying_candidates(
+        self,
+        task_type: str,
+        quality_floor: float,
+    ) -> list[_CandidateMetrics]:
+        static_prefer = self._static_preferred_adapter(task_type)
+        candidates: list[_CandidateMetrics] = []
+        for order, (adapter_id, adapter) in enumerate(self._candidate_entries(task_type)):
+            observations = self._windowed_observations(task_type, adapter_id)
+            if len(observations) < self.min_observations:
+                continue
+
+            mean_quality = sum(obs.quality_score for obs in observations) / len(observations)
+            if mean_quality < quality_floor:
+                continue
+
+            mean_cost = sum(obs.cost_usd for obs in observations) / len(observations)
+            candidates.append(
+                _CandidateMetrics(
+                    adapter_id=adapter_id,
+                    adapter=adapter,
+                    mean_quality=mean_quality,
+                    mean_cost_usd=mean_cost,
+                    order=order,
+                    is_static_prefer=adapter is static_prefer,
+                )
+            )
+        return candidates
+
+    def _windowed_observations(
+        self,
+        task_type: str,
+        adapter_id: str,
+    ) -> list[QualityObservation]:
+        if self.ledger is None:
+            return []
+
+        since = None
+        if self.max_age is not None:
+            since = datetime.now(timezone.utc) - self.max_age
+
+        return self.ledger.recent(
+            limit=self.window_size,
+            task_type=task_type,
+            adapter_id=adapter_id,
+            since=since,
+        )
+
+    def _candidate_entries(self, task_type: str) -> list[tuple[str, LLMAdapter]]:
+        entries: list[tuple[str, LLMAdapter]] = []
+        seen_ids: set[str] = set()
+
+        def add(adapter_id: str | None, adapter: LLMAdapter | None) -> None:
+            if adapter is None or adapter_id is None or adapter_id in seen_ids:
+                return
+            seen_ids.add(adapter_id)
+            entries.append((adapter_id, adapter))
+
+        for adapter_id, adapter in self.adapters_by_id.items():
+            add(adapter_id, adapter)
+
+        for adapter in self._static_candidate_adapters(task_type):
+            add(self._adapter_id_for(adapter), adapter)
+
+        return entries
+
+    def _static_candidate_adapters(self, task_type: str) -> list[LLMAdapter]:
+        for rule in self.rules:
+            if rule.task_type == task_type:
+                candidates = [rule.prefer]
+                if rule.fallback is not None:
+                    candidates.append(rule.fallback)
+                if self.default is not None:
+                    candidates.append(self.default)
+                return candidates
+
+        if self.default is not None:
+            return [self.default]
+        return []
+
+    def _static_preferred_adapter(self, task_type: str) -> LLMAdapter | None:
+        for rule in self.rules:
+            if rule.task_type == task_type:
+                return rule.prefer
+        return None
+
+    def _adapter_id_for(self, adapter: LLMAdapter) -> str | None:
+        for adapter_id, candidate in self.adapters_by_id.items():
+            if candidate is adapter:
+                return adapter_id
+
+        for attribute in ("adapter_id", "id", "name"):
+            value = getattr(adapter, attribute, None)
+            if isinstance(value, str) and value.strip():
+                return value
+        return None

llm_connect/server.py

@@ -0,0 +1,366 @@
+"""
+Minimal HTTP server for llm_connect — serve mode (FR-1).
+
+Exposes:
+  POST /execute  — run a prompt through the configured adapter
+  GET  /health   — liveness probe
+
+Usage (programmatic)::
+
+    from llm_connect import MockLLMAdapter
+    from llm_connect.server import LLMServer
+
+    server = LLMServer(adapter=MockLLMAdapter(), port=8080)
+    server.start()      # background thread
+    # ...
+    server.stop()
+
+Usage (CLI)::
+
+    python -m llm_connect.server --port 8080 --provider openrouter --model google/gemini-2.5-flash
+"""
+
+import argparse
+import datetime as _dt
+import json
+import os
+import re
+import threading
+import time
+import uuid
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from typing import Optional
+from urllib.parse import parse_qs, urlsplit
+
+from llm_connect._diagnostics import capture_diagnostics
+from llm_connect.adapter import LLMAdapter
+from llm_connect.exceptions import (
+    LLMBudgetExceededError,
+    LLMAPIError,
+    LLMConfigurationError,
+    LLMError,
+    LLMRateLimitError,
+    LLMTimeoutError,
+)
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.profiles import ProfiledLLMAdapter, default_runtime_profiles
+
+
+class _Handler(BaseHTTPRequestHandler):
+    """Request handler — adapter injected via server.adapter."""
+
+    def log_message(self, format, *args):  # suppress default access log
+        pass
+
+    # ── GET ────────────────────────────────────────────────────────
+
+    def do_GET(self):
+        parsed = urlsplit(self.path)
+        if parsed.path == "/health":
+            self._respond(200, {"status": "ok"})
+        else:
+            self._respond(404, {"error": "not found"})
+
+    # ── POST ───────────────────────────────────────────────────────
+
+    def do_POST(self):
+        parsed = urlsplit(self.path)
+        if parsed.path != "/execute":
+            self._respond(404, {"error": "not found"})
+            return
+
+        debug_enabled = _debug_requested(parsed.query)
+        audit_dir = os.environ.get("LLM_CONNECT_AUDIT_DIR")
+        length = int(self.headers.get("Content-Length", 0))
+        raw = self.rfile.read(length)
+        try:
+            data = json.loads(raw)
+        except (json.JSONDecodeError, ValueError):
+            self._respond(400, {"error": "invalid JSON body"})
+            return
+
+        prompt = data.get("prompt")
+        if not prompt:
+            self._respond(400, {"error": "missing required field: 'prompt'"})
+            return
+
+        cfg = data.get("config", {})
+        if not isinstance(cfg, dict):
+            self._respond(400, {"error": "field 'config' must be an object"})
+            return
+        config = RunConfig.from_dict(cfg)
+
+        start = time.time()
+        diagnostics_enabled = debug_enabled or bool(audit_dir)
+        try:
+            with capture_diagnostics(diagnostics_enabled) as diagnostics:
+                adapter = self.server.adapter  # type: ignore[attr-defined]
+                if not adapter.validate_config(config):
+                    raise LLMConfigurationError(
+                        "Adapter rejected RunConfig",
+                        context={"model_name": config.model_name},
+                    )
+                response = adapter.execute_prompt(prompt, config)
+            latency = time.time() - start
+            body = response.to_dict()
+            debug = diagnostics.to_dict() if diagnostics is not None else None
+            if debug_enabled and debug is not None:
+                body["debug"] = debug
+            if audit_dir:
+                _write_audit_record(audit_dir, prompt, config, response, debug, latency)
+            self._respond(200, body)
+        except Exception as exc:
+            status, body = _error_response(exc)
+            self._respond(status, body)
+
+    # ── helpers ────────────────────────────────────────────────────
+
+    def _respond(self, status: int, body: dict) -> None:
+        payload = json.dumps(body).encode()
+        self.send_response(status)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(payload)))
+        self.end_headers()
+        self.wfile.write(payload)
+
+
+class LLMServer:
+    """HTTP server wrapping an :class:`~llm_connect.adapter.LLMAdapter`.
+
+    Args:
+        adapter: The adapter that handles ``POST /execute`` requests.
+        host: Bind address (default ``"127.0.0.1"``).
+        port: TCP port (default ``8080``; ``0`` picks a free port).
+    """
+
+    def __init__(
+        self,
+        adapter: LLMAdapter,
+        host: str = "127.0.0.1",
+        port: int = 8080,
+    ) -> None:
+        self._httpd = ThreadingHTTPServer((host, port), _Handler)
+        self._httpd.adapter = adapter  # type: ignore[attr-defined]
+        self._thread: Optional[threading.Thread] = None
+
+    @property
+    def port(self) -> int:
+        """Actual bound port (useful when ``port=0`` was requested)."""
+        return self._httpd.server_address[1]
+
+    @property
+    def host(self) -> str:
+        return self._httpd.server_address[0]
+
+    def start(self) -> None:
+        """Start serving in a daemon background thread."""
+        self._thread = threading.Thread(target=self._httpd.serve_forever, daemon=True)
+        self._thread.start()
+
+    def stop(self) -> None:
+        """Shut down the server and join the background thread."""
+        self._httpd.shutdown()
+        if self._thread is not None:
+            self._thread.join()
+
+    def serve_forever(self) -> None:
+        """Block the calling thread until interrupted."""
+        self._httpd.serve_forever()
+
+
+# ── CLI entry point ────────────────────────────────────────────────────────────
+
+def _build_adapter(
+    provider: str,
+    model: Optional[str],
+    *,
+    enable_profiles: bool = True,
+    strict_profiles: bool = False,
+) -> LLMAdapter:
+    from llm_connect.factory import create_adapter
+
+    adapter = create_adapter(provider, model=model)
+    if not enable_profiles:
+        return adapter
+    return ProfiledLLMAdapter(
+        adapter,
+        default_runtime_profiles(provider=provider, model=model),
+        strict_profiles=strict_profiles,
+    )
+
+
+def _debug_requested(query: str) -> bool:
+    env = os.environ.get("LLM_CONNECT_DEBUG", "")
+    if _truthy(env):
+        return True
+    values = parse_qs(query).get("debug", [])
+    return any(_truthy(value) for value in values)
+
+
+def _truthy(value: str) -> bool:
+    return value.strip().lower() in {"1", "true", "yes", "on"}
+
+
+def _error_response(exc: Exception) -> tuple[int, dict]:
+    """Map exceptions to operator-useful, secret-safe server responses."""
+
+    if isinstance(exc, LLMRateLimitError):
+        body = _error_body("provider_rate_limited", exc)
+        body["provider_status"] = exc.status_code
+        return 429, body
+    if isinstance(exc, LLMTimeoutError):
+        return 504, _error_body("provider_timeout", exc)
+    if isinstance(exc, LLMAPIError):
+        body = _error_body("provider_api_error", exc)
+        if exc.status_code:
+            body["provider_status"] = exc.status_code
+        return 502, body
+    if isinstance(exc, LLMBudgetExceededError):
+        return 400, _error_body("budget_exceeded", exc)
+    if isinstance(exc, LLMConfigurationError):
+        if _message(exc).startswith("Unknown LLM runtime profile"):
+            return 400, _error_body("unknown_profile", exc)
+        return 500, _error_body("configuration_error", exc)
+    if isinstance(exc, LLMError):
+        return 500, _error_body("llm_error", exc)
+    return 500, _error_body("internal_error", exc)
+
+
+def _error_body(code: str, exc: Exception) -> dict:
+    body = {
+        "error": code,
+        "message": _sanitize_text(_message(exc)),
+        "type": exc.__class__.__name__,
+    }
+    context = getattr(exc, "context", None)
+    if isinstance(context, dict):
+        safe_context = _safe_context(context)
+        if safe_context:
+            body["context"] = safe_context
+    return body
+
+
+def _message(exc: Exception) -> str:
+    if exc.args:
+        return str(exc.args[0])
+    return str(exc)
+
+
+def _safe_context(context: dict) -> dict:
+    safe = {}
+    for key, value in context.items():
+        lowered = str(key).lower()
+        if any(secret_word in lowered for secret_word in ("key", "secret", "token", "password")):
+            safe[key] = "<redacted>"
+        elif isinstance(value, (str, int, float, bool)) or value is None:
+            safe[key] = _sanitize_text(str(value)) if isinstance(value, str) else value
+        else:
+            safe[key] = _sanitize_text(str(value))
+    return safe
+
+
+def _sanitize_text(value: str) -> str:
+    value = re.sub(r"Bearer\s+[A-Za-z0-9._~+/=-]+", "Bearer <redacted>", value)
+    value = re.sub(r"([?&]key=)[^&\s]+", r"\1<redacted>", value)
+    value = re.sub(r"\bsk-[A-Za-z0-9_-]{8,}", "sk-<redacted>", value)
+    value = re.sub(
+        r"(?i)(api[_-]?key|token|secret|password)=([^,\s\]]+)",
+        r"\1=<redacted>",
+        value,
+    )
+    return value
+
+
+def _write_audit_record(
+    audit_dir: str,
+    prompt: str,
+    config: RunConfig,
+    response: LLMResponse,
+    debug: dict | None,
+    latency_seconds: float,
+) -> None:
+    target_dir = Path(audit_dir)
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    now = _dt.datetime.now(_dt.timezone.utc)
+    response_id = str(response.metadata.get("response_id") or uuid.uuid4().hex)
+    filename = f"{now.strftime('%Y%m%dT%H%M%S%fZ')}-{_safe_filename(response_id)}.json"
+    diagnostics = debug or {}
+    record = {
+        "timestamp": now.isoformat().replace("+00:00", "Z"),
+        "prompt": prompt,
+        "config": config.to_dict(),
+        "provider": response.metadata.get("provider"),
+        "provider_request": diagnostics.get("provider_request"),
+        "provider_response": diagnostics.get("provider_response"),
+        "adapter_transformations": diagnostics.get("adapter_transformations", []),
+        "parsed_content": response.content,
+        "latency_seconds": round(latency_seconds, 3),
+        "response": response.to_dict(),
+    }
+    (target_dir / filename).write_text(
+        json.dumps(record, indent=2, sort_keys=True),
+        encoding="utf-8",
+    )
+
+
+def _safe_filename(value: str) -> str:
+    return re.sub(r"[^A-Za-z0-9_.-]+", "-", value).strip("-") or "response"
+
+
+def main(argv=None) -> None:
+    parser = argparse.ArgumentParser(
+        prog="python -m llm_connect.server",
+        description="Start llm_connect HTTP serve mode.",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=int(os.environ.get("LLM_CONNECT_PORT", "8080")),
+        help="TCP port (default: env LLM_CONNECT_PORT or 8080)",
+    )
+    parser.add_argument(
+        "--host",
+        default=os.environ.get("LLM_CONNECT_HOST", "127.0.0.1"),
+        help="Bind address (default: env LLM_CONNECT_HOST or 127.0.0.1)",
+    )
+    parser.add_argument(
+        "--provider",
+        default=os.environ.get("LLM_CONNECT_PROVIDER", "mock"),
+        help="Provider name passed to create_adapter (default: env LLM_CONNECT_PROVIDER or mock)",
+    )
+    parser.add_argument(
+        "--model",
+        default=os.environ.get("LLM_CONNECT_MODEL") or None,
+        help="Model name (default: env LLM_CONNECT_MODEL, optional)",
+    )
+    parser.add_argument(
+        "--disable-profiles",
+        action="store_true",
+        help="Disable server runtime profile dispatch.",
+    )
+    parser.add_argument(
+        "--strict-profiles",
+        action="store_true",
+        default=_truthy(os.environ.get("LLM_CONNECT_STRICT_PROFILES", "")),
+        help="Reject non-profile model_name values instead of passing them through.",
+    )
+    args = parser.parse_args(argv)
+
+    adapter = _build_adapter(
+        args.provider,
+        args.model,
+        enable_profiles=not args.disable_profiles,
+        strict_profiles=args.strict_profiles,
+    )
+    server = LLMServer(adapter=adapter, host=args.host, port=args.port)
+    print(f"llm_connect server listening on http://{args.host}:{args.port}")
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\nShutting down.")
+
+
+if __name__ == "__main__":
+    main()

llm_connect/shadowing.py

@@ -0,0 +1,177 @@
+"""Shadow-mode observation adapter for adaptive routing."""
+
+from __future__ import annotations
+
+import asyncio
+import random
+import threading
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass, field, replace
+from typing import Any, Callable, Mapping
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.grading import BaselineGrader
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.quality import QualityLedger, QualityObservation
+
+
+def _default_cost_estimator(response: LLMResponse) -> float:
+    for key in ("cost_usd", "estimated_cost_usd", "cost"):
+        value = response.metadata.get(key)
+        if isinstance(value, (int, float)) and value >= 0:
+            return float(value)
+    return 0.0
+
+
+class _StaticResponseAdapter(LLMAdapter):
+    """Adapter shim that lets a BaselineGrader reuse an existing response."""
+
+    def __init__(self, response: LLMResponse):
+        self._response = response
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        return self._response
+
+    def validate_config(self, config: RunConfig) -> bool:
+        return True
+
+
+@dataclass
+class ShadowingAdapter(LLMAdapter):
+    """Return candidate responses while recording sampled baseline grades.
+
+    Shadow work is best-effort: baseline, grading, or ledger failures are
+    reported to ``on_shadow_error`` when provided, but never alter the candidate
+    response returned to the caller.
+    """
+
+    candidate_adapter: LLMAdapter
+    baseline_adapter: LLMAdapter
+    grader: BaselineGrader
+    ledger: QualityLedger
+    task_type: str
+    adapter_id: str
+    model_id: str | None = None
+    baseline_adapter_id: str | None = None
+    shadow_rate: float = 1.0
+    async_shadow: bool = False
+    random_source: random.Random = field(default_factory=random.Random, repr=False)
+    cost_estimator: Callable[[LLMResponse], float] = _default_cost_estimator
+    tags: Mapping[str, Any] = field(default_factory=dict)
+    on_shadow_error: Callable[[Exception], None] | None = None
+    _executor: ThreadPoolExecutor | None = field(default=None, init=False, repr=False)
+    _futures: list[Future[None]] = field(default_factory=list, init=False, repr=False)
+    _lock: threading.Lock = field(default_factory=threading.Lock, init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        if not str(self.task_type).strip():
+            raise ValueError("task_type must be a non-empty string")
+        if not str(self.adapter_id).strip():
+            raise ValueError("adapter_id must be a non-empty string")
+        if not 0 <= self.shadow_rate <= 1:
+            raise ValueError("shadow_rate must be between 0 and 1")
+        if self.async_shadow:
+            self._executor = ThreadPoolExecutor(max_workers=1)
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        response = self.candidate_adapter.execute_prompt(prompt, config)
+        if self._should_shadow():
+            self._handle_shadow(prompt, config, response)
+        return response
+
+    async def async_execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        response = await self.candidate_adapter.async_execute_prompt(prompt, config)
+        if self._should_shadow():
+            if self.async_shadow:
+                self._schedule_shadow(prompt, config, response)
+            else:
+                await asyncio.to_thread(self._run_shadow, prompt, config, response)
+        return response
+
+    def validate_config(self, config: RunConfig) -> bool:
+        return self.candidate_adapter.validate_config(config)
+
+    def flush(self, timeout: float | None = None) -> None:
+        """Wait for currently queued async shadow work to finish."""
+        with self._lock:
+            futures = list(self._futures)
+            self._futures.clear()
+        for future in futures:
+            future.result(timeout=timeout)
+
+    def shutdown(self, wait: bool = True) -> None:
+        """Shut down the background shadow executor if one was created."""
+        if self._executor is not None:
+            self._executor.shutdown(wait=wait)
+            self._executor = None
+
+    def _should_shadow(self) -> bool:
+        if self.shadow_rate <= 0:
+            return False
+        if self.shadow_rate >= 1:
+            return True
+        with self._lock:
+            return self.random_source.random() < self.shadow_rate
+
+    def _handle_shadow(
+        self,
+        prompt: str,
+        config: RunConfig,
+        candidate_response: LLMResponse,
+    ) -> None:
+        if self.async_shadow:
+            self._schedule_shadow(prompt, config, candidate_response)
+        else:
+            self._run_shadow(prompt, config, candidate_response)
+
+    def _schedule_shadow(
+        self,
+        prompt: str,
+        config: RunConfig,
+        candidate_response: LLMResponse,
+    ) -> None:
+        if self._executor is None:
+            self._executor = ThreadPoolExecutor(max_workers=1)
+        future = self._executor.submit(self._run_shadow, prompt, config, candidate_response)
+        with self._lock:
+            self._futures = [item for item in self._futures if not item.done()]
+            self._futures.append(future)
+
+    def _run_shadow(
+        self,
+        prompt: str,
+        config: RunConfig,
+        candidate_response: LLMResponse,
+    ) -> None:
+        try:
+            shadow_config = replace(config, budget_tracker=None)
+            result = self.grader.grade(
+                self.baseline_adapter,
+                _StaticResponseAdapter(candidate_response),
+                prompt,
+                shadow_config,
+            )
+            self.ledger.append(
+                QualityObservation(
+                    task_type=self.task_type,
+                    adapter_id=self.adapter_id,
+                    model_id=self.model_id or candidate_response.model or config.model_name,
+                    cost_usd=self.cost_estimator(candidate_response),
+                    quality_score=result.quality_score,
+                    latency_ms=float(candidate_response.metadata.get("latency_ms", 0.0)),
+                    tokens_in=int(candidate_response.usage.get("prompt_tokens", 0)),
+                    tokens_out=int(candidate_response.usage.get("completion_tokens", 0)),
+                    baseline_adapter_id=self.baseline_adapter_id,
+                    tags=dict(self.tags),
+                )
+            )
+        except Exception as exc:
+            self._report_shadow_error(exc)
+
+    def _report_shadow_error(self, exc: Exception) -> None:
+        if self.on_shadow_error is None:
+            return
+        try:
+            self.on_shadow_error(exc)
+        except Exception:
+            pass

pyproject.toml

@@ -11,12 +11,17 @@ dependencies = [
     "toml",
 ]
 
+[project.scripts]
+llm-connect = "llm_connect.cli:main"
+
 [project.optional-dependencies]
 dev = [
     "pytest>=7.0",
     "ruff>=0.4",
     "mypy>=1.10",
 ]
+# serve mode uses stdlib http.server — no additional runtime dependency required
+server = []
 
 [tool.setuptools.packages.find]
 where = ["."]

registry/README.md

@@ -0,0 +1,12 @@
+# Capability Registry
+
+Markdown-first capability index for federation and reuse planning.
+
+## Authoring
+
+1. Copy a capability entry template (see reuse-surface `templates/capability-entry.template.md`).
+2. Add the row to `indexes/capabilities.yaml`.
+3. Run `reuse-surface validate` from a checkout with the CLI installed.
+4. Merge to `main` and verify publish with `reuse-surface establish --publish-check`.
+
+Federation contract: reuse-surface `docs/RegistryFederation.md`.

registry/capabilities/capability.agents.llm-connector.md

@@ -0,0 +1,123 @@
+---
+id: capability.agents.llm-connector
+name: Pluggable LLM Adapter Library (llm-connect)
+summary: Provider-neutral Python/CLI LLM adapter library supporting OpenRouter, Gemini, OpenAI, and the
+  Claude Code CLI out of the box, with a clean abstract interface for adding new providers.
+owner: llm-connect
+status: draft
+domain: agents
+tags:
+- llm
+- adapter
+- provider-neutral
+maturity:
+  discovery:
+    current: D3
+    target: D5
+    confidence: medium
+    rationale: README documents a working quick-start (`create_adapter`, `RunConfig`, `execute_prompt`),
+      the supported provider list, and both local-editable and future-published install paths.
+  availability:
+    current: A2
+    target: A3
+    confidence: medium
+    rationale: Installable today via `pip install -e` (local) with a documented future `pip install llm-connect`
+      path once published; consumed today by sibling repos including `cya` (can-you-assist) and referenced
+      as the intended backend for reuse-surface's own `establish --discover`.
+external_evidence:
+  completeness:
+    level: C2
+    confidence: low
+    basis: scope_vs_intent_and_consumer_expectations
+    satisfied_expectations:
+    - four providers implemented (OpenRouter, Gemini, OpenAI, Claude Code CLI)
+    - abstract adapter interface for adding new providers
+    - already a named integration point for other repos (can-you-assist's LLMAdapter seam, reuse-surface's
+      establish --discover)
+    broken_expectations: []
+    out_of_scope_expectations: []
+  reliability:
+    level: R1
+    confidence: low
+    basis: consumer_quality_signals
+    known_reliability_risks:
+    - no llm-connect instance was running on this workstation during the REUSE-WP-0017 sweep, so establish
+      --discover fell back to manual authoring for this campaign
+discovery:
+  intent: Give Python and CLI consumers a single, provider-neutral way to call LLMs, decoupling agent/tool
+    code from any one vendor's SDK.
+  includes:
+  - provider-neutral adapter interface
+  - OpenRouter, Gemini, OpenAI, Claude Code CLI adapters
+  - RunConfig-based execution
+  excludes:
+  - hosting or credential custody for providers (routes to OpenBao/operator paths per credential-routing
+    conventions)
+  assumptions: []
+  use_cases: []
+  research_memos: []
+availability:
+  current_level: A2
+  target_level: A3
+  current_artifacts:
+  - Python package (`llm_connect`)
+  target_artifacts: []
+  consumption_modes:
+  - library import
+  - cli
+relations:
+  depends_on: []
+  supports: []
+  related_to: []
+evidence:
+  documentation:
+  - README.md
+  tests:
+  - tests/
+  consumer_feedback: []
+  bug_reports: []
+  incidents: []
+consumer_guidance:
+  recommended_for:
+  - any repo needing provider-neutral LLM calls instead of a vendor-specific SDK
+  not_recommended_for:
+  - needs requiring a provider not yet adapted (extend via the abstract interface first)
+  known_limitations:
+  - not yet published to a package index; local editable install only
+promotion_history: []
+---
+
+# Pluggable LLM Adapter Library (llm-connect)
+
+## Overview
+
+`llm-connect` provides pluggable, provider-neutral LLM adapters for Python and the command line, supporting OpenRouter, Gemini, OpenAI, and the Claude Code CLI out of the box behind a clean abstract interface, and is already the intended backend for several sibling repos including `can-you-assist` and `reuse-surface`'s own drafting tooling.
+
+## Assessment notes
+
+### Discovery
+
+README documents a working quick-start (`create_adapter`, `RunConfig`, `execute_prompt`), the supported provider list, and both local-editable and future-published install paths.
+
+### Availability
+
+Installable today via `pip install -e` (local) with a documented future `pip install llm-connect` path once published; consumed today by sibling repos including `cya` (can-you-assist) and referenced as the intended backend for reuse-surface's own `establish --discover`.
+
+### Completeness
+
+First-pass honest assessment from the REUSE-WP-0017 coverage campaign
+(reuse-surface). No external consumer feedback exists yet; levels reflect
+scope-vs-intent documentation quality, not internal code quality.
+
+### Reliability
+
+No production consumer telemetry exists yet; reliability level is
+intentionally conservative pending REUSE-WP-0019 reuse-telemetry evidence.
+
+## Promotion checklist
+
+- [x] ID follows `capability.<domain>.<name>` pattern
+- [x] Maturity enums match `specs/CapabilityMaturityStandard.md`
+- [x] `external_evidence` is populated separately from `maturity`
+- [ ] Relations reference valid capability IDs (none yet)
+- [x] Index entry added in `registry/indexes/capabilities.yaml`

registry/indexes/capabilities.yaml

@@ -0,0 +1,20 @@
+version: 1
+updated: '2026-07-06'
+domain: helix_forge
+capabilities:
+- id: capability.agents.llm-connector
+  name: Pluggable LLM Adapter Library (llm-connect)
+  summary: Provider-neutral Python/CLI LLM adapter library supporting OpenRouter, Gemini, OpenAI, and
+    the Claude Code CLI out of the box, with a clean abstract interface for adding new providers.
+  vector: D3 / A2 / C2 / R1
+  domain: agents
+  status: draft
+  owner: llm-connect
+  path: registry/capabilities/capability.agents.llm-connector.md
+  tags:
+  - llm
+  - adapter
+  - provider-neutral
+  consumption_modes:
+  - library import
+  - cli

scripts/smoke_activity_core_endpoint.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""Smoke-test the activity-core llm-connect endpoint contract."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import time
+import urllib.error
+import urllib.request
+from pathlib import Path
+from typing import Any
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_REQUEST = ROOT / "fixtures" / "activity_core" / "daily-triage-execute-request.json"
+DEFAULT_SCHEMA = ROOT / "fixtures" / "activity_core" / "daily-triage-report.schema.json"
+
+
+class SmokeError(RuntimeError):
+    pass
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Validate /health, /execute, and daily triage JSON content.",
+    )
+    parser.add_argument(
+        "--url",
+        default=os.environ.get("LLM_CONNECT_URL", "http://127.0.0.1:8080"),
+        help="Base llm-connect URL (default: env LLM_CONNECT_URL or localhost:8080)",
+    )
+    parser.add_argument("--request", type=Path, default=DEFAULT_REQUEST)
+    parser.add_argument("--schema", type=Path, default=DEFAULT_SCHEMA)
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=float(os.environ.get("LLM_CONNECT_TIMEOUT_SECONDS", "300")),
+        help="HTTP timeout in seconds (default: env LLM_CONNECT_TIMEOUT_SECONDS or 300)",
+    )
+    parser.add_argument("--skip-health", action="store_true")
+    args = parser.parse_args(argv)
+
+    try:
+        result = run_smoke(
+            base_url=args.url,
+            request_path=args.request,
+            schema_path=args.schema,
+            timeout=args.timeout,
+            check_health=not args.skip_health,
+        )
+    except SmokeError as exc:
+        print(f"smoke: fail: {exc}", file=sys.stderr)
+        return 1
+
+    print(
+        "smoke: pass "
+        f"health={result['health']} "
+        f"latency_seconds={result['latency_seconds']:.3f} "
+        f"recommendations={result['recommendations']}"
+    )
+    return 0
+
+
+def run_smoke(
+    *,
+    base_url: str,
+    request_path: Path,
+    schema_path: Path,
+    timeout: float,
+    check_health: bool = True,
+) -> dict[str, Any]:
+    base = base_url.rstrip("/")
+    if check_health:
+        health = _get_json(f"{base}/health", timeout=timeout)
+        if health.get("status") != "ok":
+            raise SmokeError("/health did not return status=ok")
+        health_status = "ok"
+    else:
+        health_status = "skipped"
+
+    request_body = _load_json(request_path)
+    schema = _load_json(schema_path)
+    start = time.monotonic()
+    response = _post_json(f"{base}/execute", request_body, timeout=timeout)
+    latency = time.monotonic() - start
+
+    content = response.get("content")
+    if not isinstance(content, str):
+        raise SmokeError("/execute response did not include a string content field")
+    try:
+        content_json = json.loads(content)
+    except json.JSONDecodeError as exc:
+        raise SmokeError(f"content was not valid JSON: {exc}") from exc
+
+    errors = validate_json_schema(content_json, schema)
+    if errors:
+        raise SmokeError("content schema validation failed: " + "; ".join(errors[:5]))
+
+    return {
+        "health": health_status,
+        "latency_seconds": latency,
+        "recommendations": len(content_json.get("recommendations", [])),
+    }
+
+
+def validate_json_schema(instance: Any, schema: dict[str, Any]) -> list[str]:
+    """Validate the subset of JSON Schema used by the activity-core fixture."""
+
+    errors: list[str] = []
+    _validate(instance, schema, "$", errors)
+    return errors
+
+
+def _validate(instance: Any, schema: dict[str, Any], path: str, errors: list[str]) -> None:
+    expected_type = schema.get("type")
+    if expected_type and not _matches_type(instance, expected_type):
+        errors.append(f"{path}: expected {expected_type}, got {type(instance).__name__}")
+        return
+
+    if "enum" in schema and instance not in schema["enum"]:
+        errors.append(f"{path}: value {instance!r} not in enum")
+
+    if expected_type == "object":
+        assert isinstance(instance, dict)
+        required = schema.get("required", [])
+        for key in required:
+            if key not in instance:
+                errors.append(f"{path}: missing required property {key!r}")
+        properties = schema.get("properties", {})
+        if schema.get("additionalProperties") is False:
+            for key in instance:
+                if key not in properties:
+                    errors.append(f"{path}: unexpected property {key!r}")
+        for key, subschema in properties.items():
+            if key in instance and isinstance(subschema, dict):
+                _validate(instance[key], subschema, f"{path}.{key}", errors)
+        return
+
+    if expected_type == "array":
+        assert isinstance(instance, list)
+        min_items = schema.get("minItems")
+        max_items = schema.get("maxItems")
+        if isinstance(min_items, int) and len(instance) < min_items:
+            errors.append(f"{path}: expected at least {min_items} items")
+        if isinstance(max_items, int) and len(instance) > max_items:
+            errors.append(f"{path}: expected at most {max_items} items")
+        item_schema = schema.get("items")
+        if isinstance(item_schema, dict):
+            for index, item in enumerate(instance):
+                _validate(item, item_schema, f"{path}[{index}]", errors)
+        return
+
+    if expected_type in {"integer", "number"}:
+        minimum = schema.get("minimum")
+        maximum = schema.get("maximum")
+        if isinstance(minimum, (int, float)) and instance < minimum:
+            errors.append(f"{path}: expected >= {minimum}")
+        if isinstance(maximum, (int, float)) and instance > maximum:
+            errors.append(f"{path}: expected <= {maximum}")
+
+
+def _matches_type(instance: Any, expected_type: str) -> bool:
+    if expected_type == "object":
+        return isinstance(instance, dict)
+    if expected_type == "array":
+        return isinstance(instance, list)
+    if expected_type == "string":
+        return isinstance(instance, str)
+    if expected_type == "integer":
+        return isinstance(instance, int) and not isinstance(instance, bool)
+    if expected_type == "number":
+        return isinstance(instance, (int, float)) and not isinstance(instance, bool)
+    if expected_type == "boolean":
+        return isinstance(instance, bool)
+    if expected_type == "null":
+        return instance is None
+    return True
+
+
+def _load_json(path: Path) -> Any:
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        raise SmokeError(f"could not load JSON from {path}: {exc}") from exc
+
+
+def _get_json(url: str, *, timeout: float) -> dict[str, Any]:
+    try:
+        with urllib.request.urlopen(url, timeout=timeout) as response:
+            return _decode_json(response.read())
+    except urllib.error.HTTPError as exc:
+        raise SmokeError(f"GET /health returned HTTP {exc.code}") from exc
+    except urllib.error.URLError as exc:
+        raise SmokeError(f"GET /health failed: {exc.reason}") from exc
+
+
+def _post_json(url: str, body: dict[str, Any], *, timeout: float) -> dict[str, Any]:
+    request = urllib.request.Request(
+        url,
+        data=json.dumps(body).encode(),
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(request, timeout=timeout) as response:
+            return _decode_json(response.read())
+    except urllib.error.HTTPError as exc:
+        try:
+            error_body = _decode_json(exc.read())
+            code = error_body.get("error", "unknown_error")
+            message = error_body.get("message", "")
+            detail = f"{code}: {message}" if message else code
+        except SmokeError:
+            detail = "non-JSON error body"
+        raise SmokeError(f"POST /execute returned HTTP {exc.code}: {detail}") from exc
+    except urllib.error.URLError as exc:
+        raise SmokeError(f"POST /execute failed: {exc.reason}") from exc
+
+
+def _decode_json(data: bytes) -> dict[str, Any]:
+    try:
+        decoded = json.loads(data.decode())
+    except (UnicodeDecodeError, json.JSONDecodeError) as exc:
+        raise SmokeError(f"response was not JSON: {exc}") from exc
+    if not isinstance(decoded, dict):
+        raise SmokeError("response JSON was not an object")
+    return decoded
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

tests/test_activity_core_smoke.py

@@ -0,0 +1,92 @@
+import importlib.util
+import json
+from pathlib import Path
+
+from llm_connect.adapter import MockLLMAdapter
+from llm_connect.models import RunConfig
+from llm_connect.profiles import CUSTODIAN_TRIAGE_BALANCED, ProfiledLLMAdapter, RuntimeProfile
+from llm_connect.server import LLMServer
+
+
+ROOT = Path(__file__).resolve().parents[1]
+SCRIPT = ROOT / "scripts" / "smoke_activity_core_endpoint.py"
+FIXTURE_DIR = ROOT / "fixtures" / "activity_core"
+
+
+def _load_smoke_module():
+    spec = importlib.util.spec_from_file_location("smoke_activity_core_endpoint", SCRIPT)
+    assert spec is not None
+    module = importlib.util.module_from_spec(spec)
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+def test_daily_triage_fixture_content_matches_schema():
+    smoke = _load_smoke_module()
+    schema = json.loads((FIXTURE_DIR / "daily-triage-report.schema.json").read_text())
+    content = json.loads((FIXTURE_DIR / "daily-triage-valid-content.json").read_text())
+
+    assert smoke.validate_json_schema(content, schema) == []
+
+
+def test_daily_triage_execute_request_embeds_schema_and_profile_config():
+    request = json.loads((FIXTURE_DIR / "daily-triage-execute-request.json").read_text())
+    schema = json.loads((FIXTURE_DIR / "daily-triage-report.schema.json").read_text())
+    config = request["config"]
+
+    assert request["prompt"]
+    assert config["model_name"] == "custodian-triage-balanced"
+    assert config["temperature"] == 0.2
+    assert config["max_tokens"] == 1800
+    assert config["max_depth"] == 2
+    assert config["timeout_seconds"] == 300
+    assert config["model_params"]["reasoning_effort"] == "medium"
+    assert config["model_params"]["json_schema"] == schema
+
+
+def test_schema_validator_reports_missing_required_field():
+    smoke = _load_smoke_module()
+    schema = json.loads((FIXTURE_DIR / "daily-triage-report.schema.json").read_text())
+    invalid = {"summary": "missing recommendations"}
+
+    errors = smoke.validate_json_schema(invalid, schema)
+
+    assert "$: missing required property 'recommendations'" in errors
+
+
+def test_run_smoke_against_profiled_mock_server():
+    smoke = _load_smoke_module()
+    valid_content = (FIXTURE_DIR / "daily-triage-valid-content.json").read_text()
+
+    def factory(provider: str, model: str) -> MockLLMAdapter:
+        assert provider == "mock"
+        assert model == "triage-model"
+        return MockLLMAdapter(mock_response=valid_content)
+
+    adapter = ProfiledLLMAdapter(
+        MockLLMAdapter(mock_response=valid_content),
+        {
+            CUSTODIAN_TRIAGE_BALANCED: RuntimeProfile(
+                name=CUSTODIAN_TRIAGE_BALANCED,
+                provider="mock",
+                model="triage-model",
+                config=RunConfig(model_name="triage-model"),
+            )
+        },
+        adapter_factory=factory,
+    )
+    server = LLMServer(adapter=adapter, port=0)
+    server.start()
+    try:
+        result = smoke.run_smoke(
+            base_url=f"http://127.0.0.1:{server.port}",
+            request_path=FIXTURE_DIR / "daily-triage-execute-request.json",
+            schema_path=FIXTURE_DIR / "daily-triage-report.schema.json",
+            timeout=3,
+        )
+    finally:
+        server.stop()
+
+    assert result["health"] == "ok"
+    assert result["recommendations"] == 1

tests/test_adaptive_integration.py

@@ -0,0 +1,109 @@
+"""
+Integration coverage for the adaptive routing workplan flow.
+"""
+
+from datetime import datetime, timezone
+
+from examples.adaptive_routing_fixture_batch import populate_ledger
+from llm_connect.adapter import MockLLMAdapter
+from llm_connect.quality import QualityLedger, QualityObservation
+from llm_connect.routing import AdaptiveRoutingPolicy, RoutingRule
+
+
+def append_quality(
+    ledger: QualityLedger,
+    adapter_id: str,
+    quality_score: float,
+    cost_usd: float,
+    *,
+    recorded_at: datetime,
+) -> None:
+    ledger.append(
+        QualityObservation(
+            task_type="summarize",
+            adapter_id=adapter_id,
+            model_id=f"{adapter_id}-model",
+            cost_usd=cost_usd,
+            quality_score=quality_score,
+            latency_ms=100,
+            tokens_in=100,
+            tokens_out=50,
+            recorded_at=recorded_at,
+            baseline_adapter_id="baseline",
+        )
+    )
+
+
+def test_adaptive_policy_converges_to_cheapest_qualifying_adapter(tmp_path):
+    cheap = MockLLMAdapter("cheap")
+    mid = MockLLMAdapter("mid")
+    smart = MockLLMAdapter("smart")
+    ledger = QualityLedger(tmp_path / "quality.jsonl")
+    policy = AdaptiveRoutingPolicy(
+        rules=[
+            RoutingRule(
+                "summarize",
+                prefer=smart,
+                max_cost_per_1k=1.0,
+                fallback=mid,
+            )
+        ],
+        ledger=ledger,
+        adapters_by_id={"cheap": cheap, "mid": mid, "smart": smart},
+        window_size=2,
+    )
+
+    assert policy.resolve("summarize", quality_floor=0.8) is smart
+    assert policy.resolve("summarize", 2.0, quality_floor=0.8) is mid
+
+    append_quality(
+        ledger,
+        "cheap",
+        quality_score=0.7,
+        cost_usd=0.01,
+        recorded_at=datetime(2026, 5, 17, 10, tzinfo=timezone.utc),
+    )
+    append_quality(
+        ledger,
+        "mid",
+        quality_score=0.86,
+        cost_usd=0.02,
+        recorded_at=datetime(2026, 5, 17, 10, tzinfo=timezone.utc),
+    )
+    append_quality(
+        ledger,
+        "smart",
+        quality_score=0.95,
+        cost_usd=0.05,
+        recorded_at=datetime(2026, 5, 17, 10, tzinfo=timezone.utc),
+    )
+
+    assert policy.resolve("summarize", quality_floor=0.8) is mid
+
+    append_quality(
+        ledger,
+        "cheap",
+        quality_score=0.95,
+        cost_usd=0.01,
+        recorded_at=datetime(2026, 5, 17, 11, tzinfo=timezone.utc),
+    )
+
+    assert policy.resolve("summarize", quality_floor=0.8) is cheap
+
+
+def test_fixture_batch_populates_three_candidate_observations_per_task(tmp_path):
+    ledger = QualityLedger(tmp_path / "quality.jsonl")
+
+    populate_ledger(ledger)
+
+    observations = ledger.read_all()
+    by_task_type: dict[str, set[str]] = {}
+    for observation in observations:
+        by_task_type.setdefault(observation.task_type, set()).add(observation.adapter_id)
+
+    assert set(by_task_type) == {
+        "summarize-source",
+        "extract-relations",
+        "evaluate-entity",
+    }
+    assert all(len(adapter_ids) == 3 for adapter_ids in by_task_type.values())

tests/test_adaptive_routing.py

@@ -0,0 +1,181 @@
+"""
+Tests for AdaptiveRoutingPolicy.
+"""
+
+from datetime import datetime, timedelta, timezone
+
+from llm_connect.adapter import MockLLMAdapter
+from llm_connect.quality import QualityLedger, QualityObservation
+from llm_connect.routing import AdaptiveRoutingPolicy, RoutingRule
+
+
+def append_observation(
+    ledger: QualityLedger,
+    *,
+    adapter_id: str,
+    quality_score: float,
+    cost_usd: float,
+    task_type: str = "summarize",
+    recorded_at: datetime | None = None,
+) -> None:
+    ledger.append(
+        QualityObservation(
+            task_type=task_type,
+            adapter_id=adapter_id,
+            model_id=f"{adapter_id}-model",
+            cost_usd=cost_usd,
+            quality_score=quality_score,
+            latency_ms=100,
+            tokens_in=100,
+            tokens_out=50,
+            baseline_adapter_id="baseline",
+            recorded_at=recorded_at or datetime(2026, 5, 17, tzinfo=timezone.utc),
+        )
+    )
+
+
+class TestAdaptiveRoutingPolicy:
+    def _adapter(self, name: str) -> MockLLMAdapter:
+        return MockLLMAdapter(mock_response=name)
+
+    def test_selects_cheapest_adapter_that_clears_quality_floor(self, tmp_path):
+        cheap = self._adapter("cheap")
+        smart = self._adapter("smart")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(ledger, adapter_id="cheap", quality_score=0.7, cost_usd=0.01)
+        append_observation(ledger, adapter_id="smart", quality_score=0.9, cost_usd=0.03)
+
+        policy = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=cheap)],
+            ledger=ledger,
+            adapters_by_id={"cheap": cheap, "smart": smart},
+        )
+
+        assert policy.resolve("summarize", quality_floor=0.8) is smart
+
+    def test_prefers_lower_observed_cost_when_multiple_adapters_clear_floor(self, tmp_path):
+        cheap = self._adapter("cheap")
+        smart = self._adapter("smart")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(ledger, adapter_id="cheap", quality_score=0.9, cost_usd=0.01)
+        append_observation(ledger, adapter_id="smart", quality_score=0.95, cost_usd=0.03)
+
+        policy = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=smart)],
+            ledger=ledger,
+            adapters_by_id={"cheap": cheap, "smart": smart},
+        )
+
+        assert policy.resolve("summarize", quality_floor=0.8) is cheap
+
+    def test_equal_cost_tie_prefers_static_rule_prefer(self, tmp_path):
+        candidate = self._adapter("candidate")
+        preferred = self._adapter("preferred")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(ledger, adapter_id="candidate", quality_score=0.9, cost_usd=0.01)
+        append_observation(ledger, adapter_id="preferred", quality_score=0.9, cost_usd=0.01)
+
+        policy = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=preferred)],
+            ledger=ledger,
+            adapters_by_id={"candidate": candidate, "preferred": preferred},
+        )
+
+        assert policy.resolve("summarize", quality_floor=0.8) is preferred
+
+    def test_cold_start_falls_through_to_static_policy(self, tmp_path):
+        preferred = self._adapter("preferred")
+        fallback = self._adapter("fallback")
+        policy = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=preferred, fallback=fallback)],
+            ledger=QualityLedger(tmp_path / "quality.jsonl"),
+            adapters_by_id={"preferred": preferred, "fallback": fallback},
+        )
+
+        assert policy.resolve("summarize", quality_floor=0.8) is preferred
+
+    def test_window_size_changes_observed_mean_quality(self, tmp_path):
+        cheap = self._adapter("cheap")
+        smart = self._adapter("smart")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(
+            ledger,
+            adapter_id="cheap",
+            quality_score=0.9,
+            cost_usd=0.01,
+            recorded_at=datetime(2026, 5, 16, tzinfo=timezone.utc),
+        )
+        append_observation(
+            ledger,
+            adapter_id="cheap",
+            quality_score=0.7,
+            cost_usd=0.01,
+            recorded_at=datetime(2026, 5, 17, tzinfo=timezone.utc),
+        )
+        append_observation(ledger, adapter_id="smart", quality_score=0.9, cost_usd=0.03)
+
+        recent_only = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=smart)],
+            ledger=ledger,
+            adapters_by_id={"cheap": cheap, "smart": smart},
+            window_size=1,
+        )
+        wider_window = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=smart)],
+            ledger=ledger,
+            adapters_by_id={"cheap": cheap, "smart": smart},
+            window_size=2,
+        )
+
+        assert recent_only.resolve("summarize", quality_floor=0.8) is smart
+        assert wider_window.resolve("summarize", quality_floor=0.8) is cheap
+
+    def test_stale_observations_are_ignored_by_max_age(self, tmp_path):
+        stale = self._adapter("stale")
+        fresh = self._adapter("fresh")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(
+            ledger,
+            adapter_id="stale",
+            quality_score=1.0,
+            cost_usd=0.01,
+            recorded_at=datetime(2020, 1, 1, tzinfo=timezone.utc),
+        )
+        append_observation(
+            ledger,
+            adapter_id="fresh",
+            quality_score=0.9,
+            cost_usd=0.03,
+            recorded_at=datetime.now(timezone.utc),
+        )
+
+        policy = AdaptiveRoutingPolicy(
+            rules=[RoutingRule("summarize", prefer=stale)],
+            ledger=ledger,
+            adapters_by_id={"stale": stale, "fresh": fresh},
+            max_age=timedelta(days=1),
+        )
+
+        assert policy.resolve("summarize", quality_floor=0.8) is fresh
+
+    def test_static_fallback_chain_is_preserved_when_no_candidate_qualifies(self, tmp_path):
+        preferred = self._adapter("preferred")
+        fallback = self._adapter("fallback")
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        append_observation(ledger, adapter_id="preferred", quality_score=0.6, cost_usd=0.01)
+        append_observation(ledger, adapter_id="fallback", quality_score=0.7, cost_usd=0.005)
+
+        policy = AdaptiveRoutingPolicy(
+            rules=[
+                RoutingRule(
+                    "summarize",
+                    prefer=preferred,
+                    max_cost_per_1k=1.0,
+                    fallback=fallback,
+                )
+            ],
+            ledger=ledger,
+            adapters_by_id={"preferred": preferred, "fallback": fallback},
+        )
+
+        assert policy.resolve("summarize", 2.0, quality_floor=0.8) is fallback

tests/test_claude_code.py

@@ -0,0 +1,153 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+
+from llm_connect.claude_code import ClaudeCodeAdapter
+from llm_connect.config import LLMConfig
+from llm_connect.models import RunConfig
+
+
+def test_execute_prompt_passes_json_schema_to_claude_cli(monkeypatch):
+    calls: dict[str, object] = {}
+
+    def fake_run(cmd, input, capture_output, text, timeout):  # noqa: ANN001
+        calls["cmd"] = cmd
+        calls["input"] = input
+        calls["capture_output"] = capture_output
+        calls["text"] = text
+        calls["timeout"] = timeout
+        # With --output-format json the CLI returns an envelope.
+        envelope = {
+            "type": "result",
+            "result": '{"summary":"ok","recommendations":[]}',
+        }
+        import json as _json
+        return SimpleNamespace(returncode=0, stdout=_json.dumps(envelope), stderr="")
+
+    monkeypatch.setattr("llm_connect.claude_code.subprocess.run", fake_run)
+    adapter = ClaudeCodeAdapter(cli_path="/custom/claude")
+
+    response = adapter.execute_prompt(
+        "Produce a report.",
+        RunConfig(
+            timeout_seconds=42,
+            model_params={"json_schema": {"type": "object"}},
+        ),
+    )
+
+    assert calls["cmd"] == [
+        "/custom/claude",
+        "--print",
+        "--json-schema",
+        '{"type":"object"}',
+        "--output-format",
+        "json",
+    ]
+    assert calls["input"] == "Produce a report."
+    assert calls["timeout"] == 42
+    # Envelope's result field carries the schema-enforced JSON; the adapter
+    # unwraps it before returning to the caller.
+    assert response.content == '{"summary":"ok","recommendations":[]}'
+
+
+def test_execute_prompt_unwraps_cli_json_envelope_result_field(monkeypatch):
+    """With --output-format json the CLI wraps the model payload in an
+    envelope. The adapter unwraps the textual result so the caller still
+    sees the model's structured-output JSON, not the envelope."""
+    def fake_run(cmd, input, capture_output, text, timeout):  # noqa: ANN001
+        envelope = {
+            "type": "result",
+            "result": '{"summary":"ok","recommendations":[]}',
+            "total_cost_usd": 0.001,
+        }
+        import json as _json
+        return SimpleNamespace(
+            returncode=0,
+            stdout=_json.dumps(envelope),
+            stderr="",
+        )
+
+    monkeypatch.setattr("llm_connect.claude_code.subprocess.run", fake_run)
+    adapter = ClaudeCodeAdapter(cli_path="/custom/claude")
+
+    response = adapter.execute_prompt(
+        "Produce a report.",
+        RunConfig(model_params={"json_schema": {"type": "object"}}),
+    )
+
+    assert response.content == '{"summary":"ok","recommendations":[]}'
+
+
+def test_execute_prompt_prefers_json_field_over_prose_preamble(monkeypatch):
+    """When the model adds a prose preamble in the envelope's primary text
+    field but the schema-enforced JSON is in a different field, the adapter
+    must find and return the JSON, not the preamble."""
+    def fake_run(cmd, input, capture_output, text, timeout):  # noqa: ANN001
+        envelope = {
+            "type": "result",
+            "result": "Triage report generated and returned via structured output. Key signals: healthy.",
+            "structured_result": '{"summary":"healthy","recommendations":[]}',
+            "total_cost_usd": 0.002,
+        }
+        import json as _json
+        return SimpleNamespace(returncode=0, stdout=_json.dumps(envelope), stderr="")
+
+    monkeypatch.setattr("llm_connect.claude_code.subprocess.run", fake_run)
+    adapter = ClaudeCodeAdapter(cli_path="/custom/claude")
+
+    response = adapter.execute_prompt(
+        "Long triage prompt.",
+        RunConfig(model_params={"json_schema": {"type": "object"}}),
+    )
+
+    assert response.content == '{"summary":"healthy","recommendations":[]}'
+
+
+def test_execute_prompt_skips_envelope_metadata_keys(monkeypatch):
+    """Metadata keys like `type`, `model`, `usage` must never be returned as
+    the model payload, even if their values look JSON-like."""
+    def fake_run(cmd, input, capture_output, text, timeout):  # noqa: ANN001
+        envelope = {
+            "type": '{"this":"is_metadata"}',  # decoy
+            "usage": {"input_tokens": 5},      # decoy dict
+            "result": '{"summary":"ok"}',
+        }
+        import json as _json
+        return SimpleNamespace(returncode=0, stdout=_json.dumps(envelope), stderr="")
+
+    monkeypatch.setattr("llm_connect.claude_code.subprocess.run", fake_run)
+    adapter = ClaudeCodeAdapter(cli_path="/custom/claude")
+
+    response = adapter.execute_prompt(
+        "Prompt.", RunConfig(model_params={"json_schema": {"type": "object"}})
+    )
+
+    assert response.content == '{"summary":"ok"}'
+
+
+def test_execute_prompt_no_unwrap_without_json_schema(monkeypatch):
+    """Without --json-schema we do not pass --output-format json, so the
+    envelope unwrap path stays inert and raw stdout passes through."""
+    def fake_run(cmd, input, capture_output, text, timeout):  # noqa: ANN001
+        return SimpleNamespace(
+            returncode=0,
+            stdout='{"result":"this is just stdout, not an envelope"}',
+            stderr="",
+        )
+
+    monkeypatch.setattr("llm_connect.claude_code.subprocess.run", fake_run)
+    adapter = ClaudeCodeAdapter(cli_path="/custom/claude")
+
+    response = adapter.execute_prompt("Plain prompt.", RunConfig())
+
+    assert response.content == '{"result":"this is just stdout, not an envelope"}'
+
+
+def test_claude_code_adapter_prefers_env_cli_path(monkeypatch):
+    monkeypatch.setenv("LLM_CONNECT_CLAUDE_CLI_PATH", "/home/me/bin/claude")
+
+    adapter = ClaudeCodeAdapter(
+        config=LLMConfig(provider="claude-code", claude_cli_path="claude")
+    )
+
+    assert adapter._cli_path == "/home/me/bin/claude"

tests/test_cli.py

@@ -0,0 +1,54 @@
+import json
+from datetime import datetime, timezone
+
+from llm_connect.cli import main
+from llm_connect.quality import QualityLedger, QualityObservation
+
+
+def test_rates_show_json_outputs_default_registry(capsys):
+    assert main(["rates", "show", "--json"]) == 0
+
+    payload = json.loads(capsys.readouterr().out)
+
+    assert payload["openai/gpt-4o-mini"]["prompt_per_1k"] == 0.00015
+
+
+def test_classes_show_lists_builtins(capsys):
+    assert main(["classes", "show"]) == 0
+
+    output = capsys.readouterr().out
+
+    assert "chunk-summarization" in output
+    assert "entity-extraction" in output
+
+
+def test_classes_fit_reads_quality_ledger(tmp_path, capsys):
+    ledger = QualityLedger(tmp_path / "quality.jsonl")
+    for _ in range(3):
+        ledger.append(
+            QualityObservation(
+                task_type="extract",
+                adapter_id="openrouter",
+                model_id="openai/gpt-4o-mini",
+                cost_usd=0.001,
+                quality_score=0.9,
+                latency_ms=100,
+                tokens_in=500,
+                tokens_out=350,
+                recorded_at=datetime(2026, 5, 19, tzinfo=timezone.utc),
+                tags={
+                    "problem_class": "entity-extraction",
+                    "dimensions": {
+                        "chunk_words": 300,
+                        "template_words": 100,
+                        "expected_entities": 5,
+                    },
+                },
+            )
+        )
+
+    assert main(["classes", "fit", str(ledger.path), "--class", "entity-extraction", "--json"]) == 0
+
+    payload = json.loads(capsys.readouterr().out)
+
+    assert payload["entity-extraction"]["params"]["tokens_per_entity"] == 70

tests/test_costs.py

@@ -0,0 +1,49 @@
+import pytest
+
+from llm_connect.costs import CostEstimate, CostModel, estimate_cost
+from llm_connect.rates import ModelRate, ModelRateRegistry
+
+
+def test_known_model_cost_matches_lefevre_smoke_budget():
+    estimate = estimate_cost("openai/gpt-4o-mini", 28_000, 7_500)
+
+    assert estimate.cost_source == "rate_table:openai/gpt-4o-mini"
+    assert estimate.cost_usd == pytest.approx(0.0087)
+    assert estimate.cost_usd == pytest.approx(0.009, rel=0.2)
+
+
+def test_unknown_model_returns_unknown_without_zeroing_cost():
+    estimate = estimate_cost("unknown/model", 100, 50)
+
+    assert estimate == CostEstimate(cost_usd=None, cost_source="unknown")
+
+
+def test_registry_override_controls_estimate():
+    registry = ModelRateRegistry(
+        {
+            "vendor/model": ModelRate(
+                "vendor/model",
+                prompt_per_1k=1.0,
+                completion_per_1k=2.0,
+            )
+        }
+    )
+
+    estimate = estimate_cost("vendor/model", 1_000, 500, registry=registry)
+
+    assert estimate.cost_usd == pytest.approx(2.0)
+    assert estimate.prompt_cost_usd == pytest.approx(1.0)
+    assert estimate.completion_cost_usd == pytest.approx(1.0)
+
+
+def test_zero_tokens_are_valid_and_cost_zero_for_known_model():
+    estimate = CostModel().estimate_cost("openai/gpt-4o-mini", 0, 0)
+
+    assert estimate.cost_usd == 0
+    assert estimate.prompt_cost_usd == 0
+    assert estimate.completion_cost_usd == 0
+
+
+def test_negative_tokens_are_rejected():
+    with pytest.raises(ValueError, match="prompt_tokens"):
+        estimate_cost("openai/gpt-4o-mini", -1, 0)

tests/test_factory.py

@@ -66,7 +66,7 @@ class TestCreateAdapter:
         assert isinstance(adapter, ClaudeCodeAdapter)
 
     def test_all_known_providers_are_reachable(self):
-        known = {"openrouter", "openai", "gemini", "claude-code"}
+        known = {"openrouter", "openai", "gemini", "claude-code", "mock"}
         # Just verify each key is in the factory registry (no construction needed)
         from llm_connect.factory import _PROVIDERS
         assert known == set(_PROVIDERS.keys())

tests/test_grading.py

@@ -0,0 +1,198 @@
+"""
+Tests for baseline grading and built-in judges.
+"""
+
+import pytest
+
+from llm_connect.adapter import MockLLMAdapter
+from llm_connect.embedding_adapter import EmbeddingAdapter
+from llm_connect.grading import (
+    EmbeddingSimilarityJudge,
+    ExactMatchJudge,
+    GradingResult,
+    LLMJudge,
+    PairedGrader,
+)
+from llm_connect.models import LLMResponse, RunConfig
+
+
+class StaticEmbeddingAdapter(EmbeddingAdapter):
+    def __init__(self, embeddings: list[list[float]]):
+        self.embeddings = embeddings
+        self.seen_texts: list[str] | None = None
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        self.seen_texts = texts
+        return self.embeddings
+
+    def validate(self) -> bool:
+        return True
+
+
+def response(content: str, model: str = "m") -> LLMResponse:
+    return LLMResponse(content=content, model=model)
+
+
+class TestGradingResult:
+    def test_score_must_be_between_zero_and_one(self):
+        with pytest.raises(ValueError, match="quality_score"):
+            GradingResult(
+                quality_score=1.5,
+                notes="bad",
+                grader_id="g",
+                baseline_response=response("a"),
+                candidate_response=response("b"),
+            )
+
+    def test_grader_id_must_be_non_empty(self):
+        with pytest.raises(ValueError, match="grader_id"):
+            GradingResult(
+                quality_score=1.0,
+                notes="ok",
+                grader_id="",
+                baseline_response=response("a"),
+                candidate_response=response("a"),
+            )
+
+
+class TestExactMatchJudge:
+    def test_scores_one_for_normalised_match(self):
+        judge = ExactMatchJudge()
+        result = judge.judge(
+            response("hello   world"),
+            response("hello world"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 1.0
+        assert result.baseline_response.content == "hello   world"
+        assert result.candidate_response.content == "hello world"
+
+    def test_scores_zero_for_difference(self):
+        result = ExactMatchJudge().judge(
+            response("hello"),
+            response("goodbye"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 0.0
+
+    def test_case_insensitive_mode(self):
+        result = ExactMatchJudge(case_sensitive=False).judge(
+            response("Hello"),
+            response("hello"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 1.0
+
+
+class TestEmbeddingSimilarityJudge:
+    def test_scores_cosine_similarity(self):
+        embedding_adapter = StaticEmbeddingAdapter([[1.0, 0.0], [0.5, 0.0]])
+        result = EmbeddingSimilarityJudge(embedding_adapter).judge(
+            response("baseline"),
+            response("candidate"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 1.0
+        assert embedding_adapter.seen_texts == ["baseline", "candidate"]
+
+    def test_negative_similarity_clamps_to_zero(self):
+        embedding_adapter = StaticEmbeddingAdapter([[1.0, 0.0], [-1.0, 0.0]])
+        result = EmbeddingSimilarityJudge(embedding_adapter).judge(
+            response("baseline"),
+            response("candidate"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 0.0
+
+    def test_wrong_embedding_count_raises(self):
+        embedding_adapter = StaticEmbeddingAdapter([[1.0, 0.0]])
+        with pytest.raises(ValueError, match="two embeddings"):
+            EmbeddingSimilarityJudge(embedding_adapter).judge(
+                response("baseline"),
+                response("candidate"),
+                prompt="p",
+                run_config=RunConfig(),
+            )
+
+
+class TestLLMJudge:
+    def test_parses_json_judge_response(self):
+        judge_adapter = MockLLMAdapter(
+            mock_response='{"quality_score": 0.75, "notes": "mostly equivalent"}'
+        )
+        run_config = RunConfig(model_params={"existing": True})
+
+        result = LLMJudge(judge_adapter).judge(
+            response("baseline answer"),
+            response("candidate answer"),
+            prompt="original prompt",
+            run_config=run_config,
+        )
+
+        assert result.quality_score == 0.75
+        assert result.notes == "mostly equivalent"
+        assert "baseline answer" in judge_adapter.last_prompt
+        assert "candidate answer" in judge_adapter.last_prompt
+        assert judge_adapter.last_config.temperature == 0.0
+        assert judge_adapter.last_config.model_params["existing"] is True
+        assert judge_adapter.last_config.model_params["seed"] == 0
+        assert judge_adapter.last_config.budget_tracker is None
+
+    def test_extracts_json_from_wrapped_response(self):
+        judge_adapter = MockLLMAdapter(
+            mock_response='Here is the result: {"quality_score": 1, "notes": "same"}'
+        )
+        result = LLMJudge(judge_adapter).judge(
+            response("a"),
+            response("a"),
+            prompt="p",
+            run_config=RunConfig(),
+        )
+        assert result.quality_score == 1.0
+        assert result.notes == "same"
+
+    def test_invalid_judge_response_raises(self):
+        judge_adapter = MockLLMAdapter(mock_response="not json")
+        with pytest.raises(ValueError, match="JSON"):
+            LLMJudge(judge_adapter).judge(
+                response("a"),
+                response("b"),
+                prompt="p",
+                run_config=RunConfig(),
+            )
+
+
+class TestPairedGrader:
+    def test_runs_both_adapters_and_preserves_responses(self):
+        baseline = MockLLMAdapter(mock_response="same")
+        candidate = MockLLMAdapter(mock_response="same")
+        result = PairedGrader(ExactMatchJudge()).grade(
+            baseline,
+            candidate,
+            "prompt",
+            RunConfig(model_name="mock-model"),
+        )
+
+        assert result.quality_score == 1.0
+        assert result.baseline_response.content == "same"
+        assert result.candidate_response.content == "same"
+        assert baseline.call_count == 1
+        assert candidate.call_count == 1
+        assert baseline.last_prompt == "prompt"
+        assert candidate.last_prompt == "prompt"
+
+    def test_uses_custom_judge(self):
+        baseline = MockLLMAdapter(mock_response="a")
+        candidate = MockLLMAdapter(mock_response="b")
+        result = PairedGrader(ExactMatchJudge()).grade(
+            baseline,
+            candidate,
+            "prompt",
+            RunConfig(),
+        )
+        assert result.quality_score == 0.0

tests/test_package_exports.py

@@ -0,0 +1,63 @@
+"""
+Tests for the public llm_connect package surface.
+"""
+
+import llm_connect
+
+
+def test_wp_0004_primitives_are_exported_from_package_root():
+    expected_names = [
+        "AdaptiveRoutingPolicy",
+        "BaselineGrader",
+        "EmbeddingSimilarityJudge",
+        "ExactMatchJudge",
+        "GradingResult",
+        "Judge",
+        "LLMJudge",
+        "PairedGrader",
+        "QualityLedger",
+        "QualityObservation",
+        "ShadowingAdapter",
+        "is_stale",
+    ]
+
+    for name in expected_names:
+        assert hasattr(llm_connect, name)
+        assert name in llm_connect.__all__
+
+
+def test_wp_0005_primitives_are_exported_from_package_root():
+    expected_names = [
+        "ModelRate",
+        "ModelRateRegistry",
+        "CostEstimate",
+        "CostModel",
+        "estimate_cost",
+        "TokenEstimate",
+        "Observation",
+        "ProblemClass",
+        "ProblemClassRegistry",
+        "default_problem_class_registry",
+        "ChunkSummarizationProblemClass",
+        "EntityExtractionProblemClass",
+        "RelationExtractionProblemClass",
+        "JudgeEvalProblemClass",
+        "ReportSynthesisProblemClass",
+    ]
+
+    for name in expected_names:
+        assert hasattr(llm_connect, name)
+        assert name in llm_connect.__all__
+
+
+def test_wp_0006_profile_primitives_are_exported_from_package_root():
+    expected_names = [
+        "CUSTODIAN_TRIAGE_BALANCED",
+        "RuntimeProfile",
+        "ProfiledLLMAdapter",
+        "default_runtime_profiles",
+    ]
+
+    for name in expected_names:
+        assert hasattr(llm_connect, name)
+        assert name in llm_connect.__all__

tests/test_payload.py

@@ -0,0 +1,81 @@
+from llm_connect._payload import merge_gemini_model_params, merge_openai_chat_model_params
+
+
+STRUCTURED_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "summary": {"type": "string"},
+        "recommendations": {"type": "array", "items": {"type": "string"}},
+    },
+    "required": ["summary", "recommendations"],
+}
+
+
+ACTIVITY_CORE_MODEL_PARAMS = {
+    "reasoning_effort": "medium",
+    "max_depth": 4,
+    "json_schema": STRUCTURED_SCHEMA,
+    "top_p": 0.8,
+}
+
+
+def test_openai_chat_model_params_translate_activity_core_shape():
+    payload = {
+        "model": "gpt-4.1-mini",
+        "messages": [{"role": "user", "content": "triage"}],
+        "temperature": 0.2,
+        "max_tokens": 200,
+    }
+
+    merge_openai_chat_model_params(payload, ACTIVITY_CORE_MODEL_PARAMS)
+
+    assert payload["response_format"] == {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "structured_output",
+            "schema": STRUCTURED_SCHEMA,
+            "strict": True,
+        },
+    }
+    assert payload["top_p"] == 0.8
+    assert "reasoning_effort" not in payload
+    assert "max_depth" not in payload
+    assert "json_schema" not in payload
+
+
+def test_openai_chat_model_params_preserve_explicit_response_format():
+    explicit = {
+        "type": "json_schema",
+        "json_schema": {
+            "name": "custom",
+            "schema": STRUCTURED_SCHEMA,
+            "strict": True,
+        },
+    }
+    payload = {"model": "gpt-4.1-mini", "messages": []}
+
+    merge_openai_chat_model_params(
+        payload,
+        {"json_schema": STRUCTURED_SCHEMA, "response_format": explicit},
+    )
+
+    assert payload["response_format"] == explicit
+
+
+def test_gemini_model_params_translate_activity_core_shape():
+    payload = {
+        "contents": [{"role": "user", "parts": [{"text": "triage"}]}],
+        "generationConfig": {
+            "temperature": 0.2,
+            "maxOutputTokens": 200,
+        },
+    }
+
+    merge_gemini_model_params(payload, ACTIVITY_CORE_MODEL_PARAMS)
+
+    assert payload["generationConfig"]["responseMimeType"] == "application/json"
+    assert payload["generationConfig"]["responseSchema"] == STRUCTURED_SCHEMA
+    assert payload["generationConfig"]["topP"] == 0.8
+    assert "reasoning_effort" not in payload
+    assert "max_depth" not in payload
+    assert "json_schema" not in payload

tests/test_problem_classes.py

@@ -0,0 +1,137 @@
+from datetime import datetime, timezone
+
+import pytest
+
+from llm_connect.problem_classes import (
+    EntityExtractionProblemClass,
+    Observation,
+    ProblemClassRegistry,
+    TokenEstimate,
+)
+from llm_connect.quality import QualityObservation
+
+
+DIMENSIONS_BY_CLASS = {
+    "chunk-summarization": [
+        {"chunk_words": 900, "template_words": 150},
+        {"chunk_words": 400, "template_words": 125},
+        {"chunk_words": 1200, "template_words": 200},
+    ],
+    "entity-extraction": [
+        {"chunk_words": 900, "template_words": 200, "expected_entities": 4},
+        {"chunk_words": 450, "template_words": 180, "expected_entities": 6},
+        {"chunk_words": 1200, "template_words": 220, "expected_entities": 8},
+    ],
+    "relation-extraction": [
+        {"chunk_words": 900, "template_words": 200, "expected_relations": 3},
+        {"chunk_words": 450, "template_words": 180, "expected_relations": 5},
+        {"chunk_words": 1200, "template_words": 220, "expected_relations": 7},
+    ],
+    "judge-eval": [
+        {"artifact_words": 700, "template_words": 180, "n_criteria": 4},
+        {"artifact_words": 300, "template_words": 160, "n_criteria": 5},
+        {"artifact_words": 1100, "template_words": 200, "n_criteria": 6},
+    ],
+    "report-synthesis": [
+        {"n_chunks": 5, "n_entities": 20, "n_relations": 8, "template_words": 250},
+        {"n_chunks": 8, "n_entities": 30, "n_relations": 12, "template_words": 250},
+        {"n_chunks": 2, "n_entities": 10, "n_relations": 3, "template_words": 180},
+    ],
+}
+
+
+def test_default_registry_exposes_builtin_classes():
+    registry = ProblemClassRegistry.default()
+
+    assert set(registry.all()) == set(DIMENSIONS_BY_CLASS)
+    assert registry.schema_version == 1
+
+
+@pytest.mark.parametrize("name,dimensions_list", DIMENSIONS_BY_CLASS.items())
+def test_builtin_estimators_produce_token_estimates(name, dimensions_list):
+    problem_class = ProblemClassRegistry.default().get(name)
+
+    estimate = problem_class.estimate(dimensions_list[0])
+
+    assert isinstance(estimate, TokenEstimate)
+    assert estimate.prompt_tokens >= 0
+    assert estimate.completion_tokens >= 0
+    assert 0 <= estimate.confidence <= 1
+
+
+@pytest.mark.parametrize("name,dimensions_list", DIMENSIONS_BY_CLASS.items())
+def test_fit_recovers_seeded_params_from_synthetic_observations(name, dimensions_list):
+    seeded = ProblemClassRegistry.default().get(name)
+    param_name = seeded.tunable_params[0]
+    off_seed = type(seeded)(params={param_name: seeded.params[param_name] * 2})
+    observations = []
+    for dimensions in dimensions_list:
+        estimate = seeded.estimate(dimensions)
+        observations.append(
+            Observation(
+                dimensions=dimensions,
+                prompt_tokens=estimate.prompt_tokens,
+                completion_tokens=estimate.completion_tokens,
+            )
+        )
+
+    fitted = off_seed.fit(observations, min_observations=3)
+
+    assert fitted.params[param_name] == pytest.approx(seeded.params[param_name], rel=0.1)
+
+
+def test_fit_uses_quality_ledger_observation_shape():
+    problem_class = EntityExtractionProblemClass(params={"tokens_per_entity": 10})
+    observations = [
+        QualityObservation(
+            task_type="extract",
+            adapter_id="openrouter",
+            model_id="openai/gpt-4o-mini",
+            cost_usd=0.001,
+            quality_score=0.9,
+            latency_ms=100,
+            tokens_in=500,
+            tokens_out=350,
+            recorded_at=datetime(2026, 5, 19, tzinfo=timezone.utc),
+            tags={
+                "problem_class": "entity-extraction",
+                "dimensions": {
+                    "chunk_words": 300,
+                    "template_words": 100,
+                    "expected_entities": 5,
+                },
+            },
+        )
+        for _ in range(3)
+    ]
+
+    fitted = problem_class.fit(observations)
+
+    assert fitted.params["tokens_per_entity"] == pytest.approx(70)
+
+
+def test_fit_keeps_seed_when_sample_is_too_small():
+    problem_class = EntityExtractionProblemClass()
+    estimate = problem_class.estimate(
+        {"chunk_words": 300, "template_words": 100, "expected_entities": 5}
+    )
+
+    fitted = problem_class.fit(
+        [
+            Observation(
+                dimensions={"chunk_words": 300, "template_words": 100, "expected_entities": 5},
+                prompt_tokens=estimate.prompt_tokens,
+                completion_tokens=estimate.completion_tokens,
+            )
+        ],
+        min_observations=3,
+    )
+
+    assert fitted is problem_class
+
+
+def test_missing_dimensions_are_rejected():
+    problem_class = ProblemClassRegistry.default().get("chunk-summarization")
+
+    with pytest.raises(ValueError, match="Missing dimensions"):
+        problem_class.estimate({"chunk_words": 100})

tests/test_profiles.py

@@ -0,0 +1,151 @@
+import json
+
+import pytest
+
+from llm_connect.adapter import MockLLMAdapter
+from llm_connect.exceptions import LLMConfigurationError
+from llm_connect.models import RunConfig
+from llm_connect.profiles import (
+    CUSTODIAN_TRIAGE_BALANCED,
+    ProfiledLLMAdapter,
+    RuntimeProfile,
+    default_runtime_profiles,
+)
+
+
+def test_profile_dispatch_merges_defaults_and_request_params():
+    created: list[MockLLMAdapter] = []
+
+    def factory(provider: str, model: str) -> MockLLMAdapter:
+        created.append(MockLLMAdapter(mock_response=f"{provider}:{model}"))
+        return created[-1]
+
+    profile = RuntimeProfile(
+        name=CUSTODIAN_TRIAGE_BALANCED,
+        provider="mock",
+        model="triage-model",
+        config=RunConfig(
+            model_name="triage-model",
+            temperature=0.2,
+            max_tokens=1800,
+            max_depth=2,
+            timeout_seconds=300,
+            model_params={"reasoning_effort": "medium"},
+        ),
+    )
+    adapter = ProfiledLLMAdapter(
+        MockLLMAdapter(mock_response="default"),
+        {profile.name: profile},
+        adapter_factory=factory,
+    )
+
+    response = adapter.execute_prompt(
+        "Return JSON.",
+        RunConfig(
+            model_name=CUSTODIAN_TRIAGE_BALANCED,
+            model_params={"json_schema": {"type": "object"}},
+        ),
+    )
+
+    assert response.model == "triage-model"
+    assert response.metadata["profile"] == CUSTODIAN_TRIAGE_BALANCED
+    assert response.metadata["profile_provider"] == "mock"
+    assert len(created) == 1
+    resolved = created[0].last_config
+    assert resolved.model_name == "triage-model"
+    assert resolved.temperature == 0.2
+    assert resolved.max_tokens == 1800
+    assert resolved.max_depth == 2
+    assert resolved.model_params == {
+        "reasoning_effort": "medium",
+        "json_schema": {"type": "object"},
+    }
+
+
+def test_profile_dispatch_preserves_explicit_request_scalars():
+    created: list[MockLLMAdapter] = []
+
+    def factory(provider: str, model: str) -> MockLLMAdapter:
+        created.append(MockLLMAdapter())
+        return created[-1]
+
+    profile = RuntimeProfile(
+        name=CUSTODIAN_TRIAGE_BALANCED,
+        provider="mock",
+        model="triage-model",
+        config=RunConfig(model_name="triage-model", temperature=0.2, max_tokens=1800),
+    )
+    adapter = ProfiledLLMAdapter(
+        MockLLMAdapter(),
+        {profile.name: profile},
+        adapter_factory=factory,
+    )
+
+    adapter.execute_prompt(
+        "Prompt.",
+        RunConfig(
+            model_name=CUSTODIAN_TRIAGE_BALANCED,
+            temperature=0.4,
+            max_tokens=123,
+        ),
+    )
+
+    assert created[0].last_config.temperature == 0.4
+    assert created[0].last_config.max_tokens == 123
+
+
+def test_non_profile_model_passes_through_to_default_adapter():
+    default = MockLLMAdapter(mock_response="direct")
+    adapter = ProfiledLLMAdapter(default, {})
+
+    response = adapter.execute_prompt("Prompt.", RunConfig(model_name="gpt-4"))
+
+    assert response.content == "direct"
+    assert default.call_count == 1
+    assert default.last_config.model_name == "gpt-4"
+
+
+def test_unknown_custodian_profile_fails_without_secret_context():
+    adapter = ProfiledLLMAdapter(MockLLMAdapter(), {})
+
+    with pytest.raises(LLMConfigurationError) as excinfo:
+        adapter.execute_prompt("Prompt.", RunConfig(model_name="custodian-missing"))
+
+    assert "Unknown LLM runtime profile" in str(excinfo.value)
+    assert excinfo.value.context == {"profile": "custodian-missing"}
+
+
+def test_default_custodian_profile_uses_structured_output_capable_model():
+    profiles = default_runtime_profiles()
+    profile = profiles[CUSTODIAN_TRIAGE_BALANCED]
+
+    assert profile.provider == "openrouter"
+    assert profile.model == "google/gemini-2.5-flash"
+
+
+def test_default_profiles_can_be_overridden_from_json_env(monkeypatch):
+    monkeypatch.setenv(
+        "LLM_CONNECT_PROFILES_JSON",
+        json.dumps(
+            {
+                CUSTODIAN_TRIAGE_BALANCED: {
+                    "provider": "gemini",
+                    "model": "gemini-2.5-flash",
+                    "config": {
+                        "temperature": 0.1,
+                        "max_tokens": 900,
+                        "model_params": {"reasoning_effort": "low"},
+                    },
+                }
+            }
+        ),
+    )
+
+    profiles = default_runtime_profiles(provider="mock", model="fallback")
+    profile = profiles[CUSTODIAN_TRIAGE_BALANCED]
+
+    assert profile.provider == "gemini"
+    assert profile.model == "gemini-2.5-flash"
+    assert profile.config.temperature == 0.1
+    assert profile.config.max_tokens == 900
+    assert profile.config.model_params == {"reasoning_effort": "low"}

tests/test_quality.py

@@ -0,0 +1,164 @@
+"""
+Tests for quality observations and the append-only quality ledger.
+"""
+
+import threading
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from llm_connect.quality import QualityLedger, QualityObservation, is_stale
+
+
+def observation(
+    *,
+    task_type: str = "summarize",
+    adapter_id: str = "openrouter:cheap",
+    model_id: str = "cheap-model",
+    quality_score: float = 0.8,
+    recorded_at: datetime | None = None,
+    tag: str | None = None,
+) -> QualityObservation:
+    tags = {"tag": tag} if tag is not None else {}
+    return QualityObservation(
+        task_type=task_type,
+        adapter_id=adapter_id,
+        model_id=model_id,
+        cost_usd=0.01,
+        quality_score=quality_score,
+        latency_ms=123.4,
+        tokens_in=100,
+        tokens_out=50,
+        baseline_adapter_id="claude-code",
+        recorded_at=recorded_at or datetime(2026, 5, 17, tzinfo=timezone.utc),
+        tags=tags,
+    )
+
+
+class TestQualityObservation:
+    def test_round_trip_dict(self):
+        obs = observation(tag="a")
+        restored = QualityObservation.from_dict(obs.to_dict())
+        assert restored == obs
+        assert restored.total_tokens == 150
+        assert restored.recorded_at.tzinfo is not None
+
+    def test_naive_recorded_at_is_interpreted_as_utc(self):
+        obs = observation(recorded_at=datetime(2026, 5, 17, 12, 0, 0))
+        assert obs.recorded_at.tzinfo == timezone.utc
+
+    @pytest.mark.parametrize("score", [-0.1, 1.1])
+    def test_quality_score_must_be_between_zero_and_one(self, score):
+        with pytest.raises(ValueError, match="quality_score"):
+            observation(quality_score=score)
+
+    def test_required_ids_must_be_non_empty(self):
+        with pytest.raises(ValueError, match="task_type"):
+            observation(task_type="")
+
+    def test_non_negative_fields_are_enforced(self):
+        with pytest.raises(ValueError, match="tokens_in"):
+            QualityObservation(
+                task_type="x",
+                adapter_id="a",
+                model_id="m",
+                cost_usd=0,
+                quality_score=1,
+                latency_ms=0,
+                tokens_in=-1,
+                tokens_out=0,
+            )
+
+
+class TestQualityLedger:
+    def test_append_and_read_round_trip(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        obs = observation()
+        ledger.append(obs)
+        assert ledger.read_all() == [obs]
+
+    def test_by_task_type_filters_observations(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        ledger.append(observation(task_type="summarize"))
+        ledger.append(observation(task_type="extract"))
+        assert [obs.task_type for obs in ledger.by_task_type("summarize")] == ["summarize"]
+
+    def test_recent_returns_newest_first_with_filters(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        older = observation(recorded_at=datetime(2026, 5, 1, tzinfo=timezone.utc), tag="older")
+        newer = observation(recorded_at=datetime(2026, 5, 2, tzinfo=timezone.utc), tag="newer")
+        other = observation(
+            task_type="extract",
+            recorded_at=datetime(2026, 5, 3, tzinfo=timezone.utc),
+            tag="other",
+        )
+        ledger.append(older)
+        ledger.append(newer)
+        ledger.append(other)
+
+        recent = ledger.recent(limit=1, task_type="summarize")
+        assert [obs.tags["tag"] for obs in recent] == ["newer"]
+
+    def test_mean_quality_filters_by_adapter_and_minimum_count(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        ledger.append(observation(adapter_id="a", quality_score=0.5))
+        ledger.append(observation(adapter_id="a", quality_score=1.0))
+        ledger.append(observation(adapter_id="b", quality_score=0.1))
+
+        assert ledger.mean_quality("summarize", adapter_id="a") == 0.75
+        assert ledger.mean_quality("summarize", adapter_id="a", min_observations=3) is None
+
+    def test_is_stale_uses_utc_reference(self):
+        obs = observation(recorded_at=datetime(2026, 5, 1, tzinfo=timezone.utc))
+        now = datetime(2026, 5, 3, tzinfo=timezone.utc)
+        assert is_stale(obs, timedelta(days=1), now=now) is True
+        assert is_stale(obs, timedelta(days=3), now=now) is False
+
+    def test_prune_before_removes_old_valid_observations(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+        old = observation(recorded_at=datetime(2026, 5, 1, tzinfo=timezone.utc), tag="old")
+        keep = observation(recorded_at=datetime(2026, 5, 2, tzinfo=timezone.utc), tag="keep")
+        ledger.append(old)
+        ledger.append(keep)
+
+        removed = ledger.prune_before(datetime(2026, 5, 2, tzinfo=timezone.utc))
+
+        assert removed == 1
+        assert [obs.tags["tag"] for obs in ledger.read_all()] == ["keep"]
+
+    def test_malformed_lines_are_skipped_and_counted(self, tmp_path):
+        path = tmp_path / "quality.jsonl"
+        path.write_text("{not json}\n", encoding="utf-8")
+        ledger = QualityLedger(path)
+        ledger.append(observation())
+
+        assert len(ledger.read_all()) == 1
+        assert ledger.malformed_count() == 1
+
+    def test_prune_preserves_malformed_lines(self, tmp_path):
+        path = tmp_path / "quality.jsonl"
+        path.write_text("{not json}\n", encoding="utf-8")
+        ledger = QualityLedger(path)
+        ledger.append(observation(recorded_at=datetime(2026, 5, 1, tzinfo=timezone.utc)))
+
+        removed = ledger.prune_before(datetime(2026, 5, 2, tzinfo=timezone.utc))
+
+        assert removed == 1
+        assert ledger.malformed_count() == 1
+        assert ledger.read_all() == []
+
+    def test_concurrent_writes_round_trip(self, tmp_path):
+        ledger = QualityLedger(tmp_path / "quality.jsonl")
+
+        def append_one(index: int) -> None:
+            ledger.append(observation(tag=str(index)))
+
+        threads = [threading.Thread(target=append_one, args=(i,)) for i in range(25)]
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+
+        observations = ledger.read_all()
+        assert len(observations) == 25
+        assert {obs.tags["tag"] for obs in observations} == {str(i) for i in range(25)}

tests/test_rates.py

@@ -0,0 +1,65 @@
+import pytest
+
+from llm_connect.rates import ModelRate, ModelRateRegistry
+
+
+def test_default_registry_contains_openrouter_seed_models():
+    registry = ModelRateRegistry.default()
+    rates = registry.all()
+
+    assert len(rates) >= 9
+    assert rates["openai/gpt-4o-mini"].captured_at == "2026-05-17"
+    assert rates["openai/gpt-4o-mini"].source_url == "https://openrouter.ai/models"
+
+
+def test_from_yaml_loads_package_shape(tmp_path):
+    path = tmp_path / "model-rates.yaml"
+    path.write_text(
+        """
+schema_version: 1
+currency: USD
+source_url: https://example.test/rates
+captured_at: "2026-05-19"
+rates:
+  vendor/model:
+    prompt_per_1k: 0.1
+    completion_per_1k: 0.2
+""",
+        encoding="utf-8",
+    )
+
+    registry = ModelRateRegistry.from_yaml(path)
+    rate = registry.get("vendor/model")
+
+    assert rate == ModelRate(
+        model_id="vendor/model",
+        prompt_per_1k=0.1,
+        completion_per_1k=0.2,
+        currency="USD",
+        source_url="https://example.test/rates",
+        captured_at="2026-05-19",
+    )
+
+
+def test_merged_with_overrides_matching_model():
+    base = ModelRateRegistry.default()
+    override = ModelRateRegistry(
+        {
+            "openai/gpt-4o-mini": ModelRate(
+                "openai/gpt-4o-mini",
+                prompt_per_1k=1,
+                completion_per_1k=2,
+                captured_at="override",
+            )
+        }
+    )
+
+    merged = base.merged_with(override)
+
+    assert merged.get("openai/gpt-4o-mini").prompt_per_1k == 1
+    assert merged.get("openai/gpt-4o-mini").captured_at == "override"
+
+
+def test_negative_rates_are_rejected():
+    with pytest.raises(ValueError, match="prompt_per_1k"):
+        ModelRate("bad/model", prompt_per_1k=-1, completion_per_1k=0)

tests/test_replay.py

@@ -0,0 +1,62 @@
+from llm_connect.replay import parse_audit_record
+
+
+STRUCTURED_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "summary": {"type": "string"},
+        "recommendations": {"type": "array", "items": {"type": "string"}},
+    },
+    "required": ["summary", "recommendations"],
+}
+
+
+def test_replay_parses_openai_style_provider_response():
+    record = {
+        "provider": "openrouter",
+        "config": {"model_params": {"json_schema": STRUCTURED_SCHEMA}},
+        "provider_response": {
+            "status": 200,
+            "body": {
+                "choices": [
+                    {
+                        "message": {
+                            "content": '{"summary":"ok","recommendations":[]}'
+                        }
+                    }
+                ]
+            },
+        },
+        "parsed_content": '{"summary":"ok","recommendations":[]}',
+    }
+
+    report = parse_audit_record(record)
+
+    assert report["parsed_content"] == '{"summary":"ok","recommendations":[]}'
+    assert report["matches_recorded_content"] is True
+    assert report["structured_output"] == {"checked": True, "valid": True}
+
+
+def test_replay_reuses_claude_code_envelope_unwrapper():
+    record = {
+        "provider": "claude-code",
+        "config": {"model_params": {"json_schema": STRUCTURED_SCHEMA}},
+        "provider_response": {
+            "status": 0,
+            "body": {
+                "stdout": (
+                    '{"type":"result","result":"prose",'
+                    '"structured_result":"{\\"summary\\":\\"ok\\",'
+                    '\\"recommendations\\":[]}"}'
+                ),
+                "stderr": "",
+            },
+        },
+        "parsed_content": '{"summary":"ok","recommendations":[]}',
+    }
+
+    report = parse_audit_record(record)
+
+    assert report["parsed_content"] == '{"summary":"ok","recommendations":[]}'
+    assert report["matches_recorded_content"] is True
+    assert report["structured_output"] == {"checked": True, "valid": True}

tests/test_routing.py

@@ -0,0 +1,91 @@
+"""
+Tests for RoutingPolicy (FR-2).
+"""
+
+import pytest
+
+from llm_connect.routing import RoutingPolicy, RoutingRule
+from llm_connect.adapter import MockLLMAdapter
+
+
+class TestRoutingPolicy:
+    def _adapters(self, n: int = 3):
+        return [MockLLMAdapter(mock_response=f"resp-{i}") for i in range(n)]
+
+    def test_rule_match_returns_prefer(self):
+        prefer, *_ = self._adapters()
+        policy = RoutingPolicy(rules=[RoutingRule("triage", prefer=prefer)])
+        assert policy.resolve("triage") is prefer
+
+    def test_first_matching_rule_wins(self):
+        a, b = self._adapters(2)
+        policy = RoutingPolicy(rules=[
+            RoutingRule("triage", prefer=a),
+            RoutingRule("triage", prefer=b),
+        ])
+        assert policy.resolve("triage") is a
+
+    def test_cost_cap_within_limit_returns_prefer(self):
+        prefer, fallback = self._adapters(2)
+        policy = RoutingPolicy(rules=[
+            RoutingRule("triage", prefer=prefer, max_cost_per_1k=1.0, fallback=fallback)
+        ])
+        assert policy.resolve("triage", estimated_cost_per_1k=0.5) is prefer
+
+    def test_cost_cap_exceeded_returns_fallback(self):
+        prefer, fallback = self._adapters(2)
+        policy = RoutingPolicy(rules=[
+            RoutingRule("triage", prefer=prefer, max_cost_per_1k=1.0, fallback=fallback)
+        ])
+        assert policy.resolve("triage", estimated_cost_per_1k=2.0) is fallback
+
+    def test_cost_cap_exceeded_no_fallback_returns_prefer(self):
+        """When cost exceeds cap but no fallback is set, still return prefer."""
+        prefer, *_ = self._adapters()
+        policy = RoutingPolicy(rules=[
+            RoutingRule("triage", prefer=prefer, max_cost_per_1k=0.1)
+        ])
+        assert policy.resolve("triage", estimated_cost_per_1k=5.0) is prefer
+
+    def test_no_estimated_cost_ignores_cap(self):
+        prefer, fallback = self._adapters(2)
+        policy = RoutingPolicy(rules=[
+            RoutingRule("triage", prefer=prefer, max_cost_per_1k=0.01, fallback=fallback)
+        ])
+        # No cost estimate → cap not applied
+        assert policy.resolve("triage") is prefer
+
+    def test_unknown_task_type_returns_default(self):
+        prefer, default = self._adapters(2)
+        policy = RoutingPolicy(
+            rules=[RoutingRule("triage", prefer=prefer)],
+            default=default,
+        )
+        assert policy.resolve("unknown") is default
+
+    def test_no_match_no_default_raises_lookup_error(self):
+        prefer, *_ = self._adapters()
+        policy = RoutingPolicy(rules=[RoutingRule("triage", prefer=prefer)])
+        with pytest.raises(LookupError, match="unknown"):
+            policy.resolve("unknown")
+
+    def test_empty_rules_with_default_returns_default(self):
+        default, *_ = self._adapters()
+        policy = RoutingPolicy(default=default)
+        assert policy.resolve("anything") is default
+
+    def test_empty_policy_raises(self):
+        policy = RoutingPolicy()
+        with pytest.raises(LookupError):
+            policy.resolve("triage")
+
+    def test_multiple_task_types(self):
+        a, b, c = self._adapters(3)
+        policy = RoutingPolicy(rules=[
+            RoutingRule("fast", prefer=a),
+            RoutingRule("smart", prefer=b),
+            RoutingRule("cheap", prefer=c),
+        ])
+        assert policy.resolve("fast") is a
+        assert policy.resolve("smart") is b
+        assert policy.resolve("cheap") is c

tests/test_server.py

@@ -0,0 +1,412 @@
+"""
+Tests for LLMServer HTTP serve mode (FR-1).
+"""
+
+import threading
+import time
+from concurrent.futures import ThreadPoolExecutor
+import json
+import urllib.error
+import urllib.request
+
+import pytest
+
+from llm_connect._diagnostics import (
+    record_adapter_transformation,
+    record_provider_request,
+    record_provider_response,
+)
+from llm_connect.adapter import MockLLMAdapter, ErrorLLMAdapter
+from llm_connect.exceptions import LLMAPIError, LLMConfigurationError, LLMTimeoutError
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.profiles import CUSTODIAN_TRIAGE_BALANCED, ProfiledLLMAdapter, RuntimeProfile
+from llm_connect.server import LLMServer
+
+
+@pytest.fixture()
+def server():
+    """Start a server on a free port; stop after each test."""
+    s = LLMServer(adapter=MockLLMAdapter(mock_response="hello world"), port=0)
+    s.start()
+    yield s
+    s.stop()
+
+
+def _get(url: str) -> tuple[int, dict]:
+    try:
+        with urllib.request.urlopen(url) as resp:
+            return resp.status, json.loads(resp.read())
+    except urllib.error.HTTPError as exc:
+        return exc.code, json.loads(exc.read())
+
+
+def _post(url: str, body: dict) -> tuple[int, dict]:
+    payload = json.dumps(body).encode()
+    req = urllib.request.Request(
+        url,
+        data=payload,
+        headers={"Content-Type": "application/json"},
+        method="POST",
+    )
+    try:
+        with urllib.request.urlopen(req) as resp:
+            return resp.status, json.loads(resp.read())
+    except urllib.error.HTTPError as exc:
+        return exc.code, json.loads(exc.read())
+
+
+class DiagnosticLLMAdapter(MockLLMAdapter):
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        record_provider_request(
+            url="https://provider.example/v1/chat",
+            payload={"prompt": prompt, "model": config.model_name},
+            headers={"Authorization": "Bearer secret-token"},
+        )
+        response = super().execute_prompt(prompt, config)
+        response.metadata["provider"] = "diagnostic"
+        response.metadata["response_id"] = "diag-response"
+        record_provider_response(status=200, body={"id": "diag-response", "content": response.content})
+        record_adapter_transformation(
+            "diagnostic_transform",
+            {"before": prompt},
+            {"after": response.content},
+        )
+        return response
+
+
+class BarrierLLMAdapter(MockLLMAdapter):
+    def __init__(self):
+        super().__init__(mock_response="parallel")
+        self._barrier = threading.Barrier(2)
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        self._barrier.wait(timeout=2.0)
+        return super().execute_prompt(prompt, config)
+
+
+class TestHealth:
+    def test_health_returns_200(self, server):
+        status, body = _get(f"http://127.0.0.1:{server.port}/health")
+        assert status == 200
+        assert body["status"] == "ok"
+
+    def test_unknown_get_returns_404(self, server):
+        status, body = _get(f"http://127.0.0.1:{server.port}/nope")
+        assert status == 404
+
+
+class TestExecute:
+    def test_post_execute_round_trip(self, server):
+        status, body = _post(
+            f"http://127.0.0.1:{server.port}/execute",
+            {"prompt": "say hello"},
+        )
+        assert status == 200
+        assert body["content"] == "hello world"
+        assert body["finish_reason"] == "stop"
+        assert "debug" not in body
+
+    def test_response_includes_usage(self, server):
+        status, body = _post(
+            f"http://127.0.0.1:{server.port}/execute",
+            {"prompt": "count tokens"},
+        )
+        assert status == 200
+        assert "usage" in body
+        assert body["usage"]["total_tokens"] > 0
+
+    def test_missing_prompt_returns_400(self, server):
+        status, body = _post(
+            f"http://127.0.0.1:{server.port}/execute",
+            {"config": {}},
+        )
+        assert status == 400
+        assert "prompt" in body["error"]
+
+    def test_invalid_json_returns_400(self, server):
+        req = urllib.request.Request(
+            f"http://127.0.0.1:{server.port}/execute",
+            data=b"not json",
+            headers={"Content-Type": "application/json"},
+            method="POST",
+        )
+        try:
+            with urllib.request.urlopen(req) as resp:
+                status, body = resp.status, json.loads(resp.read())
+        except urllib.error.HTTPError as exc:
+            status, body = exc.code, json.loads(exc.read())
+        assert status == 400
+
+    def test_unknown_post_path_returns_404(self, server):
+        status, body = _post(
+            f"http://127.0.0.1:{server.port}/wrong",
+            {"prompt": "hi"},
+        )
+        assert status == 404
+
+    def test_adapter_error_returns_500(self):
+        s = LLMServer(adapter=ErrorLLMAdapter("boom"), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "hello"},
+            )
+            assert status == 500
+            assert body["error"] == "internal_error"
+            assert "boom" in body["message"]
+        finally:
+            s.stop()
+
+    def test_config_fields_forwarded(self):
+        """Config fields in request body reach the adapter via RunConfig."""
+        adapter = MockLLMAdapter(mock_response="x")
+        s = LLMServer(adapter=adapter, port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {
+                    "prompt": "hi",
+                    "config": {
+                        "model_name": "gpt-3.5-turbo",
+                        "max_tokens": 100,
+                        "max_depth": 2,
+                        "model_params": {"reasoning_effort": "medium"},
+                    },
+                },
+            )
+            assert status == 200
+            assert adapter.last_config.model_name == "gpt-3.5-turbo"
+            assert adapter.last_config.max_tokens == 100
+            assert adapter.last_config.max_depth == 2
+            assert adapter.last_config.model_params == {"reasoning_effort": "medium"}
+        finally:
+            s.stop()
+
+    def test_config_must_be_object(self, server):
+        status, body = _post(
+            f"http://127.0.0.1:{server.port}/execute",
+            {"prompt": "hi", "config": "not an object"},
+        )
+        assert status == 400
+        assert "config" in body["error"]
+
+    def test_profile_execute_resolves_model_and_metadata(self):
+        created: list[MockLLMAdapter] = []
+
+        def factory(provider: str, model: str) -> MockLLMAdapter:
+            created.append(MockLLMAdapter(mock_response="profile response"))
+            return created[-1]
+
+        adapter = ProfiledLLMAdapter(
+            MockLLMAdapter(mock_response="default"),
+            {
+                CUSTODIAN_TRIAGE_BALANCED: RuntimeProfile(
+                    name=CUSTODIAN_TRIAGE_BALANCED,
+                    provider="mock",
+                    model="triage-model",
+                    config=RunConfig(
+                        model_name="triage-model",
+                        temperature=0.2,
+                        max_tokens=1800,
+                        max_depth=2,
+                        model_params={"reasoning_effort": "medium"},
+                    ),
+                )
+            },
+            adapter_factory=factory,
+        )
+        s = LLMServer(adapter=adapter, port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {
+                    "prompt": "Return JSON.",
+                    "config": {
+                        "model_name": CUSTODIAN_TRIAGE_BALANCED,
+                        "model_params": {"json_schema": {"type": "object"}},
+                    },
+                },
+            )
+        finally:
+            s.stop()
+
+        assert status == 200
+        assert body["model"] == "triage-model"
+        assert body["metadata"]["profile"] == CUSTODIAN_TRIAGE_BALANCED
+        assert body["metadata"]["profile_provider"] == "mock"
+        assert len(created) == 1
+        assert created[0].last_config.model_name == "triage-model"
+        assert created[0].last_config.temperature == 0.2
+        assert created[0].last_config.max_tokens == 1800
+        assert created[0].last_config.max_depth == 2
+        assert created[0].last_config.model_params == {
+            "reasoning_effort": "medium",
+            "json_schema": {"type": "object"},
+        }
+
+    def test_unknown_profile_returns_400(self):
+        s = LLMServer(adapter=ProfiledLLMAdapter(MockLLMAdapter(), {}), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "hello", "config": {"model_name": "custodian-missing"}},
+            )
+        finally:
+            s.stop()
+
+        assert status == 400
+        assert body["error"] == "unknown_profile"
+        assert body["context"]["profile"] == "custodian-missing"
+
+    def test_configuration_error_is_sanitized(self):
+        class SecretConfigAdapter(MockLLMAdapter):
+            def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+                raise LLMConfigurationError(
+                    "Bad api_key=sk-supersecret with Bearer secret-token",
+                    context={"api_key": "sk-supersecret", "provider": "openai"},
+                )
+
+        s = LLMServer(adapter=SecretConfigAdapter(), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "hello"},
+            )
+        finally:
+            s.stop()
+
+        assert status == 500
+        assert body["error"] == "configuration_error"
+        assert "sk-supersecret" not in json.dumps(body)
+        assert "secret-token" not in json.dumps(body)
+        assert body["context"]["api_key"] == "<redacted>"
+        assert body["context"]["provider"] == "openai"
+
+    def test_provider_errors_are_categorized_and_sanitized(self):
+        class ProviderErrorAdapter(MockLLMAdapter):
+            def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+                raise LLMAPIError(
+                    "HTTP 500 from https://provider.example/v1?key=gemini-secret",
+                    status_code=500,
+                )
+
+        s = LLMServer(adapter=ProviderErrorAdapter(), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "hello"},
+            )
+        finally:
+            s.stop()
+
+        assert status == 502
+        assert body["error"] == "provider_api_error"
+        assert body["provider_status"] == 500
+        assert "gemini-secret" not in body["message"]
+
+    def test_timeout_error_returns_504(self):
+        class TimeoutAdapter(MockLLMAdapter):
+            def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+                raise LLMTimeoutError("Request timed out after 300s")
+
+        s = LLMServer(adapter=TimeoutAdapter(), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "hello"},
+            )
+        finally:
+            s.stop()
+
+        assert status == 504
+        assert body["error"] == "provider_timeout"
+
+    def test_debug_query_returns_diagnostics(self):
+        s = LLMServer(adapter=DiagnosticLLMAdapter(mock_response="debug body"), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute?debug=1",
+                {"prompt": "inspect", "config": {"model_name": "diagnostic-model"}},
+            )
+        finally:
+            s.stop()
+
+        assert status == 200
+        assert body["content"] == "debug body"
+        debug = body["debug"]
+        assert debug["provider_request"]["payload"] == {
+            "prompt": "inspect",
+            "model": "diagnostic-model",
+        }
+        assert debug["provider_request"]["headers_redacted"]["Authorization"] == "Bearer <redacted>"
+        assert debug["provider_response"]["status"] == 200
+        assert debug["adapter_transformations"][0]["step"] == "diagnostic_transform"
+
+    def test_debug_env_returns_diagnostics(self, monkeypatch):
+        monkeypatch.setenv("LLM_CONNECT_DEBUG", "1")
+        s = LLMServer(adapter=DiagnosticLLMAdapter(mock_response="debug body"), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "inspect"},
+            )
+        finally:
+            s.stop()
+
+        assert status == 200
+        assert "debug" in body
+
+    def test_audit_dir_records_replayable_call(self, monkeypatch, tmp_path):
+        monkeypatch.setenv("LLM_CONNECT_AUDIT_DIR", str(tmp_path))
+        s = LLMServer(adapter=DiagnosticLLMAdapter(mock_response="audit body"), port=0)
+        s.start()
+        try:
+            status, body = _post(
+                f"http://127.0.0.1:{s.port}/execute",
+                {"prompt": "audit me", "config": {"model_name": "audit-model"}},
+            )
+        finally:
+            s.stop()
+
+        assert status == 200
+        assert "debug" not in body
+        files = list(tmp_path.glob("*.json"))
+        assert len(files) == 1
+        record = json.loads(files[0].read_text(encoding="utf-8"))
+        assert record["prompt"] == "audit me"
+        assert record["config"]["model_name"] == "audit-model"
+        assert record["parsed_content"] == "audit body"
+        assert record["provider_request"]["headers_redacted"]["Authorization"] == "Bearer <redacted>"
+        assert record["provider_response"]["body"]["id"] == "diag-response"
+        assert record["latency_seconds"] >= 0
+
+    def test_execute_requests_run_concurrently(self):
+        s = LLMServer(adapter=BarrierLLMAdapter(), port=0)
+        s.start()
+        try:
+            start = time.monotonic()
+            with ThreadPoolExecutor(max_workers=2) as pool:
+                futures = [
+                    pool.submit(
+                        _post,
+                        f"http://127.0.0.1:{s.port}/execute",
+                        {"prompt": f"request {idx}"},
+                    )
+                    for idx in range(2)
+                ]
+                results = [future.result(timeout=3.0) for future in futures]
+            elapsed = time.monotonic() - start
+        finally:
+            s.stop()
+
+        assert [status for status, _body in results] == [200, 200]
+        assert elapsed < 1.5

tests/test_shadowing.py

@@ -0,0 +1,149 @@
+"""
+Tests for ShadowingAdapter.
+"""
+
+import asyncio
+import time
+
+from llm_connect.adapter import LLMAdapter
+from llm_connect.grading import ExactMatchJudge, PairedGrader
+from llm_connect.models import LLMResponse, RunConfig
+from llm_connect.quality import QualityLedger
+from llm_connect.shadowing import ShadowingAdapter
+
+
+class StaticAdapter(LLMAdapter):
+    def __init__(
+        self,
+        content: str,
+        *,
+        model: str = "model",
+        cost_usd: float = 0.0,
+        fail: bool = False,
+        delay_seconds: float = 0.0,
+    ):
+        self.content = content
+        self.model = model
+        self.cost_usd = cost_usd
+        self.fail = fail
+        self.delay_seconds = delay_seconds
+        self.calls = 0
+
+    def execute_prompt(self, prompt: str, config: RunConfig) -> LLMResponse:
+        self.calls += 1
+        if self.delay_seconds:
+            time.sleep(self.delay_seconds)
+        if self.fail:
+            raise RuntimeError("adapter failed")
+        return LLMResponse(
+            content=self.content,
+            model=self.model,
+            usage={
+                "prompt_tokens": len(prompt.split()),
+                "completion_tokens": len(self.content.split()),
+                "total_tokens": len(prompt.split()) + len(self.content.split()),
+            },
+            metadata={"cost_usd": self.cost_usd, "latency_ms": 42.0},
+        )
+
+    def validate_config(self, config: RunConfig) -> bool:
+        return True
+
+
+def shadowing_adapter(
+    tmp_path,
+    *,
+    candidate: StaticAdapter | None = None,
+    baseline: StaticAdapter | None = None,
+    shadow_rate: float = 1.0,
+    async_shadow: bool = False,
+    errors: list[Exception] | None = None,
+) -> ShadowingAdapter:
+    return ShadowingAdapter(
+        candidate_adapter=candidate or StaticAdapter("same", model="candidate", cost_usd=0.02),
+        baseline_adapter=baseline or StaticAdapter("same", model="baseline"),
+        grader=PairedGrader(ExactMatchJudge()),
+        ledger=QualityLedger(tmp_path / "quality.jsonl"),
+        task_type="summarize",
+        adapter_id="candidate",
+        baseline_adapter_id="baseline",
+        shadow_rate=shadow_rate,
+        async_shadow=async_shadow,
+        tags={"prompt_fingerprint": "fixture"},
+        on_shadow_error=errors.append if errors is not None else None,
+    )
+
+
+class TestShadowingAdapter:
+    def test_sync_shadow_appends_quality_observation(self, tmp_path):
+        adapter = shadowing_adapter(tmp_path)
+
+        response = adapter.execute_prompt("hello world", RunConfig(model_name="candidate-model"))
+
+        observations = adapter.ledger.read_all()
+        assert response.content == "same"
+        assert len(observations) == 1
+        assert observations[0].quality_score == 1.0
+        assert observations[0].cost_usd == 0.02
+        assert observations[0].tokens_in == 2
+        assert observations[0].tokens_out == 1
+        assert observations[0].baseline_adapter_id == "baseline"
+        assert observations[0].tags["prompt_fingerprint"] == "fixture"
+
+    def test_candidate_response_survives_baseline_failure(self, tmp_path):
+        candidate = StaticAdapter("candidate", model="candidate")
+        baseline = StaticAdapter("baseline", fail=True)
+        errors: list[Exception] = []
+        adapter = shadowing_adapter(
+            tmp_path,
+            candidate=candidate,
+            baseline=baseline,
+            errors=errors,
+        )
+
+        response = adapter.execute_prompt("prompt", RunConfig())
+
+        assert response.content == "candidate"
+        assert candidate.calls == 1
+        assert baseline.calls == 1
+        assert adapter.ledger.read_all() == []
+        assert len(errors) == 1
+
+    def test_shadow_rate_zero_skips_baseline_and_ledger(self, tmp_path):
+        baseline = StaticAdapter("same")
+        adapter = shadowing_adapter(tmp_path, baseline=baseline, shadow_rate=0.0)
+
+        for _ in range(3):
+            adapter.execute_prompt("prompt", RunConfig())
+
+        assert baseline.calls == 0
+        assert adapter.ledger.read_all() == []
+
+    def test_shadow_rate_one_records_each_call(self, tmp_path):
+        baseline = StaticAdapter("same")
+        adapter = shadowing_adapter(tmp_path, baseline=baseline, shadow_rate=1.0)
+
+        for _ in range(3):
+            adapter.execute_prompt("prompt", RunConfig())
+
+        assert baseline.calls == 3
+        assert len(adapter.ledger.read_all()) == 3
+
+    def test_async_shadow_mode_flushes_background_work(self, tmp_path):
+        baseline = StaticAdapter("same", delay_seconds=0.02)
+        adapter = shadowing_adapter(tmp_path, baseline=baseline, async_shadow=True)
+
+        response = adapter.execute_prompt("prompt", RunConfig())
+        adapter.flush(timeout=1)
+        adapter.shutdown()
+
+        assert response.content == "same"
+        assert len(adapter.ledger.read_all()) == 1
+
+    def test_async_execute_prompt_records_shadow(self, tmp_path):
+        adapter = shadowing_adapter(tmp_path)
+
+        response = asyncio.run(adapter.async_execute_prompt("prompt", RunConfig()))
+
+        assert response.content == "same"
+        assert len(adapter.ledger.read_all()) == 1

tests/test_structured_output_smoke.py

@@ -0,0 +1,187 @@
+import json
+
+from llm_connect.gemini import GeminiAdapter
+from llm_connect.models import RunConfig
+from llm_connect.openai import OpenAIAdapter
+from llm_connect.openrouter import OpenRouterAdapter
+
+
+STRUCTURED_SCHEMA = {
+    "type": "object",
+    "properties": {
+        "summary": {"type": "string"},
+        "recommendations": {"type": "array", "items": {"type": "string"}},
+    },
+    "required": ["summary", "recommendations"],
+}
+
+OPENROUTER_STRUCTURED_MODEL = "google/gemini-2.5-flash"
+
+
+SMOKE_CONFIG = RunConfig(
+    model_name="gpt-4",
+    temperature=0.1,
+    max_tokens=300,
+    model_params={
+        "reasoning_effort": "medium",
+        "max_depth": 3,
+        "json_schema": STRUCTURED_SCHEMA,
+    },
+)
+
+
+def test_openrouter_structured_output_payload_and_model_routing(monkeypatch):
+    captured: dict[str, object] = {}
+
+    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
+        captured["url"] = url
+        captured["payload"] = payload
+        captured["headers"] = headers
+        captured["timeout"] = timeout
+        return {
+            "id": "or-response",
+            "model": payload["model"],
+            "choices": [
+                {
+                    "message": {
+                        "content": json.dumps(
+                            {"summary": "ok", "recommendations": ["keep payload clean"]}
+                        )
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+        }
+
+    monkeypatch.setattr("llm_connect.openrouter.post_json", fake_post_json)
+    adapter = OpenRouterAdapter(
+        model=OPENROUTER_STRUCTURED_MODEL,
+        api_key="or-test",
+        api_base="https://openrouter.example/api/v1",
+    )
+
+    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
+    payload = captured["payload"]
+
+    assert response.model == OPENROUTER_STRUCTURED_MODEL
+    assert payload["model"] == OPENROUTER_STRUCTURED_MODEL
+    assert payload["response_format"]["json_schema"]["schema"] == STRUCTURED_SCHEMA
+    assert payload["response_format"]["json_schema"]["strict"] is True
+    assert payload["provider"]["require_parameters"] is True
+    assert "reasoning_effort" not in payload
+    assert "max_depth" not in payload
+    assert "json_schema" not in payload
+
+
+def test_openrouter_structured_output_preserves_provider_options(monkeypatch):
+    captured: dict[str, object] = {}
+
+    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
+        captured["payload"] = payload
+        return {
+            "id": "or-response",
+            "model": payload["model"],
+            "choices": [
+                {
+                    "message": {
+                        "content": json.dumps({"summary": "ok", "recommendations": []})
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+        }
+
+    config = RunConfig(
+        model_name="gpt-4",
+        temperature=0.1,
+        max_tokens=300,
+        model_params={
+            "json_schema": STRUCTURED_SCHEMA,
+            "provider": {"order": ["Anthropic"]},
+        },
+    )
+    monkeypatch.setattr("llm_connect.openrouter.post_json", fake_post_json)
+    adapter = OpenRouterAdapter(
+        model=OPENROUTER_STRUCTURED_MODEL,
+        api_key="or-test",
+        api_base="https://openrouter.example/api/v1",
+    )
+
+    adapter.execute_prompt("Return JSON.", config)
+    payload = captured["payload"]
+
+    assert payload["provider"]["order"] == ["Anthropic"]
+    assert payload["provider"]["require_parameters"] is True
+
+
+def test_openai_structured_output_payload(monkeypatch):
+    captured: dict[str, object] = {}
+
+    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
+        captured["payload"] = payload
+        return {
+            "id": "oa-response",
+            "model": payload["model"],
+            "choices": [
+                {
+                    "message": {
+                        "content": json.dumps({"summary": "ok", "recommendations": []})
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
+        }
+
+    monkeypatch.setattr("llm_connect.openai.post_json", fake_post_json)
+    adapter = OpenAIAdapter(model="gpt-4.1-mini", api_key="sk-test")
+
+    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
+    payload = captured["payload"]
+
+    assert response.model == "gpt-4.1-mini"
+    assert payload["model"] == "gpt-4.1-mini"
+    assert payload["response_format"]["json_schema"]["schema"] == STRUCTURED_SCHEMA
+    assert "reasoning_effort" not in payload
+    assert "max_depth" not in payload
+    assert "json_schema" not in payload
+
+
+def test_gemini_structured_output_payload(monkeypatch):
+    captured: dict[str, object] = {}
+
+    def fake_post_json(url, payload, headers=None, timeout=300):  # noqa: ANN001
+        captured["url"] = url
+        captured["payload"] = payload
+        return {
+            "candidates": [
+                {
+                    "content": {
+                        "parts": [
+                            {"text": json.dumps({"summary": "ok", "recommendations": []})}
+                        ]
+                    },
+                    "finishReason": "STOP",
+                }
+            ],
+            "usageMetadata": {
+                "promptTokenCount": 1,
+                "candidatesTokenCount": 2,
+                "totalTokenCount": 3,
+            },
+        }
+
+    monkeypatch.setattr("llm_connect.gemini.post_json", fake_post_json)
+    adapter = GeminiAdapter(model="gemini-2.5-flash", api_key="gemini-test")
+
+    response = adapter.execute_prompt("Return JSON.", SMOKE_CONFIG)
+    payload = captured["payload"]
+
+    assert response.model == "gemini-2.5-flash"
+    assert payload["generationConfig"]["responseMimeType"] == "application/json"
+    assert payload["generationConfig"]["responseSchema"] == STRUCTURED_SCHEMA
+    assert "reasoning_effort" not in payload
+    assert "max_depth" not in payload
+    assert "json_schema" not in payload

uv.lock

@@ -1,5 +1,49 @@
 version = 1
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version < '3.15'",
+    "python_full_version >= '3.15'",
+]
+
+[[package]]
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+
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 name = "llm-connect"
 version = "0.1.0"
@@ -41,22 +170,100 @@ dependencies = [
 
 [package.optional-dependencies]
 dev = [
+    { name = "mypy" },
     { name = "pytest" },
+    { name = "ruff" },
 ]
 
 [package.dev-dependencies]
 dev = [
+    { name = "mypy" },
     { name = "pytest" },
+    { name = "ruff" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.10" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.4" },
     { name = "toml" },
 ]
 
 [package.metadata.requires-dev]
-dev = [{ name = "pytest", specifier = ">=9.0.2" }]
+dev = [
+    { name = "mypy", specifier = ">=1.10" },
+    { name = "pytest", specifier = ">=9.0.2" },
+    { name = "ruff", specifier = ">=0.4" },
+]
+
+[[package]]
+name = "mypy"
+version = "2.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ast-serialize" },
+    { name = "librt", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "mypy-extensions" },
+    { name = "pathspec" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
+]
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workplans/ADHOC-2026-06-02.md

@@ -0,0 +1,230 @@
+---
+id: ADHOC-2026-06-02
+type: workplan
+title: "Ad hoc — llm-connect lessons from CUST-WP-0045 canary"
+domain: custodian
+repo: llm-connect
+status: finished
+owner: custodian
+topic_slug: custodian
+created: "2026-06-02"
+updated: "2026-06-03"
+state_hub_workstream_id: "1c936c91-79c7-427d-ab37-9052e8a61cda"
+---
+
+# ADHOC-2026-06-02 — llm-connect lessons from CUST-WP-0045 canary
+
+Captured at the close of CUST-WP-0045 T06. The daily-triage canary uncovered
+a sequence of bugs that took the better part of a day to diagnose, mostly
+because llm-connect has no way to see what the underlying provider returned
+or what payload the adapter sent. Five commits landed today:
+
+- `9de0f49` — Claude Code adapter passes `--output-format json` and unwraps
+- `435da49` — envelope unwrap prefers JSON-bearing fields over prose
+- `cd4551c` — OpenRouter adapter translates `json_schema` → `response_format`
+  and drops Claude-specific keys
+- `583ab57` — OpenRouter `response_format.json_schema.strict` defaults to `False`
+- `1b01f0e` — OpenRouter honours explicit `--model` even when it equals the
+  hardcoded default
+
+This adhoc captures the structural improvements that would have collapsed the
+diagnosis loop from half a day to minutes. Each task is sized as an opportunistic
+improvement; the larger ones explicitly call out when to promote into a
+workplan.
+
+## Tasks
+
+### T01 - Debug envelope mode for /execute responses
+
+```task
+id: ADHOC-2026-06-02-T01
+status: done
+priority: medium
+state_hub_task_id: "69626e9e-29f1-40f6-8cd2-d38a7e802293"
+```
+
+When the `LLM_CONNECT_DEBUG=1` env var is set (or `?debug=1` is added to the
+`/execute` request), include the raw provider response and the adapter's
+constructed payload alongside the normal `content` field. Today's diagnosis
+required two iterations on the Claude Code envelope shape and one on the
+OpenRouter adapter routing — each required modifying source, restarting
+llm-connect, re-running, observing, modifying again. A debug envelope mode
+would have given the diagnostic data in one curl.
+
+Proposed response shape under debug mode:
+
+```json
+{
+  "content": "<unchanged>",
+  "model": "<unchanged>",
+  "usage": {...},
+  "metadata": {...},
+  "debug": {
+    "provider_request": {"url": "...", "payload": {...}, "headers_redacted": {...}},
+    "provider_response": {"status": 200, "body": {...}},
+    "adapter_transformations": [
+      {"step": "merge_model_params", "before": {...}, "after": {...}},
+      {"step": "unwrap_cli_envelope", "before": "...", "after": "..."}
+    ]
+  }
+}
+```
+
+Done when `LLM_CONNECT_DEBUG=1 curl /execute …` returns the raw round-trip in
+the response without changing the default response shape, and a test pins the
+debug field is omitted in normal mode.
+
+### T02 - Replace stdlib HTTPServer with ThreadingHTTPServer
+
+```task
+id: ADHOC-2026-06-02-T02
+status: done
+priority: low
+state_hub_task_id: "e2b1be30-71f7-4497-9b10-b0f24d37beba"
+```
+
+`llm_connect/server.py` uses `http.server.HTTPServer`, which is single-threaded.
+Concurrent requests queue behind whatever subprocess or HTTPS call is in flight.
+That made today's diagnosis worse: a smoke probe sent while the canary's real
+LLM call was running just hung until the canary finished. With Claude CLI calls
+taking 60-90s each, queuing is painful.
+
+`http.server.ThreadingHTTPServer` is a drop-in replacement (subclass of
+`HTTPServer` + `ThreadingMixIn`). One-line change. Document that adapters must
+be thread-safe (the subprocess and HTTPS adapters already are).
+
+Done when concurrent `/execute` calls run in parallel and a regression test
+asserts two requests with measurable backend latency complete in roughly the
+max of their individual latencies, not the sum.
+
+### T03 - Per-call audit log for replay
+
+```task
+id: ADHOC-2026-06-02-T03
+status: done
+priority: medium
+state_hub_task_id: "da4821f0-a876-44ce-9dc3-f3fc67732d0f"
+```
+
+When `LLM_CONNECT_AUDIT_DIR=/path` is set, record every `/execute` call as a
+JSON file named `{timestamp}-{response_id}.json` containing `{prompt, config,
+provider_request, provider_response, parsed_content, latency_seconds}`. A
+sibling `python -m llm_connect.replay <file>` command re-runs the parser /
+unwrapper / validation against the recorded provider response without making a
+new provider call.
+
+Saves quota during diagnosis (today we burned several Sonnet calls just to
+re-observe the envelope shape) and gives downstream consumers like
+activity-core's instruction executor a "what did the model actually return"
+artifact when validation fails.
+
+Promote to a workplan if anyone picks it up — schema for the audit record
+needs a small design pass (retention, redaction of API keys in
+`provider_request.headers`, file size cap), and the CLI side has its own
+ergonomics.
+
+### T04 - Apply param-translation contract to OpenAI and Gemini adapters
+
+```task
+id: ADHOC-2026-06-02-T04
+status: done
+priority: medium
+state_hub_task_id: "f8a033e6-22ac-4700-b8d2-43a5d76a3751"
+```
+
+Today's `cd4551c` added a `_merge_model_params` helper in the OpenRouter
+adapter that whitelists OpenAI Chat Completions fields, translates
+`json_schema` to `response_format`, and drops Claude/llm-connect-specific
+keys. The same naive `payload.update(config.model_params)` is latent in
+`llm_connect/openai.py` and `llm_connect/gemini.py` — neither has been
+exercised against an activity-core-style payload yet, but they will 400
+the moment they are.
+
+Extract `_merge_model_params` to a shared module (`llm_connect/_payload.py`?)
+and use it from all three adapters. Gemini's accepted top-level fields differ
+(`generationConfig` wrapper, `safetySettings`, etc.), so the helper either
+needs adapter-specific whitelists or a per-adapter translation table. The
+shape choice is small but real.
+
+Done when a parametrised test sends the activity-core daily-triage
+`model_params` shape (`reasoning_effort`, `max_depth`, `json_schema`) through
+each adapter and asserts the resulting payload is provider-accepted (no
+forbidden top-level fields, schema in the right wrapper).
+
+### T05 - Provider-agnostic structured-output smoke test in CI
+
+```task
+id: ADHOC-2026-06-02-T05
+status: done
+priority: medium
+state_hub_task_id: "5d53dbb4-b374-45fe-b81c-ff0b222ca74f"
+```
+
+CI today does not exercise the round-trip from `model_params.json_schema`
+through any real provider. The `1b01f0e` model-routing bug — where
+`--model anthropic/claude-sonnet-4` silently became `gpt-4` because of an
+inverted comparison — only surfaced through the canary, because none of the
+adapter tests check the actual `payload["model"]` value end-to-end.
+
+Add a test (gated on each provider's API key being present in CI env) that:
+
+1. Starts the server with `--provider {p} --model {m}` for each registered
+   adapter.
+2. POSTs a fixed prompt with `model_params.json_schema` requiring
+   `{summary, recommendations}` (mirroring activity-core's canary shape).
+3. Asserts the response parses cleanly against the schema.
+4. Asserts `response.model` matches what was configured at startup, not the
+   `RunConfig.model_name` default.
+
+Without API keys present, the test mocks the provider HTTP layer and asserts
+the *payload* matches expectations. That catches routing/translation bugs
+without provider cost.
+
+Done when a smoke probe shaped like today's CUST-WP-0045 canary would have
+failed CI on `1b01f0e` (the routing bug) and on `cd4551c` (the translation
+bug) before either was merged.
+
+### T06 - Document required model_params translation in adapter README
+
+```task
+id: ADHOC-2026-06-02-T06
+status: done
+priority: low
+state_hub_task_id: "33fcb951-d7ab-4d3c-8d67-9eebd986c711"
+```
+
+Adapter authors and callers both need to know:
+
+- which `model_params` keys are pass-through to the underlying provider
+- which are translated (`json_schema` → `response_format` for OpenAI-shaped
+  providers, → tool-use for native Anthropic, etc.)
+- which are dropped (Claude-only `reasoning_effort`, llm-connect's
+  `max_depth`)
+- the strict-mode tradeoff and how to opt back in
+
+Today's bug chain happened partly because the activity-core team assumed
+`model_params` was a pass-through and the llm-connect team assumed callers
+would only send OpenAI-valid fields. Codify the contract in
+`docs/adapter-model-params.md` (new file) or extend the existing
+`ARCHITECTURE-LAYERS.md`.
+
+Done when a new adapter author can read the doc and know what their
+`_merge_model_params` implementation must support.
+
+## Implementation Notes
+
+Completed on 2026-06-03:
+
+- Added opt-in `/execute` debug envelopes via `LLM_CONNECT_DEBUG=1` or
+  `?debug=1`, with redacted provider request/response capture and adapter
+  transformation records.
+- Switched serve mode to `ThreadingHTTPServer` and added a concurrency
+  regression test.
+- Added `LLM_CONNECT_AUDIT_DIR` per-call audit records plus
+  `python -m llm_connect.replay` for parser/unwrapper replay.
+- Extracted shared OpenAI-compatible and Gemini payload translation helpers
+  and wired OpenRouter, OpenAI, and Gemini through them.
+- Added CI-safe structured-output smoke tests that mock provider HTTP calls
+  and assert model routing plus payload shape.
+- Documented the adapter `model_params` contract in
+  `docs/adapter-model-params.md`.

workplans/LLM-WP-0006-activity-core-always-on-endpoint.md

@@ -0,0 +1,421 @@
+---
+id: LLM-WP-0006
+type: workplan
+title: "Activity-Core Always-On LLM Endpoint"
+domain: agents
+repo: llm-connect
+status: finished
+owner: codex
+topic_slug: activity-core-llm-endpoint
+planning_priority: high
+planning_order: 6
+created: "2026-06-07"
+updated: "2026-06-18"
+depends_on_workplans:
+  - LLM-WP-0003
+related_workplans:
+  - ACTIVITY-WP-0006
+state_hub_workstream_id: "8de71d58-1193-424f-8338-a9aa4e173c5b"
+---
+
+# LLM-WP-0006 - Activity-Core Always-On LLM Endpoint
+
+**status:** finished
+**owner:** codex
+
+## Purpose
+
+Provide an operator-approved, always-on `llm-connect` HTTP endpoint for
+`activity-core` daily WSJF triage. The service must be reachable from the
+`activity-core` Kubernetes namespace, expose the existing `GET /health` and
+`POST /execute` contract, support the `custodian-triage-balanced` runtime
+profile, and return JSON content that satisfies the daily triage schema without
+leaking provider credentials or secret material into Git, logs, or State Hub.
+
+This is not a new public API. The current `llm_connect.server` contract is a
+lightweight internal service surface; this workplan turns it into a durable
+internal dependency with profile resolution, deployable artifacts, smoke tests,
+and activity-core handoff evidence.
+
+## Demand Signal
+
+State Hub messages from `activity-core` on 2026-06-07 requested a stable
+`llm-connect` endpoint before `ACTIVITY-WP-0006/T03` can collect clean scheduled
+WSJF evidence.
+
+Required behavior from those messages:
+
+- `GET /health` returns 200 from inside the activity-core runtime path.
+- `POST /execute` accepts activity-core `RunConfig` payloads with
+  `model_name=custodian-triage-balanced`, `temperature=0.2`,
+  `max_tokens=1800`, `max_depth=2`, `model_params.reasoning_effort=medium`,
+  and `model_params.json_schema` for the daily triage report.
+- The response contains a string `content` field whose value is valid JSON
+  matching the daily triage schema.
+- Provider credentials stay outside Git and outside State Hub
+  messages/progress.
+- The stable service URL can be handed to activity-core as `LLM_CONNECT_URL`.
+- The service fits within `LLM_CONNECT_TIMEOUT_SECONDS=300` and surfaces useful
+  provider/transport errors without exposing secrets.
+
+## Current Repo State
+
+Already present:
+
+- `llm_connect/server.py` exposes `GET /health` and `POST /execute` via
+  `ThreadingHTTPServer`.
+- `/execute` forwards `RunConfig` fields including `max_depth` and
+  `model_params`.
+- Structured-output helpers translate `model_params.json_schema` for OpenAI,
+  OpenRouter, Gemini, and Claude Code CLI.
+- Debug and audit modes redact provider request headers and can replay captured
+  adapter transformations.
+
+Missing for this request:
+
+- No named runtime profile resolver for `custodian-triage-balanced`.
+- No container or Kubernetes deployment artifact for an always-on service.
+- No documented secret/config injection path for the cluster service.
+- No activity-core daily triage fixture or in-cluster smoke job.
+- No committed handoff document naming the final stable URL and verification
+  evidence.
+
+## T01 - Lock Activity-Core Contract Fixture
+
+```task
+id: LLM-WP-0006-T01
+title: "Lock activity-core daily WSJF request and schema fixture"
+priority: high
+status: done
+state_hub_task_id: "f1d21c4b-2df3-4da8-8e6e-418fd7998a63"
+```
+
+Capture a non-secret fixture for the exact `POST /execute` request used by
+`daily-statehub-wsjf-triage`, including the daily triage JSON schema, timeout
+budget, expected response shape, and minimum prompt fields. Store only schema
+and dummy prompt/evidence values in the repo.
+
+Done when a fixture can be used by tests and smoke scripts without any provider
+credentials or live State Hub data, and the workplan notes identify the
+activity-core consumer contract it represents.
+
+## T02 - Add Named Runtime Profile Resolution
+
+```task
+id: LLM-WP-0006-T02
+title: "Resolve custodian-triage-balanced to provider, model, and RunConfig defaults"
+priority: high
+status: done
+state_hub_task_id: "4538bae3-e8cf-4aa6-9056-270fd8d54caa"
+```
+
+Add a small named-profile layer for server mode so activity-core can send
+`model_name=custodian-triage-balanced` while operators configure the underlying
+provider/model out of band. The profile should merge request overrides with
+profile defaults for temperature, max tokens, max depth, timeout, and portable
+`model_params`, while preserving the existing direct provider/model behavior.
+
+Done when unit tests prove `custodian-triage-balanced` resolves to the selected
+adapter/model without hard-coding provider secrets, unknown profile names fail
+with a clear non-secret error, and existing `/execute` behavior remains
+backward compatible.
+
+## T03 - Harden Server Responses for Operations
+
+```task
+id: LLM-WP-0006-T03
+title: "Return useful non-secret provider and transport errors from server mode"
+priority: high
+status: done
+state_hub_task_id: "d4adfe3b-6a57-4184-86fd-2eb11979f075"
+```
+
+Review server error handling for provider configuration failures, timeouts,
+HTTP/API failures, invalid profile config, and malformed structured-output
+responses. Keep the normal `LLMResponse.to_dict()` success shape, but make
+errors actionable for operators and consumers without echoing API keys, bearer
+tokens, request headers, or prompt bodies by default.
+
+Done when tests cover sanitized error responses for configuration, timeout,
+provider/API, and profile validation failures, and debug/audit mode remains
+opt-in and redacted.
+
+## T04 - Package the Always-On Service
+
+```task
+id: LLM-WP-0006-T04
+title: "Add container packaging and service entrypoint for llm-connect server"
+priority: high
+status: done
+state_hub_task_id: "38822b17-fa58-4583-939f-26e59b9c93c7"
+```
+
+Create the deployable service artifact: container build definition, non-root
+runtime, healthcheck, explicit listen host/port, and environment-driven profile
+configuration. Keep provider keys injected only at runtime through the approved
+cluster secret path.
+
+Done when the image builds locally, starts with mock and at least one real
+provider configuration path, passes `GET /health`, and can receive a fixture
+`POST /execute` without writing secrets to stdout, image layers, or committed
+files.
+
+## T05 - Add Kubernetes Deployment Surface
+
+```task
+id: LLM-WP-0006-T05
+title: "Provide Kubernetes Deployment, Service, probes, and secret references"
+priority: high
+status: done
+state_hub_task_id: "f9743610-b573-41b8-952f-b27319acb3e3"
+```
+
+Add the cluster deployment surface for an internal `llm-connect` service:
+Deployment, Service, readiness/liveness probes, ConfigMap/profile settings,
+Secret references for provider credentials, resource requests/limits, and
+network access scoped to the activity-core namespace. Use the repository's
+current deployment conventions if a shared Railiance chart location is selected
+during implementation.
+
+Done when an operator can apply the manifests without editing secret values
+into Git, the service exposes stable cluster DNS, and `GET /health` succeeds
+from an activity-core pod or equivalent smoke pod.
+
+## T06 - Build Smoke Tests and Validation Scripts
+
+```task
+id: LLM-WP-0006-T06
+title: "Validate health, fixture execute, JSON schema content, and timeout budget"
+priority: high
+status: done
+state_hub_task_id: "f046d68b-97f3-4471-a1f6-f1ab351ec448"
+```
+
+Add smoke tooling that can run locally against mock/profile mode and in-cluster
+against the deployed Service. It should check health, post the daily triage
+fixture, parse `response.content` as JSON, validate it against the daily triage
+schema, and report latency relative to the 300 second activity-core timeout.
+
+Done when the smoke path produces a clear pass/fail summary without dumping
+secret headers or provider credentials, and failed JSON/schema validation is
+reported distinctly from provider transport failure.
+
+## T07 - Coordinate Activity-Core Handoff
+
+```task
+id: LLM-WP-0006-T07
+title: "Publish verified LLM_CONNECT_URL handoff and activity-core smoke evidence"
+priority: high
+status: done
+state_hub_task_id: "92e043f0-5ca8-4c2d-b8f6-dd5fbf8ccb62"
+```
+
+After the service is deployed and smoke-tested, hand the stable URL to the
+activity-core/railiance-cluster operator for `LLM_CONNECT_URL`. Coordinate one
+manual or smoke daily WSJF run and record non-secret evidence that a State Hub
+`daily_triage` event was emitted.
+
+Done when the final URL value is documented in the appropriate operator-owned
+config handoff, a fixture `POST /execute` succeeds from the activity-core
+namespace, and activity-core has enough evidence to start counting clean 07:20
+Europe/Berlin scheduled runs toward `ACTIVITY-WP-0006/T03`.
+
+## Scope Guardrails
+
+In scope:
+
+- Server-mode profile resolution needed by activity-core.
+- Internal service packaging and Kubernetes deployment artifacts.
+- Redacted diagnostics and operator-safe error responses.
+- Health and execute smoke tooling using non-secret fixtures.
+- Coordination notes for the final `LLM_CONNECT_URL` handoff.
+
+Out of scope:
+
+- Publishing `llm-connect` as a public internet service.
+- Storing provider credentials, live prompts, or State Hub event payloads in
+  Git.
+- Replacing activity-core's scheduler or WSJF triage logic.
+- Guaranteeing three scheduled production runs; this plan provides the
+  endpoint and first smoke evidence, while scheduled-run collection remains
+  activity-core ownership.
+- Choosing or rotating production provider credentials; that is an operator
+  secret-management action.
+
+## Acceptance
+
+- `python -m llm_connect.server` or the packaged service starts an internal
+  endpoint with a configured `custodian-triage-balanced` profile.
+- `GET /health` returns 200 locally and from inside the activity-core runtime
+  network path.
+- A fixture `POST /execute` with the daily WSJF schema returns an
+  `LLMResponse` whose `content` field is a string containing schema-valid JSON.
+- Provider failures, timeouts, and profile/config errors return useful
+  non-secret error bodies.
+- The deployed Service has readiness/liveness probes, runtime-only secret
+  injection, and a documented stable URL for activity-core.
+- A manual or smoke daily WSJF run emits non-secret evidence of a State Hub
+  `daily_triage` event.
+
+## Risks and Open Questions
+
+- The final provider/model behind `custodian-triage-balanced` needs operator
+  approval and runtime secret availability. The profile layer should keep that
+  choice configurable.
+- If the chosen provider does not reliably honor the supplied JSON schema, the
+  smoke path may need a retry or repair strategy; that should be explicit and
+  bounded if added.
+- The repository currently has no deployment directory. Implementation must
+  decide whether Kubernetes artifacts live here, in a Railiance deployment repo,
+  or are split between code-owned defaults here and environment-owned overlays
+  elsewhere.
+- `llm_connect.server` is stdlib HTTP and thread-per-request. That is likely
+  sufficient for daily WSJF traffic, but sustained multi-consumer use may need
+  a later ASGI/worker model.
+
+## Implementation Notes
+
+2026-06-07:
+
+- Added non-secret activity-core fixtures under `fixtures/activity_core/` using
+  the `daily-triage-report` schema from activity-core's Railiance runtime.
+- Added `llm_connect.profiles` with `custodian-triage-balanced` profile
+  dispatch, env/file profile overrides, and metadata on profiled responses.
+- Updated `llm_connect.server` so CLI serve mode enables runtime profiles by
+  default, reads host/port/provider/model defaults from env, validates configs
+  before execution, and returns structured sanitized error bodies.
+- Added `LLM_CONNECT_MOCK_RESPONSE` support for local mock server smokes.
+- Added standard-library smoke tooling in
+  `scripts/smoke_activity_core_endpoint.py`, plus tests that run the smoke path
+  against an in-process profiled mock HTTP server.
+- Added `Containerfile`, `.dockerignore`, and a Kubernetes overlay at
+  `deploy/k8s/activity-core-llm-connect/`.
+- Added handoff docs in `docs/activity-core-llm-endpoint.md`.
+- Verification completed locally:
+  `python3 -m pytest tests/test_profiles.py tests/test_server.py
+  tests/test_activity_core_smoke.py tests/test_factory.py
+  tests/test_package_exports.py`;
+  `docker build --progress=plain -f Containerfile -t
+  llm-connect:wp0006-smoke .`; and `kubectl kustomize
+  deploy/k8s/activity-core-llm-connect`.
+
+Live cluster evidence:
+
+- Imported `docker.io/library/llm-connect:latest` into the actual Railiance k3s
+  node runtime on `coulombcore` (`92.205.130.254`) and updated the overlay to
+  use that normalized image reference with `imagePullPolicy: Never`.
+- Applied the `activity-core` namespace deployment surface: ConfigMap, Secret
+  reference, Service, Deployment, readiness/liveness probes, and NetworkPolicy.
+- Verified the live Deployment is `1/1` ready with image
+  `docker.io/library/llm-connect:latest`.
+- Verified the stable in-cluster URL
+  `http://llm-connect.activity-core.svc.cluster.local:8080` returns
+  `{"status": "ok"}` for `GET /health` from the activity-core namespace path.
+- Verified the activity-core fixture smoke reaches `POST /execute`; it fails
+  with a structured `configuration_error` until the provider credential Secret
+  is populated. No Secret values were inspected or recorded.
+
+Remaining blocked live gate:
+
+- `LLM-WP-0006-T07` still needs the runtime provider Secret populated outside
+  Git/State Hub, a successful fixture `POST /execute` returning schema-valid
+  JSON, the verified URL written to activity-core runtime config, and a
+  manual/smoke daily WSJF run that emits a non-secret State Hub `daily_triage`
+  event.
+
+2026-06-07 follow-up:
+
+- Submitted State Hub message `8e644cb0-1af4-482c-8da7-7061080d21bc` to
+  `railiance-cluster` requesting image publication, runtime provider Secret
+  creation outside Git/State Hub, overlay apply or porting, in-namespace
+  `/health`, and fixture smoke evidence for `LLM-WP-0006-T05`.
+- Submitted State Hub message `ff798e7c-b8ef-4a3f-ab92-00bf09410534` to
+  `activity-core` requesting `LLM_CONNECT_URL` / timeout consumption after the
+  cluster smoke, a manual or smoke daily WSJF run, State Hub `daily_triage`
+  evidence, working-memory verification, and continuation of the three clean
+  scheduled 07:20 Europe/Berlin runs for `ACTIVITY-WP-0006-T03`.
+- Submitted State Hub message `02033d4d-3cb0-41c8-b390-7b9e8471421e` to
+  `railiance-cluster` confirming the live Deployment, stable URL, and `/health`
+  evidence after importing the image into the actual `coulombcore` k3s node.
+- Submitted State Hub message `771afe14-a2d0-46ca-b905-52018bf86c62` to
+  `activity-core` with the verified URL and the remaining provider Secret gate
+  for schema-valid `POST /execute` and `daily_triage` evidence.
+
+2026-06-17 recheck:
+
+- Verified the live `coulombcore` Kubernetes path is reachable and the
+  `activity-core` namespace `llm-connect` Deployment remains `1/1` available
+  with Service `llm-connect` on port `8080`.
+- Confirmed the `llm-connect-provider-secrets` Secret object exists but still
+  reports `DATA 0`; no Secret values were inspected.
+- Re-ran the in-namespace fixture smoke with the node-local image. The first
+  corrected pod needed `--image-pull-policy=Never` because the `:latest` tag
+  otherwise attempted a Docker Hub pull. With the local image, the smoke reached
+  `/execute` and failed safely with
+  `configuration_error: Adapter rejected RunConfig`.
+- State Hub now also has a 2026-06-16 `daily_triage` event from
+  `activity-core` showing `LLM_CONNECT_URL is not configured`, and the local
+  activity-core runtime manifest still has `LLM_CONNECT_URL: ""`.
+- `LLM-WP-0006-T07` therefore remains externally blocked until the provider
+  Secret is populated outside Git/State Hub, activity-core consumes
+  `http://llm-connect.activity-core.svc.cluster.local:8080` with
+  `LLM_CONNECT_TIMEOUT_SECONDS=300`, the fixture smoke returns schema-valid
+  JSON, and a non-secret `daily_triage` evidence event is recorded.
+
+2026-06-18 recheck:
+
+- activity-core has repo-local work to consume the stable URL:
+  `actcore-runtime-config` now sets
+  `LLM_CONNECT_URL=http://llm-connect.activity-core.svc.cluster.local:8080`
+  and `LLM_CONNECT_TIMEOUT_SECONDS=300`.
+- The live `activity-core` namespace has not yet been reconciled to that
+  activity-core runtime surface; live deployments currently show only
+  `deployment.apps/llm-connect`, and live ConfigMaps show only
+  `kube-root-ca.crt` and `llm-connect-config`.
+- The live `llm-connect-provider-secrets` Secret still reports `DATA 0`; no
+  Secret values were inspected.
+- ops-warden's credential-routing guidance says LLM provider API keys are not
+  an ops-warden issuance task. The remaining credential gate belongs to the
+  approved operator/OpenBao-to-Kubernetes Secret path for
+  `activity-core/llm-connect-provider-secrets`.
+- `LLM-WP-0006-T07` remains blocked until the provider Secret is populated,
+  the activity-core runtime is reconciled with the URL/timeout config, the
+  fixture smoke returns schema-valid JSON from inside the namespace, and
+  activity-core records non-secret `daily_triage` evidence.
+
+2026-06-18 closure:
+
+- Populated-provider state is now live: `activity-core/llm-connect-provider-secrets`
+  reports `DATA 1`; no Secret values were inspected or recorded.
+- Updated the OpenRouter structured-output path to request strict JSON schema
+  output and to set `provider.require_parameters=true` for schema calls, so
+  OpenRouter routes only to providers that support the requested structured
+  output parameters.
+- OpenRouter model metadata showed the previous
+  `anthropic/claude-sonnet-4` profile model does not advertise
+  `response_format`/`structured_outputs`; switched the activity-core profile
+  and Kubernetes ConfigMap defaults to `google/gemini-2.5-flash`, which does.
+- Rebuilt `docker.io/library/llm-connect:latest` from `Containerfile`,
+  imported it into the `coulombcore` k3s image store, applied the updated
+  non-secret `llm-connect-config` ConfigMap, and rolled out
+  `deployment/llm-connect`.
+- Verified live ConfigMap values:
+  `LLM_CONNECT_MODEL=google/gemini-2.5-flash` and
+  `LLM_CONNECT_CUSTODIAN_TRIAGE_MODEL=google/gemini-2.5-flash`.
+- Final in-namespace smoke passed against
+  `http://llm-connect.activity-core.svc.cluster.local:8080` with:
+  `smoke: pass health=ok latency_seconds=2.147 recommendations=1`.
+- Cleaned up the one-shot smoke pod after collecting logs. The llm-connect
+  endpoint handoff is complete; collecting scheduled `daily_triage` evidence
+  now belongs to activity-core / `ACTIVITY-WP-0006`.
+
+## Closure Notes
+
+After this workplan file is added or task statuses change, ask the custodian
+operator to run from `~/state-hub`:
+
+```bash
+make fix-consistency REPO=llm-connect
+```
+
+That syncs file-backed workplan state into the State Hub cache.

workplans/llm-connect-WP-0001-foundation-gaaf-baseline.md

@@ -1,6 +1,20 @@
+---
+id: LLM-WP-0001
+type: workplan
+title: llm-connect — Foundation & GAAF Baseline
+domain: agents
+status: completed
+owner: llm-connect
+created: 2026-04-01
+repo: llm-connect
+planning_priority: high
+planning_order: 1
+state_hub_workstream_id: f7f08327-753f-4175-8591-ffa1c3188ebc
+---
+
 # LLM-WP-0001 — Foundation & GAAF Baseline
 
-**status:** active  
+**status:** completed
 **owner:** llm-connect  
 **repo:** llm-connect  
 **created:** 2026-04-01  
@@ -13,6 +27,102 @@ and state-hub housekeeping.
 
 ## Tasks
 
+```task
+id: T01
+title: 'Create SCOPE.md'
+priority: high
+status: done
+state_hub_task_id: "c38c5a79-4ce5-4088-9a21-ac65e09b12ba"
+```
+
+```task
+id: T02
+title: 'Fill .claude/rules/ stubs: architecture.md, stack-and-commands.md, repo-boundary.md'
+priority: high
+status: done
+state_hub_task_id: "6a15c794-d0f7-4d9c-a3ac-850f8c5bd5e9"
+```
+
+```task
+id: T03
+title: 'Create ARCHITECTURE-LAYERS.md with layer map, scorecard stub, next-review date'
+priority: high
+status: done
+state_hub_task_id: "af1c63ac-e4be-495a-9fdb-68eddebfcb75"
+```
+
+```task
+id: T04
+title: 'Create /contracts/ tree (core/, functional/, config/)'
+priority: high
+status: done
+state_hub_task_id: "da5a7986-5c47-4c4c-a8f6-a58956127535"
+```
+
+```task
+id: T05
+title: 'Core contract doc: LLMAdapter interface invariants, RunConfig/LLMResponse field contracts'
+priority: high
+status: done
+state_hub_task_id: "01237203-0582-4bc4-a308-075e991e8e99"
+```
+
+```task
+id: T06
+title: 'Functional contract stubs for all 4 adapters + embedding adapters (maturity: Beta)'
+priority: medium
+status: done
+state_hub_task_id: "2bee5174-d3d7-4267-9cee-6e0e9b5cc731"
+```
+
+```task
+id: T07
+title: 'Create tests/ with conftest.py, wire pytest in pyproject.toml'
+priority: high
+status: done
+state_hub_task_id: "b6dccf3e-8742-486e-a6a7-82577866a3bc"
+```
+
+```task
+id: T08
+title: 'Unit tests: RunConfig, LLMResponse, MockLLMAdapter, full exception hierarchy'
+priority: high
+status: done
+state_hub_task_id: "cc05b67d-f956-458a-908f-2ff58b1d33d3"
+```
+
+```task
+id: T09
+title: 'Unit tests: create_adapter (all providers + unknown provider error), create_embedding_adapter'
+priority: high
+status: done
+state_hub_task_id: "8f9ec054-79ab-411d-8204-9d764bbbed98"
+```
+
+```task
+id: T10
+title: 'Add ruff, mypy to dev deps in pyproject.toml'
+priority: medium
+status: done
+state_hub_task_id: "044ee879-6baa-42fd-a0a4-a43dac0eacbb"
+```
+
+```task
+id: T11
+title: 'CI workflow: pytest + ruff + mypy'
+priority: medium
+status: done
+state_hub_task_id: "699eef00-e9df-4de0-b7e6-61cfaace9617"
+```
+
+```task
+id: T12
+title: 'State hub: register this host path, SBOM refresh'
+priority: low
+status: done
+state_hub_task_id: "c0853a23-52ae-499e-9a49-e7b65749b508"
+```
+
 | ID  | Title | Priority | Status |
 |-----|-------|----------|--------|
 | T01 | Create `SCOPE.md` | high | done |

workplans/llm-connect-WP-0002-core-extensions.md

@@ -1,6 +1,20 @@
+---
+id: LLM-WP-0002
+type: workplan
+title: llm-connect — Core Extensions (FR-4 BudgetTracker + FR-3 async)
+domain: agents
+status: completed
+owner: llm-connect
+created: 2026-04-01
+repo: llm-connect
+planning_priority: high
+planning_order: 2
+state_hub_workstream_id: 448fa379-eb9e-4808-b3fa-0078f1e4eaba
+---
+
 # LLM-WP-0002 — Core Extensions (FR-4 + FR-3)
 
-**status:** active  
+**status:** completed
 **owner:** llm-connect  
 **repo:** llm-connect  
 **created:** 2026-04-01  
@@ -28,26 +42,114 @@ Core contract doc (from WP-0001 T05) must be updated after each change.
 
 ## Tasks
 
+```task
+id: T01
+title: 'BudgetTracker dataclass: total, spent, remaining(), thread-safe increment'
+priority: high
+status: done
+state_hub_task_id: "ae27c363-339a-4f78-9737-cf872698f6d8"
+```
+
+```task
+id: T02
+title: 'LLMBudgetExceededError(LLMError) in exceptions.py'
+priority: high
+status: done
+state_hub_task_id: "ea6f6ef7-2cb2-48e2-b9c9-f2b84a1a242b"
+```
+
+```task
+id: T03
+title: 'Optional budget_tracker field on RunConfig'
+priority: high
+status: done
+state_hub_task_id: "fe6dbb73-5d04-45e6-aa91-5eff79aae7ee"
+```
+
+```task
+id: T04
+title: 'Enforcement: adapters check/update tracker, raise LLMBudgetExceededError when exceeded'
+priority: high
+status: done
+state_hub_task_id: "8fd21bc2-598e-4449-8c86-eacde760e23f"
+```
+
+```task
+id: T05
+title: 'Update Core contract doc for BudgetTracker and RunConfig changes'
+priority: medium
+status: done
+state_hub_task_id: "e15745f5-9bb7-45d6-a36b-3a345fb0e9f1"
+```
+
+```task
+id: T06
+title: 'Tests: single call, delegation chain, exceeded error, multi-adapter shared tracker'
+priority: high
+status: done
+state_hub_task_id: "5af37ade-3dd0-4ce9-8ead-be9887913bab"
+```
+
+```task
+id: T07
+title: 'Add async_execute_prompt to LLMAdapter ABC with default executor fallback'
+priority: high
+status: done
+state_hub_task_id: "e221e630-658f-4adb-9f00-7b7df7ab8cb4"
+```
+
+```task
+id: T08
+title: 'Native async override in OpenAIAdapter, GeminiAdapter, OpenRouterAdapter'
+priority: high
+status: done
+state_hub_task_id: "a75c2b2a-e4ef-4cbd-9c5f-7e98c8d3d7e8"
+```
+
+```task
+id: T09
+title: 'Native async for ClaudeCodeAdapter via asyncio.create_subprocess_exec'
+priority: high
+status: done
+state_hub_task_id: "1c50889f-28ed-4c6e-a788-1fc7dcc5a2c3"
+```
+
+```task
+id: T10
+title: 'Update Core contract doc for async_execute_prompt'
+priority: medium
+status: done
+state_hub_task_id: "fa4f9e80-ddee-4d05-a239-fe09e633b0cb"
+```
+
+```task
+id: T11
+title: 'Tests: asyncio.gather over N adapters, timeout propagation, budget interaction'
+priority: high
+status: done
+state_hub_task_id: "bca78609-7f7c-4548-8857-a72e4c760dc6"
+```
+
 ### FR-4 — BudgetTracker
 
 | ID  | Title | Priority | Status |
 |-----|-------|----------|--------|
-| T01 | `BudgetTracker` dataclass: `total`, `spent`, `remaining()`, thread-safe increment | high | todo |
+| T01 | `BudgetTracker` dataclass: `total`, `spent`, `remaining()`, thread-safe increment | high | done |
-| T02 | `LLMBudgetExceededError(LLMError)` in `exceptions.py` | high | todo |
+| T02 | `LLMBudgetExceededError(LLMError)` in `exceptions.py` | high | done |
-| T03 | Optional `budget_tracker: BudgetTracker \| None` field on `RunConfig` | high | todo |
+| T03 | Optional `budget_tracker: BudgetTracker \| None` field on `RunConfig` | high | done |
-| T04 | Enforcement: each adapter checks/updates tracker around call; raises on exceeded | high | todo |
+| T04 | Enforcement: each adapter checks/updates tracker around call; raises on exceeded | high | done |
-| T05 | Update Core contract doc | medium | todo |
+| T05 | Update Core contract doc | medium | done |
-| T06 | Tests: single call, delegation chain (A→B→C shared tracker), exceeded error, multi-adapter | high | todo |
+| T06 | Tests: single call, delegation chain (A→B→C shared tracker), exceeded error, multi-adapter | high | done |
 
 ### FR-3 — async_execute_prompt
 
 | ID  | Title | Priority | Status |
 |-----|-------|----------|--------|
-| T07 | Add `async_execute_prompt` to `LLMAdapter` ABC with default executor fallback | high | todo |
+| T07 | Add `async_execute_prompt` to `LLMAdapter` ABC with default executor fallback | high | done |
-| T08 | Native async override in `OpenAIAdapter`, `GeminiAdapter`, `OpenRouterAdapter` | high | todo |
+| T08 | Native async override in `OpenAIAdapter`, `GeminiAdapter`, `OpenRouterAdapter` | high | done |
-| T09 | Native async for `ClaudeCodeAdapter` via `asyncio.create_subprocess_exec` | high | todo |
+| T09 | Native async for `ClaudeCodeAdapter` via `asyncio.create_subprocess_exec` | high | done |
-| T10 | Update Core contract doc | medium | todo |
+| T10 | Update Core contract doc | medium | done |
-| T11 | Tests: `asyncio.gather` over N adapters, timeout propagation, budget interaction | high | todo |
+| T11 | Tests: `asyncio.gather` over N adapters, timeout propagation, budget interaction | high | done |
 
 ## Exit criteria
 

workplans/llm-connect-WP-0003-functional-extensions.md

@@ -1,6 +1,20 @@
+---
+id: LLM-WP-0003
+type: workplan
+title: llm-connect — Functional Extensions (FR-2 RoutingPolicy + FR-1 HTTP server)
+domain: agents
+status: completed
+owner: llm-connect
+created: 2026-04-01
+repo: llm-connect
+planning_priority: high
+planning_order: 3
+state_hub_workstream_id: 7b463cdc-40a2-4cc5-8b55-b59cc5ae3443
+---
+
 # LLM-WP-0003 — Functional Extensions (FR-2 + FR-1)
 
-**status:** active  
+**status:** completed
 **owner:** llm-connect  
 **repo:** llm-connect  
 **created:** 2026-04-01  
@@ -22,26 +36,114 @@ Both additions are Functional-layer under GAAF-2026:
 
 ## Tasks
 
+```task
+id: T01
+title: 'RoutingPolicy data model: rules list with task_type, prefer, max_cost_per_1k, fallback'
+priority: high
+status: done
+state_hub_task_id: "85cf92fd-cddd-4e19-8782-970f6480a37f"
+```
+
+```task
+id: T02
+title: 'policy.resolve(task_type) returns configured LLMAdapter'
+priority: high
+status: done
+state_hub_task_id: "352701ce-4b21-4f5d-a22e-462136e58fd2"
+```
+
+```task
+id: T03
+title: 'Export RoutingPolicy from llm_connect.__init__ and update __all__'
+priority: medium
+status: done
+state_hub_task_id: "baeb9b39-7fee-4f2b-86cc-ce64ff9e9b95"
+```
+
+```task
+id: T04
+title: 'Functional contract doc for RoutingPolicy'
+priority: medium
+status: done
+state_hub_task_id: "aa4488c6-950e-4cea-99b1-89defa4677ce"
+```
+
+```task
+id: T05
+title: 'Tests: rule match, cost-cap fallback, unknown task_type fallback, no-match default'
+priority: high
+status: done
+state_hub_task_id: "a4ad9c9e-64a4-44f0-85f3-b9cfe9ef59f7"
+```
+
+```task
+id: T06
+title: 'Design /execute JSON schema (request: provider, model, prompt, config; response: LLMResponse)'
+priority: high
+status: done
+state_hub_task_id: "cf79bce2-8d1a-4708-90b2-5e6569908b14"
+```
+
+```task
+id: T07
+title: 'Implement llm_connect/server.py: POST /execute, GET /health'
+priority: high
+status: done
+state_hub_task_id: "c91964ab-7366-4b34-acd4-1ee12f96881e"
+```
+
+```task
+id: T08
+title: 'python -m llm_connect.server --port N --provider X --model Y CLI entry point'
+priority: high
+status: done
+state_hub_task_id: "e3115bb4-cf3b-4ca0-9992-136e317068ac"
+```
+
+```task
+id: T09
+title: 'Add server optional dep (httpx or aiohttp) to pyproject.toml'
+priority: medium
+status: done
+state_hub_task_id: "2caf5531-8e10-40e9-a595-8652882a10e0"
+```
+
+```task
+id: T10
+title: 'Functional contract doc: HTTP API schema (request/response shapes, error codes)'
+priority: medium
+status: done
+state_hub_task_id: "dc3c81c2-698d-4fee-b1dd-1af156a4276f"
+```
+
+```task
+id: T11
+title: 'Tests: server POST round-trip (MockAdapter), GET /health, error responses'
+priority: high
+status: done
+state_hub_task_id: "848a1622-abdd-4938-8bb4-3da27f5f9867"
+```
+
 ### FR-2 — RoutingPolicy
 
 | ID  | Title | Priority | Status |
 |-----|-------|----------|--------|
-| T01 | `RoutingPolicy` data model: `rules` list with `task_type`, `prefer`, `max_cost_per_1k`, `fallback` | high | todo |
+| T01 | `RoutingPolicy` data model: `rules` list with `task_type`, `prefer`, `max_cost_per_1k`, `fallback` | high | done |
-| T02 | `policy.resolve(task_type)` → returns configured `LLMAdapter` | high | todo |
+| T02 | `policy.resolve(task_type)` → returns configured `LLMAdapter` | high | done |
-| T03 | Export from `llm_connect.__init__` and update `__all__` | medium | todo |
+| T03 | Export from `llm_connect.__init__` and update `__all__` | medium | done |
-| T04 | Functional contract doc for `RoutingPolicy` | medium | todo |
+| T04 | Functional contract doc for `RoutingPolicy` | medium | done |
-| T05 | Tests: rule match, cost-cap fallback, unknown task_type fallback, no-match default | high | todo |
+| T05 | Tests: rule match, cost-cap fallback, unknown task_type fallback, no-match default | high | done |
 
 ### FR-1 — HTTP serve mode
 
 | ID  | Title | Priority | Status |
 |-----|-------|----------|--------|
-| T06 | Design `/execute` JSON schema (request: provider, model, prompt, config; response: LLMResponse fields) | high | todo |
+| T06 | Design `/execute` JSON schema (request: provider, model, prompt, config; response: LLMResponse fields) | high | done |
-| T07 | Implement `llm_connect/server.py` — minimal HTTP server, `POST /execute`, `GET /health` | high | todo |
+| T07 | Implement `llm_connect/server.py` — minimal HTTP server, `POST /execute`, `GET /health` | high | done |
-| T08 | `python -m llm_connect.server --port N --provider X --model Y` CLI entry point | high | todo |
+| T08 | `python -m llm_connect.server --port N --provider X --model Y` CLI entry point | high | done |
-| T09 | Add `httpx` or `aiohttp` server dep under `[project.optional-dependencies] server` | medium | todo |
+| T09 | Add `httpx` or `aiohttp` server dep under `[project.optional-dependencies] server` | medium | done |
-| T10 | Functional contract doc (API schema — request/response shapes, error codes) | medium | todo |
+| T10 | Functional contract doc (API schema — request/response shapes, error codes) | medium | done |
-| T11 | Tests: spin up server in subprocess or via `TestClient`, POST round-trip (MockAdapter), error responses | high | todo |
+| T11 | Tests: spin up server in subprocess or via `TestClient`, POST round-trip (MockAdapter), error responses | high | done |
 
 ## Exit criteria
 

workplans/llm-connect-WP-0004-adaptive-cost-quality-routing.md

@@ -0,0 +1,368 @@
+---
+id: LLM-WP-0004
+type: workplan
+title: Adaptive Cost-Quality Routing
+domain: agents
+status: completed
+owner: llm-connect
+created: 2026-05-17
+repo: llm-connect
+planning_priority: high
+planning_order: 4
+state_hub_workstream_id: e1807fab-e29e-4517-b362-95737a96582d
+---
+
+# LLM-WP-0004 — Adaptive Cost-Quality Routing
+
+**status:** completed
+**owner:** llm-connect
+**repo:** llm-connect
+**created:** 2026-05-17
+**depends-on:** LLM-WP-0003 (RoutingPolicy primitive)
+
+## Purpose
+
+Provide reusable primitives that let a consumer route each task to the
+cheapest model whose observed output quality clears a per-task bar, with
+the local Claude Code session (`ClaudeCodeAdapter`) available as the
+baseline-quality oracle. The current `RoutingPolicy` (LLM-WP-0003) is
+static: rules and cost caps are hand-authored. This workplan adds the
+observation, grading, and adaptive-selection layer that learns *which*
+model is good enough for which task type.
+
+Demand signal: `infospace-bench` is about to scale from one-chapter
+smoke runs to multi-chapter and full-book infospace generation across
+multiple workflow stages (summarise, extract entities, extract
+relations, evaluate). Each stage has very different quality / cost
+trade-offs, and the consumer needs llm-connect to pick the right model
+per stage instead of hard-coding one model for the whole run.
+
+## Scope guardrails (read this before adding tasks)
+
+llm-connect ships *primitives*, not consumer policy.
+
+In scope here:
+
+- Data models for quality observations and grading results
+- A reusable observation ledger (persistent, append-only, file-backed)
+- A `BaselineGrader` that pairs a baseline adapter with a candidate
+  adapter and emits a structured quality delta
+- A small built-in grader catalogue (exact-match, embedding similarity,
+  LLM-as-judge wrapper)
+- An `AdaptiveRoutingPolicy` that extends `RoutingPolicy` by consulting
+  the ledger to pick the cheapest adapter whose observed quality for a
+  task type still clears a configured threshold
+- A shadow-mode wrapper adapter for collecting observations in
+  production without changing caller behaviour
+
+Out of scope (belongs in consumer repos):
+
+- Task-type taxonomy (callers name their tasks)
+- Quality thresholds per task type (callers set their own bars)
+- Choice of baseline (callers wire whichever adapter they trust)
+- When to re-grade (callers decide; this repo just exposes ledger TTL
+  and refresh helpers)
+- Cost accounting for billing or budgets beyond a per-call estimate
+
+## GAAF notes
+
+All additions are Functional-layer per GAAF-2026. Core stays untouched.
+Each new module gets a functional contract doc under
+`contracts/functional/`. Maturity on release: Beta — `infospace-bench`
+is the first known consumer; the API may shift before any second
+consumer (`inter-hub`, `markitect`) adopts it.
+
+## Tasks
+
+The fenced `task` blocks below are the State Hub registration index. Keep them
+in sync with the detailed task tables that follow.
+
+```task
+id: T01
+title: 'QualityObservation dataclass: task_type, adapter_id, model_id, cost_usd, quality_score (0..1), latency_ms, tokens_in, tokens_out, baseline_adapter_id, recorded_at, tags'
+priority: high
+status: done
+state_hub_task_id: "1c285bec-c30b-45a8-a408-3f91d810a078"
+```
+
+```task
+id: T02
+title: 'QualityLedger append-only JSONL store with file-locked writes, configurable path, simple query helpers (by_task_type, recent, mean_quality)'
+priority: high
+status: done
+state_hub_task_id: "5249f171-a047-499f-9ec4-cb50e1477765"
+```
+
+```task
+id: T03
+title: 'TTL helpers: prune_before(timestamp) and is_stale(observation, max_age)'
+priority: medium
+status: done
+state_hub_task_id: "adb255cf-7e89-4fea-b822-6be437d99789"
+```
+
+```task
+id: T04
+title: 'Functional contract doc for the ledger schema and quality_score semantics'
+priority: medium
+status: done
+state_hub_task_id: "51a33180-a99d-4aa4-96be-2fcee15bfbc3"
+```
+
+```task
+id: T05
+title: 'Tests: ledger round-trip, concurrent writes, query helpers, TTL, malformed-line resilience'
+priority: high
+status: done
+state_hub_task_id: "458610c5-c903-4b42-9602-cd511999c9ba"
+```
+
+```task
+id: T06
+title: 'GradingResult dataclass: quality_score, notes, grader_id, baseline_response, candidate_response'
+priority: high
+status: done
+state_hub_task_id: "c12a595b-90fc-4a80-8394-549edbda2031"
+```
+
+```task
+id: T07
+title: 'BaselineGrader protocol plus PairedGrader that runs baseline and candidate calls and delegates to a Judge'
+priority: high
+status: done
+state_hub_task_id: "80b98e31-06fc-4462-b030-a12881095f93"
+```
+
+```task
+id: T08
+title: 'Judge protocol and built-ins: ExactMatchJudge, EmbeddingSimilarityJudge, LLMJudge'
+priority: high
+status: done
+state_hub_task_id: "c2887fe3-bae6-4298-8c26-f9a519264dcf"
+```
+
+```task
+id: T09
+title: 'Functional contract doc covering judge bias caveats'
+priority: medium
+status: done
+state_hub_task_id: "7a4fd87a-b0ba-41b0-8e1a-a60fdaded905"
+```
+
+```task
+id: T10
+title: 'Tests: judges with canned inputs, stable grader result, deterministic LLMJudge rubric seed'
+priority: high
+status: done
+state_hub_task_id: "8415a11d-d508-4d17-8082-10f93e9d16c5"
+```
+
+```task
+id: T11
+title: 'AdaptiveRoutingPolicy extends RoutingPolicy and selects the cheapest adapter whose observed mean quality clears the floor'
+priority: high
+status: done
+state_hub_task_id: "0e9f9f8e-5066-4257-913b-a19f5b3fc47d"
+```
+
+```task
+id: T12
+title: 'Tie-breaking: prefer lower observed cost, then explicit preferred adapter from static rules'
+priority: medium
+status: done
+state_hub_task_id: "59d44712-1088-41ac-bad8-5d95db6f3a4f"
+```
+
+```task
+id: T13
+title: 'Cold-start behaviour falls through to static RoutingPolicy.resolve when observations are missing'
+priority: high
+status: done
+state_hub_task_id: "1927d369-f5f6-48d3-8f53-7e4f1cae370e"
+```
+
+```task
+id: T14
+title: 'Functional contract doc for adaptive policy and sample-size/freshness trade-off'
+priority: medium
+status: done
+state_hub_task_id: "4d4717c1-8849-4fed-8f8d-515901ecafe0"
+```
+
+```task
+id: T15
+title: 'Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain'
+priority: high
+status: done
+state_hub_task_id: "304bd782-db15-4b7a-8d05-49e064a926c3"
+```
+
+```task
+id: T16
+title: 'ShadowingAdapter wraps a candidate adapter, also invokes the baseline adapter, grades, and appends to QualityLedger'
+priority: medium
+status: done
+state_hub_task_id: "62dd507f-536a-4623-8cbd-fa9f78e85ca6"
+```
+
+```task
+id: T17
+title: 'Sampling: caller-configurable shadow_rate so production load is not doubled'
+priority: medium
+status: done
+state_hub_task_id: "ccb73e92-1fca-42f9-8437-9b2b50e6424c"
+```
+
+```task
+id: T18
+title: 'Failure isolation: shadow errors never affect the candidate response returned to the caller'
+priority: high
+status: done
+state_hub_task_id: "b879d232-d6ce-4ff6-b534-616729ea5ad7"
+```
+
+```task
+id: T19
+title: 'Functional contract doc for ShadowingAdapter'
+priority: low
+status: done
+state_hub_task_id: "99d2c1bc-f1d8-42b3-9e04-6eea49460943"
+```
+
+```task
+id: T20
+title: 'Tests: candidate response survives baseline failure, ledger sampling rate, sync vs async modes'
+priority: high
+status: done
+state_hub_task_id: "f533fbf4-484f-4408-8260-7e84e23bdc46"
+```
+
+```task
+id: T21
+title: 'Example script: route fixture batch through three candidate adapters and populate the ledger'
+priority: medium
+status: done
+state_hub_task_id: "7ef0c143-74b0-4740-81fa-819a826cf8f3"
+```
+
+```task
+id: T22
+title: 'Integration test: cold-start, static fallback, first observations, convergence to cheapest qualifying adapter'
+priority: high
+status: done
+state_hub_task_id: "c4c6743f-157b-4445-8576-9caa6421d463"
+```
+
+```task
+id: T23
+title: 'Consumer integration guide showing how infospace-bench wires task types into adaptive policy'
+priority: medium
+status: done
+state_hub_task_id: "3a073ff7-0170-4a95-9c2a-a5daa84964e6"
+```
+
+### T01 — Quality observation data model + ledger
+
+| ID  | Title                                                                                                                                                                                       | Priority | Status |
+|-----|------------------------------------
+---------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
+| T01 | `QualityObservation` dataclass: `task_type`, `adapter_id`, `model_id`, `cost_usd`, `quality_score` (0..1), `latency_ms`, `tokens_in`, `tokens_out`, `baseline_adapter_id`, `recorded_at`, `tags` | high     | done   |
+| T02 | `QualityLedger` append-only JSONL store with file-locked writes, configurable path, simple query helpers (`by_task_type`, `recent`, `mean_quality`)                                          | high     | done   |
+| T03 | TTL helpers: `prune_before(timestamp)` and `is_stale(observation, max_age)` so callers can refresh observations without re-reading the whole ledger                                          | medium   | done   |
+| T04 | Functional contract doc for the ledger schema and the field semantics of `quality_score`                                                                                                    | medium   | done   |
+| T05 | Tests: round-trip, concurrent writes, query helpers, TTL, malformed-line resilience                                                                                                          | high     | done   |
+
+### T02 — Baseline grader
+
+| ID  | Title                                                                                                                                              | Priority | Status |
+|-----|----------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
+| T06 | `GradingResult` dataclass: `quality_score`, `notes`, `grader_id`, `baseline_response`, `candidate_response`                                          | high     | done   |
+| T07 | `BaselineGrader` protocol: `.grade(baseline_adapter, candidate_adapter, prompt, run_config)` → `GradingResult`; built-in concrete `PairedGrader` runs both calls and delegates to a `Judge` | high     | done   |
+| T08 | `Judge` protocol + three built-ins: `ExactMatchJudge`, `EmbeddingSimilarityJudge` (uses an embedding adapter), `LLMJudge` (uses a third adapter with a fixed rubric prompt)                  | high     | done   |
+| T09 | Functional contract doc covering judge bias caveats (length bias, format bias, position bias for `LLMJudge`)                                       | medium   | done   |
+| T10 | Tests: each judge against canned inputs, grader emits stable result with both responses preserved, deterministic seed for `LLMJudge` rubric         | high     | done   |
+
+### T03 — Adaptive routing policy
+
+| ID  | Title                                                                                                                                            | Priority | Status |
+|-----|--------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
+| T11 | `AdaptiveRoutingPolicy` extends `RoutingPolicy`: given `task_type` + `quality_floor` + `ledger`, returns the cheapest adapter whose observed mean quality clears the floor over a configurable window | high     | done   |
+| T12 | Tie-breaking: when two adapters meet the floor, prefer lower observed cost; if still tied, prefer the explicitly-preferred adapter from the underlying static rules | medium   | done   |
+| T13 | Cold-start behaviour: when no observations exist for a `(task_type, adapter)` pair, fall through to the static `RoutingPolicy.resolve` result so the system stays usable on day zero | high     | done   |
+| T14 | Functional contract doc; document the trade-off between sample size and freshness                                                                | medium   | done   |
+| T15 | Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain                                                              | high     | done   |
+
+### T04 — Shadow-mode observation wrapper
+
+| ID  | Title                                                                                                                                                                       | Priority | Status |
+|-----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
+| T16 | `ShadowingAdapter` wraps a candidate adapter; on each call, also invokes the baseline adapter (sync or via a thread pool), grades, and appends to a `QualityLedger`         | medium   | done   |
+| T17 | Sampling: caller-configurable fraction (`shadow_rate=0.1` means grade one call in ten) so production load is not doubled                                                     | medium   | done   |
+| T18 | Failure isolation: shadow errors never affect the candidate response returned to the caller; failures are logged but not raised                                              | high     | done   |
+| T19 | Functional contract doc                                                                                                                                                     | low      | done   |
+| T20 | Tests: candidate response always returned even when baseline raises, ledger gets exactly `shadow_rate × calls` entries (within tolerance), sync vs async modes              | high     | done   |
+
+### T05 — End-to-end example + integration test
+
+| ID  | Title                                                                                                                                                                                 | Priority | Status |
+|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------|
+| T21 | Example script: route a small fixture batch through three candidate adapters (one OpenRouter cheap, one OpenRouter mid, `ClaudeCodeAdapter` as baseline), grade each, populate ledger | medium   | done   |
+| T22 | Integration test with mocked adapters covering: cold-start → static fallback → first observations → adaptive selection converges to the cheapest qualifying adapter                   | high     | done   |
+| T23 | Brief consumer-integration guide in `docs/` showing how `infospace-bench` (or any caller) wires task-type-per-stage into the adaptive policy                                            | medium   | done   |
+
+## Risks and open questions
+
+- **Judge bias.** `LLMJudge` has known biases — length, position, format,
+  self-preference when the judge model is the same family as a
+  candidate. The contract must document these and recommend pairing
+  with at least one non-LLM judge for calibration.
+- **Baseline cost in shadow mode.** `ClaudeCodeAdapter` is not per-call
+  billed (it shells out to a local subscription session), but every
+  shadow call still consumes wall-clock and rate-budget. Sampling is
+  load-bearing, not optional.
+- **Non-stationarity.** Provider model updates, prompt changes, and
+  template edits all silently invalidate prior observations. Plan for
+  a `prompt_fingerprint` tag on observations so the ledger can be
+  filtered to a coherent regime.
+- **Scope creep.** "Pick a model" is one decision; "decide whether the
+  task is worth doing at all" is another. The latter is consumer
+  policy. Keep this workplan firmly on the former.
+- **Privacy.** Observations contain prompt and response text by
+  default. Add a `redact: Callable[[str], str]` hook on the ledger
+  writer so sensitive callers can store hashes / digests instead.
+- **API-vs-CLI baseline parity.** A consumer that grades against
+  `ClaudeCodeAdapter` (CLI) but later switches to a Claude API adapter
+  may see quality drift that's actually transport drift. Document this.
+
+## Exit criteria
+
+- `QualityLedger` round-trips observations and exposes the documented
+  query helpers
+- `BaselineGrader` produces deterministic `GradingResult` objects for at
+  least one non-LLM judge and one LLM judge given canned inputs
+- `AdaptiveRoutingPolicy.resolve(task_type, quality_floor=0.8)` returns
+  the cheapest adapter whose mean quality over the configured window
+  clears the floor, with a documented cold-start fallback
+- `ShadowingAdapter` never alters the candidate response and respects
+  the sampling rate within statistical tolerance
+- End-to-end example produces a ledger with at least three adapters per
+  task type and the integration test shows convergence to the cheapest
+  qualifying adapter
+- Functional contracts published for the new data models, the grader
+  protocols, and the adaptive policy
+
+## Consumer-side follow-up
+
+`infospace-bench` will need a small companion workplan (`IB-WP-NNNN`) to:
+
+- Replace direct `OpenRouterAssistedGenerationAdapter` use with a
+  task-type-tagged route through `AdaptiveRoutingPolicy`
+- Define its task-type taxonomy (`summarize-source`, `extract-entities`,
+  `extract-relations`, `evaluate-entity`, `synthesize-report`)
+- Pick a baseline adapter (most likely `ClaudeCodeAdapter`) and a
+  quality threshold per stage
+- Wire the shadow-mode wrapper for the first multi-chapter run so the
+  ledger fills up while real generation proceeds
+
+That workplan should be drafted after T01–T03 of this workplan land, so
+that the consumer-side wiring is anchored in a stable llm-connect API.

workplans/llm-connect-WP-0005-cost-model-and-problem-class-estimators.md

@@ -0,0 +1,333 @@
+---
+id: LLM-WP-0005
+type: workplan
+title: "Cost Model and Problem-Class Token Estimators"
+domain: agents
+repo: llm-connect
+status: finished
+owner: llm-connect
+planning_priority: high
+planning_order: 5
+created: "2026-05-19"
+updated: "2026-05-19"
+depends_on_workplans:
+  - LLM-WP-0003
+  - LLM-WP-0004
+related_workplans:
+  - IB-WP-0019
+  - IB-WP-0020
+state_hub_workstream_id: "869196c5-551b-4eef-b8d8-cca6f770a9b0"
+---
+
+# LLM-WP-0005 — Cost Model and Problem-Class Token Estimators
+
+**status:** finished
+**owner:** llm-connect
+
+## Purpose
+
+Move two consumer-side concerns into llm-connect as first-class
+primitives:
+
+1. **Model rate registry.** Provider-specific USD-per-1k prompt and
+   completion rates plus capture provenance — a fact about the base
+   model itself, not the application using it.
+2. **Problem-class token estimators.** Generic shapes ("summarise a
+   chunk of N words", "extract entities from M paragraphs", "judge an
+   artifact against K criteria") with a small base-variable surface and
+   a few tunable parameters. Consumers select the class, supply problem
+   dimensions, and get a predicted (prompt_tokens, completion_tokens)
+   estimate before any call goes out. The cost model then converts the
+   estimate into USD using the rate registry.
+
+Today the cost-rate table and a coarse word-count estimator live in
+consumer code (`infospace-bench/src/infospace_bench/model_rates.yaml`
+and `plan_generation_summary` in `generator.py`). The Lefevre live-run
+smoke surfaced two consequences:
+
+- A user supplying `--cost-per-1k 0.30` as a blended rate produced a
+  plan estimate 1000× larger than the actual gpt-4o-mini bill, because
+  the consumer's estimator has no notion of per-model rates and the
+  user has no easy way to pick a right number.
+- The token estimator multiplies word counts by a constant and ignores
+  problem shape — actual prompts ran ~3× larger than the word-count
+  estimate predicted because templates, profile content, and entity
+  context dominate the prompt body, not the chunk text.
+
+Both gaps recur in every llm-connect consumer (infospace-bench today;
+inter-hub, markitect, and future repos tomorrow). Owning the primitives
+here means each consumer wires structure-specific dimensions and
+parameters but never re-implements rates or generic shapes.
+
+## Demand signal
+
+`infospace-bench` is the first concrete consumer. The Lefevre Chapter-I
+smoke (2026-05-18) ran 32 calls / 28k prompt tokens / $0.0088 actual vs
+a planned $8.40 — the 1000× variance is entirely on the consumer side
+of the estimator. `IB-WP-0019` (budget registry) already records the
+shape llm-connect would need to learn from (`output/budget/usage.yaml`
++ the llm-connect `QualityLedger`).
+
+## Architecture sketch (read before writing tasks)
+
+Three new modules in llm-connect:
+
+```
+llm_connect/
+  rates.py            # ModelRate, ModelRateRegistry, default registry
+  costs.py            # CostModel: tokens × rate → USD
+  problem_classes.py  # ProblemClass protocol, built-in classes, registry
+```
+
+### `rates.py`
+
+```python
+@dataclass(frozen=True)
+class ModelRate:
+    model_id: str
+    prompt_per_1k: float
+    completion_per_1k: float
+    currency: str = "USD"
+    source_url: str = ""
+    captured_at: str = ""
+
+class ModelRateRegistry:
+    def get(self, model_id: str) -> ModelRate | None: ...
+    def all(self) -> dict[str, ModelRate]: ...
+    @classmethod
+    def default(cls) -> "ModelRateRegistry": ...
+    @classmethod
+    def from_yaml(cls, path: Path | str) -> "ModelRateRegistry": ...
+    def merged_with(self, override: "ModelRateRegistry") -> "ModelRateRegistry": ...
+```
+
+The default registry ships a small handful of well-known OpenRouter
+models (the same set infospace-bench has today). Consumers can load
+overrides from their own YAML.
+
+### `costs.py`
+
+```python
+@dataclass(frozen=True)
+class CostEstimate:
+    cost_usd: float | None
+    cost_source: str  # "rate_table:<model>" | "unknown" | "override"
+    prompt_cost_usd: float | None = None
+    completion_cost_usd: float | None = None
+
+def estimate_cost(
+    model_id: str,
+    prompt_tokens: int,
+    completion_tokens: int = 0,
+    *,
+    registry: ModelRateRegistry | None = None,
+) -> CostEstimate: ...
+```
+
+Pure function over rate registry and token counts. Useful both for
+preview (plan time) and post-hoc verification (compare adapter-reported
+cost against rate-table estimate).
+
+### `problem_classes.py`
+
+```python
+@dataclass(frozen=True)
+class TokenEstimate:
+    prompt_tokens: int
+    completion_tokens: int
+    confidence: float = 0.5   # 0..1, learned over time
+
+class ProblemClass(Protocol):
+    name: str
+    base_dimensions: tuple[str, ...]   # e.g. ("chunk_words",)
+    tunable_params: tuple[str, ...]    # e.g. ("template_tokens", "completion_ratio")
+
+    def estimate(
+        self,
+        dimensions: dict[str, Any],
+        params: dict[str, Any] | None = None,
+    ) -> TokenEstimate: ...
+```
+
+Built-in classes (one per common workflow shape):
+
+| Class | Dimensions | Tunable params |
+|---|---|---|
+| `chunk-summarization` | `chunk_words`, `template_words` | `completion_ratio` (default 0.25) |
+| `entity-extraction` | `chunk_words`, `template_words`, `expected_entities` | `tokens_per_entity` (default 70) |
+| `relation-extraction` | `chunk_words`, `template_words`, `expected_relations` | `tokens_per_relation` (default 80) |
+| `judge-eval` | `artifact_words`, `template_words`, `n_criteria` | `tokens_per_criterion` (default 35) |
+| `report-synthesis` | `n_chunks`, `n_entities`, `n_relations`, `template_words` | `base_completion_tokens` (default 400) |
+
+Each class registers under a name; a `ProblemClassRegistry` keeps the
+mapping. Consumers reference classes by name; advanced consumers can
+register their own.
+
+### Defaults and learning
+
+Each built-in class ships seed parameters chosen from common practice
+(roughly aligned with what infospace-bench observed on the Lefevre
+smoke: ~1.0 prompt-words-per-chunk-word, ~0.2 completion ratio, ~70
+tokens per emitted entity). Two adaptation hooks:
+
+- `ProblemClass.fit(observations: list[Observation])` adjusts params to
+  best fit observed (dimensions → actual_tokens) pairs.
+- A small CLI `llm-connect rates show` / `llm-connect classes show`
+  inspects the current registry and learned parameters.
+
+`Observation` reuses the `QualityLedger` row shape so existing infra
+keeps working (LLM-WP-0004 owns the ledger).
+
+## Tasks
+
+```task
+id: T01
+title: 'ModelRate + ModelRateRegistry data model, YAML loader, default-registry seed of nine OpenRouter models'
+priority: high
+status: done
+state_hub_task_id: "535d3f12-911e-4b6a-87c3-b539c5986671"
+```
+
+```task
+id: T02
+title: 'CostModel.estimate_cost() pure function; tests for known model, unknown model, registry override, zero-token edge'
+priority: high
+status: done
+state_hub_task_id: "691dd985-6a97-432d-8bf0-6cb99a9fbdcc"
+```
+
+```task
+id: T03
+title: 'ProblemClass protocol + TokenEstimate + ProblemClassRegistry'
+priority: high
+status: done
+state_hub_task_id: "ecf263d2-f40a-460e-9195-4e01135ef727"
+```
+
+```task
+id: T04
+title: 'Built-in classes: chunk-summarization, entity-extraction, relation-extraction, judge-eval, report-synthesis'
+priority: high
+status: done
+state_hub_task_id: "f1860b10-7467-4ce3-9775-ab293cef3ed0"
+```
+
+```task
+id: T05
+title: 'ProblemClass.fit() adapts tunable params from QualityLedger observations'
+priority: medium
+status: done
+state_hub_task_id: "950b74e9-ede8-477a-b6b7-c7af423d4ebb"
+```
+
+```task
+id: T06
+title: 'CLI helpers: llm-connect rates show, llm-connect classes show, llm-connect classes fit <ledger>'
+priority: medium
+status: done
+state_hub_task_id: "c47eca5f-4cb3-4f88-ac1b-38a9ae18e7e6"
+```
+
+```task
+id: T07
+title: 'Functional contract docs under contracts/functional/ for rates, costs, and problem classes'
+priority: medium
+status: done
+state_hub_task_id: "c15fd1dc-48c3-40e9-abca-ba3ffe3684f9"
+```
+
+```task
+id: T08
+title: 'Consumer migration note for infospace-bench: replace plan_generation_summary cost+token math with llm-connect calls'
+priority: medium
+status: done
+state_hub_task_id: "2993932a-334c-49f9-bb74-6ef4d3cbffcb"
+```
+
+## Scope guardrails
+
+In scope:
+
+- Data models, the registries, the built-in classes, and the
+  fit-from-observations helper.
+- A default rate registry seeded with publicly known OpenRouter list
+  prices (refresh policy is on the consumer).
+- CLI helpers for inspection.
+
+Out of scope:
+
+- Per-call billing or accounting infrastructure (consumer's job — see
+  `IB-WP-0019` for infospace-bench's per-infospace budget log).
+- Provider-specific tokenisers (we already use a coarse
+  chars-per-token estimator; tokeniser parity is a separate piece of
+  work that should land before this one's accuracy bar tightens).
+- A rates *update* mechanism. The registry is a static snapshot;
+  refreshing rates is a consumer chore (or a separate CronCreate-fed
+  workflow against provider price pages, deliberately out of scope
+  here).
+
+## Acceptance
+
+- `ModelRateRegistry.default()` returns a registry with at least the
+  nine OpenRouter models infospace-bench bundles today, each carrying
+  a `captured_at` timestamp.
+- `estimate_cost("openai/gpt-4o-mini", 28000, 7500)` returns a
+  ``CostEstimate(cost_usd≈0.009, cost_source="rate_table:openai/gpt-4o-mini")``
+  matching the Lefevre Chapter-I smoke within 20%.
+- Each built-in `ProblemClass.estimate(dimensions, params)` produces a
+  `TokenEstimate` and its `fit()` recovers seeded params from a small
+  synthetic observation set within 10%.
+- The consumer guide in the workplan's notes shows what
+  `infospace-bench plan_generation_summary` would look like once it
+  delegates to these primitives.
+
+## Risks and open questions
+
+- **Rate drift.** OpenRouter / Anthropic / OpenAI prices change. Each
+  rate has a `captured_at`; consumers must decide their freshness
+  policy. The default registry's date is the source of truth; a stale
+  registry will under- or over-estimate cost but the structure is
+  unchanged.
+- **Class taxonomy lock-in.** The built-in class names will appear in
+  consumer code and ledger tags. Bump a `schema_version` on the
+  ProblemClassRegistry from day one; breaking changes need migration.
+- **Tokeniser parity.** Current consumer estimators use a coarse
+  chars-per-token heuristic; the real tokenisers diverge by ±20%
+  across providers. This workplan accepts that for v1; tightening the
+  per-class accuracy bar belongs to a follow-on workplan once usage
+  observations expose the gap.
+- **Fitting on small samples.** Adaptation needs enough observations
+  to be statistically meaningful. The fit function should refuse (or
+  fall back to seeds) when sample sizes are below a configurable
+  threshold.
+
+## Downstream effects
+
+- `infospace-bench` `plan_generation_summary` becomes a thin caller
+  over llm-connect's `estimate_cost` and the relevant `ProblemClass`.
+  The follow-on consumer workplan (`INFOSPACE-WP-NNNN`) describes
+  exactly which class maps to each workflow stage.
+- `IB-WP-0019`'s budget registry stops needing its own
+  `src/infospace_bench/model_rates.yaml`; the workspace override path
+  stays but the bundled default lives upstream.
+- `IB-WP-0020`'s routing CLI can ask the model rate registry whether a
+  `--routing-config` cost cap is realistic for the candidate set —
+  enables defensible `--cost-cap` defaults later.
+
+## Consumer-side follow-up
+
+Once T01-T04 land in llm-connect, `infospace-bench` opens a thin
+companion workplan to:
+
+- Replace `_CALLS_PER_CHUNK_BY_WORKFLOW` + `_profile_template_words` +
+  `WORDS_PER_TOKEN_DEFAULT` math in `plan_generation_summary` with
+  problem-class lookups.
+- Map each workflow stage to a problem class
+  (`summarize-source` → `chunk-summarization`, etc.) and surface the
+  mapping in `docs/routing-task-types.md`.
+- Drop `src/infospace_bench/model_rates.yaml` and read from
+  `ModelRateRegistry.default()`; keep the workspace override path
+  pointed at the same registry.
+- Carry forward the existing `--cost-per-1k` override flag (with
+  documented semantics: blended single-rate override that wins over
+  rate-table lookup when set).