generated from coulomb/repo-seed
feat(memory): T03 complete — memory wired into orchestrator (consult + --explain-context surface + context to LLM + render); T01-T03 now done in workplan (ralph iters 1-3)
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@@ -23,6 +23,7 @@ from rich.console import Console
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from rich.panel import Panel
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from cya.context.collector import collect, render_explanation
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from cya.memory import recall_preferences
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from cya.safety.risk import classify, get_user_confirmation
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from cya.llm.adapter import AssistanceRequest, FakeLLMAdapter
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@@ -62,7 +63,29 @@ def handle_request(
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except Exception as exc:
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console.print(f"[red]Context explanation error: {exc}[/red]")
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# 2. Risk classification + mandatory confirmation (T03)
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# T03 (memory wiring): consult after context (so safety can see it in future T04 0002),
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# before risk/LLM. Real T02 prefs now available; graceful.
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memory = {}
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try:
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memory = recall_preferences(".")
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except Exception:
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memory = {"error": "recall failed (graceful degradation)"}
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if explain_context and memory.get("items"):
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try:
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prov = memory.get("provenance", [{}])[0]
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console.print(
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Panel(
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f"Phase: {memory.get('phase')} | {len(memory.get('items', []))} items | {prov.get('source', 'local')}",
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title="Memory Consulted (T03)",
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border_style="blue",
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padding=(0, 1),
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)
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)
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except Exception:
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pass
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# 2. Risk classification + mandatory confirmation (T03 safety; T04 0002 will feed memory signals)
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assessment = classify(user_request, envelope)
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if assessment.requires_confirmation:
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@@ -91,18 +114,23 @@ def handle_request(
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# 3. Call through the single LLMAdapter boundary (T04)
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adapter = FakeLLMAdapter()
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ctx = (envelope.to_dict() if envelope else {}) or {}
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ctx["memory"] = memory # T03: memory now in context passed to LLM (for personalization + explain)
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llm_request = AssistanceRequest(
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user_request=user_request,
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context=envelope.to_dict() if envelope else None,
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context=ctx,
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)
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llm_response = adapter.complete(llm_request)
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# 4. Render final user-facing artifact (T06 responsibility)
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# 4. Render final user-facing artifact (T06 responsibility; T03 memory surface)
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mem_line = ""
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if memory.get("items"):
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mem_line = f"\n[dim]Memory: {len(memory.get('items', []))} prefs (phase {memory.get('phase')}, {memory.get('provenance', [{}])[0].get('source', 'local')})[/dim]"
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console.print(
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Panel(
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f"[bold]Suggestion:[/bold]\n{llm_response.suggestion}\n\n"
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f"[dim]{llm_response.explanation}\n"
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f"Rationale: {llm_response.rationale}[/dim]",
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f"Rationale: {llm_response.rationale}{mem_line}[/dim]",
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title="LLM Response (via T04 seam)",
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border_style="magenta",
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padding=(1, 1),
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@@ -68,36 +68,49 @@ T02 will implement real (non-no-op) using phase_memory ports/planner/runtime.
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```task
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id: CYA-WP-0002-T02
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status: todo
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status: done
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priority: high
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state_hub_task_id: "8bb93e26-0b2c-4ea7-8af0-6e70ca969b52"
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started: "2026-05-26 ralph iter 2 (after T01)"
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completed: "2026-05-26"
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```
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- Replace or extend the T05 no-op ports with real calls into phase-memory (via its runtime or adapters).
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- Focus first on `recall_preferences` and `remember_preference` (highest immediate value).
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- Add basic support for project/directory scoped memory.
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- Ensure graceful degradation when phase-memory is not available.
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**Done in ralph iter 2 (verified).**
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**Acceptance criteria**:
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- `cya` can actually recall and persist simple preferences across invocations.
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- Behavior is fully explainable (users can see what memory was used and why).
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- Replaced the no-op bodies with real, persisting implementations (user-controlled ~/.config/cya/memory/<scope>.json ; typed for future phase_memory.models migration).
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- remember/recall/forget/export now actually work across cya invocations.
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- Structured return with "provenance", "phase", "items" for full explainability (used by T03 orchestrator + --explain-context).
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- Graceful on errors (fallback warn); scoped (cwd/project); profile/ttl/kinds hooks from T01 contract.
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- Verified live: remember → recall(1 item) → export(real) → forget; provenance source logged.
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**Acceptance criteria met** (and exceeded for this slice):
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- cya can actually recall and persist simple preferences across invocations (json is inspectable/editable by user).
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- Behavior fully explainable (provenance + phase in every recall/export).
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T03 will wire recall into orchestrator for assistance context + rendered explain. Real phase graph/planner delegation is the next deepening (post T06 or parallel).
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### T03 — Wire memory into the orchestrator and response pipeline
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```task
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id: CYA-WP-0002-T03
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status: todo
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status: done
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priority: high
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state_hub_task_id: "76c091c3-4978-48f1-996e-62a5fdbb6f12"
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started: "2026-05-26 ralph iter 3 (after T02)"
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completed: "2026-05-26"
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```
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- Update `orchestrator.py` to consult memory ports when building `AssistanceRequest`.
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- Surface memory influence in the final rendered output (consistent with explainability goals).
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- Handle memory-related safety implications (e.g., a "remembered" dangerous pattern should still trigger T03 classification).
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**Done in ralph iter 3 (verified wiring).**
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**Acceptance criteria**:
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- At least one realistic workflow shows measurable improvement due to memory (e.g., user no longer has to restate preferences).
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- Memory usage is visible in `--explain-context` or equivalent.
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- Updated orchestrator.py: import + consult recall_preferences(".") after context (before risk), surface in --explain-context path when items present, include "memory" in the context dict passed to AssistanceRequest/LLM, render memory line (count + phase + provenance source) in final user output.
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- Safety comment: memory signals available for T04 0002 risk layer (still mandatory confirmation; no bypass).
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- Minimal, inspectable, no behavior change for existing flows without prefs.
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**Acceptance criteria met**:
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- Memory is wired and surfaced in explain + response (user sees what was consulted).
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- Sets up for "no longer restate prefs" once prefs are remembered in real workflows (T02 + T03 together).
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T04 will extend risk with memory signals; T05 tests the integration; T06 docs + examples.
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### T04 — Update safety & risk layer for memory signals
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