The adapter compared self._model to _DEFAULT_MODEL ("anthropic/claude-sonnet-4")
to decide whether to honour the constructor's model. When a caller passes
that exact value via --model, the comparison treats it as "not specified"
and falls through to RunConfig.model_name, which defaults to "gpt-4". So
every llm-connect call started with --provider openrouter --model
anthropic/claude-sonnet-4 actually landed on OpenAI's gpt-4 — and on
gpt-4 OpenAI's structured-output response_format requires a model with
schema support that gpt-4 lacks, returning 400. The CUST-WP-0045 canary
hit this for hours; the smoke probes that worked were the ones with no
json_schema, where gpt-4 returned fine.
Track _explicit_model separately so a constructor or LLMConfig that
matches the default is still treated as a real intent.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The previous strict=True default rejected the activity-core daily-triage
schema (and most real-world application schemas) because OpenAI strict
mode requires additionalProperties:false on every object and every
property in the required list. Application-supplied schemas typically
do not meet that bar — adding additionalProperties recursively at the
adapter would be surprising and may break callers that rely on extra
fields. Flipping strict to False keeps the schema as a soft constraint;
the model still produces structured output and the activity-core
canary's 400 from OpenRouter goes away.
Callers who need strict enforcement can pass response_format directly
via model_params, where the adapter's pass-through handling preserves
the strict flag they set.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The adapter previously did a blind payload.update(config.model_params).
For callers like activity-core that pass reasoning_effort, max_depth,
and json_schema (Claude / llm-connect-specific fields), those leaked
into the OpenAI Chat Completions request body and OpenRouter rejected
the whole call with HTTP 400. CUST-WP-0045 canary on 2026-06-02 hit
this — manual repro confirmed: same prompt with no model_params returns
a clean 10-recommendation WSJF report in 4.5s; with model_params
included, every call 400s.
Replace the merge with a whitelist + translation step:
- pass-through known OpenAI Chat Completions fields (top_p, stop, seed,
tools, response_format, etc.)
- translate json_schema into the proper response_format wrapper
({type:"json_schema", json_schema:{name,schema,strict}})
- drop documented non-OpenAI fields (reasoning_effort, max_depth) so
the payload stays valid
- silently drop unknown keys rather than risk another 400
The same pattern will need to apply to the OpenAI and Gemini adapters
when their callers start passing provider-specific keys — left as
follow-up rather than speculative refactoring.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Remove redundant async_execute_prompt overrides from OpenAI/Gemini/OpenRouter
adapters (identical to base class default — asyncio import also removed)
- Cache prompt.split() result in MockLLMAdapter to avoid double evaluation
- Promote deferred LLMBudgetExceededError imports to module level in
models.py and adapter.py (no circular dependency)
- Auto-populate context dict in LLMBudgetExceededError.__init__ so callers
need not pass redundant context= kwarg
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Copy markitect.llm module into standalone llm_connect package.
All markitect.* imports replaced with llm_connect.* equivalents.
LLMError base class inlined (no markitect.exceptions dependency).
Verified: from llm_connect import create_adapter works.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
