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>
