- name
- aimlapi-llm-reasoning
- description
- Run AIMLAPI LLM and reasoning workflows through chat completions with retries, structured outputs, and explicit User-Agent headers. Use when Codex needs scripted prompting/reasoning calls against AIMLAPI models.
- env
- primaryEnv
- AIMLAPI_API_KEY
AIMLAPI LLM + Reasoning
Overview
Use run_chat.py to call AIMLAPI chat completions with retries, optional API key file fallback, and a User-Agent header on every request.
Quick start
export AIMLAPI_API_KEY="sk-aimlapi-..."
python3 {baseDir}/scripts/run_chat.py --model aimlapi/openai/gpt-5-nano-2025-08-07 --user "Summarize this in 3 bullets."Tasks
Run a basic chat completion
python3 {baseDir}/scripts/run_chat.py \
--model aimlapi/openai/gpt-5-nano-2025-08-07 \
--system "You are a concise assistant." \
--user "Draft a project kickoff checklist." \
--user-agent "openclaw-custom/1.0"Add reasoning parameters
python3 {baseDir}/scripts/run_chat.py \
--model aimlapi/openai/gpt-5-nano-2025-08-07 \
--user "Plan a 5-step rollout for a new chatbot feature." \
--extra-json '{"reasoning": {"effort": "medium"}, "temperature": 0.3}'Structured JSON output
python3 {baseDir}/scripts/run_chat.py \
--model aimlapi/openai/gpt-5-nano-2025-08-07 \
--user "Return a JSON array of 3 project risks with mitigation." \
--extra-json '{"response_format": {"type": "json_object"}}' \
--output ./out/risks.jsonReferences
references/aimlapi-llm.md: payload and troubleshooting notes.README.md: changelog-style summary of new instructions.