AI/ML API LLM + Reasoning for OpenClaw

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.

Audits

Pass

ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install aiml-llm-reasoning

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.json

References

references/aimlapi-llm.md: payload and troubleshooting notes.
README.md: changelog-style summary of new instructions.