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How to let your own LLM pick and refine engine parameters with ReadyLLM

Connect an LLM API to AI Agent tuning so it reasons out parameters and improves them over several rounds.

Short answer: Run cd backend then pip install -r requirements.txt, then uvicorn app.main:app --host 127.0.0.1 --port 8000; then in a second terminal, run cd frontend then npm install && npm run dev, and open http://localhost:3000; on the Tuning page, enter your LLM API URL, key, and model name, then save them. All steps.

Credentials. Your LLM API key is sent to the API you configure, so use a key you can rotate and keep it out of shared screenshots.

Who it is forAn engineer who already has an LLM API and wants a tuning pass that uses reasoning instead of a fixed search.

The problemManual parameter guessing is slow, and a fixed search may miss settings that suit a particular model and scenario.

The outcomeParameter suggestions from your LLM, refined over multiple rounds with benchmark results fed back each time.

What you need first

  • Python 3.9+ and Node.js 18+ on the control machine
  • A target machine with llama.cpp, vLLM, or ComfyUI already installed, or installable through the tool
  • Network or SSH access to any remote target you want to manage

Steps

  1. Run cd backend then pip install -r requirements.txt, then uvicorn app.main:app --host 127.0.0.1 --port 8000
  2. In a second terminal, run cd frontend then npm install && npm run dev, and open http://localhost:3000
  3. On the Tuning page, enter your LLM API URL, key, and model name, then save them
  4. Choose AI Agent tuning for the target and model, and start the iterative run
  5. Review the benchmark history across rounds and apply the best parameters on Deploy

Prompt for your agent

Using the repo htobty/ReadyLLM, start the backend and frontend, configure the AI tuning API with the URL <api-url> and model name <model-name>, and run AI Agent tuning on target <target-name>. Report the best round's parameters and its benchmark result, and confirm the API key is masked when the settings are read back.

Caveat. AI tuning quality depends on the LLM API you supply, and the README does not name which models it works best with.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for htobty/ReadyLLM and "Let your own LLM pick and refine engine parameters"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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