Turning local agents into self-optimizing agents

I was experimenting with a self-optimizing agentic pipeline to climb the benchmark leaderboard (TerminalBench).

On a 10-task subset, I got the performance to rise from ~30% → ~90%.

That loop worked, so I asked: can the same reflect-and-rewrite step run continuously against everyday chats instead of a benchmark?

How it works

• Every chat with your local LLM goes through a small proxy and is logged.
• autoswarm reflect has the same local model review those logs, distill concrete lessons, and write them to skills.yaml.
• Lessons auto-inject into the system prompt of future chats.

Run it (LM Studio path)

1. Start LM Studio's local server and load a model.
2. ```bash pip install -e . autoswarm doctor # verifies LM Studio is reachable autoswarm start # auto-detects upstream + model, listens on :8080

I'm genuinely fascinated by the idea of self-optimizing agents, and I believe there's something bigger to uncover there.

That said, this is just a hobby project and I'm still experimenting with it. Would love your feedback!

Link: https://github.com/arteemg/autoswarm

I'm actively working on the project, so please ⭐ the repo to stay updated.