agent-lightning

I'm just exhausted. So I've today now learned about four new harness: https://github.com/exoharness/exo/ https://github.com/laude-institute/headlong https://github.com/microsoft/agent-lightning and now https://github.com/PrimeIntellect-ai/prime-agent Of course the don't have exactly the same scopes but they are in general all about persistent memory and / or continous agent loops. Like I miss those times where only once a week a new js framework was promoted.

I trained the agent itself to write SQL, run it, check results, then rewrite until correct. The write and rewrite policies are optimized with RL, using a client–server setup in Agent Lightning and a LangGraph state machine. On a 500-sample Spider eval subset, Qwen2.5-Coder-3B with 4096 context reaches 80.4% at three turns of write and rewrite, 80.2% at one turn. After training, model Qwen2.5-Coder-1.5B can be even better than Qwen2.5-Coder-3B (untrained). I have compared multiple models and settings, hoping to shed light on tuning AI agents. Full article: https://medium.com/@yugez/training-ai-agents-to-write-and-se... Related projects: - Agent Lightning as the glue: https://github.com/microsoft/agent-lightning - verl for RL algorithms: https://github.com/volcengine/verl - vLLM for efficient rollouts: https://github.com/vllm-project/vllm -