Prime Agent: The Self-Improving AI Coding Assistant
Prime Agent: A self-improving RLM agent
Prime Intellect launches Prime Agent, an open-source coding harness that redefines AI agent architecture. It uses a persistent IPython kernel as the sole tool, enabling recursive sub-agent calls and a self-improving harness that can modify its own prompts, skills, and memory. The system supports multi-agent communication, autonomous operation, and is designed to scale with future model capabilities.
Modern harness designs were built around the capabilities of earlier generations of models, and they do not reflect what frontier models can do today: fixed tool-calling schemas and context compaction force the model to work around its own scaffolding instead of leveraging it.
- embedding-shape
LLM-generated code that seemingly went without much review or design is always such an interesting dive into just how bloated you can make code. In this repository, multiple files are close to 10K LOC, one file contains a switch statement that has so many case statements it spans more than 1000 lines, and lots of other fun stuff.
I guess it depends on the model you're trying to use, but seems most of them prefer smaller codebases, they work a lot better with less code, which kind of makes sense. With that in mind, I'd probably aim for something way smaller to bootstrap a self-improving agent. Then I'd use this "Prime Agent" as an example to my self-improving agent for what it should not evolve to.
- supermdguy
It'll be really interesting when they run RL training on the harness self-improvement loop. I've tried using LLMs for harness engineering, but it often creates too much bloat that weighs things down in the end. Guessing it's just not something the models are tuned to do by default.
Curious if anyone's tried using RL for harness engineering? I think we're still pretty far away from the optimal harness, especially when it comes to long-context memory management.
- axus
- andai
A write up on RLM (Recursive Language Models) by one of the authors of the RLM paper:
- sexyketchup777
As models get stronger, huge harnesses may become less useful. An overly opinionated harness could even constrain the model’s reasoning instead of improving it.