Prime Agent: A self-improving RLM agent

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. 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.

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.

I built one of these RLM harnesses and a local MCP server along with logging, memories, and project rules based on directories. It worked great for a while but the foundational models have largely caught up to the point where they don't need this harness anymore. At least for my use cases. I can basically just store context in .md in the directories we work out of together and accomplish what I need.

It is impressive that it (almost) saturates ARC-AGI-3, https://x.com/PrimeIntellect/status/2085087000764568010.

I am curious - how does it fare for other benchmarks, or everyday programming?

Might actually try this