Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.
Prime Agent is a general-purpose coding harness On ARC-AGI-3, it scores 95.5%, surpassing the human-expert baseline, but the gain is not benchmark-specific. We see major improvements across models when compared to their proprietary harnesses:
Prime Agent combines three ideas: 1. Recursive Language Models-native programmatic tool calling 2. Persistent multi-agent orchestration 3. A self-improving Continual Harness Together, they let the model act on its own context and harness
The RLM treats context as a variable. A persistent IPython kernel is the model's only tool. It can program over history, call tools, launch sub-agents, and keep useful state outside the active context. Long sessions become a programming problem.
We recommend using Prime Agent for both day-to-day work and standalone evals. We find strong downstream performance gains for open and closed frontier models using Prime Agent on standard long-context and long-horizon tasks, despite modern models not being trained around Prime Agent.
On EmulatorBench, it built SEGA Genesis and Game Boy Color emulators from scratch in Rust, reproducing target hardware behavior against diagnostic tests.
Prime Agent was designed as a coding agent, but also can be used for general agentic tasks in any domain. Prime Agent can autonomously run on challenging long-horizon settings like Factorio and MazeBench and yield strong results relative to existing harnesses.
Prime Agent is built on pi and fully open-source with an open license github.com/PrimeIntellect-ai/prime-agent
Install prime-agent curl -fsSL app.primeintellect.ai/prime-agent/install.sh | sh
