GoKawiilPrime Agent: A self-improving RLM agent
Today, we are launching Prime Agent, our self-improving coding harness designed around two abstractions, the Recursive Language Model (RLM) [citation] and Continual Harness [citation]. 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. Static, hand-engineered sub-agents, prompts, skills, and memory are set once at design time and never adapt to what the agent learns while running. We believe that harnesses should instead extrapolate on current model capabilities toward the next frontier of reasoning patterns.
Prime Agent is built around this principle through two main abstractions:
The Recursive Language Model (RLM) treats context as a variable and subagent delegation as function calls inside a REPL. The persistent REPL gives the model programmatic access to its history, sub-agents, and tools, allowing it to write language model programs as actions over its own context. This design allows the agent to process arbitrarily long sessions without losing access to its own past information stored in variables. Continual Harness treats the harness's own state, abstracted as its prompts, skills, memory, and sub-agents, as something the agent can create, read, update, and delete (CRUD) from its own trajectory. When combined with agent-to-agent communication, this mechanism enables orchestration across sub-agents and even across Prime Agent sessions. For example, Prime Agent can spawn persistent sub-agents, message them later in the trajectory, and communicate directly with a different Prime Agent session.
These abstractions are powerful for bootstrapping model capabilities. Prime Agent is built to be effective as a general coding assistant, as a default runtime for long-horizon autonomous evaluation, and as a collaborator for research and autoresearch.
Prime Agent is fully open-source, and can be installed via:
curl -fsSL https://app.primeintellect.ai/prime-agent/install.sh | sh
Prime Agent
The performance of agent harnesses are tied to both the design of the harness and the capability of the model trained around the harness. We designed Prime Agent to be immediately usable with modern open and closed frontier models, while also providing a feature set that we expect to provide further performance gains as newer generations of models are trained around it.
At its core, Prime Agent is designed around programmatic tool and sub-agent calling. Models in Prime Agent use a persistent IPython kernel as their only tool. Other standard harness features are called as functions in the kernel, including sub-agents, which are each implemented as another prime-agent instance.
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