Prime Agent: A self-improving RLM agent<br>LoginStart training

h1:first-child+*]:mt-0 [&>h1:first-child]:hidden">Prime 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.

Prime Agent's Architecture

RLM and Continual Harness are the two core abstractions; sub-agent CRUD plus Agent2Agent messaging compose them into orchestration.<br>Background Daemon and Agents View. The default view is a text-user interface (TUI) similar to other coding agent harnesses. By default, IPython actions made by the agent are condensed for brevity, but can be expanded to view actions made by the harness. Sub-agents launched in the REPL can also be accessed below the user chatbox.

Prime Agent runs a background daemon that owns all live agent sessions over a local socket. You can attach and detach from the session without affecting the underlying agent loop. Each root session tree runs in a recoverable worker process; if a worker crashes, the daemon recovers it from the session JSONL and kernel state snapshot.

The Agents View allows you to see and select other live sessions from the daemon. It can be opened by pressing the Left Arrow key (←) on an empty prompt, and lists sessions that are currently running, idle sessions with the daemon still active, and inactive sessions that are currently not loaded in memory. Any of these chats can immediately be entered and interacted with, and pressing space allows users to chat with a session in any state, including steering and queuing of prompts and commands such as /compact.

The Agents View is constructed as the central connecting point between agents and subagents, recursively. Any agent is discoverable in an Agents View. Users navigate from an Agents View into an agent's chat, then into the Agents View of its subagents, into a subagent chat, and so on.

Because subagents share the same Running-Idle-Inactive state machine as the root agents, they can be removed from memory after 30 minutes of inactivity, and the moment a user or agent addresses any of them, they are reloaded from disk. In highly nested chats, this can save a lot of memory.

Session and Context Management. The entire session history of the agent is stored as...