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Prime Agent: A Self-Improving RLM Agent

Prime Intellect released Prime Agent, an open-source coding and research agent designed for long-running tasks, featuring a persistent IPython environment, recursive subagents, and a self-improving harness that refines supplemental state without altering the base system prompt. The agent supports background sessions, direct inter-agent communication, and executable skills, and is installable on macOS and Linux via a curl command.

read4 min views2 publishedAug 10, 2026
Prime Agent: A Self-Improving RLM Agent
Image: source

Documentation β€’ Verifiers β€’ PRIME-RL β€’ pi-mono

Prime Agent is an open-source coding and research agent for general and long-running work. It is designed around two core abstractions:

  • The treats context as variables (Recursive Language Model (RLM)prompt-as-a-variable) and tools like recursive subagents as function calls (programmatic tool /sub-agent calling) inside a persistent REPL. - The stores supplemental prompts, memories, skill descriptions, and reusable subagent specifications as durable state that Prime Agent can refine through small, evidence-backed updates, local to the session by default.Continual Harness

Prime Agent combines a persistent Python control environment with durable harness state, so useful working context and reusable operating patterns can outlive a single chat window.

Everything is programmatic: persistent IPython is the built-in model tool; file operations, shell commands, tool use, subagents, and context management happen through code.Subagents are built in:rlm(...)

spawns real child agents for parallel or background work and returns their results programmatically.The harness can improve:/refine

reviews the current trajectory and can apply small, evidence-backed updates to supplemental harness state. It never rewrites the immutable base system prompt, and recorded snapshots support rollback.Skills are executable: skills are importable Python packages, and the built-in skill creator can turn recurring workflows into project or personal skills.Sessions run in the background: daemon-backed agents keep running when the terminal disconnects and can be reattached later.Agents communicate directly: running agents can exchange messages and orchestrate one another without routing everything through the user.Long tasks keep moving: automatic compaction, persistent goals, heartbeats, schedules, autonomous mode, and retained subagents preserve progress across turns and terminal sessions.

Install the latest stable release on macOS or Linux:

curl -fsSL https://app.primeintellect.ai/prime-agent/install.sh | sh

The installer downloads a versioned release, verifies its SHA-256 checksum, installs the prime-agent

command, and can prepare the IPython runtime used by the agent.

Start Prime Agent from the repository or directory you want it to work in:

cd /path/to/project
prime-agent

On first launch, run /login

to choose a subscription or API-key provider. Prime Agent works in the current directory and can run commands and modify files there. Use a disposable clone, clean worktree, or another checkpoint you can inspect and restore.

Warning

Prime Agent executes model-generated Python and project commands with your user permissions. Its worker and kernel processes improve lifecycle isolation and recovery; they are not a security sandbox. Review changes and use trusted repositories, instructions, skills, and extensions only. Run untrusted code or instructions in an external sandbox or restricted environment.

Useful commands:

prime-agent agents                   # Browse running, idle, and saved sessions
prime-agent attach <agent>           # Reattach to a running session
prime-agent --resume <path|id>       # Resume a saved session
prime-agent status                   # Inspect background service state
prime-agent doctor [--fix]           # Inspect or repair background services
prime-agent update [--force]         # Update Prime Agent
prime-agent shutdown [--force]       # Stop every agent, worker, and background service

Prime Agent is built for long-running work, especially for evaluations in research. These features are available in the TUI, and when run autonomously.

Continual Harness:/refine

can persist focused, reviewable lessons as supplemental prompts, memories, reusable skill descriptions, or subagent specifications, with recorded refinement history. It does not replace packaging and reviewing new executable skills.Direct agent-to-agent communication: running agents and retained subagents can discover one another, exchange messages, and steer active work.Daemon-backed continuity: active sessions, IPython state, schedules, and subagents keep running when the terminal detaches and can be reattached later.Heartbeats and schedules:/heartbeat

,rlm_heartbeat

, andprime-agent schedule

can re-enter a session periodically or at a specific time.Persistent goals:/goal

keeps an objective and its progress active across turns until it is completed, d, or cleared.Bounded autonomous mode:/autonomous

continues within configured turn, token, and time budgets and can run user-defined quality gates. A passed gate checks only what that gate verifies; reaching a limit does not imply task success.

Quickstartβ€” install, authenticate, and run a first sessionUsage and CLI referenceβ€” commands, sessions, autonomous limits, and output modesLong-running and background agentsβ€” detach and reattach, goals, heartbeats, and schedulesRLM programming modelβ€” persistent IPython, subagents, skills, and the trust modelJSON modeandRPC modeβ€” headless automation and integrationsSkillsβ€” install and create reusable capabilitiesProvider setupβ€” subscription and API-key providersArchitecture overviewβ€” daemon, worker, kernel, and persistence boundariesDevelopmentβ€” build and run from source

Our agent and TUI is built on top of pi. We thank the authors of

pi

for their valuable work.Prime Agent is fully open source and released under the MIT License.

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