prime-agent

 A self-improving RLM agent for coding workflows and long-running autonomous tasks.

 

Prime Agent: A Self-Improving RLM Agent

Documentation • Verifiers • PRIME-RL • 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 Recursive Language Model (RLM) treats context as variables (prompt-as-a-variable) and tools like recursive subagents as function calls (programmatic tool /sub-agent calling) inside a persistent REPL.
• The Continual Harness 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.

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.

Getting Started

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

Built for Long-Running Work

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, and prime-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, paused, 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.

Documentation

• Quickstart — install, authenticate, and run a first session
• Usage and CLI reference — commands, sessions, autonomous limits, and output modes
• Long-running and background agents — detach and reattach, goals, heartbeats, and schedules
• RLM programming model — persistent IPython, subagents, skills, and the trust model
• JSON mode and RPC mode — headless automation and integrations
• Skills — install and create reusable capabilities
• Provider setup — subscription and API-key providers
• Architecture overview — daemon, worker, kernel, and persistence boundaries
• Development — build and run from source

Acknowledgements

Our agent and TUI is built on top of pi. We thank the authors of pi for their valuable work.

from  https://github.com/PrimeIntellect-ai/prime-agent