Prime Agent: A Self-Improving RLM Harness

Documentation • Verifiers • PRIME-RL

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.

Install

Install the latest stable version with the one-command installer:

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

On Windows, install from PowerShell:

irm https://app.primeintellect.ai/prime-agent/install.ps1 | iex

The Windows installer downloads the latest stable Windows x64 build and verifies its checksum before installation.

Why Prime Agent

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: a persistent Python REPL is the built-in model tool; file operations, shell commands, tool use, subagents, and context management happen through code.
• Subagents are built in: rlm.spawn(...) 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.

Slack

Connect Slack from /mcp or run /mcp login slack inside Prime Agent to search your workspace and use Slack tools. See the Slack connector guide for OAuth setup and troubleshooting.

Google Drive and Google Sheets

Connect Google's official Drive and Sheets MCP servers with your own Google OAuth client: add them to mcpServers with clientId, clientSecret, callbackPort, and scopes, then run /mcp login <name>. See the Google Drive and Google Sheets guide for the Google Cloud setup and the tool mapping.

Clipboard over SSH and tmux

Prime Agent uses the local clipboard tool (such as pbcopy) when available. In tmux it asks the attached tmux client to forward a clipboard buffer if that client advertises the Ms capability. This works with tmux's default set-clipboard external, unlike application-origin OSC 52. Without tmux, remote copies request delivery using OSC 52. Terminal requests cannot confirm that your local clipboard changed, so Prime Agent reports them as unconfirmed.

Nested tmux servers and terminals may still block delivery. An outer tmux with set-clipboard external can reject the inner server's OSC 52. In that case, run tmux set -s set-clipboard on on the outer server, or use native terminal selection (Shift-drag in most terminals; Option-drag in iTerm2) followed by Cmd-C. macOS Terminal.app does not support OSC 52; iTerm2 requires its “Applications in terminal may access clipboard” setting.

Custom certificate authorities

HTTPS requests trust your operating system's trust store together with the bundled Mozilla roots. For servers behind a custom CA, point NODE_EXTRA_CA_CERTS at a PEM bundle of your CA certificates — the same variable the Node/TypeScript build honors:

export NODE_EXTRA_CA_CERTS=/path/to/your-ca.pem

Each transport reads the variable once when its first client is initialized. The background daemon keeps the environment it started with, so run prime-agent shutdown after changing it.

Getting Started

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, and skills 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]       # Browse sessions or resume one directly
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, Python REPL 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.

Contributing

Start with a GitHub Discussion for general questions, bug reports, and feature requests. Maintainers promote accepted work into Issues, and pull requests are reviewed from maintainers and vouched contributors.

License

Prime Agent is fully open source and released under the MIT License.

Citation

If you use this codebase in your research, please cite Prime Agent:

@article{karten2026prime,
  title={Prime Agent: A Self-Improving RLM Harness},
  author={Karten, Seth and Zhang, Alex L. and Thomas, Kevin and Müller, Sebastian and Bakouch, Elie and Auras, Daniel and Senghaas, Mika and Obeid, Fares and Dunas, Konstantin and Hagemann, Johannes and Jaghouar, Sami},
  journal={arXiv preprint arXiv:2608.23552},
  year={2026}
}

Available at https://arxiv.org/abs/2608.23552.

Automatic compaction settings

/settings exposes the global automatic compaction percentage in five-point steps, with a 95% default. A saved off-grid percentage stays selectable. The effective trigger also reserves room for retained history and summaries; the model's input and requested-output ceiling always takes priority. Manual compaction budgets are unchanged. The underlying compaction.thresholdPercent setting accepts 1–100, including fractional values; project settings can override the global setting.

For per-model early compaction, use the existing provider-scoped modelOverrides.<id>.contextWindow option in models.json, for example:

{
  "providers": {
    "prime-inference": {
      "modelOverrides": {
        "your-model-id": { "contextWindow": 524288 }
      }
    }
  }
}

Use the actual model ID for your provider. This sets that model's effective context window; it does not change every model's global compaction percentage.