Prime Agent

Run coding and research work in a persistent environment, then retain evidence-backed lessons for reuse.

Stars
★ 22k
Last updated
today
License
NOASSERTION
Primary language
Rust

At a glance

How it runs
CLI
Works with
Universal · cross-platform
Setup effort
Medium · a few setup steps
You'll need
Shell / CLINetwork accessLocal filesystemMCP Server
Typical use
Developers handling multi-step changes in a codebase can let the agent run commands and edit files in the project directory.
Not a fit if
  • Teams that need untrusted code to run in a security sandbox
  • Users who need a clearly documented zero-cost setup

What does this agent do, and when should you use it?

Prime Agent is an open-source CLI for general coding, research, and long-running tasks. It uses a persistent Python REPL as the model tool, treats context as variables, and can delegate work through recursive subagents. Its Continual Harness stores supplemental prompts, memories, skill descriptions, and reusable subagent specifications as durable state; updates are local to the session by default and recorded snapshots support rollback. A background daemon keeps sessions running after the terminal disconnects and lets users reattach later. The agent runs commands and modifies files in the current directory, so generated Python and project commands execute with the user's permissions; its worker processes are not a security sandbox.

Start prime-agent in the target project directory, then use /login on first launch to choose a subscription or API-key provider. The model works through a persistent Python REPL, where code can perform file operations, run shell commands, use tools, manage context, and spawn subagents. rlm.spawn(...) starts parallel or background child agents and returns their results programmatically; running agents can also message each other directly. /refine reviews the current trajectory and can apply evidence-backed updates to supplemental prompts, memories, skill descriptions, or subagent specifications, with snapshots available for rollback. Sessions can also use automatic compaction, persistent goals, heartbeats, schedules, and bounded autonomous mode.

  1. Developers handling multi-step changes in a codebase can let the agent run commands and edit files in the project directory.
  2. Researchers running lengthy evaluations can retain progress with background sessions, automatic compaction, and persistent goals.
  3. Teams splitting investigation or implementation work can use rlm.spawn(...) to launch subagents and receive programmatic results.
  4. Engineers repeating project workflows can use /refine to save evidence-backed lessons for later sessions.
  5. Developers working over SSH or tmux can reattach to background sessions after disconnecting.

How do you install or deploy this agent?

Run the installer from a compatible shell on macOS or Linux. On Windows, install from PowerShell; the installer downloads the latest stable Windows x64 build and verifies its checksum. The README does not specify provider subscription prices or API charges.

How do you use this agent?

Go to the project directory you want the agent to work in, start the CLI, and use /login on first launch to choose a subscription or API-key provider.

What are this agent's strengths and limitations?

Pros
  • The persistent Python REPL is the built-in model tool, making file, shell, tool, and context operations programmable.
  • rlm.spawn(...) supports parallel or background subagents and returns results programmatically.
  • The Continual Harness stores supplemental prompts, memories, skill descriptions, and subagent specifications with rollback snapshots.
  • The background daemon supports continued sessions, reattachment, heartbeats, and schedules.
Limitations
  • Generated Python and project commands run with the user's permissions; workers and kernels are not a security sandbox.
  • First use requires selecting a subscription or API-key provider through /login; the README does not state specific costs.
  • Work and tool operations are centered on a Python REPL and terminal CLI, which assumes coding and command-line familiarity.
  • The README does not list supported models, providers, or a minimum runtime version.

How does this agent compare with similar options?

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
Prime Agent This agent 57 · Major gaps CLI ★ 22k today Rust —
Oh My Hermes (OMH) 78 · Good CLIFree + model costs ★ 3.5k today Python —
Planning with Files 73 · Some gaps Agent plugin / skillFree ★ 28k today Python Codex · Claude Code
MiMoCode 50 · Major gaps CLIFreemium ★ 14k today TypeScript ChatGPT · Codex · Claude Code

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
57/ 100 5-point scale 2.9 / 5
Trust 13/29
Reliability 8/14
Adaptability 12/18
Convention 12/18
Effectiveness 7/13
Verifiability 5/8
Why each dimension lost points
Trust13 / 29 · 2.2/5

The README explicitly says model-generated Python and project commands run with the current user's permissions, worker isolation is not a security sandbox, and recommends an inspectable, recoverable worktree. This supports risk disclosure, but the evidence does not show restricted privileges or routine confirmation for high-impact actions, so least privilege and confirmation are only partial. It discloses Slack connection, API-key login, and clipboard delivery limits, but not general sensitive-data handling or end-to-end data flows. CI uses read-only permissions, disables persisted credentials, and pins action versions by commit SHA: useful but scoped supply-chain evidence. /refine snapshots support rollback, while general recovery for code changes and external actions is not shown. License and port origin are stated; publisher identity remains unknown.

Reliability8 / 14 · 2.9/5

The README gives concrete boundaries for the persistent REPL, daemon, resumption, bounded autonomous mode, and quality gates. The workflow describes formatting, compilation, static checks, sharded tests, and runtime-suite gates, with an audit of shard coverage. The differential script specifies fields compared between TypeScript and Rust, but the supplied material contains no execution result, so it cannot establish a successful run. Installation depends on a remote script and external providers; checksum verification is stated for Windows, while equivalent Linux/macOS detail is absent from the supplied material. Behavior when dependencies or network services are unavailable is only partly documented; the differential script gives a specific message when its TypeScript dependencies are missing.

Adaptability12 / 18 · 3.3/5

The project targets coding, research, and long-running work, and lists sessions, schedules, subagents, compaction, and budgeted autonomous operation. The README says commands and file operations use current-directory/user permissions and that autonomous mode has turn, token, and time budgets, but it does not provide a full permissions boundary or feature enable/disable matrix. Environment guidance covers Windows installation, SSH/tmux clipboard handling, and custom CAs, and directs untrusted code to an external restricted environment; platform coverage and configuration conditions remain incomplete.

Convention12 / 18 · 3.3/5

The README organizes installation, getting started, long-running work, integrations, and settings, with commands, examples, and a Slack troubleshooting guide. Windows installation, clipboard constraints, and CA configuration are concrete limitations. Repository license metadata is NOASSERTION, while LICENSE and Cargo metadata say MIT, so license text exists but the metadata mismatch reduces clarity. The README says the installer gets the latest stable version and provides an update command, but the supplied material has no separate changelog or named maintenance owner; the contribution discussion process provides a partial maintenance path.

Effectiveness7 / 13 · 2.7/5

A persistent REPL, reusable skills and memories, subagents, and background continuation have recognizable potential value for long coding and research tasks; results can be consumed through agent results, session resumption, and the CLI. The material provides no comparative benefit evidence, resource-cost data, or representative task-output examples, so cost-benefit is only partly assessable, and static evidence cannot establish result quality.

Verifiability5 / 8 · 3.1/5

The README makes specific, locatable claims about execution permissions, the absence of sandboxing, budget boundaries, and unconfirmed clipboard delivery. The CI configuration gives inspectable gate definitions, and the differential script names its comparison fields, providing limited corroboration across file types. Badges, paper citations, and CI configuration show repository claims or configured processes, not that this revision passed or that research claims hold. The text separates some design claims from limitations, but quantitative effectiveness and result correctness lack evidence in the supplied material.

Risks and how to mitigate them
  • Model-generated code and commands run with user permissions, and the project explicitly says process isolation is not a security sandbox; use trusted repositories and an inspectable, recoverable worktree.
  • The README says the Windows installer verifies a checksum; the supplied material does not show equivalent verification for installers on other platforms.
  • The license file and Cargo manifest say MIT, while supplied license metadata is NOASSERTION; publisher identity is unverified.
Evidence confidence: Low Reviewed Oct 21, 2026 Reviewed revision 0e8236274707
See the full review method →

FAQ

Is it free to use?
The README describes open-source software under the MIT License, but first use requires choosing a subscription or API-key provider. Costs depend on that option; the README gives no prices.
Can it change my project files?
Yes. It runs commands and modifies files in the current directory. The README recommends using a disposable clone or clean worktree you can inspect and restore.
Is it safe for untrusted code?
The README says its workers and kernels are not a security sandbox. Run untrusted code or instructions in an external sandbox or restricted environment.
Will work stop when I disconnect the terminal?
The background daemon can keep sessions running, and you can reattach with prime-agent attach <agent>.
View on GitHub ↗ Install ↓

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