PrimeIntellect-ai/prime-agent Examples: 6 Practical Ways to Use This Self-Improving AI Coding Assistant

Prime Agent is a self-improving, long-running AI coding assistant that enables programmatic sub-agent calls through its Recursive Language Model (RLM) and persistent memory via the Continual Harness.

This guide demonstrates six practical examples of using PrimeIntellect-ai/prime-agent, from installation and CLI workflows to advanced RLM scripting and skill development. Each example references the actual source implementation so you can trace how the system works under the hood.

Installing and Launching Prime Agent

The recommended installation method uses a shell script that pulls the latest stable release for macOS or Linux.

# Install the latest stable release

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

Per the README installation instructions【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L48-L52】, this script places the prime-agent binary on your PATH.

Once installed, start the agent from any project directory:

cd /path/to/project
prime-agent

The quick start command【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L56-L61】initializes the daemon-backed architecture. The entry-point script prime-agent.sh【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/prime-agent.sh】 handles launching the background service, attaching the IPython kernel, and starting the TUI client.

Authenticating with Your LLM Provider

On first run, authenticate with your chosen provider:

/login

This command【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L63-L64】triggers the provider authentication flow. The AI layer—implemented in packages/ai/src/stream.ts【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/packages/ai/src/stream.ts】—normalizes streaming responses across OpenAI, Anthropic, Bedrock, and other supported providers.

Managing Sessions with CLI Commands

The prime-agent binary exposes a comprehensive CLI for session and service management. These commands【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L68-L78】are parsed in packages/ai/src/cli.ts【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/packages/ai/src/cli.ts】:

prime-agent agents                   # List running, idle, and saved sessions

prime-agent attach <agent>           # Re-attach to a running session

prime-agent --resume [path|id]       # Browse or resume a session

prime-agent status                   # Inspect background service state

prime-agent doctor [--fix]           # Diagnose / repair services

prime-agent update [--force]         # Update the binary

prime-agent shutdown [--force]       # Stop all agents & services

These commands demonstrate Prime Agent examples for production workflows where you need to inspect background state, recover interrupted sessions, or maintain the daemon service.

Using the Recursive Language Model (RLM) for Sub-Agent Calls

The RLM treats the prompt as a mutable variable and enables programmatic tool/sub-agent calls inside a persistent REPL【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L31-L34】. From within an active session (TUI or attached IPython kernel), spawn child agents programmatically:

# Spawn a child agent that runs a background task

result = rlm("run_long_task.py", timeout=300)
print(result)   # The sub-agent returns its final output when finished

The rlm() helper creates a dedicated worker process for the given script and returns results programmatically. This enables composable agent workflows where parent agents orchestrate multiple specialized sub-agents, each with isolated state and configurable timeouts.

Refining the Continual Harness Without Altering Base Prompts

The Continual Harness is a durable store for supplemental prompts, memories, skill definitions, and sub-agent specifications that persist across sessions【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L36-L41】.

Run the refinement interface:

/refine

This opens a guided TUI for editing harness components. Critical safety property: refinements are stored in JSON files under .prime-agent/ (created at runtime) without touching the immutable base system prompt. This design enables safe iterative improvement—you can experiment with prompt variations and roll back without corrupting core behavior.

Creating and Registering Reusable Skills

Skills are importable Python packages that extend Prime Agent's capabilities. Create a skill from scratch:

# In a new directory, create a Python package named myskill

mkdir myskill && cd myskill
python -m pip install --quiet --upgrade build

Implement the required run function:

# myskill/__init__.py

def run(task):
    # custom logic here

    return f"Completed: {task}"

Build and install:

python -m build
pip install .

Invoke from the agent:

/run myskill.run "process dataset.csv"

Skills integrate into the harness system and can reference stored memories or call other skills via rlm(). The skill authoring workflow is documented in packages/coding-agent/docs/skills.md.

Architecture Overview: How These Examples Connect

Understanding the source structure clarifies how the Prime Agent examples above interoperate:

Component	Source Path	Role in Examples
Entry script	prime-agent.sh	Boots daemon, starts TUI, attaches IPython
AI streaming	packages/ai/src/stream.ts	Normalizes LLM responses for /login and chat
CLI dispatcher	packages/ai/src/cli.ts	Parses all prime-agent subcommands
TUI rendering	packages/tui/src/*	Interactive interface for /refine, chat, markdown
Harness persistence	.prime-agent/*.json (runtime)	Stores memories, skills, refinements

The RLM and Continual Harness abstractions unify these components into a self-improving system: the daemon maintains long-running state, the RLM enables dynamic agent composition, and the harness captures incremental improvements without destabilizing core functionality.

Summary

• PrimeIntellect-ai/prime-agent combines a Recursive Language Model with a Continual Harness for persistent, self-improving AI assistance
• Installation via curl script and launch with prime-agent initializes the daemon-backed architecture
• The CLI supports session management (agents, attach, --resume), diagnostics (doctor, status), and lifecycle control (update, shutdown)
• RLM scripting via rlm("script.py", timeout=...) enables programmatic sub-agent orchestration
• /refine safely extends the harness without modifying immutable base prompts
• Custom skills are standard Python packages with a run() function, installable via pip and invocable through /run

Frequently Asked Questions

What is the Recursive Language Model (RLM) in Prime Agent?

The RLM is a core abstraction that treats the prompt as a mutable variable and lets the model call sub-agents as functions. It enables programmatic tool/sub-agent calls inside a persistent REPL, allowing you to spawn isolated worker processes and compose complex multi-agent workflows【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L31-L34】.

How does the Continual Harness persist data across sessions?

The Continual Harness stores supplemental prompts, memories, skill definitions, and sub-agent specifications in JSON files under .prime-agent/ at runtime. It can be refined with /refine without altering the immutable base system prompt, enabling safe iterative improvement that survives session restarts【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L36-L41】.

What backend services run when I start Prime Agent?

The prime-agent.sh script launches a daemon that manages background agents, an IPython kernel for interactive Python execution, and a TUI client for the chat interface. These services enable long-running sessions that you can detach from and re-attach to later【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/prime-agent.sh】.

How do I resume a previous Prime Agent session?

Use prime-agent --resume to browse saved sessions by path or ID, or prime-agent attach <agent> to reconnect to a specific running session. The session state—including harness contents and conversation history—is maintained by the background daemon【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/README.md#L68-L78】.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}