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

> Explore 6 practical PrimeIntellect-ai/prime-agent examples. Discover how this self-improving AI coding assistant uses RLM and Continual Harness for advanced coding tasks.

- Repository: [Prime Intellect/prime-agent](https://github.com/PrimeIntellect-ai/prime-agent)
- Tags: tutorial
- Published: 2026-08-16

---

**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.

```bash

# 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:

```bash
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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/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:

```text
/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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/packages/ai/src/cli.ts)【/cache/repos/github.com/PrimeIntellect-ai/prime-agent/main/packages/ai/src/cli.ts】:

```bash
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:

```python

# 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:

```text
/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:

```bash

# 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:

```python

# myskill/__init__.py

def run(task):
    # custom logic here

    return f"Completed: {task}"

```

Build and install:

```bash
python -m build
pip install .

```

Invoke from the agent:

```text
/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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/prime-agent.sh) | Boots daemon, starts TUI, attaches IPython |
| AI streaming | [`packages/ai/src/stream.ts`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/packages/ai/src/stream.ts) | Normalizes LLM responses for `/login` and chat |
| CLI dispatcher | [`packages/ai/src/cli.ts`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/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`](https://github.com/PrimeIntellect-ai/prime-agent/blob/main/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】.