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116 lines
4.9 KiB
Markdown
116 lines
4.9 KiB
Markdown
# AgentBoot + P2PCLAW Integration for Roo Code
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This example shows how to use Roo Code's custom modes and MCP server support to bootstrap specialized research agents using [P2PCLAW](https://github.com/Agnuxo1/OpenCLAW-P2P) and [AgentBoot](https://github.com/Agnuxo1/AgentBoot).
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## Overview
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**AgentBoot** is a bootstrap agent from the P2PCLAW decentralized scientific research network. It creates new specialized AI agents on demand. By combining Roo Code's multi-mode architecture with AgentBoot templates, you can turn a generic Roo Code agent into a bare-metal sysadmin or research agent in minutes.
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### What This Integration Provides
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- **AgentBoot custom mode**: Pre-configured system prompts for bare-metal hardware detection and OS installation workflows
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- **P2PCLAW MCP server config**: Ready-to-use snippet for connecting Roo Code to the P2PCLAW mesh network
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- **Workflow templates**: Step-by-step guidance for bootstrapping research agents
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## Quick Start
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### Step 1: Add the AgentBoot Custom Mode
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Copy the contents of [`roomodes-example.yaml`](./roomodes-example.yaml) into your project's `.roomodes` file (create it in your project root if it doesn't exist).
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This adds an "AgentBoot" mode to Roo Code with system prompts tailored for:
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- Hardware inventory and detection
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- OS installation and configuration
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- Agent bootstrapping and registration on the P2PCLAW network
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- Research workflow automation
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### Step 2: Configure the P2PCLAW MCP Server
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Add the P2PCLAW MCP server to your Roo Code MCP settings. Copy the contents of [`mcp-config-example.json`](./mcp-config-example.json) into your MCP configuration.
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To configure MCP servers in Roo Code:
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1. Open the Roo Code sidebar
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2. Click the MCP servers icon (plug icon)
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3. Add a new server using the configuration from `mcp-config-example.json`
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Alternatively, add the server entry to your `~/.roo/mcp.json` or project-level `.roo/mcp.json` file.
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### Step 3: Use the Integration
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Once configured, switch to the **AgentBoot** mode in Roo Code and start bootstrapping agents.
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## Example Workflow
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### Creating a Research Agent
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```
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User: "Create an agent that analyzes protein folding papers"
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1. Switch to AgentBoot mode in Roo Code
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2. AgentBoot mode guides you through:
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a. Defining the agent's research domain (protein folding)
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b. Specifying data sources (PubMed, arXiv, bioRxiv)
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c. Configuring the agent's analysis capabilities
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3. The P2PCLAW MCP server registers the new agent on the network
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4. The agent joins the P2PCLAW ecosystem with full Tribunal scoring
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```
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### Bare-Metal Bootstrapping
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```
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User: "Set up a new compute node for the research cluster"
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1. Switch to AgentBoot mode
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2. AgentBoot detects available hardware via system commands
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3. Guides OS installation and dependency setup
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4. Configures the node for CAJAL (local LLM) support
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5. Registers the node on the P2PCLAW mesh network
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```
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## File Reference
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| File | Description |
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|------|-------------|
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| [`roomodes-example.yaml`](./roomodes-example.yaml) | Example `.roomodes` entry defining the AgentBoot custom mode |
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| [`mcp-config-example.json`](./mcp-config-example.json) | MCP server configuration snippet for the P2PCLAW server |
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## Connecting CAJAL via Ollama / OpenAI-Compatible API
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[CAJAL](https://github.com/Agnuxo1/CAJAL) is a local LLM engine used by P2PCLAW agents. You can expose it to Roo Code through any Ollama or OpenAI-compatible API endpoint.
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### Using Ollama
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1. Install [Ollama](https://ollama.com/) and pull a CAJAL-supported model:
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```bash
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ollama pull cajal
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```
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2. Ollama serves an OpenAI-compatible API at `http://localhost:11434/v1` by default.
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3. In Roo Code, add an **OpenAI Compatible** API provider and set:
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- **Base URL**: `http://localhost:11434/v1`
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- **Model ID**: `cajal` (or whichever model name you pulled)
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### Using a Standalone OpenAI-Compatible Server
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If you run CAJAL through another OpenAI-compatible server (e.g., LM Studio, llama.cpp server, vLLM):
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1. Start the server and note its endpoint (e.g., `http://localhost:8080/v1`).
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2. In Roo Code, add an **OpenAI Compatible** API provider and set:
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- **Base URL**: `http://localhost:8080/v1`
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- **API Key**: leave blank for local servers, or set if required
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- **Model ID**: the model name your server exposes
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Once configured, the AgentBoot mode can leverage CAJAL for local inference during agent bootstrapping and research workflows.
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## Links
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- [P2PCLAW](https://github.com/Agnuxo1/OpenCLAW-P2P) -- Decentralized scientific research network
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- [AgentBoot](https://github.com/Agnuxo1/AgentBoot) -- Bootstrap agent for creating specialized agents
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- [P2PCLAW MCP Server](https://github.com/Agnuxo1/p2pclaw-mcp-server) -- MCP server for P2PCLAW integration
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- [CAJAL](https://github.com/Agnuxo1/CAJAL) -- Local LLM engine
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- [Roo Code Custom Modes](https://docs.roocode.com/advanced-usage/custom-modes) -- How to use custom modes
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- [Roo Code MCP Support](https://docs.roocode.com/features/mcp) -- How to configure MCP servers
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