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This repository was archived by the owner on May 15, 2026. It is now read-only.
This repository was archived by the owner on May 15, 2026. It is now read-only.

feat: P2PCLAW AgentBoot Integration — Bootstrap Research Agents from Roo-Code #12307

Description

@Agnuxo1

Proposal: AgentBoot + Roo-Code = Instant Specialized Research Agents

Hi Roo-Code team! Roo-Code has become my go-to for complex coding tasks — the adaptive autonomy and multi-mode switching are genuinely useful.

What is P2PCLAW?

P2PCLAW is a decentralized scientific research network with 14 autonomous AI agents. One of them is AgentBoot — a bootstrap agent that creates new specialized agents on demand.

The Idea

AgentBoot + Roo-Code = Instant Specialized Agents

  1. User needs a new agent (e.g., "BioinformaticsAnalyzer")
  2. Roo-Code generates the agent code using its coding capabilities
  3. AgentBoot (via MCP) registers the new agent on P2PCLAW network
  4. New agent immediately joins the 14-agent ecosystem with full Tribunal scoring

Why Roo-Code Specifically?

  • Multi-mode architecture — Roo-Code's Ask/Architect/Code modes map perfectly to AgentBoot's design/spec/implement phases
  • MCP support — Can call P2PCLAW's MCP server directly from Roo-Code
  • Self-improving — Roo-Code can iterate on agent code based on Tribunal feedback
  • Local-first — Both projects run locally (Ollama + CAJAL)

Workflow Example

User: "Create an agent that analyzes protein folding papers"

Roo-Code (Architect mode): Designs agent architecture, writes core logic
Roo-Code (Code mode): Implements PaperClaw integration, adds CAJAL local LLM support
AgentBoot (MCP): Registers "ProteinFoldAnalyzer" on network, assigns agent ID

Result: New agent live in under 5 minutes

Links

Category

Feature Request / Integration


Disclaimer: I am the creator of P2PCLAW. Happy to provide a working MCP server config or demo video.

cc: @mrubens @samhvw8

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