InfoMesh MCP Server

Local setup required. This server has to be cloned and prepared on your machine before you register it in Claude Code.
1

Set the server up locally

Run this once to clone and prepare the server before adding it to Claude Code.

Run in terminal
pip install infomesh
2

Register it in Claude Code

After the local setup is done, run this command to point Claude Code at the built server.

Run in terminal
claude mcp add infomesh -- node "<FULL_PATH_TO_INFOMESH>/dist/index.js"

Replace <FULL_PATH_TO_INFOMESH>/dist/index.js with the actual folder you prepared in step 1.

README.md

Fully Decentralized P2P Search Engine for LLMs

InfoMesh

Fully Decentralized P2P Search Engine for LLMs No credit card. No API key. No usage cap. Forever free.

Quick Start • Why InfoMesh • Features • What's New • Architecture • Security • Credits • Contributing • Docs


[!TIP] P2P Bootstrap Nodes Active InfoMesh ships with multiple bootstrap nodes across Azure regions — your node connects automatically on first start. To add more peers manually:

infomesh peer add /ip4/<IP>/tcp/4001/p2p/
infomesh peer test

💡 Why InfoMesh?

The Problem

Every AI assistant needs real-time web access — but that access is gated behind expensive, proprietary search APIs:

Type Typical Cost Limitation
LLM-bundled web search Hidden in token cost Locked to one vendor's API, no standalone access
Custom search API ~$3–5 / 1,000 queries API key + billing account required, rate-limited
AI search SaaS ~$0.01–0.05 / query SaaS dependency, monthly usage caps
Search scraping proxy ~$50+/month Fragile, breaks on upstream changes
InfoMesh $0 — Forever Free None. You own the node, you own the index

This creates a paywall barrier for independent AI developers, open-source assistants, and researchers. Small projects and local LLMs simply cannot afford real-time web search.

The Solution

I started building AI agents and quickly hit a wall: there was no free web search API. Every provider wanted a credit card, a billing account, or a monthly subscription — just to let an AI agent look something up on the web. That felt wrong.

So I built InfoMesh — a decentralized search engine where the community is the infrastructure:

  • No central server — every participant is both a crawler and a search node.
  • No per-query cost — contribute crawling, earn search credits. The more you give, the more you can search.
  • No vendor lock-in — standard MCP protocol integration, works offline with your local index.
  • No data harvesting — search queries never leave your node. There is no central entity to collect them.

InfoMesh does not compete with existing commercial search providers. Those companies serve human search at massive scale with ads-based monetization. InfoMesh provides minimal, sufficient search capabilities for AI assistants — for free, via MCPdemocratizing real-time web access without per-query billing.

Tools (5)

web_searchPerforms a web search across the P2P network.
fetch_pageFetches the content of a specific URL.
crawl_urlCrawls a URL to index its content.
fact_checkVerifies information against the P2P index.
statusReturns the current status of the P2P node.

Configuration

claude_desktop_config.json
{"mcpServers": {"infomesh": {"command": "infomesh", "args": ["mcp"]}}}

Try it

Search for the latest developments in decentralized AI infrastructure.
Fetch the content of this URL and summarize the key points: https://example.com
Crawl this documentation page to add it to my local search index.
Fact check the following claim using the P2P network: 'The sky is green'.
Check the status of my InfoMesh node to ensure I am connected to the network.

Frequently Asked Questions

What are the key features of InfoMesh?

Fully decentralized P2P architecture with no central server. No per-query costs or API keys required. Standard MCP protocol integration for AI assistants. Local indexing capabilities for offline search. Privacy-focused design where search queries remain on the local node.

What can I use InfoMesh for?

Providing real-time web access to local LLMs without expensive API subscriptions. Building AI agents that require autonomous web crawling and indexing. Researchers needing a cost-free, transparent search infrastructure. Independent developers creating open-source AI assistants with web capabilities.

How do I install InfoMesh?

Install InfoMesh by running: pip install infomesh

What MCP clients work with InfoMesh?

InfoMesh works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Turn this server into reusable context

Keep InfoMesh docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Need the old visual installer? Open Conare IDE.
Open Conare