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/<PEER_ID>
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 MCP — democratizing real-time web access without per-query billing.

I just wanted my AI agent to search the web without reaching for my wallet. If you've felt the same way, InfoMesh is for you.

🆓 Free. Every Interface. No Exceptions.

How you use it	Cost	Example
MCP (AI assistants)	Free	Claude, VS Code Copilot, any MCP client calls `search()` — zero API fees
CLI (terminal)	Free	`uv run infomesh search "python asyncio"` — instant results from your index
Python package (code)	Free	`from infomesh.index.local_store import LocalStore` — embed search in your app
Local API (HTTP)	Free	`curl localhost:8080/search?q=...` — REST endpoint for any language

No API keys. No billing accounts. No usage caps. No rate limits per dollar. You run a node, you contribute to the network, and search is free — forever.

InfoMesh vs Other Web Search MCP Servers

Looking for a free web search MCP server? Here's how InfoMesh compares to common alternatives:

Feature	InfoMesh	API-based MCP servers	Scraper-based MCP servers	Meta-search engines
Free tier	♾️ Unlimited (credit-based)	Limited (1,000–2,000/mo typical)	Unlimited (no API)	Unlimited
API key	❌ Not required	✅ Required (signup needed)	❌ Not required	❌ Not required
Decentralized	✅ Fully P2P	❌ Centralized	❌ Centralized	❌ Single instance
Offline search	✅ Local index works offline	❌	❌	❌
Privacy	✅ Queries never leave node	⚠️ Logged by provider	Varies	✅ Self-hosted
Self-hosted	✅ You own everything	❌	❌	✅ Docker required
Crawl your own URLs	✅ `crawl_url()` tool	❌	❌	❌
Full page fetch	✅ `fetch_page()` tool	Varies	❌	❌
Install	`pip install infomesh`	Varies	Varies	Docker Compose
Open source	✅ MIT	Varies	Varies	Varies

InfoMesh is the only web search MCP server that is fully decentralized, works offline, requires no API key, and lets you crawl and index your own content — all for free.

🔐 Safe by Design — The Most Secure Search Engine You Can Run

Most search engines ask you to trust them. InfoMesh asks you to trust math.

There is no central server that collects your queries. There is no company that stores your search history. There is no database of user behavior waiting to be breached. Your data never leaves your machine unless you choose to share it.

Contribute to the network → earn credits → search for free, forever, with no limits.

That's the entire deal. No catch.

Why InfoMesh Is Enterprise-Grade Secure

Unified Trust Score

Every peer earns a continuously updated trust score based on behavior, not identity:

Trust = 0.15 × uptime  +  0.25 × contribution  +  0.40 × audit_pass_rate  +  0.20 × summary_quality

Tier	Score	What Happens
🟢 Trusted	≥ 0.8	Priority routing, lowest search cost
🔵 Normal	0.5 – 0.8	Standard operation
🟡 Suspect	0.3 – 0.5	Higher audit frequency, limited features
🔴 Untrusted	< 0.3	Network isolation after 3× consecutive failures

Compliance Built In

Regulation	How InfoMesh Handles It
robots.txt	Strictly enforced — no exceptions, automatic blocklist
DMCA	Signed takedown requests propagated via DHT, 24hr compliance
GDPR	Distributed deletion records, right-to-be-forgotten support
Copyright	Full text stored as cache only; search returns snippets with attribution

Bottom line: InfoMesh doesn't ask you to trust a company. It uses cryptography, audits, and game theory to make cheating harder than playing fair. Your queries are private, your data stays local, and your search is free — no strings attached.

🚀 Quick Start

Install & Run (Two Steps — No Git Required)

All you need is a Linux terminal (Ubuntu, Debian, etc.). No prior Python or developer experience required.

Step 1 — Install uv (Python package manager, one-time setup):

curl -LsSf https://astral.sh/uv/install.sh | sh

After this finishes, close and reopen your terminal (or run source ~/.bashrc). This ensures the uv and uvx commands are available.

Step 2 — Run InfoMesh:

uvx infomesh status

uvx automatically downloads and runs InfoMesh — no git clone, no pip install, no virtual environments. On the first run it may take a few seconds to download; subsequent runs are instant.

Try It Out

# Crawl a webpage and index it
uvx infomesh crawl https://docs.python.org/3/library/asyncio.html

# Search your local index
uvx infomesh search "asyncio"

# View the node dashboard (works over SSH too)
uvx infomesh dashboard --text

Install Permanently (Optional)

If you use InfoMesh regularly, install it as a persistent tool so you don't need the uvx prefix:

uv tool install infomesh

# Now run directly:
infomesh status
infomesh crawl https://example.com
infomesh search "example"
infomesh dashboard --text

Connect to Your AI Assistant (MCP)

Add InfoMesh as an MCP server in VS Code (Copilot), Claude Desktop, Cursor, or Windsurf — no API key needed:

{
  "mcpServers": {
    "infomesh": {
      "command": "uvx",
      "args": ["infomesh", "mcp"]
    }
  }
}

Your AI assistant can now search the web for free via MCP.

From Source (Contributors / Developers)

If you want to contribute code or run from source:

System Prerequisites

The P2P optional dependency (libp2p) includes C extensions (fastecdsa, coincurve, pynacl) that require native build tools. These are only needed if you install with pip install 'infomesh[p2p]'.

Linux (Debian / Ubuntu):

sudo apt-get update && sudo apt-get install -y build-essential python3-dev libgmp-dev

macOS:

brew install gmp
# Xcode Command Line Tools are usually pre-installed

Windows: Use WSL2 (recommended) or install Visual Studio Build Tools + GMP.

Note: These system packages are only required for the p2p optional dependency. The base install (uv sync) does not need them.

Clone & Run

# Clone and install with dev dependencies
git clone https://github.com/dotnetpower/infomesh.git
cd infomesh
uv sync

# Start InfoMesh with the TUI dashboard
uv run infomesh start

# Or run headless (servers / CI)
uv run infomesh start --no-dashboard

Docker

docker build -t infomesh .
docker run -d --name infomesh \
  -p 4001:4001 -p 8080:8080 \
  -v infomesh-data:/data \
  infomesh

Verify It Works

# Search your local index
uvx infomesh search "python asyncio tutorial"

# Check node status
uvx infomesh status

# Crawl a specific URL on demand
uvx infomesh crawl https://docs.python.org/3/

# Export your index as a portable snapshot
uvx infomesh index export backup.zst

Examples

Ready-to-run Python scripts are available in the examples/ directory:

# Local search
uv run python examples/basic_search.py "python tutorial"

# Crawl → index → search pipeline
uv run python examples/crawl_and_search.py https://docs.python.org/3/

# Programmatic MCP client
uv run python examples/mcp_client.py "async programming"

See examples/README.md for the full list.

✨ Features

Core Capabilities

Feature	Description
🌐 Fully Decentralized	No central server. Every node is both a hub and a participant — cooperative tit-for-tat architecture
🤖 LLM-First Design	Pure text API via MCP, optimized for AI consumption. No browser UI needed
🔍 Dual Search	Keyword search (SQLite FTS5 + BM25) and optional semantic vector search (ChromaDB)
🕷️ Smart Crawler	Async crawling with robots.txt compliance, politeness delays, and 3-layer deduplication
📡 P2P Network	libp2p-based with Kademlia DHT, mDNS local discovery, and encrypted transport
💾 Offline-Capable	Your local index works without internet — search your crawled knowledge anytime
🏆 Credit Incentives	Earn credits by crawling and serving peers. More contribution = more search quota
🔐 Content Integrity	SHA-256 + Ed25519 attestation on every page. Random audits + Merkle proofs
🤏 zstd Compression	Index snapshots and network transfers compressed with zstandard
📊 Console Dashboard	Beautiful Textual TUI with 6 tabs: Overview, Crawl, Search, Network, Credits, Settings

MCP Integration — Free Web Search for AI Assistants

Most commercial search APIs charge per query or require a paid subscription. InfoMesh exposes 15 MCP tools completely free — no API key, no billing:

Tool	Description
`search(query, limit)`	Full network search — merges local + remote results, ranked by BM25 + freshness + trust
`search_local(query, limit)`	Local-only search (works offline, < 10ms)
`fetch_page(url)`	Return full extracted text for a URL (from index cache or live crawl)
`crawl_url(url, depth)`	Submit a URL to be crawled and indexed by the network
`network_stats()`	Network status: peer count, index size, credit balance
`batch_search(queries)`	Run up to 10 search queries in one call
`suggest(prefix)`	Autocomplete / search suggestions
`register_webhook(url)`	Register webhook for crawl completion notifications
`analytics()`	Search/crawl/fetch counts and average latency
`explain(query)`	NEW — Score breakdown: BM25, freshness, trust components per result
`search_history(action)`	NEW — View or clear past search queries with latency stats
`search_rag(query)`	NEW — RAG-optimized chunked output with source attribution
`extract_answer(query)`	NEW — Direct answer extraction with confidence scores
`fact_check(claim)`	NEW — Cross-reference claims against indexed sources

Configure in VS Code / Copilot / Claude Desktop / Cursor

{
  "mcpServers": {
    "infomesh": {
      "command": "uvx",
      "args": ["infomesh", "mcp"]
    }
  }
}

Optional Add-ons

# Vector search with ChromaDB + sentence-transformers
uv sync --extra vector

# Local LLM summarization via Ollama
uv sync --extra llm

� What's New in v0.2.0

v0.2.0 adds 100+ features across search intelligence, RAG support, security, observability, and developer experience. Here are the highlights:

Search Intelligence

Feature	Description
🧠 NLP Query Processing	Stop-word removal (9 languages), synonym expansion, natural language parsing
✏️ Did-you-mean	Edit-distance spelling correction when no results found
📊 Search Facets	Domain, language, and date-range facet counts per query
🎯 Result Clustering	Groups results by domain for organized browsing
🔦 Snippet Highlighting	Query terms highlighted in result snippets
🧹 Smart Deduplication	Jaccard similarity-based near-duplicate removal
🔍 Search Explain	Transparent score breakdowns for every result

RAG & Answer Extraction

Feature	Description
📚 RAG Output	Chunked, source-attributed context windows for LLM consumption
💡 Answer Extraction	Direct answers with confidence scores and source URLs
✅ Fact Checking	Cross-reference claims against multiple indexed sources
🏷️ Entity Extraction	Identifies persons, organizations, URLs, emails
🛡️ Toxicity Filtering	Content safety scoring for search results

Crawler Enhancements

Feature	Description
📄 PDF Extraction	Text extraction from crawled PDF documents
🏗️ Structured Data	JSON-LD, OpenGraph, and meta tag parsing
🌍 Language Detection	Script + word-frequency detection (9 languages)
📡 RSS/Atom Feeds	Auto-discovery and parsing of feeds
📝 Content Diffing	Change detection between crawl versions
💻 Code Blocks	Extracts `<pre><code>` with language detection
📊 Table Extraction	HTML tables → structured data (CSV/dict)

Security & API

Feature	Description
🔑 API Key Management	Create, validate, revoke, rotate keys
👥 Role-Based Access	Admin/Reader/Crawler permission matrix
📋 Audit Logging	SQLite-backed audit trail for all tool calls
🔒 Webhook Signatures	HMAC-SHA256 payload verification
📊 Prometheus Metrics	`/metrics` endpoint for monitoring
📖 OpenAPI Spec	Auto-generated OpenAPI 3.1 at `/openapi-spec`

Developer Experience

Feature	Description
🐍 Python SDK	`InfoMeshClient` with sync/async search, crawl, suggest
🔌 Plugin System	Register custom plugins with lifecycle hooks
🦜 LangChain	`InfoMeshRetriever` integration
🦙 LlamaIndex	`InfoMeshReader` integration
🏗️ Haystack	`InfoMeshDocumentStore` integration
⎈ Helm Chart	Kubernetes deployment with configurable resources
🐳 Docker Compose	Multi-container setup with volumes

See CHANGELOG.md for the complete list of changes.

�🏗️ Architecture

Tech Stack

Layer	Technology	Why
Language	Python 3.12+	Modern async, type hints, `match/case`, `StrEnum`
P2P Network	libp2p (py-libp2p)	Battle-tested P2P stack with Kademlia DHT, Noise encryption
DHT	Kademlia (160-bit)	XOR distance-based routing, well-understood guarantees
Crawling	httpx + trafilatura	Best async HTTP + highest-accuracy content extraction
Keyword Search	SQLite FTS5	Zero-install, embedded, BM25 out of the box
Vector Search	ChromaDB (optional)	Semantic / embedding search with `all-MiniLM-L6-v2`
MCP Server	mcp-python-sdk	Standard protocol for LLM tool integration
Admin API	FastAPI	Local health, status, config endpoints
Serialization	msgpack	2–5× faster and 30% smaller than JSON
Compression	zstandard	Level-tunable, dictionary mode for similar documents
Dashboard	Textual	Rich TUI with tabs, sparklines, EQ visualization, BGM
Local LLM	ollama / llama.cpp	On-node summarization (Qwen 2.5, Llama 3.x, Gemma 3)
Logging	structlog	Structured, machine-parseable logs
Packaging	uv	10–100× faster than pip, handles everything

Search Flow (Target Latency: ~1 second)

🔒 Security & Trust

InfoMesh is designed with a zero-trust assumption — every peer is potentially adversarial. The system provides multiple layers of defense:

Content Integrity

Mechanism	Description
Content Attestation	Every crawled page gets `SHA-256(raw_html)` + `SHA-256(extracted_text)`, signed with the crawler's Ed25519 private key
Merkle Tree	Index-wide integrity proofs with membership verification — anyone can audit any document's inclusion
Random Audits	~1/hr per node. 3 independent auditors re-crawl a random URL and compare `content_hash`. Mismatch = trust penalty
P2P Credit Verification	Signed credit entries with Merkle proofs, verifiable by any peer

Network Security

Threat	Defense
Sybil Attack	Proof-of-Work node ID generation (~30s on avg CPU) + max 3 nodes per /24 subnet per DHT bucket
Eclipse Attack	≥3 independent bootstrap sources + routing table subnet diversity + periodic refresh
DHT Poisoning	Per-keyword publish rate limit (10/hr/node) + signed publications + content hash verification
Credit Farming	24hr probation for new nodes + statistical anomaly detection + raw HTTP hash audits
Man-in-the-Middle	All P2P transport encrypted via libp2p Noise protocol

Key Management

Ed25519 key pairs stored in ~/.infomesh/keys/
Key rotation: infomesh keys rotate — generates new key pair, publishes dual-signed revocation record to DHT
Peer identity derived from public key hash (consistent with libp2p PeerId)

Unified Trust Score

Every peer has a continuously updated trust score:

Trust = 0.15 × uptime  +  0.25 × contribution  +  0.40 × audit_pass_rate  +  0.20 × summary_quality

Tier	Score	Treatment
Trusted	≥ 0.8	Priority routing, lower search cost
Normal	0.5 – 0.8	Standard operation
Suspect	0.3 – 0.5	Higher audit frequency, limited features
Untrusted	< 0.3	Network isolation after 3× consecutive audit failures

🏢 Enterprise Readiness

InfoMesh is designed for production use, not just experimentation:

Split Deployment (DMZ / Private Network)

Enterprise environments can separate crawlers from indexers across network zones:

┌─────────── DMZ ──────────────┐       ┌──────── Private Network ────────┐
│                              │       │                                 │
│  infomesh --role crawler ──────────────▶  infomesh --role search      │
│  (crawls the public web)     │  P2P  │  (indexes + serves queries)    │
│                              │ auth  │                                 │
│  infomesh --role crawler ──────────────▶  infomesh --role search      │
│                              │       │                                 │
└──────────────────────────────┘       └─────────────────────────────────┘

Three node roles:

Role	Components	Use Case
`full` (default)	Crawler + Indexer + Search	Single-node or simple deployments
`crawler`	Crawler only, forwards pages to indexers	DMZ nodes with internet access
`search`	Indexer + Search only, accepts submissions	Private network, no internet needed

Configuration example (~/.infomesh/config.toml):

# DMZ Crawler node
[node]
role = "crawler"
listen_address = "0.0.0.0"

[network]
index_submit_peers = ["/ip4/10.0.0.1/tcp/4001", "/ip4/10.0.0.2/tcp/4001"]

# Private Search/Index node
[node]
role = "search"
listen_address = "10.0.0.1"

[network]
peer_acl = ["QmCrawler1PeerId...", "QmCrawler2PeerId..."]

CLI usage:

# Start as DMZ crawler
infomesh start --role crawler --seeds tech-docs

# Start as private indexer
infomesh start --role search --no-dashboard

Operational

Resource Governor — CPU, memory, disk I/O, and bandwidth limits with 4 preset profiles (minimal, balanced, contributor, dedicated). Dynamic throttling based on real-time system load
Pre-flight Checks — Disk space and network connectivity verified before startup
Load Guard — QPM (queries per minute) + concurrency limiting to prevent node overload
WAL Mode SQLite — Safe concurrent reads during dashboard refresh without locking crawl writes
Structured Logging — All library code uses structlog with machine-parseable output
Docker Support — Production-ready Dockerfile with volume mounts for persistent data

Configurable

TOML Configuration (~/.infomesh/config.toml) with environment variable overrides (INFOMESH_CRAWL_MAX_CONCURRENT=20)
Value Validation — All config values clamped to safe ranges with structured warnings
Dashboard Settings — All configuration editable via the TUI Settings tab (no file editing required)
Energy-aware Scheduling — LLM-heavy tasks preferentially scheduled during configured off-peak hours (1.5× credit multiplier)

Compliance

robots.txt strictly enforced — respects all crawl directives
DMCA Takedown — Signed takedown requests propagated via DHT; nodes comply within 24 hours
GDPR — Distributed deletion records for personal data; right-to-be-forgotten support
Content Attribution — AI-generated summaries labeled with content_hash + source URL
Paywall Detection — fetch_page() detects and respects paywalled content
Terms of Use — Clear TERMS_OF_USE.md covering crawler behavior and data handling

Scale

Designed for thousands of nodes with Kademlia DHT routing
3-layer deduplication prevents index bloat (URL normalization → SHA-256 exact → SimHash near-duplicate)
zstd-compressed snapshots for efficient index sharing between nodes
Common Crawl data import for bootstrapping large indexes

💰 Earning Credits

Credits are the incentive mechanism that keeps the network healthy. They are tracked locally per node — no blockchain, no central ledger.

How Credits Work

Credits Earned = Σ (Weight × Quantity × TimeMultiplier)

Earning Actions

Action	Weight	Category	How to Earn
Crawling	1.0 /page	Base	Just run InfoMesh — it auto-crawls from seed URLs
Query Processing	0.5 /query	Base	Other peers route search queries through your node
Document Hosting	0.1 /hr	Base	Passive — your indexed documents serve the network
Network Uptime	0.5 /hr	Base	Keep your node running. That's it
LLM Summarization	1.5 /page	LLM	Enable local LLM to auto-summarize crawled content
LLM for Peers	2.0 /request	LLM	Serve summarization requests from other nodes
PR — docs/typo	1,000 /merged PR	Bonus	Fix a typo or improve documentation
PR — bug fix	10,000 /merged PR	Bonus	Fix a bug with tests
PR — feature	50,000 /merged PR	Bonus	Implement a new feature
PR — major	100,000 /merged PR	Bonus	Core architecture or major feature

Time Multiplier

Base actions: Always 1.0×
LLM actions during off-peak hours (configurable, default 23:00–07:00): 1.5×
Off-peak scheduling is energy-conscious — the network preferentially routes batch LLM work to nodes currently in off-peak

Search Cost

Tier	Contribution Score	Search Cost	Effective Ratio
Tier 1	< 100	0.100 / query	10 crawls → 100 searches
Tier 2	100 – 999	0.050 / query	10 crawls → 200 searches
Tier 3	≥ 1,000	0.033 / query	10 crawls → 300 searches

Fairness Guarantees

Non-LLM nodes are never starved: A node doing only crawling at 10 pages/hr earns 100 searches/hr at worst tier
LLM earnings capped: LLM-related credits never exceed ~60% of total — LLM is a network bonus, not a participation requirement
Uptime rewards: 0.5 credits/hr just for keeping your node online, regardless of hardware
Search is never blocked: Even with zero credits, you can still search — see Zero-Dollar Debt below

💳 Zero-Dollar Debt — No Credit Card, No Real Money

What happens when your credits run out? You keep searching.

InfoMesh doesn't cut you off. There's no paywall, no "please enter your credit card," no upgrade button. Instead, there's a simple, human-friendly recovery path:

Phase	Duration	What Happens
✅ Normal	While balance > 0	Search at normal cost. Business as usual.
⏳ Grace Period	First 72 hours at zero	Search works exactly as before. Your balance goes negative, but there's no penalty. Take your time.
📉 Debt Mode	After 72 hours	Search continues, but at 2× cost. Debt accumulates — incentivizing recovery, never blocking.
🔄 Recovery	Whenever you want	Just run your node. Earn credits by crawling, hosting, or contributing. Once your balance is positive again, you're back to normal.

Credits ran out
     │
     ▼
┌─────────────────────────────────────┐
│  🟢 Grace Period (72 hours)         │
│  Search works normally.             │
│  Balance goes negative — no penalty.│
└──────────────┬──────────────────────┘
               │ 72h passed, still negative?
               ▼
┌─────────────────────────────────────┐
│  🟡 Debt Mode                       │
│  Search continues at 2× cost.      │
│  Debt accumulates.                  │
└──────────────┬──────────────────────┘
               │ Earn credits → balance > 0
               ▼
┌─────────────────────────────────────┐
│  🟢 Back to Normal                  │
│  Debt cleared. Grace reset.         │
│  Full speed ahead.                  │
└─────────────────────────────────────┘

The key principle: Debt in InfoMesh is measured in credits, not money. You recover by contributing, not by paying. Run your node, crawl some pages, keep the network alive — and your debt disappears naturally.

No credit card. No dollars. No subscription. No "trial expired" popup. Just run your node, and you're back.

🤝 Contributing

We welcome contributions of all kinds — code, documentation, bug reports, feature ideas, and seed URL lists.

Getting Started

# Clone and install
git clone https://github.com/dotnetpower/infomesh.git
cd infomesh
uv sync --dev

# Run the test suite (1,307 tests)
uv run pytest

# Run linter + formatter
uv run ruff check infomesh/ tests/
uv run ruff format .

# Run type checker
uv run mypy infomesh/

Ways to Contribute

Contribution	Difficulty	Impact
🐛 Report a bug	Easy	High — helps everyone
📝 Improve docs / translations	Easy	High — lowers entry barrier
🌱 Add seed URLs	Easy	Medium — expands crawl coverage
🧪 Write tests	Medium	High — currently 1,307 tests, always need more
🔧 Fix an issue	Medium	Direct impact
✨ Implement a feature	Hard	Moves the project forward
🔐 Security audit	Hard	Critical for trust

Code Style

Formatter: ruff format (black-compatible, 88 char lines)
Linter: ruff with E, F, I, UP, B, SIM rules
Type hints: Required on all public functions
Docstrings: Required on all public classes and functions
Tests: Every PR should include tests for new functionality
No print() in library code — use structlog

Pull Request Workflow

Fork the repository
Create a feature branch: git checkout -b feat/my-feature
Write code + tests
Run uv run pytest && uv run ruff check .
Submit a PR — you earn 1,000 – 100,000 credits per merged PR!

See CONTRIBUTING.md for the full guide.

📖 Documentation

Detailed documentation is available in the docs/ directory:

Document	Description
Overview	Project vision, principles, and mission
Architecture	System design, data flow, and component interaction
Credit System	Full incentive mechanics and fairness analysis
Tech Stack	Technology choices and rationale
Legal	robots.txt, DMCA, GDPR, compliance
Trust & Integrity	Security model and threat analysis
Security Audit	Vulnerability analysis and enterprise hardening
Console Dashboard	TUI dashboard, tabs, widgets, shortcuts
MCP Integration	MCP server setup, IDE configuration guide
Publishing	PyPI packaging, CI/CD, release process

📌 Documentation is also available in Korean (한국어).

📊 Project Stats

Metric	Value
Source modules	130+
Test files	67
Source lines	~27,000
Test lines	~14,000
Tests passing	1,307
MCP tools	15
Test coverage	Core modules fully tested
Development phases	10 (Phase 0 → 6, all complete)
Python version	3.12+
License	MIT

🗺️ Roadmap

All core phases are complete. Current focus is on community growth and production hardening.

Phase	Focus	Status
0	MVP — single-node crawl + index + MCP + CLI	✅ Complete
1	Index sharing — snapshots, Common Crawl, vector search, SimHash	✅ Complete
2	P2P network — libp2p, DHT, distributed crawl & index, Sybil/Eclipse defense	✅ Complete
3	Quality + incentives — ranking, credits, trust, attestation, audits, LLM	✅ Complete
4	Production — link graph, LLM re-ranking, attribution, legal compliance	✅ Complete
5A	Core stability — resource governor, auto-recrawl, query cache, load guard	✅ Complete
5B	Search quality — latency-aware routing, Merkle Tree integrity	✅ Complete
5C	Release readiness — Docker, key rotation, mDNS, LICENSE, CONTRIBUTING	✅ Complete
5D	Polish — LLM reputation, timezone verification, dashboard settings, P2P credit verification	✅ Complete
6	Search intelligence, RAG, security, observability, SDK, integrations, DX	✅ Complete

What's Next

🌍 Public bootstrap nodes — community-maintained seed nodes across multiple Azure regions
✅ Active: Bootstrap nodes are live in US East and US East 2. Your node connects automatically via bootstrap/nodes.json. No manual configuration needed.
🎭 JS rendering — Playwright-based SPA crawling for JS-heavy sites
📱 Web dashboard — optional browser UI alongside the TUI
🔍 Semantic search fusion — BM25 + vector hybrid ranking with RRF
🌐 Multi-language stemming — language-specific tokenization and stemming

⚖️ Legal

robots.txt: Strictly enforced. Sites that prohibit crawling are never crawled.
Copyright: Full text stored as cache only; search results return snippets with source attribution.
DMCA: Signed takedown requests propagated via DHT. All nodes must comply within 24 hours.
GDPR: Distributed deletion records. Nodes can exclude pages with personal data.
AI Summaries: Labeled as AI-generated, linked to source via content_hash, original URL always provided.
Terms of Use: See TERMS_OF_USE.md for full terms.

🙏 Acknowledgements

InfoMesh stands on the shoulders of excellent open-source projects:

httpx • trafilatura • libp2p • SQLite • ChromaDB • Textual • FastAPI • mcp-python-sdk • uv • structlog • zstandard

💡 Why InfoMesh?

The Problem

The Solution

🆓 Free. Every Interface. No Exceptions.

InfoMesh vs Other Web Search MCP Servers

🔐 Safe by Design — The Most Secure Search Engine You Can Run

Why InfoMesh Is Enterprise-Grade Secure

Unified Trust Score

Compliance Built In

🚀 Quick Start

Install & Run (Two Steps — No Git Required)

Try It Out

Install Permanently (Optional)

Connect to Your AI Assistant (MCP)

From Source (Contributors / Developers)

System Prerequisites

Clone & Run

Docker

Verify It Works

Examples

✨ Features

Core Capabilities

MCP Integration — Free Web Search for AI Assistants

Configure in VS Code / Copilot / Claude Desktop / Cursor

Optional Add-ons

� What's New in v0.2.0

Search Intelligence

RAG & Answer Extraction

Crawler Enhancements

Security & API

Developer Experience

�🏗️ Architecture

Tech Stack

Search Flow (Target Latency: ~1 second)

🔒 Security & Trust

Content Integrity

Network Security

Key Management

Unified Trust Score

🏢 Enterprise Readiness

Split Deployment (DMZ / Private Network)

Operational

Configurable

Compliance

Scale

💰 Earning Credits

How Credits Work

Earning Actions

Time Multiplier

Search Cost

Fairness Guarantees

💳 Zero-Dollar Debt — No Credit Card, No Real Money

🤝 Contributing

Getting Started

Ways to Contribute

Code Style

Pull Request Workflow

📖 Documentation

📊 Project Stats

🗺️ Roadmap

What's Next

⚖️ Legal

🙏 Acknowledgements

Resources

Latest Blog Posts

MCP directory API