Your Local Second Brain: Index, search, and synthesize your entire digital life.

ClawdHub: GNO skills bundled for Clawdbot — clawdhub.com/gmickel/gno

GNO is a local knowledge engine that turns your documents into a searchable, connected knowledge graph. Index notes, code, PDFs, and Office docs. Get hybrid search, AI answers with citations, and wiki-style note linking—all 100% offline.

• Quick Start
• Installation
• Search Modes
• Agent Integration
• Web UI
• REST API
• How It Works
• Features
• Local Models
• Architecture
• Development

• Knowledge Graph: Interactive force-directed visualization of document connections
• Graph with Similarity: See semantic similarity as golden edges (not just wiki/markdown links)
• CLI: gno graph command with collection filtering and similarity options
• Web UI: /graph page with zoom, pan, collection filter, similarity toggle
• MCP: gno_graph tool for AI agents to explore document relationships
• REST API: /api/graph endpoint with full query parameters

• Note Linking: Wiki-style [[links]], backlinks, and AI-powered related notes
• Tag System: Filter searches by frontmatter tags with --tags-any/--tags-all
• Web UI: Outgoing links panel, backlinks panel, related notes sidebar
• CLI: gno links, gno backlinks, gno similar commands
• MCP: gno_links, gno_backlinks, gno_similar tools

gno init ~/notes --name notes    # Point at your docs
gno index                        # Build search index
gno query "auth best practices"  # Hybrid search
gno ask "summarize the API" --answer  # AI answer with citations

Requires Bun >= 1.0.0.

bun install -g @gmickel/gno

macOS: Vector search requires Homebrew SQLite:

brew install sqlite3

Verify everything works:

gno doctor

One command to add GNO to your AI assistant:

gno mcp install                      # Claude Desktop (default)
gno mcp install --target cursor      # Cursor
gno mcp install --target claude-code # Claude Code CLI
gno mcp install --target zed         # Zed
gno mcp install --target windsurf    # Windsurf
gno mcp install --target codex       # OpenAI Codex CLI
gno mcp install --target opencode    # OpenCode
gno mcp install --target amp         # Amp
gno mcp install --target lmstudio    # LM Studio
gno mcp install --target librechat   # LibreChat

Check status: gno mcp status

Skills integrate via CLI with no MCP overhead:

gno skill install --scope user       # User-wide
gno skill install --target codex     # Codex
gno skill install --target all       # Both Claude + Codex

Full setup guide: MCP Integration · CLI Reference

BM25 indexes full documents (not chunks) with Snowball stemming, so "running" matches "run". Vector embeds chunks with document titles for context awareness.

gno search "handleAuth"              # Find exact matches
gno vsearch "error handling patterns" # Semantic similarity
gno query "database optimization"    # Full pipeline
gno ask "what did we decide" --answer # AI synthesis

Output formats: --json, --files, --csv, --md, --xml

Give your local LLM agents a long-term memory. GNO integrates as a Claude Code skill or MCP server, allowing agents to search, read, and cite your local files.

Skills add GNO search to Claude Code/Codex without MCP protocol overhead:

gno skill install --scope user

Then ask your agent: "Search my notes for the auth discussion"

Skill setup guide →

Connect GNO to Claude Desktop, Cursor, Raycast, and more:

GNO exposes tools via Model Context Protocol:

Design: MCP tools are retrieval-only. Your AI assistant (Claude, GPT-4) synthesizes answers from retrieved context. Best retrieval (GNO) + best reasoning (your LLM).

MCP setup guide →

Visual dashboard for search, browsing, editing, and AI answers. Right in your browser.

gno serve                    # Start on port 3000
gno serve --port 8080        # Custom port

Open http://localhost:3000 to:

• Search: BM25, vector, or hybrid modes with visual results
• Browse: Paginated document list, filter by collection
• Edit: Create, edit, and delete documents with live preview
• Ask: AI-powered Q&A with citations
• Manage Collections: Add, remove, and re-index collections
• Switch presets: Change models live without restart

Three retrieval modes: BM25 (keyword), Vector (semantic), or Hybrid (best of both). Adjust search depth for speed vs thoroughness.

Full-featured markdown editor with:

View documents with full context: outgoing links, backlinks, and AI-powered related notes sidebar.

Interactive visualization of document connections. Wiki links, markdown links, and optional similarity edges rendered as a navigable constellation.

• Add collections with folder path input
• View document count, chunk count, embedding status
• Re-index individual collections
• Remove collections (documents preserved)

Ask questions in natural language. GNO searches your documents and synthesizes answers with inline citations linking to sources.

Everything runs locally. No cloud, no accounts, no data leaving your machine.

Detailed docs: Web UI Guide

Programmatic access to all GNO features via HTTP.

# Hybrid search
curl -X POST http://localhost:3000/api/query \
  -H "Content-Type: application/json" \
  -d '{"query": "authentication patterns", "limit": 10}'

# AI answer
curl -X POST http://localhost:3000/api/ask \
  -H "Content-Type: application/json" \
  -d '{"query": "What is our deployment process?"}'

# Index status
curl http://localhost:3000/api/status

No authentication. No rate limits. Build custom tools, automate workflows, integrate with any language.

Full reference: API Documentation

graph TD
    A[User Query] --> B(Query Expansion)
    B --> C{Lexical Variants}
    B --> D{Semantic Variants}
    B --> E{HyDE Passage}

    C --> G(BM25 Search)
    D --> H(Vector Search)
    E --> H
    A --> G
    A --> H

    G --> I(Ranked Results)
    H --> J(Ranked Results)
    I --> K{RRF Fusion}
    J --> K

    K --> L(Top 20 Candidates)
    L --> M(Cross-Encoder Rerank)
    M --> N[Final Results]

0. Strong Signal Check: Skip expansion if BM25 has confident match (saves 1-3s)
1. Query Expansion: LLM generates lexical variants, semantic rephrases, and a HyDE passage
2. Parallel Retrieval: Document-level BM25 + chunk-level vector search on all variants
3. Fusion: RRF with 2× weight for original query, tiered bonus for top ranks
4. Reranking: Qwen3-Reranker scores best chunk per document (4K), blended with fusion

Deep dive: How Search Works

Models auto-download on first use to ~/.cache/gno/models/.

gno models use slim
gno models pull --all  # Optional: pre-download models (auto-downloads on first use)

Configuration: Model Setup

┌─────────────────────────────────────────────────┐
│            GNO CLI / MCP / Web UI / API         │
├─────────────────────────────────────────────────┤
│  Ports: Converter, Store, Embedding, Rerank    │
├─────────────────────────────────────────────────┤
│  Adapters: SQLite, FTS5, sqlite-vec, llama-cpp │
├─────────────────────────────────────────────────┤
│  Core: Identity, Mirrors, Chunking, Retrieval  │
└─────────────────────────────────────────────────┘

Details: Architecture

git clone https://github.com/gmickel/gno.git && cd gno
bun install
bun test
bun run lint && bun run typecheck

Contributing: CONTRIBUTING.md

MIT

made with ❤️ by @gmickel