engraph — Vault Intelligence for AI Agents

Turn your Obsidian vault into a knowledge API. 5-lane hybrid search, MCP server, HTTP REST API, ChatGPT Actions — all local, all offline.

engraph turns your markdown vault into a searchable knowledge graph that any AI agent can query — Claude Code via MCP, ChatGPT via Actions, or any tool via REST API. It combines semantic embeddings, full-text search, wikilink graph traversal, temporal awareness, and LLM-powered reranking into a single local binary. Same model stack as qmd. No API keys, no cloud — everything runs on your machine.

Why engraph?

Plain vector search treats your notes as isolated documents. But knowledge isn't flat — your notes link to each other, share tags, reference the same people and projects. engraph understands these connections.

• 5-lane hybrid search — semantic embeddings + BM25 full-text + graph expansion + cross-encoder reranking + temporal scoring, fused via Reciprocal Rank Fusion. An LLM orchestrator classifies queries and adapts lane weights per intent. Time-aware queries like "what happened last week" or "March 2026 notes" activate the temporal lane automatically.
• MCP server for AI agents — engraph serve exposes 25 tools (search, read, section-level editing, frontmatter mutations, vault health, context bundles, note creation, PARA migration, identity) that Claude, Cursor, or any MCP client can call directly.
• HTTP REST API — engraph serve --http adds an axum-based HTTP server alongside MCP with 26 REST endpoints, API key authentication, rate limiting, and CORS. Web-based agents and scripts can query your vault with simple curl calls.
• Section-level editing — AI agents can read, replace, prepend, or append to specific sections by heading. Full note rewriting with frontmatter preservation. Granular frontmatter mutations (set/remove fields, add/remove tags and aliases).
• Vault health diagnostics — detect orphan notes, broken wikilinks, stale content, and tag hygiene issues. Available as MCP tool and CLI command.
• Obsidian CLI integration — auto-detects running Obsidian and delegates compatible operations. Circuit breaker (Closed/Degraded/Open) ensures graceful fallback.
• Real-time sync — file watcher keeps the index fresh as you edit in Obsidian. No manual re-indexing needed.
• Smart write pipeline — AI agents can create, edit, rewrite, and delete notes with automatic tag resolution, wikilink discovery, and folder placement based on semantic similarity.
• Fully local — llama.cpp inference with GGUF models (~300MB mandatory, ~1.3GB optional for intelligence). Metal GPU-accelerated on macOS (88 files indexed in 70s). No API keys, no cloud.

What problem it solves

You have hundreds of markdown notes. You want your AI coding assistant to understand what you've written — not just search keywords, but follow the connections between notes, understand context, and write new notes that fit your vault's structure.

Existing options are either cloud-dependent (Notion AI, Mem), limited to keyword search (Obsidian's built-in), or require you to copy-p

... View full README on GitHub