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.

[](https://github.com/devwhodevs/engraph/actions/workflows/ci.yml) [](LICENSE) [](https://github.com/devwhodevs/engraph/releases)

engraph turns your markdown vault into a searchable knowledge graph that any AI agent can query — Claude Code via [MCP](https://modelcontextprotocol.io), ChatGPT via [Actions](https://platform.openai.com/docs/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](https://github.com/tobi/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