Obsidian Hybrid Search MCP server

Obsidian Hybrid Search

Your Obsidian vault already contains your best thinking. Obsidian Hybrid Search makes that thinking easier to find, reuse, and bring into AI-assisted work.

It gives your vault one retrieval engine and three practical ways to use it. The native [Obsidian plugin][obsidian-plugin] gives you fast search, previews, similar notes, link discovery, and graph views while you write. The MCP server lets AI agents search and read your notes as tool calls. The CLI gives power users the same engine for indexing, filtering, reranking, reading, and scripting.

The search understands how real vaults are built. It combines semantic search, BM25 full text, fuzzy title and alias matching, tags, folders, frontmatter, wikilinks, backlinks, and similar-note lookup. You can search by idea, phrase, title, relationship, or metadata without remembering the exact words you wrote.

That turns Obsidian into a stronger personal knowledge system and a better starting point for AI work. Agents can begin from your own notes, pull cited context from source files, follow related material, and work with knowledge you already trust. OHS runs locally by default with SQLite, FTS5, sqlite-vec, RRF ranking, and optional OpenAI-compatible embedding APIs.

Search quality

Evaluated on the Obsidian Help vault (171 notes, 58 queries, local model):

¹ CPU (Apple Silicon), hybrid mode, no rerank. ² GPU (Apple Silicon Metal), LLM query expansion + reranking.

OHS uses Xenova/multilingual-e5-small. How to reproduce → · Full benchmark →

Real knowledge-vault benchmark

OHS is also evaluated on Andy Matuschak’s public evergreen notes, converted into an Obsidian vault with title-based note filenames, source URLs in frontmatter, local attachments, and 5,000+ internal note links across 1,357 notes.

The curated golden set includes 78 hand-judged queries across known-item lookup, paraphrases, quote fragments, ambiguous topics, citation lookup, and multi-note evidence.

Using the default local embedding model, OHS performs strongly on this dense note network.

The benchmark exercises retrieval over a highly connected real-world knowledge vault, including queries that do not simply repeat note titles.

Result JSON · Reproduce and interpret →

Large memory benchmark

Shortened. The full README is on GitHub.