Vectr MCP server

<!-- mcp-name: io.github.swapnanil/vectr -->

Vectr

Delivery, not storage. A local daemon that gives an AI code editor semantic search over your codebase, plus working memory that shows up on its own when it is relevant.

Version 1.12.0 · Last updated 2026-09-03 · CHANGELOG

In 30 seconds

Your AI code editor pays for the same knowledge twice. It re-reads files it read yesterday, re-greps symbols it already found, and loses the exact signature it discovered at turn 5 the moment the conversation is compacted. Vectr is one local process that removes both halves of that bill:

• Retrieval that understands code. AST-aware chunks, a symbol and call graph, and hybrid semantic plus BM25 ranking. Describe a behaviour and get the whole function back, in one call instead of a grep loop and three blind file reads.
• Working memory that gets delivered. Save a finding once. It comes back verbatim in under 50ms, and on editors that expose session hooks it arrives automatically at the moment it applies, without the agent choosing to ask for it.
• Local, keyless, zero config. One pip install, one vectr start. The embedding model runs on your machine. Nothing is transmitted anywhere, and there is no API key.

pip install vectr && cd /path/to/your/project && vectr start

That is the whole setup. Vectr writes the MCP config for your editor, then indexes in the background.

How vectr differs from other AI agent memory tools

There is a healthy category of general-purpose memory layers for AI agents, and the good ones solve a real problem well. Vectr is most often compared to them, so here is the difference stated plainly. The left column describes the design these tools tend to share, not any one product; individual tools vary, so check the one you are considering.

Shortened. The full README is on GitHub.