memini-ai
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@memini-airecall what we learned about the authentication module"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
memini-ai
Local-first memory for AI coding agents. Three MCP tools: remember, recall, orient.
Postgres + pgvector for storage, BGE-M3 embeddings on CPU, hybrid vector + full-text search.
Quick start
uv tool install memini-ai --index https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match
memini-ai db up # pgvector/pgvector:pg18 on 127.0.0.1:5555
memini-ai migrate # optional; serve applies migrations on first connection
memini-ai warm # one-time ~2.2 GB BGE-M3 download, then embeds one stringThe --index flags install CPU torch instead of the default CUDA build. Run warm before wiring
up a client: the first remember or recall would otherwise download BGE-M3 (about 2.2 GB) inside
a tool call and time out until it finishes.
Then, from the project you want memory for:
cd ~/Projects/my-repo
memini-ai init --client claude-code --project my-repoRestart the client and call orient.
Related MCP server: memento
Docs
License
MIT. See LICENSE.
This server cannot be deployed
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