Adaptive Memory

Private, local memory for your coding agents. Free and open source. No cloud, no API key.

Adaptive Memory is an MCP server that gives Claude Code, Cursor, Windsurf, Cline and other local agents a long-term memory. Memories live in one SQLite file on your machine. Search is semantic (by meaning) plus keyword, using a small embedding model that runs locally. No API key, no account, no server, no Docker.

Install

Requires Python 3.10+.

pip install git+https://github.com/rakesh1308/AdaptiveMemoryEngine.git
adaptive-memory warmup   # optional: downloads the ~130 MB embedding model now, once

Check it works: adaptive-memory stats should print a JSON summary.

Claude Code

claude mcp add adaptive-memory -- adaptive-memory

Cursor / Windsurf / Cline / Claude Desktop

Add to your MCP config:

{
  "mcpServers": {
    "adaptive-memory": { "command": "adaptive-memory" }
  }
}

If your agent can't find the adaptive-memory command, use the full Python path instead: "command": "python", "args": ["-m", "adaptive_memory"].

Teach your agent when to use it (optional but recommended)

Copy skill/adaptive-memory/SKILL.md into your agent's skills folder, for example ~/.claude/skills/adaptive-memory/SKILL.md for Claude Code. It tells the agent when to search, what to store, and what never to store.

Try it

Restart your agent, then say: "Remember that we use PostgreSQL for the main database." In a later session ask: "Which database do we use?"

Tools

Where your data lives

Use a different ADAPTIVE_MEMORY_DB per project if you want separate memories. To back up, copy the database file or run adaptive-memory export > backup.json.

How it works

• One SQLite file with an FTS5 keyword index and stored vectors.
• Search merges keyword and semantic rankings. If the embedding model can't load, it falls back to keyword search and says so.
• Changing the embedding model re-embeds your memories automatically. Nothing is lost.
• Brute-force vector search is fast up to tens of thousands of memories, which is far more than a coding agent will write.

How good is the search?

study/eval_embeddings.py scores retrieval on 20 memories, 20 paraphrased questions (no shared wording, e.g. "who signs off schema changes?" for "Priya approves all database migrations") and 10 exact-term queries. Top-1 accuracy:

Set OPENAI_API_KEY and rerun the script to add OpenAI text-embedding-3-small to the table. The set is small, so treat the numbers as a sanity check, not a benchmark. Switch models with ADAPTIVE_MEMORY_MODEL; memories are re-embedded automatically.

Is SQLite enough? (Do I need Postgres?)

Yes for this use. Search keeps all vectors in memory and compares them with one matrix multiply. Measured median search time (384-dim vectors, 8-word memories):

A coding agent writes hundreds or low thousands of memories. Postgres + pgvector only pays off with millions of memories or many users sharing one server.

Works where

Agents that run on your machine and can start a local process: Claude Code, Cursor, Windsurf, Cline, VS Code Copilot, Zed, Claude Desktop. It does not work in the claude.ai website or phone apps, which can't reach a process on your computer.

Privacy

Memories never leave your device. The only network call is the one-time model download from Hugging Face. Do not store passwords or API keys in memory.

License

Apache License 2.0. See LICENSE.