Give your AI agent persistent memory. One line in your MCP config — corrections, preferences, and conventions persist across sessions. No workflow changes, no cloud, no API costs for search.

Part of PLUR — where, in our tool-routing and local-knowledge benchmark, Haiku with memory outperformed Opus without it at 10x less cost.

Setup (30 seconds)

Claude Code

One command — sets up storage, MCP config, and hooks:

npx @plur-ai/mcp init

Restart Claude Code. Done. Your agent now has persistent memory with automatic injection.

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "plur": { "command": "npx", "args": ["-y", "@plur-ai/mcp"] }
  }
}

Windsurf / any MCP client

Same pattern — point it at npx -y @plur-ai/mcp.

That's it. Your agent now has memory. Use your tools as usual — corrections accumulate automatically.

What happens

You correct your agent  →  engram created       →  YAML on your disk
Next session starts     →  relevant ones injected →  agent remembers
You rate the result     →  engram strengthens    →  quality improves

Knowledge is stored as engrams — small assertions that strengthen with use and decay when irrelevant. Search is fully local (BM25 + embeddings), so memory recall costs nothing and works offline. Benchmark methodology →

Tools

By default (lean profile), your agent gets 11 tools. Everything else is reachable through plur_admin:

Tool	What it does
`plur_session_start`	Start a session — injects relevant engrams for your task
`plur_learn`	Store a memory — correction, preference, convention, or decision
`plur_recall_hybrid`	Best default — BM25 + embeddings merged via RRF. Zero cost.
`plur_feedback`	Rate a memory — trains relevance over time
`plur_forget`	Retire a memory (history preserved)
`plur_session_end`	End a session — captures summary and new learnings
`plur_status`	System health
`plur_doctor`	Diagnose embedder, hybrid search, and remote-store auth
`plur_packs_uninstall`	Remove an installed pack
`plur_tensions_purge`	Clear stale/resolved tensions
`plur_admin`	Dispatch to any other tool: `{ action: "plur_packs_install", args: {...} }`

Less commonly needed tools (plur_recall, plur_inject_hybrid, plur_learn_batch, plur_ingest, plur_sync, plur_packs_install, plur_packs_list, plur_capture, plur_timeline, and more) are all reachable via plur_admin. Set PLUR_TOOL_PROFILE=full to expose all 40 tools directly.

Sync across machines

Your agent can sync memory to any git remote:

Agent: plur_sync({ remote: "git@github.com:you/plur-memory.git" })
→ "Initialized and pushed."

# On another machine, same remote:
Agent: plur_sync()
→ "Synced. Pulled 12 remote commits."

Works with GitHub, GitLab, Gitea, any git host. Your data, your repo.

Configuration

Custom storage path:

{
  "mcpServers": {
    "plur": {
      "command": "npx",
      "args": ["-y", "@plur-ai/mcp"],
      "env": { "PLUR_PATH": "/path/to/storage" }
    }
  }
}

Default: ~/.plur/. Everything is plain YAML — open it, read it, edit it.

Benchmark

Retrieval (LongMemEval R@5): 76.7% out-of-the-box · 97.0% with openai-3-large embeddings

Agent task impact: Haiku + PLUR outperforms Opus without memory at ~10× less cost. House rules: 12–0 across Haiku, Sonnet, Opus. A/B win rate: 89%.

Full methodology →