Smriti

Local-first persistent memory for AI agents via MCP.

Smriti (स्मृति) — Sanskrit for "memory, remembrance"

One brain. Every agent. Zero cloud. Zero cost.

What is this?

A standalone MCP server backed by sqlite-vec and a local embedding model. Install it, point any MCP-compatible agent at it, and every AI you use shares one persistent, semantically searchable memory.

| | Smriti | Cloud alternatives | |---|---|---| | Setup | npx smriti | Accounts + API keys + config | | Cost | $0 | Variable | | Privacy | 100% local | Data on external servers | | Offline | Full functionality | Needs internet | | Portability | Single .db file | DB export/migration |

Install

npm install -g smriti

Usage

# stdio mode (for Claude Code, Cursor, etc.)
smriti

# HTTP mode (for remote agents)
smriti --http --port 3838

MCP Client Configuration

Claude Code

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "memory": {
      "command": "smriti"
    }
  }
}

Cursor

Add to MCP settings:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["smriti"]
    }
  }
}

Any MCP client (HTTP mode)

smriti --http --port 3838

Then point your client at http://localhost:3838/mcp.

Tools

| Tool | Description | |------|-------------| | capture | Store a thought with auto-extracted metadata | | search | Semantic search — find thoughts by meaning | | recall | Browse recent memories with filters | | forget | Delete a specific memory by ID | | context | Get structured context bundle for a topic | | stats | Memory patterns and insights |

Resources

| URI | Description | |-----|-------------| | memory://recent | Last 24h of thoughts | | memory://topics | Topic index with counts | | memory://people | People mentioned + context | | memory://stats | Overall memory statistics |

Prompts

| Name | Description | |------|-------------| | brain-dump | Guided capture session | | weekly-review | End-of-week synthesis | | migrate | Import memories from other sources |

Configuration

Config lives at ~/.smriti/config.json:

{
  "db_path": "~/.smriti/brain.db",
  "embedding": {
    "provider": "onnx",
    "model": "Xenova/all-MiniLM-L6-v2"
  },
  "extraction": {
    "provider": "rules"
  },
  "server": {
    "transport": "stdio",
    "port": 3838
  }
}

How it works

1. You (or an agent) call capture with text
2. Smriti generates a vector embedding locally (all-MiniLM-L6-v2 via ONNX)
3. Regex-based extraction pulls out people, topics, actions, and classifies the type
4. Everything is stored in a single SQLite file with sqlite-vec for vector search
5. search finds thoughts by semantic similarity, not just keywords
6. All data stays on your machine — nothing leaves localhost

License

MIT