Sediment

Semantic memory for AI agents. Local-first, MCP-native.

Combines vector search, a relationship graph, and access tracking into a unified memory intelligence layer — all running locally as a single binary.

Why Sediment?

• Single binary, zero config — no Docker, no Postgres, no Qdrant. Just sediment.
• Sub-16ms recall — local embeddings and vector search at 100 items, no network round-trips.
• 4-tool focused API — store, recall, list, forget. That's it.
• Works everywhere — macOS (Intel + ARM), Linux x86_64. All data stays on your machine.

Comparison

	Sediment	OpenMemory MCP	mcp-memory-service
Install	Single binary	Docker + Postgres + Qdrant	Python + pip
Dependencies	None	3 services	Python runtime + deps
Tools	4	10+	24
Embeddings	Local (all-MiniLM-L6-v2)	API-dependent	API-dependent
Graph features	Built-in	No	No
Memory decay	Built-in	No	No

Install

# Via crates.io
cargo install sediment-mcp

# Via Homebrew
brew tap rendro/tap
brew install sediment

# Via shell installer
curl -fsSL https://raw.githubusercontent.com/rendro/sediment/main/install.sh | sh

# From source
cargo install --path .

Setup

Add Sediment to your MCP client configuration:

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "sediment": {
      "command": "sediment"
    }
  }
}

Claude Code

Run sediment init in your project, or add manually to ~/.claude/settings.json:

{
  "mcpServers": {
    "sediment": {
      "command": "sediment"
    }
  }
}

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "sediment": {
      "command": "sediment"
    }
  }
}

VS Code (Copilot)

Add to .vscode/mcp.json in your project:

{
  "servers": {
    "sediment": {
      "command": "sediment"
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "sediment": {
      "command": "sediment"
    }
  }
}

JetBrains IDEs

Go to Settings > Tools > AI Assistant > MCP Servers, click +, and add:

{
  "sediment": {
    "command": "sediment"
  }
}

Tools

Tool	Parameters	Description
store	content, scope?	Save content to memory
recall	query, limit?	Search by semantic similarity
list	limit?, scope?	List stored items
forget	id	Delete an item by ID

CLI

sediment           # Start MCP server
sediment init      # Set up Claude Code integration
sediment stats     # Show database statistics
sediment list      # List stored items

How It Works

Two-Database Hybrid

All local, embedded, zero config:

• LanceDB — Vector embeddings and semantic similarity search
• SQLite (access.db) — Relationship graph, access tracking, decay scoring, consolidation queue

Key Features

• Memory decay: Results re-ranked by freshness (30-day half-life) and access frequency. Old memories rank lower but are never auto-deleted.
• Trust-weighted scoring: Validated and well-connected memories score higher.
• Project scoping: Automatic context isolation between projects. Same-project items get a similarity boost.
• Relationship graph: Items linked via RELATED, SUPERSEDES, and CO_ACCESSED edges. Recall expands results with 1-hop graph neighbors and co-access suggestions.
• Background consolidation: Near-duplicates (≥0.95 similarity) auto-merged; similar items (0.85–0.95) linked.
• Type-aware chunking: Intelligent splitting for markdown, code, JSON, YAML, and plain text.
• Conflict detection: Items with ≥0.85 similarity flagged on store.
• Cross-project recall: Results from other projects flagged.
• Local embeddings: all-MiniLM-L6-v2 via Candle (384-dim vectors, no API keys).
• Model integrity: SHA-256 verification of all model files on every load, pinned to a specific revision.
• Auto-migration: Database schema automatically migrated when upgrading from older versions.

Security

• Input bounds: Content (1MB), queries (10KB), JSON-RPC lines (10MB).
• Rate limiting: 600 tool calls per minute.
• SQL injection prevention: Sanitized filter expressions for LanceDB; parameterized queries for SQLite.
• Cross-project access control: Forget enforces project isolation. Cross-project content is flagged in recall results.
• Error sanitization: Internal errors logged to stderr; only generic messages returned to MCP clients.
• Retry with backoff: Transient failures retried with exponential backoff (3 attempts, 100ms–2s).

Performance

Sub-16ms recall at 100 items with full graph features enabled. See BENCHMARKS.md for detailed numbers.

DB Size	Graph Off	Graph On
100	~12ms	~15ms
1,000	~36ms	~65ms

Data Location

• Vector store: ~/.sediment/data/
• Graph + access tracking: ~/.sediment/access.db

Everything runs locally. Your data never leaves your machine.

Contributing

See CONTRIBUTING.md for build instructions and PR guidelines.

License

MIT