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-25ms recall — local embeddings and vector search, no network round-trips.
5-tool focused API — store, recall, list, forget, connections. 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	5	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	Description
`store`	Save content with optional title, tags, metadata, expiration, scope, replace, and related item links
`recall`	Search memories by semantic similarity with decay scoring, trust weighting, graph expansion, and co-access suggestions
`list`	List stored items by scope (project/global/all) with tag filtering
`forget`	Delete an item by ID (removes from vector store and graph)
`connections`	Show relationship graph for an item (related, supersedes, co-accessed edges)

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

Three-Database Hybrid

All local, embedded, zero config:

LanceDB — Vector embeddings and semantic similarity search
SQLite (graph) — Relationship tracking: RELATED, SUPERSEDES, CO_ACCESSED, CLUSTER_SIBLING edges
SQLite (access) — Mutable counters: 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.
Auto-tagging: Items without tags inherit tags from similar existing items.
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 with provenance metadata.
Local embeddings: all-MiniLM-L6-v2 via Candle (384-dim vectors, no API keys).

Performance

Sub-25ms recall latency at 10K items with full graph features enabled. See BENCHMARKS.md for detailed numbers.

DB Size	Graph Off	Graph On
100	~8ms	~10ms
1,000	~12ms	~15ms
10,000	~18ms	~23ms

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

sediment-mcp 0.2.2