MCP-native semantic memory
Memory for AI agents
Single binary. Zero config. Sub-25ms recall.
Zero dependencies
No Docker. No Python. No cloud. One Rust binary with local embeddings. macOS (Intel + ARM) and Linux. All data stays in ~/.sediment/.
Intelligent recall
Semantic search with memory decay, trust scoring, relationship graph, and auto-consolidation. Not just vector search.
MCP native
Works with Claude Code, Claude Desktop, Cursor, VS Code Copilot, Windsurf, JetBrains — any MCP client.
How Sediment compares
The simplest path to persistent AI memory.
Sediment
Single Rust binary, zero config
- Single binary install
- Zero dependencies
- 5 focused MCP tools
- Local embeddings
- Relationship graph
- Memory decay & trust scoring
OpenMemory MCP
Mem0's local MCP server
- Docker + Postgres + Qdrant
- 3 services required
- 10+ MCP tools
- API-dependent embeddings
- No relationship graph
- No memory decay
mcp-memory-service
Python MCP memory server
- Python + pip install
- Python runtime + dependencies
- 24 MCP tools
- API-dependent embeddings
- No relationship graph
- No memory decay
Performance
Sub-25ms recall at 10K items. All local, no network round-trips.
With full graph features enabled. Apple M3 Max. Methodology
5 tools. That's it.
A focused API that LLMs can actually use well.
store Save content with title, tags, metadata, expiration, scope, and related item links
recall Semantic search with decay scoring, trust weighting, graph expansion, and co-access suggestions
list Browse stored items by scope with tag filtering
forget Delete an item from the vector store and relationship graph
connections Explore the relationship graph for any item
Under the hood
Three-database hybrid
LanceDB for vectors, SQLite for the relationship graph, SQLite for access tracking. All embedded, zero config.
Memory decay
30-day half-life freshness scoring combined with log-scaled access frequency. Old memories rank lower but are never deleted.
Trust-weighted scoring
Validated and well-connected memories score higher. The more you use a memory, the more trustworthy it becomes.
Auto-consolidation
Near-duplicates auto-merged. Similar items linked. Runs in the background, non-blocking.
Project scoping
Automatic context isolation per project. Same-project items boosted, cross-project results flagged.
Local embeddings
all-MiniLM-L6-v2 via Candle. 384-dim vectors, no API keys, no network calls.
Type-aware chunking
Intelligent splitting for markdown, code, JSON, YAML, and plain text. Long content is chunked with individual embeddings.
Cross-project recall
Results from other projects are surfaced and flagged with provenance metadata. Knowledge flows across your work.
Auto-tagging
Items stored without tags automatically inherit tags from similar existing items. Your memory organizes itself.
Get started
Add to your MCP client
{
"mcpServers": {
"sediment": {
"command": "sediment"
}
}
} Works with Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, JetBrains
CLI included
Manage your memory from the terminal.