YantrikDB MCP Server

Cognitive memory for AI agents. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible client.

Website: yantrikdb.com · Docs: yantrikdb.com/guides/mcp · GitHub: yantrikos/yantrikdb-mcp

Install

pip install yantrikdb-mcp

Configure

Add to your MCP client config:

{
  "mcpServers": {
    "yantrikdb": {
      "command": "yantrikdb-mcp"
    }
  }
}

That's it. The agent auto-recalls context, auto-remembers decisions, and auto-detects contradictions — no prompting needed.

Remote Server Mode

Run as a shared server accessible from multiple machines:

# Generate a secure API key
export YANTRIKDB_API_KEY=$(python -c "import secrets; print(secrets.token_urlsafe(32))")

# Start SSE server
yantrikdb-mcp --transport sse --port 8420

Connect clients to the remote server:

{
  "mcpServers": {
    "yantrikdb": {
      "type": "sse",
      "url": "http://your-server:8420/sse",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Supports sse and streamable-http transports. Bearer token auth via YANTRIKDB_API_KEY env var.

Environment Variables

Variable	Default	Description
YANTRIKDB_DB_PATH	~/.yantrikdb/memory.db	Database file path
YANTRIKDB_EMBEDDING_MODEL	all-MiniLM-L6-v2	Sentence transformer model
YANTRIKDB_EMBEDDING_DIM	384	Embedding dimension
YANTRIKDB_API_KEY	(none)	Bearer token for network transports

Why Not File-Based Memory?

File-based memory (CLAUDE.md, memory files) loads everything into context every conversation. YantrikDB recalls only what's relevant.

Benchmark: 15 queries × 4 scales

Memories	File-Based	YantrikDB	Savings	Precision
100	1,770 tokens	69 tokens	96%	66%
500	9,807 tokens	72 tokens	99.3%	77%
1,000	19,988 tokens	72 tokens	99.6%	84%
5,000	101,739 tokens	53 tokens	99.9%	88%

Selective recall is O(1). File-based memory is O(n).

• At 500 memories, file-based exceeds 32K context windows
• At 5,000, it doesn't fit in any context window — not even 200K
• YantrikDB stays at ~70 tokens per query, under 60ms latency
• Precision improves with more data — the opposite of context stuffing

Run the benchmark yourself: python benchmarks/bench_token_savings.py

Tools

15 tools, full engine coverage:

Tool	Actions	Purpose
remember	single / batch	Store memories — decisions, preferences, facts, corrections
recall	search / refine / feedback	Semantic search, refinement, and retrieval feedback
forget	single / batch	Tombstone memories
correct	—	Fix incorrect memory (preserves history)
think	—	Consolidation + conflict detection + pattern mining
memory	get / list / search / update_importance / archive / hydrate	Manage individual memories + keyword search
graph	relate / edges / link / search / profile / depth	Knowledge graph operations
conflict	list / get / resolve / reclassify	Handle contradictions and teach substitution patterns
trigger	pending / history / acknowledge / deliver / act / dismiss	Proactive insights and warnings
session	start / end / history / active / abandon_stale	Session lifecycle management
temporal	stale / upcoming	Time-based memory queries
procedure	learn / surface / reinforce	Procedural memory — learn and reuse strategies
category	list / members / learn / reset	Substitution categories for conflict detection
personality	get / set	AI personality traits from memory patterns
stats	stats / health / weights / maintenance	Engine stats, health, weights, and index rebuilds

See yantrikdb.com/guides/mcp for full documentation.

Examples

1. Auto-recall at conversation start

User: "What did we decide about the database migration?"

The agent automatically calls recall("database migration decision") and retrieves relevant memories before responding — no manual prompting needed.

2. Remember decisions + build knowledge graph

User: "We're going with PostgreSQL for the new service. Alice will own the migration."

The agent calls:

• remember(text="Decided to use PostgreSQL for the new service", domain="architecture", importance=0.8)
• remember(text="Alice owns the PostgreSQL migration", domain="people", importance=0.7)
• graph(action="relate", entity="Alice", target="PostgreSQL Migration", relationship="owns")

3. Contradiction detection

After storing "We use Python 3.11" and later "We upgraded to Python 3.12", calling think() detects the conflict. The agent surfaces it:

"I found a contradiction: you previously said Python 3.11, but recently mentioned Python 3.12. Which is current?"

Then resolves with conflict(action="resolve", conflict_id="...", strategy="keep_b").

Privacy Policy

YantrikDB MCP Server stores all data locally on your machine (default: ~/.yantrikdb/memory.db). No data is sent to external servers, no telemetry is collected, and no third-party services are contacted during operation.

• Data collection: Only what you explicitly store via the remember tool or what the AI agent stores on your behalf.
• Data storage: Local SQLite database on your filesystem. You control the path via YANTRIKDB_DB_PATH.
• Third-party sharing: None. Data never leaves your machine in local (stdio) mode.
• Network mode: When using SSE/HTTP transport, data travels between your client and your self-hosted server. No Anthropic or third-party servers are involved.
• Embedding model: Uses a local ONNX model (all-MiniLM-L6-v2). Model files are downloaded once from Hugging Face Hub on first use, then cached locally.
• Retention: Data persists until you delete it (forget tool) or delete the database file.
• Contact: [email protected]

Full policy: yantrikdb.com/privacy

Support

• Issues: github.com/yantrikos/yantrikdb-mcp/issues
• Email: [email protected]
• Docs: yantrikdb.com/guides/mcp

License

This MCP server is licensed under MIT — use it freely in any project.

Note: This package depends on yantrikdb (the cognitive memory engine), which is licensed under AGPL-3.0. The AGPL applies to the engine itself — if you modify the engine and distribute it or provide it as a network service, those modifications must also be AGPL-3.0. Using the engine as-is via this MCP server does not trigger AGPL obligations on your code.