YantrikDB MCP Server

YantrikDB — Cognitive memory for AI agents. Persistent semantic recall, knowledge graph, contradiction detection, and procedural learning. Ships as embeddable engine, network database, or MCP server.

Works with Claude Code, Cursor, Windsurf, Hermes Agent, Prime Agent, and any MCP-compatible client. Ships a portable Agent Skills skill — skills/persistent-memory — that teaches any compliant harness the memory golden path.

Website: yantrikdb.com · Docs: yantrikdb.com/guides/mcp · GitHub: yantrikos/yantrikdb-mcp · Paper: Skill as Memory, Not Document

Every value on screen is the server's own answer over MCP — driver: docs/demo/demo.py, recorded with docs/demo/demo.tape.

At a glance

Install

# Default — uses the engine's bundled 64-dim embedder. ~10 MB install,
# ~80 ms cold start, no native ML deps.
pip install yantrikdb-mcp

# Optional: higher-quality 384-dim ONNX MiniLM-L6-v2 embedder (~150 MB install).
# Auto-used when an existing pre-v0.6 database is detected.
pip install 'yantrikdb-mcp[onnx]'

Upgrading from v0.5.x? Your existing database stays at 384 dim — install the [onnx] extra to keep using it transparently. New installs default to the lean bundled embedder. v0.7.0+ pins the engine migration fix automatically. See Embedder backends below.

Configure

The MCP server has three deployment modes. Pick the one that fits your setup.

Mode 1 — Local (default, recommended for single user)

The MCP server runs the engine in-process with a local SQLite database. Fast, private, zero dependencies.

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

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

Mode 2 — HTTP Cluster (recommended for shared/multi-machine setups)

Forward all tool calls to a YantrikDB HTTP cluster instead of using an embedded engine. The MCP server is a thin stateless client — all memories live on the cluster, accessible from any machine.

Benefits: shared memory across machines, high availability, no local embedder download, no local database.

{
  "mcpServers": {
    "yantrikdb": {
      "command": "yantrikdb-mcp",
      "env": {
        "YANTRIKDB_SERVER_URL": "http://node1:7438,http://node2:7438",
        "YANTRIKDB_TOKEN": "ydb_your_database_token"
      }
    }
  }
}

• Comma-separate multiple nodes for Raft cluster auto-discovery
• Automatic leader-following on failover
• 15s request timeout
• Get the token from the cluster: yantrikdb token create --db your_database

Mode 3 — SSE Server (legacy, single remote instance)

Run the MCP server itself as a long-running SSE server with its own embedded database. Clients connect via HTTP streaming.

# 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

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

Supports sse and streamable-http transports. Note: SSE connections can drop on idle — Mode 2 (HTTP Cluster) is more reliable for shared deployments.

Environment Variables

Embedder backends

Local mode ships three embedders. The MCP picks one automatically; override with YANTRIKDB_EMBEDDER.

auto (default) reads the SQLite file at YANTRIKDB_DB_PATH and picks onnx if it already contains memories — preserving recall quality on upgrades — and bundled otherwise. Multilingual is never auto-selected because its 256-dim vectors are incompatible with existing bundled (64-dim) or ONNX (384-dim) databases; opt-in only on fresh databases.

Set YANTRIKDB_EMBEDDER=bundled|onnx|multilingual to override. If you set YANTRIKDB_EMBEDDER=onnx (or auto-detection picks it) without installing the extras, the server fails fast with an install hint:

RuntimeError: Existing DB has memories embedded with the 384-dim ONNX
model, but ONNX deps are missing.
  Install with:  pip install 'yantrikdb-mcp[onnx]'

For the multilingual backend, the engine downloads potion-multilingual-128M (~460 MB tarball) from github.com/yantrikos/yantrikdb-models on first use. The download is SHA-256 verified, extracted into the engine's cache dir, and reused on subsequent starts. No extra Python deps required — the model runs entirely inside the Rust engine.

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

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

Recommended agent workflow (golden path)

The server injects a golden-path playbook into the agent's system prompt. Since v0.10.0 the default is digest-first: