dakera-mcp
Self-hosted MCP-native agent memory server. Gives AI agents persistent, decay-weighted memory via 83 MCP tools — no cloud, full control. RocksDB+HNSW backend. Works with Claude Code, Cursor, and any MCP-compatible agent.
README
⚡ dakera-mcp
MCP server for Dakera AI. 83 tools. Gives any MCP-compatible AI agent persistent, queryable memory in minutes.
Works with Claude, Claude Code, and any MCP-compatible framework.
Part of Dakera AI — the memory engine for AI agents.
The Dakera memory engine scores 87.6% on LoCoMo (1,540 questions, standard eval) — benchmark details
Run Dakera
The MCP server connects to a Dakera memory server. You need one running first:
docker run -d \
--name dakera \
-p 3300:3300 \
-e DAKERA_ROOT_API_KEY=dk-mykey \
ghcr.io/dakera-ai/dakera:latest
For persistent storage (recommended):
curl -sSfL https://raw.githubusercontent.com/Dakera-AI/dakera-deploy/main/docker-compose.yml \
-o docker-compose.yml
DAKERA_API_KEY=dk-mykey docker compose up -d
curl http://localhost:3300/health # → {"status":"ok"}
Full deployment guide (Docker Compose, Kubernetes, Helm): dakera-deploy
Install
cargo install dakera-mcp
Or with Docker:
docker pull ghcr.io/dakera-ai/dakera-mcp:latest
Connect
Add to .mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):
{
"mcpServers": {
"dakera": {
"command": "dakera-mcp",
"env": {
"DAKERA_API_URL": "http://localhost:3300",
"DAKERA_API_KEY": "your-key"
}
}
}
}
What You Get
83 tools across 15 categories:
- Memory — store, recall, search, decay, importance scoring
- Sessions — create and manage agent sessions
- Agents — namespaces, stats, memory health
- Knowledge — graph construction, entity extraction, clustering, cross-agent network
- Vectors — upsert, query, hybrid search, batch operations
- Full-Text — BM25 index, search, stats
- Operations — health, metrics, backup, audit
Why This Exists
AI agents forget everything when the session ends. Dakera fixes that. This MCP server gives your agent a persistent memory layer with zero infrastructure overhead — point it at a Dakera instance and it works.
→ dakera.ai for hosted instance
→ Self-host with dakera-deploy
Documentation
Related
| Repo | What it is |
|---|---|
| dakera-py | Python SDK |
| dakera-js | TypeScript SDK |
| dakera-cli | CLI |
| dakera-deploy | Self-host Dakera |
Part of the Dakera AI open core. The engine is proprietary. The tools are yours.
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