u/BandicootLost104

Built an MCP server specifically for agent memory. 83 tools across 8 categories:

•	Memory store/recall/search  
•	Hybrid retrieval (vector + BM25 + graph)  
•	Knowledge graph (entity extraction, similarity edges, cluster summaries)  
•	Session management  
•	Importance decay and contradiction resolution  
•	Built-in embeddings (no external API)

Architectural decision: memory tools are on-demand, not inject-at-session-start. The agent queries what it needs mid-task. Token cost scales with actual need instead of burning 10k on orientation.

Connects to a Dakera memory server (self-hosted Rust binary):

docker run -d -p 3300:3300 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest

MCP config:

{
"mcpServers": {
"dakera": {
"command": "dakera-mcp",
"env": {
"DAKERA_API_URL": "http://localhost:3300",
"DAKERA_API_KEY": "dk-mykey"
}
}
}
}

Works with Claude Desktop, Claude Code, Cursor, Windsurf, anything MCP-compatible.

87.6% on LoCoMo benchmark. MIT-licensed SDKs.

https://github.com/Dakera-AI/dakera-mcp | https://dakera.ai