Dragon Brain — persistent long-term memory for AI agents via MCP (Model Context Protocol). Knowledge graph (FalkorDB) + vector search (Qdrant) + CUDA GPU embeddings. Works with Claude, Gemini CLI, Cursor, Windsurf, VS Code Copilot. 31 tools, 1116 tests.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"dragon-brain": {
"env": {
"QDRANT_HOST": "localhost",
"QDRANT_PORT": "6333",
"FALKORDB_HOST": "localhost",
"FALKORDB_PORT": "6379",
"EMBEDDING_API_URL": "http://localhost:8001"
},
"args": [
"-m",
"claude_memory.server"
],
"command": "python"
}
}
}Are you the author?
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Memory infrastructure for AI agents — that fails loud, by design.
100% LongMemEval R@5 · 34 MCP tools · sub-200ms hybrid search · CI-gated fail-loud contracts · No LLM required
An open-source MCP server that gives any LLM long-term memory using a knowledge graph + vector search hybrid. Store entities, observations, and relationships — then recall them semantically across sessions. Works with any MCP client: Claude Code, Claude Desktop, Cursor, Windsurf, Cline, Gemini CLI, VS Code Copilot, or any LLM that speaks Model Context Protocol.
Unlike flat chat history or simple RAG, Dragon Brain understands relationships between memories — not just similarity. An autonomous agent ("The Librarian") periodically clusters and synthesizes memories into higher-order concepts.
And it tells you when it can't remember — instead of pretending the memory was never there. (why this matters →)

Prerequisites: Docker and Docker Compose. Detailed setup: See docs/SETUP.md for comprehensive instructions including prerequisites, platform-specific notes, and troubleshooting.
docker compose up -d
This spins up 4 containers:
GPU users:
docker compose --profile gpu up -dfor NVIDIA CUDA acceleration.
Verify everything is healthy:
docker ps --filter "name=claude-memory"
pip install dragon-brain
Note: Dragon Brain requires FalkorDB and Qdrant running as Docker services. The pip package installs the MCP server — run
docker compose up -dfirst for the infrastructure. The embedding model (~1GB) is served via Docker, not downloaded locally.
Claude Code (recommended):
claude mcp add dragon-brain -- python -m claude_memory.server
Add to your MCP client config:
{
"mcpServers": {
"dragon-brain": {
"command": "python",
"args": ["-m", "claude_memory.server"],
"env": {
"FALKORDB_HOST": "localhost",
"FALKORDB_PORT": "6379",
"QDRANT_HOST": "lo
... [View full README on GitHub](https://github.com/iikarus/Dragon-Brain#readme)