ladybug-memory-mcp

# 🐞 Ladybug Memory MCP Server [](https://pypi.org/project/ladybug-memory-mcp/) [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) Persistent graph memory for AI agents using [LadybugDB](https://ladybugdb.com/) — an embedded graph database with native vector search and full-text search. Give your AI agent memory that persists across sessions, deduplicates automatically, and models knowledge as a graph with typed relationships. ## Why Ladybug Memory? - **Graph memory** — memories linked via Topic nodes and relationships (RELATED_TO, SUPERSEDES) with Cypher queries - **Three-layer auto-dedup** — exact hash + semantic similarity + LLM-driven consolidation - **Hybrid search** — HNSW vector search + full-text search combined - **Topic auto-linking** — tags become graph nodes, enabling traversal queries - **Embedded** — no Docker, no server process, single database directory - **Zero config** — sensible defaults, just install and run - **Importance & access tracking** — memories ranked by relevance and usage ## Quick Start ```bash # Run directly with uvx (no install needed) uvx ladybug-memory-mcp ``` Or install and run: ```bash pip install ladybug-memory-mcp ladybug-memory-mcp ``` ## MCP Configuration Add to your MCP client config (Kiro, Claude Desktop, Cursor, etc.): ```json { "mcpServers": { "ladybug-memory": { "command": "uvx", "args": ["ladybug-memory-mcp@latest"], "env": { "FASTMCP_LOG_LEVEL": "ERROR" } } } } ``` That's it — zero config required. All settings have sensible defaults. ## Tools | Tool | Description | |------|-------------| | `memory_store` | Store a memory with auto-dedup and auto-link to Topic nodes | | `memory_search` | Hybrid semantic + keyword search, ranked by relevan…