A single local MCP gateway for Neo4j that exposes both proxied generic Neo4j tools (schema, Cypher, GDS) and custom YAML-defined use-case tools behind one stdio endpoint.
Enables querying a Neo4j knowledge graph built from Git repositories and relational database schemas via MCP, with tools for schema inspection, Cypher queries, and searching code and database entities.
Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.
Manages a Neo4j knowledge graph of reusable Python functions, documentation, and environment variables, enabling dynamic script composition and execution with automatic dependency management and secret protection.
An MCP server that ingests semiconductor PDFs into a Neo4j knowledge graph, enabling AI agents to query domain knowledge, verify claims against source text, and record design reasoning.
memex is a developer context-continuity MCP server — it watches your git repos and builds a temporal knowledge graph (modules, symbols, decisions, open problems) via Graphiti + Neo4j, then serves it to any AI coding agent over MCP. Every edge carries a validity window and a confidence score that decays over time. 12 tools across read and write. Install via npx -y stifler-memex-mcp. MIT licensed.
Provides a graph-based code analysis to coding agents, enabling tools to trace call dependencies, assess change impact, and identify hotspots using tree-sitter and Neo4j.
An MCP server that acts as a knowledge engine for software projects, delivering relevant context at the start of a task and accumulating knowledge at its end through tools like start_task, context, finish_task, remember, and search, with a file-based source of truth and optional semantic retrieval via Graphiti/Neo4j.
Provides persistent knowledge graph memory for AI agents with local semantic search using Neo4j and ONNX embeddings, enabling offline operation with zero API costs.
Augments an LLM with Ontolocy cyber graph capabilities, enabling natural language queries against a Neo4j graph database populated with MITRE ATT\&CK data.
Enables an AI with persistent consciousness through a dual-layer architecture using Neo4j and Google Gemini, allowing natural communication, learning, and personality development.
A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.
Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.
Enables multi-agent trip planning with coordinated specialized agents for team management, food preferences, and travel recommendations. Integrates Neo4j employee graphs, Postgres data, real-time weather APIs, and country information for comprehensive travel planning.