# knowledge graph

Codebase knowledge graph MCP server for AI coding agents

Codebase Memory MCP is an MIT-licensed MCP server that turns a repository into a persistent code knowledge graph for AI coding agents. It gives Claude Code, Cursor, Codex-style agents, and other MCP clients structural queries for functions, classes, call chains, routes, and architecture, helping them explore large projects without repeatedly rereading files or relying only on broad search.

Knowledge graph memory engine for AI agents

Cognee is an open-source knowledge engine that builds persistent memory for AI agents by combining vector search with graph databases. It ingests data from 38+ source formats, structures information into a knowledge graph with embeddings, and enables semantic and relational queries through its ECL pipeline. Its cognitive science-inspired architecture provides superior cross-document entity identification compared to traditional RAG approaches.

Turn code and docs into a queryable knowledge graph

Graphify is an open-source AI coding assistant skill that transforms folders of code, documentation, research papers, and images into queryable knowledge graphs. It works as a skill for Claude Code, Codex, OpenCode, and other AI coding assistants. Using tree-sitter AST parsing for 19 programming languages and Claude vision for documents and images, it builds NetworkX graphs with Leiden community detection, outputting interactive HTML visualizations and structured JSON for codebase exploration.

Build real-time temporal knowledge graphs for AI agents

Graphiti is an open-source Python framework by Zep for building temporally-aware knowledge graphs for AI agents. It continuously integrates conversations, business data, and external information into queryable graphs with bi-temporal tracking. The hybrid retrieval combines semantic search, BM25 keywords, and graph traversal for sub-300ms queries without LLM calls at retrieval time.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

Build codebase context graphs and AI agents for engineering

Engineering context graph and agent platform that parses codebases into living knowledge graphs for automated SDLC tasks. Connects to repositories, issue trackers, and team communications to empower AI agents with deep architecture understanding for debugging, PR generation, and system design. Offers an open-source core with full IDE extensions, CLI integrations, and enterprise on-premises deployment options.

Interactive codebase knowledge graph generator and architecture navigator

Understand Anything is an open-source tool that transforms software codebases into interactive knowledge graphs. It analyzes dependencies, maps domain workflows, generates guided tours, and feeds structured context into AI coding assistants.

Context engineering platform for AI agents with temporal knowledge graphs

Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.