ApeRAG is an open-source, production-ready GraphRAG platform from ApeCloud for building retrieval-augmented generation applications and knowledge graphs. Hosted on GitHub with about 1,300 stars under the Apache-2.0 license and actively developed through 2026, it combines graph-based retrieval, vector search and full-text search with AI agents and MCP support.
Plain vector RAG often misses relationships between facts. ApeRAG indexes documents into a knowledge graph alongside vector and full-text indexes, so its hybrid retrieval can follow entities and relations as well as semantic similarity. It supports multimodal document processing, so mixed content such as text and images can be indexed together, and it exposes its capabilities to agents through the Model Context Protocol, letting autonomous agents search and reason across your knowledge base. Deployment is designed for real operations: you can start in minutes with Docker Compose on a modest machine, or scale out on Kubernetes for production, with enterprise-grade management features for running the platform as shared infrastructure. The documented minimum requirements are modest โ two CPU cores and 4 GiB of RAM with Docker installed โ which keeps local evaluation cheap before you commit to a larger Kubernetes deployment.
ApeRAG is free and open source under the Apache-2.0 license. You deploy it on your own infrastructure with Docker or Kubernetes at no license cost; your only expenses are your servers and any LLM or embedding APIs you choose to connect.
Engineering teams building serious RAG applications โ enterprise knowledge bases, document assistants and agent systems โ that need graph-aware retrieval, multimodal indexing and a platform they can operate in production themselves.