ApeRAG
apecloud/ApeRAG
RAG platform combining Graph RAG, vector search and full-text search for building knowledge graphs and context engineering.
- Local integrations
- Cloud integrations
Installation, supported tools and required credentials are maintained by the project.
Project details
- GitHub stars
- 1,322
- Language
- Python
- License
- Apache-2.0
- Forks
- 147
- Latest release
- v0.5.0-alpha.141y ago
- Activity
- updated 1mo ago
- agents
- graphrag
- knowledge-graph
- mcp
- context-engineering
First MCP server?Start with the basics
Knowledge graph-based persistent memory for maintaining context.
Up-to-date code documentation for LLMs and AI code editors.
Hindsight: long-term memory for AI agents modeled on human memory.
Memory manager for AI apps and agents using graph and vector stores, with ingestion from many data sources.
Turn docs, GitHub repos, PDFs, videos, Confluence, Notion and Slack/Discord into AI skills and RAG knowledge, exportable to vector databases like Qdrant.
Local-first AI memory: a semantic knowledge graph of Markdown files that humans and LLMs both read and write, with optional cloud sync and team workspaces.
Self-hosted Markdown knowledge base with a web UI: search, read, create, edit, tag and template notes.
Universal memory agent for AI: provides semantic search, namespace management, text/vector operations and RAG for local or remote environments.