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ApeRAG
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
1.2kstars
132forks
Python
agentscontext-engineeringgraphragknowledge-graphmcp
Quality signals
StaleNo testsNo CIThin docsNo licensePopular
AI summary
Auto-generated overview of what this skill does and when to reach for it.
Production-ready GraphRAG platform combining vector, graph, and full-text search with AI agents and MCP support for building knowledge-intensive AI applications.
Use this when
- Need hybrid search across documents, graphs, and images
- Want to deploy a scalable RAG system with Kubernetes
- Building AI agents that autonomously search and reason over knowledge bases
- Processing complex documents with tables, formulas, or visuals
- Integrating AI assistants via Model Context Protocol (MCP)
Don't use when
- Need a simple chatbot without knowledge graphs
- Working with small datasets (<10k docs) where simpler RAG suffices
- Requiring real-time responses with sub-second latency
Capabilities
- Multi-modal indexing (text, graph, vision, tables)
- Hybrid retrieval combining vector, full-text, graph, and summary search
- Built-in AI agents with MCP tool support for autonomous reasoning
- Kubernetes deployment with Helm charts for production
- Advanced document parsing via MinerU for complex content
- Graph-based entity normalization for cleaner knowledge graphs
InstallMCPRun via uvx (Python)
Universal installANY TOOL
$ npx skillcity@latest apecloud/ApeRAGAuto-detects the skill or MCP server in this repo and installs it for Claude Code, Codex, Cursor, Cline, OpenCode, Gemini CLI, Anti-Gravity, OpenClaw and Hermes.
Requires Node.js 18+ (ships with npx).
Or install manually
uvx aperag
MCP Configuration
Add this to your claude_desktop_config.json
{
"mcpServers": {
"aperag": {
"command": "uvx",
"args": [
"aperag"
]
}
}
}Loading README…
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