raphaelmansuy/edgequake
EdegQuake 🌋 High-performance GraphRAG inspired from LightRag written in Rust; Transform documents into intelligent knowledge graphs for superior retrieval and generation
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 1 day ago, with 14 contributors on GitHub.
Peaked at #42 in the weekly ranking the week of Mar 8, its best in the past year.
Use Cases & Benefits
- Transforms documents into intelligent knowledge graphs enabling advanced retrieval and generation using a high-performance Rust implementation of the LightRAG algorithm.
- Combines vector similarity search with graph traversal for multi-hop reasoning and thematic queries, delivering faster, more accurate, and structurally aware document retrieval.
- Use for building scalable, production-ready knowledge graph retrieval augmented generation (RAG) systems with multi-tenant REST API and real-time streaming support.
- Use for extracting and querying complex entity relationships from large document corpora, including PDFs, with zero-config cross-platform vision-LLM PDF processing.
- Use for integrating knowledge graph enhanced retrieval into AI agents and applications via Model Context Protocol (MCP) with official SDKs in multiple languages.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
High-Performance Graph-RAG Framework in Rust Transform documents into intelligent knowledge graphs for superior retrieval and generation
Traditional RAG systems retrieve document chunks using vector similarity alone. This works for simple lookups but fails on multi-hop reasoning ("How does X relate to Y through Z?"), thematic questions ("What are the major themes?"), and relationship queries. The core problem: vectors capture semantic similarity but lose structural relationships between concepts.
EdgeQuake solves this by implementing the LightRAG algorithm in Rust: documents are not just chunked and embedded — they are decomposed into a knowledge graph of entities and relationships. At query time, the system traverses both the vector space and the graph structure, combining the speed of vector search with the reasoning power of graph traversal.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
274+35
Open issues
66+41
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| NVIDIA/auto-ontology | 7.5K | +5.6K | 37 | — |
| HKUDS/DeepTutor | 55.5K | +5.5K | 65 | 5 days ago |
| affaan-m/ECC | 297.9K | +5.2K | 63 | 35 days ago |
| DietrichGebert/ponytail | 185.3K | +5.1K | 60 | 7 days ago |
| raphaelmansuy/edgequake | 2.3K | — | 65 | 1 day ago |
| Category median | 113.6K | +5.5K | 60 | — |
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