CATEGORY INTELLIGENCE / WORLDWIDE
Retrieval / RAG
Grounding model answers in useful knowledge.
Red ocean · Measured classification
Search falling · established supply
Many active repositories match this scope. Check the search direction, alternatives and specific user problems before choosing an entry point.
Report dated · Method 1.2.0 · moderate evidence confidence
- 456 active projects match the published GitHub search scope.
- The keyword “retrieval augmented generation” has 104 complete weeks; 0% are reported as zero.
- Matching active GitHub projects
- 456
- Search-interest growth
- -74% · Last 8 complete weeks vs previous 8
- Search term and region
- retrieval augmented generation · Worldwide
- Complete weekly observations
- 104
Why this classification
- Median weekly search interest fell 74% across two consecutive eight-week windows.
- 456 matching active repositories; this is search coverage, not a count of direct competitors.
- Four-week search change: -49%; thirteen-week change: -41%. These windows check the direction of the eight-week comparison.
Source evidence
GitHub repository search · topic:retrieval-augmented-generation fork:false archived:false stars:>=5 pushed:>=2026-03-20
Collected 2026-09-16T04:47:48.475Z
Google Trends search interest · Collected 2026-09-16T04:47:48.471Z
Leading repositories
| Repository | Stars | Description |
|---|---|---|
| infiniflow/ragflow | 90,773 | RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs |
| pathwaycom/llm-app | 58,927 | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. |
| HKUDS/LightRAG | 39,683 | [EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation |
| VectifyAI/PageIndex | 35,654 | 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG |
| NirDiamant/RAG_Techniques | 29,503 | This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial. |
| deepset-ai/haystack | 26,518 | Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems. |
| HKUDS/RAG-Anything | 23,333 | "RAG-Anything: All-in-One RAG Framework" |
| memvid/memvid | 16,542 | Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory. |
| llmware-ai/llmware | 14,843 | Unified framework for building enterprise RAG pipelines with small, specialized models |
| neuml/txtai | 12,949 | 💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows |
Limits of this result
- Google Trends measures relative search attention, not customers, revenue or willingness to pay.
- Supply counts active repositories matching the displayed GitHub topic and phrase queries; unmatched and closed-source competitors are outside this coverage.
- Repository density and search attention are separate observations. Neither proves commercial competition or demand.
Search interest measures attention, not paying customers. Classification thresholds are published heuristics and still require empirical calibration.
Use and share the evidence
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