CATEGORY INTELLIGENCE / WORLDWIDE
Retrieval / RAG.
Grounding model answers in useful knowledge.
Established red ocean
A crowded category without sustained search growth. Enter only with a concrete switching advantage.
Report dated · Method 1.0.2 · high evidence confidence.
- Matching active GitHub projects
- 459
- Search-interest growth
- -73% · last 8 complete weeks versus the previous 8
- Search term and region
- retrieval augmented generation · Worldwide
- Complete weekly observations
- 104
Why this classification
- Median weekly search interest fell 73% across two consecutive eight-week windows.
- 459 matching active repositories; the published dense-supply threshold is 50.
Source evidence
GitHub repository search · Collected 2026-09-15T16:35:44.822Z
topic:retrieval-augmented-generation fork:false archived:false stars:>=5 pushed:>=2026-03-19
Google Trends search interest · Collected 2026-09-15T16:30:22.275Z
Leading repositories
| Repository | Stars | Description |
|---|---|---|
| infiniflow/ragflow | 90,741 | 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 | 58,930 | 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 | 39,666 | [EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation |
| VectifyAI/PageIndex | 35,653 | 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG |
| NirDiamant/RAG_Techniques | 29,490 | This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial. |
| deepset-ai/haystack | 26,514 | 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 | 23,329 | "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 | 14,844 | 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 carrying the selected GitHub topic; unlabeled and closed-source competitors are outside this coverage.
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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