[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-github-rag-production-list-battle-tested-tools-en":3,"article-related-github-rag-production-list-battle-tested-tools-en":31,"series-tools-fb2600b9-89a3-493e-9d04-cd7823ac10cc":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"fb2600b9-89a3-493e-9d04-cd7823ac10cc","github-rag-production-list-battle-tested-tools-en","49 stars for GitHub’s RAG production list","\u003Cp data-speakable=\"summary\">\u003Ca href=\"\u002Ftag\u002Fgithub\">GitHub\u003C\u002Fa> repo Yigtwxx\u002Fawesome-\u003Ca href=\"\u002Ftag\u002Frag\">rag\u003C\u002Fa>-production catalogs production RAG tools, stacks, and benchmarks.\u003C\u002Fp>\u003Cp>49 stars and 17 forks mark \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FYigtwxx\u002FAwesome-RAG-Production\" target=\"_blank\" rel=\"noopener\">Yigtwxx\u002Fawesome-rag-production\u003C\u002Fa>, a GitHub-curated list for building and operating retrieval-augmented generation systems in production. The repo was last reviewed on 2026-05-30 and says its freshness is audited weekly.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>GitHub stars\u003C\u002Ftd>\u003Ctd>49\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Forks\u003C\u002Ftd>\u003Ctd>17\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Last reviewed\u003C\u002Ftd>\u003Ctd>2026-05-30\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Commits\u003C\u002Ftd>\u003Ctd>180\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What changed\u003C\u002Fh2>\u003Cp>The repository is not a single framework. It is a long-form reference map that groups tools by job: orchestration, ingestion, embeddings, vector databases, reranking, evaluation, observability, deployment, caching, security, and cost control.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780870666109-xv1v.png\" alt=\"49 stars for GitHub’s RAG production list\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Its decision guide pushes readers toward different stacks based on maturity. For complex agents, it points to \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flanggraph\" target=\"_blank\" rel=\"noopener\">LangGraph\u003C\u002Fa>; for indexing-heavy apps, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Frun-llama\u002Fllama_index\" target=\"_blank\" rel=\"noopener\">LlamaIndex\u003C\u002Fa>; for auditable pipelines, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack\" target=\"_blank\" rel=\"noopener\">Haystack\u003C\u002Fa>; and for fast prototypes, \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\" target=\"_blank\" rel=\"noopener\">LangChain\u003C\u002Fa>.\u003C\u002Fp>\u003Cul>\u003Cli>Local stack: Ollama + Chroma + Ragas for laptop-only testing.\u003C\u002Fli>\u003Cli>Mid-scale stack: Qdrant or Weaviate, plus Cohere Rerank and Langfuse or Arize Phoenix.\u003C\u002Fli>\u003Cli>Enterprise stack: Milvus, vLLM, DeepEval, and OpenLIT.\u003C\u002Fli>\u003Cli>Evaluation and tracing are treated as first-class parts of the stack, not add-ons.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The repo also includes production case studies from LinkedIn, DoorDash, and Discord. Those examples stress hybrid search, domain-specific embeddings, reranking, and A\u002FB testing before \u003Ca href=\"\u002Ftag\u002Fllm\">LLM\u003C\u002Fa> rollout.\u003C\u002Fp>\u003Ch2>Why it matters\u003C\u002Fh2>\u003Cp>For developers, the value is practical: it compresses a messy tool search into a production checklist. Instead of starting from tutorial code, teams can compare tradeoffs in latency, vendor lock-in, observability, and deployment complexity before they commit.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780870664016-nzk1.png\" alt=\"49 stars for GitHub’s RAG production list\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>For the market, the repo reflects where RAG work is moving now. The focus is less on demo quality and more on operating cost, traceability, and scale. That is useful for teams choosing between managed services and self-hosted infrastructure, especially when retrieval quality and latency have to hold up under real traffic.\u003C\u002Fp>\u003Cp>The main question the list raises is simple: which RAG stack fits your scale without creating a maintenance burden you cannot support?\u003C\u002Fp>","Yigtwxx’s GitHub repo maps production RAG stacks, from LangGraph and Qdrant to Milvus, with benchmarks, pitfalls, and case studies.","github.com","https:\u002F\u002Fgithub.com\u002FYigtwxx\u002FAwesome-RAG-Production",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780870666109-xv1v.png","tools","en","4f0b90ab-f554-474e-9efd-ecec55257302",[17,18,19,20,21,22],"RAG","GitHub","LangGraph","Qdrant","Milvus","observability",[24,25,26],"Maps RAG tools by production role, not by hype.","Shows three stack tiers: local, mid-scale, and enterprise.","Highlights evaluation, tracing, and cost as core design choices.",0,"2026-06-07T22:17:20.678948+00:00","2026-06-07T22:17:20.671+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":17,"slug":34},"rag",{"name":18,"slug":36},"github",{"name":19,"slug":38},"langgraph",{"name":20,"slug":40},"qdrant",{"name":21,"slug":42},"milvus",{"id":15,"slug":44,"title":45,"language":46},"github-rag-production-list-battle-tested-tools-zh","GitHub 49 星 RAG 生產清單","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"267be20a-b87f-45fd-a6ec-79d136955b91","open-source-rag-stack-build-plan-en","Open Source RAG Stack Turns Chaos Into a Build Plan","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780872537488-owb6.png","2026-06-07T22:47:55.794337+00:00",{"id":55,"slug":56,"title":57,"cover_image":58,"image_url":58,"created_at":59,"category":13},"fc7e377e-bb67-449e-addd-bb52faff26fc","how-to-build-akiraos-wasm-apps-for-zephyr-en","How to build AkiraOS WASM apps for Zephyr","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780862578461-6d3p.png","2026-06-07T20:02:24.896777+00:00",{"id":61,"slug":62,"title":63,"cover_image":64,"image_url":64,"created_at":65,"category":13},"393e4df1-4ee8-4581-b1b2-dbe7d3322ee9","foundry-mcp-remote-tools-agent-endpoint-en","Foundry MCP turns remote tools into one agent endpoint","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780848209482-y0nz.png","2026-06-07T16:02:58.263739+00:00",{"id":67,"slug":68,"title":69,"cover_image":70,"image_url":70,"created_at":71,"category":13},"d5f55e6c-39d2-4eb6-85cb-326d2255d014","leverage-meaning-no-more-buzzword-mistakes-en","Leverage lets you stop sounding like a buzzword","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780806025318-s62c.png","2026-06-07T04:02:47.777895+00:00",{"id":73,"slug":74,"title":75,"cover_image":76,"image_url":76,"created_at":77,"category":13},"4065ada8-125b-4286-85c5-85cfe7d6369a","llm-leaderboard-2026-300-models-ranked-en","LLM Leaderboard 2026: 300+ Models Ranked","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780776189065-qk79.png","2026-06-06T20:02:37.334702+00:00",{"id":79,"slug":80,"title":81,"cover_image":82,"image_url":82,"created_at":83,"category":13},"92a22a3d-6d0c-4884-9865-c1fe0f2e5e78","llama-benchy-llama-bench-style-api-benchmarks-en","llama-benchy brings llama-bench tests to APIs","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780775297695-nchl.png","2026-06-06T19:47:54.675055+00:00",[85,90,95,100,105,110,115,120,125,130],{"id":86,"slug":87,"title":88,"created_at":89},"8008f1a9-7a00-4bad-88c9-3eedc9c6b4b1","surepath-ai-mcp-policy-controls-en","SurePath AI's New MCP Policy Controls Enhance AI Security","2026-03-26T01:26:52.222015+00:00",{"id":91,"slug":92,"title":93,"created_at":94},"27e39a8f-b65d-4f7b-a875-859e2b210156","mcp-standard-ai-tools-2026-en","MCP Standard in 2026: Integrating AI Tools","2026-03-26T01:27:43.127519+00:00",{"id":96,"slug":97,"title":98,"created_at":99},"165f9a19-c92d-46ba-b3f0-7125f662921d","rag-2026-transforming-enterprise-ai-en","How RAG in 2026 is Transforming Enterprise AI","2026-03-26T01:28:11.485236+00:00",{"id":101,"slug":102,"title":103,"created_at":104},"6a2a8e6e-b956-49d8-be12-cc47bdc132b2","mastering-ai-prompts-2026-guide-en","Mastering AI Prompts: A 2026 Guide for Developers","2026-03-26T01:29:07.835148+00:00",{"id":106,"slug":107,"title":108,"created_at":109},"3ab2c67e-4664-4c67-a013-687a2f605814","garry-tan-open-sources-claude-code-toolkit-en","Garry Tan Open-Sources a Claude Code Toolkit","2026-03-26T08:26:20.245934+00:00",{"id":111,"slug":112,"title":113,"created_at":114},"66a7cbf8-7e76-41d4-9bbf-eaca9761bf69","github-ai-projects-to-watch-in-2026-en","20 GitHub AI Projects to Watch in 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