azure-search-openai-demo vs chat-langchain
Two of the top rag and search, side by side: score, setup, license, activity and what each review found.
azure-search-openai-demo
Azure RAG chat reference on AI Search and Azure OpenAI
chat-langchain
LangChain docs assistant as a Managed Deep Agent with Next.js UI
| What we compare | azure-search-openai-demo | chat-langchain |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 73, popular | 67, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 45, patchy |
| Easy to run | 0, hard | 0, hard |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 7.8k | 6.5k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | TypeScript |
| Docker | No | No |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
azure-search-openai-demo
The canonical Azure RAG sample: a Python (Quart) backend and React frontend answering multi-turn questions over your documents with citations and a visible thought process, using Azure AI Search for retrieval and Azure OpenAI for generation. azd up provisions Container Apps, AI Search, Document Intelligence and Blob storage, with optional Cosmos DB chat history, Entra login with document ACLs, multimodal and speech. For teams already on Azure.
Who it is for: Teams on Azure wanting the vendor-maintained RAG starting point
Strengths
- Optional Entra login with per-document access control and Cosmos DB chat history
- Evaluation, safety evaluation, monitoring and productionizing guides in docs/
- Multimodal, speech and agentic retrieval are switchable features
- Commits within the last week; tests included
Weaknesses
- Cannot run locally until azd up has provisioned Azure resources
- Provisions paid services by default (AI Search, Document Intelligence); run azd down
- Azure OpenAI only; no other provider path
- README itself says not production-ready without extra security work
- Python, azure-openai
- Needs azure-subscription, azd, azure-ai-search, azure-openai, azure-document-intelligence, azure-blob-storage
chat-langchain
A documentation assistant for LangChain, LangGraph and LangSmith: a Python agent built with LangChain middleware (guardrails, ingress guards, retry) and deployed through Managed Deep Agents, which owns identity, ingress and the checkpointer. Tools search the docs through a managed MCP connector, a Pylon support knowledge base and a URL validator, and a Next.js chat UI sits in frontend/. For teams wanting a reference for a guarded docs assistant.
Who it is for: Teams building a guarded docs assistant on the LangChain stack
Strengths
- Guardrails and link validation are implemented as reusable middleware
- Supabase token plus guest identity handled in identity.py
- Frontend proxies LangSmith feedback so the API key never reaches the browser
- Tests included
Weaknesses
- Tied to Managed Deep Agents (mda CLI) for identity, ingress and state
- Needs a Pylon account and knowledge base ID to run as written
- Docs retrieval depends on a managed MCP connector, not your own index
- Product-specific: you replace the LangChain docs with your own corpus
- TypeScript, anthropic, langchain, langgraph
- Needs anthropic-api-key, pylon-api-key, managed-deep-agents, supabase
- env example file