RAG isn't dead: a full map of the LangChain data ecosystem
mdancho84 · x · 2026-10-13
Data scientist Matt Dancho, promoting his free Python AI/DS workshop, argues that context engineering reshapes how you architect AI systems in 2026: which data sources your agent can access, how retrieval tools are designed and combined, how you manage token budgets, and how you debug bad retrievals.
The accompanying chart maps the LangChain data ecosystem end to end: 135+ document loader integrations (public and proprietary data), 25+ embedding integrations, 40+ vector store integrations, plus document transformers, compressors and re-rankers (e.g. Cohere Re-rank, clustering), feeding 60+ LLM integrations for retrieval and QA.
His takeaway: RAG isn't dead — it's just one tool inside a rich context window.
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