Vol. VI · Context & Memory · VI.4
Retrieval and Document Agents
LlamaIndex's claim: the hard problem is often the corpus, not the orchestrator. Agentic RAG is a router over query engines, not a smarter chatbot.
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Doctrine
- If the knowledge lives in documents, start with indexes and evals on retrieval, not with a crew.
- Agentic RAG: the model chooses which retriever, rewrite, or subquery — with traces.
- Firecrawl and similar tools feed the index. They are not a substitute for one.
When LlamaIndex, when LangGraph
Corpus-heavy Q&A: LlamaIndex (or Dify for visual). Multi-step operations with tools and state: LangGraph. Many products need both: retrieve, then act. Keep the retrieval eval separate from the agent eval or you will not know which half broke.
Anti-patterns
- Dumping crawl results into the prompt and calling it RAG.
- No citation path from answer to chunk.