LangGraph 0.5 + Weaviate Agentic RAG 2026: Build It Right

Your RAG demo answered ten cherry-picked questions perfectly, then hit production and started confidently citing the wrong tenant’s documents. Single-shot retrieval can’t tell when it’s wrong, so it never retries — it just fills the context window with three mediocre chunks and lets the model hallucinate the rest. Meanwhile your agentic rewrite loops forever on ambiguous queries, your Weaviate hybrid search alpha was set to 0.5 because someone read it in a blog post, and nobody can explain why p95 latency doubled after adding a reranker. In 2026 the gap between a RAG prototype and a system that survives real users is architectural, and most teams are still shipping the prototype.

This is written for developers who ship: backend and ML engineers building retrieval systems that carry real load. You should be comfortable with Python, async, and typed state, and you should have called an LLM API before. You do not need prior LangGraph experience — the graph model is built from first principles. Out of scope: fine-tuning embedding models, training your own reranker, Kubernetes operations beyond deployment basics, and non-Weaviate vector stores. This is an opinionated build on a specific stack, not a survey of every vector database on the market.

Honest framing: LLMs are excellent at query rewriting, relevance grading, and deciding which tool to call next — that’s why agentic loops beat static pipelines. They are unreliable at knowing when their own retrieval is insufficient without explicit grading nodes, and they will happily loop until your budget is gone if you don’t bound them. Automated evaluation catches regressions, not correctness. Human review is non-negotiable in three places: the tenancy and access-control filters that decide who sees what, the fallback path that reaches outside your corpus, and the golden dataset your CI gates depend on. Every architectural claim here is presented with its tradeoff, not as a universal answer.

What This Guide Covers

• Why reasoning-loop retrieval replaced single-shot RAG, and how to judge when your use case actually needs it
• The LangGraph 0.5 mental model — state, nodes, edges, and durable execution — explained so you can debug a graph, not just copy one
• Weaviate schema design decisions that determine whether agentic retrieval is fast or painful six months in
• Chunking strategies compared on the same corpus, including late chunking, with the retrieval quality numbers behind each choice
• Hybrid search tuning that goes past default alpha values: BM25 fusion behavior and when multi-vector retrieval earns its cost
• A head-to-head look at current reranking options — hosted rerankers versus cross-encoders — scored on quality, latency, and price per query
• How to assemble the agentic graph itself: routing, query rewriting, and retrieval exposed as a tool the model calls deliberately
• Corrective and self-correcting RAG patterns, including grading criteria, retry limits, and a disciplined fallback to web search
• Query-time metadata filtering and multi-tenancy that holds up under a security review instead of leaking across customers
• Human-in-the-loop interrupts and event streaming, so your UI shows reasoning in progress rather than a spinner
• A node-by-node cost and latency budget, plus the caching layers that cut spend without poisoning results
• The failure modes that kill agentic RAG in production — retrieval collapse, context poisoning, runaway loops — and the guardrails for each
• An evaluation harness wired into CI: metric selection, trace inspection, and regression gates that block a bad merge
• Deployment tradeoffs between managed LangGraph Platform and self-hosted FastAPI, with a migration path if you choose wrong

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