RAG Agent with LangGraph 2026: Build a Research Agent That Uses Tools

Simple RAG answers from one retrieval. A RAG agent decides when to search, what to search, and when to answer. LangGraph makes this structured.

## RAG Agent with LangGraph 2026: Build a Research Agent That Uses Tools Plain RAG does one retrieval, then answers. A RAG agent (or agentic RAG) decides dynamically: it can search multiple sources, rephrase queries, and decide when it has enough information. LangGraph (38,600 GitHub stars, MIT) is the standard framework for building these stateful agent loops in 2026. ## RAG vs RAG Agent | Aspect | Basic RAG | RAG Agent | |--------|-----------|-----------| | Retrieval | Once, fixed | Repeated, adaptive | | Query | As typed | Can be rewritten | | Sources | One store | Multiple tools | | Decides when to answer | Never | Yes | | Follow-up handling | Stateless | Full state | ## The Graph Structure ``` [start] -> retrieve -> grade_documents ^ | | v | relevant? --no--> rewrite_query -> retrieve | | | yes | v +----- generate -> [end] ``` Key nodes: retrieve (search the vector store), grade (check if results answer the question), rewrite (improve the query if not), generate (final answer). ## Minimal Implementation ```python from langgraph.graph import StateGraph def retrieve(state): docs = vectorstore.search(state["question"]) return {"docs": docs} def generate(state): answer = llm.invoke(f"Answer using: {state['docs']}") return {"answer": answer} graph = StateGraph(ResearchState) graph.add_node("retrieve", retrieve) graph.add_node("generate", generate) graph.add_edge("retrieve", "generate") graph.set_entry_point("retrieve") app = graph.compile() ``` ## When You Actually Need an Agent - Queries span multiple knowledge domains - Users ask vague questions that need query rewriting - You have multiple tools (vector search + SQL + web search) - You want the system to say "I don't know" instead of hallucinating ## FAQ **LangGraph or LangChain?** LangGraph is the newer, graph-based framework built by the LangChain team; use it for agents. Plain LangChain chains are fine for simple linear RAG. **What is the retriever + grader pattern?** The most common RAG agent: retrieve, score the docs, and if scores are low, rewrite the query and retry - improving accuracy on hard questions. **Does it work with local models?** Yes - swap the LLM for Ollama and the vectorstore for Chroma; the graph logic is model-agnostic.