LangGraph vs Chroma for RAG: Which Should You Use?

LangGraph and Chroma solve different problems. LangGraph is an orchestration framework for multi-step LLM workflows; Chroma is a vector database for storing and querying embeddings. If you’re building RAG, use Chroma for retrieval and LangGraph only when you need control flow around retrieval, tools, retries, and human review.

Quick Comparison

When LangGraph Wins

• •

  You need multi-step RAG, not just retrieve-and-generate.

  Example: retrieve documents, rerank them, ask a clarification question if confidence is low, then generate an answer only after validation.

• •

  You need branching logic around retrieval.

  LangGraph’s StateGraph lets you route based on state: if no relevant chunks are found, call a fallback retriever; if the answer is risky, send it to review.

• •

  You need human-in-the-loop approvals.

  In regulated environments like banking or insurance, you can pause execution before final response generation and require approval on specific nodes.

• •

  You need durable workflow control.

  If your RAG pipeline includes retries, tool calls, or multiple agents that must preserve state across steps, LangGraph gives you that orchestration layer.

A typical pattern looks like this:

from langgraph.graph import StateGraph, END

def retrieve(state):
    # call retriever
    return {"docs": docs}

def generate(state):
    # synthesize answer from docs
    return {"answer": answer}

graph = StateGraph(dict)
graph.add_node("retrieve", retrieve)
graph.add_node("generate", generate)
graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "generate")
graph.add_edge("generate", END)

app = graph.compile()

That is not a vector store replacement. It is workflow control around your RAG system.

When Chroma Wins

• •

  You want a real vector store for retrieval.

  Chroma’s job is to store embeddings and run similarity search with APIs like collection.add() and collection.query(). That is the core of RAG.

• •

  You want to ship fast with minimal moving parts.

  For most teams, the simplest path is: chunk documents, embed them, store them in Chroma, query top-k chunks at runtime, then feed them into your prompt.

• •

  You want local development that mirrors production behavior.

  Chroma works well with local persistence through PersistentClient, which makes it easy to build and test retrieval logic without standing up heavy infrastructure.

• •

  You want straightforward metadata filtering.

  In RAG systems where access control matters — department tags, policy type, product line — Chroma’s metadata filters are enough for many practical cases.

Example:

import chromadb

client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_or_create_collection(name="policies")

collection.add(
    ids=["doc1", "doc2"],
    documents=["Policy A text...", "Policy B text..."],
    metadatas=[{"type": "claims"}, {"type": "underwriting"}]
)

results = collection.query(
    query_texts=["What does the claims policy say about escalation?"],
    n_results=3,
    where={"type": "claims"}
)

That gets you the retrieval layer of RAG without adding orchestration overhead.

For RAG Specifically

Use Chroma as the default choice for retrieval in RAG. It solves the actual storage-and-search problem directly and keeps your architecture simple enough to debug under load.

Add LangGraph only when the RAG flow has real branching, like clarification loops, fallback retrievers, compliance checks, or human approval gates. For plain document Q&A, LangGraph is extra machinery; for regulated multi-step workflows around retrieval, it earns its place.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.