How to Integrate LangGraph for fintech with Redis for RAG

Combining LangGraph for fintech with Redis gives you a practical pattern for regulated AI agents: LangGraph handles the orchestration and stateful decision flow, while Redis stores retrieval data, conversation context, and fast-access memory for RAG. In a banking or insurance workflow, that means your agent can route between policy lookup, transaction context, and document retrieval without rebuilding state on every turn.

Prerequisites

• •Python 3.10+
• •A running Redis instance
  • •Local: redis-server
  • •Or managed Redis with TLS enabled
• •LangGraph installed
• •Redis Python client installed
• •An embeddings model or embedding API key for indexing documents
• •Access to your fintech knowledge base:
  • •policy PDFs
  • •product docs
  • •support runbooks
  • •compliance FAQs

Install the packages:

pip install langgraph redis langchain-openai langchain-community langchain-text-splitters

Set environment variables:

export OPENAI_API_KEY="your-key"
export REDIS_URL="redis://localhost:6379"

Integration Steps

1. •Create the Redis vector store for RAG

Use Redis as the retrieval layer so your agent can fetch relevant chunks before making a decision. This is the part that makes the system fast enough for production use.

import os
from langchain_community.vectorstores import Redis
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

redis_url = os.environ["REDIS_URL"]
embeddings = OpenAIEmbeddings()

docs = [
    "KYC must be refreshed every 12 months for retail accounts.",
    "Wire transfers above $10,000 require enhanced due diligence.",
    "Credit card disputes must be filed within 60 days of the statement date."
]

splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
chunks = splitter.create_documents(docs)

vectorstore = Redis.from_documents(
    documents=chunks,
    embedding=embeddings,
    redis_url=redis_url,
    index_name="fintech-rag"
)

2. •Build a retriever from Redis

LangGraph nodes should not query raw documents directly. Give them a retriever so each step gets only the top matches.

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

query = "What is the deadline for filing a credit card dispute?"
results = retriever.invoke(query)

for doc in results:
    print(doc.page_content)

3. •Define a LangGraph state and retrieval node

This is where LangGraph earns its keep. You define state once, then build nodes that enrich that state with retrieved context before downstream reasoning.

from typing import TypedDict, List
from langgraph.graph import StateGraph, END

class GraphState(TypedDict):
    question: str
    context: List[str]
    answer: str

def retrieve_context(state: GraphState):
    docs = retriever.invoke(state["question"])
    return {"context": [d.page_content for d in docs]}

4. •Add an answer node and connect the graph

In fintech, keep this step deterministic enough to audit. The answer node can call your LLM with retrieved context and return a concise response.

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

def generate_answer(state: GraphState):
    prompt = f"""
You are a fintech support assistant.
Question: {state['question']}

Context:
{chr(10).join(state['context'])}

Answer using only the provided context.
"""
    response = llm.invoke(prompt)
    return {"answer": response.content}

graph = StateGraph(GraphState)
graph.add_node("retrieve", retrieve_context)
graph.add_node("answer", generate_answer)

graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "answer")
graph.add_edge("answer", END)

app = graph.compile()

5. •Run the full LangGraph + Redis RAG flow

At this point, Redis handles retrieval and LangGraph handles orchestration. That separation is what makes it easy to add approval steps, compliance checks, or escalation branches later.

result = app.invoke({
    "question": "What is the deadline for filing a credit card dispute?",
    "context": [],
    "answer": ""
})

print(result["answer"])

Testing the Integration

Use a real query that should hit your indexed content. If Redis retrieval works and LangGraph wiring is correct, you should see an answer grounded in one of your stored chunks.

test_input = {
    "question": "When must credit card disputes be filed?",
    "context": [],
    "answer": ""
}

output = app.invoke(test_input)
print("ANSWER:", output["answer"])
print("CONTEXT:", output["context"])

Expected output:

ANSWER: Credit card disputes must be filed within 60 days of the statement date.
CONTEXT: ['Credit card disputes must be filed within 60 days of the statement date.', ...]

If context comes back empty, check these first:

• •Redis URL and connectivity
• •Whether documents were actually indexed into fintech-rag
• •Embedding model consistency between indexing and retrieval
• •Search k value if your corpus is small

Real-World Use Cases

• •

  Customer support copilot

  • •Retrieve policy language from Redis and route through LangGraph steps for billing, KYC, chargebacks, or loan servicing.
• •

  Compliance assistant

  • •Use LangGraph to branch into approval or escalation nodes after Redis returns relevant AML/KYC guidance.
• •

  Advisor or banker workspace

  • •Keep account notes, product docs, and internal playbooks in Redis-backed RAG so the agent can answer questions with low latency and predictable state transitions.

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.