How to Integrate LangGraph for investment banking with Kubernetes for RAG

Combining LangGraph for investment banking with Kubernetes gives you a clean way to run retrieval-augmented agents that can answer deal, compliance, and market-intelligence questions with controlled execution. The useful part is not just orchestration; it is being able to scale graph-based workflows on Kubernetes while keeping your RAG pipeline isolated, observable, and easy to roll back.

Prerequisites

• •Python 3.10+
• •A running Kubernetes cluster
  • •kubectl configured
  • •Namespace created for your agent workloads
• •Access to a vector store for RAG
  • •Examples: Pinecone, pgvector, Weaviate, Elasticsearch
• •LangGraph installed
  • •langgraph
  • •langchain
  • •langchain-openai or your model provider SDK
• •Kubernetes Python client installed
  • •kubernetes
• •A service account or kubeconfig with permissions to:
  • •create jobs/pods
  • •read pod logs
  • •inspect services if needed
• •Environment variables set:
  • •OPENAI_API_KEY or equivalent model key
  • •KUBECONFIG if you are not using in-cluster auth

Integration Steps

1) Define the RAG state and graph nodes in LangGraph

For investment banking workflows, keep the graph explicit. You want separate nodes for retrieval, synthesis, and compliance checks so you can audit each step.

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

class AgentState(TypedDict):
    question: str
    retrieved_docs: List[Dict[str, Any]]
    answer: str

def retrieve(state: AgentState) -> AgentState:
    # Replace with your vector DB call
    docs = [
        {"title": "Q2 Earnings", "text": "Revenue increased by 12% YoY."},
        {"title": "Debt Update", "text": "Net leverage remains below covenant threshold."},
    ]
    return {**state, "retrieved_docs": docs}

def synthesize(state: AgentState) -> AgentState:
    context = "\n".join([d["text"] for d in state["retrieved_docs"]])
    answer = f"Based on retrieved filings:\n{context}\n\nAnswer: The company looks within covenant range."
    return {**state, "answer": answer}

graph = StateGraph(AgentState)
graph.add_node("retrieve", retrieve)
graph.add_node("synthesize", synthesize)
graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "synthesize")
graph.add_edge("synthesize", END)

app = graph.compile()

This gives you a deterministic workflow. In banking use cases, that matters because every response should be traceable back to retrieved sources.

2) Wrap the LangGraph execution in a Kubernetes-friendly worker

Run the graph inside a containerized worker process. The worker accepts a question payload and executes the compiled graph.

import json
from langgraph.graph import StateGraph

def run_agent(question: str):
    result = app.invoke({"question": question, "retrieved_docs": [], "answer": ""})
    return result["answer"]

if __name__ == "__main__":
    import sys
    payload = json.loads(sys.stdin.read())
    question = payload["question"]
    print(run_agent(question))

Build this into an image and deploy it as a Kubernetes Job or long-running service depending on your traffic pattern. For batch-heavy banking workflows like earnings summarization or credit memo drafting, Jobs are often the better fit.

3) Use the Kubernetes Python client to launch the agent job

If you want your control plane to spawn agent runs on demand, use the official Kubernetes client. This is the part that connects orchestration to execution.

from kubernetes import client, config

config.load_kube_config()

batch_api = client.BatchV1Api()

job_manifest = client.V1Job(
    metadata=client.V1ObjectMeta(name="langgraph-rag-job"),
    spec=client.V1JobSpec(
        template=client.V1PodTemplateSpec(
            metadata=client.V1ObjectMeta(labels={"app": "langgraph-rag"}),
            spec=client.V1PodSpec(
                restart_policy="Never",
                containers=[
                    client.V1Container(
                        name="agent",
                        image="your-registry/langgraph-rag:latest",
                        command=["python", "/app/worker.py"],
                        stdin=True,
                    )
                ],
            ),
        ),
        backoff_limit=2,
    ),
)

batch_api.create_namespaced_job(namespace="ai-agents", body=job_manifest)
print("Job created")

Use this when you need isolation per request or per deal team. It also makes it easy to apply resource limits per run.

4) Stream input into the pod and read results back

For production systems, your controller usually submits a job and then reads logs or stores outputs in object storage. Here is the log-based version using Kubernetes APIs.

from kubernetes import client, config
import time

config.load_kube_config()
core_api = client.CoreV1Api()

pod_name = None
for _ in range(30):
    pods = core_api.list_namespaced_pod(
        namespace="ai-agents",
        label_selector="app=langgraph-rag"
    )
    if pods.items:
        pod_name = pods.items[0].metadata.name
        break
    time.sleep(2)

if pod_name:
    logs = core_api.read_namespaced_pod_log(
        name=pod_name,
        namespace="ai-agents"
    )
    print(logs)
else:
    print("No pod found")

This is simple and reliable for internal tooling. If you need stronger guarantees, write results to Postgres or S3 from inside the worker instead of relying on logs.

5) Add retrieval data injection through environment variables or mounted secrets

Banking systems should never hardcode credentials in code. Inject vector store credentials and model keys through Kubernetes Secrets and read them inside LangGraph nodes.

import os

def retrieve(state):
    vector_url = os.environ["VECTOR_DB_URL"]
    api_key = os.environ["VECTOR_DB_API_KEY"]

    # Example placeholder for your actual retriever call
    docs = [
        {"title": "Proxy Statement", "text": f"Connected to {vector_url}"},
        {"title": "Risk Factors", "text": f"Authenticated with key length {len(api_key)}"},
    ]
    return {**state, "retrieved_docs": docs}

In practice, wire this to your retriever client inside the node. Keep secrets out of the graph definition so the same workflow can run across dev, staging, and prod.

Testing the Integration

Run a local smoke test before pushing to cluster. This verifies that LangGraph executes end-to-end and that your controller can reach Kubernetes.

from kubernetes import client, config

config.load_kube_config()

# Local graph test
result = app.invoke({
    "question": "Can we summarize leverage risk from the latest filing?",
    "retrieved_docs": [],
    "answer": ""
})
print(result["answer"])

# Cluster connectivity test
v1 = client.CoreV1Api()
namespaces = [ns.metadata.name for ns in v1.list_namespace().items]
print("ai-agents" in namespaces)

Expected output:

Based on retrieved filings:
Revenue increased by 12% YoY.
Net leverage remains below covenant threshold.

Answer: The company looks within covenant range.
True

Real-World Use Cases

• •

  Deal desk RAG assistant

  • •Pulls from CIMs, earnings transcripts, and internal notes.
  • •Runs as isolated Kubernetes Jobs per request so each banker gets clean execution boundaries.
• •

  Compliance-aware research copilot

  • •Uses LangGraph nodes for retrieval plus policy checks before final response.
  • •Deploys on Kubernetes with separate namespaces for legal review and production usage.
• •

  Credit memo drafting pipeline

  • •Retrieves borrower financials, covenant history, and sector notes.
  • •Scales horizontally on Kubernetes when multiple analysts submit memo requests at once.

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.