How to Integrate LangGraph for investment banking with Kubernetes for startups

Combining LangGraph for investment banking with Kubernetes gives you a clean way to run regulated, multi-step AI workflows as production services. The practical win is simple: LangGraph handles the decisioning and stateful orchestration, while Kubernetes handles scaling, isolation, rollout control, and fault recovery for startup infrastructure.

Prerequisites

• •Python 3.10+
• •A Kubernetes cluster:
  • •local: kind, minikube, or Docker Desktop Kubernetes
  • •cloud: EKS, GKE, or AKS
• •kubectl configured and pointing at your cluster
• •A container registry for pushing images
• •Access to your investment banking data sources:
  • •market data API
  • •internal document store
  • •trade blotter or CRM if needed
• •Python packages:
  • •langgraph
  • •langchain-openai or another model provider package
  • •kubernetes
  • •fastapi
  • •uvicorn

Install the core dependencies:

pip install langgraph langchain-openai kubernetes fastapi uvicorn

Integration Steps

1. •Build a LangGraph workflow for an investment banking task.

Start with a graph that routes between market research, risk checks, and a final recommendation. In investment banking systems, you want deterministic structure around LLM calls, not free-form chat loops.

from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI

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

class IBState(TypedDict):
    deal_summary: str
    risk_notes: str
    recommendation: str

def analyze_deal(state: IBState):
    prompt = f"Summarize investment banking risks for: {state['deal_summary']}"
    result = llm.invoke(prompt)
    return {"risk_notes": result.content}

def recommend(state: IBState):
    prompt = (
        f"Given deal summary: {state['deal_summary']}\n"
        f"Risk notes: {state['risk_notes']}\n"
        "Write a concise investment recommendation."
    )
    result = llm.invoke(prompt)
    return {"recommendation": result.content}

graph = StateGraph(IBState)
graph.add_node("analyze_deal", analyze_deal)
graph.add_node("recommend", recommend)
graph.add_edge(START, "analyze_deal")
graph.add_edge("analyze_deal", "recommend")
graph.add_edge("recommend", END)

app = graph.compile()

2. •Wrap the graph in an API service that Kubernetes can run.

Kubernetes needs a stateless container entrypoint. FastAPI is enough here; the graph state is passed in per request, which keeps the service horizontally scalable.

from fastapi import FastAPI
from pydantic import BaseModel

api = FastAPI()

class DealRequest(BaseModel):
    deal_summary: str

@api.post("/analyze")
def analyze(req: DealRequest):
    result = app.invoke({"deal_summary": req.deal_summary, "risk_notes": "", "recommendation": ""})
    return {
        "deal_summary": req.deal_summary,
        "risk_notes": result["risk_notes"],
        "recommendation": result["recommendation"],
    }

Run it locally first:

uvicorn main:api --host 0.0.0.0 --port 8000

3. •Add Kubernetes client logic for deployment awareness.

For startup teams, you often need the agent to know whether it’s running in dev, staging, or prod. The official Kubernetes Python client exposes cluster metadata through the CoreV1Api.

from kubernetes import client, config

def get_cluster_context():
    try:
        config.load_incluster_config()
    except Exception:
        config.load_kube_config()

    v1 = client.CoreV1Api()
    pods = v1.list_namespaced_pod(namespace="default", limit=5)
    return [pod.metadata.name for pod in pods.items]

if __name__ == "__main__":
    print(get_cluster_context())

Use this to attach runtime metadata to your agent logs or route requests by environment. Don’t make the graph depend on Kubernetes calls directly unless you need cluster-aware behavior inside the workflow.

4. •Package and deploy the service to Kubernetes.

Your container should expose the FastAPI app and let Kubernetes manage replicas. A minimal deployment uses a Deployment plus a Service.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ib-langgraph-agent
spec:
  replicas: 2
  selector:
    matchLabels:
      app: ib-langgraph-agent
  template:
    metadata:
      labels:
        app: ib-langgraph-agent
    spec:
      containers:
        - name: api
          image: your-registry/ib-langgraph-agent:latest
          ports:
            - containerPort: 8000
---
apiVersion: v1
kind: Service
metadata:
  name: ib-langgraph-agent-svc
spec:
  selector:
    app: ib-langgraph-agent
  ports:
    - port: 80
      targetPort: 8000

Apply it with:

kubectl apply -f k8s.yaml

5. •Use Kubernetes from Python to verify rollout health.

In production banking workflows, you want automated checks before sending traffic to the agent. The Python client can confirm replica readiness and pod status after deploys.

from kubernetes import client, config

config.load_kube_config()
apps_v1 = client.AppsV1Api()

deployment = apps_v1.read_namespaced_deployment(
    name="ib-langgraph-agent",
    namespace="default",
)

print("desired:", deployment.spec.replicas)
print("available:", deployment.status.available_replicas)
print("ready:", deployment.status.ready_replicas)

Testing the Integration

Send a request to the API and verify that LangGraph returns structured output while Kubernetes keeps the service available.

import requests

payload = {
    "deal_summary": "Mid-market acquisition of a software company with high customer concentration."
}

resp = requests.post("http://localhost:8000/analyze", json=payload)
print(resp.status_code)
print(resp.json())

Expected output:

200
{
  'deal_summary': 'Mid-market acquisition of a software company with high customer concentration.',
  'risk_notes': '...',
  'recommendation': '...'
}

If you deployed to Kubernetes, port-forward first:

kubectl port-forward svc/ib-langgraph-agent-svc 8000:80

Then rerun the same test against localhost:8000.

Real-World Use Cases

• •

  Investment memo drafting

  • •Use LangGraph to collect financial inputs, summarize risks, and draft an IC memo.
  • •Run multiple replicas on Kubernetes so analysts don’t wait on one overloaded worker.
• •

  Deal screening pipeline

  • •Route inbound opportunities through enrichment, compliance checks, and valuation heuristics.
  • •Scale workers independently during peak sourcing periods.
• •

  Portfolio monitoring agent

  • •Poll market signals and internal positions on a schedule.
  • •Use Kubernetes jobs or cron jobs to trigger LangGraph runs without keeping long-lived processes alive.

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.