This LangGraph workflow creates a customer support system with: intent classification, knowledge base Q&A, ticket creation, and human agent escalation for complex issues.
TL;DR — Key Takeaways
- Multi-Agent Customer Support with LangGraph — Advanced level
- Tool: LangGraph
- 5 steps with detailed instructions
- Prerequisites: Python 3.11+, LangGraph, LangChain, FastAPI, OpenAI API, knowledge base docs
Use Case
SaaS companies wanting to deflect 50-70% of support tickets automatically.
Prerequisites
Python 3.11+, LangGraph, LangChain, FastAPI, OpenAI API, knowledge base docs
Workflow Diagram
Multi-Agent Customer Support with LangGraph
Step-by-Step Guide
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Intent Classification Node
Classify incoming customer message: 1) FAQ (answer from knowledge base), 2) Technical issue (troubleshooting flow), 3) Billing (CRM lookup), 4) Complaint (escalation), 5) Other (human).
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Knowledge Base Q&A Node
For FAQ intent: RAG retrieval from company knowledge base. Generate answer with citations. If confidence < 70%, route to human.
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Troubleshooting Flow Node
For technical issues: guided diagnostic flow. Ask clarifying questions, suggest solutions step by step. Track issue resolution attempts.
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CRM Integration Node
For billing/account issues: lookup customer data, subscription status, payment history. Generate response with specific account context.
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Human Escalation Node
For complex issues or low-confidence responses: create support ticket with full conversation context, customer history, AI analysis summary. Notify human agent.
Tags
This workflow answers:
- How to set up multi-agent customer support with langgraph
- What tools and access you need
- How to troubleshoot common issues