Customer Journey Agent with LangGraph

This LangGraph workflow creates a stateful agent graph that tracks customer onboarding progress, adapts responses based on customer type and stage, and escalates to human support when needed. Uses LangGraph state management for context persistence.

Workflow Diagram

Step-by-Step Guide

1. 1

   Define the State Schema

   Create a TypedDict with fields: customer_id, stage (signup/onboarding/active), messages, customer_type, satisfaction_score. This state persists across all nodes in the graph.

2. 2

   Build the Graph Nodes

   Create nodes: greeting_node, assess_node, guide_node, qa_node, escalate_node. Each node receives the state, processes it, and returns updated state. Connect nodes with conditional edges.

3. 3

   Add Conditional Routing

   Use conditional edges: if satisfaction_score < 3, route to escalate_node. If customer asks a question, route to qa_node. Otherwise, continue to guide_node. This makes the agent adaptive.

4. 4

   Implement Memory Checkpointing

   Use LangGraph MemorySaver to persist conversation state. This allows the agent to resume conversations across sessions and maintain context. Store checkpoints in SQLite or Redis.

5. 5

   Deploy with FastAPI

   Wrap the LangGraph agent in a FastAPI endpoint. Add streaming for real-time responses. Deploy to your preferred platform (Vercel, Railway, self-hosted). Test with 20 simulated customer journeys.

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