LangGraph for Workflows
Master stateful, graph-based agent workflows with cycles, branching, and human-in-the-loop patterns
Your Progress
0 / 5 completedAdvanced Workflow Patterns
LangGraph unlocks powerful patterns beyond basic graphs: human-in-the-loop for critical decisions, nested subgraphs for modularity, and streaming for real-time updates. These patterns make production agents safer, more maintainable, and more user-friendly.
Interactive: Pattern Explorer
Human-in-the-Loop
Pause execution and wait for human approval before critical actions
Workflow Steps:
1
Agent plans action
2
Pause for approval
3
Human reviews
4
Continue or modify
Code Example:
graph.add_node("approval", interrupt=True)
🚀 Production Best Practices
⏸️
Interrupt Before Critical Actions
Pause before sending emails, making purchases, or deleting data. Let humans review and approve.
Use case: Email agent pauses before sending to 1000+ recipients
💾
Checkpoint State to Database
Save state after each node. If workflow crashes, resume from last checkpoint instead of restarting.
Use case: Long-running research agent that takes 30+ minutes
🔍
Add Observability & Logging
Log state transitions, node execution times, and errors. Integrate with LangSmith or custom dashboards.
Use case: Debug why agent keeps looping infinitely
💡 When to Use LangGraph
✅ Use LangGraph When:
- • Need cycles/loops (retry logic)
- • Complex conditional branching
- • Human approval required
- • Long-running workflows
- • Need to inspect/debug state
❌ Use LangChain When:
- • Simple linear workflows
- • One-shot agent calls
- • No state persistence needed
- • Rapid prototyping
- • Minimal complexity