LangGraph Architecture Decisions

When to Use LangGraph

Use LangGraph When You Need:

• - Stateful conversations - Multi-turn interactions with memory
• Human-in-the-loop - Approval gates, corrections, interventions
• Complex control flow - Loops, branches, conditional routing
• Multi-agent coordination - Multiple LLMs working together
• Persistence - Resume from checkpoints, time travel debugging
• Streaming - Real-time token streaming, progress updates
• Reliability - Retries, error recovery, durability guarantees

Consider Alternatives When:

State Schema Decisions

TypedDict vs Pydantic

Recommendation: Use TypedDict for most cases. Use Pydantic when you need validation or complex nested structures.

Reducer Selection

State Size Considerations

CODEBLOCK0

Graph Structure Decisions

Single Graph vs Subgraphs

Single Graph when:

• - All nodes share the same state schema
• Simple linear or branching flow
• < 10 nodes

Subgraphs when:

• - Different state schemas needed
• Reusable components across graphs
• Team separation of concerns
• Complex hierarchical workflows

Conditional Edges vs Command

CODEBLOCK1

Static vs Dynamic Routing

Static Edges (add_edge):

• - Fixed flow known at build time
• Clearer graph visualization
• Easier to reason about

Dynamic Routing (add_conditional_edges, Command, Send):

• - Runtime decisions based on state
• Agent-driven navigation
• Fan-out patterns

Persistence Strategy

Checkpointer Selection

Checkpointing Scope

CODEBLOCK2

When to Disable Checkpointing

• - Short-lived subgraphs that should be atomic
• Subgraphs with incompatible state schemas
• Performance-critical paths without need for resume

Multi-Agent Architecture

Supervisor Pattern

Best for:

• - Clear hierarchy
• Centralized decision making
• Different agent specializations

CODEBLOCK3

Peer-to-Peer Pattern

Best for:

• - Collaborative agents
• No clear hierarchy
• Flexible communication

CODEBLOCK4

Handoff Pattern

Best for:

• - Sequential specialization
• Clear stage transitions
• Different capabilities per stage

CODEBLOCK5

Streaming Strategy

Stream Mode Selection

Subgraph Streaming

CODEBLOCK6

Human-in-the-Loop Design

Interrupt Placement

Resume Patterns

CODEBLOCK7

Error Handling Strategy

Retry Configuration

CODEBLOCK8

Fallback Patterns

CODEBLOCK9

Scaling Considerations

Horizontal Scaling

• - Use PostgresSaver for shared state
• Consider LangGraph Platform for managed infrastructure
• Use stores for large data outside checkpoints

Performance Optimization

1. 1. Minimize state size - Use references for large data
2. Parallel nodes - Fan out when possible
3. Cache expensive operations - Use CachePolicy
4. Async everywhere - Use ainvoke, astream

Resource Limits

CODEBLOCK10

Decision Checklist

Before implementing:

1. 1. [ ] Is LangGraph the right tool? (vs simpler alternatives)
2. [ ] State schema defined with appropriate reducers?
3. [ ] Persistence strategy chosen? (dev vs prod checkpointer)
4. [ ] Streaming needs identified?
5. [ ] Human-in-the-loop points defined?
6. [ ] Error handling and retry strategy?
7. [ ] Multi-agent coordination pattern? (if applicable)
8. [ ] Resource limits configured?