langgraph-architecturelisted

# 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: | Scenario | Alternative | Why | |----------|-------------|-----| | Single LLM call | Direct API call | Overhead not justified | | Linear pipeline | LangChain LCEL | Simpler abstraction | | Stateless tool use | Function calling | No persistence needed | | Simple RAG | LangChain retrievers | Built-in patterns | | Batch processing | Async tasks | Different execution model | ## State Schema Decisions ### TypedDict vs Pydantic | TypedDict | Pydantic | |-----------|----------| | Lightweight, faster | Runtime validation | | Dict-like access | Attribute access | | No validation overhead | Type coercion | | Simpler serialization | Complex nested models | **Recommendation**: Use TypedDict for most cases. Use Pydantic when you need validation or complex nested structures. ### Reducer Selection | Use Case | Reducer | Example | |----------|---------|---------| | Chat messages | `add_messages` | Handle