Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

LangChain & LangGraph Architecture Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration. When to Use This Skill Building autonomous AI agents with tool access Implementing complex multi step LLM workflows Managing conversation memory and state Integrating LLMs with external data sources and APIs Creating modular, reusable LLM application components Implementing document processing pipelines Building production grade LLM applications Package Structure (LangChain 1.x) Core Concepts 1. LangGraph Agents LangGraph is the standard for building agents in 2026. It provides: Key Features: StateGraph : Explicit state management with typed state Durable Execution : Agents persist through failures Human in the Loop : Inspect and modify state at any point Memory : Short term and long term memory across sessions Checkpointing : Save and resume agent state Agent Patterns: ReAct : Reasoning + Acting with create react agent Plan and Execute : Separate planning and execution nodes Multi Agent : Supervisor routing between specialized agents Tool Calling : Structured tool invocation with Pydantic schemas 2. State Management LangGraph uses TypedDict for explicit state: 3. Memory Systems Modern memory implementations: ConversationBufferMemory : Stores all messages (short conversations) ConversationSummaryMemory : Summarizes older messages (long conversations) ConversationTokenBufferMemory : Token based windowing VectorStoreRetrieverMemory : Semantic similarity retrieval LangGraph Checkpointers : Persistent state across sessions 4. Document Processing Loading, transforming, and storing documents: Components: Document Loaders : Load from various sources Text Splitters : Chunk documents intelligently Vector Stores : Store and retrieve embeddings Retrievers : Fetch relevant documents 5. Callbacks & Tracing LangSmith is the standard for observability: Request/response logging Token usage tracking Latency monitoring Error tracking Trace visualization Quick Start Modern ReAct Agent with LangGraph Detailed patterns and worked examples Detailed pattern documentation lives in references/details.md . Read that file when the navigation tier above is insufficient. Testing Strategies Performance Optimization 1. Caching with Redis 2. Async Batch Processing 3. Connection Pooling

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