LangChain & LangGraph Architecture

Build sophisticated LLM applications with LangChain 1.x and LangGraph for agents, memory, and tool integration.

- LangGraph provides the standard agent framework with StateGraph for explicit state management, durable execution, human-in-the-loop inspection, and checkpointing across sessions

- Supports ReAct agents, plan-and-execute workflows, multi-agent supervision, and structured tool invocation with Pydantic schemas

- Memory systems include ConversationBufferMemory, ConversationSummaryMemory, VectorStoreRetrieverMemory, and persistent PostgreSQL checkpointers for production deployments

- Integrates with LangSmith for request logging, token tracking, latency monitoring, and trace visualization; includes custom callback handlers for fine-grained observability

- Document processing pipeline covers loading, chunking, embedding, and retrieval; supports RAG patterns with vector stores like Pinecone and Chroma

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)