langchain-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)
langchain (1.2.x) # High-level orchestration
langchain-core (1.2.x) # Core abstractions (messages, prompts, tools)
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