langchain-architecture

Multi-agent workflows are orchestrated in LangGraph using patterns for ReAct, Plan-and-Execute, and multi-agent coordination with tool calls. This design manages state and memory to execute complex LLM tasks across data sources.

Memory and state in LangChain agents are managed through typed state, durability, memory layers, and checkpoints. This structure ensures explicit state management and durable execution across multi-step autonomous AI workflows.

Document processing pipelines in LangChain load, transform, and store documents using embeddings and retrieval. These pipelines integrate with agents to process data sources in a repeatable, testable manner for production settings.

LangGraph supports durable execution for autonomous agent workflows through explicit state management and checkpoints. This enables production-grade agents to operate across tools and data sources reliably across multi-step tasks.

Use LangGraph for LLM application architecture when building production-grade autonomous agents that require explicit state management, tool schemas, and observability across multi-step workflows operating across diverse data sources.