langchain-architecture

Build autonomous agents with LangGraph by defining state machines for execution, utilizing durable execution to persist state, and integrating tools for complex, multi-step workflows.

Implement long-term memory in LLM applications by transitioning from simple buffer memory to vector store persistence, allowing agents to retain context and historical data across sessions.

Add human-in-the-loop approval to LangChain workflows by configuring LangGraph interrupts, pausing durable execution to wait for external user validation before resuming the agent pipeline.

Trace and debug Retrieval-Augmented Generation pipelines using LangSmith to monitor LLM application performance, inspect tool utilization, and evaluate agent orchestration step-by-step.

Connect external APIs to LLMs by defining structured schemas that map tool inputs and outputs, enabling LangChain agents to seamlessly call external services and utilize tools during execution.

LangChain supports durable execution for complex multi-step processing through LangGraph, ensuring autonomous agents maintain state reliability and recover from interruptions during workflows.