Tracing and Evaluating LangGraph Agents

LangGraph is a versatile library for building stateful multi-actor applications within large language models (LLMs). It supports cycles, which are crucial for creating agents, and provides greater control over the flow and state of an application. Key abstractions include nodes, edges, and conditional edges, which structure agent workflows. State is central to LangGraph's operation, allowing it to maintain context and memory. Arize offers an auto-instrumentor for Langchain that works with LangGraph, capturing and tracing calls made to the framework. This level of traceability is crucial for monitoring agent performance and identifying bottlenecks. By evaluating agents using LLMs as judges, developers can measure their effectiveness and improve performance over time.