Building a LangGraph Agent from Scratch

LangGraph is a library within the LangChain ecosystem that extends LangChain's capabilities by providing a framework for creating stateful, multi-actor applications with LLMs[1]. While LangChain provides pre-built agent abstractions and composable components for LLM application development, LangGraph focuses on lower-level primitives for agent orchestration, enabling developers to build complex autonomous agent behaviors as state machines with features like durable execution, human-in-the-loop oversight, and comprehensive memory management[4]. LangGraph's graph-based structure represents agents as nodes and communication channels as edges, allowing for sophisticated control flows including single-agent, multi-agent, and hierarchical architectures[2].

LangGraph provides several production-critical features: **durable execution** allows agents to persist through failures and resume from where they left off[4]; **state management** automatically tracks and maintains context across multiple interactions, enabling agents to adapt dynamically[1]; **human-in-the-loop** capabilities enable human oversight by allowing inspection and modification of agent state at any point[4]; **streaming support** provides token-by-token output and intermediate step visibility for real-time user feedback[2]; and **comprehensive memory** options support both short-term working memory for ongoing reasoning and long-term memory across sessions[4]. Additionally, LangGraph Platform offers managed deployment and monitoring solutions specifically designed for long-running, stateful workflows[3].