LangChain vs LangGraph: Differences, Code & When to Use Each (2026)

LangChain and LangGraph are complementary frameworks for building AI agents: LangChain offers a high-level, quick-start approach through its `create_agent` API, while LangGraph provides low-level control over state, nodes, routing, loops, persistence, and long-running workflows. Since their 1.0 releases in October 2025, LangChain agents have run on the LangGraph runtime, allowing developers to begin with built-in tool-calling loops and middleware for approvals, retries, summarization, PII handling, and context management, then adopt explicit LangGraph graphs when custom branching, validation, multi-model flows, or complex multi-agent topologies are needed. Both support checkpoint-based memory, human intervention, testing, and deployment through LangSmith or self-hosted services, but LangGraph enables finer control such as interrupting any workflow stage, replaying checkpoints, and composing subgraphs. The comparison illustrates equivalent weather-agent implementations, showing that LangChain hides most orchestration while LangGraph exposes each model and tool step, and it recommends migrating deprecated pre-1.0 APIs to `create_agent` or custom `StateGraph` workflows. The text also describes testing approaches ranging from unit and integration testing to trajectory evaluation and external agent assurance tools, including TestMu AI products for adversarial, conversational, and end-to-end application testing.

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