The industry standard framework for developing context-aware AI applications. With the release of LangGraph 1.0, it excels at building robust, stateful multi-ag
LangChain and LangGraph form the dominant open-source orchestration stack: LangChain for components (models, retrievers, tools) and LangGraph for production agent architectures, stateful graphs with checkpoints, human-in-the-loop nodes, and durable execution. The ecosystem's breadth made it the default starting point for LLM applications.
Pricing: Open-source (MIT) frameworks are free; the commercial LangGraph Platform and LangSmith observability run freemium with usage-based paid tiers (published pricing, mid-2026).
LangChain vs LlamaIndex (2026): They compose rather than compete: LlamaIndex leads on the data side, document parsing (LlamaParse), ingestion, and retrieval quality, while LangChain/LangGraph leads on orchestration: stateful agents, checkpoints, and human-in-the-loop control. Many production stacks use exactly that split.
LangGraph for anything agentic or stateful: it's where the project's production energy lives. Classic LangChain components still serve simple chains and integrations underneath.
Many do, especially with LangGraph and LangSmith; others build thin custom orchestration after prototyping with it. Either way, fluency in its patterns is near-universal interview currency.