LangChain agents
LangChain agents are autonomous workflows that let a large language model decide which tool—API, database, calculator, or Python function—to call next until a user goal is met. The agent receives the prompt, inspects available tools via a schema, plans a step, executes it, and feeds the result back into the LLM for the next decision, forming a reflection loop. Since LangChain 1.0 (October 2025), agents are built with create_agent, a LangGraph-backed constructor; the older AgentType constants, initialize_agent, and AgentExecutor were moved to the langchain-classic package and are treated as legacy. Developers register tools with a one-line decorator, set stop conditions, and rely on LangChain’s callback system for tracing, retries, and cost tracking. Guardrails enforce rate limits and filter unsafe actions, while vector memory lets agents recall prior context across sessions. By chaining reasoning with tool use, LangChain agents automate complex tasks such as data enrichment, code generation, or multi-step RAG pipelines—cutting manual glue code and speeding time-to-production.
The loop follows the ReAct pattern (reasoning and acting): the agent alternates between a reasoning step and a tool call instead of producing only a final answer. Its behavior is bounded by the tools and environment the application provides.
Related terms
Related services: LangChain development company, AI agent development company.
Ready to put agentic AI to work?
Book a free 30-minute consultation. We'll map one real process worth automating with production-grade AI.