How

    How to Use Agents in LangChain

    Agents are LangChain's most powerful feature — AI that can decide which tools to use and in what order.

    Quick Answer

    LangChain agents use an LLM as a reasoning engine to decide which actions to take. You define tools (functions the agent can call), and the agent autonomously plans and executes steps to achieve a goal.

    Agent Architecture

    An agent consists of an LLM (the brain), tools (available actions), and a prompt (instructions). The agent loops: observe → think → act → observe, until the task is complete.

    Built-in vs Custom Tools

    LangChain includes tools for web search, calculations, file operations, and API calls. You can also create custom tools by wrapping any Python function.

    Use Cases

    • Research assistants that search and synthesize information
    • Data analysis bots that query databases and create reports
    • Automation workflows that interact with multiple APIs

    When Not to Use

    • When the workflow is deterministic and doesn't need reasoning
    • Cost-sensitive applications (agents make multiple LLM calls)
    • When you need guaranteed execution paths

    Build this properly → Start the LangChain Course

    Go from concepts to production-ready AI applications with our structured, hands-on course.

    Start the Course