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

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