What is the LangChain agent, and how does it work?

A LangChain agent is a component within the LangChain framework that uses a large language model (LLM) to decide how to execute tasks by breaking them into steps. Instead of relying solely on the LLM’s static knowledge, the agent dynamically interacts with external tools—like APIs, databases, or calculators—to gather information or perform actions. For example, if asked to calculate the current weather in a city, the agent might first call a weather API to fetch data, then use a math tool to convert temperatures between units. The agent’s core function is to determine which tools to use, in what order, and how to process their outputs to achieve the desired result.

The agent works by combining the reasoning capabilities of an LLM with a structured workflow. When given a task, the LLM generates a plan based on available tools and the user’s input. For instance, if a user asks, “What’s the population of Tokyo divided by 2?” the agent might first recognize the need to retrieve Tokyo’s population using a search tool, then pass that number to a calculator tool for division. The agent uses a loop: the LLM decides the next action, the action is executed using a tool, and the result is fed back into the LLM to determine if further steps are needed. This loop continues until the agent concludes the task is complete or hits a stopping condition, like reaching a maximum number of steps.

A key detail is how the agent’s decision-making is guided. Developers define the tools available (e.g., web search, Python code execution) and configure prompts to instruct the LLM on how to use them. For example, a prompt might say, “Use the search tool to find recent data, then the calculator for math operations.” The agent’s effectiveness depends on how well these prompts and tools are designed. A common pattern is the ReAct framework, where the LLM alternates between generating reasoning steps (“I need to find X first”) and actions (“Call search API with query X”). By structuring interactions this way, the agent can handle complex, multi-step tasks while staying adaptable to different use cases.