What is the difference between chains and agents in LangChain?

In LangChain, chains and agents are two distinct approaches for structuring interactions with language models. Chains are predefined sequences of operations that execute in a fixed order, making them ideal for straightforward, predictable tasks. Agents, in contrast, dynamically decide their next steps based on context, allowing them to handle complex, open-ended scenarios. The key difference lies in flexibility: chains follow a rigid workflow, while agents adapt their behavior using reasoning and external tools.

A chain is like a scripted pipeline. For example, you might create a chain to summarize text by first splitting a document into chunks, sending each chunk to a language model for processing, then combining the results. Each step is explicitly defined, and there’s no deviation from the sequence. Chains work well for tasks like data extraction, where steps like parsing inputs, validating formats, and formatting outputs are consistent. Another example is a customer service chatbot that follows a strict flow: classify the user’s intent, retrieve a predefined response from a database, and send a reply. Chains are easy to debug and optimize since their behavior is predictable, but they lack adaptability when unexpected inputs or multi-step reasoning are required.

Agents use a language model to make decisions in real time. For instance, an agent might determine whether to search the web, query a database, or perform a calculation based on a user’s question. Imagine a travel-planning agent: given a query like “Find a weekend trip under $500,” it could first check flight prices, then look up hotel availability, and finally suggest activities—all while adjusting its approach if a step fails. Agents rely on tools (e.g., APIs, calculators) and a decision loop where they evaluate the current state, choose an action, and repeat until the task is complete. This makes them powerful for ambiguous tasks but harder to control, as their behavior depends on the model’s intermediate choices. Developers often use agents for applications requiring problem-solving, like troubleshooting technical issues or analyzing unstructured data.

In summary, chains are best for linear, repeatable processes, while agents excel at dynamic scenarios requiring judgment. For example, use a chain to format and translate a document, but an agent to answer a research question that requires combining data from multiple sources. Choosing between them depends on whether the task benefits from rigidity (chains) or adaptability (agents). Understanding this distinction helps developers design systems that balance efficiency and flexibility.