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Agents | 🦜️🔗 LangChain

The core idea of agents is to use a language model to choose a sequence of actions to take. In chains, a sequence of actions is hardcoded (in code). In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.

For a quick start to working with agents, please check out this getting started guide. This covers basics like initializing an agent, creating tools, and adding memory.

There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. For an in depth explanation, please check out this conceptual guide

There are many different types of agents to use. For a overview of the different types and when to use them, please check out this section.

Agents are only as good as the tools they have. For a comprehensive guide on tools, please see this section.

Agents have a lot of related functionality! Check out comprehensive guides including:

• Building a custom agent
• Streaming (of both intermediate steps and tokens)
• Building an agent that returns structured output
• Lots functionality around using AgentExecutor, including: using it as an iterator, handle parsing errors, returning intermediate steps, capping the max number of iterations, and timeouts for agents

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