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58 lines
2.9 KiB
Markdown
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# Agent types
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## Action agents
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Agents use an LLM to determine which actions to take and in what order.
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An action can either be using a tool and observing its output, or returning a response to the user.
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Here are the agents available in LangChain.
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### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
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This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
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based solely on the tool's description. Any number of tools can be provided.
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This agent requires that a description is provided for each tool.
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**Note**: This is the most general purpose action agent.
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### [Structured input ReAct](/docs/modules/agents/agent_types/structured_chat.html)
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The structured tool chat agent is capable of using multi-input tools.
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Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
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schema to create a structured action input. This is useful for more complex tool usage, like precisely
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navigating around a browser.
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### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
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Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
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function should to be called and respond with the inputs that should be passed to the function.
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The OpenAI Functions Agent is designed to work with these models.
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### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)
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This agent is designed to be used in conversational settings.
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The prompt is designed to make the agent helpful and conversational.
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It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
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### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
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This agent utilizes a single tool that should be named `Intermediate Answer`.
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This tool should be able to lookup factual answers to questions. This agent
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is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
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where a Google search API was provided as the tool.
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### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
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This agent uses the ReAct framework to interact with a docstore. Two tools must
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be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
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The `Search` tool should search for a document, while the `Lookup` tool should lookup
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a term in the most recently found document.
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This agent is equivalent to the
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original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
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## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
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Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
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