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34 lines
1.5 KiB
Markdown
# Agent Types
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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-description`
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This agent uses the ReAct 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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## `react-docstore`
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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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## `self-ask-with-search`
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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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### `conversational-react-description`
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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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