Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsAgent TypesConversationalOpenAI functionsOpenAI Multi Functions AgentReActReAct document storeSelf-ask with searchStructured tool chatXML AgentHow-toToolsToolkitsCallbacksModulesSecurityGuidesMoreModulesAgentsAgent TypesOn this pageAgent TypesAgents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here are the agents available in LangChain.Zero-shot ReAct‚ÄãThis agent uses the ReAct framework to determine which tool to use
based solely on the tool's description. Any number of tools can be provided.
This agent requires that a description is provided for each tool.Note: This is the most general purpose action agent.Structured input ReAct‚ÄãThe structured tool chat agent is capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
schema to create a structured action input. This is useful for more complex tool usage, like precisely
navigating around a browser.OpenAI Functions‚ÄãCertain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
function should be called and respond with the inputs that should be passed to the function.
The OpenAI Functions Agent is designed to work with these models.Conversational‚ÄãThis agent is designed to be used in conversational settings.
The prompt is designed to make the agent helpful and conversational.
Agents use an LLM to determine which actions to take and in what order.
Agents use an LLM to determine which actions to take and in what order. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsAgent TypesConversationalOpenAI functionsOpenAI Multi Functions AgentReActReAct document storeSelf-ask with searchStructured tool chatXML AgentHow-toToolsToolkitsCallbacksModulesSecurityGuidesMoreModulesAgentsAgent TypesOn this pageAgent TypesAgents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here are the agents available in LangChain.Zero-shot ReAct‚ÄãThis agent uses the ReAct framework to determine which tool to use
based solely on the tool's description. Any number of tools can be provided.
This agent requires that a description is provided for each tool.Note: This is the most general purpose action agent.Structured input ReAct‚ÄãThe structured tool chat agent is capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
schema to create a structured action input. This is useful for more complex tool usage, like precisely
navigating around a browser.OpenAI Functions‚ÄãCertain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
function should be called and respond with the inputs that should be passed to the function.
The OpenAI Functions Agent is designed to work with these models.Conversational‚ÄãThis agent is designed to be used in conversational settings.
The prompt is designed to make the agent helpful and conversational.