`agents`#

Agent is a class that uses an LLM to choose a sequence of actions to take.

In Chains, a sequence of actions is hardcoded. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order.

Agents select and use Tools and Toolkits for actions.

Class hierarchy:

BaseSingleActionAgent --> LLMSingleActionAgent
                          OpenAIFunctionsAgent
                          XMLAgent
                          Agent --> <name>Agent  # Examples: ZeroShotAgent, ChatAgent


BaseMultiActionAgent  --> OpenAIMultiFunctionsAgent

Main helpers:

AgentType, AgentExecutor, AgentOutputParser, AgentExecutorIterator,
AgentAction, AgentFinish

Classes

`agents.agent.AgentExecutor`	Agent that is using tools.
`agents.agent.AgentOutputParser`	Base class for parsing agent output into agent action/finish.
`agents.agent.BaseMultiActionAgent`	Base Multi Action Agent class.
`agents.agent.BaseSingleActionAgent`	Base Single Action Agent class.
`agents.agent.ExceptionTool`	Tool that just returns the query.
`agents.agent.MultiActionAgentOutputParser`	Base class for parsing agent output into agent actions/finish.
`agents.agent.RunnableAgent`	Agent powered by Runnables.
`agents.agent.RunnableMultiActionAgent`	Agent powered by Runnables.
`agents.agent_iterator.AgentExecutorIterator`(...)	Iterator for AgentExecutor.
`agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo`	Information about a VectorStore.
`agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit`	Toolkit for routing between Vector Stores.
`agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit`	Toolkit for interacting with a Vector Store.
`agents.chat.output_parser.ChatOutputParser`	Output parser for the chat agent.
`agents.conversational.output_parser.ConvoOutputParser`	Output parser for the conversational agent.
`agents.conversational_chat.output_parser.ConvoOutputParser`	Output parser for the conversational agent.
`agents.mrkl.base.ChainConfig`(action_name, ...)	Configuration for a chain to use in MRKL system.
`agents.mrkl.output_parser.MRKLOutputParser`	MRKL Output parser for the chat agent.
`agents.openai_assistant.base.OpenAIAssistantAction`	AgentAction with info needed to submit custom tool output to existing run.
`agents.openai_assistant.base.OpenAIAssistantFinish`	AgentFinish with run and thread metadata.
`agents.openai_assistant.base.OpenAIAssistantRunnable`	Run an OpenAI Assistant.
`agents.openai_functions_agent.agent_token_buffer_memory.AgentTokenBufferMemory`	Memory used to save agent output AND intermediate steps.
`agents.output_parsers.json.JSONAgentOutputParser`	Parses tool invocations and final answers in JSON format.
`agents.output_parsers.openai_functions.OpenAIFunctionsAgentOutputParser`	Parses a message into agent action/finish.
`agents.output_parsers.openai_tools.OpenAIToolsAgentOutputParser`	Parses a message into agent actions/finish.
`agents.output_parsers.react_json_single_input.ReActJsonSingleInputOutputParser`	Parses ReAct-style LLM calls that have a single tool input in json format.
`agents.output_parsers.react_single_input.ReActSingleInputOutputParser`	Parses ReAct-style LLM calls that have a single tool input.
`agents.output_parsers.self_ask.SelfAskOutputParser`	Parses self-ask style LLM calls.
`agents.output_parsers.tools.ToolAgentAction`
`agents.output_parsers.tools.ToolsAgentOutputParser`	Parses a message into agent actions/finish.
`agents.output_parsers.xml.XMLAgentOutputParser`	Parses tool invocations and final answers in XML format.
`agents.react.output_parser.ReActOutputParser`	Output parser for the ReAct agent.
`agents.schema.AgentScratchPadChatPromptTemplate`	Chat prompt template for the agent scratchpad.
`agents.structured_chat.output_parser.StructuredChatOutputParser`	Output parser for the structured chat agent.
`agents.structured_chat.output_parser.StructuredChatOutputParserWithRetries`	Output parser with retries for the structured chat agent.
`agents.tools.InvalidTool`	Tool that is run when invalid tool name is encountered by agent.

Functions

`agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent`(...)	A convenience method for creating a conversational retrieval agent.
`agents.format_scratchpad.log.format_log_to_str`(...)	Construct the scratchpad that lets the agent continue its thought process.
`agents.format_scratchpad.log_to_messages.format_log_to_messages`(...)	Construct the scratchpad that lets the agent continue its thought process.
`agents.format_scratchpad.openai_functions.format_to_openai_function_messages`(...)	Convert (AgentAction, tool output) tuples into FunctionMessages.
`agents.format_scratchpad.openai_functions.format_to_openai_functions`(...)	Convert (AgentAction, tool output) tuples into FunctionMessages.
`agents.format_scratchpad.tools.format_to_tool_messages`(...)	Convert (AgentAction, tool output) tuples into ToolMessages.
`agents.format_scratchpad.xml.format_xml`(...)	Format the intermediate steps as XML.
`agents.json_chat.base.create_json_chat_agent`(...)	Create an agent that uses JSON to format its logic, build for Chat Models.
`agents.openai_functions_agent.base.create_openai_functions_agent`(...)	Create an agent that uses OpenAI function calling.
`agents.openai_tools.base.create_openai_tools_agent`(...)	Create an agent that uses OpenAI tools.
`agents.output_parsers.openai_tools.parse_ai_message_to_openai_tool_action`(message)	Parse an AI message potentially containing tool_calls.
`agents.output_parsers.tools.parse_ai_message_to_tool_action`(message)	Parse an AI message potentially containing tool_calls.
`agents.react.agent.create_react_agent`(llm, ...)	Create an agent that uses ReAct prompting.
`agents.self_ask_with_search.base.create_self_ask_with_search_agent`(...)	Create an agent that uses self-ask with search prompting.
`agents.structured_chat.base.create_structured_chat_agent`(...)	Create an agent aimed at supporting tools with multiple inputs.
`agents.tool_calling_agent.base.create_tool_calling_agent`(...)	Create an agent that uses tools.
`agents.utils.validate_tools_single_input`(...)	Validate tools for single input.
`agents.xml.base.create_xml_agent`(llm, tools, ...)	Create an agent that uses XML to format its logic.

Deprecated classes

`agents.agent.Agent`
`agents.agent.LLMSingleActionAgent`
`agents.agent_types.AgentType`(value[, names, ...])
`agents.chat.base.ChatAgent`
`agents.conversational.base.ConversationalAgent`
`agents.conversational_chat.base.ConversationalChatAgent`
`agents.mrkl.base.MRKLChain`
`agents.mrkl.base.ZeroShotAgent`
`agents.openai_functions_agent.base.OpenAIFunctionsAgent`
`agents.openai_functions_multi_agent.base.OpenAIMultiFunctionsAgent`
`agents.react.base.DocstoreExplorer`(docstore)
`agents.react.base.ReActChain`
`agents.react.base.ReActDocstoreAgent`
`agents.react.base.ReActTextWorldAgent`
`agents.self_ask_with_search.base.SelfAskWithSearchAgent`
`agents.self_ask_with_search.base.SelfAskWithSearchChain`
`agents.structured_chat.base.StructuredChatAgent`
`agents.xml.base.XMLAgent`

Deprecated functions

`agents.agent_toolkits.vectorstore.base.create_vectorstore_agent`(...)
`agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent`(...)
`agents.initialize.initialize_agent`(tools, llm)
`agents.loading.load_agent`(path, **kwargs)
`agents.loading.load_agent_from_config`(config)

agents#

`agents`#