action_description="useful for doing math"
    )
]
mrkl = MRKLChain.from_chains(llm, chains)
pydantic model langchain.agents.ReActChain[source]#
Chain that implements the ReAct paper.
Example
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
pydantic model langchain.agents.ReActTextWorldAgent[source]#
Agent for the ReAct TextWorld chain.
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool]) → langchain.prompts.base.BasePromptTemplate[source]#
Return default prompt.
pydantic model langchain.agents.SelfAskWithSearchChain[source]#
Chain that does self ask with search.
Example
from langchain import SelfAskWithSearchChain, OpenAI, GoogleSerperAPIWrapper
search_chain = GoogleSerperAPIWrapper()
self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
pydantic model langchain.agents.StructuredChatAgent[source]#
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html