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# `langchain_cohere.react_multi_hop.agent`.create_cohere_react_agent

langchain_cohere.react_multi_hop.agent.create_cohere_react_agent(llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate) → Runnable

  Create an agent that enables multiple tools to be used in sequence to complete a task.

  Parameters

    - llm (BaseLanguageModel) – The ChatCohere LLM instance to use.

    - tools (Sequence[BaseTool]) – Tools this agent has access to.

    - prompt (ChatPromptTemplate) – The prompt to use.

  Returns

    A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does and returns a List[AgentAction] or a single AgentFinish.

    The AgentFinish will have two fields: * output: str - The output string generated by the model * citations: List[CohereCitation] - A list of citations that refer to the

    > output and observations made by the agent. If there are no citations this list will be empty.

  Return type

    Runnable

  Example

  . code-block:: python

    from langchain.agents import AgentExecutor from langchain.prompts import ChatPromptTemplate from langchain_cohere import ChatCohere, create_cohere_react_agent

    prompt = ChatPromptTemplate.from_template(“{input}”) tools = [] # Populate this with a list of tools you would like to use.

    llm = ChatCohere()

    agent = create_cohere_react_agent(

      llm, tools, prompt

    ) agent_executor = AgentExecutor(agent=agent, tools=tools)

    agent_executor.invoke({

      “input”: “In what year was the company that was founded as Sound of Music added to the S&P 500?”,

    })

# Examples using create_cohere_react_agent

- Cohere