langchain.agents.react.agent.create_react_agent

langchain.agents.react.agent.create_react_agent(llm: ~langchain_core.language_models.base.BaseLanguageModel, tools: ~typing.Sequence[~langchain_core.tools.BaseTool], prompt: ~langchain_core.prompts.base.BasePromptTemplate, output_parser: ~typing.Optional[~langchain.agents.agent.AgentOutputParser] = None, tools_renderer: ~typing.Callable[[~typing.List[~langchain_core.tools.BaseTool]], str] = <function render_text_description>) → Runnable[source]

Create an agent that uses ReAct prompting.

Args:

llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. output_parser: AgentOutputParser for parse the LLM output. tools_renderer: This controls how the tools are converted into a string and

then passed into the LLM. Default is render_text_description.

Returns:

A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an AgentAction or AgentFinish.

Examples:

from langchain import hub
from langchain_community.llms import OpenAI
from langchain.agents import AgentExecutor, create_react_agent

prompt = hub.pull("hwchase17/react")
model = OpenAI()
tools = ...

agent = create_react_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

agent_executor.invoke({"input": "hi"})

# Use with chat history
from langchain_core.messages import AIMessage, HumanMessage
agent_executor.invoke(
    {
        "input": "what's my name?",
        # Notice that chat_history is a string
        # since this prompt is aimed at LLMs, not chat models
        "chat_history": "Human: My name is Bob
AI: Hello Bob!”,

}

)

Prompt:

The prompt must have input keys:

  • tools: contains descriptions and arguments for each tool.

  • tool_names: contains all tool names.

  • agent_scratchpad: contains previous agent actions and tool outputs as a string.

Here’s an example:

from langchain_core.prompts import PromptTemplate

template = '''Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}'''

prompt = PromptTemplate.from_template(template)
Parameters
Return type

Runnable