langchain.agents.react.agent
.create_react_agent¶
- langchain.agents.react.agent.create_react_agent(llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate) 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.
- 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)