March 29, 2026 - Without it, the agent won’t know how to invoke the dynamically added tool. To learn more about tools, see Tools. To customize how tool errors are handled, use the @wrap_tool_call decorator to create middleware: from langchain.agents import create_agent from langchain.agents.middleware import wrap_tool_call from langchain.messages import ToolMessage @wrap_tool_call def handle_tool_errors(request, handler): """Handle tool execution errors with custom messages.""" try: return handler(request) except Exception as e: # Return a custom error message to the model return ToolMessage( content=f"Tool error: Please check your input and try again.

We've worked through each step of our agent code, but it doesn't run without us running every step. We must write a class to handle all the logic we just worked through. ... from langchain_core.messages import BaseMessage, HumanMessage, AIMessage class CustomAgentExecutor: chat_history: list[BaseMessage] def __init__(self, max_iterations: int = 3): self.chat_history = [] self.max_iterations = max_iterations self.agent: RunnableSerializable = ( { "input": lambda x: x["input"], "chat_history": lambda x: x["chat_history"], "agent_scratchpad": lambda x: x.get("agent_scratchpad", []) } | prompt | l

JavaScript/TypeScript API reference for index.ReactAgent.invoke in langchain. Part of the LangChain ecosystem.

This page contains reference documentation for Agents. See the docs for conceptual guides, tutorials, and examples on using Agents.

Unified API reference documentation for LangChain, LangGraph, Deep Agents, LangSmith, and Integrations.

Main entrypoint into LangChain. ... Utils file included for backwards compat imports. ... Entrypoint to building Agents with LangChain.

March 28, 2024 - Consequently, it employs the Agent to obtain the next action, executes the returned action iteratively, and continues this process until a conclusive answer is generated for the given input. Let’s delve into a simple example to illustrate the process! from langchain_openai import OpenAI import langchain from langchain import hub from langchain.agents import AgentExecutor, create_react_agent, Tool from langchain_community.utilities import GoogleSearchAPIWrapper # use google as a tool google = GoogleSearchAPIWrapper() def top5_results(query): return google.results(query, 5) TOOL_GOOGLE = Tool( name="google-search", description="Search Google for recent results.", func=top5_results, ) tools = [TOOL_GOOGLE] # prompt prompt = hub.pull("hwchase17/react-chat") # llm llm = OpenAI(temperature=0) # agent agent = create_react_agent( llm=llm, tools=tools, prompt=prompt, )

August 28, 2024 - Build powerful multi-agent systems by applying emerging agentic design patterns in the LangGraph framework. ... Before we get into anything, let’s set up our environment for the tutorial. ... Testing that everything is working correctly by querying GPT-3.5 (the default language model) of OpenAI: from langchain_openai import OpenAI llm = OpenAI(openai_api_key=api_key) question = "Is Messi the best footballer of all time?" output = llm.invoke(question) print(output[:75])

January 11, 2024 - Your agent can be multipurpose one. For example, it might be searching for some kind of information on the internet, doing the reasoning step, and after all invoking action to create a Jira issue.

August 25, 2024 - I have written one more article on Overview on AI Agents and different types of Agentic Frameworks to work with and it gives us a good flavour of agentic applications. ... from dotenv import load_dotenv from langchain import hub from langchain.agents import ( AgentExecutor, create_react_agent, ) from langchain_core.tools import Tool from langchain_openai import ChatOpenAI # Load environment variables from .env file load_dotenv()

Our LLM/agent will read this and use it to decide when and how to use the tool. Clear parameter names that ideally tell the LLM what each parameter is. If the parameter names aren't clear, we ensure the docstring explains what the parameter is for and how to use it. Both parameter and return type annotations. ... from langchain_core.tools import tool @tool def add(x: float, y: float) -> float: """Add 'x' and 'y'.""" return x + y @tool def multiply(x: float, y: float) -> float: """Multiply 'x' and 'y'.""" return x * y @tool def exponentiate(x: float, y: float) -> float: """Raise 'x' to the power of 'y'.""" return x ** y @tool def subtract(x: float, y: float) -> float: """Subtract 'x' from 'y'.""" return y - x