React agent langchain example. tool_names: contains all tool names.
- React agent langchain example. Benefits of using ReAct: ReAct allows agents to generate numerous thought This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. 7 langchain-text-splitters==0. 馃馃帳 Voice ReAct Agent This is an implementation of a ReAct -style agent that uses OpenAI's new Realtime API. This project combines two functionalities: a Code Interpreter using LLM Agent Orchestration and Tool Utilization, and a ReAct LangChain Agent example. Nov 18, 2024 路 Learn to build a LangChain ReAct agent using the Requests Toolkit. This class is designed to handle ReAct-style LLM calls and ensures that the output is parsed correctly This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. LangChain helps you compose ReAct framework. The goal is to enable the agent to process user queries, interact with an SQL database, and return coherent, context-aware Jan 23, 2025 路 In this blog, we explored the process of building a ReAct Agent using langgraph. Jan 6, 2024 路 For example the React research paper covers three action space. agent. For a more robust and feature-rich implementation, we recommend using the create_react_agent function from the LangGraph library. Unfortunately, the LangChain docs are so high-level that they are almost useless. Documentation for LangChain. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Execute the agent with a basic math query Tested with Ollama version 0. 2 - langgraph >= 0. There are many things LangChain can help us with, but in this tutorial we will focus just on getting the first ReactJs and LangChain example up and running. Agents select and use Tools and Toolkits for actions. We will build a small ReactJs app … Continue reading "Example app with LangChain and ReactJs" In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. py Copy Code Output: Lookup agent For the lookup (i. This project showcases the creation of a ReAct (Reasoning and Acting) agent using the LangChain library. Jul 4, 2025 路 LangChain similarly defines them as systems using an LLM to “decide the control flow of an application. Run the client: python client. - Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet Dec 11, 2024 路 Hey there, @BEbillionaireUSD! I'm here to help you with any bugs, questions, or contributions you have. However, adding history to this, and invoking agents, is a specific feature combination without a representative example in the documentation. Let's tackle this together! To combine an agent with bonded tools and memory using create_react_agent with a few-shot prompt, you can follow these steps: Define the Tools and Model: Initialize the tools and the language model you want to use. Below is a high-level example of how the ReAct prompting approach works in practice. Jun 4, 2025 路 A Langchain agent is an LLM-based decision-maker that receives user prompts, thinks step-by-step (usually using ReAct or similar frameworks), chooses what action to take, and executes it using a set of available tools. One of the most important improvements we made is function calling. Engineered an autonomous multi-agent system by integrating Code Interpreter, ReAct, and LangChain frameworks, which streamlined dynamic code execution and reasoning, resulting in a 35% boost in operational efficiency. tool_names: contains all tool names. This project provides a user-friendly interface for deploying ReAct agents that can access various data sources and APIs For example, if looking for coffee beans between 5 and 10 dollars, the tool input would be `coffee beans, 5, 500, 1000`. This agent uses a two step process: First, the agent uses an LLM to create a plan to answer Aug 13, 2024 路 Hello, @invalidexplorer! I'm here to assist you with any bugs, questions, or contributions you might have. 3. First, grasp the essence of ReAct Prompting with our guided example. 16 langchain-core==0. 4 Documentation for LangChain. I am trying to understand how ReAct agents are implemented on a high level to understand the concepts behind this approach. Includes an LLM, tools, and prompt. 2. What’s a ReAct agent? Dec 5, 2023 路 Master LangChain Agents and React Framework with our ultimate guide! Transform your AI skills, unleash intelligent automation. Below we assemble a minimal SQL agent. Conversational ReAct This agent is designed for use in conversational settings. Aug 1, 2025 路 Building LangGraph Agents with Geminiimport os # Read your API key from the environment variable or set it manually api_key = os. output_parser. This idea is largely inspired by BabyAGI and then the "Plan-and-Solve" paper. Specifically, we enable this model to call tools by providing it a list of LangChain tools. agents import initialize_agent, load_tools from langchain. Apr 29, 2025 路 Langchain ReAct agent example. Apr 20, 2025 路 This allows the agent to handle queries that the LLM alone might not answer, by dynamically invoking tools for additional information . 16 langchain-community==0. This often makes it important to keep the human “in the loop” when building agents. Mar 1, 2025 路 Learn how LangGraph, an AI agent framework built by LangChain, allows developers to create complex and flexible agent workflows using stateful graphs and built-in memory management. May 21, 2025 路 Learn to build AI agents using Langchain MCP’s model context protocol, including code examples, and best practices|ProjectPro Aug 22, 2023 路 2. Here's a pseudo-code example demonstrating LangChain MCP Adapters This library provides a lightweight wrapper that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph. We will create a ReAct agent that answers questions about publicly traded stocks and write a comprehensive test suite for it. Let's work together to resolve your issue. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. The core logic, defined in src/react_agent/graph. For example, if asked “What’s the GDP of Spain in 2024?”, a ReAct agent could decide to call a Wikipedia search tool to fetch the latest data. js framework makes it easy to integrate LLMs (Large Language Models) such as OpenAi's GTP with our JavaScript-based apps. A few-shot prompt template can be constructed from either a set of examples, or Apr 25, 2024 路 In this post, we will delve into LangChain’s capabilities for Tool Calling and the Tool Calling Agent, showcasing their functionality through examples utilizing Anthropic’s Claude 3 model. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Warning This implementation is based on the foundational ReAct paper but is older and not well-suited for production applications. The agent is integrated with a set of tools, such as an SQL tool, and utilizes a memory buffer to maintain conversation history across sessions. Can someone please help me figure out how I can use memory with create_react_agent? Am I using wrong agent for this use case? System Info langchain==0. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Code from langchain. 1. LangSmith - LangChain provides a template for implementing ReAct logic using LangChain. The prompt must have input keys: tools: contains descriptions and arguments for each tool. Nov 29, 2024 路 Today we are going to discuss about Agentic Framework — ReAct Pattern and it’s implementation using two different approaches: Apr 12, 2025 路 Langchain hub is one of the centralized to store templatized prompt Here is an example of a ReAct prompt that we used in part 1: React System Prompt As you can see, we can pass tools, tool names and input as parameters The main advantage of using prompts from the centralized data store is that we can adapt the prompt on the fly without having to make a new software deployment just to update Here's an example: . , message filtering, etc. Aug 8, 2023 路 Customize a zero-shot agent that uses the ReAct architecture in this code-along tutorial. May 1, 2025 路 """ This example demonstrates using LangGraph's ReAct agent with Ollama models. It design a simple Wikipedia web API with three types of actions to support interactive information retrieval. In a recent article, I used the new LangChain expression language to create a pipeline-like invocation of prompts, LLMs and output parser. The example shows how to: 1. Understand how the ReAct chain works in LangChain by understanding the reasoning and action iterations being used. Set up a ReAct agent using LangGraph 3. Starting from the basic building blocks like defining a language model and tools, we advanced to designing a Jan 23, 2025 路 Get started in minutes using MCP and ReAct agent for intelligent tool handling in LangChain. Some example questions You can copy and paste the example questions from this table to replace the question at the bottom of the "ai-langchain-react-agent. It is easy to write custom tools, and you can easily pass these to the model. This guide demonstrates how to implement a ReAct agent using the LangGraph Functional API. prompts import PromptTemplate template = '''Answer the following questions as best you can. Get thine LLM outputs in Shakespearian English. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. Setup This tutorial uses LangGraph for agent orchestration, OpenAI's Feb 13, 2024 路 Plan and execute agents promise faster, cheaper, and more performant task execution over previous agent designs. The ReAct framework is a powerful approach that combines reasoning capabilities with actionable outputs, enabling language models to interact with external tools and answer complex questions May 22, 2024 路 This tutorial explores how three powerful technologies — LangChain’s ReAct Agents, the Qdrant Vector Database, and Llama3 Language Model. ReActOutputParser [source] # Bases: AgentOutputParser Output parser for the ReAct agent. getenv("GEMINI_API_KEY") To better understand how to implement a ReAct agent using LangGraph, let's walk through a practical example. Jun 2, 2024 路 We introduced three types of agents: the Zero-shot ReAct Agent, Conversational Agent, ReAct Docstore and Self-ask with search catering to beginners. Mar 31, 2025 路 Current ReAct Agents Since the introduction of ReAct Agent the capabilities of LLMs has evolved. 5-sonnet, gpt-4o, o1, and o3-mini across two domains of tasks. A Python library for creating hierarchical multi-agent systems using LangGraph. 38 langchain-openai==0. This shows a complete MCP client server using langchain example. py, demonstrates a flexible ReAct agent that iteratively reasons about user queries and executes actions, showcasing the power of this approach for complex problem-solving tasks. It covers the following topics, along with complete code examples (using triple backticks) and the names of the required packages: Using the Prebuilt ReAct Agent Adding Thread-Level Memory Adding a Custom System Prompt Returning Structured Output Adding Semantic Search to Apr 12, 2025 路 Common Tools of LangChain Prompt Templates Cyclic graphs (LangGraphs) Built-in persistence Human-in-the-loop Prompt templates Langchain hub is one of the centralized to store templatized prompt Here is an example of a ReAct prompt that we used in part 1: React System Prompt As you can see, we can pass tools, tool names and input as parameters Jul 4, 2025 路 LangChain similarly defines them as systems using an LLM to “decide the control flow of an application. Create ReAct agent Now that you have installed the required packages and set your environment variables, we can code our ReAct agent! Define graph state We are going to define the most basic ReAct state in this example, which will just contain a list of messages. Build resilient language agents as graphs. GitHub Gist: instantly share code, notes, and snippets. Tool : A class from LangChain that represents a tool the agent can use. See how to feed agents a self-defined prompt template. Aug 25, 2024 路 AgentExecutor and create_react_agent : Classes and functions used to create and manage agents in LangChain. Dec 14, 2024 路 While agents can be powerful, they are not perfect. Feb 28, 2025 路 This document consolidates all core instructions and examples for using and extending LangGraph’s prebuilt ReAct agent. code-block:: python from langchain_core. They can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). In this notebook we'll explore agents and how to use them in LangChain. e. Lay the foundation… Jul 28, 2025 路 What Is the LangChain ReAct Framework and How Does It Work? LangChain ReAct represents a sophisticated prompting technique that synergizes reasoning and action elements within large language models. ReActOutputParser # class langchain. To get structured output from a ReAct Agent in LangChain without encountering JSON parsing errors, you can use the ReActOutputParser class. For your specific use case, feel free to add any other state keys that you need. Create a simple tool (add function) 2. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. Designed a robust LangGraph Mar 7, 2023 路 ReAct (Reasoning + Acting) is a flexible LLM chain framework and essential for today's advanced LLM reasoning. This framework enables LLMs to analyze problems systematically while executing specific actions through external tool integration, creating a dynamic problem-solving environment that mirrors human Jan 31, 2024 路 In this blog, we will delve into the implementation of the ReAct framework within Langchain and provide a detailed, step-by-step guide on the functioning of a simple agent. The ReAct agent: Takes a user query as input Reasons about the query and decides on an action Executes the chosen action using available tools Observes the result of the action Repeats steps 2-4 until it can provide a final answer By default, it's set up with a basic set of tools, but can be easily extended with custom tools to suit various use cases. What’s a ReAct agent? Aug 27, 2023 路 However, the ReAct framework takes an extra step and makes use of the external environment as a wellspring of information. llms import OpenAI from langchain. . LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. The ReAct agent is a tool-calling agent that operates as follows: Queries are issued to a chat model; If the model generates no tool calls, we return the model response. We'll start by installing the prerequisite libraries that we'll be using in this example. The most well-known agent implementation are ReAct Agents. You will create a simple agent whose goal is to use a tool to find the current weather for a specified location. This section demonstrates basic setup — full implementation details follow in later sections. ) To manage message history in create_react_agent, you need to define a pre_model_hook function or runnable that takes graph state an returns a state update: Trimming example: Agent # class langchain. May 29, 2025 路 Migration Example: From initialize_agent to create_react_agent (LangGraph) Here’s how to update your deprecated agent creation logic using LangGraph’s pre-built ReAct agent. g. Using LangGraph's pre-built ReAct agent constructor, we can do this in one line. Make APIs work with natural language for easy, real-time data retrieval. Finally, agent. question-answering) agent, we'll use a simple ReACT architecture and give the agent tools for looking up track names, artist names, and album names based on various filters. ainvoke sends the query, letting the agent potentially use the server’s tools to find the answer. 4. These classes allow you to provide examples of desired interactions, which the language model can use to generate responses that align with your expectations. Apr 8, 2025 路 This article documents my journey building a multi-tool ReAct-style agent that can solve math problems and fetch real-time weather information using local subprocess and SSE-based MCP servers. react. summarization — summarize earlier messages in the history and replace them with a summary custom strategies (e. In this issue, we will build a simple ReAct-style agent from scratch using LangGraph (LangChain's graph-based This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools. ” In essence, agents are systems that can independently make decisions, use tools, take actions, and pursue a goal without direct human guidance. Their framework enables you to build layered LLM-powered applications that are context-aware and able to interact dynamically with their environment as agents, leading to simplified code for you and a more dynamic user experience for your customers. agents. For example, using a custom tool and OpenAI's Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet ReAct agent using LangChain, integrated with a custom tool to calculate the length of a given text. The execution is usually done by a separate agent (equipped with tools). May 7, 2025 路 A create_react_agent uses the LLM and tools to process a user query. Agents 馃 Agents are like "tools" for LLMs. Feb 22, 2024 路 The Langchain. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. Although I found an example how to add memory in LHCL following Test a ReAct agent with Pytest/Vitest and LangSmith This tutorial will show you how to use LangSmith's integrations with popular testing tools Pytest and Vitest/Jest to evaluate your LLM application. Return them as a markdown formatted list with each recommendation from tool results, being sure to include the full PDP URL. For example, in our fireside chat we did with Michele Catasta (President of Replit) on their Replit Agent, he speaks several times about the human-in-the-loop component being crucial to their agent design. Jun 17, 2025 路 LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. py" script. It excels at maintaining context, making dynamic decisions, and handling intricate logic. memory import ConversationBufferMemory llm = OpenAI(openai_api_key Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. 6 and the following models: - llama3. agent_scratchpad: contains previous agent actions and tool outputs as a string. ReAct agent using LangChain, integrated with a custom tool to calculate the length of a given text. This allows you to verify, if the agent uses the best tool for the question. However, our exploration doesn’t conclude here. This walkthrough demonstrates how to use an agent optimized for conversation. It serves as a foundational example for building more complex, tool-augmented LLM applications. The agent can store, retrieve, and use memories to enhance its interactions with users. This agent is the most general-purpose action agent available in LangChain and can be highly beneficial in situations where multiple tools are available, and selecting the right tool is time-consuming. LangChain has several built agents that wrap around the ReAct framework. Start learning now! The ReAct agent in LangChain is a versatile agent that utilizes the ReAct framework to select the appropriate tool based on its description. If the model generates tool calls, we execute the tool calls with available tools, append them as tool messages to our message list Aug 25, 2024 路 AgentExecutor and create_react_agent : Classes and functions used to create and manage agents in LangChain. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. Here’s an example: This walkthrough showcases using an agent to implement the ReAct logic. Learn how to build 3 types of planning agents in LangGraph in this post. Nov 24, 2024 路 To add few-shot examples to a prebuilt React agent in LangChain, you can use the FewShotPromptTemplate or FewShotChatMessagePromptTemplate classes. Nov 22, 2024 路 React agents represent an exciting frontier in AI development, offering developers the ability to create sophisticated, interactive agents… Sep 16, 2023 路 Don’t rush into agent applications in LangChain. Dec 9, 2024 路 Here's an example: . - Jan 9, 2025 路 LangGraph builds on LangChain and ReAct frameworks, enabling complex, stateful AI applications. Aug 21, 2023 路 A step-by-step guide to building a LangChain enabled SQL database question answering agent. Contribute to langchain-ai/langgraph development by creating an account on GitHub. It incorporates the React framework to determine which tool to use and utilizes memory to remember previous conversation interactions. 1 - qwen3:8b Tested with: - langchain >= 0. Sep 11, 2024 路 But create_react_agent does not have an option to pass memory. We will be using OpenAI for the LLM and LangChain as it already has built-in functionality that leverages the ReAct framework to build agents that perform tasks by combining the power of LLMs and different tools. We will equip it with a set of tools using LangChain's SQLDatabaseToolkit. 1 Sep 16, 2024 路 The LangChain library is in constant evolution. We’ve set up the environment, pulled a React prompt, initialized the language model, and added the capability to This guide demonstrates how to implement a ReAct agent using the LangGraph Functional API. Since the tools in the semantic layer use slightly more complex inputs, I had to dig a little deeper. Here’s an example: Apr 18, 2025 路 Example of Single-Agent code with LangChain Here, we are building a simple general-purpose agent using ReAct framework. This post will give you the answer for the questions: "What is ReAct?", "How ReAct works?", and "How to build ReAct?". A Aug 28, 2024 路 A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI. Sep 3, 2024 路 Here is an example of how you can set up and use the AgentComponent to build and run a Langchain ReAct agent: Define the Configuration: Use the build_config method to define the configuration for your agent, including the agent name, LLM, tools, and prompts. Mar 30, 2025 路 LangChain-MCP-Adapters is a toolkit provided by LangChain AI that enables AI agents to interact with external tools and data sources through the Model Context Protocol (MCP). Jun 27, 2024 路 In this post, we’ve created a responsive AI agent using Langchain and OpenAI. Why do LLMs need to use Tools? Feb 4, 2025 路 How do I now build a Langchain or Langgraph AI agent with a tool using Deepseek-R1 available in AzureOpenAI? Is it also possible to use the React framework available in Langchain or Langgraph to build this AI agent with a tool? Feb 10, 2025 路 We explore how increasing the number of instructions and tools available to a single ReAct agent affects its performance, benchmarking models like claude-3. jsParams required to create the agent. 6. 24 - langchain-ollama >= 0. For this weather Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Function calling allows us to to connect LLMs to external tools in a structured way, which is more reliable than parsing raw text and reduces the likelihood of errors and hallucinations. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. This project is designed to create and configure a ReAct (Reasoning and Acting) agent using LangChain and OpenAI's GPT-4o model. The planning is almost always done by an LLM. Feb 20, 2024 路 The examples in LangChain documentation (JSON agent, HuggingFace example) use tools with a single string input. LangGraph implements workflows as directed graphs, allowing for sophisticated reasoning, adaptability, and multi-agent collaboration in LLM-powered systems. Sep 18, 2024 路 Let’s walk through a simple example of building a Langchain Agent that performs two tasks: retrieves information from Wikipedia and executes a Python function. For example, you can look up albums by a particular artist, artists who released songs with a specific name, etc. May 2, 2023 路 LangChain is a framework for developing applications powered by language models. Unlike basic chains, agents can: Decide what actions to take Call tools in a dynamic, non-linear order Handle intermediate Mar 25, 2024 路 To initialize a CONVERSATIONAL_REACT_DESCRIPTION agent using the create_react_agent method in LangChain, you can follow these steps: Import the necessary modules and classes from langchain and other libraries as shown in the example. aiin hxh tonr wamtk rmo tcrp hgqt jwy xfay miic