Langchain agent tool. We'll use the tool calling agent, which is generally the most reliable kind and the recommended one for most use cases. Ensure that the LLM understands when and how to invoke these tools. , whether it selects the appropriate first tool for a given step). LangChain comes with a number of built-in agents that are optimized for different use cases. The primary supported way to do this is with LCEL. from model outputs. Tools are essentially functions that extend the agent’s capabilities by When constructing your own agent, you will need to provide it with a list of Tools that it can use. Function calling is a key skill for effective tool use, but there aren’t many good benchmarks for measuring function calling performance. Designed for versatility, the agent can tackle tasks like generating random numbers, sharing philosophical insights, and dynamically fetching and extracting content from webpages. Oct 29, 2024 · This guide dives into building a custom conversational agent with LangChain, a powerful framework that integrates Large Language Models (LLMs) with a range of tools and APIs. How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. agents import AgentExecutor, create_tool_calling_agent, tool from langchain_anthropic import ChatAnthropic from langchain_core. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. Class hierarchy: Tools # Tools are functions that agents can use to interact with the world. Agents are systems that take a high-level task and use an LLM as a reasoning engine to decide what actions to take and execute those actions. It is described to the agent as Sep 9, 2024 · LangChain agents are meta-abstraction combining data loaders, tools, memory, and prompt management. This guide will walk you through some ways you can create custom tools. Agents select and use Tools and Toolkits for actions. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. Quickstart In this guide, we will go over the basic ways to create Chains and Agents that call Tools. Chat models that support tool calling features implement a . May 2, 2023 · This prompted us to reassess the limitations on tool usage within LangChain's agent framework. Using agents This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. from_messages( [ ("system", "You are a helpful assistant"), ("placeholder", "{chat_history}"), ("human LangChain allows you to enforce tool choice (using tool_choice), ensuring the model uses either a particular tool or any tool from a given list. AgentExecutor [source] # Bases: Chain Agent that is using tools. Apr 10, 2024 · Let’s build a simple agent in LangChain to help us understand some of the foundational concepts and building blocks for how agents work there. prompts import PromptTemplate template = '''Answer the following questions as best you can. How to: pass in callbacks at runtime How to: attach callbacks to a module How to: pass callbacks into a module constructor How to: create custom callback handlers How to: use callbacks in Apr 6, 2024 · 「LangChain」の「Agent」「Tool」「Toolkits」の概要をまとめました。 ・langchain 0. callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain_core. Agentの概要 「Agent」は、LLMで実行する一連の行動を決定するChainです。通常の「Chain」では一連の行動が (コード内に) ハードコーディングされていますが、「Agent」では「LLM」がどの行動をどの順序で実行 How to create tools When constructing an agent, you will need to provide it with a list of Tools that it can use. The tool decorator is an easy way to create tools. A tool is an association between a function and its schema. Next, we will use the high level constructor for this type of agent. g. 6k次，点赞9次，收藏15次。本文介绍了如何在LLM驱动的应用中使用工具链解决非AI功能和AI逻辑的集成问题，通过示例展示了如何加载工具、自定义工具和使用代理进行零样本反应决策。关键点包括工具类定义、代理类型以及工具选择的依赖于提示工程的特性。 Dec 9, 2024 · Source code for langchain. 📄️ ArXiv This notebook goes over how to use the arxiv tool with an agent. This is usually powered by a language model, a prompt, and an output parser. But for certain use cases, how many times we use tools depends on the input. Class hierarchy: LangChain’s ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. You will be able to ask this agent questions, watch it call tools, and have conversations with it. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. tools """Interface for tools. Understand how LangChain agents enhance LLM applications by dynamically integrating external tools, APIs, and real-time data access. """ 这种既可以接受多个，也可以接受单个. We recommend that you use LangGraph for building agents. Trajectory: Evaluate whether the agent took the expected path (e. One of its most exciting aspects is the Agents Choosing between multiple tools In our Quickstart we went over how to build a Chain that calls a single multiply tool. tools # Tools are classes that an Agent uses to interact with the world. In Sep 10, 2023 · はじめに langchainのAgentは言語モデルに使用する関数（tool)を決定させるためのクラスです。Agentはtoolを決定するだけで実行はしません。タスクを完了するためにはtoolを実行し、その実行結果を言語モデルに渡す必要があり、その処理はAgentではなく Tools Tools are interfaces that an agent can use to interact with the world. While LangChain includes some prebuilt tools, it can often be more useful to use tools that use custom logic. Finally, we will walk through how to construct a conversational retrieval agent from components. Feb 23, 2023 · LangChain の Agent を利用すると、単独の言語モデルだけでは実現できない計算や最新の情報を考慮した上での回答をすることができます。 LangChain の Agent がどのように Tool を選択しているかを確認してみました。 前提条件 langchain==0. tools import BaseTool, tool. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. Aug 7, 2024 · Implementing Shell/Bash Tool from Langchain for windows OS using ReAct agent , Groq LLM api (free) In the realm of LLM frameworks , LangChain offers an underrated feature for connecting Large Jun 15, 2023 · Agents are autonomous by utilising a Large Language Models (LLM) to decide which sequence of actions to pursue & which tools to use. agent. Classes Chains refer to sequences of calls - whether to an LLM, a tool, or a data preprocessing step. 📄️ Polygon IO Toolkit This notebook shows how to use agents to interact with the Polygon IO toolkit. Toolkits are supported Aug 5, 2024 · LangChain入門 (6) – Tool/Agent - 外部世界への橋渡し 28 このように生成AI単体でできないこと、特に外部への働きかけを行う場合は、ツールとエージェントを使用するとよいでしょう。 Future Coders Future Codersではほかにも多くの独自教材を用意しています。 Aug 13, 2024 · Beginner tutorial on how to design, create powerful, tool-calling AI agents chatbot workflow with LangGraph and LangChain. They have convenient loading methods. They combine a few things: The name of the tool A description of what the tool is Schema of what the inputs to the tool are The function to call Whether the result of a tool should be returned directly to the user It is useful to have all this information because this information can be used to build action-taking Dec 19, 2023 · Agents may be the “killer” LLM app, but building and evaluating agents is hard. prompts import ChatPromptTemplate prompt = ChatPromptTemplate. As we can see, the agent will first choose which tables are relevant and then add the schema for those tables and a few sample rows to the prompt. We will first create it WITHOUT memory, but we will then show how to add memory in. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. s: str = Field(description= "输入字符串") """Multiply two numbers. Tools can be passed to chat models that support tool calling allowing the model to request the execution of a specific function with specific inputs. For an in depth explanation, please check out this conceptual Example: . For details, refer to the LangGraph documentation as well as guides for 2 days ago · This page shows you how to develop an agent by using the framework-specific LangChain template (the LangchainAgent class in the Vertex AI SDK for Python). load_tools. It uses LangChain’s ToolCall interface to support a wider range of provider implementations, such as Anthropic, Google Gemini, and Mistral in addition to OpenAI. This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. Mar 17, 2025 · In conclusion, LangChain’s tools and agents represent a significant leap forward in the development of AI applications. Tools allow agents to interact with various resources and services like APIs Documentation for LangChain. bind_tools() method for passing tool schemas to the model. Tavily We have a built-in tool in LangChain to easily use Tavily search engine as a tool. Agent We'll use an OpenAI chat model and an "openai-tools" agent, which will use OpenAI's function-calling API to drive the agent's tool selection and invocations. Besides the actual function that is called, the Tool consists of several components: Agents let us do just this. This tutorial is recommended for developers who are just getting into building with Azure AI Services or professionals who are Aug 28, 2024 · A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI. Deprecated since version 0. Defining Custom Tools When constructing your own agent, you will need to provide it with a list of Tools that it can use. """ LangGraph provides control for custom agent and multi-agent workflows, seamless human-in-the-loop interactions, and native streaming support for enhanced agent reliability and execution. 165) Python API DeepLearning ChatGPT LangChain Last updated at 2023-05-11 Posted at 2023-05-11 The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. We will use two tools: Tavily (to search online) and then a retriever over a local index we will create. search), other chains, or even other agents. tools import BaseTool, StructuredTool, Tool, tool Defining tool schemas For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Agent 1-1. # Import things that are needed generically from langchain import LLMMathChain, SerpAPIWrapper from langchain. The Webbrowser Tool gives your agent the ability to visit a website and extract information. By keeping it simple we can get a better grasp of the foundational ideas behind these agents, allowing us to build more complex agents in the future. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. jsParams required to create the agent. This is useful for structuring the model's behavior and guiding it towards a desired outcome. Load the LLM First, let's load the language model we're going to Dec 12, 2024 · Build LangChain agents step by step to create AI assistants that automate tasks and integrate advanced tools seamlessly. Jan 3, 2025 · An agent in Langchain is a dynamic system that can make decisions based on a given task, interact with external resources (referred to as tools), and perform multiple steps to complete a task. What is Langchain? LangChain is a framework for developing applications powered by language models. A toolkit is a collection of tools meant to be used together. Read about all the agent types here. Apr 11, 2024 · Quickstart To best understand the agent framework, let's build an agent that has two tools: one to look things up online, and one to look up specific data that we've loaded into a index. In Chains, a sequence of actions is hardcoded. Setup: LangSmith By definition, agents take a self-determined, input-dependent 【LangChainと外部APIの連携】 Agents×Tools完全攻略 (v0. chat_models import ChatOpenAI from langchain. load_tools(tool_names: List[str], llm: BaseLanguageModel | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, allow_dangerous_tools: bool = False, **kwargs: Any) → List[BaseTool] [source] # Load tools based on their name. from langchain_core. 📄️ AWS Lambda Amazon AWS Lambda is a Define tools We first need to create the tools we want to use. They combine a few things: The name of the tool A description of what the tool is JSON schema of what the inputs to the tool are The function to call Whether the result of a tool should be returned directly to the user It is useful to have all this information because this This chapter will explore how to build custom tools for agents in LangChain. Now let's take a look at how we might augment this chain so that it can pick from a number of tools to call. A large collection of built-in Tools. In an API call, you can describe tools and have the model intelligently choose to output a structured object like JSON containing arguments to call these tools. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the Apr 11, 2024 · LangChain already has a create_openai_tools_agent() constructor that makes it easy to build an agent with tool-calling models that adhere to the OpenAI tool-calling API, but this won’t work for models like Anthropic and Gemini. code-block:: python from langchain. They combine a few things: The name of the tool A description of what the tool is JSON schema of what the inputs to the tool are The function to call Whether the result of a tool should be returned directly to the user It is useful to have all this information because this information can be used to Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. 0. Single step: Evaluate any agent step in isolation (e. Apr 29, 2025 · Discover how LangChain powers advanced multi-agent AI systems in 2025 with orchestration tools, planner-executor models, and OpenAI integration. 💡 Customization of tool retrieval: Optionally define custom functions for tool retrieval. Apr 25, 2024 · 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. """ from typing import List, Optional from langchain_core. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. These tools can be generic utilities (e. 构建 LLM 代理 (Agents) 的定制工具 Building Custom Tools for LLM Agents 代理 (Agents) 是使用大型语言模型（LLM）最强大和最有趣的方法之一。 LLM 的兴起使得代理 (Agents) 在基于人工智能的应用中变得非常普遍。 使用代理 (Agents) 可以让 LLM 访问工具。 Oct 25, 2023 · 文章浏览阅读7. The toolkit provides access to Polygon's Stock Market Data API. Includes an LLM, tools, and prompt. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the right tools and Tools and Toolkits Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. For a quick start to working with agents, please check out this getting started guide. This covers basics like initializing an agent, creating tools, and adding memory. 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 LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. 定义一个搜索的工具. Memory is needed to enable conversation. Includes support for in-memory and Postgres backends. Note that this requires a Tavily API key set as an environment variable named TAVILY_API_KEY - they have a free tier, but if you don’t agent_toolkits # Toolkits are sets of tools that can be used to interact with various services and APIs. Jul 24, 2024 · langchain 中提供了内置工具的，但是基本不能用，除了一个计算器和一个执行 python 代码的，其他的都要 api. 1. agents. That means there are two main considerations when thinking about different multi-agent workflows: What are the multiple independent agents? How are those agents connected? This thinking lends itself incredibly well to a graph representation, such as that provided by langgraph. Key concepts Tools are a way to encapsulate a function and its schema in a way that can be How to use tools in a chain In this guide, we will go over the basic ways to create Chains and Agents that call Tools. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. Jun 2, 2024 · LangChain offers a robust framework for working with agents, including: - A standard interface for agents. Earlier this year, we introduced a "multi-action" agent framework, where agents can plan multiple actions to perform on each step of the agent executor. Sep 18, 2024 · Best Practices for Using Langchain Agents Tool Selection: Choose the right tools for your agent based on the task at hand. Custom agent This notebook goes through how to create your own custom agent. Agent # class langchain. Agents are used when a single input/output process is not enough, and the task requires reasoning, planning, or interaction with external systems. We’ll start with a couple of simple tools to help us understand the typical tool building pattern before moving on to more complex tools using other ML models to give us even more abilities like describing images. Learn how to build LangChain agents in Python. By combining pre-built tools with custom 🧰 Scalable access to tools: Equip agents with hundreds or thousands of tools. Tool 模块相当于是使用外部工具，或者自定义工具. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. 📝 Storage of tool metadata: Control storage of tool descriptions, namespaces, and other information through LangGraph's built-in persistence layer. Setup Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. Agents Agents can be thought of as the chain responsible for deciding what step to take next. - A variety of pre-built agents to choose from. Tools Tools are interfaces that an agent, chain, or LLM can use to interact with the world. Setup We'll need to install the following packages for this guide: load_tools # langchain_community. This gives the model awareness of the tool and the associated input schema required by the tool. (3) Tool Calling: When appropriate, the model can decide to call a tool and While other tools (like the Requests tools) are fine for static sites, PlayWright Browser toolkits let your agent navigate the web and interact with dynamically rendered sites. In these cases, we want to let the model itself decide how many times to use tools and in what order. Tool calling agent Tool calling allows a model to detect when one or more tools should be called and respond with the inputs that should be passed to those tools. 定义一个计算 a * b 的计算器. js to build a simple AI agent that will help us do price conversion for MyTreat website. Class hierarchy: Oct 24, 2024 · How to build Custom Tools in LangChain 1: Using @tool decorator: There are several ways to build custom tools. Different agents have different prompting styles for reasoning, different ways Nov 22, 2024 · LangChain is a powerful framework designed to build AI-powered applications by connecting language models with various tools, APIs, and data sources. This is driven by a LLMChain. 92 Agent を利用した質問応答 Mar 1, 2025 · 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. Agent that calls the language model and deciding the action. Currently, tools can be loaded with the following snippet: from langchain. query: str = Field(description= "search query") """Look up things online. By combining robust building blocks with intelligent orchestrators, LangChain empowers developers to create dynamic, context-aware, and scalable solutions that can transform industries and enhance user experiences. 1 1. LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. 官方文档： Defining Custom Tools | 🦜️🔗 LangChain. Agent uses the description to choose the right tool for the job. LangChain is great for building such interfaces because it has: Good model output parsing, which makes it easy to extract JSON, XML, OpenAI function-calls, etc. In particular, you'll be able to create LLM agents that use custom tools to answer user queries. Mar 1, 2023 · Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Concepts Concepts we will cover are: Using language models, in particular their tool calling ability Tools 📄️ Alpha Vantage Alpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. For an in depth explanation, please check out this conceptual Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. 】 18 LangChain Chainsとは？ 【Simple・Sequential・Custom】 19 LangChain Memoryとは？ 【Chat Message History・Conversation Buffer Memory】 20 LangChain Agentsとは？ Feb 15, 2025 · This article explores LangChain’s Tools and Agents, how they work, and how you can leverage them to build intelligent AI-powered applications. Intermediate agent actions and tool output messages will be passed in here. Besides the actual function that is called, the Tool consists of several components: name (str), is required and must be unique within a set of tools provided to an agent description (str), is optional but recommended, as it is used by an agent to determine tool use args Toolkits are collections of tools that are designed to be used together for specific tasks. This article quickly goes over the basics of agents Tool use and agents An exciting use case for LLMs is building natural language interfaces for other "tools", whether those are APIs, functions, databases, etc. Here are the steps: Define and configure a model Define and use a tool (Optional) Store chat history (Optional) Customize the prompt template (Optional Mar 14, 2025 · In this tutorial we are going to look at how to create model instances supported by Azure OpenAI Service on Azure AI Foundry and look at how to use Lang Graph from Langchain. May 30, 2023 · If you’ve just started looking into LangChain and wonder how you could use agents as tools for other agents, you’ve come to the right place. They recognize and prioritize individual tasks, execute LLM invocations and tool interactions, to orchestrate the synthesizing of results. Provides a lot of 16 LangChain Model I/Oとは？ 【Prompts・Language Models・Output Parsers】 17 LangChain Retrievalとは？ 【Document Loaders・Vector Stores・Indexing etc. This is generally the most reliable way to create agents. In this tutorial, we will use pre-built LangChain tools for an agentic ReAct agent to showcase its ability to differentiate appropriate use cases for each tool. (2) Tool Binding: The tool needs to be connected to a model that supports tool calling. This library is How to use LangChain tools Tools are interfaces that an agent, chain, or LLM can use to interact with the world. We hope to continue developing different toolkits that can enable agents to do amazing feats. , of tool calls) to arrive at the final answer. Each tool has a description. Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. agent_toolkits. That's where Agents come in! LangChain comes with a number of built-in agents that are optimized for different use Feb 4, 2025 · To create a LangChain AI agent with a tool using any LLM available in LangChain's AzureOpenAI or AzureChatOpenAI class, follow these steps: Instantiate the LLM: Use the AzureChatOpenAI class to create an instance of the language model. . We'll focus on Chains since Agents can route between multiple tools by default. May 2, 2023 · This notebook takes you through how to use LangChain to augment an OpenAI model with access to external tools. The agent returns the exchange rate between two currencies on a specified date. 📄️ Apify This notebook shows how to use the Apify integration for LangChain. Key concepts (1) Tool Creation: Use the tool function to create a tool. AgentExecutor # class langchain. In this example, we will use OpenAI Tool Calling to create this agent. Today, we are excited to release four new test environments for benchmarking LLMs’ ability Final response: Evaluate the agent's final response. Tool是使用LangChain4J开发Agent应用的重头戏，也是Function calling调用后，实际的业务逻辑代码所在： 每一块Function calling的实作，都只是朴素的function，但这里出现了两个新的Annotation，@Tool与@P。 @Tool接受一段话，这与一般LLM对话中的提示词（Prompt）作用相当，不同的是@Tool里的文字是引导LLM能根据用户发 For a quick start to working with agents, please check out this getting started guide. Tools can be just about anything — APIs, functions, databases, etc. For a list of toolkit integrations, see this page. agents import AgentType, initialize_agent from langchain. agents import AgentType, initialize_agent, load_tools from langchain_openai import OpenAI May 24, 2024 · Discover how LangChain empowers developers to create sophisticated AI agents by integrating with 10 powerful tools, from financial data analysis and image generation to SEO optimization and biomedical research. What Are LangChain Tools? Overview The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. This is a more generalized version of the OpenAI tools agent, which was designed for OpenAI’s specific style of tool calling. LangGraph offers a more flexible and full-featured framework for building agents, including support for tool-calling, persistence of state, and human-in-the-loop workflows. Apr 24, 2024 · In this tutorial, we will build an agent that can interact with multiple different tools: one being a local database, the other being a search engine. Aug 16, 2024 · In this tutorial, we will explore how to build a multi-tool agent using LangGraph within the LangChain framework to get a better… Agents Chains are great when we know the specific sequence of tool usage needed for any user input. You have to define a function and The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. lxxwrsfssshavoybrcyhmlcuqdpmefgnvwetisjszjlxj