Langchain agent tools. Learn to build AI agents with LangChain and LangGraph.

Langchain agent tools. They have convenient loading methods. If a tool_calls parameter is passed, then that is used to get the tool names and tool inputs. May 19, 2025 · Learn about LangChain's Open Agent Network, its features, and how to get stared to make first no-code AI agent for free. 📄️ Polygon IO Toolkit This notebook shows how to use agents to interact with the Polygon IO toolkit. 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. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Defining Custom Tools When constructing your own agent, you will need to provide it with a list of Tools that it can use. This notebook shows how to set up a Pinecone vectorstore, a Search tool, and a ChatOpenAI model with chain-of-thought reasoning. But for certain use cases, how many times we use tools depends on the input. All Toolkits expose a get_tools method which returns a list of tools. output_parsers. In an earlier article, I investigated LangChain in the context of solving classical NLP tasks. Classes 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. langgraph-bigtool is a Python library for creating LangGraph agents that can access large numbers of tools. This guide will cover how to bind tools to an LLM, then invoke the LLM to generate these arguments. You have to define a function and 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. from langchain. agents import AgentType, initialize_agent, load_tools from langchain_openai import OpenAI May 7, 2025 · Learn how to build agentic systems using Python and LangChain. agent. A collection of Tools in LangChain are called a Toolkit. 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). You can therefore do: Features 🛠️ Convert MCP tools into LangChain tools that can be used with LangGraph agents 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them Aug 5, 2024 · ToolとAgentは生成AIが外部に働きかけるときの窓口のような働きをします。モデルの可能性を大きく広げてくれます。あらかじめ用意されているツールもありますが、自分で拡張することも可能です。今回はそんなツールとツールを活用するAgentについてみてゆきましょう。 本記事はFuture Coders独自 Sep 9, 2024 · A remarkable library for using LLMs is LangChain. Currently, tools can be loaded with the following snippet: Tools # Tools are functions that agents can use to interact with the world. In these cases, we want to let the model itself decide how many times to use tools and in what order. Bind the Tools: Use the bind_tools Dec 12, 2024 · Build LangChain agents step by step to create AI assistants that automate tasks and integrate advanced tools seamlessly. Jul 24, 2024 · 文章浏览阅读1. To pass in our tools to the agent, we just need to format them to the OpenAI function format and pass them to our model. The agent returns the observation to the LLM, which can then be used to generate the next action. Setup Hyperbrowser is a platform for running, running browser agents, and scaling headless browsers. Chat models that support tool Tools # Tools are functions that agents can use to interact with the world. For a list of agent types and which ones work with more complicated inputs, please see. See Prompt section below for more. - A variety of pre-built agents to choose from. In Chains, a sequence of actions is hardcoded. g. Class hierarchy: Many agents will only work with tools that have a single string input. Currently, tools can be loaded with the following snippet: Jun 4, 2025 · 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. May 2, 2023 · TL;DR: we're introducing a new abstraction to allow for usage of more complex tools. prompt (BasePromptTemplate) – The prompt to use. invoke({ input: "what is LangChain?", }); console. Designed for versatility, the agent can tackle tasks like generating random numbers, sharing philosophical insights, and dynamically fetching and extracting content from webpages. These tools present an infinite number of possibilities. Define the Tools: Create classes for the tools you want to use, inheriting from BaseModel and defining the necessary fields. Agents are one of the most powerful and fascinating approaches to using L arge L anguage M odels (LLMs). This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. 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. They recognize and prioritize individual tasks, execute LLM invocations and tool interactions, to orchestrate the synthesizing of results. Create autonomous workflows using memory, tools, and LLM orchestration. const agentExecutor = new AgentExecutor({ agent, tools, }); const result = await agentExecutor. Class hierarchy: The agent executes the action (e. We are also introducing a new agent class that works well with these agents Repeated tool use with agents Chains are great when we know the specific sequence of tool usage needed for any user input. Class hierarchy: Apr 13, 2023 · Langchain Agents, powered by advanced Language Models (LLMs), are transforming the way we interact with data, perform searches, and execute tasks. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. """ from typing import List, Optional from langchain_core. tools. Toolkits are collections of tools that are designed to be used together for specific tasks. Find out how to use built-in tools, custom tools, and tools from different providers. 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. This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools. For this, only basic LangChain features were required, namely model loading, prompt management, and invoking the model with rendered prompt. Key concepts Tools are a way to encapsulate a function and its schema in a way that can be passed Jun 17, 2025 · Build a smart agent with LangChain that allows LLMs to look for the latest trends, search the web, and summarize results using real-time tool calling. 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. agents import load_tools # Imports the load_tools function for loading various tools in the langchain package from langchain_openai import ChatOpenAI # Imports the ChatOpenAI class to interact with OpenAI's models Define tools We first need to create the tools we want to use. , runs the tool), and receives an observation. The DynamicTool and DynamicStructuredTool classes takes as input a name, a description, and a function. 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. From tools to agent loops—this guide covers it all with real code, best practices, and advanced tips. Apr 10, 2024 · We can build out tools as needed, depending on the nature of tasks we are trying to carry out with the agent to fulfil. openai_tools. A large collection of built-in Tools. Setup We'll need to install the following packages for this guide: tools # Tools are classes that an Agent uses to interact with the world. 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 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. 1. Using tools allows the model to request that more than one function will be called upon when appropriate. For details, refer to the LangGraph documentation as well as guides for Sep 9, 2024 · LangChain agents are meta-abstraction combining data loaders, tools, memory, and prompt management. Mar 17, 2025 · In conclusion, LangChain’s tools and agents represent a significant leap forward in the development of AI applications. 6. Memory is needed to enable conversation. Using agents allows us to give LLMs access to tools. LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls. In chains, a sequence of actions is hardcoded (in code). 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. The schemas for the agents themselves are defined in langchain. tavily_search import TavilySearchResults from langchain_openai import OpenAI 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. For a list of toolkit integrations, see this page. Financial Data Analysis with Alpha Vantage. 4. Jun 19, 2025 · Build AI agents from scratch with LangChain and OpenAI. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. Implementation wise, this is literally just an array of the Tools that are available for the agent. In this example, we will use OpenAI Tool Calling to create this agent. We'll focus on Chains since Agents can route between multiple tools by default. Connecting Google Drive Data with LangChain. In this notebook we'll explore agents and how to use them in LangChain. Using LangChain Tools ADK provides the LangchainTool wrapper to integrate tools from the LangChain ecosystem into 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. (By bind -ing the functions, we This is a more generalized version of the OpenAI tools agent, which was designed for OpenAI’s specific style of tool calling. The toolkit provides access to Polygon's Stock Market Data API. Oct 24, 2024 · How to build Custom Tools in LangChain 1: Using @tool decorator: There are several ways to build custom tools. For detailed documentation of all GithubToolkit features and configurations head to the API reference. By understanding the core architecture — LLMs, tools, chains, memory, and the agent loop — developers can create sophisticated agents tailored to specific use cases. from model outputs. In this guide, we will go over the basic ways to create Chains and Agents that call Tools. 📄️ Apify This notebook shows how to use the Apify integration for LangChain. ToolsAgentOutputParser [source] # Bases: MultiActionAgentOutputParser Parses a message into agent actions/finish. These tools can be generic utilities (e. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string from langchain. It provides create_openai_tools_agent # langchain. Lifelike Speech Synthesis from ElevenLabs. LangChain comes with a number of built-in agents that are agents # Agent is a class that uses an LLM to choose a sequence of actions to take. If you're creating agents using OpenAI models, you should be using this OpenAI Tools agent rather than the OpenAI functions agent. Acquire skills in fetching and processing live data from the web for accurate responses. 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. Dec 9, 2024 · Source code for langchain. Aug 25, 2024 · The basic code to create an agent in LangChain involves defining tools, loading a prompt template, and initializing a language model. Aug 28, 2024 · LangChain’s 90k GitHub stars are all the credibility it needs—right now, it is the hottest framework to build LLM-based applications. Perhaps at the heart of LangChain’s capabilities are LangChain agents. Finally, we will walk through how to construct a conversational retrieval agent from components. Toolkits are supported 03プロンプトエンジニアの必須スキル5選04プロンプトデザイン入門【質問テクニック10選】05LangChainの概要と使い方06LangChainのインストール方法【Python】07LangChainのインストール方法【JavaScript・TypeScript】08LCEL（LangChain Expression Language）の概要と使い方09LangSmithの Using agents This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. That's where Agents come in! LangChain comes with a number of built-in agents that are optimized for different use 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. We'll start by installing the prerequisite libraries that we'll be using in this example. Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. 📄️ AWS Lambda Amazon AWS Lambda is a 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. You will be able to ask this agent questions, watch it call tools, and have conversations with it. Github Toolkit The Github toolkit contains tools that enable an LLM agent to interact with a github repository. 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. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Learn to build AI agents with LangChain and LangGraph. The LangChain Overview The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. search), other chains, or even other agents. 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 In the OpenAI Chat API, functions are now considered a legacy options that is deprecated in favor of tools. Load the LLM First, let's load the language model we're going to Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. Agent uses the description to choose the right tool for the job. We hope to continue developing different toolkits that can enable agents to do amazing feats. 📄️ ArXiv This notebook goes over how to use the arxiv tool with an agent. 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. Its comprehensive set of tools and components allows you to build end-to-end AI solutions using almost any LLM. By leveraging the power of these agents, users Using LangChain’s @tool decorators we define 3 simple tools for our agent to use. 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. In some situations, this can help signficantly 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. Learn how to create and use tools, chains and agents with LangChain, a Python library for building conversational AI applications. Next, we will use the high level constructor for this type of agent. The goal of tools APIs is to more reliably return valid and useful tool May 2, 2023 · LangChain is a framework for developing applications powered by language models. May 2, 2023 · Learn how to use LangChain to create LLM agents that can access external tools to answer user queries. Each tool has a description. Agents let us do just this. tools (Sequence[BaseTool]) – Tools this agent has access to How to use toolkits Toolkits are collections of tools that are designed to be used together for specific tasks. Comprehensive SEO Data from DataForSEO. May 2, 2023 · LangChain is a framework for developing applications powered by language models. Provides a lot of 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. By combining pre-built tools with custom 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. 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. tools_renderer (Callable[[list[BaseTool]], str]) – This controls how the tools are Bind tools to LLM How does the agent know what tools it can use? In this case we're relying on OpenAI function calling LLMs, which take functions as a separate argument and have been specifically trained to know when to invoke those functions. This covers basics like initializing an agent, creating tools, and adding memory. The tool is a wrapper for the PyGitHub library. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. Use LangGraph Platform to templatize your cognitive architecture so that tools, prompts, and models are easily Custom agent This notebook goes through how to create your own custom agent. 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. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. The tool decorator is an easy way to create tools. base. Importantly, the name, description, and JSON schema (if used) are all used in the prompt. Setup: LangSmith By definition, agents take a self-determined, input-dependent 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. Agents select and use Tools and Toolkits for actions. ToolsAgentOutputParser # class langchain. While previous tools took in a single string input, new tools can take in an arbitrary number of inputs of arbitrary types. Agents 🤖 Agents are like "tools" for LLMs. This interoperability is crucial because it allows for faster development time and allows you to reuse existing tools. Parameters: llm (BaseLanguageModel) – LLM to use as the agent. A toolkit is a collection of tools meant to be used together. This is generally the most reliable way to create agents. 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. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. AgentExecutor [source] # Bases: Chain Agent that is using tools. agents import AgentExecutor, create_react_agent from langchain_community. Setup At a high-level, we will: Install the pygithub library Create a Github app Set your environmental variables Pass the tools to Learn the latest advancements in LLM APIs and LangChain Expression Language (LCEL) to build powerful conversational agents. This article was published as a part of the Data Science Blogathon. callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain_core. For an in depth explanation, please check out this conceptual Jun 2, 2024 · LangChain offers a robust framework for working with agents, including: - A standard interface for agents. 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. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. Explore agents, tools, memory, and real-world AI applications in this practical guide. Oct 29, 2024 · Learn to create and implement custom tools for specialized tasks within a conversational agent. With tools, LLMs can search the web, do math, run code, and more. May 4, 2025 · Learn how to build agentic AI systems using LangChain, including agents, memory, tool integrations, and best practices to from langchain import hub from langchain. 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. The explosion of interest in LLMs has made agents incredibly prevalent in AI-powered use cases. Tools can be just about anything — APIs, functions, databases, etc. agents. 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: await callbacks Parameters: llm (BaseLanguageModel) – LLM to use as the agent. 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. Why do LLMs need to use Tools? Self-ask Tools for every task LangChain offers an extensive library of off-the-shelf tools u2028and an intuitive framework for customizing your own. 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. When the agent reaches a stopping condition, it returns a final return value. Develop a conversational agent that maintains context for coherent and relevant interactions. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. 5. 1. tools import BaseTool, tool May 9, 2025 · Conclusion LangChain provides a robust framework for building AI agents that combine the reasoning capabilities of LLMs with the functional capabilities of specialized tools. Importantly, the name and the description will be used by the language model to determine when to call this function and with what parameters, so make sure to set these to some values the Jun 12, 2025 · Learn how to use MCP (Model Context Protocol) in LangChain to build modular, secure, and scalable LLM agents—complete setup guide. Tavily We have a built-in tool in LangChain to easily use Tavily search engine as a tool. It lets you launch and manage browser sessions at scale and provides easy to use solutions for any webscraping needs, such as scraping a single page or crawling an entire site. In the OpenAI Chat API, functions are now considered a legacy options that is deprecated in favor of tools. Agents Chains are great when we know the specific sequence of tool usage needed for any user input. LangGraph's flexible framework supports diverse control flows – single agent, multi-agent, hierarchical, sequential – and robustly handles realistic, complex scenarios. In For a quick start to working with agents, please check out this getting started guide. May 24, 2024 · In this blog post, we’ll explore 10 powerful tools that seamlessly integrate with LangChain, unlocking a wide range of capabilities for your AI agents. 2w次，点赞47次，收藏62次。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 apiTool 模块相当于是使用外部工具，或者自定义工具。_langchain agent tool AgentExecutor # class langchain. Apr 29, 2025 · Discover how LangChain powers advanced multi-agent AI systems in 2025 with orchestration tools, planner-executor models, and OpenAI integration. log(result); /* { input: 'what is LangChain?', output: 'LangChain is a platform that offers a complete set of powerful building blocks for building context-aware, reasoning applications with flexible abstractions and an AI-first toolkit. 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 Third Party Tools ADK is designed to be highly extensible, allowing you to seamlessly integrate tools from other AI Agent frameworks like CrewAI and 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 will use two tools: Tavily (to search online) and then a retriever over a local index we will create. If one is not passed, then the AIMessage is assumed to be the final output. However, understanding how to use them can be valuable for debugging and testing. create_openai_tools_agent( llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate, strict: bool | None = None, ) → Runnable [source] # Create an agent that uses OpenAI tools. They are autonomous or semi-autonomous tools that can perform tasks, make Deprecated since version 0. We will first create it WITHOUT memory, but we will then show how to add memory in. tools """Interface for tools. Defining custom tools One option for creating a tool that runs custom code is to use a DynamicTool. Ensure reliability with easy-to-add moderation and quality loops that prevent agents from veering off course. Tools are functions or APIs that extend the capabilities of a model, chains are fixed sequences of tools, and agents are flexible agents that decide how to use tools based on input. 3. This article quickly goes over the basics of agents If you're using pre-built LangChain or LangGraph components like create_react_agent,you might not need to interact with tools directly. Learn how to use LangChain Tools to create and call functions that can be passed to language models. tools (Sequence[BaseTool]) – Tools this agent has access to. It leverages LangGraph's long-term memory store to allow an agent to search for and retrieve relevant tools for a given problem. sktj jetgt peridi blmk awr jpfv ielumi nnsaox ngnas mccpxq