Langchain agents and tools. agents import AgentAction from langchain_core. Chat models that support tool calling features implement a . 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. 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. 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. Why do LLMs need to use Tools? 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. The schemas for the agents themselves are defined in langchain. Provides a lot of When constructing an agent, you will need to provide it with a list of Tools that it can use. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. LangChain is a framework for building LLM-powered applications. This is generally the most reliable way to create agents. Learn to build AI agents with LangChain and LangGraph. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. From tools to agent loops—this guide covers it all with real code, best practices, and advanced tips. chat import ChatPromptTemplate from langchain_core. Jun 15, 2023 · There is a whole array of Action options available to the LangChain Agent. May 4, 2025 · Tool integrations: Connect LLMs to APIs, search engines, databases, and more. prompt (ChatPromptTemplate) – The prompt to use. Agent # class langchain. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. This is often achieved via tool-calling. For this, only basic LangChain features were required, namely model loading, prompt management, and invoking the model with rendered prompt. We can take advantage of this structured output, combined with the fact that you can bind multiple tools to a tool calling chat model and allow the model to choose which one to call, to create an agent that repeatedly calls tools and receives results until a query is resolved. The agent returns the observation to the LLM, which can then be used to generate the next action. 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 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. While LangChain includes some prebuilt tools, it can often be more useful to use tools that use custom logic. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. LangChain products are designed to be used independently or stack for multiplicative benefit. 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. 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. The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. Besides the actual function that is called, the Tool consists of several components: 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. Using agents This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. Finally, we will walk through how to construct a conversational retrieval agent from components. 1, which is no longer actively maintained. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. Here’s an example: agent_toolkits # Toolkits are sets of tools that can be used to interact with various services and APIs. In this tutorial we LangChain products are designed to be used independently or stack for multiplicative benefit. See Prompt section below for more on the expected input variables. They combine a few things: It is useful to have all this information because this information can be used to build action-taking systems! Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. agent. There are several key components here: Schema LangChain has several abstractions to make working with agents easy Build controllable agents with LangGraph, our low-level agent orchestration framework. 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. 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. Intermediate agent actions and tool output messages will be passed in here. In 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. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. In an earlier article, I investigated LangChain in the context of solving classical NLP tasks. Using LangChain Tools ADK provides the LangchainTool wrapper to integrate tools from the LangChain ecosystem into Jan 23, 2024 · Each agent can have its own prompt, LLM, tools, and other custom code to best collaborate with the other agents. Custom agent This notebook goes through how to create your own custom agent. The more tools are available to an Agent, the more actions can be taken by the Agent. Concepts There are several key concepts to understand when building agents: Agents, AgentExecutor, Tools, Toolkits. 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. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. Parameters: llm (BaseLanguageModel) – LLM to use as the agent. This guide will walk you through some ways you can create custom tools. Next, we will use the high level constructor for this type of agent. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. Use LCEL, which simplifies the customization of chains and agents, to build applications; Apply function calling to tasks like tagging and data extraction; Understand tool selection and routing using LangChain tools and LLM function calling – and much more. For a quick start to working with agents, please check out this getting started guide. In this guide, we will go over the basic ways to create Chains and Agents that call Tools. What Are LangChain Tools? If you're using pre-built LangChain or LangGraph components like create_react_agent,you might not need to interact with tools directly. This is driven by a LLMChain. Curated list of tools and projects using LangChain. tools (Sequence[BaseTool]) – Tools this agent has access to. Class hierarchy: ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool <name> # Examples: BraveSearch, HumanInputRun Main helpers: Concepts The core idea of agents is to use a language model to choose a sequence of actions to take. LangChain makes it significantly easier to build AI agents that are not only smart but also actionable and contextual. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. 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. May 2, 2023 · LangChain is a framework for developing applications powered by language models. Tools can be just about anything — APIs, functions, databases, etc. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. 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。 Feb 24, 2025 · This comprehensive guide showed how to create a fully functional weather chatbot agent that combines the strengths of OpenAI's GPT, LangChain, and FastAPI. In Chains, a sequence of actions is hardcoded. Each tool has a description. May 24, 2024 · By providing a unified framework for integrating various AI tools, LangChain simplifies the process of building intelligent agents that can understand and interact using natural language interfaces. For an in depth explanation, please check out this conceptual May 2, 2023 · This prompted us to reassess the limitations on tool usage within LangChain's agent framework. The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. 📄️ Apify This notebook shows how to use the Apify integration for LangChain. 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 tools # Tools are classes that an Agent uses to interact with the world. Agent uses the description to choose the right tool for the job. Memory is needed to enable conversation. For details, refer to the LangGraph documentation as well as guides for May 7, 2025 · Learn how to build agentic systems using Python and LangChain. We will first create it WITHOUT memory, but we will then show how to add memory in. 1. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves. 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. A big use case for LangChain is creating agents. Includes an LLM, tools, and prompt. 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. When the agent reaches a stopping condition, it returns a final return value. 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. Setup Deprecated since version 0. 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. 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. 1. The biggest difference here is that the first function requires an object with multiple input fields, while the 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. Create an agent that uses tools. Load the LLM First, let's load the language model we're going to Sep 9, 2024 · LangChain agents are meta-abstraction combining data loaders, tools, memory, and prompt management. LangChain Runnables that accept string or dict input can be converted to tools using the as_tool method, which allows for the specification of names, descriptions, and additional schema information for arguments. AgentExecutor [source] # Bases: Chain Agent that is using tools. bind_tools() method for passing tool schemas to the model. This is a more generalized version of the OpenAI tools agent, which was designed for OpenAI’s specific style of tool calling. from model outputs. abc import Sequence from typing import Callable from langchain_core. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. 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 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. This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools. 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. , runs the tool), and receives an observation. The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish. When constructing your own agent, you will need to provide it with a list of Tools that it can use. Below is a list of some of the tools available to LangChain agents. LangChain is the tool that you and your team might use to develop automated systems that review and moderate user-generated content by identifying and filtering inappropriate or harmful material. Tools are essentially functions that extend the agent’s capabilities by Oct 29, 2024 · Gain knowledge of the LangChain framework and its integration with Large Language Models and external tools. In this example, we will use OpenAI Tool Calling to create this agent. Agent that calls the language model and deciding the action. tools import BaseTool from . 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). Mar 17, 2025 · LangChain—a revolutionary framework designed to simplify and enhance the development of language-based AI applications. 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. 📄️ ArXiv This notebook goes over how to use the arxiv tool with an agent. May 1, 2024 · Source code for langchain. 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. The agent executes the action (e. Agent modules: Create autonomous systems that decide which tool to use and when. This covers basics like initializing an agent, creating tools, and adding memory. 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. It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. Here is an attempt to keep track of the initiatives around LangChain. This is just one of the many uses of LangChain, which offers a whole arsenal of tools to take your generative AI projects to the next level. We recommend that you use LangGraph for building agents. 📄️ AWS Lambda Amazon AWS Lambda is a Tools Tools are interfaces that an agent can use to interact with the world. Explore agents, tools, memory, and real-world AI applications in this practical guide. 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. They recognize and prioritize individual tasks, execute LLM invocations and tool interactions, to orchestrate the synthesizing of results. - A variety of pre-built agents to choose from. agents. However, understanding how to use them can be valuable for debugging and testing. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. AgentExecutor # class langchain. Subscribe to the newsletter to stay informed about the Awesome LangChain. This is documentation for LangChain v0. LangGraph is an extension of LangChain specifically aimed at creating highly controllable and customizable agents. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. In this blog post, we’ll explore the core components of LangChain, specifically focusing on its powerful tools and agents that make it a game-changer for developers and businesses alike. Classes The agent prompt must have an agent_scratchpad key that is a MessagesPlaceholder. It is through these tools that it is able to interact with its environment. g. load_tools # flake8: noqa """Tools provide access to various resources and services. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). Create autonomous workflows using memory, tools, and LLM orchestration. Acquire skills in fetching and processing live data from the web for accurate responses. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. Dec 12, 2024 · Build LangChain agents step by step to create AI assistants that automate tasks and integrate advanced tools seamlessly. from collections. In chains, a sequence of actions is hardcoded (in code). LlamaIndex forms part of this list of tools, with LlamaIndex acting as a framework to access and search different Documentation for LangChain. 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. Learn to create and implement custom tools for specialized tasks within a conversational agent. messages import BaseMessage from langchain_core. The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. 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 Toolkits are collections of tools that are designed to be used together for specific tasks. Agents select and use Tools and Toolkits for actions. 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. prompts. LangChain has a large ecosystem of integrations with various external resources like local and remote file systems, APIs and databases. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. Sep 9, 2024 · A remarkable library for using LLMs is LangChain. By leveraging these tools, developers can expand their projects to include features like stock tracking, personalized recommendations, and task automation. Start applying these new capabilities to build and improve your applications today. language_models import BaseLanguageModel from langchain_core. Apr 10, 2024 · In order to carry out its task, and operate on things and retrieve information, the agent has what are called Tool’s in LangChain, at its disposal. Jun 17, 2025 · Build an Agent 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. We send a couple of emails per month about the articles, videos, projects, and Hyperbrowser is a platform for running, running browser agents, and scaling headless browsers. Classes Aug 28, 2024 · A comprehensive tutorial on building multi-tool LangChain agents to automate tasks in Python using LLMs and chat models using OpenAI. LangChain is a framework for developing applications powered by large language models (LLMs). Oct 16, 2024 · はじめに こんにちは。 PharmaX でエンジニアをしている諸岡（@hakoten）です。 この記事では、 LangChain の「Tool Calling」の基本的な使い方と仕組みについてご紹介しています。LangChainをこれから始める方や、Tool Callingをまだあまり使ったことがない方に、ぜひ最後まで読んでいただけると嬉しいです Aug 13, 2024 · Beginner tutorial on how to design, create powerful, tool-calling AI agents chatbot workflow with LangGraph and LangChain. Ensure that the LLM understands when and how to invoke these tools. For the current stable version, see this version (Latest). They have convenient loading methods. 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. jsParams required to create the agent. Class hierarchy: Jun 19, 2025 · Build AI agents from scratch with LangChain and OpenAI. Jul 24, 2024 · 文章浏览阅读1. 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 Feb 16, 2025 · This article explores LangChain’s Tools and Agents, how they work, and how you can leverage them to build intelligent AI-powered applications. A large collection of built-in Tools. This interoperability is crucial because it allows for faster development time and allows you to reuse existing tools. LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. Actions are taken by the agent via various tools. Memory modules: Let agents remember past interactions. Defining Custom Tools When constructing your own agent, you will need to provide it with a list of Tools that it can use. 📄️ Polygon IO Toolkit This notebook shows how to use agents to interact with the Polygon IO toolkit. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. runnables import Runnable, RunnablePassthrough from langchain_core. The toolkit provides access to Polygon's Stock Market Data API. Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be. ksw bfsoc wxktfx vkv ympp hobcpn gcdg ifgsroj lfrhnmmr idvha