Langchain structured tool. However, LangChain provides other ways to build custom tools that can handle more complex objects as inputs and outputs. Jul 30, 2024 · LangChain APIs now allow using Pydantic v2 models for BaseTool and StructuredTool. 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 Dec 19, 2024 · Firstly, if the model has tool calling (function calling) support then LangChain supports a structured output method which uses the tool-calling API under the hood to return JSON formatted output. ChatPromptTemplate, tools_renderer: ~typing. This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. The issue arises because with_structured_output returns a RunnableSequence that doesn't support the bind_tools method. bind_tools() method for passing tool schemas to the model. How to use output parsers to parse an LLM response into structured format Language models output text. We will also demonstrate how to use few-shot prompting in this context to improve performance. Documentation for LangChain. 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 A tool that can be created dynamically from a function, name, and description, designed to work with structured data. In this tutorial we LangChain includes a suite of built-in tools and supports several methods for defining your own custom tools. This is the easiest and most reliable way to get structured outputs. Tool Calling: When appropriate, the model can decide to call a tool and ensure its The tool response format. tool # langchain_core. Create a new model by parsing and validating input data from keyword arguments. (2) Tool Binding: The tool needs to be connected to a model that supports tool calling. dev TLDR : tool_callsに新しいAIMessage属性を導入しています。信頼性の高いツール呼び出しのための API を公開する LLM プロバイダーが増えています。新しい属性 create_structured_chat_agent # langchain. The central concept is that the output structure of model responses needs to be represented in some way. If "content" then the output of the tool is interpreted as the contents of a ToolMessage. While some model providers support built-in ways to return structured output, not all do. LangChain supports the creation of tools from: Functions; LangChain Runnables; By sub-classing from BaseTool -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code. A number of chat models also support accepting Pydantic v2 models in bind_tools and with_structured_output (including Anthropic, OpenAI, Fireworks, and Mistral). This article explores what is ToolMessage, how it works and how to use it effectively with practical examples and advanced use cases like handling CSV data and debugging. A large collection of built-in Tools. callbacks import ( AsyncCallbackManagerForToolRun StructuredTool # class langchain_core. Oct 27, 2023 · 这就使得 LangChain 开始考虑放宽工具使用的限制。 2023年初，LangChain 引入了“多操作”代理框架，允许代理计划执行多个操作。 在此基础上，LangChain 推出了结构化工具对话代理，允许更复杂、多方面的交互。 May 13, 2023 · Q: Can I use structured tools with existing agents? A: If your structured tool accepts one string argument: YES, it will still work with existing agents. These updates require langchain-core>=0. description. I plan to make more videos with these tools being used in a more ro May 21, 2023 · By Jacob Lee Over the past few months, I had the opportunity to do some cool exploratory work for a client that integrated LLMs like GPT-4 and Claude into their internal workflow, rather than exposing them through a chat interface. This schema has only three fields name - The name of the tool. # I use the Structured tool in Langchain agents as follows: def create_agent ( llm: BaseLanguageModel, tools: Sequence [BaseTool], prompt: ChatPromptTemplate, task_context: str = "", **agent_kwargs, missing_vars = {"tools", "tool_names", "agent_scratchpad"}. There are two main methods an output Aug 16, 2024 · はじめに LangChainのChatOCIGenAIでTool callingとStructured outputがサポートされたので検証してみます。 Tool callingはいわゆるFunction callingの機能です。OCI SDK単体ではすでに同じ機能が使 A tool that can be created dynamically from a function, name, and description, designed to work with structured data. This is often achieved via tool-calling. A structured tool is a tool that uses a schema to define the structure of the arguments that the LLM generates as part of its ToolCall. The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. Class hierarchy: It is often crucial to have LLMs return structured output. A StreamEvent is a dictionary with the following schema: event: string - Event names are of the format: on_ [runnable_type The simplest way to create a tool is through the StructuredToolParams schema. For best results, pydantic. See the schema below for a DynamicStructuredTool. For more information on how to build agentic workflows in Oct 24, 2024 · How to build Custom Tools in LangChain 1: Using @tool decorator: There are several ways to build custom tools. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Feb 26, 2025 · AI models often communicate in natural language when interacting with humans. 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. List [~langchain_core. Instead, you should create separate LLM instances for different purposes: one for structured output and another for using tools [1]. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. 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 Feb 12, 2025 · You cannot directly combine with_structured_output and bind_tools in LangChain's ChatOpenAI. g. Oct 30, 2024 · The structured chat agent in LangChain can handle multiple tool invocations in a single user input using a multi-hop approach. Today, the LangChain team released what they call: LangChain Templates. - WeatherInputs: A Pydantic model defining the The term "Structured Outputs" is overloaded and can refer to two things: The general ability of the LLM to generate outputs in a structured format (this is what we cover on this page) The Structured Outputs feature of OpenAI, which applies to both response format and tools (function calling). tools # Tools are classes that an Agent uses to interact with the world. Defaults to None These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. Apr 13, 2024 · この記事は、2024/4/11 LangChain blog 掲載記事の日本語翻訳です。 Tool Calling with LangChain TLDR: We are introducing a new tool_calls attribute on AIMess blog. tool(*args: str | Callable | Runnable, return_direct: bool = False, args_schema: Type | None = None, infer_schema: bool = True, response_format: Literal['content', 'content_and_artifact'] = 'content', parse_docstring: bool = False, error_on_invalid_docstring: bool = True) → Callable [source] # Make tools out of functions, can be used with or without Optional list of tags associated with the tool. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain Tool objects Defining custom tools One option for creating a tool that runs custom code is to use a DynamicTool. Oct 7, 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。 LangChainには、各LLMモデルのレスポンスをあらかじめ決まった構造のクラスで受け取ることができる with_structured_output というメソッドがあります。 この記事では、 with_structured_output を使用してPydanticのクラスを Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. (3) Tool Calling: When appropriate, the model can decide to call a tool and This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. Creating tools from functions may be sufficient for most use cases, and can be done via a simple @tool decorator. , regular expressions), and custom fine-tuned ML models. with_structured_output() is implemented for models that provide native APIs for structuring outputs, like tool/function calling or JSON mode, and makes use of these capabilities under the hood. This schema does not include This is the easiest and most reliable way to get structured outputs. StructuredTool ¶ Note StructuredTool implements the standard Runnable Interface. 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. Dec 6, 2023 · In the LangChain framework, to ensure that your tool function is called asynchronously, you need to define it as a coroutine function using async def. You can use these to eg identify a specific instance of a tool with its use case. The framework will automatically use the _arun method for coroutine functions. Jun 30, 2025 · Learn the best methods for working with LangChain structured outputs in real-world applications, from parsing to validation. if missing_vars: LangChain provides a method, withStructuredOutput(), that automates the process of binding the schema to the model and parsing the output. May 2, 2023 · Structured tool’s enable more complex, multi-faceted interactions between language models and tools, making it easier to build innovative, adaptable, and powerful applications. Classical solutions to information extraction rely on a combination of people, (many) hand-crafted rules (e. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the 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. How to return structured data from a model It is often useful to have a model return output that matches some specific schema. Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' schema to populate the action input. js offers a powerful solution with DynamicStructuredTool Oct 22, 2024 · The StructuredOutputParser class is a versatile tool in LangChain that allows you to extract structured information from LLM outputs using custom-defined schemas. Output parsers are classes that help structure language model responses. Tool calling is a powerful technique that allows developers to build sophisticated applications that can leverage LLMs to access, interact and manipulate external resources like databases, files and APIs. Setup Install the OpenAI integration package, retrieve your key, and store it as an environment variable named OPENAI_API_KEY: Create a runnable for extracting structured outputs. Extracting structured output Overview Large Language Models (LLMs) are emerging as an extremely capable technology for powering information extraction applications. Tools can be just about anything — APIs, functions, databases, etc. base. How to: create tools How to: use built-in tools and toolkits How to: use chat models to call tools How to: pass tool outputs to chat models How to: few shot prompt tool behavior Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Initialize the tool. In this guide we'll go over the basic ways to create a Q&A system over tabular data with_structured_output: A helper method for chat models that natively support tool calling to get structured output matching a given schema specified via Pydantic, JSON schema or a function. with_structured_output and . It extends the StructuredTool class and overrides the _call method to execute the provided function when the tool is called. Agent uses the description to choose the right tool for the job. The general idea was to take some input data, analyze Nov 16, 2024 · While working with LangChain you will most likely come across the ToolMessage class which provides a structured way to relay tool outputs back to the model. This guide will show you a few different strategies you can use to do this. Dec 9, 2024 · langchain. These are applications that can answer questions about specific source information. BaseTool]], str] = <function render_text 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. langchain. Every chat model which supports tool calling in LangChain accepts binding tools to the model through this schema. from model outputs. callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain_core. If a dictionary is passed in, it’s assumed to already be a valid JsonSchema. 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. bind_tools is that . Tools allow us to extend the capabilities of a model beyond just outputting text/messages. TL;DR: 我们正在引入一个新的抽象概念，以允许使用更复杂的工具。之前的工具只接受单个字符串输入，而新工具可以接受任意数量、任意类型的输入。我们还引入了一个新的代理类，它可以很好地与这些新型工具配合使用。 重要链接 工具列表 新代理 早在 2022 年 11 月我们首次发布 LangChain 时，代理 LangChain 提供了一种方法 with_structured_output()，该方法可以自动执行将模式绑定到 模型 并解析输出的过程。 此辅助函数适用于所有支持结构化输出的模型提供商。 How to use LangChain tools Tools are interfaces that an agent, chain, or LLM can use to interact with the world. agents. But what if they need to interact with external systems that require a structured format? Tool calling, also known as tools # Tools are classes that an Agent uses to interact with the world. Next, check out the guide on tool calling, a more built-in way of obtaining structured output that some model providers support, or read more about output parsers for other types of structured data like XML. metadata?: LangChain provides abstractions to work with structured tool calling capabilities across different LLM providers (like OpenAI, Google Gemini, Anthropic Claude, etc. How to: create tools How to: use built-in tools and toolkits How to: use chat models to call tools How to: pass tool outputs to chat models Apr 11, 2024 · Intro Large Language Models (LLMs) can interact with external data sources via tool calling functionality. Feb 15, 2025 · The rise of AI-powered applications has brought significant advancements in natural language processing (NLP) and automation. 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. 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. BaseModel class. Tool Binding: The tool needs to be connected to a model that supports tool calling. An Introduction to ToolMessage Class The ToolMessage class in Mar 26, 2024 · I'm trying to alter the args_schema of a StructuredTool in Langchain. Such systems tend to get complex over time and become One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. prompts. A tool that can be created dynamically from a function, name, and description, designed to work with structured data. 🏃 The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. Sequence [~langchain_core. Class hierarchy: 定义自定义工具 当构建自己的代理时，您需要为其提供一组工具列表，代理可以使用这些工具。除了调用的实际函数之外，工具由几个组件组成： name（str），是必需的，并且在提供给代理的工具集中必须是唯一的 description（str），是可选的但建议的，因为代理使用它来确定工具的使用方式 return Jun 8, 2024 · Setting Up a Structured Tool First, let's define a simple structured tool using LangChain: Explanation - BaseTool: The base class for creating tools. This is because oftentimes the outputs of the LLMs are used in downstream applications, where specific arguments are required. Key concepts Tool Creation: Use the @tool decorator to create a tool. One way is to use the StructuredTool class, which allows you to define a tool that takes structured arguments. schema - The schema of the tool, defined with a Zod object. Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. create_structured_chat_agent(llm: ~langchain_core. Tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. 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 In our Quickstart we went over how to build a Chain that calls a single multiply tool. pydantic_v1 import BaseModel, Field from langchain_core Tools LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. LangChain, an open-source framework, has emerged as a powerful tool Aug 23, 2024 · If you’re working with Prisma and need a flexible way to integrate structured data handling into your AI-driven applications, LangChain. This helper function is available for all model providers that support structured output. Aug 14, 2024 · langchain_core. BaseModels should have docstrings describing what the schema represents and descriptions for the parameters. These templates are downloadable customizable components and are directly accessible within your codebase which allows for quick and easy customization wherever needed. Key concepts (1) Tool Creation: Use the tool function to create a tool. Base class for Tools that accept input of any shape defined by a Zod schema. The tool abstraction in LangChain associates a TypeScript function with a schema that defines the function's name, description and input. convert. This gives the model awareness of the tool and the associated input schema required by the tool. The tool schema. If more configuration is needed-- e. new StructuredTool<SchemaT, SchemaOutputT, SchemaInputT, ToolOutputT> (fields?): StructuredTool <SchemaT, SchemaOutputT, SchemaInputT, ToolOutputT> callbacks?: Callbacks. """ from __future__ import annotations import textwrap from collections. A description of the tool. 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. BaseLanguageModel, tools: ~typing. In this article I include a complete working example of the LangChain parser in its simplest form. This allows the agent to use multiple tools in sequence to complete a task. structured_chat. input_variables . Chat models that support tool calling features implement a . The DynamicTool and DynamicStructuredTool classes takes as input a name, a description, and a function. Providers have been introducing native tool calling capability into their models Jun 19, 2024 · 🤖 Hello, You're correct that the @tool decorator requires the function to have a string as input and output. description (optional) - A description of the tool. This guide will walk you through how we stream agent data to the client using React Server Components inside this directory. A tool is an association between a function and its schema. Refer here for a list of pre-built tools. The tool response format. with_structured_output: Ensures that the output conforms to a specific schema, either a dictionary or a Pydantic model. chat. One common use-case is extracting data from arbitrary text to insert into a traditional database or use with some other downstream system. This both binds the schema to the model as a tool and parses the output to the specified output schema. Parameters: output_schema (dict[str, Any] | type[BaseModel]) – Either a dictionary or pydantic. You have to define a function and May 5, 2023 · In this video, I take a cursory look into Langchain's newest release of structured tools. With these challenges in mind, LangChain provides a helper function (with_structured_output()) to streamline the process. But there are times where you want to get more structured information than just text back. StructuredTool implements the standard Runnable Interface. This can enhance type safety, improve code readability, and simplify the integration of tools and chat models. BaseTool], prompt: ~langchain_core. Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results. . The structured chat agent is capable of using multi-input tools. structured. These applications use a technique known as Retrieval Augmented Generation, or RAG. ), allowing you to define tools once and use them with compatible models and agents. Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. The Runnable Interface has additional methods that are available on runnables, such as with_config, with_types, with_retry, assign, bind, get_graph, and more. StructuredChatAgent ¶ class langchain. abc import Awaitable from inspect import signature from typing import ( TYPE_CHECKING, Annotated, Any, Callable, Literal, Optional, Union, ) from pydantic import Field, SkipValidation from typing_extensions import override from langchain_core. 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 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. The goal of tools APIs is to more reliably return valid and useful tool calls than what can Aug 11, 2024 · Enhance LangChain by integrating support for this OpenAI's new Structured Outputs mode, this strict Structured Outputs for 100% conformance to developer-supplied JSON Schemas. The input arguments’ schema. 1. Structured Chat Agent. Each tool has a description. bind_tools method be Oct 15, 2023 · A dynamic structured tool is an extension of LangChain’s StructuredTool class which basically overrides the _call method with the function provided. StructuredTool [source] # Bases: BaseTool Tool that can operate on any number of inputs. jsGenerate a stream of events emitted by the internal steps of the runnable. bind_tools allows calling external tools. Tool that can operate on any number of inputs. tools. Implementation of StructuredToolInterface. with_structured_output ensures structured output while . Callable [ [~typing. Provides a lot of . 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. , specification """Structured tool. Tools are interfaces that an agent, chain, or LLM can use to interact with the world. I get the following error: ValueError: This function requires a . jsInterface that defines the shape of a LangChain structured tool. 结构化结果和工具调用 # 结构化结果 # LLM返回结构化数据这一功能极具实用价值，例如从数据中提取接口参数，亦或是将数据存储于数据库。 接下来介绍几种相关方法 with_structured_output方法 # 这是模型所提供的原生 API 用于输出结构化数据，此方式是最为简单且可靠的。不管是工具调用、函数调用 Documentation for LangChain. Tools LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. difference ( prompt. If "content_and_artifact" then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage. 0: Use create_structured_chat_agent instead. 🏃. The tool decorator is an easy way to create tools. Having the LLM return structured output reliably is necessary for that. language_models. param verbose: bool = False ¶ Whether to log the tool’s progress. We'll focus on Chains since Agents can route between multiple tools by default. messages import ToolCall from langchain_core. However, structured tool with more than one argument are not directly compatible with the following agents without further customization: zero-shot-react-description react-docstore self-ask Here is a simple example of an agent which uses LCEL, a web search tool (Tavily) and a structured output parser to create an OpenAI functions agent that returns source chunks. llm (Runnable Dec 9, 2024 · langchain_core. I have a dynamic args_schema depending on the tool to use, but I want to remove an attribute from the args_schema if it exists. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. Aug 28, 2024 · Yes, the unique difference between . May 12, 2024 · Description Hi! I'm trying to use Langchain to create a negotiation agent that can call tools, and then output a final json of the negotiation decision. The method StructuredTool # class langchain_core. Apr 18, 2025 · This article dives into how to return structured outputs in LangChain, covering key concepts, schema definitions, tool calling, JSON mode, and LangChain’s built-in helper functions to streamline Tools Tools are interfaces that an agent can use to interact with the world. I'm using create_tool_calling_agent, and I want the output to be structured like the Output object, and so I bind the llm using with_structured_output. Apr 30, 2024 · The LangChain structured output parser in its simplest form closely resembles OpenAI’s function calling. 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. Defining Custom Tools When constructing your own agent, you will need to provide it with a list of Tools that it can use. StructuredChatAgent [source] ¶ Bases: Agent Deprecated since version 0. Dec 9, 2024 · from __future__ import annotations import textwrap from inspect import signature from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Type, Union from langchain_core. saofc ydbx bhnzqu xdazte cdwwsg lzyo vtezwu uxat vxrq oaou
|