Langchain Tool Example. LangChain Forum: Connect with the community and share all of you

LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback. embeddings import HuggingFaceEmbeddings from langchain. 0. messages import HumanMessage from langchain_google_genai import ChatGoogleGenerativeAI # Define the tool @tool(description="Get the current weather in a given location") def get_weather(location: str) -> str: return "It's sunny. Jan 13, 2026 · This page documents the file and directory organization of the langchain-aiplugin repository. LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves Jan 19, 2025 · Getting Started with LangChain Tools Your starting point for creating tools Are you curious about AI agents and ready to build them from scratch? This post will be a great start for you. LangChain Academy: Learn the basics of LangGraph in our free, structured course. 6 days ago · More examples and explanations related to the LangChain and LangGraph libraries can be found in my dedicated one series of articles. 1 day ago · LangChain provides framework for building applications powered by large language models that simplify prompt management, model interaction, memory handling, data retrieval, and tool integration. Jul 9, 2025 · Troubleshoot LangChain and LangGraph tracing issues with common causes and clear fixes to keep your observability on point. LangChain simplifies streaming from chat models by automatically enabling streaming mode in certain cases, even when you’re not explicitly calling the streaming methods. I think in terms of three layers: Jun 9, 2025 · LangChain Agents Relevant source files This page covers the LangChain-based agent system in RAI, which provides AI-powered robotics agents that integrate Large Language Models (LLMs) with ROS2 tools and capabilities. The tools node executes the tools and adds the responses to the messages list as ToolMessage objects. If you w 6 days ago · Learn to build AI agents with n8n using LangChain integration. This emits an event after every agent step. LangChain Ecosystem Integration The documentation has been unified at docs. Prerequisites Basic knowledge of Python or JavaScript Familiarity with LLM concepts (prompts, tokens, tools) Optional but helpful: Prior attendance of "Build Your First AI Agent – Part 1" The agent node calls the language model with the messages list (after applying the system prompt). Complete guide covers workflow architecture, LLM configuration, and production deployment. Tools extend what agents can do—letting them fetch real-time data, execute code, query external databases, and take actions in the world. This page documents the tool implementations that wrap memgraph-toolbox core tools into LangChain's BaseTool interface, and the MemgraphToolkit that bundles these tools for agent use. from langchain. Meaning we must modify existing tools or build entirely new ones. The process repeats until no more tool_calls are present in LangChain provides the engineering platform and open source frameworks developers use to build, test, and deploy reliable AI agents. We are also introducing a new agent class that works well with these LangChain simplifies streaming from chat models by automatically enabling streaming mode in certain cases, even when you’re not explicitly calling the streaming methods. com, with all Python and JavaScript examples living side-by-side for the first time. chains import RetrievalQA from langchain. By storing these in the graph’s state, the agent can access the full context for a given conversation while maintaining separation between different threads. agents import create_agent from langchain. While previous tools took in a single string input, new tools can take in an arbitrary number of inputs of arbitrary types. g. You can use zod to define the tool’s input schema: The agent node calls the language model with the messages list (after applying the system prompt). More features related to LangChain v1. messages import AIMessage, HumanMessage # Tools from langchain. Oct 29, 2024 · Learning Objectives Gain knowledge of the LangChain framework and its integration with Large Language Models and external tools. Contribute to langchain-ai/langgraph development by creating an account on GitHub. Acquire skills in fetching and processing live data from the web for accurate responses. agents. These Dec 8, 2023 · This notebook shows how to use LLMs in combination with Neo4j, a graph database, to perform Retrieval Augmented Generation (RAG). 190 Redirecting Completely New Tools # First, we show how to create completely new tools from scratch. for example: Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to LLMs. 190 Redirecting Tutorials, conceptual guides, and resources to help you get started. Contribute to patterns-ai-core/langchainrb development by creating an account on GitHub. In particular, you’ll be able to create LLM agents that use custom tools to answer user queries. Jan 6, 2026 · LangChain Tools and Toolkit Relevant source files The LangChain integration provides a set of tools and a toolkit for using Memgraph with LangChain agents and chains. A quick mental model for LangChain in 2026 LangChain in 2026 is more modular than its early versions. Short-term memory updates when the agent is LangChain offers an extensive ecosystem with 1000+ integrations across chat & embedding models, tools & toolkits, document loaders, vector stores, and more. The LangChain library provides a substantial selection of prebuilt tools. CloudVoyager is an interactive cloud platform comparison tool that transforms the complex decision-making process of choosing between AWS, Google Cloud, and Microsoft Azure into an intuitive, visually stunning experience. Learn how to integrate Replicate with LangChain using the Model Context Protocol (MCP). document_loaders import TextLoader 1 day ago · Examples: gptuber-by-langchain, kanji-flashcard-app-gpt4 Domain-Specific Frameworks Specialized frameworks for particular use cases (avatars, VR) Handles domain-specific requirements (3D rendering, voice sync) Provides pre-built components and patterns Examples: ChatdollKit, aiavatarkit Enterprise Integration via Azure LangChain Docs MCP server If you’re using an AI coding assistant or IDE (e. Tool dataclass # The LangChain library provides a substantial selection of prebuilt tools. agents in recent version. Product teams evaluating LangChain, LangFlow, or custom agent frameworks This session is technical in nature and assumes basic programming familiarity. Simple Example: Q&A over Custom Docs (LangChain + LLaMA) Python:- from langchain. Functions can have any signature - the tool will automatically infer input schemas unless disabled. invoke({ "messages": [{"role LangChain 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. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. agents instead, which is the new standard for building tool-calling agents in LangChain v1. I will Welcome to LangChain — 🦜🔗 LangChain 0. These providers have standalone langchain-provider packages for improved versioning Feb 16, 2025 · LangChain, an open-source framework, has emerged as a powerful tool for developing applications that integrate language models with external tools, knowledge bases, and APIs. Multi-agent systems coordinate specialized components to tackle complex workflows. 190 Redirecting LangChain offers an extensive ecosystem with 1000+ integrations across chat & embedding models, tools & toolkits, document loaders, vector stores, and more. The framework makes it easier to connect data sources, APIs, and machine learning models so businesses can use AI effectively. chat_models import init_chat_model model = init_chat_model( "claude-sonnet-4-5-20250929", temperature=0 ) # Define tools @tool def multiply(a: int, b: int) -> int: """Multiply `a` and `b`. # Agent building from langchain. It describes the purpose of each directory and key file, showing how the codebase is organized to separate 4 days ago · LangChain components turn LLMs from “chatbots” into structured, scalable, and production-ready AI systems by separating concerns like reasoning, retrieval, memory, and tool usage. Project 2: Simple Tools Notebook: langchain_simple_tools. For example, if you have an agent that calls a tool once, you should see the following updates: LLM node: AIMessage with tool call requests Tool node: ToolMessage with execution result LLM node: Final AI response 9 hours ago · Cons: usage costs, network latency, data residency constraints In both cases, you’ll wrap the Hugging Face pipeline in LangChain so the rest of your code can call a single interface. In LangGraph agents, for example, you can call Open source tracing and monitoring for your LangChain application. To improve your LLM application development, pair LangChain with: May 2, 2023 · This notebook takes you through how to use LangChain to augment an OpenAI model with access to external tools. You can use create_agent from langchain. Aug 27, 2025 · LangChain is a software company that offers developers the tools to build applications using large language models. However, in many real-world projects, we’ll often find that only so many requirements can be satisfied by existing tools. In LangGraph agents, for example, you can call . 190 Redirecting May 2, 2023 · TL;DR: we're introducing a new abstraction to allow for usage of more complex tools. Powered by LangChain, it features: - Ready-to-use app templates - Conversational agents that remember - Seamless deployment on cloud platforms Before using LangGraph, we recommend you familiarize yourself with some of the components used to build agents, starting with models and tools. That means you don’t need to build your agent on LangChain to take advantage of LangSmith’s tracing and evaluation features. vectorstores import FAISS from langchain. 5 days ago · 今晚20:30，想看真实效果的，晚上准点来，下方预约，开播有提醒哦！！！目前版本是1. Apr 10, 2024 · For example an SQLToolkit might contain a tool for generating an SQL query, validating an SQL query, and executing an SQL query. It LangChain isn’t just another AI tool — it’s the backbone for building powerful applications with LLMs 🔗 Think: chatbots, AI agents, knowledge search, and more — all powered by language models that can reason, recall, and act. If the resulting AIMessage contains tool_calls, the graph will then call the tools. These providers have standalone langchain-provider packages for improved versioning Build resilient language agents as graphs. chat_models import init_chat_model from langchain. 0正式开源。记得收藏哦 目录1 4 days ago · Unlike some of the observability tools, which are often tightly coupled to a particular LLM application framework, LangSmith is framework-agnostic. Under the hood, tools are callable functions with well-defined inputs and outputs that get passed to a chat model. Create tools Basic tool definition The simplest way to create a tool is by importing the tool function from the langchain package. Deep Agents are equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - making them well-equipped Examples: Guided examples on getting started with LangGraph. XX can be found in the official LangChain documentation. These agents enable natural language interaction with robotic systems through reasoning, tool calling, and multimodal communication. There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class. A provider is a third-party service or platform that LangChain integrates with to access AI capabilities like chat models, embeddings, and vector stores. LangChain is an open source framework with a pre-built agent architecture and integrations for any model or tool — so you can build agents that adapt as fast as the ecosystem evolves python. The Integrations Toolkit page on the LangChain docs has a large list of toolkits developed by the community that might be useful for you. 2+ and is not available in langchain. Claude Code or Cursor), you should install the LangChain Docs MCP server to get the most out of it. " Deep Agents is an agent harness built on langchain and langgraph. I will LangChain：控制流通常靠你写 Python if/while，或靠 Agent 内部策略(交给LLM去控制)LangGraph：控制流是“一等公民” 分支、回路、跳转、并行、终止条件都在图里表达 LangChain：更多是“上下文”概念（memory/me… Flowise is trending on GitHub It's an open-source drag & drop UI tool that lets you build custom LLM apps in just minutes. agents import create_agent # Messages and content from langchain. It happens in four main steps: creating the tool, binding it to a model, letting the model decide when to use it, and finally executing the tool. We will commonly use LangChain components throughout the documentation to integrate models and tools, but you don’t need to use LangChain to use LangGraph. llms import HuggingFaceHub from langchain. Oct 24, 2024 · In this example, we are creating a tool to get percentage marks, given obtained and total marks. Understand their key importance in AI development. State is persisted to a database (or memory) using a checkpointer so the thread can be resumed at any time. Deep Agents is an agent harness built on langchain and langgraph. This is particularly useful when you use the non-streaming invoke method but still want to stream the entire application, including intermediate results from the chat model. LangChain agents can use tools defined on MCP servers using the langchain-mcp-adapters library. Automatically capture rich traces and metrics and evaluate outputs. LangChain’s agent manages short-term memory as a part of your agent’s state. structured_output import ToolStrategy class ContactInfo(BaseModel): name: str email: str phone: str agent = create_agent( model="gpt-4o-mini", tools=[search_tool], response_format=ToolStrategy(ContactInfo) ) result = agent. Welcome to LangChain — 🦜🔗 LangChain 0. The agent node then calls the language model again. ipynb This project shows how to use pre-built tools in LangChain and create an agent that can respond to user questions using those tools. Deep Agents are equipped with a planning tool, a filesystem backend, and the ability to spawn subagents - making them well-equipped 6 days ago · create_tool_calling_agent was removed in LangChain v0. 6 days ago · In this post, we’ll explore when multi-agent architectures become necessary, the four main patterns we’ve observed, and how LangChain empowers you to effectively build multi-agent systems. Step-by-step guide with Python and TypeScript code examples. What is Langchain? LangChain is a framework for developing applications powered by language models. To stream agent progress, use the stream or astream methods with stream_mode="updates". Completely New Tools # First, we show how to create completely new tools from scratch. As Remote refines these tools, the team is planning to contribute generic improvements back to LangChain's open-source ecosystem and adopt new community innovations as they emerge. Tool dataclass # Welcome to LangChain — 🦜🔗 LangChain 0. There are two int inputs and a float output. document_loaders import TextLoader 1 day ago · Examples: gptuber-by-langchain, kanji-flashcard-app-gpt4 Domain-Specific Frameworks Specialized frameworks for particular use cases (avatars, VR) Handles domain-specific requirements (3D rendering, voice sync) Provides pre-built components and patterns Examples: ChatdollKit, aiavatarkit Enterprise Integration via Azure The core agent loop involves calling a model, letting it choose tools to execute, and then finishing when it calls no more tools: Middleware exposes hooks before and after each of those steps: Oct 27, 2025 · LangChain is a framework that makes it easier to build applications using large language models (LLMs) by connecting them with data, tools and APIs. Python and JS/TS. Convert Python functions and Runnables to LangChain tools. 4，2. Retrieval tools are not limited to a single string query argument, as in the above example. The process repeats until no more tool_calls are present in from langchain. Ready to build real AI agents—beyond simple chatbots? I just published “Fundamentals of Building AI Agents (2026 Edition)”, a practical e‑book for developers and AI practitioners. com Redirecting Dec 26, 2025 · Explore AI Agent Frameworks like Langchain, CrewAI, and Microsoft Semantic Kernel. You can use it with LangChain, LangGraph, or other custom code. It helps developers move beyond simple text generation and create intelligent workflows. tools import tool from langchain. It runs on LangGraph under the hood and supports the ReAct loop for tool calling. This will let us access document metadata in our application, separate from the stringified representation that is sent to the model. tools import tool # Model initialization from langchain. Case studies: Hear how industry leaders use LangGraph to ship AI applications at scale. Build LLM-powered applications in Ruby. Learn to create and implement custom tools for specialized tasks within a conversational agent. 6 days ago · 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. embeddings import init_embeddings Jan 19, 2025 · Getting Started with LangChain Tools Your starting point for creating tools Are you curious about AI agents and ready to build them from scratch? This post will be a great start for you. Can be used as a decorator with or without arguments to create tools from functions. However, not every complex task requires this approach — a single agent with the right (sometimes dynamic) tools and prompt can often achieve similar results. #langchain #llm #artificialintelligence #machinelearning #generativeai #largelanguagemodels #ai #LangChain # Dec 26, 2025 · Explore AI Agent Frameworks like Langchain, CrewAI, and Microsoft Semantic Kernel. from pydantic import BaseModel from langchain. 1 day ago · A recent example is an Agentic OCR-to-JSON Schema prototype, which combines document parsing with an agentic workflow to outperform basic OCR by a wide margin. This chapter will explore how to build custom tools for agents in LangChain. The invoke function can be used to get results from May 13, 2025 · Tool calling in LangChain follows a simple but powerful pattern. This ensures your agent has access to up-to-date LangChain documentation and examples. Here we use the tool decorator to configure the tool to attach raw documents as artifacts to each ToolMessage. langchain.

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