Langchain agents documentation github. Build resilient language agents as graphs.

Langchain agents documentation github. A basic agent works in the following manner: Given a prompt an agent uses an LLM to request an action to take (e. To read more about how the interrupt function works, see the LangGraph documentation: conceptual guide how-to guide (TypeScript docs coming soon, but the concepts & implementation are the same). Parameters: llm (BaseLanguageModel) – LLM to use as the agent. It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. LangChain is an open source orchestration framework for application development using large language models (LLMs). Specifically, we enable this model to call tools by providing it a list of LangChain tools. 1. Productionization LangGraph Studio is a specialized agent IDE that enables visualization, interaction, and debugging of agentic systems that implement the LangGraph Server API protocol. In this case, we save all memories scoped to a configurable user_id, which lets the bot learn a user's preferences across conversational threads. LangChain 🔌 MCP. js, a library for building stateful, multi-actor applications with LLMs. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. Complete LangChain Guide: Covers all key concepts, including chains, agents, and document loaders. Key Enhancements: LangChain Integration: Native support for LangChain models and tools Multi-LLM Support: GigaChat, OpenAI, DeepSeek, Qwen, and more via LangChain Maintained Compatibility: Full backward compatibility with original MCP patterns Inspiration Dynamic Agent Delegation: The supervisor can decide whether to handle a user query itself or delegate it to a configured specialist agent. 1 billion valuation, helps developers at companies like Klarna and Rippling use off-the-shelf AI models to create new applications. 0: LangChain agents will continue to be supported, but it is recommended for new use cases to be built with LangGraph. I used the GitHub search to find a similar question and LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. Framework to build resilient language agents as graphs. LangChain provides a standard interface for agents, along with LangGraph. Build controllable agents with LangGraph, our low-level agent orchestration framework. Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. Setup: LangSmith By definition, agents take a self-determined, input-dependent This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. This lets your agents continuously Oct 1, 2023 · How to build a LangChain agents that can interact with data from a postgresql database of an Human Resources Systems. langchain-core This package contains base abstractions for different components and ways to compose them together. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents. Azure Database for PostgreSQL for data storage and querying. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). prebuilt. 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. LangChain / LangGraph SQL Agent Demo This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. As per the requirements for a language model to be compatible with LangChain's CSV and pandas dataframe agents, the language model should be an instance of BaseLanguageModel or a subclass of it. A Python library for creating swarm-style multi-agent systems using LangGraph. For these applications, LangChain simplifies the entire application lifecycle: Open-source libraries: Build your applications using LangChain's open-source components and third-party integrations. It’s designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. This project aims to simplify data manipulation tasks by providing a natural language interface for executing complex pandas operations. Graph mode exposes the full feature-set 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. It's grouped into 4 sections, each with a notebook and accompanying code in the src/email_assistant directory. Essentially, langchain makes it easier to build chatbots for your own data and "personal assistant" bots that Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples An LLM agent built using LangChain and OpenAI API, integrated with tools like DuckDuckGo, Wikipedia, and Arxiv for real-time web search, factual information, and academic research. The tool is a wrapper for the PyGitHub library. This tutorial builds upon the foundation of the existing tutorial available here: link written in Korean. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. This tutorial delves into LangChain, starting from an overview then providing practical examples. LangChain provides abstractions for building agents that use language models alongside arbitrary tools. For more details, please refer to the Langchain documentation. It also includes a simple web interface for interacting with the agent. Classes Overview and tutorial of the LangChain Library. Contribute to VRSEN/langchain-agents-tutorial development by creating an account on GitHub. Agent that calls the language model and deciding the action. 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. The agent returns the observation to the LLM, which can then be used to generate the next action. For detailed documentation of all GithubToolkit features and configurations head to the API reference. Follow their code on GitHub. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. The agent operates by maintaining an internal state and iteratively performing actions based on the input and the results of previous actions. Contribute to langchain-ai/langgraph development by creating an account on GitHub. The interfaces for core components like chat models, vector stores, tools and more are defined here. Customizable and Scalable: Designed to adapt to various use cases, from Q&A to autonomous Welcome to the LangChain 101 repository! This project serves as an accessible entry point for beginners eager to explore the world of agentic AI, focusing on the crucial concept of tools. LangGraph Visualizations: Easily visualize the reasoning and workflow of your agents. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support Architecture LangChain is a framework that consists of a number of packages. 4 days ago · Learn the key differences between LangChain, LangGraph, and LangSmith. Agent Framework: Leverages LangChain's agent framework with OpenAI's GPT-4o-mini for query processing. Contribute to AI-App/LangChain-AI. These section build from the basics of 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. This will clone a frontend chat application (Next. Additionally, I noticed a recurring pattern in Sep 26, 2024 · Checked other resources I added a very descriptive title to this question. Configurable Agents: Easily define and configure multiple child agents, selecting from the list of existing agents you have deployed and configured in Open Agent Platform (OAP). AutoGen for coordinating AI agents in collaborative workflows. 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. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. Studio also integrates with LangSmith to enable tracing, evaluation, and prompt engineering. 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). This YouTube tutorial goes over the architecture and concepts used for easily spinning up agents with using LangChain using OpenAI's API - edrickdch/langchain-agents This code demo's how you can connect to an SQL database using langchain SQL agent, query the data with natural language and send it to the LLM for generating a insightful response Github Toolkit The Github toolkit contains tools that enable an LLM agent to interact with a github repository. This project demonstrates how to 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. I searched the LangChain documentation with the integrated search. mcp-agent is a simple, composable framework to build agents using Model Context Protocol with extended support for LangChain integrations. 5 to build an agent that can interact with pandas DataFrames. tools_renderer (Callable[[list[BaseTool]], str]) – This controls how the tools are LangChain is a framework for developing applications powered by large language models (LLMs). Jan 17, 2025 · Hi everyone, I’ve partially updated the documentation to replace deprecated references to initialize_agent with langgraph. The agent executes the action (e. For details, refer to the LangGraph documentation as well as guides for ReAct Agents Overview ReAct agents in LangChain are designed to handle natural language inputs, process them, and determine the appropriate actions to take using a set of integrated tools. g. env Curated list of tools and projects using LangChain. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. This repository demonstrates how to build a multi-agent AI system using: LangChain for natural language to SQL translation. 3. It leverages LangGraph's long-term memory store to allow an agent to search for and retrieve relevant tools for a given problem. Jul 15, 2024 · Checked other resources I added a very descriptive title to this question. I used the GitHub search to find a similar question and In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. The repo is a guide to building agents from scratch. Discover how each tool fits into the LLM application stack and when to use them. These agents enable Large Language Models (LLMs) to perform complex tasks by integrating with external APIs, generating personalized images, and more, providing a comprehensive approach to bridging AI with real-world data. prompt (BasePromptTemplate) – The prompt to use. The AWS Bedrock stack includes a conversational chain About langchain ReAct agent代码示例，展示了如何定义custom tools来让llm使用。 详情请参照langchain文档。 The Langchain ReAct Agent code example demonstrates how to define custom tools for LLM usage. New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. The application showcases a shipping company LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. What is Open Agent Platform? Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. We send a couple of emails per month about the articles, videos, projects, and In simple terms, langchain is a framework and library of useful templates and tools that make it easier to build large language model applications that use custom data and external tools. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. 27 agents RAG Integration: Uses LangChain and FAISS to retrieve relevant documents from a knowledge base. Mar 6, 2024 · Checked other resources I added a very descriptive title to this question. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle 🌟 Features Dynamic AI Agent Creation: Build agents with custom prompts and logic. Currently the OpenAI stack includes a simple conversational Langchain agent running on AWS Lambda and using DynamoDB for memory that can be customized with tools and prompts. Customizable System Prompt: Tailor the supervisor's behavior and instructions Deprecated since version 0. I used the GitHub search to find a similar question and LangMem helps agents learn and adapt from their interactions over time. You'll know that the indexing is complete when the indexer "delete"'s the content from its graph memory (since it's been persisted in your configured storage provider). If you want to get started quickly check out mcp-use. ai Overview and tutorial of the LangChain Library. LangGraph development by creating an account on GitHub. Here is an attempt to keep track of the initiatives around LangChain. note This is a starter project to help you get started with developing a retrieval agent using LangGraph in LangGraph Studio. When the agent reaches a stopping condition, it returns a final return value. Contribute to gkamradt/langchain-tutorials development by creating an account on GitHub. The LangChain community in Seoul is excited to announce the LangChain OpenTutorial, a brand-new resource designed for everyone. LangChain Integration: Harness the power of LangChain for streamlined AI pipelines. Python Code Examples: Practical and easy-to-follow code snippets for each topic. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. This is a simple way to let an agent persist important information to reuse later. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. , a tool to run). LangSmith documentation is hosted on a separate site. This will assume knowledge of LLMs and retrieval so if you haven't already explored those sections, it is recommended you do so. It demonstrates how to create, test, and add features like Human-in-the-Loop (HITL) and persistent memory to an AI agent. A Python library for creating hierarchical multi-agent systems using LangGraph. I plan to work on pages 2 and 3 shortly to complete the updates. Setup At a high-level, we will: Install the pygithub library Create a Github app Set your environmental variables Pass the tools to Contribute to theodo-group/langchain-agent development by creating an account on GitHub. The langchain_pandas_agent project integrates LangChain and OpenAI 3. The schemas for the agents themselves are defined in langchain. Jun 17, 2025 · 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. Contribute to langchain-ai/langchain-mcp-adapters development by creating an account on GitHub. LangChain provides the smoothest path to high quality agents. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. js for building custom agents. LangChain + Next. Collection of Langchain agents. . output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. No third-party integrations are defined here. These agents are designed to streamline and enhance various research tasks, leveraging advanced AI capabilities. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. 0: Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. I'm happy to share the code with you! The Stripe Agent Toolkit enables popular agent frameworks including OpenAI's Agent SDK, LangChain, CrewAI, Vercel's AI SDK, and Model Context Protocol (MCP) to integrate with Stripe APIs through function calling. Specifically: I addressed the instances for page 1 of 3 in the search: repo:langchain-ai/langchain path:/^docs\// initialize_agent. It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build. Contribute to Cdaprod/langchain-cookbook development by creating an account on GitHub. This repository contains an 'agent' which can take in a URL, and generate a Twitter & LinkedIn post based on the content of the URL. Deploy and scale with LangGraph Platform, with APIs for state management, a visual studio for debugging, and multiple deployment options. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. LangGraph ReAct Agent Template This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. You can peruse LangSmith how-to guides here, but we'll highlight a few sections that are particularly relevant to LangChain below: Evaluation langgraph-bigtool is a Python library for creating LangGraph agents that can access large numbers of tools. Jul 9, 2025 · The startup, which sources say is raising at a $1. com website to build and deploy agents with your favorite MCP servers Feb 5, 2024 · Checked other resources I added a very descriptive title to this question. Agents select and use Tools and Toolkits for actions. Subscribe to the newsletter to stay informed about the Awesome LangChain. This is driven by a LLMChain. It is easy to write custom tools, and you can easily pass these to the model. A CLI tool to quickly set up a LangGraph agent chat application. LangChain is a powerful framework for building applications with large language models (LLMs), and this tutorial Langchain_CrewAI_Gemini-AI_Agents This GitHub repository houses a project where the Langchain platform, powered by Google's Gemini AI, collaborates with CREWAI to develop AI agents tailored for automating research activities. Additionally, it integrates with Introduction LangChain is a framework for developing applications powered by large language models (LLMs). LangGraph ReAct Memory Agent This repo provides a simple example of a ReAct-style agent with a tool to save memories. Agent [source] # Bases: BaseSingleActionAgent Deprecated since version 0. js or Vite), along with up to 4 pre-built agents. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. The Github toolkit contains tools that enable an LLM agent to interact with a github repository. It includes support for both This prebuilt graph is an agent that uses a reflection-style architecture to check and improve an initial agent's output. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. You can use this code to get started with a LangGraph application, or to test out the pre-built agents! Usage: create-agent-chat-app What is Open Agent Platform? Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. , runs the tool), and receives an observation. Chroma DB & Pinecone: Learn how to integrate Chroma DB and Pinecone with OpenAI embeddings for powerful data management. js application Social media agent - agent for sourcing, curating, and scheduling social media posts with human-in-the-loop (TypeScript) Agent Protocol - Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production To use the Agent Inbox, you'll have to use the interrupt function, instead of raising a NodeInterrupt exception in your codebase. It builds up to an "ambient" agent that can manage your email with connection to the Gmail API. This is not possible if you want to go to production, because it requires every user to have their own LangSmith API key, and set the LangGraph configuration themselves. By default, the Agent Chat UI is setup for local development, and connects to your LangGraph server directly from the client. The retrieval chat bot manages a chat history and The core idea of agents is to use a language model to choose a sequence of actions to take. Setup At a high-level, we will: Install the pygithub library Create a Github app Set your environmental variables Pass the tools to A Python library for creating hierarchical multi-agent systems using LangGraph. js + Next. It contains example graphs exported from src/retrieval_agent/graph. Copy the . The library is not exhaustive of the entire Stripe API. This repository contains implementations of AI email assistants built using LangGraph. agents. The dependencies are kept purposefully very lightweight You can just invoke it with an empty list (default) to index sample documents from LangChain and LangGraph documentation. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. The system remembers which agent was last active, ensuring that on subsequent Langchain Agents. It utilizes LangChain's CSV Agent and Pandas DataFrame Agent, alongside OpenAI and Gemini APIs, to facilitate natural language interactions with structured data, aiming to uncover hidden insights through conversational AI. The example in this repository demonstrates how to expose those The agent executes the action (e. js template - template LangChain. create_react_agent. According to the official LangChain website, their code‑generation capabilities “accelerate software development by automating code writing, refactoring, and documentation for your team” 【46645545831789†L296-L299】. I used the GitHub search to find a similar question and This project enables chatting with multiple CSV documents to extract insights. A swarm is a type of multi-agent architecture where agents dynamically hand off control to one another based on their specializations. FastAPI Endpoint: Provides a simple API to interact with the agent. 💡 Let developers easily connect any LLM to tools like web browsing, file operations, and more. Structured Learning Path: Start from the basics and progress to advanced topics. See Prompt section below for more. Build resilient language agents as graphs. 🌐 MCP-Use is the open source way to connect any LLM to any MCP server and build custom MCP agents that have tool access, without using closed source or application clients. LangChain has 208 repositories available. agent. It's designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. Contribute to antoinewg/langchain-agent-collection development by creating an account on GitHub. Agents use language models to choose a sequence of actions to take. LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. Available in both Python- and Javascript-based libraries, LangChain’s tools and APIs simplify the process of building LLM-driven applications like chatbots and AI agents. Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. tools (Sequence[BaseTool]) – Tools this agent has access to. It provides tooling to extract important information from conversations, optimize agent behavior through prompt refinement, and maintain long-term memory. It uses a human-in-the-loop (HITL) flow to handle authentication with different social media platforms, and to allow the user to make changes, or accept/reject the Sep 26, 2023 · I understand you're trying to use the LangChain CSV and pandas dataframe agents with open-source language models, specifically the LLama 2 models. Contribute to lloydchang/langchain-ai-langgraph development by creating an account on GitHub. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. Contribute to TheAILearner/Langchain-Agents development by creating an account on GitHub. The LangChain agents will be queried for use cases like employee password reque Agent # class langchain. 🤖 Agents: Agents allow an LLM autonomy over how a task is accomplished. An agent is a custom 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). 🦜🎤 Voice ReAct Agent This is an implementation of a ReAct -style agent that uses OpenAI's new Realtime API. Docker Support: Containerized setup for easy deployment. LangChain Python API Reference langchain-community: 0. LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. Feb 14, 2024 · I developed a multi-modal chatbot that leverages agents to address this issue. py that implement a retrieval-based question answering system. 💻 Welcome to the "Functions, Tools and Agents with LangChain" course! Instructed by Harrison Chase, Co-Founder and CEO at LangChain, this course will keep you updated with the latest advancements in Large Language Models (LLMs) and the libraries supporting them. It offers both functional primitives you can use with any storage system and native integration with LangGraph's storage layer. Course Website: 📚 deeplearning. LangChain implements a standard interface for large language models and related technologies, such as embedding models and vector stores, and integrates with hundreds of providers. jjjqh wpwcpus ebeg cmx jsel vemf dbgkhuyw uooiy fbofsa qicuutb