Langchain agent example github. json is indexed instead.

Langchain agent example github. Build effective agents with Model Context Protocol using simple, composable patterns - now with LangChain integration. - langchain-ai/langgraphjs Open Agent Platform provides a modern, web-based interface for creating, managing, and interacting with LangGraph agents. - langgraphjs/examples/multi_agent/agent_supervisor. Amazon Bedrock Custom LangChain Agent Create a custom LangChain agent dubbed "Agent AWS" that queries the AWS Well-Architected Framework and deploys Lambda functions, all backed by Amazon Bedrock and housed in a Streamlit chatbot. The application showcases a shipping company Curated list of agents built on LangChain. This is a starter project to help you get started with developing a RAG research agent using LangGraph in LangGraph Studio. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. For detailed documentation of all GithubToolkit features and configurations head to the API reference. json is indexed instead. An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. 🦜通过演示 LangChain 最具有代表性的应用范例，带你快速上手 LangChain 各个使用场景。（包含完整代码和数据集） - larkwins/langchain-examples LangChain is a library that utilizes natural language processing and machine learning algorithms to create agents to answer questions from CSV data. A collection of generative UI agents written with LangGraph. A swarm is a type of multi-agent architecture where agents dynamically hand off control to one another based on their specializations. agents import AgentType, initialize_agent, load_tools from langchain. Feb 27, 2025 · This project combines two functionalities: a Code Interpreter using LLM Agent Orchestration and Tool Utilization, and a ReAct LangChain Agent example. - langgraphjs/examples/multi_agent/hierarchical_agent_teams. ipynb Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. The language model used is OpenAIs GPT-4o mini. langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture. Based on https://learn. Multi-Agent Workflow with LangChain and LangGraph This project demonstrates a collaborative multi-agent system using LangChain and LangGraph. @langchain/core: Base abstractions and LangChain Expression Language. Contribute to langchain-ai/agent-protocol development by creating an account on GitHub. Apr 29, 2025 · Langchain ReAct agent example. - GitHub - easonlai/azure_o The Github toolkit contains tools that enable an LLM agent to interact with a github repository. import os from langchain. The LangChain libraries themselves are made up of several different packages. Welcome to "Awesome LagnChain Agents" repository! This repository is dedicated to showcasing the most amazing, innovative, and intriguing LangChain Agents from all over the world. Contribute to johnsnowdies/langchain-sql-agent-example development by creating an account on GitHub. ipynb at main · langchain-ai/langgraphjs LangGraph ReAct Agent Template This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. Examples | Building Effective Agents | MCP | LangChain Framework to build resilient language agents as graphs. Contribute to openai/openai-cookbook development by creating an account on GitHub. 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 Nov 14, 2023 · LangChain SQL - Agent Setup. ai/courses/building-your-own-database-agent/ - azure_langchain. ipynb # Entry point for the project ├── data/ # Dataset files or sample inputs ├── . It is designed to process user queries by leveraging two specialized AI agents: a Research Agent and a Writer Agent. Contribute to langchain-ai/langgraph development by creating an account on GitHub. It's designed with simplicity in mind, making it accessible to users without technical expertise, while still offering advanced capabilities for developers. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. (Update when i a Aug 30, 2023 · Explores the implementation of a LangChain Agent using Azure Cosmos DB for MongoDB vCore to handle traveler inquiries and bookings. To address these issues and facilitate communication with external applications, we introduce the concept of an Agent as a processor. Rather, it serves as an illustrative example for developers aiming to create personalized conversational agents for diverse applications like virtual workers and customer support systems. Why do LLMs need to use Tools? Build resilient language agents as graphs. The Agent can be considered a centralized manager This repository contains sample code to demonstrate how to create a ReAct agent using Langchain. LangGraph ReAct Memory Agent This repo provides a simple example of a ReAct-style agent with a tool to save memories. deeplearning. Here's an example of how you can use the LangChain framework to build a RAG model. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. I implement and compare three main architectures: Plan and Execute, Multi-Agent Supervisor Multi-Agent Collaborative. The main use cases for LangGraph are conversational agents, and long-running, multi-step LLM applications or any LLM application that Jan 20, 2025 · LangChain + OpenAI + Azure SQL. @langchain/community: Third party integrations. js or Vite), along with up to 4 pre-built agents. Studio also integrates with LangSmith to enable tracing, evaluation, and prompt engineering. 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. 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. Contribute to gkamradt/langchain-tutorials development by creating an account on GitHub. My goal is to support the LangChain community by giving these fantastic 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. Contribute to amalshehu/langchain-js-realworld development by creating an account on GitHub. Contribute to VRSEN/langchain-agents-tutorial development by creating an account on GitHub. Jan 30, 2024 · Let's see what we can do about your RAG requirements. It is easy to write custom tools, and you can easily pass these to the model. Dec 20, 2023 · I am using langchain version '0. It's designed to be simple yet informative, guiding you through the essentials of integrating custom tools with Langchain. 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 A Python library for creating swarm-style multi-agent systems using LangGraph. Lambda instruments the Financial Services agent logic as a LangChain Conversational Agent that can access customer-specific data stored on DynamoDB, curate opinionated responses using your documents and webpages indexed by Kendra, and provide general knowledge answers through the FM on Bedrock. Looks great! We're also able to ask questions that refer to previous interactions in the conversation and the agent is able to refer to the conversation history to as a source of information. Python Code Examples: Practical and easy-to-follow code snippets for each topic. AI PDF Chatbot & Agent Powered by LangChain and LangGraph This monorepo is a customizable template example of an AI chatbot agent that "ingests" PDF documents, stores embeddings in a vector database (Supabase), and then answers user queries using OpenAI (or another LLM provider) utilising LangChain and LangGraph as orchestration frameworks. LangChain is an open-source framework created to aid the development of applications leveraging the power of large language models (LLMs). The Build resilient language agents as graphs. This project aims to demonstrate the potential use of Neo4j graph database as memory of Langchain agent, which contains An implementation of entity graph similar to NetworkxEntityGraph, with Neo4j as storage An implementation of BaseChatMemory which make use Neo4j knowledge graph, extending existing ConversationKGMemory which uses NetworkxEntityGraph (in memory entity graph implemented with Framework to build resilient language agents as graphs. Additionally, it integrates with Overview and tutorial of the LangChain Library. Complete LangChain Guide: Covers all key concepts, including chains, agents, and document loaders. This repository contains a collection of apps powered by LangChain. An agent is a custom Contribute to theodo-group/langchain-agent development by creating an account on GitHub. 🦜🔗 Build context-aware reasoning applications. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. The tool is a wrapper for the PyGitHub library. txt # List of dependencies Tableau tools for Agentic use cases with Langchain & Langgraph. LangGraph Studio is a specialized agent IDE that enables visualization, interaction, and debugging of agentic systems that implement the LangGraph Server API protocol. Designed a robust LangGraph Aug 22, 2024 · Example of using Langchain with Azure OpenAI LLM. More examples from the community can be found here. 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). LangGraph Visualizations: Easily visualize the reasoning and workflow of your agents. Each agent performs a distinct role and collaborates to generate high-quality answers. An Agentic RAG implementation using Langchain and a telegram client to send/receive messages from the chatbot - riolaf05/langchain-rag-agent-chatbot Langchain realworld examples in JS. 350'. ipynb at main · langchain-ai/langgraphjs This is an example monorepo with multiple agents to deploy with LangGraph Cloud. Azure OpenAI GPT-4 for intelligent language understanding and generation of SQL queries in PostgreSQL. Enfuse agents with updated data so they make better informed decisions that scale with your analytics practice - tableau/tableau_. GitHub Gist: instantly share code, notes, and snippets. This repository contains examples of using LangChain, a framework for building applications with large language models (LLMs), to create various types of agents. For this, four datasets from the European Statistical Office (Eurostat) are loaded Build resilient language agents as graphs. Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) Text Splitter Playground - Play with various types of text splitting for RAG (Source code) Tweet 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. Although the solution in this post showcases the capabilities of a generative AI financial services agent powered by Amazon Bedrock, it is essential to recognize that this solution is not production-ready. 🦜🎤 Voice ReAct Agent This is an implementation of a ReAct -style agent that uses OpenAI's new Realtime API. It provides a unified interface to create agents based on different language models such as OpenAI. Those sample documents are based on the conceptual guides for 🦜🔗 Build context-aware reasoning applications. The system remembers which agent was last active, ensuring that on subsequent 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. js. We would like to show you a description here but the site won’t allow us. 0. Each approach has distinct strengths Collection of Langchain agents. Chroma DB & Pinecone: Learn how to integrate Chroma DB and Pinecone with OpenAI embeddings for powerful data management. Build resilient language agents as graphs. - tryAGI/LangChain A Python library for creating hierarchical multi-agent systems using LangGraph. This will clone a frontend chat application (Next. These evaluators expect you to format your agent's trajectory as a list of OpenAI format dicts or as a list of LangChain BaseMessage classes, and handle message formatting under the hood. Contribute to ThreeRiversAINexus/sample-agents development by creating an account on GitHub. Graph mode exposes the full feature-set LangChain结合了大型语言模型、知识库和计算逻辑，可以用于快速开发强大的AI应用。这个仓库包含了我对LangChain的学习和实践经验，包括教程和代码案例。让我们一起探索LangChain的可能性，共同推动人工智能领域的进步！ - aihes/LangChain-Tutorials-and-Examples Agent-IA Project │ ├── main. Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. Example application for the construction and inference of an LLM-based LangChain SQL Agent that can dynamically query a database and invoke multiple visualization tools. This repository contains an 'agent' which can take in a URL, and generate a Twitter & LinkedIn post based on the content of the URL. If an empty list is provided (default), a list of sample documents from src/sample_docs. LangChain Integration: Harness the power of LangChain for streamlined AI pipelines. Structured Learning Path: Start from the basics and progress to advanced topics. 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. Here is an attempt to keep track of the initiatives around LangChain. A good place to start includes: Tutorials More examples Examples of using advanced RAG techniques Example of an agent with memory, tools and RAG If you have any issues or feature requests, please submit them here. Setup At a high-level, we will: Install the pygithub library Create a Github app Set your environmental variables Pass the tools to Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples The main use cases for LangGraph are conversational agents, and long-running, multi-step LLM applications or any LLM application that would benefit from built-in support for persistent checkpoints, cycles and human-in-the-loop interactions (ie. LangGraph is a library for building stateful, multi-actor applications with LLMs. Engineered an autonomous multi-agent system by integrating Code Interpreter, ReAct, and LangChain frameworks, which streamlined dynamic code execution and reasoning, resulting in a 35% boost in operational efficiency. 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 This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. 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. env # Environment variables └── requirements. A Build resilient language agents as graphs. AutoGen for coordinating AI agents in collaborative workflows. LangChain is a framework for developing applications powered by large language models (LLMs). Based on your request, I understand that you're looking to build a Retrieval-Augmented Generation (RAG) model with memory and multi-agent communication capabilities using the LangChain framework. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support Here is a simple example of using the MCP tools with a LangGraph agent LangServe 🦜️🏓. js application which enables chatting with any LangGraph server with a messages key through a chat interface. These agents leverage the power of LLMs to perform tasks such as music recommendations, financial data retrieval, and mathematical reasoning. Contribute to n-mhatre/ReAct-Agent-Implementation-from-Scratch-with-LangChain development by creating an account on GitHub. Contribute to langchain-ai/langserve development by creating an account on GitHub. Contribute to langchain-ai/langchain development by creating an account on GitHub. Specifically, we enable this model to call tools by providing it a list of LangChain tools. The assistant can fetch the current time, perform web searches, and create notes based on search results, with in-built test cases to ensure functionality. C# implementation of LangChain. May 2, 2023 · LangChain is a framework for developing applications powered by language models. This repository demonstrates how to build a multi-agent AI system using: LangChain for natural language to SQL translation. llms import OpenAI import mlflow # Note: Ensure that the package 'google-search-results' is installed via pypi to run this example # and that you have a accounts with SerpAPI and OpenAI to use their APIs. Using one of langchain's pre-built agents involves three variables: defining the tools or the toolkit defining the llm defining the agent type This is all really easy to do in langchain, as we will see in the following example. Subscribe to the newsletter to stay informed about the Awesome LangChain. Each project is presented in a Jupyter notebook and showcases various functionalities such as creating simple chains, using tools, querying CSV files, and interacting with SQL databases. This repository provides several examples using the LangChain4j library. Agent Chat UI is a Next. This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. 🌟 Features Dynamic AI Agent Creation: Build agents with custom prompts and logic. That's all for this example of building a retrieval augmented conversational agent with OpenAI and Pinecone (the OP stack) and LangChain. The file has the column Customer with 101 unique names from Cust1 to Cust101. Contribute to antoinewg/langchain-agent-collection development by creating an account on GitHub. It also includes a simple web interface for interacting with the agent. The AWS Bedrock stack includes a conversational chain Unlimited Open-source Gemini Agents With Langchain - GitHub - ZeroXClem/Gemini-agent-example: Unlimited Open-source Gemini Agents With Langchain Agent trajectory match evaluators are used to judge the trajectory of an agent's execution either against an expected trajectory or using an LLM. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. This project is a voice-controlled AI assistant powered by LangChain agents, Vosk for speech recognition, OpenAI for text generation and TTS, and SerpAPI for web searches. We send a couple of emails per month about the articles, videos, projects, and Github Toolkit The Github toolkit contains tools that enable an LLM agent to interact with a github repository. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. This is a simple way to let an agent persist important information to reuse later. A CLI tool to quickly set up a LangGraph agent chat application. Ready to support ollama. Examples and guides for using the OpenAI API. It can be used for chatbots, text summarisation, data generation, code understanding, question answering, evaluation, and more. In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. The project provides detailed instructions for setting up the environment and loading travel data, aiming to empower developers to integrate similar agents into their The repository contains a bare minimum code example to get started with the Agent Inbox with LangGraph. Framework to build resilient language agents as graphs. Azure Database for PostgreSQL for data storage and querying. I am using a sample small csv file with 101 rows to test create_csv_agent. Now we will use the LangChain library to create a The repository contains a bare minimum code example to get started with the Agent Inbox with LangGraph. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. The role of Agent in LangChain is to help solve feature problems, which include tasks such as numerical operations, web search, and terminal invocation that cannot be handled internally by the language model. js - langchain-ai/langgraphjs-gen-ui-examples LangChain and LangGraph SQL agents example. LangServe 🦜️🏓. Aug 25, 2024 · In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. Customizable and Scalable: Designed to adapt to various use cases, from Q&A to autonomous To use the Agent Inbox, you'll have to use the interrupt function, instead of raising a NodeInterrupt exception in your codebase. Curated list of tools and projects using LangChain. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. This repository contains various examples of how to use LangChain, a way to use natural language to interact with LLM, a large language model from Azure OpenAI Service. Tools are essentially functions that extend the agent’s capabilities by Lambda instruments the Financial Services agent logic as a LangChain Conversational Agent that can access customer-specific data stored on DynamoDB, curate opinionated responses using your documents and webpages indexed by Kendra, and provide general knowledge answers through the FM on Bedrock. zsiho sqao glhje oziunp rieyf vecmi dhuqhq cnkc vhlyn hdolzf