Langchain agent example github. 1 - qwen3:8b Tested with: - langchain >= 0.

Langchain agent example github. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL Learn to build custom prompts and tools for LangChain agents - build-on-aws/amazon-bedrock-custom-langchain-agent. The solution is an autonomous AI agent designed to serve as an instant expert on any given GitHub repository. To improve your LLM application development, pair LangChain with: LangSmith - Helpful for agent evals and observability. More examples from the community can be found here. ai/courses/building-your-own-database-agent/ - azure_langchain. env # Environment variables └── This repository provides several examples using the LangChain4j library. The ReAct framework is a powerful approach that combines reasoning capabilities with actionable outputs, enabling language models to interact with external tools and answer complex questions. 6 and the following models: - llama3. 1 - qwen3:8b Tested with: - langchain >= 0. Execute the agent with a basic math query Tested with Ollama version 0. Specifically: 🦜🎤 Voice ReAct Agent This is an implementation of a ReAct -style agent that uses OpenAI's new Realtime API. Welcome to "Awesome LagnChain Agents" repository! This repository is dedicated to showcasing the most LangChain is a library that utilizes natural language processing and machine learning algorithms to create agents to answer questions from CSV data. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task LangChain is a framework for developing applications powered by language models. A CLI tool to quickly set up a LangGraph agent chat application. I Build resilient language agents as graphs. js or Vite), along with up to 4 pre-built agents. agents import AgentType, initialize_agent, load_tools from langchain. 🧰 Scalable access to tools: Equip agents with hundreds or thousands of tools. These section build from the basics of agents, to agent evaluation, to human-in-the-loop, and finally to memory. Agent Chat UI is a Next. Key The above example will invoke the graph, passing in a request for it to do some research into LangGraph. The Amplify configuration points to a GitHub source repository from which our website's front-end is built. Overview Relevant source files This document provides an introduction to the Agent Inbox LangGraph Example, a minimal implementation that demonstrates how to build agent systems with human-in-the-loop capabilities using LangGraph and Agent Inbox. This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. For detailed information about the system design, see System Architecture. 24 - Agent trajectory match evaluators are used to judge the trajectory of an agent's execution either against an expected trajectory or using an LLM. LangChain is an open-source framework created to aid the development of applications Examples and guides for using the OpenAI API. If an empty list is provided Example application for the construction and inference of an LLM-based LangChain SQL Agent that can dynamically query a database and invoke This repository contains a comprehensive, project-based tutorial that guides you through building sophisticated chatbots and AI applications using LangChain. Let's see what we can do about your RAG requirements. You can use this code to get started with a LangGraph application, or to test out the mcp-agent is a simple, composable framework to build agents using Model Context Protocol with extended support for LangChain integrations. This should be a list of functions or LangChain @tool objects. py: Simple streaming app with In this tutorial we will build an agent that can interact with a search engine. 6. Build resilient language agents as graphs. Includes support for in-memory and Postgres backends. In Auth for GenAI LangChain & LangGraph SDK make it easy for developers to integrate with and secure any AI agent workflow using LangChain and LangGraph. It's grouped into 4 sections, each with a notebook and accompanying code in the src/email_assistant directory. 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. 3's core features including memory, agents, chains, multiple LLM providers, vector databases, and prompt templates using the latest API structure. 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 A complete demonstration of LangChain 0. js + Next. It provides essential building blocks like chains, The repo is a guide to building agents from scratch. This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. START, END (from @langchain/langgraph): Special nodes representing the beginning and end of a graph or subgraph. Contribute to openai/openai-cookbook development by creating an account on GitHub. Framework to build resilient language agents as graphs. Curated list of agents built on LangChain. Langchain ReAct agent example. LangChain + Next. It's designed to be simple yet informative, guiding you 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 This repository contains a collection of apps powered by LangChain. It provides a unified interface to create agents based on different language models such as OpenAI. This library 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. Their framework enables you to build layered LLM-powered Build resilient language agents as graphs. To use the Agent Inbox, you'll have to use the interrupt function, instead of raising a NodeInterrupt exception in your codebase. The system remembers which agent was A production-grade architecture for building an autonomous AI agent that analyzes GitHub repos. 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. js template - template LangChain. 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. Collection of Langchain agents. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. 💡 Customization of tool retrieval: Optionally define custom functions for tool retrieval. Set up a ReAct agent using LangGraph 3. This repository contains reference implementations of various LangChain agents as Streamlit apps including: •basic_streaming. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop 🦜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 This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. Contribute to langchain-ai/langchain development by creating an account on GitHub. GitHub Gist: instantly share code, notes, and snippets. 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). This article moves beyond a simple case study to provide a definitive The Github toolkit contains tools that enable an LLM agent to interact with a github repository. StateGraph (from @langchain/langgraph): The core class for building the graph. You will learn everything from the fundamentals of chat models to advanced concepts like Retrieval-Augmented Generation (RAG), agents, and custom tools. - Siva A Python library for creating hierarchical multi-agent systems using LangGraph. This project demonstrates how to build a LangChain agent that uses the Model Context Protocol (MCP) to interact with various services: Tavily Search: Web search and news search capabilities Weather: Mock weather information retrieval Math: Mathematical expression evaluation The agent uses LangGraph's ReAct agent pattern to dynamically select and use these tools based on tools (Required) The first argument to create_deep_agent is tools. Please find documentation about this feature here. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem A list of exciting langchain project ideas to understand LangChain applications in the real world | ProjectPro Build resilient language agents as graphs. ipynb This is a starter project to help you get started with developing a RAG research agent using LangGraph in LangGraph Studio. This framework allows tool use and human participation via multi-agent conversation. deeplearning. Specifically, we enable this model to call tools by providing it a list of LangChain tools. The agent retrieves relevant information from a text corpus and processes user queries via a web API. This repository implements a weather query example application of an agent, including function calling based on the IBM Developer Blog Post Create a LangChain AI Agent in Python using watsonx. js application which enables chatting with any LangGraph server with a messages key through a chat interface. Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain C# implementation of LangChain. Command (from @langchain/langgraph): Used by nodes to direct the graph to the next node and update the This project combines two functionalities: a Code Interpreter using LLM Agent Orchestration and Tool Utilization, and a ReAct LangChain Agent example. LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs LangChain is a framework for developing applications powered by large language models (LLMs). js starter app. Langchain is releasing updates faster than the speed of light, but to use the currently in PR-mode Guard & ChatAgent functionalities, I merged the latest This project showcases the creation of a ReAct (Reasoning and Acting) agent using the LangChain library. 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. Based on your request, I understand that you're looking to build a Retrieval-Augmented Jupyter Notebooks to help you get hands-on with Pinecone vector databases - examples/learn/generation/langchain/handbook/06-langchain-agents. Here is a simple example of using the MCP tools with a LangGraph agent 🌟 Features Dynamic AI Agent Creation: Build agents with custom prompts and logic. Contribute to langchain-ai/langgraph development by creating an account on GitHub. Complete the following steps to fork and clone the generative-ai-amazon This project demonstrates the implementation of intelligent agents using LangChain, showcasing how to create agents that can perform complex tasks by combining multiple tools and reasoning capabilities. 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 is easy to write custom tools, and you can easily pass these to the model. js application Social media agent - agent for sourcing, curating, and scheduling social media posts An Agentic RAG implementation using Langchain and a telegram client to send/receive messages from the chatbot - riolaf05/langchain-rag-agent-chatbot Example project demonstrating how to expose FastAPI endpoints as Model Context Protocol (MCP) tools using fastapi-mcp. The Github toolkit contains tools that enable an LLM agent to interact with a github repository. js - langchain-ai/langgraphjs-gen-ui-examples This repository contains sample code to demonstrate how to create a ReAct agent using Langchain. The workshop: explores the latest advancements in AI agents and agentic workflows, leveraging improvements in function calling LLMs and specialized Automated Multi Agent ChatExamples Automated Multi Agent Chat AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. 📝 Storage of tool metadata: Control storage of tool descriptions, namespaces, and other information through LangGraph's built-in persistence layer. This notebook takes you through how to use LangChain to augment an OpenAI model with access to external tools. 3. The agent (and any subagents) will have access to these tools. Additionally, it integrates with Langsmith for tracing and feedback collection. These LangChain and LangGraph SQL agents example. LangChain Integration: Harness the power of LangChain for streamlined This project demonstrates the integration of a Large Language Model (LLM) with the Google Search API via LangChain agents to automate data retrieval and This template scaffolds a LangChain. Create a custom LangChain agent dubbed "Agent AWS" that queries the AWS Well-Architected Framework and deploys Lambda functions, all backed by Agent-IA Project │ ├── main. Create a simple tool (add function) 2. The core logic, defined in src/react_agent/graph. 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. Includes a basic LangChain This project implements a Retrieval-Augmented Generation (RAG) agent using LangChain, OpenAI's GPT model, and FastAPI. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to many tools. ipynb # Entry point for the project ├── data/ # Dataset files or sample inputs ├── . The code will log the stream URL, which you can open in your browser to view the CUA stream. py, demonstrates a flexible ReAct agent that iteratively reasons about user queries and executes actions, showcasing the power 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. Learn to use LangChain, RAG, and FAISS for code Q&A. It builds up to an "ambient" agent that can manage your email with connection to the Gmail API. Curated list of tools and projects using LangChain. You will be able to ask this agent questions, watch it call the search Build resilient language agents as graphs. It is 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. This YouTube tutorial goes over the architecture and concepts used for easily spinning up agents with using LangChain using OpenAI's API - Before you deploy the solution, you need to create your own forked version of the solution repository with a token-secured webhook to automate continuous deployment of your Amplify website. js from the standpoint of a new contributor. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. ToolCall represents an LLM's request to use a tool. In particular, you'll be It demonstrates how to create a custom tool (search_ticker) with LangChain and integrate it into an agent. It uses a human-in Multi-Agent Workflow with LangChain and LangGraph This project demonstrates a collaborative multi-agent system using LangChain and LangGraph. Contribute to johnsnowdies/langchain-sql-agent-example development by creating an A collection of generative UI agents written with LangGraph. This implementation leverages the ChatOpenAI model from import os from langchain. We try to be as close to the original as possible in terms of abstractions, but are open to new entities. Purpose and Scope The Agent 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 """ This example demonstrates using LangGraph's ReAct agent with Ollama models. It showcases how to use and combine LangChain modules for several use cases. LangGraph ReAct Memory Agent This repo provides a simple example of a ReAct-style agent with a tool to save memories. The example shows how to: 1. - tryAGI/LangChain A Python library for creating swarm-style multi-agent systems using LangGraph. This repository contains an 'agent' which can take in a URL, and generate a Twitter & LinkedIn post based on the content of the URL. 🦜🔗 Build context-aware reasoning applications. This will clone a frontend chat application (Next. Math Agent: This agent can solve mathematical problems and answer logic-based Agents are like "tools" for LLMs. To create a LangChain AI agent with a tool using any LLM available in LangChain's AzureOpenAI or AzureChatOpenAI class, follow these steps: 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 Contribute to VRSEN/langchain-agents-tutorial development by creating an account on GitHub. Contribute to antoinewg/langchain-agent-collection development by creating an account on GitHub. Links to notebook examples: Agents 🤖 Agents are like "tools" for LLMs. js. A swarm is a type of multi-agent architecture where agents dynamically hand off control to one another based on their specializations. - langgraphjs/examples/multi_agent/agent_supervisor. Based on https://learn. This is a simple way to let an The repository contains a bare minimum code example to get started with the Agent Inbox with LangGraph. The tool is a wrapper for the PyGitHub library. ipynb at master Contribute to langchain-ai/agent-protocol development by creating an account on GitHub. LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). These Github Toolkit The Github toolkit contains tools that enable an LLM agent to interact with a github repository. ipynb at main · langchain-ai/langgraphjs Example of using Langchain with Azure OpenAI LLM. juku zngn xgae utxkff jsrnaw yczj qyjllnz csziy ltaed pmmcuz