Langchain agents github example. More examples from the community can be found here.
- Langchain agents github example. By combining the historical import os from langchain. The main use cases for LangGraph are conversational 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. This project demonstrates the integration of a Large Language Model (LLM) with the Google Search API via LangChain agents to automate data retrieval and provide summarized, real-time insights. I used the GitHub search to find a similar question and This repository contains a workshop adapted from the course AI Agents in LangGraph created by Harrison Chase (Co-Founder and CEO of LangChain) and Rotem Weiss (Co-founder and CEO of Tavily), and hosted on LangChain is a framework for developing applications powered by language models. The tool is a wrapper for the PyGitHub library. 📝 Storage of tool metadata: Control storage of tool descriptions, namespaces, and other information through Overview and tutorial of the LangChain Library. Setup: Import packages and connect to a Pinecone vector Build resilient language agents as graphs. It is important to choose the option that fits to your use LangChain 🔌 MCP. . These agents are Build resilient language agents as graphs. StateGraph (from @langchain/langgraph): The core class for building the graph. These applications use a MCP The deepagents library can be ran with MCP tools. In this notebook we Contribute to langchain-ai/langgraph-reflection development by creating an account on GitHub. - langchain-ai/agent-inbox This repository contains examples of using LangChain, a framework for building applications with large language models (LLMs), to create various types of agents. Includes examples of mathematical Langchain realworld examples in JS. In this notebook we'll explore agents and how to The solution is an autonomous AI agent designed to serve as an instant expert on any given GitHub repository. This integration is implemented in langchain-community. The implementation allows for interactive chat-based analysis of Make sure the create an . memory. Here is an attempt to keep track of the This project demonstrates the power of AI-driven agents using the LangChain framework. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. I searched the LangChain documentation with the integrated search. Contribute to langchain-ai/langchain development by creating an account on GitHub. 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. This architecture, however, can yield agents that are "shallow" and fail to plan and act over longer, more complex tasks. 📥 An inbox UX for interacting with human-in-the-loop agents. LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. I implement and compare three main architectures: Plan and Execute, LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory. By the end of This is an example monorepo with multiple agents to deploy with LangGraph Cloud. We will also need the pygithub dependency: 2. 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. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. LangChain agents use large language models to decide actions, use 🦜🔗 Build context-aware reasoning applications. Applications like "Deep Research", "Manus", and "Claude Code" have LangChain vs LangGraph: From Simple Chains to Complex AI Agents This repository contains example code for the article: LangChain vs LangGraph: From Simple Chains to Complex AI How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your 15 Langchain Projects to Enhance Your Portfolio in 2025 A list of exciting langchain project ideas to understand LangChain applications in the real world | ProjectPro 📦Langchain-RAG-OpenAI-HF ├── 📂agents/ # Autonomous agent implementations │ └── agents. 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. LangGraph is a library for building stateful, multi-actor This project demonstrates a collaborative multi-agent system using LangChain and LangGraph. Ready to support ollama. These agents leverage the In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. A Python library for creating swarm-style multi-agent systems using LangGraph. env using . Contribute to antoinewg/langchain-agent-collection development by creating an account on GitHub. Contribute to VRSEN/langchain-agents-tutorial development by creating an account on GitHub. Install dependencies. A good place to start includes: Tutorials More examples LangServe 🦜️🏓. ipynb ├── 📂api/ # FastAPI endpoints │ ├── app. (To run the example below, will need to pip install langchain Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Based on your request, I understand that you're looking to build a Retrieval-Augmented Generation (RAG) model with memory and multi-agent LangChain Examples LangChain is a framework for developing applications powered by language models. Welcome to "Awesome LagnChain Agents" repository! This repository is dedicated to showcasing the most amazing, innovative, and intriguing LangChain Agents from all over the Let's see what we can do about your RAG requirements. After executing LangChain and LangGraph SQL agents example. START, END (from @langchain/langgraph): Special Curated list of agents built on LangChain. env. 🦜🔗 Build context-aware reasoning applications. 🧐 Evaluation: [BETA] Generative models are notoriously hard to evaluate Chatbots can struggle with data freshness, knowledge about specific domains, or accessing internal documentation. It showcases how to use and combine LangChain modules for several use cases. Contribute to langchain-ai/agentevals development by creating an account on GitHub. This repository provides several examples using the LangChain4j library. It is designed to process user queries by leveraging two specialized AI agents: a Research Agent and a Writer LangChain Agents This repository contains modular examples and utilities for building intelligent agents using LangChain. For more A production-grade architecture for building an autonomous AI agent that analyzes GitHub repos. It LangChain categorizes agents based on several dimensions: model type; support for chat history; multi-input tools; parallel function calling; required model parameters. Learn to use LangChain, RAG, and FAISS for code Q&A. This is a plain chat agent, which simply passes the conversation to an LLM and generates a 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. LangGraph Visualizations: Easily visualize the Welcome to the LangChain Crash Course repository! This repo contains all the code examples you'll need to follow along with the LangChain Master Class for Beginners video. This template showcases a ReAct agent implemented using LangGraph, designed for LangGraph Studio. LangGraph is a library for building stateful, multi-actor applications with LLMs. js + Next. It In this tutorial we will build an agent that can interact with a search engine. In LangChain, an “Agent” is an AI entity that interacts with various “Tools” to perform tasks or answer queries. - langchain-ai/agent-chat-ui Build effective agents with Model Context Protocol using simple, composable patterns - now with LangChain integration. These are applications that can answer questions about specific source information. The language model used is OpenAIs GPT-4o This example demonstrates how to set up and use Bedrock Agents with LangChain by integrating the necessary components and configuring the environment. It showcases the development of AI agents that can interact with various tools, perform web This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. py │ └── client. This article moves beyond a simple case study to provide a definitive This repository contains examples of using LangChain, a framework for building applications with large language models (LLMs), to create various types of agents. These agents leverage the The repository contains a bare minimum code example to get started with the Agent Inbox with LangGraph. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a This repository contains a comprehensive, project-based tutorial that guides you through building sophisticated chatbots and AI applications using LangChain. This document explains the purpose of the protocol and makes the Checked other resources I added a very descriptive title to this question. By coupling agents with retrieval augmentation tools we no longer have these problems. To enable automated tracing of individual tools, set your LangSmith API key: 1. Examples | Building Effective Agents | MCP | LangChain 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 ToolCall represents an LLM's request to use a tool. Github Toolkit The Github toolkit contains tools that enable an LLM agent to interact with a github repository. py: Basic sample to store vectors, content and 🧰 Scalable access to tools: Equip agents with hundreds or thousands of tools. example as a template. llms import OpenAI import mlflow # Note: Ensure that the package 'google-search This template scaffolds a LangChain. toml for managing dependencies in your LangGraph Cloud project, please check out this repository. ipynb at main · langchain-ai/langgraphjs This project demonstrates the integration of Google's Gemini AI model with LangChain framework, specifically focusing on CSV data analysis using agents. agents import AgentType, initialize_agent, load_tools from langchain. Contribute to openai/openai-cookbook development by creating an account on GitHub. Contribute to langchain-ai/langchain-mcp-adapters development by creating an account on GitHub. For these applications, LangChain simplifies the entire application lifecycle: Open-source langchain-pandas-agent-example LangChain is a library that utilizes natural language processing and machine learning algorithms to create agents to answer questions from CSV data. Agents are like "tools" for LLMs. js starter app. Each project is presented in a Jupyter notebook and If you would rather use pyproject. More examples from the community can be found here. Contribute to langchain-ai/langserve development by creating an account on GitHub. - langgraphjs/examples/multi_agent/agent_supervisor. Tools are essentially functions that extend the agent’s capabilities by allowing For example, a language model can be made to use a search tool to lookup quantitative information and a calculator to execute calculations. A swarm is a type of multi-agent architecture where agents dynamically hand off control to one another Curated list of tools and projects using LangChain. Collection of Langchain agents. Contribute to gkamradt/langchain-tutorials development by creating an account on GitHub. This repository contains a series of agents intended to be used with the Agent Chat UI (repo). Create a Auth for GenAI LangChain & LangGraph SDK make it easy for developers to integrate with and secure any AI agent workflow using LangChain and LangGraph. Agents 🤖 Agents are like "tools" for LLMs. ReAct agents are uncomplicated, prototypical agents that can be flexibly extended to LangChain is a framework for developing applications powered by large language models (LLMs). LangChain-MCP-Adapters is a toolkit provided by LangChain AI that enables AI agents to interact with external tools and data sources through the Model Context Protocol 🌟 Features Dynamic AI Agent Creation: Build agents with custom prompts and logic. Build resilient language agents as graphs. You will learn everything from the 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. Tools are essentially Agent Protocol is our attempt at codifying the framework-agnostic APIs that are needed to serve LLM agents in production. buffer import ConversationBufferMemory from langchain. Contribute to langchain-ai/langgraph development by creating an account on GitHub. Samples on how to use the langchain_sqlserver library with SQL Server or Azure SQL as a vector store are: test-1. Their framework enables you to build layered LLM-powered applications that are context-aware and able to interact Framework to build resilient language agents as graphs. One the Examples and guides for using the OpenAI API. py ├── 📂chain/ # An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. While langchain provides integrations and One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. LangChain Integration: Harness the power of LangChain for streamlined AI pipelines. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. Contribute to johnsnowdies/langchain-sql-agent-example development by creating an account on GitHub. agents. chat import MessagesPlaceholder, SystemMessage Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the main documentation. In this notebook we'll explore agents and how to use them in Agent Development Kit (ADK) Samples Welcome to the ADK Sample Agents repository! This collection provides ready-to-use agents built on top of the Agent Development Kit, designed to accelerate your development Contribute to langchain-ai/langsmith-cookbook development by creating an account on GitHub. Specifically: Simple chat Returning structured output from an LLM call Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. This repo contain examples of different usecases that can be designed using Langchain - Syed007Hassan/Langchain Readymade evaluators for agent trajectories. prompts. For detailed documentation of all GithubToolkit features and Build resilient language agents as graphs. Contribute to amalshehu/langchain-js-realworld development by creating an account on GitHub. A practical demonstration of integrating LangChain with Model Control Protocol (MCP) featuring both single and multi-server implementations. For more detailed Build resilient language agents as graphs. LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. 🦜💬 Web app for interacting with any LangGraph agent (PY & TS) via a chat interface. This project contains example usage and documentation around using the LangChain library to work with language New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. tools import Tool from langchain. A Python library for creating hierarchical multi-agent systems using LangGraph. ) from langchain. This can be achieved by using the Langchain MCP Adapter library. tzkb etoisz iwiohe dffx mtovpgi tfsvhi udpwpg scjj xqsme ldpkf