Jupyter Notebook agents

Empower Large Language Models (LLM) using Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) for knowledge intensive tasks

Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI), (Using ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini)

Large Language Models (LLMs) tutorials & sample scripts, ft. langchain, openai, llamaindex, gpt, chromadb & pinecone

Repo for AI Agents The Definitive Guide

RAG Time: A 5-week Learning Journey to Mastering RAG

LLMs in Finance - Generative AI - AI Agents

Learn Modern AI Assisted Python with Type Hints

xpander.ai is the runtime and control plane to build, run, and ship reliable AI agents fast and anywhere

Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.

End-to-end RAG system design, evaluation, and optimization. 极客时间RAG训练营，RAG 10大组件全面拆解，4个实操项目吃透 RAG 全流程。RAG的落地，往往是面向业务做RAG，而不是反过来面向RAG做业务。这就是为什么我们需要针对不同场景、不同问题做针对性的调整、优化和定制化。魔鬼全在细节中，我们深入进去探究。

🔥 This repository contains complete application examples, including websites and other projects, developed using Firecrawl.

Building DeepSeek R1 from Scratch

This is a multi agent tutorial based on the CAMEL framework, aimed at understanding how to build an Agent Society from the ground up!

AI-Video-Cropper is a Python-based tool that leverages the power of GPT-4 (OpenAI's language model) to automatically analyze videos, extract the most interesting sections, and crop them for improved viewing experience. This project combines the capabilities of GPT-4, FFmpeg, and OpenCV to automate the process of identifying highlights in videos

小李的大模型应用开发学习路线，涵盖 RAG、Agent、面试八股与论文速读。

Microsoft Foundry (demos, documentation, accelerators).

Sample to envision intelligent apps with Microsoft's Copilot stack for AI-infused product experiences.

A virtual lab of LLM agents for science research

Using GPT to organize and access information, and generate questions. Long term goal is to make an agent-like research assistant.

Autonomously train research-agent LLMs on custom data using reinforcement learning and self-verification.

Build a bot that speaks like you!

Querying local documents, powered by LLM

Paving the way for open agents and AGI for all.

This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

AI Bank Statement Document Automation By LLM model and Personal Finanical Analysis

1 million FPS multi-agent driving simulator

Building a Simple Chatbot from Scratch in Python (using NLTK)

AI-in-a-Box leverages the expertise of Microsoft across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction.

Official implementation for "Blended Diffusion for Text-driven Editing of Natural Images" [CVPR 2022]

A simple, minimal wrapper for tensorflow's seq2seq module, for experimenting with datasets rapidly

Repository for the "Building LLMs for Production" book by Towards AI.

A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in finance.

This repository contains different LLM chatbot projects (RAG, LLM agents, etc.) and well-known techniques for training and fine tuning LLMs.

Implementing cognitive architecture and psychological memory concepts into Agentic LLM Systems

👩🏻‍🍳 A collection of example notebooks using Haystack

CoexistAI is a modular, developer-friendly research assistant framework . It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple MCP tool calls or API calls or Python functions.

首个中医大语言模型——“仲景”。受古代中医学巨匠张仲景深邃智慧启迪，专为传统中医领域打造的预训练大语言模型。 The first-ever Traditional Chinese Medicine large language model - "CMLM-ZhongJing". Inspired by the profound wisdom of the ancient Chinese medical master Zhang Zhongjing, it is a pre-trained large language model designed specifically for the field of Traditional Chinese Medicine.

Introductory examples for building LLM-based AI agents. 异步图书：《大模型应用开发 动手做AI Agent》 - 这是一些非常简单的入门示例，重在引导新手入门，目前LLM开发领域发展很快，本书只是一个提纲挈领。更多的示例和代码大家可以去OpenAI Cookbook, LangChain Example中去挖掘。

Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）

Building Agentic AI Systems, published by Packt

Task-based Agentic Framework using StrictJSON as the core

Workflow for AI Agents enables automated conversion of CAD files (such as `.rvt`, `.ifc`, `.dwg`) using command-line converters on a local Windows machine

轻松玩转LLM兼容openai&langchain，支持文心一言、讯飞星火、腾讯混元、智谱ChatGLM等

An example of multi-agent orchestration with llama-index

This repository contains advanced LLM-based chatbots for Q&A using LLM agents, and Retrieval Augmented Generation (RAG) and with different databases. (VectorDB, GraphDB, SQLite, CSV, XLSX, etc.)

Your new Telegram buddy powered by transformers

It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

LangGraph 1.0 Tutorial

Projects from basic algorithms to MARL. Implements MADDPG,MATD3,MA/HAPPO in Predator-Prey pursuit games with PettingZoo MPE environments.

A collection of notebooks, cookbooks, and recipes showcasing fun and effective ways to use CrewAI's agentic workflow implementations and tools.

Azure AI Search + Azure OpenAI Accelerator to build Agents

Official Repo for Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning

Repo for my course on Generative AI and Agentic AI in Production

Giving LLMs like GPT-4 the ability to plan and execute terminal commands

pykoi: Active learning in one unified interface

Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)

100 天搞定 Agent 开发

The codebase for the book "AI-Powered Search" (Manning Publications, 2025) and associated "AI-Powered Search: Modern Retrieval for Humans & Agents" Maven course

Low latency JSON generation using LLMs ⚡️

The Official Repo for "Quick Start Guide to Large Language Models"

A collection of agents that use Large Language Models (LLMs) to perform tasks common on our day to day jobs in cyber security.

📚 从零开始的向量数据库原理与实践教程，在线阅读地址：https://easy-vecdb.datawhale.cc/

How to use OpenAIs Whisper to transcribe and diarize audio files

Jupyter Notebooks for Mastering LLM with Advanced RAG Course

ERNIE Bot Agent is a Large Language Model (LLM) Agent Framework, powered by the advanced capabilities of ERNIE Bot and the platform resources of Baidu AI Studio.

Building a Multi-Agent AI System with LangGraph and LangSmith

[EMNLP2025] "GraphAgent: Agentic Graph Language Assistant"

GitHub Sentinel 是专为大模型（LLMs）时代打造的智能信息检索和高价值内容挖掘 AI Agent。它面向那些需要高频次、大量信息获取的用户，特别是开源爱好者、个人开发者和投资人等。GitHub Sentinel 不仅能帮助用户自动跟踪和分析 GitHub 开源项目 的最新动态，还能快速扩展到其他信息渠道，如 Hacker News 的热门话题，提供更全面的信息挖掘与分析能力。

Agent Framework Samples - showcasing ways in which agent framework can be utilized.

A unified framework to solve and analyze heterogeneous-agent macro models.

A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.

9 Different Ways to Optimize AI Agent Memories

Modern AI Agents

Chatbot system for Final Year Project. Chatbot made in Python using Natural Language Toolkit especially Machine Learning. Easy to Understand and Implement.

The IQ Series is a hands-on learning experience for Microsoft IQ: Microsoft's unified intelligence layer for the enterprise, spanning Foundry IQ, Work IQ, and Fabric IQ. The series includes video episodes, Jupyter notebooks, and Azure deployment templates.

Reference implementation of a RAG-based documentation helper using LangChain, Pinecone, and Tavily..

Code for the paper "FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents" arXiv:2502.07393

中文聊天机器人，基于10万组对白训练而成，采用注意力机制，对一般问题都会生成一个有意义的答复。已上传模型，可直接运行。

Learn how to crawl your website and build a Q/A bot with the OpenAI API

An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. Ready to support ollama.... (Update when i am free)

Getting the latest versions of Disco Diffusion to work locally, instead of colab. Including how I run this on Windows, despite some Linux only dependencies ;)

Workshop: Agentic Search for Context Engineering

Fact-checking LLM outputs with self-ask

Discord AI Chatbot using DialoGPT, trained on the game transcript of The World Ends With You

Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (ACL 2024)

吴恩达《ChatGPT Prompt Engineering for Developers》课程中英版

A framework for event based autonomous multi-agent systems.

A free open source RAG based AI legal assistant.

Converting Unstructured Data to a Knowledge Graph: An End-to-End Pipeline

An introduction to the world of AI Agents

AI ChatBot using Python Tensorflow and Natural Language Processing (NLP) along side TFLearn

Build, evaluate and observe LLM apps

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

Implementation of "Generative Agents: Interactive Simulacra of Human Behavior" paper with Guidance and Langchain. Full features and work with local LLMs.

ToolQA, a new dataset to evaluate the capabilities of LLMs in answering challenging questions with external tools. It offers two levels (easy/hard) across eight real-life scenarios.

A set of lessons aimed at anyone learning LLM and generative AI concepts, with sections on operations and security, as well as development.