Jupyter Notebook agents

A complete collection of RAG interview questions, answers (1297 questions & 52 RAG types), system design scenarios, architecture patterns, and production-ready concepts.

Apply LLMs to your data, build personal assistants, and expand your use of LLMs with agents, chains, and memories.

Benchmarking LLM agents on Cyber Threat Investigation.

Improve your questions! The AI for Inquiry - QuestionImprover Agent is an LLM-driven “tool for thought” designed to enhance the depth and quality of user-posed questions by engaging expert personas in a novel graph-based reasoning rhythm to foster a cycle of iterative inquiry improvement.

This is the corresponding code for the O'Reilly book: Harness Engineering

Learn to build and customize multi-agent systems using the AutoGen. The course teaches you to implement complex AI applications through agent collaboration and advanced design patterns.

Analysis of OpenAI's ChatGPT

Tutorial to create WhatsApp Bot using Twilio and Python

This was designed for interp researchers who want to do research on or with interp agents to give quality of life improvements and fix some of the annoying things you get from only using Claude code out of the box

The Automated LLM Speedrunning Benchmark measures how well LLM agents can reproduce previous innovations and discover new ones in language modeling.

Quran, Hadith, Translations, Tafaseer, Corpus Linguistics. Everything for NLP

Implementation of Toolformer: Language Models Can Teach Themselves to Use Tools

I'm building a virtual Insurance Agent using Retrieval Augmented Generation(RAG)

Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs" by Boris Ivanovic and Marco Pavone.

A companion guide for the blog post series, LangChain Decoded.

Agentic RAG, Multi-Agent Systems, and Vision Reasoning are three pipelines to find the perfect LLM

5-Day Gen AI Intensive Course with Google

This repository includes code and demos for session "Unlock insights with AWS GenAI services" (re:Invent BOA303)

A dataset for training/evaluating Question Answering Retrieval models on ChatGPT responses with the possibility to training/evaluating on real human responses.

Notebooks and Code about Generative Ai, LLMs, MLOPS, NLP , CV and Graph databases

Agentic Architectural Patterns for Building Multi-Agent Systems, published by Packt

小嘿作文生成器：根据主题谓语、主题宾语，生成中学考试风格的作文。

Bringing Generative AI to the way the Civil Service works

Simple introduction to LLM Agents

This repository is intended for those looking to dive deep on advanced Text-to-SQL concepts.

State of the Art results in Intent Classification using Sematic Hashing for three datasets: AskUbuntu, Chatbot and WebApplication.

RAG using Llama3, Langchain and ChromaDB

从前端到 AI：langchain.js 入门和实战

MAGNet: Multi-agents control using Graph Neural Networks

Implementing Ollama and Agents to create a blogging bot

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets

An End to End GenAI Framework

Transformer Chatbot in TensorFlow 2 with TPU support.

Mastering NLP from Foundations to LLMs, Published by Packt

Timescale Vector Cookbook. A collection of recipes to build applications with LLMs using PostgreSQL and Timescale Vector.

A Deep Thinking RAG Pipeline to Solve Complex Queries

GPT-Researcher as a LangChain tool to be used in Agents and Chains

🦙 echoOLlama: A real-time voice AI platform powered by local LLMs. Features WebSocket streaming, voice interactions, and OpenAI API compatibility. Built with FastAPI, Redis, and PostgreSQL. Perfect for private AI conversations and custom voice assistants.

Legal Research Copilot Example Solution built with Generative AI capabilities of PostgreSQL on Azure

Language AI Engineering Lab, a place where you can deeply understand and build modern Language AI systems, from fundamentals to production.

A collection of personally developed projects contributing towards the advancement of Artificial General Intelligence(AGI)

谷歌5天AI Agents强化课程

ChatGPT API Usage using LangChain, LlamaIndex, Guardrails, AutoGPT and more

DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

Multi-agent pathfinding via Conflict Based Search

This is the repository for the LatteReview python package. LatteReview is a low-code Python package designed to automate systematic literature review processes through AI-powered agents.

Our idea is to combine the power of computer vision model and LLMs. We use YOLO, CLIP and DINOv2 to extract high-level features from images. We pass the prompt, along with the extracted features, to LLM, allowing for advanced image dataset queries.

Various projects using Large Language Model (GPT & LLAMA) other open source model from HuggingFace and OpenAI. OpenAI API required for running various model

Implementation of Deep Thinking Trading System

Build Enterprise RAG (Retriver Augmented Generation) Pipelines to tackle various Generative AI use cases with LLM's by simply plugging componants like Lego pieces.

AI Agent Work shop include Agent from Corepython, using CrewAI and Using SmolAgent

AI 应用示例合集

🚀 Advanced Data & AI Engineering Portfolio: Real-world projects and production-ready patterns to level up your AI skills—from building clean data pipelines to deploying RAG systems, AI agents, and intelligent dashboards.

This is a Python package to add tool calling capabilities to newly released LLMs on LangChain's ChatOpenAI, AzureAIChatCompletionsModel and ChatBedrockConverse classes ahead of time before LangChain and LangGraph supports it!

BotSIM - a data-efficient end-to-end Bot SIMulation toolkit for evaluation, diagnosis, and improvement of commercial chatbots

Learn GenAI and Agentic AI from Zero to Production

Using LlamaIndex, Redis, and OpenAI to chat with PDF documents. Supplementary material for blog post on Microsoft Developer Blog

A comprehensive collection of LangGraph implementations, tutorials, and advanced AI workflows covering Agentic RAG systems, MCP (Model Context Protocol) development, and practical AI application patterns.

This repository is home to all the Jupyter Notebooks required for MongoDB's GenAI Developer Day.

OpenAI API官方使用指南（中文修订版）Examples and guides for using the OpenAI API（Chinese）

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

I put all my exploration around AI in reproducible notebooks in this repository

All the code and materials

Code Repository for: AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models

This is a Azure OpenAI book for .NET Developer

[IEEE TAI] Safe Multi-Agent Reinforcement Learning to Make decisions in Autonomous Driving.

[IEEE TAI] Safe Multi-Agent Reinforcement Learning to Make decisions in Autonomous Driving.

[ACL 2024] PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

Mistral + Haystack: build RAG pipelines that rock 🤘

Building a Q&A LLM Agent to Answer Questions about Your Dataset

Google's Meena transformer chatbot implementation

🎡 Pompt 提示词工程师入门指南 ~视频字幕+代码资料 [( Python、Golang、NodeJs ) x ( 中文、英文 )]

InsightSolver: Colab notebooks for exploring and solving operational issues using deep learning, machine learning, and related models.

Comprehensive tutorials for LangChain, LangGraph, and LangSmith using Groq LLM. Learn to build advanced AI systems, from basics to production-ready applications. Covers key concepts, real-world examples, and best practices. Ideal for beginners and experts alike. Elevate your AI development skills!

[ICCV 2023] ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation

pytorch를 사용하여 텍스트 전처리부터 RAG, 에이전트, LLM 파인튜닝을 정리한 Deep Learning NLP 저장소입니다.

🌴 Drop-in replacement REST API for Vertex AI (PaLM 2, Codey, Gemini) that is compatible with the OpenAI API specifications

CNN for intent classification task in a Chatbot

Complete example of how to build an Agentic RAG architecture with Redis, Amazon Bedrock, and LlamaIndex.

PCLA: A framework for testing autonomous agents in the CARLA simulator

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

A set of re-usable AI agent for document processing

This project uses Deep Reinforcement Learning (DRL) to develop and evaluate stock trading strategies. By implementing agents like PPO, A2C, DDPG, SAC, and TD3 in a realistic trading environment with transaction costs, it aims to optimize trading decisions based on return, volatility, and Sharpe ratio.

AirSim-based UAV Foundation Model Learning: Covering Agents, VLN, World Models, etc.

[EMNLP '25] A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An agentic materials scientist powered by @materialsproject, @langchain-ai, and @openai https://aclanthology.org/2025.emnlp-main.1280/

An advanced LangGraph series exploring real-world agent workflows, dynamic tools, parallel execution, long-term memory, and human-in-the-loop designs. Includes hands-on Python notebooks for building scalable, production-ready AI agent architectures.

Core concepts - where to apply parallelism in agentic solution

Build and deploy AI-driven assistants with our OpenAI Assistants Template. This tutorial provides a hands-on approach to using OpenAI's Assistant API, complete with code modules, interactive Jupyter Notebook examples, and best practices to get you started on creating intelligent conversational agents.

Agentic AI crash course using langgraph framework

Negotiation Multi-Agent System (A negotiation library designed for situated negotiations within business-like simulations)

An agent based LLM assistant that extends RAG with batch entity extraction and SQL querying to improve performance on multi-step and analytical questions.

Code and data for the paper: Competing Large Language Models in Multi-Agent Gaming Environments

使用 OpenAI API 的例子和中文指南（自动持续更新 OpenAI 官方文档）

A local LLM chatbot with RAG for PDF input files

Optimizing Dynamic Knowledge Base Using AI Agent

大型語言模型(LLM)的讀書會內容code部分整理，此github提供簡單、免費、易實作code範例，並由淺入深帶你一步步了解如何基於python建置LLM流程、部屬功能等

完整的RAG技术教程 - 从基础概念到生产部署，系统化掌握检索增强生成技术。包含4个模块、20章内容、17个Jupyter Notebooks、6个企业级实战案例