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Jupyter Notebook agents
2,004 Jupyter Notebook AI agents indexed on MeshKore — the most complete public catalog, ranked by popularity and updated daily.
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Jupyter Notebook agents — page 9 of 21
Coffee Chat Voice Assistant is a voice-driven ordering system powered by Azure OpenAI GPT-4o Realtime API, simulating the experience of ordering coffee with a café barista. It supports natural conversations, live order updates, and real-time transcription, showcasing the power of AI for seamless customer interactions.
Multi-agent simulator in Jax for research and teaching in AI & ALife
Cost-Sensitive Toolpath Agent for Multi-turn Image Editing
Agentic RAG using Crew AI.
[ICLR 2026] Official code for [EdiVal-Agent Automated, object-centric evaluation for multi-turn instruction-based image editing]
Agentic AI Engineering is a production-grade engineering resource for building modern agentic AI systems with LangChain, LangGraph, RAG, MCP, local models, and deployable Python services.
A Continual Multi-agent RL testbed based on Hanabi
Xây dựng AI Agent Website
https://youtu.be/7hQhPMPNY6A
Sample code from my blog posts on Medium and my personal website.
Key value memory network implemented using keras
Arrakis is a library to conduct, track and visualize mechanistic interpretability experiments.
AI Demo 项目，一个专门为希望学习和探索人工智能（AI）技术的开发者准备的实战案例集合。
Multi-Agent AI System with LangChain, AutoGen, Azure OpenAI GPT-4, and Azure PostgreSQL
Applying Evaluation Driven Development (EDD) to aid in the design decision of RAG pipelines
Scraping Wikipedia by combining LangChain's agents and tools with OpenAI's LLMs and function calling
Learn how multimodal AI merges text, image, and audio for smarter models
An intuitive approach towards understanding how Retrieval Augmented Generation (RAG) systems work, for the curious yet daunted reader
Tutorial on LLMs and agents
A simple multi-agent workflow for tailoring a cover letter to a specific job based on your skills/experience.
Taught by AI genius Andrew NG, this course entails the cutting edge topics such as, How generative AI works including what it can and can't do, Common uses cases such as Reading, Writing, and Chatting, Life Cycle of GenAI projects, Advanced Technology options such as RAG, Fine tunning, and Pre-Training, Implications of GenAI on business & Society.
Exercises for the Agentic RAG live course for O'Reilly.
LLM-agents benchmark set of geospatial tasks requiring multi-step tool use; and LLM-as-Judge based evaluation framework.
A Zed Agent Client Protocol (ACP) Jupyter Kernel
Encountering 14 different Naive RAG fails and using KG to solve it
Notebooks to demo the use of Azure AI Python SDK / LangChain with DeepSeek R1 reasoning model in Azure AI Foundry.
The app uses the Gemini language model to generate personalized book recommendations.
A Personal Assistant leveraging Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM). This tool is designed to revolutionize PDF document analysis tasks by combining machine learning with retrieval-based systems.
This sample demonstrates how to use Document Intelligence's Layout model to convert a PDF document, such as invoices, into Markdown, then use GPT-3.5 Turbo to extract structured JSON data using the Azure OpenAI Service.
Multi-agent Reinforcement Learning for Liquidation Strategy Analysis. ICML 2019 AI in Finance.
The state-of-the-art in multi-agent Reinforcement Learning is the MADDPG algorithm which utilises DDPG actor-critic neural networks where each agent uses centralized critic training but decentralized actor execution, and is capable of learning either cooperative or competitive environments. This is demonstrated on the Unity Tennis Environment.
This is the code for paper "Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement LearningApproach"
This repository contains hybrid-rag a LLMOPS python package
LLM-RAG-Agent-Tutorial for AI application developers and researchers.
ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning (AAMAS 2026)
Multiagent deep reinforcement learning research project
DrFAQ is a plug-and-play question answering NLP chatbot that can be generally applied to any organisation's text corpora.
official repo for AAAI ALOHA chatbot
Intelligent ChatBot built with Microsoft's DialoGPT transformer to make conversations with human users!
网络安全 LLM 智能体应用教程
AI Agents with Google's Gemini Pro and Gemini Pro Vision Models
Implement Google Deep Minds DQN for multiple agents for a grid world environment where vehicles must pick up customers.
Making LLM Tool-Calling Simpler.
Implementing a scalable content team using AI involves creating a framework that blends the strengths of AI technologies with the creative and supervisory capabilities of human team members. This strategy aims to enhance efficiency, creativity, and content output quality.
Langchain Agents
AI Agents with Google's Gemini Pro and Gemini Pro Vision Models
Auto-updating archive of Anthropic docs, engineering posts, MCP spec, cookbooks, skills, and plugins. 2,900+ files from 11 sources.
Multimodal RAG ingests PDFs and generates combined text and image outputs by retrieving and grounding relevant information from the documents.
Conflict-based search for optimal multi-agent pathfinding
Advanced receipt OCR and analysis using PaddleOCR, GPT-3.5-turbo, Plotly, and Gradio for interactive visualizations.
Generative chatbot using seq2seq
Data-Driven Cycling using Strava data and GPX data analysis. Digital Personal Trainer using old cycling workout data to predict new workouts
Mrzaizai2k Stock Assistant Bot: Your all-in-one stock analysis companion. Calculate payback time, find support/resistance, and receive market warnings.
All notebooks from the (currently) free course ChatGPT Prompt Engineering for Developers offered by DeepLearning.AI and OpenAI
Natural Language Querying using RAG LLMs with Excel Sheets as the context
Query Only Linear Adapter Training for Fine Tuned Embedding Model Query Representation
[ACL'25 Main] SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence! | 让你的LLM更好地利用上下文文档：一个基于注意力的简单方案
Demo implementation for 《Large Language Model Enhanced Multi-Agent Systems for 6G Communications》
Deep SRE Agent is a cutting-edge intelligent SRE (Site Reliability Engineering) experimental platform designed to explore the application of LLMs (Large Language Models) in the field of SRE.
A collection of 19 generative AI projects in Python, showcasing applications in text generation, image synthesis, and chatbots using frameworks like Transformers and PyTorch. Includes datasets, code, and tutorials for building and deploying cutting-edge AI models.
A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives).
Vision-first AI agent for desktop automation. Fully offline. Powered by YOLO, OCR & ResNet — building towards local intelligence.
Chunk your data into markdown text blocks for your LLM applications
Transcription from mp3 files to html with or without embedded player
This repository contains projects developed to showcase how to apply Generative AI and open-source LLMs in the construction industry
SDLC Copilot is an Agentic AI system designed to streamline and automate the Software Development Lifecycle (SDLC). From requirement gathering to deployment and maintenance, SDLC Copilot leverages AI to optimize development workflows, reduce manual effort, and ensure software quality.
Let's RAG it RAW without fancy frameworks
Competition and Agent Frameworks for the Trading Agents Competition
A Python-based chatbot project built on the autogen and tinygrad foundation, utilizing advanced agents for dynamic conversations and function orchestration, enhancing and expanding traditional chatbot capabilities.
A comprehensive collection of Google ADK implementations: Function Tools, MCP, Vertex AI Agent Engine, RAG Engine, and Agent Starter Pack templates for Cloud Run and GKE.
8 Lessons, Get Started Building with Generative AI and Gemini API
A Stock Price prediction system using LLM and Multi-agent-system
Packet Routing Simulator for Multi-Agent Reinforcement Learning
Пример реализации вопрос-ответного бота по документации на базе YandexGPT и других сервисов Yandex Cloud.
A set of jupyter notebooks
A structured learning repo for retrieval-augmented generation, from foundations to production patterns.
SDLC Copilot is an Agentic AI system designed to streamline and automate the Software Development Lifecycle (SDLC). From requirement gathering to deployment and maintenance, SDLC Copilot leverages AI to optimize development workflows, reduce manual effort, and ensure software quality.
Multimodal RAG ingests PDFs and generates combined text and image outputs by retrieving and grounding relevant information from the documents.
An innovative application designed to help pharmacists and pharmacy students quickly research FDA-approved drugs by retrieving relevant information from drug labels and adverse event datasets, and providing AI-generated summaries to streamline the learning process
This is a self-learning chatbot coded in python. The chatbot would answer questions from the article URL. The algorithm would parse the article and answer the questions.
it provides Pepper Robot conversation abilities to handle a free open-domain dialogue.
Generative AI playground using Ollama, OpenAI API and JavaScript. Try AI models in your browser!
Fine-tuning black-box OpenAI embedding models
META LLAMA3 GENAI Real World UseCases End To End Implementation Guide
A tool that converts scientific PDFs into plain text for your LLM-related needs, such as building RAGs or agents for academic knowledge. It was developed in collaboration with the LlamaIndex team.
Workshop for building intelligent AI solutions using Azure AI Foundry, featuring Vector Search, RAG, Agentic AI, and multi-agent orchestration with LangChain and Azure AI Search.
AI Learning: A comprehensive repository for Artificial Intelligence and Machine Learning resources, primarily using Jupyter Notebooks and Python. Explore tutorials, projects, and guides covering foundational to advanced concepts in AI, ML, DL and Gen/Agentic Ai.
Autonomous multi-agent system for intelligent HDB resale search — combining geospatial analytics, MRT proximity, and price intelligence using DeepAgents, LangGraph, FastAPI, Gradio UI, and MCP Toolbox. Fully Dockerized with reproducible data ingestion pipelines.
Python framework for 3D agentic AI avatars (MetaHuman/UE). Features real-time lip-sync (Audio2Face), custom text-based emotion driving expressions, and multiple LangChain agent examples (Cafe, PA, Nurse). Along with sample HTML,CSS,JS website.
a step by step pipeline of RAG with Ollama, Langchain, FAISS, investigating whether Lord Elrond is secretly Agent Smith
AgentQL's integrations with workflow automation tools and AI agent frameworks let you extract structured data from web pages using queries or natural language and interact with the web with Playwright. Resilient, fast, and AI-ready.
Generative AI Client for SAP HANA Cloud is an extension of the existing HANA ML Python client library, mainly focusing on GenAI and related use cases. It includes many leading-edge GenAI related open source libraries and provides seamless integration with HANA ML, HANA vector engine, and other SAP GenAI Hub SDK.
Learn how to use LangChain to build AI bots that can reason, use your data, and search the internet.
Experiments with Langchain using different approaches on Google colab
Building Private Healthcare AI Assistant for Clinics Using Qdrant Hybrid Cloud, DSPy and Groq - Llama3
A little tool that lets you ask questions from your pdfs, epubs, text files and word documents.
In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications.