Jupyter Notebook agents

Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks

Financial CrewAI Agents (LangChain, YF Tools, Ai Crew, Groq Inference)

Local Startup Advisor Chatbot

LLM powered agents for scanning vulnerabilities on any website - Llama 3 8B, Groq, Selenium, CrewAI, Exa AI

[KDD 2023] Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

Study for Natural Language Processing & Deep Learning Framework

ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning. BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.

A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following

A tutorial for building autonomous agents: with LangChain and from scratch

Multi-agent environments library for simulating classic vehicle routing problems.

Cooperative Energy Management and Eco-driving of Plug-in Hybrid Electric Vehicle via Multi-agent Reinforcement Learning

Write tweets with AI Agents (CrewAI) and LLMs (Llama 3, GPT-4o)

Tutorial on LLMs and agents

[ICLR 2026] Official code for [EdiVal-Agent Automated, object-centric evaluation for multi-turn instruction-based image editing]

Encountering 14 different Naive RAG fails and using KG to solve it

An AI developer that writes LangChain Expression Language (LCEL).

Simple web browsing for your Langchain agent.

RAG - Add Your Own Data to LLMs Using LangChain & LlamaIndex

Learn how to talk to Django as any human should -- e.g. Semantic Search and Text-to-SQL.

Video Voiceover with gpt-4o-mini

The official code of our paper “RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation”

Build agents and workflows with RAG, MCP, harnesses, & multi-agent orchestration in a 3-week cohort

Sample code from my blog posts on Medium and my personal website.

Leverage modern open-source tools to create better web scraping workflows.

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

[EMNLP 2026 Findings] Cost-Sensitive Toolpath Agent for Multi-turn Image Editing

Agentic RAG using Crew AI.

A simple multi-agent workflow for tailoring a cover letter to a specific job based on your skills/experience.

LLM-RAG-Agent-Tutorial for AI application developers and researchers.

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.

ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning (AAMAS 2026)

Multimodal RAG that ingests PDFs, generates grounded text, image outputs by retrieving relevant content from documents.

A Continual Multi-agent RL testbed based on Hanabi

The framework to take LLMs out of the box. Learn to use LangChain to call LLMs into new environments, and use memories, chains, and agents to take on new and complex tasks.

https://youtu.be/7hQhPMPNY6A

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

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.

This repository contains hybrid-rag a LLMOPS python package

LLM-agents benchmark set of geospatial tasks requiring multi-step tool use; and LLM-as-Judge based evaluation framework.

This repository contains projects developed to showcase how to apply Generative AI and open-source LLMs in the construction industry

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"

A structured learning repo for retrieval-augmented generation, from foundations to production patterns.

A real-world agentic AI system that combines supplier, procurement, delivery, and cost datasets to generate end-to-end supply chain insights, risk analysis, and automated decision support using RAG + multi-agent orchestration.

Xây dựng AI Agent Website

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.

Conflict-based search for optimal multi-agent pathfinding

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.

Data-Driven Cycling using Strava data and GPX data analysis. Digital Personal Trainer using old cycling workout data to predict new workouts

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

Langchain Agents

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.

Agentic AI Agents, LangChain v1, Gemini 3, Tool Calling, Agent Memory, Prompt Engineering, Guardrails, MCP, FastAPI

AI Agents with Google's Gemini Pro and Gemini Pro Vision Models

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.

Master self-improving LLM agents systematically with runnable Stanford CS329A notebooks.

Advanced receipt OCR and analysis using PaddleOCR, GPT-3.5-turbo, Plotly, and Gradio for interactive visualizations.

Generative chatbot using seq2seq

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

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 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

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.

Transcription from mp3 files to html with or without embedded player

8 Lessons, Get Started Building with Generative AI and Gemini API

Пример реализации вопрос-ответного бота по документации на базе YandexGPT и других сервисов Yandex Cloud.

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.

Forty hands-on recipes for production-grade retrieval-augmented generation, Nebius-first and provider-agnostic.

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.

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 Stock Price prediction system using LLM and Multi-agent-system

Packet Routing Simulator for Multi-Agent Reinforcement Learning

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.