Notebooks for the Built Multi-Agent Applications with AutoGen Course

SPARC-P is a multi-agent digital human training platform for clinician communication practice. This repository is the operational notebook and documentation workspace for training, backend orchestration, and deployment across UF HiPerGator and UF PubApps.

Exploratory analysis and visualization of the Moltbook data using Jupyter Notebooks.

These are various Jupyter-Lab notebooks that can help you get familiar with various AI agent frameworks. The notebooks are desinged specifically to run within the Multi-Agent-AI-Research-System environment. Please see my other repository for setting up this environment.

Interactive Jupyter notebooks for learning Praval - the Pythonic multi-agent AI framework

A Python package for safe and flexible Python code execution, supporting both Docker (isolated) and Host (fast) backends. It automatically handles code outputs like images, CSV files, and markdown, making it ideal for data analysis and notebook-style workflows.

Jupyter Notebook demo showing an agentic AI workflow for automated cold email outreach with multi-agent collaboration, tool integration, and handoffs.

AI Research Assistant Platform powered by OpenAI's gpt-oss models - Democratizing advanced data analysis through conversational AI and automated notebook generation

A beginner Codelab: build two AI agents that hand off and validate each other's work, in one Colab notebook. Gemini + Google ADK, no infrastructure.

A series of notebooks exploring the capabilities of LangGraph for building agentic workflow, from ReAct Agents to Multi-Agent Orchestration with memory and human-in-the-loop

An interactive Jupyter Notebook demonstrating AI agent collaboration using CrewAI. This project explores how multiple AI agents can research, generate content, and automate workflows through task orchestration.

Hands-on Jupyter notebooks implementing 12 modern agentic design patterns: reflection, ReAct, planning, multi-agent, memory, tree-of-thoughts & more, all of that built with LangGraph, DeepSeek-V3, and Gemma 2.

Agent Tools & Multi-Agent Orchestration (Agentic AI) This notebook demonstrates hands-on implementation of agent tools and multi-agent orchestration using Google’s Agent Development Kit (ADK) as part of a structured Agentic AI learning program.

A self-correcting multi-agent system that audits and actively fixes Kaggle notebooks. Powered by LangGraph and Gemini 2.5, it uses an autonomous feedback loop to improve code quality, documentation, and reproducibility scores in minutes.

A hands-on, from-scratch repository for learning and building multi-agent agentic AI systems. Explore Agentic-Rag, MUlti-agent RAG, ReAct, reflection, planning, human-in-the-loop, multi-agent , hierarchical , and RAG patterns using LangGraph, Python, and Jupyter notebooks.

Agent QA Mentor: an agentic QA pipeline that evaluates tool-using AI agent trajectories (scores, issue codes, safety/hallucination detection), rewrites prompts with targeted fixes, and stores long-term memory for continuous improvement—plus a CI-style eval gate and demo notebook.