Jupyter Notebook agents

Turn speech into semantic paragraphs in real time, on a CPU (no GPU) — single-pass streaming chunker + Moonshine STT + a live mic demo. Plus a transcribe/chunk/summarize API.

Extract information, summarize, ask questions, and search videos using OpenAI's Vision API 🚀🎦

Source code for paper:Multi-agent reinforcement learning for liquidation strategy analysis

Reducing Global Carbon Footprint based on Multi-Agent Reinforcement Learning - School of AI Fellowship Research

A LLama agent that builds startup ideas from your intutions (generates react codebases)

SECOM: On Memory Construction and Retrieval for Personalized Conversational Agents, ICLR 2025

The Chatbot (HealthBot) will try to solve or provide an answer to health-related issues or queries that the user is asking for. We are implementing NLP and ML to improve the efficiency of the chatbot. Tkinter is used as a frontend, and we are creating a desktop application with the help of Tkinter.

GenAI Experimentation

Running llama3 using Ollama-Python, Curl, LangChain, Chroma, and User interface.

Multi-agent simulation library in Python

This repository demonstrates the construction of a state-of-the-art multimodal search engine, leveraging Amazon Titan Embeddings, Amazon Bedrock, and LangChain.

Copilot for the terminal. Generate commands and preview them before running

Benchmarking Multi-Agent Debate between Language Models for Truthfulness in Q&A.

Agent Innovator Lab – building AI agents on Azure, covering search optimization, agent design, evaluation, and RAG best practices.

A detail Implementation of handling long-term memory in Agentic AI

Building a Claude-like agentic system.

T2I-Copilot: A Training-Free Multi-Agent Text-to-Image System for Enhanced Prompt Interpretation and Interactive Generation (ICCV'25)

RAG-powered AI agent that translates natural language into SQL for live database querying.

Ultimate AWS Data & AI Platform: Real-time flight delay predictions with complete DE, DS, MLOps, Web App & Multi-Agent LLM - All deployed via CDK self-mutating pipelines

EVE bot, a customer service chatbot to enhance virtual engagement for Twitter Apple Support

OpenAI 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 Python OpenAI API 를 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

Cookbooks and tutorials on Literal AI

An example of using Function Calling with OpenAI's API

A data discovery and manipulation toolset for unstructured data

Multi-Agent Deep RAG

Official repository for the ICLR 2026 Oral Paper🔥 “Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training”

Healthcare system to predict Diseases based on patient symptoms

An example notebook to build RAG with Excel files using SQL Agents.

👩🏻‍🔬🧪SciTonic is a highly adaptive technical operator of agents that can produce complexe analyses on technical data with high performance & on-the-fly . You can ask it what you want and it will respond with quality everytime.

Multi-agent reinforcement learning for autonomous navigation for mapping and multi-objective drone swarm exploration

Step-by-step tutorial to extract data, analyze, and decide on stocks in the market using Django, Celery, TimescaleDB, Jupyter, OpenAI, and more.

13 projects using ChatGPT API, Whisper, Embeddings, and DALL-E with Python.

A hands-on course for learning PydanticAI - the Python agent framework built on Pydantic

A curated list of all things awesome about OpenAI

A curated collection of tools to aid transcriptionists and subtitlers.

A collection of cookbooks to help developers get started quickly with the Firecrawl API.

JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.

Code for our NeurIPS'24 Dataset and Benchmark paper: Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation

通用大模型 × 文风大模型 = 多样化风格的聊天机器人

This repository implements the proposed method from IEEE 2020 Paper Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks Donate me: 8660186868668 MB Bank

A multi-agent system trained with GRPO for reliable long-horizon task planning and execution.

This project contains a step-by-step guide on how to design an advanced agentic memory for your LLM based applications.

A fully python based Streamlit development harness for ChatGPT hosted in Azure OpenAI Service.

Gathers Tensorflow deep learning models.

Jupyter notebooks for course Building and Evaluating Advanced RAG Applications, taught by Jerry Liu (Co-founder and CEO of LlamaIndex) and Anupam Datta (Co-founder and chief scientist of TruEra).

LLM Agent that leverages cheminformatics tools to provide informed responses.

AI multi-agent system for comprehensive Bitcoin (BTC) analysis, combining financial news, market performance, and AI-driven price predictions for investment recommendations.

AI Agents, LLM Fine-tuning, Developer Productivity, Governance, IBM watsonx

Example Notebook for Synthetic User Research with Persona Prompting and Autonomous Agents

인프런의 "LangGraph를 활용한 AI Agent 개발" 강의 소스코드입니다

Deep research agentic system using Time Test Diffusion

This repository will show how Langchain🦜🔗 library can be used and integrated

Simple RAG tutorials that can be run locally or using Google Colab (only Pro version).

Инициатива, посвященная безопасности агентов на базе искусственного интеллекта

A Repository chatbot and Recommendation system for Github users.

MARS is shortened for Multi-Agent Research Studio, a library for mulit-agent reinforcement learning research.

A hybrid retrieval system for RAG that combines vector search and graph search, integrating unstructured and structured data. It retrieves context using embeddings and a knowledge graph, then passes it to an LLM for generating accurate responses.

Building Spotify playlists based on vibes using LangChain and GPT

In this project, we used Langchain to create a ChatGPT for your PDF using Streamlit. We built an application that allows you to ask questions about a PDF document and get answers directly from an LLM (Large Language Model), like OpenAI's ChatGPT.

Testing speed and accuracy of RAG with, and without Cross Encoder Reranker.

Pre-built examples of Generative AI agents with Bedrock across multiple industries.

Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

This repository contain my 75Day Hard Generative AI and LLM Learning Challenge.

Using OpenAI or other LLMs via Langchain to connect that intelligence to Zapier which can access virtually every app, you might want to automate

Course on solving heterogenous agent models

Source code for the IBM Granite AI Model Workshop

A langgraph based blog writing agent

Korean Sentence Embedding Model Performance Benchmark for RAG

Training & Implementation of chatbots leveraging GPT-like architecture with the aitextgen package to enable dynamic conversations.

ChatBard: An Intelligent Customer Service Center App Using PaLM2 APIs

A tutorial showing how to train RL agents for webgames, in this case, Chrome Dino

Sample scripts to use with Agentic Document Extraction (ADE).

Train reinforcement learning agent using ML-Agents with Google Colab.

Learn how to create an AI Agent with Django, LangGraph, and Permit.

Knowledge chatbot using Agentic Retrieval Augmented Generation (RAG) techniques. Full-stack proof of concept built on langchain, llama-index, django, pgvector, with multiple advanced RAG techniques used.

Open Source Agentic AI Projects! 🤖 This repository is dedicated to learning and developing Agentic AI systems.

Using LangChain's SQL Database Chain and Agent with various LLMs to perform Natural Language Queries (NLQ) of an Amazon RDS for PostgreSQL database.

LLM Dynamic Planner - Combining LLM with PDDL Planners to solve an embodied task

Pull high-quality, efficient embeddings for PubMed, arXiv and Wikipedia from Huggingface and use for local LLM inference/Retrieval Augmented Generation (RAG)

ChemClaw — The first chemistry-native AI agent skill library.

UAV-based Cellular-Communication: Multi-Agent Deep Reinforcement Learning for Interference Management

Personal Health Insights Agent (PHIA)

Function Calling Mistral 7B. Learn how to make functions call for open source LLMs.

The purpose of the "Meta Agent with More Agents" project is to dynamically solve complex queries by breaking them down into smaller tasks and assigning each to specialized AI agents. The Meta Agent coordinates the process, leveraging a ReAct Agent for tool-based tasks and a Chain of Thought Agent for reasoning-based tasks. The system's flexibility.

This repository contains the code for the LangGraph course: "LangGraph in Action - Building Autonomous AI Agents"

LLM as Interpreter for Natural Language Programming, Pseudo-code Programming and Flow Programming of AI Agents

🧠 A production-grade, agentic RAG platform for portfolio intelligence, combining LangChain, Chroma/FAISS, Hugging Face embeddings, and Ollama with dynamic entity extraction, backend API tool-chaining, and a real-time interactive assistant across deploy-ready frontend, backend, and infrastructure stacks.

Agentic AI framework built using LangGraph and Multi-Agent Control Plane (MCP) for building structured, goal-driven multi-agent systems.

Local LLM Agent with Langchain

Fine-tune Llama3 model to support function calling

Concepts and examples on using and training LLMs