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Jupyter Notebook agents
2,100 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 6 of 21
Useful tool to build multi-agent in an easy way
It shows case studies of the LangGraph agent.
Splunk App for Data Science and Deep Learning - container images repository
Private RAG app sample using Llama3, Ollama and PostgreSQL
This is my Artificial Intelligence Project in which we build AI Contextual Chatbot
STAgent is a multimodal LLM-based AI agent that enables deep research about spatial transcriptomics data
Official repository for the ICLR 2026 Oral Paper🔥 “Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training”
Building a Claude-like agentic system.
A multi-agent system trained with GRPO for reliable long-horizon task planning and execution.
SECOM: On Memory Construction and Retrieval for Personalized Conversational Agents, ICLR 2025
Benchmarking Multi-Agent Debate between Language Models for Truthfulness in Q&A.
A detail Implementation of handling long-term memory in Agentic AI
Power up your data science workflow with ChatGPT.
Scripts/tools for dalle2 outpainting
NeurIPS'24 DB (Spotlight) | Instruction Tuning Large Language Models to Understand Electronic Health Records
Projeto de GenAI usando LLAMA V2 Local estilo ChatPDF
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.
Connect an agent here. 342 skills (Cursor, Claude Code & Codex), 107 research papers, 201 ML notebooks, 9 textbooks, 18 industry reports, 96 curated projects, 5,380 OpenClaw skills. Start at AGENTS.md.
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)
Multi-agent simulation library in Python
RAG-powered AI agent that translates natural language into SQL for live database querying.
A hands-on course for learning PydanticAI - the Python agent framework built on Pydantic
GenAI Experimentation
T2I-Copilot: A Training-Free Multi-Agent Text-to-Image System for Enhanced Prompt Interpretation and Interactive Generation (ICCV'25)
Tài liệu Agent Guide đã chuyển về phiên bản web với trải nghiệm sử dụng tối ưu hơn. Click link dưới đây
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.
Running llama3 using Ollama-Python, Curl, LangChain, Chroma, and User interface.
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
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.
Agent Innovator Lab – building AI agents on Azure, covering search optimization, agent design, evaluation, and RAG best practices.
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
Multi-Agent Deep RAG
An 18 notebook course that isolates and measures each component of agentic loop engineering on real, industry standard software datasets.
Healthcare system to predict Diseases based on patient symptoms
EVE bot, a customer service chatbot to enhance virtual engagement for Twitter Apple Support
An example of using Function Calling with OpenAI's API
An example notebook to build RAG with Excel files using SQL Agents.
A data discovery and manipulation toolset for unstructured data
Step-by-step tutorial to extract data, analyze, and decide on stocks in the market using Django, Celery, TimescaleDB, Jupyter, OpenAI, and more.
Code for our NeurIPS'24 Dataset and Benchmark paper: Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation
Deep research agentic system using Time Test Diffusion
Cookbooks and tutorials on Literal AI
👩🏻🔬🧪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.
13 projects using ChatGPT API, Whisper, Embeddings, and DALL-E with Python.
Agent-based LLM modeling of mechanics problems
Multi-agent reinforcement learning for autonomous navigation for mapping and multi-objective drone swarm exploration
A curated collection of tools to aid transcriptionists and subtitlers.
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
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
Simple RAG tutorials that can be run locally or using Google Colab (only Pro version).
A curated list of all things awesome about OpenAI
A collection of cookbooks to help developers get started quickly with the Firecrawl API.
This project contains a step-by-step guide on how to design an advanced agentic memory for your LLM based applications.
通用大模型 × 文风大模型 = 多样化风格的聊天机器人
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).
Open Source Agentic AI Projects! 🤖 This repository is dedicated to learning and developing Agentic AI systems.
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.
Knowledge-graph RAG built from structured metadata instead of LLM extraction. 929M edges, zero LLM calls, 83.2% on held-out PubMedQA. Case study: the full PubMed 2026 baseline.
A fully python based Streamlit development harness for ChatGPT hosted in Azure OpenAI Service.
Gathers Tensorflow deep learning models.
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
MARS is shortened for Multi-Agent Research Studio, a library for mulit-agent reinforcement learning research.
Example Notebook for Synthetic User Research with Persona Prompting and Autonomous Agents
A modular and AI-powered appointment booking agent designed to streamline scheduling for businesses, starting with dental clinics. Built with LangGraph, Composio, and Bland.com, it integrates Google Calendar and Gmail to manage appointments, send confirmations, and handle voice interactions.
LangGraph Typescript Agents Notebooks: email, human in the loop, memory
A hands-on, module-by-module build of the agentic AI + generative AI stack on Azure — from a single Azure OpenAI call to a production-shaped, observable, multi-agent platform.
A hands-on, module-by-module build of the agentic AI + generative AI stack on Azure — from a single Azure OpenAI call to a production-shaped, observable, multi-agent platform.
This repository will show how Langchain🦜🔗 library can be used and integrated
Инициатива, посвященная безопасности агентов на базе искусственного интеллекта
A Repository chatbot and Recommendation system for Github users.
This repository contain my 75Day Hard Generative AI and LLM Learning Challenge.
Learn how to create an AI Agent with Django, LangGraph, and Permit.
인프런의 "LangGraph를 활용한 AI Agent 개발" 강의 소스코드입니다
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.
🧠 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.
AI-powered multi-agent medical assistant with a deterministic safety layer — crisis detection, refused-topic hard-stops, dosage grounding, and post-generation diagnosis verification running before and after every LLM call, backed by parallel RAG, Wikipedia, and Tavily retrieval with symptom and drug-question routing.
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.
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
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.
A langgraph based blog writing agent
Open AI/ML and AI-literacy curriculum for engineers: tutorials, assignments, projects, and verified workflows for physical systems.
Korean Sentence Embedding Model Performance Benchmark for RAG