Repository Index

18 Lessons to Get Started Building AI Agents

• agentic-ai
• agentic-framework
• agentic-rag
• ai-agents

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

• agents
• ai
• llms
• machine-learning

Translated📚 Building large models from scratch

• agent
• llm
• rag

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

• rag
• tutorials
• langchain
• llama-index

A simple screen parsing tool towards pure vision based GUI agent

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.

• agents
• genai
• tutorials
• langchain

Translated✅ (Completed) Super comprehensive deep learning notes [Tudui PyTorch] [Li Mu Dive into Deep Learning] [Andrew Ng Deep Learning] [Dafei LLM Agent]

• book
• chinese
• computer-vision
• deep-learning

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

• agent
• agent-framework
• agents
• ai-agents

Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.

• machine-learning
• trading
• investment
• finance

Get started with building Fullstack Agents using Gemini 2.5 and LangGraph

• gemini
• gemini-api

Sample code and notebooks for Generative AI on Google Cloud, with Gemini platform

• generative-ai
• llm
• vertex-ai
• langchain

FinRL®: Financial Reinforcement Learning. 🔥

• deep-reinforcement-learning
• drl-trading-agents
• stock-trading
• drl-algorithms

An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.

• rag
• ai-agents
• alignment
• debugging

Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.

This trading-gym is the first trading for agent to train with episode of short term trading itself.

Agentic LLM CTF to test prompt injection attacks and preventions