RAGENT is designed to create autonomous AI agents that can interact with diverse tools and data sources. Under the hood, it uses retrieval-augmented generation to fetch relevant context from local files or external sources and then composes responses via OpenAI models. Developers can plug in tools for web search, browser automation with Selenium, file read/write operations, code execution in secure sandboxes, and OCR for image text extraction. The framework manages conversation memory, handles tool orchestration, and supports custom prompt templates. With RAGENT, teams can rapidly prototype intelligent agents for document Q&A, research automation, content summarization, and end-to-end workflow automation, all within a Python environment.

PerfAgents is an AI-powered synthetic monitoring and testing platform designed to simplify the setup and management of monitoring your critical applications. It supports multiple open-source frameworks like Selenium, Puppeteer, Cypress, and Playwright. By leveraging your existing scripts or effortlessly creating new ones, PerfAgents provides continuous testing, reducing downtime and ensuring optimal application performance. It seamlessly integrates with tools like Slack, Microsoft Teams, Jira, and PagerDuty for real-time alerts and notifications.

Dino Reinforcement Learning offers a comprehensive toolkit for training an AI agent to play the Chrome dinosaur game via reinforcement learning. By integrating with a headless Chrome instance through Selenium, it captures real-time game frames and processes them into state representations optimized for deep Q-network inputs. The framework includes modules for replay memory, epsilon-greedy exploration, convolutional neural network models, and training loops with customizable hyperparameters. Users can monitor training progress via console logs and save checkpoints for later evaluation. Post-training, the agent can be deployed to play live games autonomously or benchmarked against different model architectures. The modular design allows easy substitution of RL algorithms, making it a flexible platform for experimentation.