Taiga is a Python-based open-source AI agent framework designed to streamline the creation, orchestration, and deployment of autonomous large language model (LLM) agents. The framework includes a flexible plugin system for integrating custom tools and external APIs, a configurable memory module for managing long-term and short-term conversational context, and a task chaining mechanism to sequence multi-step workflows. Taiga also offers built-in logging, metrics, and error handling for production readiness. Developers can quickly scaffold agents with templates, extend functionality via SDK, and deploy across platforms. By abstracting complex orchestration logic, Taiga enables teams to focus on building intelligent assistants that can research, plan, and execute actions without manual intervention.

Agent TARS leverages a combination of advanced computer vision and natural language processing techniques to understand and manipulate graphical user interfaces. By capturing visual representations of web pages, TARS can identify buttons, forms, tables, and other page elements. Users interact with TARS through natural language prompts, instructing it to click, scroll, extract text, or fill forms across multiple pages. It supports customizable workflows that chain tasks—such as logging into accounts, scraping data, and exporting results to CSV or JSON. With support for headless and headful browser modes, TARS enables both interactive exploration and unattended automation, making it ideal for testing, data acquisition, and routine browser-based operations.