Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.

Codegen empowers developers by automating various coding tasks. It uses advanced AI techniques to interpret natural language prompts and generates corresponding code snippets or entire applications in real-time. This tool significantly reduces the time developers spend on routine coding tasks, allowing them to focus on more complex problem-solving and innovation. With support for multiple programming languages, Codegen ensures broad applicability across different software development projects.