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.

OpenSpiel is a research framework that provides a wide range of environments (from simple matrix games to complex board games such as Chess, Go, and Poker) and implements various reinforcement learning and search algorithms (e.g., value iteration, policy gradient methods, MCTS). Its modular C++ core and Python bindings allow users to plug in custom algorithms, define new games, and compare performance across standard benchmarks. Designed for extensibility, it supports single and multi-agent settings, enabling study of cooperative and competitive scenarios. Researchers leverage OpenSpiel to prototype algorithms quickly, run large-scale experiments, and share reproducible code.

AmongA and researching multi-agent AI systems. Through a simple Python API, users instantiate any number of AI agents, each equipped with tailored personas, prompts, and memory buffers. Agents engage in configurable conversation loops, supporting debates, brainstorming, decision-making, or game simulations. The framework seamlessly integrates with major LLM APIs (e.g., OpenAI, Anthropic), enabling message-based interaction and transcript logging. Developers can extend behavior by customizing agent roles, controlling turn-taking logic, and plugging in external data sources. AmongAIs also provides utilities for sentiment analysis, score-based evaluation, and session replay. Ideal for teams exploring emergent communication, collaborative ideation, and testing digital worker coordination in research and production settings.