Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

HackerGCLASS Agent API is an open-source Python framework that exposes RESTful endpoints to run AI agents. Developers can define custom tool integrations, configure prompt templates, and maintain agent state and memory across sessions. The framework supports orchestrating multiple agents in parallel, handling complex conversational flows, and integrating external services. It simplifies deployment via Uvicorn or other ASGI servers and offers extensibility with plugin modules, enabling rapid creation of domain-specific AI agents for diverse use cases.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.

LLM-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.

mini-AlphaStar demystifies the complex AlphaStar architecture by offering an accessible, open-source PyTorch framework for StarCraft II AI development. It features spatial feature encoders for screen and minimap inputs, non-spatial feature processing, LSTM memory modules, and separate policy and value networks for action selection and state evaluation. Using imitation learning to bootstrap and reinforcement learning with self-play for fine-tuning, it supports environment wrappers compatible with StarCraft II via pysc2, logging through TensorBoard, and configurable hyperparameters. Researchers and students can generate datasets from human gameplay, train models on custom scenarios, evaluate agent performance, and visualize learning curves. The modular codebase enables easy experimentation with network variants, training schedules, and multi-agent setups. Designed for education and prototyping rather than production deployment.

MultiAgentPuzzleSolver provides a modular environment where independent AI agents work together to solve puzzles such as sliding tiles, Rubik’s Cube, and logic grids. Agents share state information, negotiate subtask assignments, and apply diverse heuristics to explore the solution space more effectively than single-agent approaches. Developers can plug in new agent behaviors, customize communication protocols, and add novel puzzle definitions. The framework includes tools for real-time visualization of agent interactions, performance metrics collection, and experiment scripting. It supports Python 3.8+, standard libraries, and popular ML toolkits for seamless integration into research projects.

SuperAGI is an open-source autonomous AI agent framework designed for developers. It enables the creation, management, and execution of autonomous agents. Leveraging cutting-edge tools and technologies, SuperAGI empowers developers to build sophisticated applications that can function independently, streamlining various tasks ranging from document processing and internal support to customer experience. The framework is developer-first, providing all the tools and resources needed to build, manage, and run autonomous software systems efficiently.

FastMCP is an open-source Python framework for building MCP (Model Context Protocol) servers and clients that empower LLMs with external tools, data sources, and custom prompts. Developers define tool classes and resource handlers in Python, register them with the FastMCP server, and deploy using transport protocols like HTTP, STDIO, or SSE. The framework’s client library offers an asynchronous interface for interacting with any MCP server, facilitating seamless integration of AI agents into applications.

Promptix Python is an open-source framework for building autonomous AI agents in Python. With a simple installation via pip, you can instantiate agents powered by any major LLM, register domain-specific tools, configure in-memory or persistent data stores, and orchestrate multi-step decision loops. The SDK supports real-time streaming of token outputs, callback handlers for logging or custom processing, and built-in memory modules to retain context across interactions. Developers can leverage this library to prototype chatbot assistants, automations, data pipelines, or research agents in minutes. Its modular design allows swapping models, adding custom tools, and extending memory backends, providing flexibility for a wide range of AI agent use cases.