HexaBot is designed to simplify the development and deployment of intelligent autonomous agents. It provides modular workflow pipelines that break complex tasks into manageable steps, along with persistent memory stores to retain context across sessions. Developers can connect agents to external APIs, databases, and third-party services through a plugin ecosystem. Real-time monitoring and logging ensure visibility into agent behavior, while SDKs for Python and JavaScript enable rapid integration into existing applications. HexaBot’s scalable infrastructure handles high concurrency and supports versioned deployments for reliable production use.

llm-lab provides a flexible toolkit for creating intelligent agents using large language models. It includes an agent orchestration engine, support for custom prompt templates, memory and state tracking, and seamless integration with external APIs and plugins. Users can write scenarios, define toolchains, simulate interactions, and collect performance logs. The framework also offers a built-in testing suite to validate agent behavior against expected outcomes. Extensible by design, llm-lab enables developers to swap LLM providers, add new tools, and evolve agent logic through iterative experimentation.

MAPF_G2RL is an open-source research framework that bridges graph theory and deep reinforcement learning to tackle the multi-agent path finding (MAPF) problem. It encodes nodes and edges into vector representations, defines spatial and collision-aware reward functions, and supports various RL algorithms such as DQN, PPO, and A2C. The framework automates scenario creation by generating random graphs or importing real-world maps, and orchestrates training loops that optimize policies for multiple agents simultaneously. After learning, agents are evaluated in simulated environments to measure path optimality, makespan, and success rates. Its modular design allows researchers to extend core components, integrate new MARL techniques, and benchmark against classical solvers.

GYM_XPLANE_ML wraps the X-Plane flight simulator as an OpenAI Gym environment, exposing throttle, elevator, aileron and rudder controls as action spaces and flight parameters like altitude, speed, and orientation as observations. Users can script training workflows in Python, select predefined scenarios or customize waypoints, weather conditions, and aircraft models. The library handles low-latency communication with X-Plane, runs episodes in synchronous mode, logs performance metrics, and supports real-time rendering for debugging. It enables iterative development of ML-driven autopilots and experimental RL algorithms in a high-fidelity flight environment.

Advanced_RAG provides a modular pipeline for retrieval-augmented generation tasks, including document loaders, vector index builders, and chain managers. Users can configure different vector databases (FAISS, Pinecone), customize retriever strategies (similarity search, hybrid search), and plug in any LLM to generate contextual answers. It also supports evaluation metrics and logging for performance tuning and is designed for scalability and extensibility in production environments.