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Comprehensive モジュール式AIシステム Tools for Every Need

Get access to モジュール式AIシステム solutions that address multiple requirements. One-stop resources for streamlined workflows.

モジュール式AIシステム

  • LLM-Blender-Agent

    LLM-Blender-Agent orchestrates multi-agent LLM workflows with tool integration, memory management, reasoning, and external API support.
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    What is LLM-Blender-Agent?
    LLM-Blender-Agent enables developers to build modular, multi-agent AI systems by wrapping LLMs into collaborative agents. Each agent can access tools like Python execution, web scraping, SQL databases, and external APIs. The framework handles conversation memory, step-by-step reasoning, and tool orchestration, allowing tasks such as report generation, data analysis, automated research, and workflow automation. Built on top of LangChain, it’s lightweight, extensible, and works with GPT-3.5, GPT-4, and other LLMs.

  • DEf-MARL

    Framework for decentralized policy execution, efficient coordination, and scalable training of multi-agent reinforcement learning agents in diverse environments.
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    What is DEf-MARL?
    DEf-MARL (Decentralized Execution Framework for Multi-Agent Reinforcement Learning) provides a robust infrastructure to execute and train cooperative agents without centralized controllers. It leverages peer-to-peer communication protocols to share policies and observations among agents, enabling coordination through local interactions. The framework integrates seamlessly with common RL toolkits like PyTorch and TensorFlow, offering customizable environment wrappers, distributed rollout collection, and gradient synchronization modules. Users can define agent-specific observation spaces, reward functions, and communication topologies. DEf-MARL supports dynamic agent addition and removal at runtime, fault-tolerant execution by replicating critical state across nodes, and adaptive communication scheduling to balance exploration and exploitation. It accelerates training by parallelizing environment simulations and reducing central bottlenecks, making it suitable for large-scale MARL research and industrial simulations.

  • multi_agent_celar

    An open-source multi-agent framework enabling emergent language-based communication for scalable collaborative decision-making and environment exploration tasks.
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    What is multi_agent_celar?
    multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.
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