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Comprehensive Forschungsreproduzierbarkeit Tools for Every Need

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Forschungsreproduzierbarkeit

  • GAMA Genstar Plugin

    GAMA Genstar Plugin integrates generative AI models into GAMA simulations for automatic agent behavior and scenario generation.
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    What is GAMA Genstar Plugin?
    GAMA Genstar Plugin adds generative AI capabilities to the GAMA platform by providing connectors to OpenAI, local LLMs, and custom model endpoints. Users define prompts and pipelines in GAML to generate agent decisions, environment descriptions, or scenario parameters on the fly. The plugin supports synchronous and asynchronous API calls, caching of responses, and parameter tuning. It simplifies the integration of natural language models into large-scale simulations, reducing manual scripting and fostering richer, adaptive agent behaviors.
    GAMA Genstar Plugin Core Features
    • Connect to OpenAI and local LLMs for on-demand inference
    • Define AI-driven behaviors via GAML primitives
    • Support for synchronous and asynchronous model calls
    • Response caching and parameter tuning
    • Customizable prompt templates and pipelines

  • MARFT

    MARFT is an open-source multi-agent RL fine-tuning toolkit for collaborative AI workflows and language model optimization.
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    What is MARFT?
    MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.

  • WorFBench

    WorFBench is an open-source benchmark framework evaluating LLM-based AI agents on task decomposition, planning, and multi-tool orchestration.
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    What is WorFBench?
    WorFBench is a comprehensive open-source framework designed to assess the capabilities of AI agents built on large language models. It offers a diverse suite of tasks—from itinerary planning to code generation workflows—each with clearly defined goals and evaluation metrics. Users can configure custom agent strategies, integrate external tools via standardized APIs, and run automated evaluations that record performance on decomposition, planning depth, tool invocation accuracy, and final output quality. Built‐in visualization dashboards help trace each agent’s decision path, making it easy to identify strengths and weaknesses. WorFBench’s modular design enables rapid extension with new tasks or models, fostering reproducible research and comparative studies.
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