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Comprehensive extensible platforms Tools for Every Need

Get access to extensible platforms solutions that address multiple requirements. One-stop resources for streamlined workflows.

extensible platforms

  • SeeAct

    SeeAct is an open-source framework that uses LLM-based planning and visual perception to enable interactive AI agents.
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    What is SeeAct?
    SeeAct is designed to empower vision-language agents with a two-stage pipeline: a planning module powered by large language models generates subgoals based on observed scenes, and an execution module translates subgoals into environment-specific actions. A perception backbone extracts object and scene features from images or simulations. The modular architecture allows easy replacement of planners or perception networks and supports evaluation on AI2-THOR, Habitat, and custom environments. SeeAct accelerates research on interactive embodied AI by providing end-to-end task decomposition, grounding, and execution.
    SeeAct Core Features
    • LLM-based subgoal planning
    • Visual perception and feature extraction
    • Modular execution pipeline
    • Benchmark tasks on simulated environments
    • Configurable components
    SeeAct Pro & Cons

    The Cons

    Action grounding remains a significant challenge with a notable performance gap compared to oracle grounding.
    Current grounding methods (element attributes, textual choices, image annotation) have error cases leading to failures.
    Success rate on live websites is limited to about half the tasks, indicating room for improvement in robustness and generalization.

    The Pros

    Leverages advanced multimodal large models like GPT-4V for sophisticated web interaction.
    Combines action generation and grounding to effectively perform tasks on live websites.
    Exhibits strong capabilities in speculative planning, content reasoning, and self-correction.
    Openly available as a Python package facilitating ease of use and further development.
    Demonstrated competitive performance in online task completion with a 50% success rate.
    Accepted at a major AI conference (ICML 2024), reflecting validated research contributions.

  • AgentRails

    AgentRails integrates LLM-powered AI agents into Ruby on Rails apps for dynamic user interactions and automated workflows.
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    What is AgentRails?
    AgentRails empowers Rails developers to build intelligent agents that leverage large language models for natural language understanding and generation. Developers can define custom tools and workflows, maintain conversation state across requests, and integrate seamlessly with Rails controllers and views. It abstracts API calls to providers like OpenAI and enables rapid prototyping of AI-driven features, from chatbots to content generators, while adhering to Rails conventions for configuration and deployment.
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