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Ejecución de código

  • JARVIS-1

    JARVIS-1 is a local open-source AI agent that automates tasks, schedules meetings, executes code, and maintains memory.
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    What is JARVIS-1?
    JARVIS-1 delivers a modular architecture combining a natural language interface, memory module, and plugin-driven task executor. Built on GPT-index, it persists conversations, retrieves context, and evolves with user interactions. Users define tasks through simple prompts, while JARVIS-1 orchestrates job scheduling, code execution, file manipulation, and web browsing. Its plugin system enables custom integrations for databases, email, PDFs, and cloud services. Deployable via Docker or CLI on Linux, macOS, and Windows, JARVIS-1 ensures offline operation and full data control, making it ideal for developers, DevOps teams, and power users seeking secure, extensible automation.
    JARVIS-1 Core Features
    • Local AI agent framework
    • Natural language task automation
    • Persistent memory and context
    • Extensible plugin system
    • Multi-model support (OpenAI, local LLMs)
    • Web browsing and file operations
    • Code execution and scheduling
    JARVIS-1 Pro & Cons

    The Cons

    Some initial learning epochs show limitations such as lack of tools or fuel, indicating dependency on experience and trial.
    Details on deployment complexity and computational resource requirements are not provided.
    Specific limitations or comparisons with other AI systems outside Minecraft domain are not mentioned.

    The Pros

    Capable of perceiving and processing multimodal inputs including vision and language.
    Supports over 200 complex, diverse tasks within Minecraft.
    Exhibits superior performance especially in short-horizon tasks and outperforms other agents in longer-horizon challenges.
    Incorporates a memory system enabling continual self-improvement and life-long learning.
    Operates autonomously with sophisticated planning and control abilities.

  • Symbol-LLM

    An extensible Python framework for building LLM-based AI agents with symbolic memory, planning and tool integration.
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    What is Symbol-LLM?
    Symbol-LLM offers a modular architecture for constructing AI agents powered by large language models augmented with symbolic memory stores. It features a planner module to break down complex tasks, an executor to invoke tools, and a memory system to maintain context across interactions. With built-in toolkits like web search, calculator and code runner, plus simple APIs for custom tool integration, Symbol-LLM enables developers and researchers to rapidly prototype and deploy sophisticated LLM-based assistants for various domains including research, customer support, and workflow automation.

  • Tiny-OAI-MCP-Agent

    A minimal OpenAI-based agent that orchestrates multi-cognitive processes with memory, planning, and dynamic tool integration.
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    What is Tiny-OAI-MCP-Agent?
    Tiny-OAI-MCP-Agent provides a small, extensible agent architecture built on the OpenAI API. It implements a multi-cognitive process (MCP) loop for reasoning, memory, and tool usage. You define tools (APIs, file operations, code execution), and the agent plans tasks, recalls context, invokes tools, and iterates on results. This minimal codebase allows developers to experiment with autonomous workflows, custom heuristics, and advanced prompt patterns while handling API calls, state management, and error recovery automatically.
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