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плагин-система

  • InfantAgent is a Python framework for rapidly building intelligent AI agents with pluggable memory, tools, and LLM support.
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    What is InfantAgent?
    InfantAgent offers a lightweight structure for designing and deploying intelligent agents in Python. It integrates with popular LLMs (OpenAI, Hugging Face), supports persistent memory modules, and enables custom tool chains. Out of the box, you get a conversational interface, task orchestration, and policy-driven decision making. The framework’s plugin architecture allows easy extension for domain-specific tools and APIs, making it ideal for prototyping research agents, automating workflows, or embedding AI assistants into applications.
    InfantAgent Core Features
    • LLM integration (OpenAI, Hugging Face)
    • Persistent memory management
    • Modular tool invocation
    • Policy-based decision engine
    • Conversational interface
    • Plugin architecture
  • Local-Super-Agents enables developers to build and run autonomous AI agents locally with customizable tools and memory management.
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    What is Local-Super-Agents?
    Local-Super-Agents provides a Python-based platform for creating autonomous AI agents that run entirely locally. The framework offers modular components including memory stores, toolkits for API integration, LLM adapters, and agent orchestration. Users can define custom task agents, chain actions, and simulate multi-agent collaboration within a sandboxed environment. It abstracts complex setup by offering CLI utilities, pre-configured templates, and extensible modules. Without cloud dependencies, developers maintain data privacy and resource control. Its plugin system supports integrating web scrapers, database connectors, and custom Python functions, empowering workflows such as autonomous research, data extraction, and local automation.
  • An open-source Python framework for building customizable AI assistants with memory, tool integrations, and observability.
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    What is Intelligence?
    Intelligence empowers developers to assemble AI agents by composing components that manage stateful memory, integrate language models like OpenAI GPT, and connect to external tools (APIs, databases, and knowledge bases). It features a plugin system for custom functionalities, observability modules to trace decisions and metrics, and orchestration utilities to coordinate multiple agents. Developers install via pip, define agents in Python with simple classes, and configure memory backends (in-memory, Redis, or vector stores). Its REST API server enables easy deployment, while CLI tools assist in debugging. Intelligence streamlines agent testing, versioning, and scaling, making it suitable for chatbots, customer support, data retrieval, document processing, and automated workflows.
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