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Speicherung von Erinnerungen

  • Open-source Python framework enabling autonomous AI agents to plan, execute, and learn tasks via LLM integration and persistent memory.
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    What is AI-Agents?
    AI-Agents provides a flexible, modular platform for creating autonomous AI-driven agents. Developers can define agent objectives, chain tasks, and incorporate memory modules to store and retrieve contextual information across sessions. The framework supports integration with leading LLMs via API keys, enabling agents to generate, evaluate, and revise outputs. Customizable tool and plugin support allows agents to interact with external services like web scraping, database queries, and reporting tools. Through clear abstractions for planning, execution, and feedback loops, AI-Agents accelerates prototyping and deployment of intelligent automation workflows.
    AI-Agents Core Features
    • LLM integration with OpenAI and other providers
    • Autonomous task planning and execution engine
    • Persistent memory storage across sessions
    • Modular tool and API plugin system
    • Configurable multi-agent orchestration
  • Freysa is a personalized AI twin that grows and remembers your conversations.
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    What is Freysa?
    Freysa is the world's first evolving AI agent designed to serve as your personalized information assistant. This AI twin not only remembers your past conversations but grows alongside you as your needs change. It also offers the functionality to generate custom images based on your personalized data, making interactions more engaging and tailored. Freysa supports a creative and intuitive interface to enhance communication, understanding, and customized data management.
  • A Python framework orchestrating customizable LLM-driven agents for collaborative task execution with memory and tool integration.
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    What is Multi-Agent-LLM?
    Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.
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