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자동화된 비서

  • A modular Python starter template for building and deploying AI agents with LLM integration and plugin support.
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    What is BeeAI Framework Py Starter?
    BeeAI Framework Py Starter is an open-source Python project designed to bootstrap AI agent creation. It includes core modules for agent orchestration, a plugin system to extend functionality, and adapters for connecting to popular LLM APIs. Developers can define tasks, manage conversational memory, and integrate external tools through simple configuration files. The framework emphasizes modularity and ease of use, enabling rapid prototyping of chatbots, automated assistants, and data-processing agents without boilerplate code.
    BeeAI Framework Py Starter Core Features
    • Agent orchestration and workflow management
    • Plugin architecture for extensibility
    • Adapters for major LLM providers
    • Config-driven agent definition
    • Conversational memory management
  • A Python framework for constructing multi-step reasoning pipelines and agent-like workflows with large language models.
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    What is enhance_llm?
    enhance_llm provides a modular framework for orchestrating large language model calls in defined sequences, allowing developers to chain prompts, integrate external tools or APIs, manage conversational context, and implement conditional logic. It supports multiple LLM providers, custom prompt templates, asynchronous execution, error handling, and memory management. By abstracting the boilerplate of LLM interaction, enhance_llm streamlines the development of agent-like applications—such as automated assistants, data processing bots, and multi-step reasoning systems—making it easier to build, debug, and extend sophisticated workflows.
  • AgentCrew is an open-source platform for orchestrating AI agents, managing tasks, memory, and multi-agent workflows.
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    What is AgentCrew?
    AgentCrew is designed to streamline the creation and management of AI agents by abstracting common functionalities such as agent lifecycle, memory persistence, task scheduling, and inter-agent communication. Developers can define custom agent profiles, specify triggers and conditions, and integrate with major LLM providers like OpenAI and Anthropic. The framework provides a Python SDK, CLI tools, RESTful endpoints, and an intuitive web dashboard for monitoring agent performance. Workflow automation features allow agents to work in parallel or sequence, exchange messages, and log interactions for auditing and retraining. The modular architecture supports plugin extensions, enabling organizations to tailor the platform to diverse use cases, from customer service bots to automated research assistants and data extraction pipelines.
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