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Core_Features.md

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Core Features

  • Agent-Oriented Design: The framework is designed specifically with agents in mind, enabling customization of agents via the perception, planning, action, and memory modules.
  • Rich Multi-Agent Collaboration Modes: Offers industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express), and supports user-defined orchestration of new modes, enabling organic collaboration among multiple agents.
  • Customizable Components: All components of the framework, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents.
  • Seamless Integration of Domain Expertise: Provides capabilities for domain-specific prompts, knowledge construction and management, and supports domain-level SOP orchestration and embedding to align agents to expert levels in their fields.
  • LLM Compatibility and Extensibility: Built-in support for multiple models such as the OpenAI series, Qwen series, Baichuan series, Kimi series, etc., while offering standard extensions to integrate any third-party model services and privately deployed models.
  • Multi-Data Source Compatibility and Extensibility: Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvus. It also provides standard extension methods to integrate any third-party data services and other databases.
  • Convenient Ecosystem Integration: The framework integrates mature and practically tested large model application ecosystem capabilities, shielding most of the underlying configurations and implementations. This includes LLM ecosystem components like LangChain, llama_index, as well as technical components like gRPC and SLS.
  • Observable and Feedback Capable: With the monitor component, all service interactions and model interactions within agentUniverse can be recorded and observed. Combined with the framework's automatic agent evaluation capabilities, users can effortlessly comprehend the performance improvements resulting from agent or model iterations, and subsequently incorporate these insights back into service iterations and model training
  • Enterprise Private Component Extensions: The framework provides a mechanism for extending and loading private technical components, enabling the integration of internal elements such as custom-developed RPC protocol modules, messaging systems, and logging tools via configuration.
  • One-Click Service Deployment: Supports one-click initialization of a web server leveraging Flask and Gunicorn, facilitating the seamless integration of agent capabilities into any existing system.
  • Standard Container Delivery: Provides a standard Docker image and offers recommended integration approaches based on Docker and Kubernetes (K8S) functionalities.