Skip to content

Latest commit

 

History

History
12 lines (12 loc) · 2.69 KB

1_Core_Features.md

File metadata and controls

12 lines (12 loc) · 2.69 KB
Raw

Core Features

  • Agent-Oriented Design: The framework is designed with agents in mind, customizing agents through 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 framework components, 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.
  • Multi-LLM and Extensible: 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 and Extensible: Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvu. 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, you can easily grasp the performance gains from agent or model iterations and feed them back into service iterations and model training.
  • Enterprise Private Component Extensions: The framework offers a private technical component extension and loading mechanism, allowing internal components like self-developed RPC protocol components, messaging components, and logging components to be integrated via configuration.
  • One-Click Service Deployment: Supports one-click startup of a web server through the capabilities of Flask and Gunicorn, making it easy to integrate agent capabilities into any existing system.
  • Standard Container Delivery: Provides a standard Docker image and offers recommended integration solutions based on Docker and Kubernetes (K8S) capabilities.