/agentUniverse

agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications. Furthermore, through the community, they can exchange and share practices of patterns across different domains.

Primary LanguagePythonApache License 2.0Apache-2.0

agentUniverse

Language version: English | 中文

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Overview

agentUniverse is a framework for developing applications powered by multi-agent base on large language model. It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which serves as a pattern factory that allowing developers to buid and customize multi-agent collaboration patterns. With this framework, developers can easily construct multi-agent applications, and share the pattern practices from different technical and business fields.

The framework will come with serveral pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be enriched in the future. Patterns that are currently about to be released include:

  • PEER pattern： This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback which enhancing performance in reasoning and analytical tasks.

  • DOE pattern： This pattern consists of three agents: Data-fining agent, which is designed to solve data-intensive and high-computational-precision task; Opinion-inject agent, which combines the data results from first agent and the expert opinions which are pre-collected and structured; the third agent, Express agent generates the final result base on given document type and language style.

More patterns are coming soon...

agentUniverseSample Project

agentUniverse Sample Project

Quick Installation

Using pip:

pip install agentUniverse

Quick Start

We will show you how to:

  • Prepare the environment and application project
  • Build a simple agent
  • Use pattern components to complete multi-agent collaboration
  • Test and optimize the performance of the agent
  • Quickly serve the agent For details, please read Quick Start.

Guidebook

For more detailed information, please refer to the Guidebook.

API Reference

readthedocs

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