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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications.
| Date | Stars |
|---|---|
| 2026-07-24 | 2308 |
| 2026-07-25 | 2309 |
| 2026-07-28 | 2309 |
| 2026-07-30 | 2309 |
| 2026-07-31 | 2313 |
| 2026-08-06 | 2313 |
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# agentUniverse **************************************** Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語](./README_jp.md)   [](LICENSE) [](https://pypi.org/project/agentUniverse/)  **************************************** ## What is agentUniverse? **agentUniverse is a multi-agent framework based on large language models.** It provides flexible and easily extensible capabilities for building individual agents. The core of agentUniverse is a rich set of multi-agent collaborative pattern components (serving as a collaborative pattern factory), which allows agents to perform their respective duties and maximize their capabilities when solving problems in different fields; at the same time, agentUniverse focuses on the integration of domain experience, helping you smoothly integrate domain experience into the work of intelligent agents.🎉🎉🎉 **🌈🌈🌈agentUniverse originates from the real-world financial business practices of AntGroup (https://github.com/antgroup), dedicated to assisting developers and enterprises in effortlessly constructing domain-expert-level intelligent agents that collaborate to accomplish tasks.**  We look forward to your practice and communication and sharing of Patterns in different fields through the community. This framework has already placed many useful components that have been tested in real business scenarios in terms of multi-agent cooperation, and will continue to be enriched in the future. The pattern components that are currently open for use include: * PEER pattern component: This pattern uses agents with different responsibilities—Plan, Execute, Express, and Review—to break down complex problems into manageable steps, execute the steps in sequence, and iteratively improve based on feedback, enhancing the performance of reasoning and analysis tasks. Typical use cases: Event interpretation, industry analysis. * DOE pattern component: This pattern employs three agents—Data-fining, Opinion-inject, and Express—to improve the effectiveness of tasks that are data-intensive, require high computational precision, and incorporate expert opinions. Typical use cases: Financial report generation. More patterns are coming soon... The LLM model integration can be accomplished with simple configuration, currently agentUniverse supported models include: |-|Vendors| Models | |:-----:|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |<img src="https://github.com/user-attachments/assets/b7b0f2ce-3250-4008-b6d7-4712a983deb9" height="25">|Qwen| qwen3 Series（qwen3-235b-a22b、qwen3-32b、qwen3-30b-a3b, etc.） 、qwen2.5-72b-instruct、qwq-32b-preview、qwen-max、… | |<img src="https://github.com/user-attachments/assets/5a997feb-bef4-4e53-ac3e-d38221e5399c" height="25">|Deepseek| deepseek-r1、deepseek-v3、deepseek-r1-distill-qwen-32b、… | |<img src="https://github.com/user-attachments/assets/0b50e555-65e8-49b2-b725-f3f71ee7daed" height="25">|OpenAI| GPT-4o、GPT-4o mini、OpenAI o1、OpenAI o3-mini、… | |<img src="https://github.com/user-attachments/assets/60fe0a70-0b47-4ac7-9bc9-8e860732ace9" height="25">|Claude| claude 3.7
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f6eeefa748c84a86, topic:ai-agents, topic:multi-agent, desc:agent framework
matched fp:f6eeefa748c84a86, topic:awesome, topic:awesome-list
