OWL

CAMEL-AI开源的多智能体协作系统，旨在复刻并超越Manus

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https://github.com/camel-ai/owl

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In-depth Report

OWL is a general multi-agent collaboration system open sourced by the CAMEL-AI team, aiming to replicate and surpass Manus. The product is completely open source and topped the list of open source multi-agent frameworks on the GAIA benchmark with a score of 58.18. OWL supports the automation of complex tasks such as task planning, file operations, and cross-platform control. It can run in the cloud or local environment, and is equipped with Ubuntu and Memory Toolkit. Its core advantage is to achieve efficient task decomposition and execution through the dynamic cooperation of multiple AI Agents.
OWL is developed by the CAMEL-AI team. CAMEL (Communicative Agents for Embodied Intelligence) is an AI organization focusing on multi-agent system research and is committed to building a network of AI Agents that can collaborate like humans. The full name of OWL is Optimized Workforce Learning. Its core concept is to let multiple AI Agents work together to complete complex tasks like managing a workforce. The project is completely open source and the code is hosted on GitHub. Developers are free to contribute and customize. The CAMEL-AI team has profound technical accumulation in the field of multi-agent, and its framework has been widely used in many fields such as medical health, intelligent transportation, e-commerce, knowledge graphs, environmental monitoring and the Internet of Things. As one of its core products, OWL has received extremely high attention on GitHub since its release, becoming one of the most popular open source AI Agent projects in 2025.
OWL provides a complete multi-agent collaboration framework, with core functions including task planning, file operations, cross-platform control, web crawling, report generation, and code deployment. Unlike a single AI Agent, OWL uses a dynamic agent interaction mechanism to allow Agents with different roles to work together to effectively solve complex tasks. At the architectural level, OWL is built on the CAMEL-AI Framework and adopts a modular design, allowing developers to flexibly configure tool chains and Agent roles. The system provides a wealth of built-in tools, covering multiple fields such as file parsing, data processing, and code execution. It supports both cloud and local deployment modes, and users can choose a suitable operating environment according to their needs. Memory Toolkit is a major feature of OWL, which gives the agent the ability to store and recall past task execution experiences. This design significantly improves the system's task completion efficiency, especially in complex scenarios that require multi-step collaboration. OWL is also equipped with Ubuntu environment support to facilitate developers to perform system-level operations and development and debugging. Judging from the performance data, OWL performs well in the GAIA (General AI Assistants) benchmark. GAIA is an important standard for measuring the capabilities of AI agents, covering multiple dimensions such as reasoning, planning, and tool usage. OWL ranks first among open source multi-agent frameworks with a score of 58.18, which fully proves its technical strength in the field of task automation.
As an open source project, OWL's basic functions are currently completely free and open to community developers. As an open source product, its business model mainly relies on community support, enterprise services and technical consulting. The CAML-AI team may provide enterprise-level customized services, including privatized deployment, technical support, and custom development. Such services usually adopt a subscription or project-based charging model. For individual developers and small and medium-sized teams, the open source version of OWL can already meet most needs. Developers can clone the project directly from GitHub and deploy and use it locally according to the official documentation. The functions of the open source version are continuously updated, the community is highly active, and issues are responded to in a timely manner.
Judging from online discussions, OWL has been widely recognized by the developer community. Many developers compare it with Manus and believe that OWL has advantages in open source and flexibility. Since it is an open source project, users can deeply customize it according to their own needs, which is an advantage that closed source products cannot achieve. Positive comments are mainly concentrated in three aspects: first, high performance, and the outstanding performance of GAIA benchmark test proves its technical strength; second, multi-agent collaboration mechanism, dynamic role allocation and task distribution are reasonably designed; third, rich tool chain, covering common development scenarios. Some users mentioned that as an emerging project, OWL's documentation and tutorials are still being improved, and there is a certain learning threshold for novice users. In addition, due to its reliance on the cooperation of multiple Agents, resource consumption is relatively high and certain hardware configuration requirements are required. These are factors that potential users need to consider before deploying.
The release of OWL has attracted widespread attention in the AI Agent field. As an open source solution, it provides developers with a customizable and extensible multi-agent framework that helps promote the development of the entire ecosystem. Compared with closed source solutions, the open source model can attract more developers to participate in contributions and accelerate technology iteration. From the perspective of technology trends, multi-agent collaboration is an important direction for the development of AI Agents. OWL represents cutting-edge practice in this direction, and its design concepts and implementation methods provide valuable reference for the industry. As large language model technology matures, the application scenarios of multi-agent systems will be further expanded. The open source community generally approves of OWL's technical route and believes that it has achieved a good balance between architectural design, functional completeness and performance. At the same time, as a completely open source project, OWL's transparency also enhances user confidence and reduces the risk of technology lock-in.
As a rapidly developing technology project, the main risks faced by OWL include technology maturity and community sustainability. Since the project is still under active development, the API and functions may change. Users need to pay attention to version updates and adjust their usage in a timely manner. From the perspective of usage scenarios, multi-agent systems involve complex task coordination and data processing, and users need to properly configure permissions and security policies during deployment. Especially when handling sensitive data, best practices should be followed to avoid the risk of data leakage. In addition, as an open source project, the long-term development of OWL relies on community activity and financial support. Although the current development trend is good, the sustainability of technology projects is always a risk factor that needs attention. Users are advised to pay attention to project updates and community dynamics to obtain the latest information in a timely manner.
OWL is suitable for the following user groups: technology developers can directly use the open source version for customized development; AI researchers can conduct multi-agent related research based on its architecture; enterprises and teams can deploy private AI automation systems; individual users who have needs for complex task automation can also try to use it. For beginners, it is recommended to start with the sample project in the official GitHub repository to gradually understand the system architecture and usage. Since it involves multi-Agent collaborative debugging, certain experience in using large language models is required. For enterprise users with customization needs, it is recommended to evaluate the technical team capabilities and deployment costs to determine whether they need to seek official support. The open source version is fully functional, but enterprise-level support may require additional service fees.
OWL is a high-performance open source multi-agent collaboration framework launched by the CAMEL-AI team, which performs well in the GAIA benchmark test. Its core advantages are that it is open source and free, has high flexibility, complete functions, and is suitable for complex task automation scenarios. As one of the most watched open source AI Agent projects in 2025, OWL provides developers with a powerful multi-agent collaboration tool and has high reference value for teams and individuals interested in trying AI automation technology. As the community continues to grow, it is expected that OWL will play a role in more application scenarios.

User Reviews

HannahReyes4

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OWL 这个多智能体框架有点东西，GAIA 基准开源第一不是白给的。试了试让它做市场调研，自动搜了十几篇文章还整理了摘要，确实省事。不过配置的时候确实劝退小白，光 API Key 就填了七八个。

JCastillo_Pro

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试了一晚上被工具注册问题整麻了，调 requests 库说没注册，得自己手动加。框架思路是好的，但工程成熟度还差一截。

Raymond_Rodriguez

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跟 OpenManus 比了一圈，OWL 任务完成度确实更高，但 UI 真的一言难尽，对话记录还得翻后端日志看。

JacquelineGomez_X

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太香了！

安然796

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消耗 token 的速度太猛了，跑一个中等复杂度的任务烧掉几十万 token，比我预想的多太多了。要是频繁使用的话，每个月光 API 费用就得好几百刀，对个人开发者来说负担挺重的。如果不优化 token 消耗，大规模部署的成本问题会非常突出。

白妍丹

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多智能体协作这块的思路确实领先，User Agent 负责拆解任务目标、协调各个子智能体的工作节奏，Assistant Agent 负责具体执行。两个 agent 之间的角色扮演机制很有意思，通过自然对话来协作，而不是硬编码的流程编排。这种方式的优势在处理不确定性强、步骤灵活的任务时特别明显。

Justin422

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拿它做了个竞品分析的自动化流程，爬网页、读文档、生成报告一条龙。中间有些步骤需要人工介入调一下参数，但整体效率提升还是很明显的，原来要半天的工作量压缩到了一小时左右。

剑客_2

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用它写代码还不错，让 OWL 帮忙调研 GitHub 仓库然后生成可视化图表，确实一把过。但做动画相关的任务就不太行了，生成的 HTML 页面按钮点了没反应，试了好几次都不行。

梅花956

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开源精神值得点赞，不用邀请码直接就能用，比 Manus 那套饥饿营销舒服多了。

RJenkins_66

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架构设计确实有前瞻性。CAMEL AI 团队做 agent 基础设施已经好几年了，从最早的 multi-agent 框架到跨平台操控项目 CRAB，再到百万 agent 模拟系统 OASIS，积累很深。OWL 算是他们研究成果的集大成者。不过最近更新速度明显慢下来了，社区精力好像回到底层 Camel 框架本身的迭代上了。

JWilson_88

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跟本地文件交互的效果出乎意料地好，文件读写成功率很高，这对做数据处理类的自动化工作流来说非常实用，不用额外处理文件 IO 的兼容性问题。

MRobertsZ

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支持 MCP 协议是个大加分项，意味着可以用社区生态里各种现成的工具，接了个飞书机器人 MCP，agent 直接能发消息查文档，方便。

CDavis007

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让 OWL 查了一下伦敦最近上映的电影，它自己打开浏览器搜索、滚动页面、读取排片信息，最后给了个挺完整的报告。整个过程看下来确实像有个远程员工在帮你干活，虽然速度慢了点但胜在不用自己动手。

Sarah.Cooper_Plus4

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太吃模型能力了，底层用 GPT-4o 和 Claude 3.5 差距很大。框架本身没问题，但最终效果上限被底层模型卡脖子，模型选型直接影响任务完成质量。

Olivia_GrayIII

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配置文档写得很详细，跟着一步步来能跑通。但说实话不适合非技术人员，对新手太不友好了。

Jose_Hughes_20243

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初体验还行，让它订机票查航班，虽然最后结果没保存到本地文件有点遗憾，但查询过程挺丝滑的，自��打开了浏览器搜索上海到路易维尔的航班，给出了中转信息，结果还挺靠谱。

EvaLi

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消耗 token 太快了，同等工作量比直接调 API 贵好几倍，不过换来的是自动化的工作流，这个取舍看具体场景吧。

Lydia_Walker00

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部署成功了，流程还算顺利，就是需要填的 API Key 太多了。不过跑起来之后确实挺好用的，让 agent 写了个数据报表的脚本，比我自己写快多了。

杜君明

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适合有一定技术背景的人玩，你要是愿意折腾、愿意调试各种报错，OWL 能给你不少惊喜。但如果就想开箱即用，劝退概率很大。

Beverly.NelsonSr9

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跑复杂任务的时候确实容易出问题，8 步以上的链式调用成功率会明显下降，中间某一环出错后面全乱套。建议拆成小任务分步执行，或者加一些错误重试机制来提升稳定性。

MarkKelly_Pro884

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胡梦康团队的方向很清晰，从 OWL 到后来创立 Evolvent AI，一直在啃 Agent 数据基础设施这块硬骨头。开源社区能看到这个路径挺难得的。

Patricia.Howard_77

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GitHub 两万星不是白刷的。虽然现在迭代速度不如刚开源那会儿猛了，但框架底子摆在那，社区里还是有很多人在上面做二次开发。LLM 选型自由度很高，GPT、Claude、Qwen、DeepSeek 都能跑，还可以用 Ollama 跑本地模型保证数据安全，这点对企业用户来说很关键。

秦芳

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看了一篇深度评测，说 OWL 在金融场景下可以同时监控多个市场并自动执行交易策略，这个场景确实适合多智能体架构。可惜我暂时没有条件实操验证，有在用的老哥说说效果吗。

RuthHall_20209

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LLM 选型自由度不错，支持各种主流模型，还可以用 Ollama 本地部署，不用担心数据外泄。

rONALDpEDERSEN

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yyds！

RebeccaKoch

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跟 Agno 和 OpenManus 都对比过，OWL 在企业级多智能体任务上确实是最强的，30 多个内置工具包覆盖场景很全面。但如果你只是做轻量级的个人助手，有点杀鸡用牛刀了，用 Agno 或者直接调 API 就行。

Raymond.Nelson_7

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源码质量还不错，CAMEL 团队的工程水平是在线的。整体架构清晰，模块划分合理，扩展性做得很好。不过 web UI 部分确实简陋，后期估计得自己搭前端，目前那个 Gradio 界面对复杂任务的展示力不从心。

Matthew.Vasquez_X

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终于找到了一个靠谱的 Manus 平替，不用抢邀请码的感觉真好。性能比想象中好，日常自动化任务基本能覆盖。

Gabriel.Myers_X

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Docker 部署很方便，一条命令拉起来就能用。但模型配置那一步坑有点多，环境变量容易配错，新手建议直接用官方推荐的 conda 方式安装。

Gzwol

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多智能体之间的角色扮演机制很有意思，两个 agent 通过对话自然协作、动态分工，不是生硬的任务编排。这种方式处理开放性的复杂任务更灵活，但也导致执行过程中不确定性比较高，有时候同样的任务跑两次结果不一样。

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