代理 AI 基金会新增 4 更多开源 AI 工具
维持开源人工智能工具的财团Agentic AI Foundation今天在其翼下夺取了四个新项目。
这些技术由OpenAI集团PBC,Anthropic PBC,Hugging Face股份有限公司和Alibaba集团控股有限公司捐赠。 这使金融情报机构维持的项目总数达到10个。 Linux基金会去年12月管理了发布
开发者通过应用程序编程接口访问基于云的大型语言模型。 LLM提供商使用不同的API，这意味着为一个提供商的API编写的软件不能轻易移植到另一个提供商。 切换提供者往往要求开发人员进行广泛的代码修改。
开放对策是捐赠给AIF的四个项目中的第一个，旨在应对这一挑战。 它提供了实施AI模型API的标准化模板。 开发者可以在使用开放响应的API之间移动代码，而不进行重大修改。
该技术由OpenAI捐赠，它以API为模型，通过API提供对其LLMs的访问。 这意味着OpenAI的动力工作量可以轻松地根据开放响应转移到API，反之亦然。 此外，许多开发商已经熟悉了公司的API，这应该会缓解收养。
今天AAIF 宣布的第二次贡献来自Anthropic. 公司去年12月捐赠了Agent技能，一个开源项目它发布。 它提供了一种实施技能的标准化格式，这些技能是提高AI agent产出质量的资源集合。 这些资源可以包括说明如何执行某项任务的指示、示例和自动化脚本。
采用标准化格式的包装技能使开发团队之间更容易分享这些技能。 无需改变文件的结构，持续组织数据也使得AI agents更容易处理，这降低了幻觉的风险。
称 Anthropic工程师David Soria Parra。 校对：Soria Parra
AI实验室在沙盒中训练其模型，技术环境模拟LLM在生产中预计将运行的基础设施。 例如，一个公司建立一种气温编码模型，可能会产生模拟开发商工作站的沙盒。 Sandboxs是OpenEnv和OpenSandbox的焦点，是今天AAIF 宣布的另外两个项目。 它们分别由Hugging Face和Alibaba捐赠。
一个人工智能项目中使用的沙盒由一个自动化重复管理任务的训练系统协调。 OpenEnv减少了切换培训系统的工作量。 OpenSandbox则反过来提供一套用于管理沙盒的API. 它可以简化诸如将运行在沙盒内部的agents连接到外部应用等任务。
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Agentic AI Foundation adds four more open-source AI tools
The Agentic AI Foundation, a consortium that maintains open-source artificial intelligence tools, today took four new projects under its wing.
The technologies were donated by OpenAI Group PBC, Anthropic PBC, Hugging Face Inc. and Alibaba Group Holding Ltd. They bring the total number of projects maintained by the AAIF to 10. That’s up from the three the group managed when it was launched by the Linux Foundation last December.
Developers access cloud-based large language models through application programming interfaces. Each LLM provider uses a different API, which means that software written for one provider’s API can’t be easily ported to another. Switching providers often requires developers to make extensive code changes.
Open Responses, the first of the four projects donated to the AAIF, addresses the challenge. It provides a standardized template for implementing AI model APIs. Developers can move code between APIs that use Open Responses without making major changes.
The technology was donated by OpenAI, which modeled it after the API through which it provides access to its LLMs. That means OpenAI-powered workloads can be easily moved to APIs based on Open Responses and vice versa. Plus, the fact that many developers are already familiar with the company’s APIs should ease adoption.
The second contribution that AAIF announced today came from Anthropic. The company donated Agent Skills, an open-source project it released last December. It provides a standardized format for implementing skills, which are collections of resources that improve an AI agent’s output quality. Those resources can include instructions that explain how to carry out a task, illustrative examples and automation scripts.
Packaging skills in a standardized format makes it easier to share them between development teams. There’s no need to change how the files are structured before connecting them to a workload. Organizing data consistently also makes it easier to process for AI agents, which lowers the risk of hallucinations.
“The AAIF has done important work in giving the Model Context Protocol (MCP) a neutral home since we donated it less than a year ago, and alongside the AAIF, we’re committed to continuing to invest in open source agentic tooling for builders everywhere,” said Anthropic engineer David Soria Parra.
AI labs train their models in sandboxes, technology environments that simulate the infrastructure on which an LLM will be expected to run in production. For example, a company building a vibe coding model might create sandboxes that simulate developer workstations. Sandboxes are the focus of OpenEnv and OpenSandbox, the two other projects that the AAIF announced today. They were donated by Hugging Face and Alibaba, respectively.
The sandboxes used in an AI project are coordinated by a training system that automates repetitive management tasks. OpenEnv reduces the amount of work involved in switching training systems. OpenSandbox, in turn, provides a set of APIs for managing sandboxes. It eases tasks such as connecting agents that run inside sandboxes to external applications.
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