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Hey HN! We've just open-sourced Agent, our framework for running computer-use workflows across multiple apps in isolated macOS/Linux sandboxes.<p>After launching Computer a few weeks ago, we realized many of you wanted to run complex workflows that span multiple applications. Agent builds on Computer to make this possible. It works with local Ollama models (if you're privacy-minded) or cloud providers like OpenAI, Anthropic, and others.<p>Why we built this:<p>We kept hitting the same problems when building multi-app AI agents - they'd break in unpredictable ways, work inconsistently across environments, or just fail with complex workflows. So we built Agent to solve these headaches:<p>• It handles complex workflows across multiple apps without falling apart
• You can use your preferred model (local or cloud) - we're not locking you into one provider
• You can swap between different agent loop implementations depending on what you're building
• You get clean, structured responses that work well with other tools<p>The code is pretty straightforward:<p>async with Computer() as macos_computer:
agent = ComputerAgent(
computer=macos_computer,
loop=AgentLoop.OPENAI,
model=LLM(provider=LLMProvider.OPENAI)
)<p><pre><code> tasks = [
"Look for a repository named trycua/cua on GitHub.",
"Check the open issues, open the most recent one and read it.",
"Clone the repository if it doesn't exist yet."
]
for i, task in enumerate(tasks):
print(f"\nTask {i+1}/{len(tasks)}: {task}")
async for result in agent.run(task):
print(result)
print(f"\nFinished task {i+1}!")
</code></pre>
Some cool things you can do with it:<p>• Mix and match agent loops - OpenAI for some tasks, Claude for others, or try our experimental OmniParser
• Run it with various models - works great with OpenAI's computer_use_preview, but also with Claude and others
• Get detailed logs of what your agent is thinking/doing (super helpful for debugging)
• All the sandboxing from Computer means your main system stays protected<p>Getting started is easy:<p>pip install "cua-agent[all]"<p># Or if you only need specific providers:
pip install "cua-agent[openai]" # Just OpenAI
pip install "cua-agent[anthropic]" # Just Anthropic
pip install "cua-agent[omni]" # Our experimental OmniParser<p>We've been dogfooding this internally for weeks now, and it's been a game-changer for automating our workflows. Grab the code at <a href="https://github.com/trycua/cua" rel="nofollow">https://github.com/trycua/cua</a>, or join us on Discord if you have questions: <a href="https://discord.com/invite/mVnXXpdE85" rel="nofollow">https://discord.com/invite/mVnXXpdE85</a><p>What are you planning to automate? I'm especially curious about complex workflows that span multiple apps - those are the hardest to get right and where we've seen Agent really shine. Would love to hear your thoughts!
IRC<p>chat.farted.net/6667<p>哈哈 - 它可以工作！
嘿，HN！<p>将用户的反馈转化为产品成功。<p>这句话我经常听到，但没人真正知道这是什么意思。<p>所以我刚刚启动了 Feedback Hunter，旨在帮助每位 SaaS 业主应对这个艰巨的任务。<p>希望你们喜欢，如果能给我一些反馈，我会非常感激，大家 :)