by yomorun
🦖 Serverless AI Agent Framework with Geo-distributed Edge AI Infra.
# Add to your Claude Code skills
git clone https://github.com/yomorun/yomoLast scanned: 4/25/2026
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}yomo is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by yomorun. 🦖 Serverless AI Agent Framework with Geo-distributed Edge AI Infra. It has 1,920 GitHub stars.
Yes. yomo passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/yomorun/yomo" and add it to your Claude Code skills directory (see the Installation section above).
yomo is primarily written in Rust. It is open-source under yomorun on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh yomo against similar tools.
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YoMo is an open-source LLM Function Calling Framework for building scalable and ultra-fast AI Agents. 💚 We care about: Empowering Exceptional Customer Experiences in the Age of AI
We believe that seamless and responsive AI interactions are key to delivering outstanding customer experiences. YoMo is built with this principle at its core, focusing on speed, reliability, and scalability.
| Features | ||
|---|---|---|
| ⚡️ | Serverless LLM Tools | Deploy and Manage LLM Tools / Skills seamlessly. |
| 🔐 | Enhanced Security | TLS v1.3 encryption is applied to every data packet by design, ensuring robust security for your AI agent communications. |
| 📸 | Effortless Agents DevOps | Streamline the entire lifecycle of your LLM tools, from development to deployment. Significantly reduces operational overhead, allowing you to focus exclusively on creating innovative AI agent functionalities. |
| 🌎 | Geo-Distributed Architecture | Bring AI inference and tools closer to your users with our globally distributed architecture, resulting in significantly faster response times and a superior user experience for your AI agents. |
Let's build a simple AI agent with LLM Function Calling to provide weather information:
curl -fsSL https://get.yomo.run | sh
Verify the installation:
yomo --version
Use Ollama as the LLM provider:
ollama pull ornith
Launch the server:
yomo serve
You can also use the --config flag to specify a custom coniguration yaml file.
yomo init
Finished, now, let's run it:
yomo run -n get-weather ./app
curl http://127.0.0.1:9001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [
{
"role": "user",
"content": "I am going for a hike on the Yarra Bend Park Loop. What should I wear?"
}
]
}'
You'll receive a helpful response like this:
Yarra Bend Park is on the Yarra River and gets misty/foggy when it overflows from its channel into the park—this typically occurs on damp autumns around November. Today's conditions are mild, warm, dry, with no fog expected today but a chance of light rain or drizzle possible tomorrow afternoon.
**Clothing for your hike:**
- **Base/mid-layer:** A long-sleeve top is enough. If it gets chilly at the river bank (a 2°C drop is possible), add a fleece mid-layer rather than relying on just an autumn outer shell.
- **Pants:** Jeans are okay if you want them, but light hiking pants or athletic wear are more comfortable and dry faster.
- **Footwear:** You'll be on forest trails around the Yarra River—sturdy sneakers will do for the loop today. If there's rain tomorrow afternoon, bring a pair of boots.
- **Rain gear:** Carry an umbrella just in case you get caught on the trail after the rain passes this weekend.
**Don't worry about mosquitoes this month.** They arrive in March/April when it gets hot and dry—and that's right around the time summer solstice fog starts forming (June). In November, no mosquitoes at all.
Check out our Servereless LLM Function Calling Examples for more use cases and inspiration.
Read more about YoMo on yomo.run.
It’s no secret that today’s users want instant AI inference, every AI
application is more powerful when it response quickly. But, currently, when we
talk about distribution, it represents distribution in data center. The AI model is
far away from their users from all over the world.
If an application can be deployed anywhere close to their end users, solve the problem, this is Geo-distributed System Architecture:
First off, thank you for considering making contributions. It's people like you that make YoMo better. There are many ways in which you can participate in the project, for example:
Build
cargo build --release
./target/release/yomo --help
Use Ollama as the LLM provider:
ollama pull ornith
Run YoMo server:
./target/release/yomo serve
Initialize a Serverless LLM Tool project:
./target/release/yomo init
then edit ./app/src/app.ts in the project.
Run YoMo serverless tool:
./target/release/yomo run --name get-weather ./app
Send a request to the LLM agent:
curl \
--request POST \
--url http://127.0.0.1:9001/v1/chat/completions \
--header 'Content-Type: application/json' \
--data '{
"messages": [
{
"role": "user",
"content": "How is the weather in London?"
}
]
}'
Send a request to the serverless function directly:
curl \
--request POST \
--url http://127.0.0.1:9001/tool/get-weather \
--header 'Content-Type: application/json' \
--data '{
"args":"{\"city\":\"London\"}"
}'