yomo

🦖 Stateful Serverless Framework for Geo-distributed Edge AI Infra. with function calling support, write once, run on any model.

Stars: 1698

Visit

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

README:

YoMo

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. Built atop QUIC Transport Protocol and Stateful Serverless architecture, makes your AI application low-latency, reliable, secure, and easy.

💚 We care about: Customer Experience in the Age of AI

🌶 Features

	Features
⚡️	Low-latency Guaranteed by implementing atop QUIC QUIC
🔐	Security TLS v1.3 on every data packet by design
📸	Stateful Serverless Make your GPU serverless 10x faster
🌎	Geo-Distributed Architecture Brings AI inference closer to end users
🚀	Y3 a faster than real-time codec

🚀 Getting Started

Let's implement a function calling with sfn-currency-converter:

Step 1. Install CLI

curl -fsSL https://get.yomo.run | sh

Verify if the CLI was installed successfully

yomo version

Step 2. Start the server

Prepare the configuration as my-agent.yaml

name: ai-zipper
host: 0.0.0.0
port: 9000

auth:
  type: token
  token: SECRET_TOKEN

bridge:
  ai:
    server:
      addr: 0.0.0.0:8000 ## Restful API endpoint
      provider: openai ## LLM API Service we will use

    providers:
      azopenai:
        api_endpoint: https://<RESOURCE>.openai.azure.com
        deployment_id: <DEPLOYMENT_ID>
        api_key: <API_KEY>
        api_version: <API_VERSION>

      openai:
        api_key: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxx
        model: gpt-4-1106-preview

      gemini:
        api_key: <GEMINI_API_KEY>

      cloudflare_azure:
        endpoint: https://gateway.ai.cloudflare.com/v1/<CF_GATEWAY_ID>/<CF_GATEWAY_NAME>
        api_key: <AZURE_API_KEY>
        resource: <AZURE_OPENAI_RESOURCE>
        deployment_id: <AZURE_OPENAI_DEPLOYMENT_ID>
        api_version: 2023-12-01-preview

Start the server:

YOMO_LOG_LEVEL=debug yomo serve -c my-agent.yaml

Step 3. Write the function

First, let's define what this function do and how's the parameters required, these will be combined to prompt when invoking LLM.

type Parameter struct {
	Domain string `json:"domain" jsonschema:"description=Domain of the website,example=example.com"`
}

func Description() string {
	return `if user asks ip or network latency of a domain, you should return the result of the giving domain. try your best to dissect user expressions to infer the right domain names`
}

func InputSchema() any {
	return &Parameter{}
}

Create a Stateful Serverless Function to get the IP and Latency of a domain:

func Handler(ctx serverless.Context) {
	var msg Parameter
	ctx.ReadLLMArguments(&msg)

	// get ip of the domain
	ips, _ := net.LookupIP(msg.Domain)

	// get ip[0] ping latency
	pinger, _ := ping.NewPinger(ips[0].String())
	pinger.Count = 3
	pinger.Run()
	stats := pinger.Statistics()

	val := fmt.Sprintf("domain %s has ip %s with average latency %s", msg.Domain, ips[0], stats.AvgRtt)
	ctx.WriteLLMResult(val)
}

Finally, let's run it

$ yomo run app.go

time=2024-03-19T21:43:30.583+08:00 level=INFO msg="connected to zipper" component=StreamFunction sfn_id=B0ttNSEKLSgMjXidB11K1 sfn_name=fn-get-ip-from-domain zipper_addr=localhost:9000
time=2024-03-19T21:43:30.584+08:00 level=INFO msg="register ai function success" component=StreamFunction sfn_id=B0ttNSEKLSgMjXidB11K1 sfn_name=fn-get-ip-from-domain zipper_addr=localhost:9000 name=fn-get-ip-from-domain tag=16

Done, let's have a try

$ curl -i http://127.0.0.1:9000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "messages": [
    {
      "role": "system",
      "content": "You are a test assistant."
    },
    {
      "role": "user",
      "content": "Compare website speed between Nike and Puma"
    }
  ],
  "stream": false
}'

HTTP/1.1 200 OK
Content-Length: 944
Connection: keep-alive
Content-Type: application/json
Date: Tue, 19 Mar 2024 13:30:14 GMT
Keep-Alive: timeout=4
Proxy-Connection: keep-alive

{
  "Content": "Based on the data provided for the domains nike.com and puma.com which include IP addresses and average latencies, we can infer the following about their website speeds:
  - Nike.com has an IP address of 13.225.183.84 with an average latency of 65.568333 milliseconds.
  - Puma.com has an IP address of 151.101.194.132 with an average latency of 54.563666 milliseconds.
  
  Comparing these latencies, Puma.com is faster than Nike.com as it has a lower average latency. 
  
  Please be aware, however, that website speed can be influenced by many factors beyond latency, such as server processing time, content size, and delivery networks among others. To get a more comprehensive understanding of website speed, you would need to consider additional metrics and possibly conductreal-time speed tests.",
  "FinishReason": "stop"
}

Full Example Code

Full LLM Function Calling Codes

📚 Documentation

🎯 Focuses on Geo-distributed AI Inference Infra

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:

🦸 Contributing

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:

File a bug report. Be sure to include information like what version of YoMo you are using, what your operating system is, and steps to recreate the bug.
Suggest a new feature.
Read our contributing guidelines to learn about what types of contributions we are looking for.
We have also adopted a code of conduct that we expect project participants to adhere to.

License

Apache License 2.0

For Tasks:

Click tags to check more tools for each tasks

run function calls deploy geo-distributed ai ensure low-latency ai implement stateful serverless optimize gpu processing

For Jobs:

ai engineer software developer data scientist cloud engineer machine learning engineer

Alternative AI tools for yomo

Similar Open Source Tools

yomo

github

: 1.7k

notte

Notte is a web browser designed specifically for LLM agents, providing a language-first web navigation experience without the need for DOM/HTML parsing. It transforms websites into structured, navigable maps described in natural language, enabling users to interact with the web using natural language commands. By simplifying browser complexity, Notte allows LLM policies to focus on conversational reasoning and planning, reducing token usage, costs, and latency. The tool supports various language model providers and offers a reinforcement learning style action space and controls for full navigation control.

github

: 210

swe-rl

SWE-RL is the official codebase for the paper 'SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution'. It is the first approach to scale reinforcement learning based LLM reasoning for real-world software engineering, leveraging open-source software evolution data and rule-based rewards. The code provides prompt templates and the implementation of the reward function based on sequence similarity. Agentless Mini, a part of SWE-RL, builds on top of Agentless with improvements like fast async inference, code refactoring for scalability, and support for using multiple reproduction tests for reranking. The tool can be used for localization, repair, and reproduction test generation in software engineering tasks.

github

: 244

curator

Bespoke Curator is an open-source tool for data curation and structured data extraction. It provides a Python library for generating synthetic data at scale, with features like programmability, performance optimization, caching, and integration with HuggingFace Datasets. The tool includes a Curator Viewer for dataset visualization and offers a rich set of functionalities for creating and refining data generation strategies.

github

: 889

flo-ai

Flo AI is a Python framework that enables users to build production-ready AI agents and teams with minimal code. It allows users to compose complex AI architectures using pre-built components while maintaining the flexibility to create custom components. The framework supports composable, production-ready, YAML-first, and flexible AI systems. Users can easily create AI agents and teams, manage teams of AI agents working together, and utilize built-in support for Retrieval-Augmented Generation (RAG) and compatibility with Langchain tools. Flo AI also provides tools for output parsing and formatting, tool logging, data collection, and JSON output collection. It is MIT Licensed and offers detailed documentation, tutorials, and examples for AI engineers and teams to accelerate development, maintainability, scalability, and testability of AI systems.

github

: 73

archgw

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize AI agents with APIs. It handles tasks related to prompts, including detecting jailbreak attempts, calling backend APIs, routing between LLMs, and managing observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and observability. Users can build fast, observable, and personalized AI agents using Arch to improve speed, security, and personalization of GenAI apps.

github

: 1.3k

basiclingua-LLM-Based-NLP

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

github

: 77

call-center-ai

Call Center AI is an AI-powered call center solution leveraging Azure and OpenAI GPT. It allows for AI agent-initiated phone calls or direct calls to the bot from a configured phone number. The bot is customizable for various industries like insurance, IT support, and customer service, with features such as accessing claim information, conversation history, language change, SMS sending, and more. The project is a proof of concept showcasing the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI for an automated call center solution.

github

: 325

suno-api

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

github

: 1.7k

oasis

OASIS is a scalable, open-source social media simulator that integrates large language models with rule-based agents to realistically mimic the behavior of up to one million users on platforms like Twitter and Reddit. It facilitates the study of complex social phenomena such as information spread, group polarization, and herd behavior, offering a versatile tool for exploring diverse social dynamics and user interactions in digital environments. With features like scalability, dynamic environments, diverse action spaces, and integrated recommendation systems, OASIS provides a comprehensive platform for simulating social media interactions at a large scale.

github

: 659

ichigo

Ichigo is a local real-time voice AI tool that uses an early fusion technique to extend a text-based LLM to have native 'listening' ability. It is an open research experiment with improved multiturn capabilities and the ability to refuse processing inaudible queries. The tool is designed for open data, open weight, on-device Siri-like functionality, inspired by Meta's Chameleon paper. Ichigo offers a web UI demo and Gradio web UI for users to interact with the tool. It has achieved enhanced MMLU scores, stronger context handling, advanced noise management, and improved multi-turn capabilities for a robust user experience.

github

: 2.1k

llm-colosseum

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

github

: 1.3k

hydraai

Generate React components on-the-fly at runtime using AI. Register your components, and let Hydra choose when to show them in your App. Hydra development is still early, and patterns for different types of components and apps are still being developed. Join the discord to chat with the developers. Expects to be used in a NextJS project. Components that have function props do not work.

github

: 90

llm-context.py

LLM Context is a tool designed to assist developers in quickly injecting relevant content from code/text projects into Large Language Model chat interfaces. It leverages `.gitignore` patterns for smart file selection and offers a streamlined clipboard workflow using the command line. The tool also provides direct integration with Large Language Models through the Model Context Protocol (MCP). LLM Context is optimized for code repositories and collections of text/markdown/html documents, making it suitable for developers working on projects that fit within an LLM's context window. The tool is under active development and aims to enhance AI-assisted development workflows by harnessing the power of Large Language Models.

github

: 108

py-llm-core

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

github

: 118

odoo-expert

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

github

: 56

For similar tasks

yomo

github

: 1.7k

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 608

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.1k