Opensource Multi-Agent Orchestrator

Today’s top AI Highlights:

1. No more idle GPUs - Fast model loading for high-performance inference

2. AWS’s opensource multi-agent orchestration framework to deploy advanced multi-agent AI apps

3. xAI API in public beta - $25/month free credits until 2024 end

4. Reduce latency on rewrites and refactors with OpenAI’s Predicted Outputs feature

5. Computer Use that works with any LLM

& so much more!

Read time: 3 mins

AI Tutorials

We’re always looking for ways to automate complex workflows. Building tools that can search, synthesize, and summarize information is a key part of this, especially when dealing with ever-changing data like news.

For this tutorial, we’ll create a multi-agent AI news assistant using OpenAI’s Swarm framework along with Llama 3.2. You’ll be able to run everything locally, using multiple agents to break down the task into manageable, specialized roles—all without cost.

We will use:

• Swarm to manage the interactions between agents,

• DuckDuckGo for real-time news search, and

• Llama 3.2 for processing and summarizing news.

Each agent will handle a specific part of the workflow, resulting in a modular and flexible app that’s easy to adapt or expand.

We share hands-on tutorials like this 2-3 times a week, designed to help you stay ahead in the world of AI. If you're serious about levelling up your AI skills and staying ahead of the curve, subscribe now and be the first to access our latest tutorials.

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Latest Developments

Max Inference without Model Loading Bottlenecks ⚡️

No need to keep waiting for your massive AI models to load. Here’s a new opensource Python SDK called the Model Streamer that speeds up loading large AI models. It streams tensor data directly from storage to your GPU, minimizing downtime and boosting performance, especially for inference servers. It currently supports PyTorch models and handles various storage options, including S3 and network filesystems.

Key Highlights:

1. Parallelism for Speed - It uses parallel processing to load model tensors concurrently, minimizing the time it takes to get your models from storage to the GPU and ready for inference.

2. Storage Agnostic - Whether your models reside on local SSDs, network file systems, or cloud storage like S3, it works seamlessly across different storage mediums. 

3. Direct Safetensors Support - No more format conversions. Model Streamer works natively with the widely adopted safetensors format, further streamlining your model loading process.

4. Control Over Resource Usage - You can fine-tune the streamer's performance using environment variables. RUNAI_STREAMER_CONCURRENCY lets you adjust the number of parallel threads for loading. You can cap the CPU buffer size with RUNAI_STREAMER_MEMORY_LIMIT, offering options for unlimited, minimal, or a specific memory limit.

5. Easy Integration - Adding it to your PyTorch code is straightforward. The Python API is designed for simple integration and minimal code changes. Just wrap your loading logic with the SafetensorsStreamer context manager and you're good to go. You'll need to clone yielded tensors to avoid overwriting issues, a small price to pay for the performance gains.

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Opensource Framework for Managing Multiple AI Agents 👯‍♂️

Multi-Agent Orchestrator is a new powerful opensource framework designed for managing multiple AI agents, intelligently routing user queries, and handling complex conversations. Built for scalability and modularity, it allows you to create AI apps that can maintain coherent dialogues across multiple domains, efficiently delegating tasks to specialized agents while preserving context throughout the interaction.

The framework offers flexibility in agent implementation, storage options, and deployment environments. Built-in tools for agent overlap analysis and customizable logging further streamline the development process.

Key Highlights:

1. Diverse Agent Integration - Seamlessly incorporate various agent types, including LLMs via Amazon Bedrock and OpenAI, AWS Lambda functions, Amazon Lex bots, custom APIs, and local processing.

2. Flexible Context Management - Maintain conversation history using in-memory storage, DynamoDB, or implement your own custom storage solution. Control context length and selectively disable storage for specific agents so you can optimize for performance and cost.

3. Simplified Agent Selection - The framework intelligently routes user requests to the appropriate agent based on detailed agent descriptions and conversation history. An agent overlap analysis tool helps refine descriptions and minimize routing conflicts.

4. Agent Overlap Analysis & Optimization - Built-in tools analyze agent descriptions to identify potential overlaps in functionality, helping you optimize agent configuration and improve routing accuracy. This ensures that each agent has a distinct role and reduces ambiguity in request handling.

Quick Bites

xAI has launched its API in public beta with a preview of a new Grok model. This models comes with 128k token context and function calling support, and comparable performance to Grok 2 but with improved efficiency, speed and capabilities. It is also compatible with OpenAI and Anthropic SDKs.

xAI is offering $25 in free monthly credits through the end of 2024 for testing—sign up at console.x.ai to get started.

Meet hertz-dev, the first open-source full-duplex, audio-only base model built for real-time conversation. Standard Intelligence has released checkpoints for this 8.5 billion-parameter model, which uses cutting-edge audio encoding and generation layers to deliver instant high-quality speech synthesis. The checkpoints are available here to download.

OpenAI's Chat Completion API now offers "Predicted Outputs" to cut latency for GPT-4o and GPT-4o-mini by providing a reference string, ideal for tasks like code refactoring or content updating. This feature will help you speed up workflows by passing in existing content to optimize response times for known output formats.

Hugging Face now integrates directly into PyCharm, letting you add image and text models to Python projects with just a few clicks. With easy model selection, instant code snippets, and local model caching, you can now add advanced ML capabilities in just a few clicks, all without leaving your IDE.

Tools of the Trade

1. Awesome CursorRules: A curated collection of the most useful .cursorrules files for Cursor AI showcasing the best ways to customize AI behavior across different projects. It has ready-to-use, project-specific rules to make Cursor AI’s code suggestions more relevant.

2. Label Studio: Opensource data labeling studio that lets you annotate data types like text, audio, images, videos, and more with a simple and straightforward UI. It integrates easily into workflows and can run locally or in the cloud

3. Browser Use: Opensource Computer Use that allows any LLM to interact with websites, manage multiple tabs, and automatically detect page elements. You can use any LLM model that is supported by LangChain by adding correct environment variables.

4. Awesome LLM Apps: Build awesome LLM apps using RAG to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos through simple text. These apps will let you retrieve information, engage in chat, and extract insights directly from content on these platforms.

Hot Takes

1. My kid asks me: Daddy, should I be a programmer like u?
   I answered: no, kiddo, learn real-world skills. By the time you're 25, coding will be an obsolete profession.
   But, I wonder if physical labor will last for long. ~
   John Rush
   
   

2. Why is Ilya Sutskever SSI being so silent? Will they casually drop Superintelligence out of nowhere?
   We’ve barely heard anything significant after the hype about Ilya leaving OpenAI and launching "Safe Superintelligence"
   Like, no technical papers, no major announcements, just some unclear blog post about their mission.
   Given how he used to be pretty vocal about AI development at OpenAI, this long silence feels off. ~
   Haider.

That’s all for today! See you tomorrow with more such AI-filled content.

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