RAG with Up-to-Date Context

Today’s top AI Highlights:

1. Python toolkit to create, orchestrate, and scale swarms of AI agents

2. Open-source RAG framework for large-scale and real-time data

3. Multi-agent end-to-end research framework (100% open-source)

4. Composio’s AI SDR kit to build AI sales and business agents

5. Browser use for AI agents using WebUI

& so much more!

Read time: 3 mins

AI Tutorials

The demand for AI-powered data visualization tools is surging as businesses seek faster, more intuitive ways to understand their data. We can tap into this growing market by building our own AI-powered visualization tools that integrate seamlessly with existing data workflows.

In this tutorial, we'll build an AI Data Visualization Agent using Together AI's powerful language models and E2B's secure code execution environment. This agent will understand natural language queries about your data and automatically generate appropriate visualizations, making data exploration intuitive and efficient.

E2B is an open-source infrastructure that provides secure sandboxed environments for running AI-generated code. Using E2B's Python SDK, we can safely execute code generated by language models, making it perfect for creating an AI-powered data visualization tool

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 leveling 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

Enterprise-Grade Production-Ready Multi-Agent Framework 👯

Swarms brings enterprise-grade multi-agent orchestration to Python developers, making it easier than ever to automate complex business operations. The framework gives you everything you need to build and manage agent swarms that actually work - flexible architectures, seamless third-party integration, and developer-friendly APIs.

It's built for production with features like automatic retries and asynchronous task handling. It also comes with a comprehensive ecosystem, including a marketplace for sharing and selling agents.

Key Highlights:

1. Flexible Multi-Agent Workflows - Swarms lets you choose from various pre-built ways to organize your agents, like sequential, parallel, and hierarchical workflows. This means you can set up the right collaboration model for your particular task. You can also fully customize agent interactions and task processing, so you're not stuck with a limited set of options.

2. Integration with External Services - Connecting your agent systems to external tools is streamlined. Swarms makes it easy to integrate with APIs, databases, and LLMs like OpenAI and Anthropic, without complex coding. It handles structured data (JSON, YAML, CSV) well using its AgentParse library, and enables your agents to use live data for real-time applications.

3. Scalable & Production-Ready - Swarms is designed for real-world production use. It includes features like concurrent processing, load balancing, and horizontal scaling, so it can handle large workloads and high traffic. Additionally, automatic retries and asynchronous task support ensure stability and dependability in production environments.

4. Full Ecosystem & Memory Management - Swarms includes a full ecosystem, including a CLI, cloud-based deployment options, and a marketplace for sharing and selling agents. It uses RAG systems like ChromaDB for long-term memory. This allows your agents to access and use their past interactions and knowledge, allowing for more contextual and relevant results.

5. Developer Experience - Clean APIs and comprehensive docs make it easy to get started and build with confidence. Everything from agent creation to workflow orchestration follows clear, consistent patterns. You get the flexibility to customize when needed, but sensible defaults keep simple things simple.

Fyxer AI: Automate Emails, Meetings, and Team Tasks in Seconds

Fyxer AI automates daily email and meeting tasks:

• Email Organization: It organizes your inbox so you see important emails first.

• Automated Email Drafting: Crafts replies that sound like you—convincing, concise, and flawlessly written in any language.

• Meeting Notes: Keeps you focused by taking notes, summarizing meetings, and drafting follow-ups.

Fyxer AI adapts to teams and sets up in just 30 seconds with Gmail or Outlook.

Try Fyxer For Free!

Bring Up-to-Date Context in Your RAG App ⏰

Neum AI is an open-source comprehensive framework for managing vector embeddings at scale, making it easier to build and scale RAG applications. The framework handles the entire data pipeline from extracting content across multiple data sources to processing embeddings and managing vector storage.

Its distributed architecture enables processing billions of data points efficiently, while offering real-time synchronization to keep vectors up-to-date as underlying data changes. To minimize infrastructure headaches, Neum AI provides both local development and managed cloud options.

Key highlights:

1. Streamlined Data Pipeline - Built-in connectors for common data sources (S3, Azure Blob, SharePoint), embedding services (OpenAI, Azure OpenAI), and vector stores (Weaviate, Qdrant, Pinecone). The pipeline handles data extraction, preprocessing, embedding generation, and storage management through a unified API.

2. Real-time Data Sync - Neum AI's cloud platform automatically syncs changes in your data sources with vector embeddings in real-time so your RAG app is up-to-date with the changes in the source data. While the local environment provides the tools for synchronization, you’d need to trigger the pipeline manually to update the embeddings.

3. Data Processing - The framework optimizes throughput through parallel processing and includes features like customizable chunking strategies and automatic metadata tracking. You can process large volumes of data faster while maintaining data quality and organization.

4. Deployment - Choose between local development or the managed cloud platform. The same code works in both environments, letting you start small locally and scale up seamlessly. The cloud platform adds features like scheduling, monitoring, and automatic synchronization.

5. Integration - Provides both Python SDK and REST APIs, along with ready-to-use evaluation tools through Ragas integration. The framework plays well with popular libraries like LangChain and LlamaIndex, making it easy to incorporate into existing RAG workflows or build new ones from scratch.

Quick Bites

AMD and John Hopkins researchers released Agent Laboratory, a multi-agent framework that takes your research idea and outputs a research report and code repository. It consists of three phases - Literature Review > Experimentation > Report Writing - during which the AI agents collaborate to accomplish distinct objectives, integrating external tools like arXiv, Hugging Face, Python, and LaTeX.

The framework adapts to your computational resources, whether you're using a MacBook or a GPU cluster. It is not to replace your creativity but to complement it so you can focus on ideation and critical thinking while automating repetitive and time-intensive tasks like coding and documentation. The code is available here.

Composio released AI SDR-Kit, a comprehensive suite of 60+ integrations and templates to build powerful AI sales and business development agents. The kit provides integrations with CRMs, email platforms, and data enrichment tools, and supports frameworks like LangChain and CrewAI. It also offers enterprise-grade compliance and agent auth.

You can use the Python and TypeScript SDKs or Composio’s APIs to build these AI agents. Quickstart with pre-built templates for Lead Generator Agent, Outreach Agent, Market Research Agent, and more.

You can bring Microsoft's Phi-4 (14B) up to GPT-4o-mini performance levels using Unsloth's latest optimization that fixed critical bugs in the model's tokenizer and fine-tuning pipeline. The team converted Phi-4 to a Llama architecture and implemented dynamic 4-bit quantization, which preserves accuracy while dramatically reducing resource requirements - 70% less memory usage, and extended context window from 12K to >128K tokens. Use their free Google Colab notebook that runs on T4 16GB GPUs.

Tools of the Trade

1. Browser-use: Open-source project that lets AI agents interact with the web UI. It supports multiple AI models (like Gemini, GPT-4o, Claude, DeepSeek, local Llama 3). You can either use your own browser or run the agents in a Docker container.

2. DevPod: Create isolated developer environments in containers, allowing developers to work on their code locally, in the cloud, or on remote machines while maintaining consistency through devcontainer.json configuration files

3. Resume Matcher: Open-source AI tool that analyzes a resume and job description to find matching keywords. It parses the documents, extracts key terms, and measures similarity to help you tailor your resume to be more ATS-friendly.

4. Awesome LLM Apps: Build awesome LLM apps with RAG, AI agents, and more to interact with data sources like GitHub, Gmail, PDFs, and YouTube videos, and automate complex work.

Hot Takes

1. The future of work is decentralized, asynchronous, and permissionless. ~
   Bojan Tunguz
   
   

2. Too many people talking about LLMs replacing software engineers, not enough people talking about LLMs replacing Deloitte analysts ~
   Ishan

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

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