Graph-based Memory Layer for AI Agents

Today’s top AI Highlights:

1. New AI agent memory with 100% accuracy gains and 90% lower latency

2. Opensource end-to-end framework for building SOTA foundation models

3. The first job listing for an AI agent as an employee

4. Build full-stack apps from your phone: Replit Agents goes mobile & free

5. Local LLM-assisted text completion in VS Code using llama.cpp

& so much more!

Read time: 3 mins

AI Tutorials

For businesses looking to stay competitive, understanding the competition is crucial. But manually gathering and analyzing competitor data is time-consuming and often yields incomplete insights. What if we could automate this process using AI agents that work together to deliver comprehensive competitive intelligence?

In this tutorial, we'll build a multi-agent competitor analysis team that automatically discovers competitors, extracts structured data from their websites, and generates actionable insights. You'll create a team of specialized AI agents that work together to deliver detailed competitor analysis reports with market opportunities and strategic recommendations.

This system combines web crawling, data extraction, and AI analysis to transform raw competitor website data into structured insights. Using a team of coordinated AI agents, each specializing in different aspects of competitive analysis

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

New State of the Art In Agent Memory 💡🧠

AI agent development is hitting new levels of complexity, demanding smarter ways to manage information. Memory layer framework Zep has stepped up to replace current state-of-the-art frameworks, and making bold claims about accuracy and speed that directly address developers' pain points.

Zep uses a temporal knowledge graph, automatically extracting facts from user interactions and business data and tracking relationships. The team behind Zep has published some impressive benchmarks that show a significant reduction in latency, and accuracy gains exceeding 100% under certain tests.

Key Highlights:

1. Faster Context Retrieval - Zep boasts up to 90% lower latency compared to feeding entire chat histories into the LLM's context window. This translates to faster agent response times. In specific evaluations, Zep exhibited a latency of around 2.5 seconds compared to full-context methods that took around 29 seconds. This is achieved by utilizing around only 2% of the tokens needed by the baseline for evaluation.

2. Temporal Knowledge Graph - Forget simple fact retrieval – Zep automatically builds a temporal knowledge graph (using Graphiti) from user interactions and evolving business data. This allows your agents to reason about how facts change over time, understand the context behind decisions, and answer questions that require more than just finding a specific piece of information.

3. Integration with Existing Frameworks - Zep integrates with popular tools like LangChain, LangGraph, and AutoGen. With simple APIs for memory management, and flexible memory manipulation needs, you can seamlessly add Zep to your existing AI projects without major code rewrites.

4. Cost-Effective - By focusing on extracting key facts and relationships, Zep reduces the number of tokens sent to the LLM, leading to lower API costs and more efficient resource utilization. In evaluations, Zep utilized an average of less than 2% of the baseline tokens. You can manage your costs directly, since the speed is primarily linked to the performance of the embedding service you use.

Everything You Need to Build State-of-the-Art AI Models 🤖🌟

Building and deploying foundation models can be complex. That's why the fully open-source platform, Oumi, deserves attention. Oumi combines everything you need for building foundation models into a single open-source platform. The framework streamlines the entire lifecycle from training to deployment, making it easy to work with models ranging from 10M to 405B parameters.

Oumi handles data preparation, model training, evaluation, and deployment through a unified API that works seamlessly whether you're prototyping on a laptop or running large-scale experiments on a cluster. The platform supports both text and multimodal models like Llama, DeepSeek, and Qwen, while maintaining production-grade reliability and flexibility for research.

Key Highlights:

1. Unified API for Model Lifecycle - Oumi delivers a single, consistent API, so you can seamlessly manage your foundation models from the initial training phase to final production deployment, regardless of the scale or infrastructure.

2. Flexible Data Pipeline - Oumi provides built-in tools for data synthesis, curation, and filtering with LLM judges. This saves you time and effort when preparing your training data. Plus, support for custom datasets, chat formats, and vision-language datasets gives you great flexibility in dealing with any kind of project.

3. Built for Real Production Use - Train and fine-tune models at any scale using proven techniques like SFT, LoRA, QLoRA, and DPO. The platform handles everything from data preprocessing to deployment, with built-in support for distributed training and hardware acceleration. You get enterprise-grade stability without sacrificing the flexibility needed for ML research.

4. Deploy Anywhere - Run models on laptops, clusters, or major cloud providers through a consistent API. The platform integrates popular inference engines like vLLM and SGLang for optimal performance, while supporting both open models and commercial APIs from providers like OpenAI and Anthropic. No need to rewrite code when moving between environments.

Quick Bites

AI job listings enter new territory as companies begin recruiting AI agents directly. A recent remote position at Firecrawl offers $10,000-15,000 monthly for an AI system to autonomously create and test example applications, specifically requesting applications from "AI agents only."

This is a very exciting start to a new paradigm for workforce and it’s more important than ever to start building and deploying AI agents, RAG systems, and LLM applications – not just for staying competitive, but for actively shaping how this AI-augmented workforce will operate.

LlamaIndex has released LlamaReport, a new API-first report generation tool, in beta, to transform source documents into structured reports using a flexible templating system. The tool comes with an intelligent planning engine that creates generation strategies based on custom templates (from markdown to questionnaires), along with LLM-powered editing capabilities that can propose and justify targeted changes rather than full rewrites. You can join the waitlist to be notified when the API and UI are generally available.

Play AI just launched Dialog 1.0, a new ultra-emotional text-to-speech model, outperforming ElevenLabs in expressiveness and quality by a wide margin based on human preference tests. The model boasts impressively low 303ms Time to First Audio (TTFA) latency, and supports 30+ languages including experimental ones. Check out their voiceover studio or grab an API key to experiment with it yourself!

Replit has made its AI Agent freely available to all users, and launched a completely redesigned mobile app optimized for AI development. You can now build and deploy full-stack applications directly from your phones through a chat interface. Replit Agents will handle everything from database setup to user authentication. The agent supports hundreds of programming languages and frameworks, so you can create, host, and collaborate on projects entirely from mobile devices.

Tools of the Trade

1. llama-vscode: VS Code extension for local, LLM-powered code completions using llama.cpp as the backend server, with features like auto-suggest, configurable context management, and support for different model sizes based on available VRAM.

2. Vercel AI SDK Tools Registry: Pre-built AI tools for use with the Vercel AI SDK for LLMs to interact with external services and APIs. These tools simplify integration of functionalities like web search, platform integrations (Discord, Slack, GitHub), and utilities (Postgres, Math) into LLM applications.

3. Smolmodels: Open-source Python library that generates complete ML model training and inference code from natural language descriptions by combining graph search with LLM code generation. Handles the entire ML pipeline.

4. DeepSeek R1 vs OpenAI o1: Live demo that compares DeepSeek R1 and OpenAI o1 models in a RAG pipeline. The key technical difference is that DeepSeek R1 shows its reasoning process through streaming output, while OpenAI o1 provides complete answers at once.

5. 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. Claude 4 is gonna slap ~
   Matt Shumer

   
   

2. Just one week of managing a shared GPU server makes you understand why socialism is hell ~
   Tom Dörr

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

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