OpenMemory MCP that Works Across AI Tools

Today’s top AI Highlights:

1. Build lightning-fast web AI agents in just a few lines of code

2. Private memory MCP server that works across all MCP clients

3. Turn any AI agent into an A2A server with a single line of code

4. ByteDance’s vision-language reasoning model outperforms OpenAI CUA and Claude 3.7 Sonnet

5. Vibe-code MCP-ready tools for any AI agent

& so much more!

Read time: 3 mins

AI Tutorial

Building good research tools is hard. When you're trying to create something that can actually find useful information and deliver it in a meaningful way, you're usually stuck cobbling together different search APIs and prompt engineering for hours. It's a headache, and the results are often inconsistent.

In this tutorial, we'll build an AI Domain Deep Research Agent that does all the heavy lifting for you. This app uses three specialized agents that are built using the Agno framework, use Qwen’s new flagship model Qwen 3 235B via Together AI, and use tools via Composio to generate targeted questions, search across multiple platforms, and compile professional reports — all with a clean Streamlit interface.

What makes this deep research app different from other tools out there is its unique approach: it automatically breaks down topics into specific yes/no research questions, combines results from both Tavily and Perplexity AI for better coverage, and formats everything into a McKinsey-style report that's automatically saved to Google Docs.

We share hands-on tutorials like this every 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

Opensource Framework for Web Browsing Agents 🌐🤖

Notte is an opensource web AI agent framework designed for building, deploying, and scaling browser-using agents with a single API. It gives you access to full browser sessions, automated LLM agents, natural-language control over web pages, and secure credential handling — all wrapped in a clean SDK. The core idea is to make the internet more agent-friendly by turning web pages into structured maps that models can easily process.

Notte is optimized for real-world use: fast execution, low overhead, and flexibility. It works well with smaller models (like Llama) thanks to its perception layer, which strips away the noise from web pages. Plus, it comes with MCP support, letting you plug in Claude or Cursor to act on websites directly from the chat.

Key Highlights:

1. Perception Layer - Notte introduces a perception layer that abstracts messy HTML/DOM into structured, natural-language descriptions of a page. This allows LLMs to focus on meaningful actions instead of parsing noisy layouts, enabling smoother planning and fewer errors.

2. Full-Stack Agent API - The framework includes agent runs, browser sessions, page observation, scraping, and acting, all accessible via simple Python or REST APIs. You can run end-to-end autonomous workflows or guide your agent step by step, depending on your needs.

3. MCP Server - Notte comes with a ready-to-use MCP server implementation focused on browser control. You can connect tools like Claude Desktop to browse, search, and interact with websites directly from chat.

4. Comparison with Other Web Agents - Notte outperforms competitors like Browser-Use and Convergence across speed, accuracy, and reliability. It completes tasks in 47s on average vs. 113s for Browser-Use and 83s for Convergence, with a 96.6% task success rate.

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Memory MCP Server That Works Across Apps 🧠⚙️↔️

Most AI tools today work in silos. You plan your roadmap in Claude, code in Cursor, and debug in Windsurf, yet none of them remembers what the others did. OpenMemory MCP by Mem0 changes that. It’s a local memory server built on the MCP to help all your AI tools share and reuse context in real time.

OpenMCP acts as a shared memory layer that works across MCP clients like Claude Desktop, Cursor, Windsurf, and Cline. It runs entirely on your machine, gives you a dashboard to monitor and control what each tool remembers, and supports full control over what’s saved, who can access it, and when it expires. Memory finally becomes portable across tools, securely and under your control.

Key Highlights:

1. Local memory infrastructure - OpenMemory MCP runs 100% on your machine - no syncing, no API calls to external servers. Every bit of context stays private and under your control. Ideal for developers building secure agentic workflows or debugging tools that must keep user data confidential.

2. Standardized MCP APIs - The server exposes four core APIs—add_memories, search_memory, list_memories, and delete_all_memories. Any tool that supports MCP (like Claude or Cursor) can use these to store and retrieve memories, enabling true context continuity across sessions and apps.

3. Built-in dashboard - The included dashboard shows which clients stored what, how often specific memories were accessed, and lets you pause or restrict memory access per app. You can organize memories by topic, timestamp, or emotion, offering better traceability during development or testing.

4. Easy setup - The install process is minimal—just clone, set up your .env, and run Docker. The dashboard launches at localhost:3000, and clients like Claude or Cursor connect via a single MCP install command. No complex auth, no third-party middleware. Just plug it in and it works.

Quick Bites

Upwork is great, but this gig economy is being run by AI. 10dollarjob is a new AI agent marketplace where users can hire teams of AI agents to complete tasks, just like hiring freelancers, but faster, cheaper, and always available. When you give it a job, an orchestrator engine automatically breaks down your job, matches it with the best agents from the marketplace, and calculates a total price for the job based on the individual pricing of each agent. Developers can list their AI agents and get paid per task through the built-in Prava wallet system. It’s currently live for waitlisted users, with early access open for both users and agent builders.

Pydantic has released FastA2A, a lightweight Python library that turns any AI agent into an A2A server. With just one line of code, developers can expose a PydanticAI agent as an A2A server using the to_a2a() method. Although Pydantic AI built it, FastA2A is an agent-agnostic implementation of the A2A protocol. The library is designed to be used with any agentic framework, and is not exclusive to PydanticAI.

ByteDance has also released Seed1.5 VLM, a vision-language foundation model focused on improving multimodal understanding and reasoning capabilities. With just 532 million parameter visual encoder and an MoE language model with 20B active parameters, Seed1.5-VL excels in vision and video understanding tasks. The model outperforms multimodal agentic systems like OpenAI CUA and Claude 3.7 Sonnet, in GUI control and gameplay. You can try the demo on HF Spaces.

Manus AI agent is now available for free to try without a waitlist! Once you sign up, you'll get a one-time bonus of 1,000 credits and one free daily task for all users (300 credits).

Tools of the Trade

1. Prism: AI that watches session replays and tells developers what’s broken, who it affects, and how to fix it. It uses vision and language models to analyze user behavior, summarize pain points, and generate clear, actionable insights.

2. Willow: A voice dictation tool for Mac that lets you type anywhere on your computer using your voice, with fast and accurate transcription. It looks at what you’re working on so it can get technical terms, names, and phrases right.

3. BuildShip: Vibe code and export MCP-compatible tools for AI agents using simple prompts and a no-code canvas backed by AI-generated logic. You can self-host the tool with full code access or deploy on the cloud, ready to plug into agent frameworks like OpenAI, LangChain, or LlamaIndex.

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

Hot Takes

1. Engineering isn’t replaced by an LLM turning English into bits of code, and code isn’t engineering, nor is it technology.

   There are too many internet thought leaders who have long since or never shipped software proclaiming to know how LLMs will impact engineering. ~
   David Cramer
   
   

2. The entire tech community is under the impression that AI coding will result in power flowing from engineers to “idea guys.”

   Wrong—it will always flow to whatever still has scarcity: those who know how to get distribution ~
   Nikita Bier

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

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