Muscle Memory for AI Agents

Today’s top AI Highlights:

1. LangChain’s opensource no-code platform to build and deploy agents with RAG and MCP

2. A cache for AI agents to learn and replay complex behaviors.

3. Vibe check your code for free within your IDE

4. Google DeepMind’s AI agent solved 300-year-old math problems

5. Free, local opensource alternative to v0, Lovable, or Bolt.new

& 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

A Behavior Cache for AI Agents 🤖🧠📂

Why should your AI agent waste tokens re-solving a task it already nailed yesterday? Muscle-Mem is an opensource caching layer for AI agents that records their tool-calling patterns while solving a task, and replays them when the same task comes up again. It cuts out LLM calls on repeat runs, reducing cost and latency, while still allowing agents to handle edge cases dynamically. Think of it like a JIT compiler - not for code, but for agent behaviors.

Muscle-Mem lets you wrap tools with simple decorators and define validation checks to ensure cached actions are still safe to run. This framework intelligently determines when to use fast, script-like execution for routine tasks and when to fall back to full agent capabilities for edge cases, eliminating unnecessary LLM calls and their associated costs and latency.

Key Highlights:

1. Hybrid execution model - Muscle-Mem combines the speed of scripted automation with the flexibility of AI agents, taking a one-time efficiency hit during initial task discovery but then storing behaviors as deterministic code for later reuse. This solves the classic automation dilemma where scripts break on edge cases but pure-agent approaches are prohibitively expensive.

2. Framework-agnostic design - The SDK integrates with your existing agent setup rather than replacing it, sitting one layer below your agent and treating it as a black box. This means minimal changes to your workflow - a call to engine(task) feels identical to agent(task) with all the caching benefits built in.

3. Sophisticated environment tracking - Instead of blindly repeating cached actions, Muscle-Mem captures data about the environment in which actions were taken, then validates whether the current context matches before executing. This prevents automation failures when conditions change.

4. Extensive customization - The system provides a straightforward API with Engines, Tools, and Checks that let you define exactly how environment capture and comparison should work for your specific use case, without any "automagical" hidden behavior or opinionated defaults.

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Opensource No-Code Agent Building Platform 🧠🛠️🧩

LangChain has released Open Agent Platform (OAP), an opensource, no-code interface to build and deploy AI agents. This platform enables you to build agents on top of LangGraph, connect them to various tools via MCP, integrate RAG capabilities with LangConnect, and even build multi-agent systems with supervisor agents.

OAP runs entirely in the browser and doesn’t require a separate backend to get started. With support for Supabase auth and streamable MCP servers, it makes deploying AI agents that interact with real-world tools much more straightforward.

Key Highlights:

1. No-code agent development - The platform offers a visual interface where non-developers can build highly customizable agents without writing code. Users can connect to tools, data sources, and other agents through an intuitive UI, making advanced AI agent creation accessible to citizen developers while still giving technical users the flexibility they need.

2. Integration ecosystem - Connect your agents to MCP tools for accessing external services, LangConnect for RAG capabilities, and other LangGraph agents through a supervisor architecture. The platform handles authentication and communication between these components, simplifying what would normally require complex integration work.

3. Custom agent configuration - Build your own agents with configurable fields for model parameters, system prompts, tool access, and RAG settings. The platform supports various UI components for agent configuration including text inputs, sliders, dropdowns, and JSON editors, giving users precise control over agent behavior.

4. Deployment and auth - Deploy your agents to LangGraph Platform with built-in authentication through Supabase, secure API communication, and proper token management. The platform includes proxy routes for authenticated communication with MCP and RAG servers, keeping sensitive credentials secure and simplifying deployment.

Quick Bites

CodeRabbit’s AI code reviews are now available for free inside VS Code, Cursor, and Windsurf. This brings fast, inline feedback right into your editor, covering committed and uncommitted changes, spotting bugs early, and helping you raise cleaner PRs with less back-and-forth. It works across major languages, supports one-click fixes, and fits right into your workflow without needing to leave the IDE.

Alibaba’s WAN team has released Wan2.1-VACE, an open-source, all-in-one video creation and editing model under Apache 2.0 license. It supports text-to-video, image-to-video, video editing, and even video-audio generation tasks, packed into a single unified model that handles multimodal inputs and complex scenes with consistent performance. Wan2.1 comes in two sizes (1.3B and 14B), with the smaller model running on consumer GPUs (8.19 GB VRAM). The 14B variant delivers high-res 720p output, precise visual text rendering, and outperforms several top closed-source models across benchmarks.

Coding is still the most common use of LLMs, but the next big shift in coding with LLMs isn't autocomplete — it’s algorithm discovery. On this path, Google DeepMind has released an evolutionary coding agent, AlphaEvolve, for general-purpose algorithm discovery and optimization. It can go beyond single-function discovery to evolve entire codebases and develop much more complex algorithms. The agent leverages fast LLM like Gemini Flash for idea breadth, and Gemini Pro for deeper reasoning, paired with automated evaluators to verify and improve code over time.

AlphaEvolve has already been used to boost efficiency in Google’s data centers, chip design, and Gemini model training. An early access program is planned for selected academic users, and broader availability is in the works.

After a gazillion requests from users to release GPT 4.1 in ChatGPT, OpenAI has finally started rolling out GPT 4.1 and GPT 4.1-mini in the ChatGPT desktop app and web. The models were released a month ago via API only, with significant improvements over GPT-4o in coding and instruction following.

Tools of the Trade

1. Superexpert.AI: Opensource, no-code platform to create multi-task AI agents directly from a web interface, each with its own models, tools, and instructions. You can extend everything using TypeScript, add your own tools, use built-in RAG, and deploy the full chat app like any regular Next.js project.

2. Dyad: A free, local opensource AI app builder as an alternative to v0, Lovable, or Bolt.new. It lets you build and test full-stack AI apps with auth, database, and server functions, using your preferred models, tools, and IDE.

3. Void: An opensource alternative to Cursor, built on VS Code, that works with any LLM, giving you full control over your models, prompts, and data. It supports autocomplete, inline edits, agent-based coding, and direct model connections.

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. Me: "You don't need a developer to build this"

   The Internet: "Stupid! Developers aren't dead!"

   Me: "AI can't build this by itself"

   The Internet: "Stupid! AI will replace everyone!"

   You have to pick a side if you want to make people happy. Centrism is dead. ~
   Santiago
   
   

2. The more time you spend with AI the more you realize prompt engineering isn’t going away any time soon. For most knowledge work, there’s a very wide variance of what you can get out of AI by better understanding how you prompt it. This actually is a 21st century skill. ~
   Aaron Levie

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

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