Opensource Alternative to Manus AI

Today’s top AI Highlights:

1. Open local Manus AI alternative — No APIs, no $200 monthly bills

2. Code-first agent orchestration framework with visual workflows

3. OpenAI released lightweight Deep Research for Free users

4. Anthropic aims to create an “MRI for AI” by 2027

5. 5 tools to convert APIs to MCP servers

& so much more!

Read time: 3 mins

AI Tutorial

Financial management is a deeply personal and context-sensitive domain where one-size-fits-all AI solutions fall short. Building truly helpful AI financial advisors requires understanding the interplay between budgeting, saving, and debt management as interconnected rather than isolated concerns.

A multi-agent system provides the perfect architecture for this approach, allowing us to craft specialized agents that collaborate rather than operate in silos, mirroring how human financial advisors actually work.

In this tutorial, we'll build a Multi-Agent Personal Financial Coach application using Google’s newly released Agent Development Kit (ADK) and the Gemini model. Our application will feature specialized agents for budget analysis, savings strategies, and debt reduction, working together to provide comprehensive financial advice. The system will offer actionable recommendations with interactive visualizations.

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 100% Local and Free Alternative to Manus AI 🔓 🖥️ 🌐

Manus AI kicked off an entire ecosystem of general AI agents capable of turning thoughts into actions. It's impressive and powerful, but with a $200 monthly subscription and closed-source code, not everyone can access or customize this technology.

AgenticSeek offers a completely local alternative to Manus AI that runs entirely on your hardware. This voice-enabled AI agent can browse the web, write code, and plan complex tasks autonomously while keeping all your data private. Built specifically for local reasoning models like DeepSeek R1, it eliminates cloud dependencies and subscription costs - you only pay for the electricity to run it.

Key Highlights:

1. Complete Privacy & Local Operation - Everything stays on your machine with no data leaving your system. Your files, conversations, and searches remain completely private with zero cloud dependency, giving you full control over your AI assistant.

2. Autonomous Web Browsing - AgenticSeek navigates the internet independently to search for information, read content, extract data, and even fill out web forms - all without requiring constant guidance or supervision.

3. Code Writing & Execution - Need development help? The assistant writes, debugs, and runs programs in Python, C, Go, Java and other languages without supervision. It handles the technical work while you focus on the bigger picture.

4. Intelligent Task Management - The system automatically selects the best specialized agent for each task and can break complex projects into manageable steps. It coordinates multiple AI agents to execute multi-stage workflows efficiently.

5. Model Integration - AgenticSeek works with various local providers including Ollama, LM Studio, and local OpenAI-compatible APIs. You can run it on different hardware setups - from a decent GPU (14B models need at least 12GB VRAM) to more powerful rigs for larger models.

Structured Workflow Framework for Agent Orchestration 🤖🔀🛠️

There are 100s of AI agent frameworks today, but we face a frustrating choice: either use UI tools that look great but hit walls when building complex systems, or dive into code libraries buried under layers of abstractions that obscure what's actually happening. Neither option delivers the seamless development experience today's AI engineers need.

Grapheteria is a workflow framework that bridges this divide by offering both visual clarity and code-first flexibility. It lets you build agent orchestration systems where your code and UI stay perfectly synchronized - edit in the editor and watch your workflow diagram update instantly, or drag nodes in the UI and see your code reflect the changes. With a simple three-phase execution model and time-travel debugging, Grapheteria keeps your workflow logic clean and maintainable.

Key Highlights:

1. Clean, Flexible API - Build complex agent workflows with minimal code that follows a clear node structure. Each node is responsible for a specific task through a three-phase execution model (prepare, execute, cleanup) that prevents state corruption and improves maintainability. Edges between nodes use simple Python expressions for dynamic routing based on your workflow's shared state.

2. Visual Debugging - The built-in UI provides real-time visualization of your workflow as you code. When something goes wrong, the time-travel debugger lets you step backward to fix issues and continue without losing your place.

3. Production Readiness - Grapheteria comes with features essential for deploying to production - detailed logging, automatic state saving, and error handling. The framework includes a built-in web server, so you can containerize your workflow and trigger it via HTTP requests without additional infrastructure.

4. Ecosystem Integration - Works seamlessly with recent AI protocols like Model Context Protocol (MCP) and Agent2Agent (A2A), letting you build interoperable agent systems that can share context with models and communicate with other agents.

Quick Bites

China's Moonshot AI is on a roll 🔥 They just open-sourced Kimi-Audio, a new foundation model built for audio understanding, generation, and conversation. Trained on over 13 million hours of speech, music, and sounds, it features a hybrid tokenizer, a lightweight LLM backbone, and parallel heads for audio and text. It beats SOTA on 10+ audio benchmarks and comes with a full evaluation toolkit — code, models, and tools are all live on GitHub under permissive licenses.

Lovable has released 2.0 version with a brand new UI, Chat Agent mode, Dev mode, multi-player collaboration, and more features. The new agentic Chat Mode reasons across multiple steps and is perfect for asking questions, planning your project, and debugging, without making any edits. A robust Dev Mode lets you directly edit code in Lovable without prompting the agent to do it. There’s also a more robust Visual Editing feature to directly click on a UI component and make edits.

Anthropic CEO Dario Amodei penned an essay titled "The Urgency of Interpretability," emphasizing that researchers still know surprisingly little about how the world's most powerful models actually work. He says, “These systems will be absolutely central to the economy, technology, and national security, and will be capable of so much autonomy that I consider it basically unacceptable for humanity to be totally ignorant of how they work.” He has set an ambitious goal for the company to develop reliable AI model problem detection by 2027.

OpenAI has released a lightweight version of their Deep Research tool to serve more ChatGPT users, including Free users. This lightweight version is powered by a version of o4-mini and is nearly as intelligent as the Deep Research available till now. Responses will typically be shorter while maintaining the depth and quality. Once limits for the original version of Deep Research are reached, queries automatically default to the lightweight version.

Tools of the Trade

5 tools to convert APIs to MCP servers:

1. FastAPI-MCP: Exposes your FastAPI endpoints as MCP servers in one line of code with native auth support. It preserves all your schemas and dependencies. 100% opensource.

2. RapidMCP: Transforms your REST API into an MCP server in minutes, with zero code changes. Simply plug in your API, and convert it to an AI-agent-ready MCP server - no backend modifications needed.

3. MCPify: Build and deploy MCP servers without writing a single line of code. It is like Lovable/Bolt/V0 for building MCP servers. Supports MCP's Streamable HTTP transport. You can also share the MCP server you've built with others on the same platform.

4. Speakeasy: Generate MCP servers directly from OpenAPI docs with minimal code. It generates TypeScript MCP servers with customizable tool descriptions and scopes.

5. Higress by Alibaba: Transform your OpenAPI specs into MCP servers with one command. Higress's openapi-to-mcp tool automatically converts API documentation into servers with detailed response templates. Deploy with zero infrastructure. 100% opensource.

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. i'm genuinely sorry for vibe coders who don't understand the generated code. it must be a nightmare ~
   kitze
   
   

2. It’s always a good time to learn coding. Don’t allow anyone to convince you otherwise. ~
   elvis
   
   

3. Gemini has 350,000,000 users and Google hasn't integrated it with Search, Chrome, and Android yet. ~
   Andriy Burkov

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

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