Dedalus Labs recently launched!

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^{"Build and deploy complex AI agents with any model and any tool in just 5 lines of code. No Docker files. No YAMLs. No wasted weeks."}

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^{TL;DR: The founders are excited to launch Dedalus Labs, an AI-native cloud platform for developers to build agentic AI applications. Their SDK allows you to connect any LLM to any MCP servers – local or remotely hosted by Dedalus Labs. No Docker files or YAML configs required.}

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Blog

https://dedaluslabs.ai/blog/dedalus-seed-round

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Product Demos

• https://www.youtube.com/watch?v=s2khf1Monho‍‍
  

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• https://www.youtube.com/watch?v=4B1xuRCgLgA

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Founded by Catherine Di & Windsor Nguyen‍‍

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The Founders Story: Two Princeton CS Students with High Standards

Meet Cathy (ex-Voyage AI / Salesforce) and Windsor (ex-DeepMind / Sentient AGI).

They first ran into this problem while working with MCP during Windsor’s time at Sentient AGI. Every solution felt wrong: drag-and-drop GUIs, brittle configs, frameworks that broke under real workloads. Nothing met their standards for what good developer infrastructure should be.

So the founders built what they always wanted—a developer-first platform that makes agentic workflows composable, scalable, and effortless to ship.

If no one else is going to build it right, Dedalus Labs will.

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The Problem: Building Agents Is Hard

Building tool-using agents ≠ calling v1/chat/completions.

Today, developers are still stuck with:

• Running their MCP servers locally.
• If deploying remotely, wiring up AWS and config hell.
• Juggling model quirks, streaming issues, and auth across GPT-4o, Claude 3.5, Sonnet, Flash, etc. (♾️ API keys).
• Rebuilding everything just to add a new tool.

The Dedalus Labs founders were tired of writing hundreds of lines of code and battling Docker files and infra—all for a spaghetti agent that breaks under stress.

So, they built the tool they wished existed.

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What Dedalus Labs Offers: Complex Agents in 5 Lines of Code

Dedalus invented the simplest way to build and deploy agents—a drop-in infrastructure layer that unifies models, tools, and orchestration.

Their open-source SDK supports:

• Vendor-agnostic model handoffs.
• Chaining both local tools and hosted MCPs.
• Real-time streaming across any provider.

—all in 5 lines of code.

And their managed infra lets you deploy an MCP server in 3 clicks, so anyone can equip their agent with tools. No Docker files, no setup.

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Why MCP: Tools Made for Agents

MCP (Model Context Protocol) is becoming the standard for how models talk to tools. Think of it as an API that AI models already know how to call—reliable, predictable, and language-agnostic.

As more companies adopt MCP, exposing your product as an MCP server means you’re not just serving humans anymore—you’re serving agents.

At Dedalus Labs, they’re building the infrastructure that powers that shift.

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Learn More

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^{🌐 Visit www.dedaluslabs.ai to learn more.}

^{❤️ Dedalus Labs just launched and announced their seed round on LinkedIn & X. Please like, comment, share via DM, and repost!} 

^{💥 Get Started + Early YC Deal:}

^{• Quick install: pip install dedalus-labs}

^{• Docs: docs.dedaluslabs.ai}

^{• YC Deal: we’re giving $200 free credits to every YC company. Email the founders here to claim your deal!}

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^{👣 Follow  Dedalus Labs on LinkedIn & X.}