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Modular – drop AI features into your app with two function calls

Modular – drop AI features into your app with two function calls

by modular_dev·Apr 20, 2026·7 points·1 comment

AI Analysis

MidShip It

Yet another AI plumbing layer — LangChain and Vercel AI SDK already exist.

Strengths
  • MCP-native from day one is a timely integration choice
  • Two-method API surface (run/chat) is genuinely minimal
  • Model routing between Claude, GPT-4o, Gemini without code changes
Weaknesses
  • Waitlist only — no working product to evaluate yet
  • AI middleware category is extremely crowded with established players
Category
Target Audience

Startup engineers shipping AI features without ML teams

Similar To

LangChain · Vercel AI SDK · LlamaIndex

Post Description

I kept hitting the same wall at work every time we needed to ship an AI feature. What looked like a week of work turned into picking a model, setting up a vector DB, managing embeddings, wiring up chat history, handling retries — none of it was the actual feature. So I built Modular. You register a function that returns your app's data, then call ai.run() for one-shot features or ai.chat() for stateful conversation. Everything else — context management, embeddings, session history, model routing, retries — is handled. MCP-native from day one. Works with Claude, GPT-4o, and Gemini. Still early — collecting feedback before building the full SDK. Would love to hear if others have hit this same wall, or if you think I'm solving the wrong problem.

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