Creating Avolve.io: A Knowledge Graph for Modern Development

TL;DR: Avolve.io is a knowledge graph that solves the compatibility hell developers face when choosing framework versions. Built with Next.js 15, React 19, and graph database architecture, it provides verified patterns instead of guesswork.

The Frustration That Started Everything

Every developer has experienced this:

You want to build with modern tools. You start a new project. You check the documentation. Next.js says version X. React says version Y. TypeScript has version Z. Vercel AI SDK mentions version A. Supabase recommends version B.

Do they all work together? Nobody knows.

You install what seems right. Build breaks. Version conflict. You try different combinations. Something else breaks. You spend 3 hours debugging version mismatches instead of building your actual product.

There has to be a better way.

The Core Problem

Documentation tells you how individual tools work. It doesn't tell you what combinations actually work together.

Package managers resolve dependencies. But they can't tell you that React 19.1 + Next.js 15.5 + Vercel AI SDK 5.0 is a tested, working combination for AI applications.

Developers end up:

• Guessing based on dates ("latest should work together, right?")
• Wasting time on trial-and-error version combinations
• Getting stuck on subtle incompatibilities that don't show up until production
• Reverting to older stacks because they're "safe"

We're flying blind when we should have maps.

My Solution: Verified Compatibility Patterns

Avolve.io is a knowledge graph that maintains verified compatibility patterns for the modern web stack.

Instead of documentation that says "Next.js 15 works with React," it tells you:

"Next.js 15.5.3 + React 19.2.0 + TypeScript 5.9.3 + Vercel AI SDK 5.0.48 + Supabase 2.57.0 + Tailwind CSS 4.1.14 is a verified working combination as of October 2025."

Specific versions. Tested combinations. Verified patterns.

Why a Knowledge Graph?

I could have built a simple lookup table. But that misses the deeper problem.

Framework compatibility isn't just version matching. It's about relationships:

• Next.js depends on React (specific versions)
• Vercel AI SDK integrates with Next.js (specific patterns)
• Supabase auth requires server-side handling (specific Next.js features)
• Tailwind v4 needs different configuration than v3 (breaking changes)

These relationships form a graph, not a list. A knowledge graph is the natural structure.

Graph vs. Traditional Database

Traditional database approach:

Framework: Next.js
Version: 15.5
Compatible with: React 19, TypeScript 5.9

Simple, but shallow. It doesn't capture why they're compatible, how they integrate, or what patterns work.

Knowledge graph approach:

Next.js 15.5
  ├─ requires React 19.x (peer dependency)
  ├─ works with TypeScript 5.9+ (tested pattern)
  ├─ integrates with Vercel AI SDK 5.0 (streaming support)
  └─ optimizes Tailwind v4 (built-in support)

Each edge contains:
  - Relationship type
  - Integration pattern
  - Verified date
  - Known issues
  - Best practices

This captures the context developers actually need.

Technical Architecture

Core Stack (Eating My Own Dog Food)

Avolve.io is built with the exact stack it documents:

• Next.js 15.5 - App Router with Server Components
• React 19.2 - Latest stable with RSC
• TypeScript 5.9 - Full type safety
• Graph Database - For relationship modeling
• Vercel AI SDK - For AI-powered recommendations
• Tailwind CSS v4 - With Oxide engine

Why this matters: I'm not just documenting these tools. I'm using them in production. Every pattern in Avolve.io has been tested in Avolve.io itself.

The Data Model

Each node in the graph represents an entity:

• Framework (Next.js, React, etc.)
• Version (15.5, 19.2, etc.)
• Feature (Server Components, Streaming, etc.)
• Pattern (Auth flow, API routes, etc.)

Edges represent relationships:

• Dependencies (requires, works with)
• Integrations (connects to, extends)
• Patterns (best practice, anti-pattern)
• Status (verified, tested, experimental)

Verification Pipeline

This is what makes Avolve.io different from "community wisdom."

1. Automated testing - Real projects built with each combination
2. Build verification - Confirm it compiles and runs
3. Type checking - Ensure no type errors
4. Runtime testing - Verify features work as expected
5. Date stamping - Track when verification occurred

If a combination breaks, the graph updates. Users see current status, not outdated advice.

What Developers Get

Instead of guessing:

"I think Next.js 15 probably works with React 19..."

You get verified patterns:

"Next.js 15.5+ works with React 19.2.0. Tested October 2025. Known working patterns: Server Components, Streaming, PPR. Known issues: None. Migration notes: Update dependencies, check React Compiler config."

Instead of trial-and-error:

"Let me try different TypeScript versions until something works..."

You get tested combinations:

"TypeScript 5.9.3 is the verified version for this stack. Earlier versions miss Project Corsa features. Later versions may have compatibility issues."

Instead of scattered docs:

"Check Next.js docs, React docs, AI SDK docs, Supabase docs, hope they're all current..."

You get integrated knowledge:

"Here's the complete October 2025 stack with verified integration patterns. Server-side Supabase auth works with Next.js middleware. AI SDK streaming integrates with React Server Components. Full code examples included."

Why This Matters for AI-Native Development

We're in a transition moment. AI isn't just a feature anymore — it's a core part of the stack.

But AI integration patterns change fast:

• Vercel AI SDK adds new capabilities monthly
• LLM providers update APIs constantly
• Integration patterns evolve rapidly

Avolve.io tracks these changes. When OpenAI updates their API, when Anthropic releases Claude 3.7, when Vercel ships AI SDK 5.1 — the knowledge graph updates.

Developers get current, verified patterns instead of outdated blog posts.

Current Focus

Right now, I'm working on:

1. Expanding Coverage

• More frameworks (Vue, Svelte, Angular)
• More integrations (databases, auth, payments)
• More patterns (deployment, testing, monitoring)

2. AI-Powered Recommendations

Using LLMs to provide:

• "Best stack for your use case"
• "Migration path from your current versions"
• "Breaking changes to watch for"

3. Automated Updates

Building pipelines that:

• Monitor new releases
• Test compatibility automatically
• Update the graph in real-time

The Long-Term Vision

Phase 1 (Current): Verified compatibility for major frameworks

Phase 2 (Next 3-6 months): AI-powered recommendations and migration guidance

Phase 3 (6-12 months): Automated compatibility testing as a service — test your stack against new releases before you upgrade

Phase 4 (Beyond): Community-contributed patterns with verification — developers share what works, automated systems verify it

Key Architectural Decisions

Why Next.js?

Server Components let me fetch graph data server-side, stream it to the client, and provide instant UI updates. Perfect for knowledge exploration.

Why Graph Database?

Relationships are first-class entities. Querying "what integrates with X" is natural, not a complex JOIN.

Why Vercel AI SDK?

The future of Avolve.io includes AI-powered recommendations. Building on the AI SDK from day one means those features integrate naturally.

Why Open Knowledge?

Proprietary compatibility databases help vendors, not developers. Open knowledge (free to access, verified by testing) helps the community.

Lessons Learned

1. Documentation Lag is Universal

Even well-maintained docs lag reality by weeks or months. Automated verification catches this.

2. Community Knowledge is Scattered

Reddit threads, GitHub issues, blog posts — the knowledge exists but it's fragmented. A knowledge graph unifies it.

3. Developers Need Confidence

"Probably works" isn't good enough when you're building production apps. Verified patterns provide confidence.

Key Takeaways

• Framework compatibility is a graph problem, not a list problem
• Verification through testing beats community opinions
• Knowledge graphs capture context that traditional databases miss
• AI-native development needs AI-aware compatibility tracking
• Open, verified knowledge serves developers better than proprietary advice

Try It Yourself

Explore the knowledge graph: avolve.io

See the patterns I'm using: This site is built with the exact October 2025 stack documented in Avolve.io

Connect: Have compatibility patterns to share? Frameworks you'd like covered? Let me know.

Developers shouldn't waste time guessing which versions work together. With verified compatibility patterns and knowledge graph architecture, we can build faster and break less.

Technical Appendix: Sample Graph Query

For the technically curious, here's what a knowledge graph query looks like in Avolve.io:

query GetVerifiedStack {
  framework(name: "Next.js", version: "15.5") {
    requires {
      framework
      version
      relationship
    }
    integrates {
      framework
      pattern
      verified
      lastTested
    }
    knownIssues {
      description
      severity
      workaround
    }
  }
}

This single query returns:

• Required dependencies
• Verified integrations
• Known issues
• Last verification date

Try building that with traditional SQL. Good luck.