Gavriel Cohen didn’t set out to build a company. He was a former Wix engineer with a side project — a small, obscure GitHub package for personal use. Then one day, while browsing OpenClaw’s 400,000-to-800,000-line codebase, he found it: fragments of his own code, embedded without attribution, without consent, without a note.

That discovery set off a chain of events that led to NanoClaw, a $12M seed raise, a Docker partnership, and — most importantly for the broader AI agent ecosystem — a hard conversation about what accountability actually means when codebases grow faster than humans can audit them.

The Discovery

Cohen went public with the finding, describing how he spotted his own functions, code structure, and comments inside OpenClaw’s sprawling codebase. The code came from a personal repository that wasn’t widely known. It had been incorporated — whether by a human contributor or an AI code generation tool — without any attribution or communication.

The incident joined a longer list of concerns Cohen had accumulated about OpenClaw’s approach. A privacy-leaking log bug had previously exposed WhatsApp message contents. The codebase had grown so large, with so many small and unmaintained dependencies, that meaningful security review had become functionally impossible. When you can’t audit a system, you can’t trust it.

“OpenClaw is a Frankenstein,” Cohen reportedly told outlets covering the story. “It’s built fast and it’s built big, but no one really knows what’s in it.”

The NanoClaw Response

Rather than file a complaint or wait for attribution, Cohen rebuilt from scratch. NanoClaw is his answer: approximately 500 lines of core code, MIT-licensed, designed to do what OpenClaw does — AI agent execution with tool use and external integrations — but with security as the first-order constraint, not an afterthought.

The architectural choices are deliberate:

  • Containerized from day one — initially Apple containers, now Docker — so agent actions run in hard isolation from the host system
  • Minimal dependency surface — fewer packages means fewer attack vectors and a codebase small enough to actually read
  • Credential proxying — API keys never reach the agent process directly; a proxy layer intercepts and forwards them
  • Human-in-the-loop approvals — configurable gates before the agent takes consequential actions

NanoClaw hit Hacker News #1, earned thousands of GitHub stars, and attracted enterprise interest from executives at major tech firms who were quietly uncomfortable with the security posture of existing agent frameworks.

$12M, Docker, and What It Signals

NanoCo — the company Cohen and his brother Lazer built around NanoClaw — closed a $12M seed round in May 2026, led by Valley Capital Partners with participation from Docker, Vercel, monday.com, and Slow Ventures. Angels included Clem Delangue (Hugging Face co-founder). They reportedly turned down a ~$20M acquisition offer to stay independent.

The Docker partnership is particularly pointed. Docker built the modern container ecosystem for application deployment. That they see NanoClaw as the right model for secure agent execution is a meaningful signal about where the industry is heading — away from monolithic, fast-moving frameworks and toward auditable, containerized, minimal-footprint agents.

The Harder Question: AI-Generated Codebases

The Cohen incident isn’t primarily a story about one developer’s code being borrowed. It’s a story about what happens when AI tools generate code at a pace that outstrips human review.

OpenClaw, like many fast-moving open-source projects, uses AI-assisted development extensively. That’s not inherently problematic — but it creates a specific accountability gap. When an AI model suggests or generates code, it may draw on training data that includes proprietary or attributed-but-not-licensed code. The human committing that code may not recognize the source. The result lands in a codebase with hundreds of contributors and no one who can say with certainty where a given function came from.

This is a structural problem, not a bad-actor problem. OpenClaw’s maintainers likely didn’t knowingly take Cohen’s code. But “didn’t know” isn’t the same as “didn’t happen” — and as codebases grow to hundreds of thousands of lines with AI assistance, the gap between “didn’t know” and “can’t know” starts to close.

The industry doesn’t have great tools for this yet. Code provenance tracking, AI-generated content flagging, automated attribution checks — these are all early-stage or non-existent at the pace modern frameworks move.

What This Means for OpenClaw Users

If you’re running OpenClaw in production, this story is worth sitting with. Not because OpenClaw is necessarily malicious, but because the accountability question it raises applies to your stack broadly. Can you audit what’s running in your agent? Do you know where its dependencies came from? If an AI-generated function has a subtle bug or a security flaw inherited from its training source, would you find it?

NanoClaw is one answer to that question: stay small, stay auditable, stay isolated. It’s not the only answer — OpenClaw has made real security investments, and its scale enables capabilities that a 500-line framework can’t match.

But Cohen’s story is a useful forcing function. The best time to think about code provenance and codebase accountability is before you need to.

Sources

  1. OpenClaw Code Reuse Story — The New Stack
  2. NanoClaw Creator Turns Down $20M Buyout, Raises $12M Seed — TechCrunch
  3. NanoClaw vs. OpenClaw Security — The New Stack
  4. Cohen Brothers Raised Millions for OpenClaw Competitor — Business Insider
  5. First Claw Company to Raise Funding — Fortune

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260606-2000

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