Frequently Asked Questions

Rotifer Protocol is a universal evolution framework for AI agents. Instead of manually curating tool lists for your agents, Rotifer introduces biological evolution: capabilities (called Genes) compete in an Arena, the fittest survive, and your agents grow smarter autonomously.

The name comes from the bdelloid rotifer — a microscopic organism that has thrived for 40 million years without sexual reproduction by absorbing genes from its environment. Rotifer Protocol applies the same principle to software.

Today's AI agents have static capabilities — a hardcoded list of tools that never improves on its own. If Tool A is terrible and Tool B is great, the agent has no way to know. There's no competition, no selection, no evolution.

Rotifer fixes this by introducing:

• Arena competition — same-domain Genes fight head-to-head on fitness metrics
• Natural selection — the fittest Genes survive, the rest are eliminated
• Autonomous improvement — agents automatically upgrade their genome over time

Relationship: Complementary — MCP standardizes how agents call tools; Rotifer standardizes how tools evolve.

Synergy: MCP Tools can be upgraded to Rotifer Genes with one command (rotifer wrap --from-skill), gaining quality ranking, security scoring, and autonomous evolution. MCP is Rotifer's most important upstream ecosystem.

Relationship: Direct competitor — both build an "evolution layer" on top of MCP/Skills, but with fundamentally different definitions of what "evolution" means.

Core architectural distinction: EvoMap treats Genes as strategy documents — standardized records of how a problem was solved, shared for other agents to replicate. Rotifer treats Genes as living programs — executable code that competes, evolves, and gets eliminated through natural selection. The same word "Gene", two entirely different things.

Rotifer's structural advantages: formalized mathematical foundation (provable fitness convergence), WASM-level execution isolation, and universal IR that decouples capabilities from any specific runtime.

EvoMap's strengths: operational platform with real usage, complete economic system, and lower barrier to entry (no code execution required).

Relationship: Complementary — these are orchestration frameworks (how multiple agents collaborate); Rotifer is a capability protocol (how individual capabilities evolve).

Synergy: These frameworks can serve as upstream users — orchestrate multiple agents with AutoGen/CrewAI, while each agent's capabilities are powered by Rotifer Genes that evolve autonomously.

Relationship: Orthogonal — A2A handles agent-to-agent communication; Rotifer handles capability evolution. They operate at entirely different protocol layers.

Synergy: Agents on the A2A network can use Rotifer Genes as their capability modules. Zero overlap, full compatibility.

A Gene is the atomic capability unit in Rotifer. Think of it as a "living plugin" that can:

• Compete in the Arena against other Genes in the same domain
• Evolve through mutation and selection pressure
• Propagate to other agents via horizontal gene transfer
• Be eliminated when its fitness declines

Every Gene has a typed interface (inputSchema / outputSchema), a unique identity (content hash), and runs inside a WASM sandbox.

Similarities: Both are modular, composable, reusable AI agent capability units. Both define input/output schemas.

Key differences:

In short: Skill is the degenerate special case of Gene — turn off all "life characteristics" and you get a Skill.

The Arena is where Genes compete. Same-domain Genes are evaluated on fitness metrics (correctness, latency, efficiency, diversity). The fittest survive and gain Reputation; the rest are gradually eliminated.

It's biological natural selection — applied to software capabilities.

Native Genes have an executable express() function — they can actually run computations (grammar checking, SEO analysis, etc.).

Wrapped Genes contain only metadata and descriptions — they serve as AI prompt templates for tasks that require LLM reasoning (creative writing, architecture design).

Both participate in the ecosystem, but only Native Genes can compete in full Arena fitness evaluations.

Decision framework — if 3+ are "yes", go Native:

High-value Native domains (far beyond current catalog):

Keep as Wrapped (these rely on LLM reasoning, not deterministic algorithms):

• Creative writing, brainstorming, architecture design, product management, strategic planning
• Anything where "good" is subjective and human judgment defines quality

No. Migration is a progressive enhancement, not a rewrite:

• Your core business logic stays the same
• Your inputSchema / outputSchema maps directly to Gene Phenotype
• Your Workflow / Chain maps to Genome DataFlowGraph

The CLI provides automated tooling: rotifer scan assesses feasibility, rotifer wrap --from-skill auto-converts in one command.

Option A — Terminal:

npm install -g @rotifer/playground
rotifer init my-project
cd my-project
rotifer scan --skills

Option B — Paste this into your AI IDE:

See the Getting Started guide for a full walkthrough.

Each card contains a ready-to-paste prompt. Copy it, paste into your IDE's chat, and the AI handles the rest. Replace grammar-checker with any Gene from the catalog.

International IDEs

China IDEs

Yes. The engine is Apache 2.0 + Rotifer Safety Clause, the specification is CC BY-SA 4.0.

• Playground (CLI + Runtime)
• Protocol Specification
• Papers & Whitepapers

v0.5.0-alpha.2 — includes the Reputation System, P2P Network Foundation, Cloud Registry, and Skill Import. See the Roadmap for what's next.