dif.sh vs Doop: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of dif.sh and Doop — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
dif.sh
Dif
Feature flags and A/B tests defined as markdown files in your repo, with a generated context file coding agents read on session start.
Key features
- Markdown-Defined Experiments: One .md file per flag or test holds id, status, owner, surface, hypothesis, audience, variants, metrics and guardrails in frontmatter, with the brief and rationale written below it.
- Agent Context File: Every dif build regenerates dif/context.json listing active flags, experiments, variants and recent learnings, which coding agents pick up on session start.
- Build-Time Conflict Detection: The build resolves an exclusion graph and refuses to compile when two live tests would bucket one user into both, so clashes break in CI instead of production.
- One Shape for Four Concepts: Feature flags, A/B tests, holdouts and staged rollouts are the same file format with different frontmatter, reducing the number of concepts and failure modes.
- Generated Typed Client: dif build emits a small typed client you import once at boot, then call per flag with control and variant branches — supported for web server, React and Svelte.
- Privacy-Preserving Targeting: Audience attributes such as country, plan or returning_visitor are declared in config.yaml while values arrive at runtime from your app's user context, so no customer data is committed.
- Structured Conclusion Workflow: dif conclude archives a finished experiment, drafts its Decision block and appends a line to the surface log so the next test on that screen starts informed.
- Flexible Result Routing: Add a Dif Cloud key and dif.track() computes lift with no join code, or run dif init --events custom to own the handlers that forward events to Segment, Amplitude or your warehouse.
Best for
- Keeping Experiments in Code Review: Ship flag and experiment changes through the same pull request flow as the code they gate, using git history as the audit trail.
- Giving Coding Agents Experiment Context: Let an AI coding agent see which experiments are live on a surface and what prior tests taught before it edits that screen.
- Preventing Overlapping Tests: Use exclusion groups to guarantee no user is bucketed into two conflicting experiments, caught at build time rather than discovered in the results.
- Running Staged Rollouts: Ramp a new feature to a growing share of traffic using the same file format as an A/B test, without learning a separate rollout tool.
- Instrumenting Without a Vendor Lock-In: Forward exposure and result events to an existing analytics warehouse instead of adopting a hosted experimentation database.
- Small-Team Experimentation on a Budget: Use the free CLI and SDK with self-owned event handlers before deciding whether the hosted metrics layer is worth paying for.
Doop
Kevin Goedecke
Open-source infinite design canvas where humans and AI agents design together live, with agents joining through a built-in MCP server.
Key features
- Agent-Native MCP Canvas: Agents connect over an HTTP MCP endpoint with a single command and one browser OAuth approval, then edit the canvas as you, attributed and accountable, with no API keys handed over.
- Streaming Frames: Every section an agent writes renders on the canvas the moment it lands, so you watch the design arrive rather than waiting on a spinner.
- Comments as Tasks: A note left anywhere on the canvas becomes a task the right agent picks up, works on, and replies to with a screenshot, turning feedback directly into the backlog.
- Agent Self-Review: A built-in headless renderer gives agents screenshots of their own frames so they judge fit, spacing and contrast like a senior designer and correct issues before handoff.
- Shared Canvas Memory: Tasks, decisions and comments live on the canvas rather than in one agent's context, so any agent that joins later plugs into the same state and continues.
- Learned Taste Profile: Casual feedback such as 'rounder corners' or 'keep it to the blue' is distilled into a persistent taste profile applied to every new frame and inherited by every agent.
- Live Export URLs: Each frame is a URL that can be embedded in a doc, a post or an og:image and re-renders whenever the design changes, so shared assets never go stale.
- Reference and URL Import: Paste screenshots to have agents distill palette, type and mood into a written brief, or paste a public URL to land an editable snapshot of your existing page on the canvas for side-by-side variants.
Best for
- Agent-Assisted Landing Pages: Steering Claude Code or Codex through hero, pricing and footer frames on one canvas and watching each render live.
- Design Review Loops: Leaving contrast or spacing notes on a frame and letting an agent apply the fix and return a screenshot without a synchronous handoff.
- Redesign Comparison: Importing an existing public page as an editable snapshot so agent-generated variants sit next to the original instead of replacing it blind.
- Team Design Sessions: Multiple people and multiple agents working the same canvas, each seeing what the others' agents are doing in real time.
- Style Consistency: Building a canvas taste profile once so every subsequent frame and every new agent inherits the same corner radius, palette and type decisions.
- Always-Fresh Shared Assets: Embedding live frame URLs in documentation or social posts so the shared image updates automatically when the design changes.