AI Toolbox vs dif.sh: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AI Toolbox and dif.sh — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AI Toolbox
Infi Developments
Chrome extension that adds folders, full-text search, a prompt library, and conversation export inside ChatGPT, Gemini, Claude, and Grok.
Key features
- Cross-Platform Search: One keyboard shortcut runs a full-text search across ChatGPT, Gemini, Claude, and Grok histories at once, grouping results by assistant so a single click opens the exact chat.
- Folders and Subfolders: Sort conversations into nested folders and pin the chats you return to daily, keeping the native sidebar organized as history grows.
- Bulk Conversation Export: Download many conversations at once as PDF, Markdown, plain text, JSON, or ZIP instead of saving them one by one.
- Prompt Library and Chaining: Save your best prompts once and reuse them in a click, browse a public prompt library, and run saved prompts one after another automatically.
- Smart Tags and Auto-Categorization: Paid plans tag and categorize conversations automatically so related chats stay grouped without manual filing.
- Context Mentions: Reference past chats from inside a new conversation so earlier work can be pulled into the current thread.
- Per-Platform Modules: Separate ChatGPT, Gemini, Claude, and Grok modules can be compared side by side and licensed for the platform you actually use.
- Cross-Device Sync: Folders, bookmarks, and prompts sync across devices and Chromium browsers on paid tiers.
Best for
- Research Archiving: Keep months of AI research organized in folders and export the important threads to PDF or Markdown for a permanent record.
- Finding a Half-Remembered Answer: Search every assistant at once when you cannot recall whether a conversation happened in ChatGPT, Claude, Gemini, or Grok.
- Prompt Reuse for Repetitive Work: Store proven prompts in a library and run them in sequence instead of retyping the same instructions.
- Client and Project Separation: Give each client or project its own folder tree so unrelated conversations never mix.
- Handing Off Work: Bulk-export a set of conversations as a ZIP to share reasoning and outputs with teammates who do not have access to your account.
- Multi-Assistant Workflows: Compare how different assistants answered the same question by keeping their threads filed under one topic.
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.