chat-recall vs Modeinspect: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of chat-recall and Modeinspect — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
chat-recall
chat-recall
Makes every conversation your team has had with AI coding assistants searchable, and flags secrets leaked into those chats.
Key features
- Unified Conversation Search: Full-text search across the chats, plans, task lists and notes written by five supported AI coding tools, searchable the moment they arrive.
- Local Secret Redaction: Passwords and API keys are stripped on your own computer before anything is uploaded; only the last few characters are ever received.
- Leaked Key Reporting: Shows every key found, whether it is still live, and how many conversations it appeared in, with support for custom in-house key formats you register.
- MCP Server and Recall Tools: Exposes the history to your assistants through an MCP server so they can query past work directly rather than starting cold.
- Ranked Action Plan: Derives code findings and a prioritized list of what to fix next, written out as CODE_TASKS.md.
- Self-Closing Bug Tasks: Each detected bug becomes a task with a sketched fix, and closes itself once the problem is actually gone.
- Config Distribution: Skills and MCP configuration follow you to every machine and to whichever assistant you pick up next, with a per-machine view of what is missing.
- Per-Project Rules: Mark a project as a prototype or a live product once, and every assistant that opens it plays by the matching rules.
Best for
- Credential Incident Response: Find which keys were pasted into assistant conversations, whether they are still valid, and where they spread.
- Recovering Past Decisions: Search months of AI conversations to recover a plan or rationale instead of asking the same question again.
- Onboarding a New Machine: Sign in on a new laptop and get the full conversation history and every accumulated skill without copying files by hand.
- Switching Assistants: Try a different AI coding tool without losing the add-ons and context built up in the previous one.
- Team Knowledge Sharing: Share project history selectively with teammates and assign follow-up work from a shared task board.
- Security Review Before Shipping: Run the secret monitor and code findings over accumulated history as a pre-release check.
Modeinspect
Acreom
An AI design canvas that sits on top of your real codebase, so UI is shaped with live components and shipped as a pull request instead of a mockup.
Key features
- Codebase-Backed Canvas: Turns the product's real repository into editable canvas frames, so every element on screen is the component that actually ships.
- Components at 1:1 Fidelity: Drops in real shipped components with every variant and state intact, preventing the drift that comes from redrawn look-alikes.
- Enforced Design Tokens: Pulls every colour, spacing value and text style from the project's own library so nothing off-system can be placed on the canvas.
- Native Breakpoints: Lays out mobile, tablet and desktop side by side and reflows each one live instead of freezing a single frame per size.
- Capture to Canvas: Pulls any screen straight out of the running product onto the canvas pixel-exact and fully live, so redesign starts from current reality.
- Dynamic State Design: Shapes hover, focus, error, empty, loading and success states directly on the real component rather than guessing at them.
- AI Exploration: Uses current AI models to generate variants, restyle a section, adjust copy or apply a design direction with the designer still steering.
- Merge-Ready Pull Requests: Converts canvas changes into scoped, type-safe code changes delivered to engineering as a pull request, with no redlines or spec docs.
Best for
- Redesigning a Live Screen: Capture an existing production screen onto the canvas and rework it against the real components instead of rebuilding it in a mockup tool.
- Design QA on the Real Product: Audit spacing, tokens and responsive behaviour on the actual interface across breakpoints before a release.
- Shipping Visual Changes Without Handoff: Send small styling and layout fixes straight to engineering as a scoped pull request rather than filing a design ticket.
- Designing Every State: Build out loading, empty and error states on the real component so the interface holds together once users hit edge cases.
- Exploring Directions Quickly: Generate several AI-assisted variants of a section to compare options before committing to one.
- Stakeholder Review: Share a live prototype running on real data and components so feedback is given on the real thing rather than a static image.
- Keeping Design and Code in Sync: Enforce the existing design system automatically so new work cannot introduce off-brand colours or spacing.