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Airframe positions itself as a “product workspace for AI-native teams.” It aims to solve the problem of coding agents lacking product memory: traditional teams keep requirements in Notion, designs in Figma, tasks in Linear, and discussions in Slack, which means agents need the context re-explained every time. Airframe brings Boards, Briefs, Wireframes, and Journal & Memory into a single workspace, turning product specs into a single source of truth that agents can read, update, and continuously evolve.
Based on the main description, Airframe is not just about generating content; its real focus is connecting “specification—build—review” into a closed loop. Boards support sticky notes, diagrams, story maps, and AI images; Briefs are used to organize features, requirements, and acceptance criteria; Wireframes & Flows are described as machine-readable, making them easier for agents to query and build from; and Journal & Memory stores decisions, learnings, and context that persist across sessions. Its native MCP integration claims 90+ tools, and it supports agents proposing patch diffs that humans can review, accept, or reject. Version history, rollback, and ongoing v2/v3 iteration are highlighted as key selling points.
Airframe uses a freemium model. The Free plan is permanently free, requires no credit card, and includes 3 projects plus 50 AI chat messages per day. Pro costs $29/month and includes unlimited projects, unlimited AI chat, 10 MCP connections, AI image generation, and priority support. Enterprise is custom-priced and includes SSO/SAML, custom limits, and annual billing. Its “no per-seat pricing” approach is friendly to cross-functional teams and helps reduce collaboration costs.
Its strengths are the deep integration of product context, specs, wireframes, and agent memory, along with diff, merge, human review, and version rollback capabilities. This makes it suitable for serious product iteration rather than one-off prompt generation. The limitations are also clear: the page does not disclose the underlying AI models, data privacy policy, training data usage, compliance certifications, API details, or a concrete list of third-party tools. There is also no information on Chinese interface support or Chinese output quality. Given the Early Access context, its real-world stability and performance on complex projects still need to be validated.
Airframe is best suited for product and development teams already using Claude, v0, Bolt, or other coding agents, especially teams that want to reduce repeated requirement explanations and let agents inherit product context. Compared with a Notion + Figma + Linear setup, Airframe is more oriented toward agent-native workflows. Access from mainland China, payment methods, and localization support are not mentioned in the main text, so they should be considered unknown. If access is restricted, a temporary alternative would be to use Notion, Figma, and Linear together with domestic AI coding tools.
⚠ This review is compiled from public sources and does not constitute a purchase recommendation. Verify all facts on the vendor's official site. Verify on getairframe.com official site.
getairframe.com is an United States AI Apps provider. TG4G tracks its product information, an overall rating of 8.0/10, and a China-accessibility score of Workable. Click "Visit Official Site" to reach getairframe.com directly.