Source control integrations
Connect repositories, code review, issues, pull requests, and engineering workflows to AI assistants.
GitHubcodeengineering
AI integrations
AI plugins and integrations
Explore the integration layer that lets AI products work with source control, documents, design systems, deployment platforms, and knowledge tools.
Plugin availability depends on the AI host and connected account. AiFolder organizes integrations by the job they enable rather than claiming every integration is available in every AI product.
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AI plugins and integrations
6 entries
Knowledge integrations
Ground AI workflows in team docs, wikis, saved research, and structured knowledge sources.
docsknowledgesearch
Cloud storage integrations
Work with files and folders in connected storage systems while preserving product permission boundaries.
Drivefilesdocuments
Design integrations
Bring design files, components, tokens, screenshots, and product context into implementation workflows.
FigmadesignUI
Deployment integrations
Inspect projects, deployments, logs, environments, and delivery workflows from AI development tools.
VerceldeployDevOps
Reading & research integrations
Connect saved articles, highlights, research libraries, and reading workflows to synthesis tasks.
researchhighlightslibrary
AI plugins and integrations FAQ
What is an AI plugin?+
An AI plugin is a packaged integration or workflow that gives an AI product access to an external capability, data source, or permitted action.
Are plugins and MCP servers interchangeable?+
They can overlap in capability, but they describe different layers. A plugin is a packaged product integration; MCP is a protocol for exposing tools and context through a standard interface.
Why does plugin availability change by product?+
Each host controls its supported integrations, authentication, permissions, regions, plans, and product UX. Check the host's current integration directory before depending on a specific connection.