Iris turns a recording into a structured, queryable context object — transcripts, frame analysis, action timelines — and serves it to Claude, Cursor, your CLI, your SDK. One click. No prompt engineering.
Speaks fluent agent — connects to
How Iris works
Browser extension, CLI, or any video upload. Iris doesn't care if it's a screenshare, a Loom export, or a Quicktime clip — drop it in.
Every frame is read by Claude. Audio transcribed by Deepgram. UI actions detected automatically. The output is a structured context object — not a hallucination.
Claude, Cursor, your CLI — they all hit the same MCP and REST surface. Need to send a teammate the recording? Public link with chapters, transcript, and tags.
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The platform
"iris": {
"command": "npx",
"args": ["@iris/mcp"]
}const iris = new IrisClient({ apiKey });
const { context } = await iris.getLatestRecording();
console.log(context.summary);Why teams use Iris
"What error did I just see in the dashboard?" — Iris pinpoints the moment, the stack, the URL.
"Show me how I deployed the staging branch last Tuesday." Iris returns the recording, summary, and the commands you ran.
Give your coding agent a memory it can re-read instead of a chat log it can’t.
Integrate
{
"mcpServers": {
"iris": {
"command": "npx",
"args": ["@iris/mcp-server"],
"env": {
"IRIS_API_KEY": "ir_live_xxxxx"
}
}
}
}
// Then just tell Claude:
// "Check the most recent recording"FAQ
Loom is built for human-to-human video sharing. Iris does that too — but it also extracts structured context (timelines, actions, transcripts, OCR) that AI agents can consume via MCP, REST API, or SDK. Think of it as Loom + an AI understanding layer.
Iris uses Claude (Anthropic) for frame-by-frame vision analysis and context generation, and Deepgram for audio transcription. Both are configurable via environment variables. Self-hosters bring their own keys.
Yes. Iris is 100% open-source (MIT) and ships with a Dockerfile. Deploy on Railway, your own server, or any Docker-compatible infrastructure. Your recordings and data stay on your servers.
When self-hosted, your data never leaves your infrastructure. On the hosted version, recordings are stored in your workspace and accessible only with your credentials or API keys. We don't train models on your data.
Iris accepts WebM and MP4 recordings. The browser extension records in WebM. You can also upload any video file via the CLI, SDK, or dashboard.
The fastest way is the MCP server — add one config block to Claude Desktop or Cursor and your agent can query recordings directly. You can also use the REST API, TypeScript SDK, or CLI for custom integrations.
Ready when you are
Free plan is permanent. No credit card. 60 seconds from signup to your first agent-readable recording.