# Video import (/docs/context-capture/video-import)





Video import [#video-import]

Record a walkthrough, import it, and SageOx turns it into context your team can read back in assistant-mode, code-mode, and the web app. Design rationale, bug reproductions, architecture explanations — all searchable and accessible across the product.

<img alt="A fully processed recording with pipeline stages, summary, and keyframe thumbnails" src={__img0} placeholder="blur" />

Supported sources [#supported-sources]

Import from the tools you already use:

| Source          | How to import                           |
| --------------- | --------------------------------------- |
| **Loom**        | Paste share URL in the web app          |
| **Figma**       | Paste Figma recording URL               |
| **Cap**         | Paste share URL or upload exported file |
| **Zoom/Meet**   | Upload the downloaded recording         |
| **Local files** | Drag and drop MP4, WebM, or audio files |

How it works [#how-it-works]

You record. SageOx does the rest. Every recording goes through an extraction pipeline that turns video into structured, searchable artifacts.

<Mermaid
  chart="graph LR
    A[Record] --> B[Import]
    B --> C[Transcribe]
    C --> D[Extract Keyframes]
    D --> E[Summarize]
    E --> F[Commit to Team Context]
    F --> G[AI Coworker Access]"
/>

The pipeline runs automatically after upload. When it finishes, extracted artifacts commit to your Team Context — the shared knowledge base your team can read back in assistant-mode, code-mode, and the web app.

What gets extracted [#what-gets-extracted]

Each recording produces structured artifacts:

```
discussions/2026-03-20-ux-review/
├── transcript.vtt        # timestamped speech with speaker labels
├── summary.json          # chapters, decisions, action items
├── keyframes.json        # frame images + vision descriptions
└── metadata.json         # title, participants, duration
```

Coworkers and AI coworkers consume these artifacts to understand what was discussed, what decisions were made, and what the UI looked like. They do not watch the raw video every time — they read the structured output.

Import methods [#import-methods]

Web app (recommended) [#web-app-recommended]

The fastest way to import a single recording:

1. Go to your team's **Media** section
2. Click **Import** and paste a Loom, Figma, or Cap URL
3. Or click **Upload** to drag and drop a local file

Processing starts automatically. You'll see progress in the pipeline view.

CLI [#cli]

Import directly from your terminal without leaving your editor:

```bash
ox import https://www.loom.com/share/abc123 --title "Sprint Planning"
ox import --status rec_01234567 --watch  # track progress
```

See [Video Import via CLI](/docs/context-capture/video-import-cli) for details.

Supported formats [#supported-formats]

| Type       | Formats                                 |
| ---------- | --------------------------------------- |
| Video      | MP4, WebM, MOV, MKV                     |
| Audio      | MP3, WAV, M4A, OGG, FLAC, AAC           |
| Transcript | VTT, SRT, TXT (skip transcription step) |
| Max size   | 500MB                                   |

Recording tips [#recording-tips]

<Callout type="info">
  **Smaller files = faster everything.** 720p at 15fps is the sweet spot. Smaller files upload faster, transcribe faster, and extract cleaner keyframes. Target \~1 MB per minute. Your AI coworker doesn't need 4K.
</Callout>

**For best results:**

* **Narrate as you go** — Transcript quality drives extraction quality. Silent recordings produce no searchable context.
* **Keep it focused** — 5-10 minutes is ideal. Split longer sessions by topic.
* **Use descriptive titles** — Your AI coworkers search by title. "Sprint 12 Checkout Flow Redesign" beats "Recording 47".

See [Cap Setup](/docs/context-capture/video-import-cap) for optimal export settings.

Use cases [#use-cases]

| Record this              | Your AI coworker gets                              |
| ------------------------ | -------------------------------------------------- |
| Figma design walkthrough | Design rationale to reference when implementing UI |
| Bug reproduction         | Searchable steps + screenshot keyframes            |
| Architecture explanation | Context for future refactoring decisions           |
| Code review walkthrough  | Reasoning behind feedback and suggestions          |
| Product demo             | Feature intent and expected behavior               |

How recordings get used [#how-recordings-get-used]

In code-mode [#in-code-mode]

Repo-based AI coworkers can receive recording-derived context through Team Context and cite specific decisions when implementing features.

In assistant-mode [#in-assistant-mode]

MCP-connected assistants can search the transcript, summary, and related context without needing a local repo checkout.

In the web app [#in-the-web-app]

Humans can open the recording, transcript, summary, and keyframes directly when they want the source material itself.

Getting started guides [#getting-started-guides]

| Guide                                                                 | What you'll learn                        |
| --------------------------------------------------------------------- | ---------------------------------------- |
| [Cap Setup](/docs/context-capture/video-import-cap)                   | Optimal recording and export settings    |
| [Upload via Web](/docs/context-capture/video-import-web)              | Drag-and-drop upload in the browser      |
| [Import via CLI](/docs/context-capture/video-import-cli)              | Import from your terminal                |
| [Using in Coding Sessions](/docs/context-capture/video-import-coding) | How AI coworkers consume your recordings |

What's next [#whats-next]

* [Console Recorder](/docs/context-capture/console) — record directly from your browser
* [Discussions](/docs/context-capture/discussions) — all ways to capture team knowledge
* [Team Context](/docs/features/team-context) — where imported recordings live
* [Distillation](/docs/features/distill) — how recordings become team memory