# OpenCut Headless Mode: Automation and Batch Rendering Workflows Explained

> Discover OpenCut's headless mode for automating video processing and batch rendering. Control OpenCut via CLI and HTTP API without the GUI for efficient workflows.

- Repository: [OpenCut.app/OpenCut](https://github.com/OpenCut-app/OpenCut)
- Tags: how-to-guide
- Published: 2026-06-23

---

**OpenCut's upcoming headless mode provides a non-interactive execution environment that enables developers to automate video processing and run large-scale batch renders via CLI and HTTP API without launching the graphical interface.**

OpenCut is a free, open-source video editor built on a single Rust-based core shared across web, desktop, and mobile front-ends. While the current public release focuses on UI-driven editing, the next-generation rewrite introduces a **headless mode** specifically designed for automation and batch rendering workflows. This capability allows the high-performance video processing engine to run as a background service, making it ideal for CI pipelines, server-side workers, and AI-driven content generation pipelines.

## Architecture of OpenCut Headless Mode

The headless implementation follows a modular architecture that separates the rendering engine from interface concerns. According to the OpenCut-app/OpenCut source code, this design centers on four key components that enable programmatic control.

### Rust Core Engine

At the foundation lies the **Rust Core**, the same high-performance video processing library that powers the graphical interface. In headless mode, this core runs as a standalone process, handling media decoding, timeline manipulation, and encoding without any UI dependencies. Because the core is **stateless per job**, multiple instances can process renders in parallel on a single machine or distributed across a cluster.

### API Server Layer

The **API Server** wraps the Rust core in a lightweight HTTP (or RPC) interface. As outlined in the roadmap within [`README.md`](https://github.com/OpenCut-app/OpenCut/blob/main/README.md) (lines 25-27), this Editor API exposes endpoints for loading media, applying edits, and exporting results. The server implementation scaffold resides in [`apps/api/src/index.ts`](https://github.com/OpenCut-app/OpenCut/blob/main/apps/api/src/index.ts), where the HTTP endpoints for project creation and render job management will be defined.

### CLI Wrapper

A command-line binary (`opencut`) provides a convenient interface to the API server. This wrapper forwards arguments and script paths to the headless core, enabling simple integration into shell scripts and automation tools. The CLI supports the `--headless` flag to start the core without UI components and the `--script` flag to specify JavaScript-based editing instructions.

## How to Use OpenCut Headless Mode for Automation

Once released, the headless mode supports multiple integration patterns ranging from simple command-line rendering to complex batch processing systems.

### Command-Line Interface Rendering

The simplest approach uses the `opencut` CLI to render individual videos with preset editing scripts. This method requires no programming beyond writing the edit script and invoking the binary.

```bash

# Render a single video with a preset script

opencut \
  --headless \
  --input ./src/assets/intro.mp4 \
  --script ./scripts/trim-and-fade.js \
  --output ./out/final.mp4

```

The `--headless` flag initializes the Rust core without graphical components, while `--script` points to a JavaScript file containing timeline operations such as trimming and transitions.

### Programmatic API Integration

For dynamic workflows, applications can communicate directly with the headless API server using HTTP requests. The following TypeScript example demonstrates creating a project, importing media, applying edits, and exporting the result.

```typescript
import { OpenCutClient } from "opencut-client";

async function renderVideo() {
  const client = new OpenCutClient("http://localhost:8787"); // API server started by `opencut --headless`

  const project = await client.createProject();
  await client.importMedia(project.id, "./src/assets/intro.mp4");
  await client.applyScript(project.id, "./scripts/trim-and-fade.js");

  const result = await client.export(project.id, {
    format: "mp4",
    resolution: "1080p",
  });

  await client.download(result.fileUrl, "./out/final.mp4");
}

renderVideo().catch(console.error);

```

The `OpenCutClient` wrapper handles HTTP communication with the headless server, mirroring the UI workflow programmatically.

### Batch Processing with Job Queues

Because the Rust core remains stateless per job, you can process multiple renders concurrently using job queue libraries. The following example uses `p-queue` to manage parallel rendering tasks.

```typescript
import { OpenCutClient } from "opencut-client";
import PQueue from "p-queue";

const queue = new PQueue({ concurrency: 4 }); // run up to 4 renders in parallel
const client = new OpenCutClient("http://localhost:8787");

const jobs = [
  { input: "clip1.mp4", script: "s1.js", out: "out1.mp4" },
  { input: "clip2.mp4", script: "s2.js", out: "out2.mp4" },
  // …
];

for (const job of jobs) {
  queue.add(async () => {
    const proj = await client.createProject();
    await client.importMedia(proj.id, job.input);
    await client.applyScript(proj.id, job.script);
    const res = await client.export(proj.id, { format: "mp4" });
    await client.download(res.fileUrl, job.out);
    console.log(`✅ Rendered ${job.out}`);
  });
}

await queue.onIdle();

```

Setting `concurrency: 4` maximizes CPU utilization while maintaining isolation between render jobs, ensuring reproducible results across the batch.

### CI/CD Pipeline Integration

Headless mode integrates seamlessly into continuous integration workflows. The following GitHub Actions configuration demonstrates installing dependencies, starting the OpenCut API server, and executing a batch render script.

```yaml
name: Batch Render Videos
on: push
jobs:
  render:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install dependencies
        run: |
          curl -fsSL https://moonrepo.dev/install/proto.sh | bash
          proto use
          bun install
      - name: Start headless OpenCut server
        run: opencut --headless &
      - name: Run batch render script
        run: node ./ci/render-batch.js

```

The build pipeline defined in [`bunfig.toml`](https://github.com/OpenCut-app/OpenCut/blob/main/bunfig.toml) packages both the Rust core and the CLI wrapper, enabling automated deployment of the rendering environment in containerized CI runners.

## Key Source Files and Implementation Details

Understanding the codebase structure helps developers prepare for the headless release:

- **[`README.md`](https://github.com/OpenCut-app/OpenCut/blob/main/README.md)** (lines 25-27): Contains the Editor API roadmap and headless feature description, confirming the architectural direction for automation support.
- **[`apps/api/src/index.ts`](https://github.com/OpenCut-app/OpenCut/blob/main/apps/api/src/index.ts)**: Serves as the entry point for the upcoming HTTP API server where endpoints for programmatic control will be implemented.
- **[`apps/web/package.json`](https://github.com/OpenCut-app/OpenCut/blob/main/apps/web/package.json)**: Lists dependencies and build scripts that will eventually include the CLI wrapper distribution.
- **[`bunfig.toml`](https://github.com/OpenCut-app/OpenCut/blob/main/bunfig.toml)**: Configures the Bun and Moon build pipeline that compiles the Rust core and packages the headless binary for various platforms.

These files collectively demonstrate how OpenCut maintains a unified codebase while supporting both interactive and automated workflows.

## Summary

- **Headless mode** enables OpenCut to run as a background service without graphical dependencies, utilizing the same Rust core that powers the UI.
- The architecture consists of a **stateless Rust core**, an **HTTP API server**, and a **CLI wrapper** (`opencut`) for flexible integration options.
- Developers can trigger renders via **command-line scripts**, **HTTP API calls**, or **batch job queues** with concurrency control.
- The implementation supports **CI/CD pipelines** through container-friendly startup and scriptable interfaces.
- Source files including [`README.md`](https://github.com/OpenCut-app/OpenCut/blob/main/README.md), [`apps/api/src/index.ts`](https://github.com/OpenCut-app/OpenCut/blob/main/apps/api/src/index.ts), and [`bunfig.toml`](https://github.com/OpenCut-app/OpenCut/blob/main/bunfig.toml) define the roadmap and build system for this automation capability.

## Frequently Asked Questions

### Is headless mode available in the current stable version of OpenCut?

No, headless mode is currently part of the next-generation rewrite roadmap documented in [`README.md`](https://github.com/OpenCut-app/OpenCut/blob/main/README.md) (lines 25-27). The feature will be released as part of the Editor API update, which introduces programmatic endpoints for automation workflows alongside the existing UI-driven editor.

### What programming languages can I use with the OpenCut headless API?

While the core engine is written in Rust, the headless API exposes HTTP endpoints that accept commands from any language capable of making web requests. The examples demonstrate JavaScript and TypeScript clients, but Python, Go, or Ruby scripts can interact with the API equally well by posting JSON payloads to the server endpoints defined in [`apps/api/src/index.ts`](https://github.com/OpenCut-app/OpenCut/blob/main/apps/api/src/index.ts).

### How does OpenCut handle concurrent batch rendering jobs?

The Rust core is designed to be **stateless per job**, allowing multiple render processes to run in parallel on the same machine or across a cluster. You can implement concurrency using job queue libraries like `p-queue` (as shown in the batch examples) or orchestration tools like Kubernetes, with each job maintaining isolation and reproducibility independent of other renders.

### Can I run OpenCut headless mode on cloud servers or containers?

Yes, the headless architecture is specifically designed for server environments. The CLI binary supports `--headless` initialization without display requirements, and the API server binds to standard HTTP ports. The build configuration in [`bunfig.toml`](https://github.com/OpenCut-app/OpenCut/blob/main/bunfig.toml) supports cross-platform compilation, making it suitable for Docker containers, CI runners, and cloud VM deployments without GUI dependencies.