Produce short WeChat videos with OpenClaw agents
AI ：15 0.1 ！OpenClaw+Remotion+ ， 10 。 ，React ， ，Whisper 。 ， ， 90%！ Task: The author built an automated video production pipeline using OpenClaw agents, Remotion for React-based rendering, MiniMax for voice cloning, and Whisper for timestamp alignment. The workflow involves agents generating scripts, synthesizing audio, and rendering video files, while the human selects topics and approves the final output. This shifts video creation from manual editing to a code-driven, template-based process.; tools: OpenClaw. Human role: The original post does not say how a person stayed involved.; limitations: The system requires technical prerequisites including a server, React skills, and OpenClaw deployment experience. Key constraints include balancing TTS speed (1.15x optimal), handling Whisper timestamp drift, and ensuring Remotion font loading. The author warns against downgrading the content generation model, as it severely impacts script quality..
Tools
Published on Agent Work Atlas:
Overview
AI ：15 0.1 ！OpenClaw+Remotion+ ， 10 。 ，React ， ，Whisper 。 ， ， 90%！
Workflow
The author built an automated video production pipeline using OpenClaw agents, Remotion for React-based rendering, MiniMax for voice cloning, and Whisper for timestamp alignment. The workflow involves agents generating scripts, synthesizing audio, and rendering video files, while the human selects topics and approves the final output. This shifts video creation from manual editing to a code-driven, template-based process.
Reported result
Production time dropped from 2-3 hours to approximately 15 minutes per video, with costs reduced to about ¥0.1 per clip. Viewership metrics improved significantly, with average views rising from under 200 to 1,595 per video, and total views reaching 9,018 in three days. The author attributes this success to increased posting frequency, consistent visual style, and stable output quality.
Human role
The original post does not say how a person stayed involved.
Limitations
The system requires technical prerequisites including a server, React skills, and OpenClaw deployment experience. Key constraints include balancing TTS speed (1.15x optimal), handling Whisper timestamp drift, and ensuring Remotion font loading. The author warns against downgrading the content generation model, as it severely impacts script quality.
Try it
Act as a video production agent. I want to replicate a low-cost, high-frequency video workflow for [replaceable: target platform, e.g., WeChat Channels]. Inputs: - Topic list: [replaceable: 5 potential video topics] - Voice sample: [replaceable: URL to 30s audio file for cloning] - Template style: [replaceable: description of visual style, e.g., dark background, large text] Steps: 1. Select one topic from the list based on [replaceable: audience interest criteria]. 2. Generate a 60-second script (~200 words) using a high-quality LLM. 3. Synthesize audio using [replaceable: TTS service, e.g., MiniMax] with the provided voice sample at 1.15x speed. 4. Extract precise timestamps for each sentence using [replaceable: ASR tool, e.g., Whisper]. 5. Populate a [replaceable: video framework, e.g., Remotion] scene data file with text, timestamps, and visual cues. 6. Render the video to MP4. 7. Output the final video file and a summary of the script. Constraints: - Do not publish or send the video without my explicit confirmation. - If TTS quality is poor, stop and ask for a better voice sample. - Ensure font assets are locally hosted to prevent rendering errors. Deliverables: - Final MP4 video file. - JSON file of scene data. - Cost and time estimate report. First, perform a dry run with a 10-second sample to verify timestamp alignment before full rendering.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.