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hermes - 💡(How to fix) Fix [Feature Request] Add video content learning — ingest, transcribe, and learn from video content [1 participants]

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NousResearch/hermes-agent#12885•Fetched 2026-04-20 12:16:21
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Fix Action

Fix / Workaround

  • Video is increasingly the primary medium for educational/technical content
    • The current workaround (manual subtitle extraction) breaks the "AI agent" promise — the AI should be doing this work
    • Competitor agents (Claude, GPT) are adding native video understanding
    • For users in the Hermes ecosystem (developers, researchers, learners), video learning is a daily workflow
RAW_BUFFERClick to expand / collapse

markdown ## Use Case

 Currently, learning from video content requires a manual multi-step workflow:
 1. User finds a video (YouTube, Toutiao, etc.)
 2. User manually extracts subtitles via browser extension
 3. User pastes subtitles to the AI for translation/storage
 4. AI stores to wiki/knowledge base

 This is fragmented and requires constant user involvement.

 ---

 ## Desired Feature

 Hermes should be able to:
 1. **Receive a video URL** (YouTube, TikTok, etc.) directly in conversation
 2. **Auto-transcribe** using Whisper (already in optional-skills) or a built-in ASR model
 3. **Extract key content** — identify core concepts, timestamps of important moments, claims made
 4. **Ingest into knowledge base** — add to wiki/brain with proper tagging and linking
 5. **Learn from video** — update relevant skills or memory with insights from the video

 ---

 ## Why This Matters

 - Video is increasingly the primary medium for educational/technical content
 - The current workaround (manual subtitle extraction) breaks the "AI agent" promise — the AI should be doing this work
 - Competitor agents (Claude, GPT) are adding native video understanding
 - For users in the Hermes ecosystem (developers, researchers, learners), video learning is a daily workflow

 ---

 ## Suggested Implementation

 - Native `video_understand` or `video_learn` tool
 - YouTube: use `yt-dlp` + Whisper for transcription
 - Other platforms: browser-based extraction as fallback
 - Output: structured summary + full transcript stored to wiki
 - Trigger: simply share a URL in conversation, agent auto-detects and offers to process

 ---

 ## Priority

 Medium-High. This is a workflow bottleneck for power users who learn primarily through video.

extent analysis

TL;DR

Implement a native video_understand or video_learn tool to auto-transcribe and extract key content from videos, integrating with existing Whisper ASR model and wiki/knowledge base.

Guidance

  • Investigate using yt-dlp for YouTube video processing and Whisper for transcription, as suggested in the implementation section.
  • Consider implementing a browser-based extraction fallback for other video platforms.
  • Design a structured output format for the summary and full transcript, ensuring compatibility with the existing wiki/knowledge base.
  • Develop a trigger mechanism to auto-detect video URLs shared in conversation and offer processing, enhancing the user experience.

Example

No code snippet is provided due to the high-level nature of the issue, but an example implementation might involve integrating yt-dlp and Whisper using Python:

import yt_dlp
import whisper

# Example video URL
url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Download video subtitles using yt-dlp
ydl_opts = {}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
    info = ydl.extract_info(url, download=False)

# Transcribe audio using Whisper
model = whisper.load_model("base")
result = model.transcribe(info["formats"][0]["url"])

Note: This example is hypothetical and may not reflect the actual implementation details.

Notes

The implementation should consider factors like video platform support, transcription accuracy, and knowledge base integration. Additionally, the priority of this feature should be weighed against other development tasks.

Recommendation

Apply a workaround by implementing a native video_understand or video_learn tool, as this addresses the core issue of manual video processing and aligns with the desired feature set.

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#batch processing#GPU compatibility#latency issue#model loading#dependency error

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