# Augent

> The audio & video layer for agents. 22 local MCP tools. No cloud, no API keys.

- Skill: `lord1egypt/augent` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lord1egypt/augent`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lord1egypt/augent/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: Lord1Egypt (https://skillmd.com/u/lord1egypt)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/lord1egypt/augent

---


# Augent — Audio & Video Intelligence for AI Agents

Augent is an MCP server that gives your agent 22 tools for audio and video intelligence. Download from 1000+ sites via yt-dlp and aria2c, transcribe in 99 languages via faster-whisper, search by keyword or meaning via sentence-transformers, take notes, identify speakers via pyannote-audio, detect chapters, separate audio via Demucs v4, export clips, extract visual frames, record X/Twitter Spaces (requires user-configured auth token in `~/.augent/auth.json`), and generate speech via Kokoro TTS. All processing runs locally. Downloads are saved to `~/Downloads/`, notes and clips to `~/Desktop/`, transcription memory to `~/.augent/memory/`.

## Config

```json
{
  "mcpServers": {
    "augent": {
      "command": "augent-mcp"
    }
  }
}
```

If `augent-mcp` is not in PATH, use `python3 -m augent.mcp` as the command instead.

## Install

Install via the ClawHub install button above, or use `uv tool install augent` for the base package or `uv tool install "augent[all]"` for all features. FFmpeg is required for audio processing.

## Tools

Augent exposes 22 MCP tools:

### Core

| Tool | Description |
|------|-------------|
| `download_audio` | Download audio from video URLs at maximum speed. Supports YouTube, Vimeo, TikTok, Twitter/X, SoundCloud, and 1000+ sites. Uses aria2c multi-connection + concurrent fragments. |
| `transcribe_audio` | Full transcription of any audio file with per-segment timestamps. Returns text, language, duration, and segments. Cached by file hash. |
| `search_audio` | Search audio for keywords. Returns timestamped matches with context snippets. Supports clip export. |
| `deep_search` | Semantic search — find moments by meaning, not just keywords. Uses sentence-transformers embeddings. |
| `search_memory` | Search across ALL stored transcriptions in one query. Keyword or semantic mode. |
| `take_notes` | All-in-one: download audio from URL, transcribe, and save formatted notes. Supports 5 styles: tldr, notes, highlight, eye-candy, quiz. |
| `clip_export` | Export a video clip from any URL for a specific time range. Downloads only the requested segment. |

### Analysis

| Tool | Description |
|------|-------------|
| `chapters` | Auto-detect topic chapters with timestamps using embedding similarity. |
| `search_proximity` | Find where two keywords appear near each other (e.g., "startup" within 30 words of "funding"). |
| `identify_speakers` | Speaker diarization — identify who speaks when. No API keys required. |
| `separate_audio` | Isolate vocals from music/noise using Meta's Demucs v4. Feed clean vocals into transcription. |
| `batch_search` | Search multiple audio files in parallel. Ideal for podcast libraries or interview collections. |

### Utilities

| Tool | Description |
|------|-------------|
| `text_to_speech` | Convert text to natural speech using Kokoro TTS. 54 voices, 9 languages. Runs in background. |
| `list_files` | List media files in a directory with size info. |
| `list_memories` | Browse all stored transcriptions by title, duration, and date. |
| `memory_stats` | View memory statistics (file count, total duration). |
| `clear_memory` | Clear the transcription memory to free disk space. |
| `tag` | Add, remove, or list tags on transcriptions. Broad topic categories for organizing memories. |
| `highlights` | Export the best moments from a transcription. Auto mode picks top moments; focused mode finds moments matching a topic. |
| `visual` | Extract visual context from video at moments that matter. Query, auto, manual, and assist modes. Frames saved to Obsidian vault. |
| `rebuild_graph` | Rebuild Obsidian graph view data for all transcriptions. Migrates files, computes wikilinks, generates MOC hubs. |
| `spaces` | Download or live-record X/Twitter Spaces. Start, check status, or stop recordings. |

## Usage Examples

### Take notes from a video

> "Take notes from https://youtube.com/watch?v=xxx"

The agent calls `take_notes` which downloads, transcribes, and returns formatted notes. One tool call does everything.

### Search a podcast for topics

> "Search this podcast for every mention of AI regulation" — provide the file path or URL.

The agent uses `search_audio` for exact keyword matches, or `deep_search` for semantic matches (finds relevant discussion even without exact words).

### Transcribe and identify speakers

> "Transcribe this meeting recording and tell me who said what"

The agent calls `transcribe_audio` then `identify_speakers` to label each segment by speaker.

### Search across all transcriptions

> "Search everything I've ever transcribed for mentions of funding"

The agent uses `search_memory` to search across all stored transcriptions without needing a file path.

### Export a clip

> "Clip the part where they talk about pricing"

The agent uses `search_audio` or `deep_search` to find the moment, then `clip_export` to extract just that segment.

### Separate vocals from noisy audio

> "This recording has music in the background, clean it up and transcribe"

The agent calls `separate_audio` to isolate vocals, then `transcribe_audio` on the clean vocals track.

### Generate speech from text

> "Read these notes aloud"

The agent calls `text_to_speech` to generate an MP3 with natural speech. Supports multiple voices and languages.

## Note Styles

When using `take_notes`, the `style` parameter controls formatting:

| Style | Description |
|-------|-------------|
| `tldr` | Shortest possible summary. One screen. Bold key terms. |
| `notes` | Clean sections with nested bullets (default). |
| `highlight` | Notes with callout blocks for key insights and blockquotes with timestamps. |
| `eye-candy` | Maximum visual formatting — callouts, tables, checklists, blockquotes. |
| `quiz` | Multiple-choice questions with answer key. |

## Model Sizes

`tiny` is the default and handles nearly everything. Only use larger models for heavy accents, poor audio quality, or maximum accuracy needs.

| Model | Speed | Accuracy |
|-------|-------|----------|
| **tiny** | Fastest | Excellent (default) |
| base | Fast | Excellent |
| small | Medium | Superior |
| medium | Slow | Outstanding |
| large | Slowest | Maximum |

## File Paths

Augent reads and writes to these locations on your machine:

| Path | Purpose |
|------|---------|
| `~/Downloads/` | Default directory for downloaded audio files |
| `~/Desktop/` | Default directory for notes, clips, and TTS output |
| `~/.augent/memory/transcriptions.db` | SQLite database for persistent transcription memory |
| `~/.augent/memory/transcriptions/` | Markdown files for each stored transcription |
| `~/.augent/config.yaml` | User configuration (optional) |
| `~/.augent/auth.json` | Twitter/X authentication cookies for Spaces recording (optional, user-created) |

If Obsidian is installed, visual frames are saved to the Obsidian vault's `External Files/visual/` directory. The vault path is auto-detected from Obsidian's config.

## Network Access

Network access is used for two purposes only:

1. Downloading media from user-provided URLs via yt-dlp and aria2c
2. Downloading ML models on first use (Whisper, sentence-transformers, pyannote, Demucs, Kokoro) from Hugging Face

No telemetry. No background network activity. No data is uploaded.

## ML Dependencies

The `augent[all]` install includes these local ML components:

| Component | Purpose | Size |
|-----------|---------|------|
| faster-whisper | Speech-to-text transcription | ~75MB (tiny model) |
| sentence-transformers | Semantic search, auto-tagging, chapter detection | ~90MB |
| pyannote-audio | Speaker diarization | ~29MB |
| Demucs v4 | Audio source separation (vocals from noise) | ~80MB |
| Kokoro | Text-to-speech (54 voices, 9 languages) | ~200MB |

All models run locally. None require API keys or cloud services.

## Requirements

- Python 3.10+
- FFmpeg (audio processing)
- yt-dlp + aria2c (for audio downloads)

## Links

- [GitHub](https://github.com/AugentDevs/Augent)
- [Documentation](https://docs.augent.app)