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

CODEBLOCK0

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
INLINECODE8	Download audio from video URLs at maximum speed. Supports YouTube, Vimeo, TikTok, Twitter/X, SoundCloud, and 1000+ sites. Uses aria2c multi-connection + concurrent fragments.
INLINECODE9

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
INLINECODE15	Auto-detect topic chapters with timestamps using embedding similarity.
INLINECODE16

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
INLINECODE20	Convert text to natural speech using Kokoro TTS. 54 voices, 9 languages. Runs in background.
INLINECODE21

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
INLINECODE44	Shortest possible summary. One screen. Bold key terms.
INLINECODE45

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

INLINECODE49 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
INLINECODE50	Default directory for downloaded audio files
INLINECODE51

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. 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
• Documentation