augent

name	augent
description	Audio intelligence toolkit. Transcribe, search by keyword or meaning, take notes, detect chapters, identify speakers, separate audio, export clips, tag, highlights, visual context, and text-to-speech — all local, all private. 21 MCP tools for audio and video.
homepage	https://github.com/AugentDevs/Augent
metadata	{"openclaw":{"emoji":"🎙","os":["darwin","linux","win32"],"requires":{"bins":"[Truncated]"},"install":["[Truncated]","[Truncated]"]}}

Augent — Audio Intelligence for AI Agents

Augent is an MCP server that gives your agent all audio intelligence tools. Transcribe, search, take notes, identify speakers, detect chapters, separate audio, export clips, and generate speech — fully local, fully private.

Config

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

If augent-mcp is not in PATH, use the full Python module path:

{
  "mcpServers": {
    "augent": {
      "command": "python3",
      "args": ["-m", "augent.mcp"]
    }
  }
}

Install

One-liner (recommended): Installs augent, FFmpeg, yt-dlp, aria2, and configures MCP automatically.

curl -fsSL https://augent.app/install.sh | bash

Via uv:

uv tool install augent

For all features (semantic search, speaker diarization, TTS, source separation):

uv tool install "augent[all]"

Via pip:

pip install "augent[all]"

System dependencies: FFmpeg is required. Install with brew install ffmpeg (macOS) or apt install ffmpeg (Linux). For fast audio downloads, also install yt-dlp and aria2.

Tools

Augent exposes 21 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.

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

Memory

Transcriptions are stored by file content hash + model size. Same file = instant results on repeat searches. Memory persists at ~/.augent/memory/transcriptions.db. Source URLs from any platform are permanently stored by file hash. Use memory_stats to check usage and clear_memory to free space.

Requirements

• Python 3.10+
• FFmpeg (audio processing)
• yt-dlp + aria2 (optional, for audio downloads)

Links

• GitHub
• Documentation
• Install Script