Podcli

Open-source AI podcast clipper.
Turn a long episode into short clips with face tracking and burned-in captions. Drive it from the CLI, a web studio, or your coding agent.

podcli.com · Docs · Install · MCP

_{▶ Watch with sound on X}

podcli process episode.mp4

That one command transcribes the episode, picks the moments worth clipping, crops to whoever is speaking, and burns the captions in. Transcription and rendering run on your machine. The only network calls are the optional Claude or Codex requests when you use AI clip scoring.

Install

No prerequisites. The installer fetches a self-contained binary, and the first run provisions Python, Node, FFmpeg, whisper.cpp, and the models it needs into a managed folder.

macOS and Linux

curl -fsSL https://podcli.com/install.sh | sh

Windows (PowerShell)

irm https://podcli.com/install.ps1 | iex

Runs on macOS (Apple Silicon), Linux (x64 and arm64), and Windows (x64). Intel Mac support is in progress.

Quick start

podcli                       # interactive menu, opens the web studio
podcli process episode.mp4   # transcribe, pick moments, render clips

Clips land in podcli-clips/ in the directory you ran it from, so each show keeps its own renders. Everything else (knowledge, presets, assets, clip history, cache) lives in one managed folder that follows you between directories. Set PODCLI_OUTPUT to render somewhere fixed instead.

What you get

Clips

• 9:16, 16:9, or 1:1, with captions sized for each canvas
• Face tracking that follows the speaker, split-screen layouts included
• Multi-segment cuts that drop filler, long pauses, and tangents
• Four caption styles: branded, hormozi, karaoke, subtle
• Logos, intros, outros, and background music from a reusable asset library
• Loudness-normalized audio and hardware encoding on VideoToolbox, NVENC, and VAAPI, with a CPU fallback

Finding the moments

• Whisper transcription with speaker diarization, or bring your own transcript as .txt, .srt, or .vtt
• AssemblyAI as an alternative engine, and yt-dlp to pull an episode straight from a URL
• AI scoring against your knowledge base, checked against your episode database so it stops resuggesting moments you already published
• Audio energy and laughter detection to build highlight reels

The studio at localhost:3847

• Library, episode workspace, per-clip detail, highlights, thumbnails, content, analytics, assets, knowledge, config, integrations, and MCP setup
• ⌘K command palette across pages, clips, and assets
• Titles, descriptions, tags, and hashtags, with any section regenerated on your own guidance
• Thumbnail studio for 16:9 and 9:16, with frame and text options
• Transcript corrections that carry through to every render

Shipping it

• 26 MCP tools, so an agent can transcribe, score, render, and publish through conversation
• YouTube publishing plus performance analytics to see which clips landed
• DaVinci Resolve export as FCPXML when you want to finish by hand
• Pre

... View full README on GitHub