podifyr-ai 2026.5.24

Automated developer onboarding via podcast-style audio walkthroughs of Python codebases.

🎙️ Podifyr-AI

AI-powered CLI that transforms Python codebases into podcast-style audio walkthroughs using LangGraph agentic pipelines, AST analysis, dependency graph traversal, and neural text-to-speech synthesis

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What is Podifyr-AI?

Podifyr-AI is a CLI tool that analyzes a Python repository's architecture and generates a conversational, podcast-style audio walkthrough. It's designed to accelerate developer onboarding by letting new team members listen to an AI-generated explanation of the system architecture — like having a senior engineer give them a KT session on day one.

How It Works

Repository → AST Parsing → Dependency Graph → AI Script → TTS Audio → 🎧 Walkthrough

1. Parse: Traverses the repo and extracts structural metadata (classes, functions, imports) using Python's AST module
2. Graph: Builds a directed dependency graph to understand module relationships and reading order
3. Script: Uses a multi-agent LangGraph pipeline (Analyzer → Scriptwriter) to generate conversational explanations
4. Audio: Synthesizes speech via Edge TTS (free) or OpenAI TTS with concurrent chunk generation and FFmpeg stitching

Quick Start

Installation

pip install podifyr-ai

Generate a Walkthrough (Free — No API key for TTS)

# Set your OpenAI API key (needed for script generation)
export OPENAI_API_KEY="sk-..."

# Generate a walkthrough — uses free Edge TTS by default
podifyr-ai generate ./path/to/your/repo

Or pass everything via CLI (no env setup needed)

podifyr-ai generate ./my-project \
  --api-key sk-your-key-here \
  --tts-backend edge \
  --output ./walkthrough

Azure OpenAI

podifyr-ai generate ./my-project \
  --api-key your-azure-key \
  --azure-endpoint https://your-resource.openai.azure.com \
  --azure-deployment gpt-4o-mini \
  --tts-backend edge

CLI Reference

podifyr-ai generate <REPO_PATH>     Generate a podcast walkthrough

Options:
  --output, -o PATH              Output directory [default: ./podifyr_output]
  --api-key TEXT                  OpenAI/Azure API key (or set OPENAI_API_KEY)
  --tts-backend TEXT              TTS: 'edge' (free), 'openai', 'elevenlabs'
  --voice TEXT                    Voice: alloy, echo, fable, onyx, nova, shimmer
  --azure-endpoint TEXT           Azure OpenAI endpoint (enables Azure mode)
  --azure-deployment TEXT         Azure chat model deployment name
  --skip-audio                    Generate script only, skip audio
  --no-cache                      Disable caching for this run
  --concurrency, -c INT           Max concurrent TTS requests [1-20]
  --graph-details                 Show dependency graph metrics
  --verbose, -V                   Enable debug logging

podifyr-ai config init              Create .env configuration file
podifyr-ai config show              Display current resolved settings
podifyr-ai cache clear              Clear cached data
podifyr-ai cache stats              Show cache statistics
podifyr-ai --version                Show version

TTS Backends

Backend	Cost	API Key Required	Quality	Setup
Edge (default)	Free	No	Good (Microsoft Neural)	None
OpenAI	~$0.015/1K chars	Yes (OPENAI_API_KEY)	Good	API key
ElevenLabs	Varies	Yes (ELEVENLABS_API_KEY)	Excellent	pip install podifyr-ai[elevenlabs]

Configuration

Settings are resolved in priority order: CLI flags > Environment variables > .env file > Defaults

# Generate a template .env file
podifyr-ai config init

# View resolved configuration
podifyr-ai config show

Key Environment Variables

Variable	Default	Description
OPENAI_API_KEY	—	OpenAI API key (for LLM script generation)
PODIFYR_TTS_BACKEND	edge	TTS backend: edge, openai, elevenlabs
PODIFYR_TTS_VOICE	alloy	Voice identifier
PODIFYR_AZURE_ENABLED	false	Use Azure OpenAI for LLM
PODIFYR_AZURE_ENDPOINT	—	Azure OpenAI resource URL
PODIFYR_AZURE_API_KEY	—	Azure API key
PODIFYR_AZURE_CHAT_DEPLOYMENT	—	Azure model deployment name

See docs/getting-started/configuration.md for the full reference.

Features

• 🧠 AST-based analysis — Extracts architecture without executing code
• 🔗 Dependency graphing — Cycle-aware topological sorting with NetworkX
• 🤖 Multi-agent pipeline — LangGraph orchestration with analyzer + scriptwriter nodes
• 🔊 Free TTS included — Edge TTS (Microsoft Neural voices) works out of the box
• 🔌 Plugin backends — Swappable TTS providers (Edge, OpenAI, ElevenLabs)
• ☁️ Azure OpenAI — Full support for Azure OpenAI deployments
• 💾 Smart caching — Content-hash invalidation avoids redundant API calls
• 📊 Rich CLI — Beautiful progress bars, graph metrics, and colored output
• 🐳 Docker support — Reproducible builds with multi-stage Dockerfile
• ✅ Production-grade — Strict typing, structured logging, comprehensive test suite

Architecture

src/podifyr/
├── core/          # Exceptions, constants, protocols, types
├── config/        # Pydantic settings with layered resolution
├── logging/       # Structured logging with structlog
├── utils/         # Filesystem helpers, retry logic, async patterns
├── cache/         # Disk-based caching with content-hash invalidation
├── parsing/       # AST visitor, models, filtering engine
├── graph/         # NetworkX graph builder and analyzer
├── agents/        # LangGraph nodes, prompts, orchestrator
├── audio/         # TTS backends (Edge, OpenAI, ElevenLabs, Azure), stitcher
└── cli/           # Typer commands with rich display

Requirements

• Python 3.10+
• FFmpeg (for audio stitching)
• An LLM API key (OpenAI or Azure OpenAI) for script generation
• TTS: Edge TTS works free with no key; OpenAI/ElevenLabs require API keys

Install FFmpeg

# macOS
brew install ffmpeg

# Ubuntu/Debian
sudo apt-get install ffmpeg

# Windows
winget install ffmpeg

Development

# Clone and install in dev mode
git clone https://github.com/anunayandkumar/podifyr-ai.git
cd podifyr-ai
pip install -e ".[dev,docs,all]"

# Run tests
pytest

# Run linter and type checker
ruff check src/ tests/
mypy src/

License

MIT — see LICENSE for details.