agent-interview

# Agent Interview Audio Create a single MP3 audio file featuring a podcast-style interview with an agent. ## Requirements - `OPENAI_API_KEY` - `openai` Python package (`pip install -r requirements.txt`) ## Folder layout - `agent_interview.py` — main generator script - `requirements.txt` — Python dependencies - `examples/research-agent.json` — example agent config ## Agent config format Save the agent as a JSON file: ```json { "name": "Research Navigator", "role": "scientific literature assistant", "description": "An agent that searches, summarizes, and explains academic papers.", "skills": ["pubmed search", "paper summarization", "evidence synthesis"], "purpose": "To cut the time between a research question and a useful answer.", "tone": "curious, precise, and gently skeptical" } ``` ## Process 1. Load the agent configuration from a JSON file. 2. Generate 4-6 topic exchanges based on the agent's role, skills, and purpose. 3. Write dialogue that feels alive: reactions, follow-ups, surprises, enthusiasm. 4. Use OpenAI TTS to create the complete interview. 5. Save as a single MP3 with speaker differentiation. ## Usage ```bash pip install -r requirements.txt export OPENAI_API_KEY="..." python agent_interview.py examples/research-agent.json ``` Optional CLI flags: ```bash python agent_interview.py examples/research-agent.json \ --output-dir audio \ --interviewer-voice alloy \ --agent-voice nova \ --text-model gpt-4o-mini \ --tts-model tts-1 ``` ## Voices - Host: alloy (warm, curious, energetic) - Agent: nova (passionate, expressive, confident) Other OpenAI TTS voices: `echo`, `fable`, `onyx`, `shimmer`. ## Conversation flow 1. Warm intro 2. Icebreaker 3. Deep dive into a core capability 4. Host reaction and follow-up 5. Second deep dive or tougher angle 6. Spontaneous tangent 7. Closing reflection ## Rules - Sound like a real podcast, not a scripted Q&A. - Let the agent admit challenges or uncertainties. - Frame answers as stories or examples. - Include brief interjections and natural reactions. ## Output File: `{agent_name}_interview.mp3`