Pipecat Friday Agent

Pipecat Friday Agent

Selective Reading Rule

Start with:

• references/senior-master-standard.md
• references/usage-routing.md
• references/quality-checklist.md

Then load only the inherited docs, scripts, assets, or examples that match the user's actual task.

Overview

This skill provides a blueprint for building F.R.I.D.A.Y. (Replacement Integrated Digital Assistant Youth), a local voice assistant inspired by the tactical AI from the Iron Man films. It uses the Pipecat framework to orchestrate a low-latency pipeline:

• STT: OpenAI Whisper (whisper-1) or gpt-4o-transcribe
• LLM: Google Gemini 2.5 Flash (via a compatibility shim)
• TTS: OpenAI TTS (nova voice)
• Transport: Local Audio (Hardware Mic/Speakers)

When to Use This Skill

• Use when you want to build a real-time, conversational voice agent.
• Use when working with the Pipecat framework for pipeline-based AI.
• Use when you need to integrate multiple providers (Google and OpenAI) into a single voice loop.
• Use when building Iron Man-themed or tactical-themed voice applications.

How It Works

Step 1: Install Dependencies

You will need the Pipecat framework and its service providers installed:

pip install pipecat-ai[openai,google,silero] python-dotenv

Step 2: Configure Environment

Create a .env file with your API keys:

OPENAI_API_KEY=your_openai_key
GOOGLE_API_KEY=your_google_key

Step 3: Run the Agent

Execute the provided Python script to start the interface:

python scripts/friday_agent.py

Core Concepts

Pipeline Architecture

The agent follows a linear pipeline: Mic -> VAD -> STT -> LLM -> TTS -> Speaker. This allows for granular control over each stage, unlike end-to-end speech-to-speech models.

Google Compatibility Shim

Since Google's Gemini API has a different message format than OpenAI's standard (which Pipecat aggregators expect), the script includes a GoogleSafeContext and GoogleSafeMessage class to bridge the gap.

Best Practices

• ✅ Use Silero VAD: It is robust for local hardware and prevents background noise from triggering the LLM.
• ✅ Concise Prompts: Tactical agents should give short, data-dense responses to minimize latency.
• ✅ Sample Rate Match: OpenAI TTS outputs at 24kHz; ensure your audio_out_sample_rate matches to avoid high-pitched or slowed audio.
• ❌ No Polite Fillers: Avoid "Hello, how can I help you today?" Instead, use "Systems nominal. Ready for commands."

Troubleshooting

• Problem: Audio is choppy or delayed.
  • Solution: Check your OUTPUT_DEVICE index. Run a script like test_audio_output.py to find the correct hardware index for your OS.
• Problem: "Validation error" for message format.
  • Solution: Ensure the GoogleSafeContext shim is correctly translating OpenAI-style dicts to Gemini-style schema.

Related Skills

• @voice-agents - General principles of voice AI.
• @agent-tool-builder - Add tools (Search, Lights, etc.) to your Friday agent.
• @llm-architect - Optimizing the LLM layer.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.