LayerCode Gym

A testing toolkit for voice AI agents built on Layercode.com. Quickly spin up a testing environment to run through hundreds of scenarios and understand how your agent will perform in production.

Note: This is an unofficial, community-maintained project.

Perfect for regression testing, load testing, and automated evaluation of your voice AI agents.

Features

• Three User Simulator Types: Fixed text, pre-recorded audio, or AI-driven personas
• Smart Wait Handling: AI personas intelligently wait when assistants need processing time
• Captured Analytics: Full transcripts with TTFAB, latency stats, and audio recordings
• LogFire Integration: Real-time observability and debugging
• Batch Testing: Run hundreds of conversations concurrently
• CLI & Python API: Quick testing via CLI or programmatic control, plus api-agents CLI to swap webhook URLs for CI
• LLM-as-Judge: Bring your own quality evaluation with customizable criteria as a conversational hook
• GitHub Actions Integration: Automated CI/CD testing with parallel persona execution

See examples/ for reference!

Quick Start

Prerequisites: Backend server configured in Layercode dashboard.

No server yet? Launch one quickly:

uvx layercode-create-app run --tunnel --unsafe-update-webhook
# Displays tunnel URL to enter in Layercode dashboard

!! Caution: --unsafe-update-webhook automatically updates the webhook URL in the Layercode dashboard!

CLI Quick Test (No Installation)

# Set environment
export SERVER_URL="http://localhost:8001"
export LAYERCODE_AGENT_ID="your_agent_id"

# Run instantly with uvx (no installation)
uvx layercode-gym run --text "Hello, I need help with my account"

# Multiple messages
uvx layercode-gym run --text "Hi" --text "Tell me more" --text "Goodbye"

# Audio file
uvx layercode-gym run --file recording.wav

# AI agent with persona
uvx layercode-gym run --agent \
  --persona-background "You are a frustrated customer" \
  --persona-intent "Cancel your subscription"

Run uvx layercode-gym --help to see available commands, or uvx layercode-gym run --help for all run options.

Manage Agent Webhooks (for CI)

# List all agents
uvx layercode-gym api-agents list

# Get agent details (use --json for full pipeline config)
uvx layercode-gym api-agents get --agent-id ag-123

# Update webhook URL (useful for PR testing)
uvx layercode-gym api-agents update --agent-id ag-123 --webhook-url https://pr-backend.com/webhook

Cloudflare Tunnel (for Local Development)

Quickly expose your local server to the internet with a Cloudflare tunnel. This is useful for testing webhooks without deploying your backend.

Requires: cloudflared to be installed.

# Basic tunnel - displays URL to copy manually
uvx layercode-gym tunnel --port 8000

# Or specify a full URL directly
uvx layercode-gym tunnel --url http://localhost:8000

# Auto-update agent webhook (recommended for development)
uvx layercode-gym tunnel --port 8000 --unsafe-update-webhook

# Explicit agent ID (overrides LAYERCODE_AGENT_ID env var)
uvx layercode-gym tunnel --port 8000 --agent-id ag-123456 --unsafe-update-webhook

When using --unsafe-update-webhook:

1. The tunnel starts and gets a base URL (e.g., https://random-words.trycloudflare.com)
2. The agent path is appended to create the full webhook URL (e.g., https://random-words.trycloudflare.com/api/agent)
3. Your agent's webhook URL is automatically updated
4. When you stop the tunnel (Ctrl+C), the original webhook URL is restored

Agent path resolution: --agent-path flag → LAYERCODE_AGENT_PATH env var → path from existing webhook → default /api/agent

Environment Variables:

• LAYERCODE_AGENT_ID - Default agent ID for webhook updates
• LAYERCODE_API_KEY - API key for webhook updates (required for --unsafe-update-webhook)
• LAYERCODE_AGENT_PATH - Path to append to tunnel URL (default: extracted from existing webhook, or /api/agent)

Warning: --unsafe-update-webhook modifies your agent's configuration. Only use with development/test agents, not production.

See tunnel documentation for more details.

Python API

# Install
uv add layercode-gym

# Set environment
export SERVER_URL="http://localhost:8001"
export LAYERCODE_AGENT_ID="your_agent_id"
export OPENAI_API_KEY="sk-..."  # For TTS and AI personas

from layercode_gym import LayercodeClient, UserSimulator

# Simple text messages
simulator = UserSimulator.from_text(
    messages=["Hello!", "Tell me about pricing", "Thank you"],
    send_as_text=True
)

client = LayercodeClient(simulator=simulator)
conversation_id = await client.run()

Architecture

┌─────────────┐                    ┌──────────────┐
│  Your Test  │──1. Authorize──────▶│ Your Backend │
│    Code     │                     │   Server     │
└─────────────┘                     └──────────────┘
       │                                    │
       │                             2. Return
       │                           client_session_key
       │                                    │
       └──────3. Connect with key───────────┘
                      │
                      ▼
              ┌──────────────┐
              │  Layercode   │
              │   Platform   │
              └──────────────┘

Flow:

1. Client authorizes through YOUR backend server (SERVER_URL)
2. Backend returns client_session_key from LayerCode
3. Client connects to LayerCode WebSocket with that key

The client never hits LayerCode's API directly - it always goes through your backend first.

User Simulators

Three types for different testing needs:

1. Fixed Text Messages

Fastest option, perfect for regression testing:

simulator = UserSimulator.from_text(
    messages=["Hello", "Tell me more", "Goodbye"],
    send_as_text=True  # or False to use TTS
)

2. Pre-recorded Audio Files

Test transcription and audio handling:

from pathlib import Path

simulator = UserSimulator.from_files(
    files=[Path("greeting.wav"), Path("question.wav")]
)

3. AI Agent Personas

Realistic, dynamic conversations using PydanticAI:

from layercode_gym import Persona

simulator = UserSimulator.from_agent(
    persona=Persona(
        background_context="You are a 35-year-old small business owner",
        intent="You want to understand pricing and features"
    ),
    model="openai:gpt-5-mini",
    max_turns=5
)

Examples

The examples/ directory contains ready-to-run scripts:

• 01_text_messages.py - Simple text conversation for quick testing
• 02_audio_file.py - Stream pre-recorded audio to test transcription
• 03_agent_persona.py - AI-driven user with dynamic responses
• 04_callbacks_judge.py - CriteriaJudge for automated pass/fail evaluation
• 05_batch_evaluation.py - Run multiple conversations concurrently
• 06_outdoor_shop_eval.py - Custom data processor with domain-specific criteria
• 07_custom_judge.py - Build your own judge with custom PydanticAI output types
• 08_long_running_task.py - Testing agents with wait handling for slow operations

Run any example:

python examples/01_text_messages.py

See full documentation for detailed explanations.

LLM-as-Judge Evaluation

Evaluate conversations against pass/fail criteria using CriteriaJudge:

from layercode_gym import CriteriaJudge, LayercodeClient, Settings

judge = CriteriaJudge(
    criteria=[
        "Did the agent answer all user questions?",
        "Was the agent polite and professional?",
        "Did the conversation flow naturally?"
    ],
    # Note: gpt-5-mini is fast/cheap for testing; use gpt-5 for production
    model="openai:gpt-5-mini"
)

async def on_end(log):
    result = await judge.evaluate(log)
    print(f"Overall: {'PASS' if result.overall_pass else 'FAIL'}")
    judge.save_results(result, log.conversation_id, Settings.load().output_root)

client = LayercodeClient(
    simulator=simulator,
    conversation_callback=on_end
)

Results saved to conversations/<id>/judge_evaluation.json with full evaluation metadata:

{
  "schema_version": "1.0",
  "evaluated_at": "2025-12-05T13:15:41.124793+00:00",
  "model": "openai:gpt-5-mini",
  "criteria": [{"id": 1, "criterion": "Did the agent answer all user questions?"}],
  "additional_context": "Optional context provided to the judge",
  "judgment": {
    "criteria_results": [{"criterion_id": 1, "passed": true}],
    "overall_pass": true,
    "reasoning": "The agent answered all questions clearly..."
  },
  "results_summary": [{"id": 1, "criterion": "...", "passed": true}]
}

Batch Testing

Run hundreds of conversations concurrently:

import asyncio
from tqdm.asyncio import tqdm_asyncio

scenarios = ["Message 1", "Message 2", "Message 3"]
tasks = [run_conversation(msg) for msg in scenarios]

results = await tqdm_asyncio.gather(*tasks, desc="Running conversations")

See examples/05_batch_evaluation.py for the complete pattern.

GitHub Actions CI/CD

Run automated tests in your CI pipeline with multiple personas in parallel:

- uses: ./.github/actions/layercode-gym-test
  with:
    personas: |
      - background: You are a potential customer
        intent: Learn about pricing and features

      - background: You are a frustrated user
        intent: Get help with a problem
    judge-enabled: true
    judge-criteria: |
      - Did the agent provide clear and helpful responses?
    server-url: ${{ secrets.SERVER_URL }}
    layercode-agent-id: ${{ secrets.LAYERCODE_AGENT_ID }}
    openai-api-key: ${{ secrets.OPENAI_API_KEY }}

Features:

• Run multiple personas in parallel for maximum speed
• Automated quality evaluation with LLM judge
• Detailed artifacts with transcripts and audio recordings
• Optional LogFire observability integration

Tip: Use the api-agents CLI to update your agent's webhook URL for PR testing:

# Point agent to PR-specific backend before running tests
layercode-gym api-agents update --agent-id ag-123 --webhook-url https://pr-456.example.com/webhook

# Restore original after tests
layercode-gym api-agents update --agent-id ag-123 --webhook-url https://production.example.com/webhook

See GitHub Actions documentation for complete setup guide, or api-agents CLI docs for webhook management.

Conversation Outputs

After each conversation:

conversations/<conversation_id>/
├── transcript.json          # Full log with timing metrics
├── conversation_mix.wav     # Combined audio (user + assistant)
├── user_0.wav              # Individual user turns
├── assistant_0.wav         # Individual assistant turns
└── judge_evaluation.json   # CriteriaJudge results (if enabled)

Transcript includes TTFAB, latency stats, turn counts, and full message history.

Custom Implementations

Custom TTS Engine

from layercode_gym.simulator import TTSEngineProtocol
from pathlib import Path

class MyTTSEngine(TTSEngineProtocol):
    async def synthesize(self, text: str, **kwargs) -> Path:
        # Your TTS service (ElevenLabs, Azure, etc.)
        return audio_file_path

simulator = UserSimulator.from_text(
    messages=["Hello!"],
    send_as_text=False,
    tts_engine=MyTTSEngine()
)

Custom LLM for Agents

Use any LLM supported by PydanticAI. Important: You must define the system prompt with proper placeholders.

from pydantic_ai import Agent
from textprompts import TextTemplates

# Load the required prompt template
templates = TextTemplates("src/layercode_gym/simulator/prompts")
system_prompt = templates.render(
    "basic_agent.txt",
    background_context="Your background",
    intent="Your intent"
)

# Create custom agent with proper system prompt
agent = Agent(
    "anthropic:claude-3-5-sonnet",
    system_prompt=system_prompt
)

simulator = UserSimulator.from_agent(agent=agent, deps=my_deps)

Available models:

• openai:gpt-5 / openai:gpt-5-mini
• anthropic:claude-3-5-sonnet
• ollama:llama3 (local)
• gemini:gemini-1.5-pro

Prompt requirements: The system prompt must include {background_context} and {intent} placeholders. See src/layercode_gym/simulator/prompts/basic_agent.txt for the default template.

Custom Simulator

Full control via protocol implementation:

from layercode_gym.simulator import UserSimulatorProtocol, UserRequest, UserResponse

class MyCustomSimulator(UserSimulatorProtocol):
    async def get_response(self, request: UserRequest) -> UserResponse | None:
        # Your logic here
        return UserResponse(text="Hello!", audio_path=None, data=())

Environment Variables

Required:

SERVER_URL="http://localhost:8001"       # Your backend server
LAYERCODE_AGENT_ID="your_agent_id"       # LayerCode agent ID

Optional:

OPENAI_API_KEY="sk-..."                  # For TTS and AI agents
OPENAI_TTS_MODEL="gpt-4o-mini-tts"       # TTS model
OPENAI_TTS_VOICE="coral"                 # Voice (alloy, echo, fable, onyx, nova, shimmer, coral)
LAYERCODE_OUTPUT_ROOT="./conversations"  # Save location
LOGFIRE_TOKEN="..."                      # Enable LogFire observability

LogFire Integration

Real-time observability and debugging with LogFire:

export LOGFIRE_TOKEN="your_token_here"

Automatically instruments PydanticAI and OpenAI calls, providing:

• Real-time conversation tracking
• Performance metrics and spans
• Error tracking with stack traces
• Beautiful UI for exploring conversations

Type Safety

Enforces mypy --strict throughout. All event schemas use TypedDict or dataclasses.

uv run mypy src/layercode_gym

Related Projects

• layercode-create-app - CLI to scaffold LayerCode backends with tunneling
• layercode-examples - Agent patterns and integration recipes

Documentation

Full documentation at svilupp.github.io/layercode-gym

• Getting Started
• Core Concepts
• Examples
• API Reference
• Advanced Usage

Contributing

This is a minimal, focused toolkit. Extensions should be done via:

• Custom simulator strategies (implement UserSimulatorProtocol)
• Custom callbacks (implement TurnCallback or ConversationCallback)
• Custom TTS engines (implement TTSEngineProtocol)

Keep the core simple and extensible.

License

MIT - See LICENSE file for details.