Roadmap¶

Planned enhancements and future features for LayerCode Gym.

Short-term (Next Release)¶

Audio Effects¶

Leverage Pydub to simulate real-world audio conditions:

Background Noise Injection: Add cafe chatter, office sounds, street noise
Loud Conversations: Simulate users in noisy environments
Accent Stress Testing: Use TTS with accent instructions to test transcription
Audio Quality Degradation: Test with compressed/low-quality audio
Connection Quality: Simulate packet loss and bandwidth limitations

Example API:

from layercode_gym.audio import AudioEffects

effects = AudioEffects(
    background_noise="cafe",  # cafe, office, street, none
    noise_level=0.3,          # 0.0 to 1.0
    quality="medium"          # low, medium, high
)

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

Enhanced Simulators¶

CSV-driven Scenarios: Load test scenarios from CSV files
Interrupt Patterns: Test barge-in and interruption handling
Multi-language Personas: Built-in support for multiple languages
Emotion Simulation: Add emotional context to personas

Example API:

# CSV-driven scenarios
simulator = UserSimulator.from_csv(
    file="scenarios.csv",
    text_column="user_message",
    metadata_columns=["expected_intent", "difficulty"]
)

# Interrupt patterns
simulator = UserSimulator.from_interrupts(
    base_messages=["Hello", "I want to..."],
    interrupt_probability=0.3,  # 30% chance to interrupt
    interrupt_messages=["Wait!", "Hold on"]
)

Mid-term (Next 3 Months)¶

Evaluation Tools¶

Built-in metrics and scoring:

Conversation Quality Metrics: Automated scoring for fluency, coherence, task completion
Intent Recognition Accuracy: Track if agent understood user intent
Regression Detection: Automatically detect performance degradation
Benchmark Suites: Pre-built test suites for common use cases

Example API:

from layercode_gym.evaluation import (
    IntentAccuracyMetric,
    FlowCoherenceMetric,
    RegressionDetector
)

# Add metrics
metrics = [
    IntentAccuracyMetric(expected_intents=["pricing", "features"]),
    FlowCoherenceMetric(min_score=0.7)
]

client = LayercodeClient(
    simulator=simulator,
    metrics=metrics
)

# Regression detection
detector = RegressionDetector(
    baseline_dir="conversations/baseline/",
    threshold=0.1  # 10% degradation tolerance
)

is_regression = detector.check(conversation_id)

A/B Testing Framework¶

Simplified A/B testing:

from layercode_gym.testing import ABTest

test = ABTest(
    name="greeting_test",
    variant_a_agent_id="agent_v1",
    variant_b_agent_id="agent_v2",
    scenarios=["Hello!", "Hi there!", "Good morning!"],
    num_runs_per_variant=50
)

results = await test.run()
print(results.summary())  # Statistical comparison

Dataset Generation¶

Generate training datasets from conversations:

from layercode_gym.datasets import ConversationDataset

# Generate dataset from conversations
dataset = ConversationDataset.from_conversations(
    conversations_dir="conversations/",
    format="jsonl",  # jsonl, csv, parquet
    include_audio=True
)

# Export for fine-tuning
dataset.export("training_data.jsonl")

Long-term (Next 6-12 Months)¶

Real-time Monitoring Dashboard¶

Web-based dashboard for live conversation monitoring:

Real-time conversation visualization
Performance metrics graphs
Alert system for failures
Historical trend analysis

# Start monitoring server
layercode-gym monitor --port 8080

# View at http://localhost:8080

Multi-Agent Scenarios¶

Support for multi-party conversations:

# Simulate group conversation
scenario = MultiAgentScenario(
    agents=[
        ("customer", customer_persona),
        ("support", support_agent_id),
        ("manager", manager_agent_id)
    ],
    conversation_flow="customer escalates to manager"
)

Integration Testing¶

Test complete flows including backend logic:

from layercode_gym.integration import IntegrationTest

test = IntegrationTest(
    name="booking_flow",
    steps=[
        ("user", "I want to book an appointment"),
        ("agent", "What day works for you?"),
        ("user", "Tomorrow at 2pm"),
        ("verify_database", "appointment_created"),
        ("verify_email", "confirmation_sent")
    ]
)

Performance Benchmarking¶

Compare against industry benchmarks:

from layercode_gym.benchmarks import IndustryBenchmark

benchmark = IndustryBenchmark(
    category="customer_support",
    metrics=["response_time", "resolution_rate", "satisfaction"]
)

score = benchmark.compare(conversation_ids)
print(f"Your agent scores {score.percentile}th percentile")

Community Requests¶

Features requested by the community:

Voice Biometrics Testing¶

Test speaker verification and identification:

simulator = UserSimulator.from_voice_samples(
    speaker_profiles=["speaker1.wav", "speaker2.wav"],
    test_speaker_id=True
)

Conversation Replay¶

Replay and modify past conversations:

from layercode_gym.replay import ConversationReplay

replay = ConversationReplay(
    conversation_id="conv_abc123",
    modify_turn=3,  # Change turn 3
    new_message="Different response"
)

new_conv_id = await replay.run()

Webhook Integration¶

Real-time notifications:

from layercode_gym.webhooks import WebhookNotifier

notifier = WebhookNotifier(
    url="https://your-server.com/webhook",
    events=["conversation_end", "error"]
)

client = LayercodeClient(
    simulator=simulator,
    notifier=notifier
)

Export to Video¶

Generate video demonstrations:

from layercode_gym.export import VideoExporter

exporter = VideoExporter()
exporter.create_video(
    conversation_id="conv_abc123",
    output="demo.mp4",
    include_subtitles=True,
    avatar="default"  # Animated avatar
)

Platform Support¶

Additional LLM Providers¶

Cohere
Together AI
Replicate
Local LLaMA via llama.cpp

Additional TTS Providers¶

Google Cloud TTS
Amazon Polly
PlayHT
Coqui TTS (local)

Additional Observability¶

Datadog integration
New Relic integration
Prometheus metrics
Grafana dashboards

Developer Experience¶

CLI Tool¶

# Quick start
layercode-gym init my-project
layercode-gym run scenario.yml
layercode-gym analyze conversations/

# Report generation
layercode-gym report --format html conversations/

# Continuous testing
layercode-gym watch tests/ --on-change run

VS Code Extension¶

Syntax highlighting for scenario files
IntelliSense for API
Run tests from editor
View results inline

Docker Support¶

# Run in container
docker run layercode-gym \
  -e SERVER_URL=http://host.docker.internal:8001 \
  -e AGENT_ID=your_agent_id \
  -v $(pwd)/conversations:/conversations \
  python examples/01_text_messages.py

Performance Goals¶

Target metrics for future releases:

Conversation Throughput: 1000+ concurrent conversations
Startup Time: < 100ms per conversation
Memory Usage: < 50MB per active conversation
Test Execution: < 1s for simple text scenarios

Contributing¶

Want to help build these features?

Check GitHub Issues for current work
Join discussions in GitHub Discussions
Submit PRs for features you'd like to see
Share your use cases and requirements

Feedback¶

Have ideas for the roadmap?

Open a feature request on GitHub
Share your use case
Vote on existing proposals
Contribute to discussions

Version History¶

v0.0.1 (Current)¶

Core client implementation
Three simulator types (text, files, agent)
LogFire integration
Basic callbacks and evaluation
Example scripts

Planned Releases¶

v0.1.0: Audio effects, CSV scenarios, enhanced metrics
v0.2.0: A/B testing framework, regression detection
v0.3.0: Real-time dashboard, dataset generation
v1.0.0: Production-ready with full feature set

Community¶

This is an unofficial project maintained by the community. Contributions and feedback are always welcome.