I'm here to help you deploy your Python code on LiveKit Cloud! Just follow these friendly steps: 1. Ensure Prerequisites: First, make sure you have the LiveKit CLI (v2.5 or later) installed, have created a LiveKit Cloud project, and that your Python version is 3.9 or higher. 2. Navigate to Your Project Directory: Open your terminal and head over to the folder where your code lives by running

cd your-agent-project

. 3. Authenticate: Next, run

lk cloud auth

to connect with LiveKit Cloud. If you need, set your default project using

lk project set-default "<project-name>"

. 4. Deploy Your Agent: Now, deploy your code with

lk agent deploy

. If this is your first deployment, start with

lk agent create

. 5. Dockerfile Requirements: Make sure your Dockerfile uses a glibc-based image like Debian or Ubuntu, sets a working directory, installs all necessary dependencies, and has a startup command defined. 6. Secrets Management: For your sensitive data, it’s best to use LiveKit Cloud’s secrets management instead of embedding them in your code or Dockerfile. 7. Monitor Deployment: Keep an eye on how things are going by checking the status and logs with

lk agent status

and

lk agent logs

. To get started quickly, you might find the agent-starter-python repository super helpful as a template. If you run into any bumps or have questions along the way, just let me know—I’m here to support you! Sources: Deploying to production | LiveKit Docs | Voice AI quickstart | LiveKit Docs | taskfile.yaml | Builds and Dockerfiles | LiveKit Docs | Agent deployment CLI reference | LiveKit Docs

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I'm here to help you every step of the way! When you have a moment, could you please share the content of your Dockerfiles? That way, I can take a closer look and better understand what might be going on with your deployment setup.

# ============================================================================= # VOICE AGENT DOCKERFILE # ============================================================================= # Multi-stage build for optimized voice agent container # ============================================================================= # BUILD STAGE # ============================================================================= FROM python:3.11-slim AS builder # Set build arguments ARG DEBIAN_FRONTEND=noninteractive # Install build dependencies RUN apt-get update && apt-get install -y \ build-essential \ python3-dev \ curl \ && rm -rf /var/lib/apt/lists/* # Create virtual environment RUN python -m venv /opt/venv ENV PATH="/opt/venv/bin:$PATH" # Copy requirements and install dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # ============================================================================= # RUNTIME STAGE # ============================================================================= FROM python:3.11-slim AS runtime # Set environment variables ENV PYTHONUNBUFFERED=1 \ PYTHONDONTWRITEBYTECODE=1 \ PATH="/opt/venv/bin:$PATH" \ PYTHONPATH="/app/src" # Create application directory WORKDIR /app # Copy virtual environment and application code COPY --from=builder /opt/venv /opt/venv COPY src/ ./src/ COPY start_voice.sh ./ # Create non-root user for security RUN groupadd -r appgroup && \ useradd -r -g appgroup -u 1000 appuser && \ chown -R appuser:appgroup /app # Switch to non-root user USER appuser # Expose port EXPOSE 8082 # Health check - HTTP-based using built-in health endpoint # Note: When deployed to Kubernetes, the liveness/readiness probes in k8s-manifests.tf # will be used instead of this Docker HEALTHCHECK. However, this is still useful for: # - Local development and testing with docker run # - Non-Kubernetes deployments (Docker Compose, etc.) # - Consistency validation (both should check the same endpoint) HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ CMD curl -f http://localhost:8082/health || exit 1 # Set the entrypoint with correct Python path CMD ["./start_voice.sh"]