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Custom Agents

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How It Works

1. Create a custom agent from the dashboard
2. Write code in the built-in editor or let the AI assistant generate it
3. Build the Docker image from your code
4. Deploy as a persistent worker or Preview for testing

Dashboard Code Editor
        │
        ▼
Code stored in MinIO → Docker image built → Container spawned
                                                    │
                                                    ▼
                                            Connects to LiveKit
                                            Handles voice calls

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Creating a Custom Agent

1. Go to Dashboard → Agents → Create Agent
2. Select Custom mode
3. The agent is scaffolded with default files:

agent.py              — Main entry point (required)
requirements.txt      — Extra pip dependencies
prompts/
  system.txt          — System prompt for the LLM
tools/
  __init__.py         — Custom function tools
webhooks/
  __init__.py         — Webhook handlers
pipelines/
  __init__.py         — Custom pipeline stages
utils/
  __init__.py         — Shared helpers

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AI Coding Assistant

Create a voice agent that can search the web using Tavily to answer questions about current events, news, or facts. Always use the Tavily days parameter set to 7 so results are only from the last week. Keep responses conversational.

Add a tool that checks order status by calling our REST API at https://api.example.com/orders

Switch the STT provider to Deepgram and add sentiment analysis

• Generate all file changes
• Detect when API keys are needed and show a secure input form
• Pick an appropriate voice for the agent
• Follow LiveKit Agents SDK best practices

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Environment Variables

• Platform keys (LiveKit, Google, Resemble, STT) are injected automatically from your dashboard Settings
• Custom keys (e.g., Tavily, Stripe, your own APIs) are added per-agent via the Environment Variables tab or through the AI assistant

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Preview vs Deploy

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Preview

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Deploy

• Registers with LiveKit as arkenos-custom-{agent_id}
• Accepts dispatched calls (inbound SIP and outbound)
• Runs with restart: unless-stopped (auto-recovery)
• Can be stopped from the dashboard

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Writing Tools

from livekit.agents import function_tool, RunContext

@function_tool
async def search_web(query: str, context: RunContext) -> str:
    """Search the web for current information."""
    async with httpx.AsyncClient() as client:
        resp = await client.post("https://api.tavily.com/search", json={
            "api_key": os.environ.get("TAVILY_API_KEY"),
            "query": query,
            "days": 7,
            "max_results": 3,
        })
        results = resp.json().get("results", [])
        return "\n".join(f"- {r['title']}: {r['content'][:200]}" for r in results)

class MyAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a helpful assistant with web search.",
            tools=[search_web],
        )

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Speak-while-executing

@function_tool
async def slow_lookup(query: str, context: RunContext) -> str:
    """Look up information."""
    await context.session.say("Let me look that up, one moment.")
    # ... do the slow thing
    return result

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File Versioning

• View version history for any file
• Roll back to a previous version
• See what changed between versions

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Build and Image Caching

1. All files are downloaded from MinIO
2. A Docker image is built on top of arkenos-agent-base:latest
3. The image is tagged with a hash of requirements.txt
4. If requirements haven’t changed, the cached image is reused

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Pre-Installed Libraries