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YAML Configuration

Configure a Machina agent declaratively -- no Python needed for standard deployments.

Quick Example

Create machina.yaml:

name: "Maintenance Assistant"

plant:
  name: "North Plant"
  location: "Building A"

connectors:
  cmms:
    type: generic_cmms
    settings:
      data_dir: "./data/cmms"
  docs:
    type: document_store
    settings:
      paths: ["./data/manuals"]

channels:
  - type: cli

llm:
  provider: "ollama:llama3"
  temperature: 0.1

sandbox: false

Then load it:

from machina import Agent

agent = Agent.from_config("machina.yaml")
agent.run()

Configuration Reference

Top-level fields

Field Type Default Description
name string "Machina Agent" Agent name
description string "Maintenance AI assistant" Agent description
plant object {name: "Default Plant"} Plant configuration
connectors object {} Named connector instances
channels list [] (defaults to CLI) Communication channels
llm object {provider: "ollama:llama3"} LLM provider settings
sandbox boolean false Enable sandbox mode (writes logged, not executed)
logging object {} Logging configuration overrides

Plant

plant:
  name: "North Plant"
  location: "Building A"

Connectors

Each connector has a type, optional enabled flag, and a settings dict:

connectors:
  my_cmms:
    type: generic_cmms
    enabled: true          # default: true
    settings:
      data_dir: "./data/cmms"

Available types

Type Class Extra
generic_cmms GenericCmmsConnector --
sap_pm SapPmConnector cmms-rest
maximo MaximoConnector cmms-rest
upkeep UpKeepConnector cmms-rest
opcua OpcUaConnector opcua
mqtt MqttConnector mqtt
document_store DocumentStoreConnector docs-rag
simulated_sensor SimulatedSensorConnector --
telegram TelegramConnector telegram
slack SlackConnector slack
email EmailConnector --
calendar CalendarConnector calendar

The settings dict is passed as keyword arguments to the connector constructor. Check each connector's documentation for available settings.

Channels

channels:
  - type: cli
  - type: telegram
    settings:
      bot_token: "${BOT_TOKEN}"

If channels is empty or omitted, a CLI channel is used by default.

LLM

llm:
  provider: "ollama:llama3"     # or "openai:gpt-4o", "anthropic:claude-sonnet-4-20250514"
  temperature: 0.1
  max_tokens: 4096

Environment Variables

Use ${VAR} syntax anywhere in the YAML. Variables are resolved at load time:

connectors:
  sap:
    type: sap_pm
    settings:
      url: "https://sap.company.com/odata/v4"
      auth:
        token: "${SAP_TOKEN}"

export SAP_TOKEN=eyJhbGci...
python agent.py

If a referenced variable is not set, load_config() raises ValueError with a clear message.

When to Use YAML vs Python

YAML config is designed for knowledge-base agents — the kind that answer technician questions over CMMS data, equipment manuals, and spare part inventories. These agents need connectors, an LLM, and a chat channel, but no custom automation logic.

For agents with automated workflows (alarm response, predictive pipelines, spare part reorder), use Python. Workflows contain logic that YAML can't express: guard conditions with lambdas, error policies, LLM reasoning steps, and template variables. This is a deliberate design choice — encoding arbitrary Python logic in YAML would be fragile and hard to debug.

YAML config Python
Agent type Knowledge-base / Q&A Workflow automation
What you configure Connectors, LLM, channels, plant Everything + workflows with guards, lambdas
Workflows Not supported Full DSL (examples 01-04)
Best for Standard deployments, Docker, ops teams Complex agents, custom integrations
Example yaml_config/ quickstart/, alarm_to_workorder/

Hybrid approach

You can combine both: configure connectors and LLM via YAML, then register workflows in Python:

from machina import Agent
from machina.workflows.builtins import alarm_to_workorder

agent = Agent.from_config("machina.yaml")
agent.register_workflow(alarm_to_workorder)
agent.run()

Next Steps