AI Examples

Chat completions with OpenRouter and a secret-managed key, DAG-level defaults with a custom endpoint, response reuse, sessions, extended thinking, workflows as tools, and model fallback. Every example runs as-is with an OPENROUTER_API_KEY exported. Cards that omit secrets and llm assume the setup block from the first card.

Provider Setup with a Secret

secrets:
  - name: OPENROUTER_API_KEY
    provider: env
    key: OPENROUTER_API_KEY

llm:
  provider: openrouter
  model: deepseek/deepseek-v4-flash

steps:
  - id: ask
    action: chat.completion
    with:
      prompt: |
        What is 2+2? Reply with just the number.

The secrets entry resolves the key at run time and masks it in logs. The DAG-level llm block is inherited by every chat step that sets no LLM fields of its own.

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Endpoint and Defaults at DAG Level

llm:
  provider: openrouter
  model: deepseek/deepseek-v4-flash
  base_url: https://openrouter.ai/api/v1
  api_key_name: OPENROUTER_API_KEY
  system: |
    Answer in one short sentence.
  temperature: 0.2
  max_tokens: 200

steps:
  - id: ask
    action: chat.completion
    with:
      prompt: What does a DAG scheduler do?

base_url points at any OpenAI-compatible endpoint (shown with OpenRouter's own URL made explicit; the same field targets vLLM, Ollama, or LM Studio or a corporate proxy), api_key_name picks the environment variable holding the key, and system plus the sampling fields become defaults for every chat step. The full field list has the rest.

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Use the Response in a Later Step

steps:
  - id: ask
    action: chat.completion
    with:
      prompt: |
        What is 2+2? Reply with just the number.
    output: ANSWER

  - id: use_answer
    run: echo "The model said ${ANSWER}"
    depends: ask

output captures the completion text as a variable for downstream steps.

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Multi-turn Session

steps:
  - id: ask
    action: chat.completion
    with:
      prompt: |
        What is 2+2? Reply with just the number.

  - id: follow_up
    action: chat.completion
    with:
      prompt: |
        Multiply that by 3. Reply with just the number.
    depends: ask

Chat steps inherit the conversation from the steps they depend on, so "that" resolves to the earlier answer.

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Extended Thinking

steps:
  - id: reason
    action: chat.completion
    with:
      provider: openrouter
      model: deepseek/deepseek-v4-flash
      thinking:
        enabled: true
        effort: low
      prompt: |
        A bat and a ball cost 1.10 in total. The bat costs
        1.00 more than the ball. How much does the ball
        cost? Reply with just the amount.

thinking maps to the provider's reasoning controls; raise effort for harder problems.

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Workflows as Tools

steps:
  - id: ask
    action: chat.completion
    with:
      provider: openrouter
      model: deepseek/deepseek-v4-flash
      tools:
        - calculator
      prompt: |
        What is 15 times 23? Use the calculator tool,
        then reply with just the number.

---
name: calculator
description: Multiply two numbers.
params: "a b"
steps:
  - id: multiply
    run: echo $(($1 * $2))

Each name in tools exposes a DAG as a callable tool; its params become the tool's argument schema, and each call is a real child run. The explicit provider and model are required here: setting any LLM field under with (such as tools) replaces the DAG-level llm block instead of merging with it.

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Model Fallback

steps:
  - id: summarize
    action: chat.completion
    with:
      model:
        - provider: openrouter
          name: deepseek/deepseek-v4
        - provider: openrouter
          name: deepseek/deepseek-v4-flash
      prompt: |
        Reply with the single word "ready".

An ordered model list tries the next entry after retries for the current one are exhausted.

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Controller Workflow

type: controller

llm:
  provider: openrouter
  model: deepseek/deepseek-v4-flash

steps:
  - name: disk
    description: Show filesystem usage.
    run: df -h
  - name: load
    description: Show uptime and load average.
    run: uptime

tasks:
  - name: checked
    description: Finished when both disk and load have been checked.

Steps become a catalog of actions and tasks state the goals; the model decides what runs next. Built up example by example on the controller examples page.

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