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docs: recipe updates (#3844)
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@ -32,7 +32,7 @@ goose run --recipe trip.yaml
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## Extensions
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Goose recipes have a section where you can specify which extensions Goose can use during execution. Goose will only use the ones you specify.
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Goose recipes have a section where you can specify which [extensions](/docs/guides/recipes/recipe-reference#extensions) Goose can use during execution. Goose will only use the ones you specify.
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Let's say we want to make sure we have good weather during our Europe trip. We can just add a weather extension (this example uses the [weather-mcp-server](https://github.com/TuanKiri/weather-mcp-server) by TuanKiri under the MIT License) to our recipe, modify the prompt a bit and now Goose will check the weather before adding a city to our trip.
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@ -53,6 +53,8 @@ extensions:
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args: []
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timeout: 300
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description: "Weather data for trip planning"
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env_keys:
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- WEATHER_API_KEY
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```
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## Parameters
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214
documentation/docs/tutorials/sub-recipes-in-parallel.md
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214
documentation/docs/tutorials/sub-recipes-in-parallel.md
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---
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title: Running Sub-Recipes In Parallel
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sidebar_label: Sub-Recipes In Parallel
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description: Run multiple sub-recipes instances concurrently with real-time progress tracking
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---
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Goose recipes can execute multiple [sub-recipe](/docs/guides/recipes/sub-recipes) instances concurrently using isolated worker processes. This feature enables efficient batch operations, parallel processing of different tasks, and faster completion of complex workflows.
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:::warning Experimental Feature
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Running sub-recipes in parallel is an experimental feature in active development. Behavior and configuration may change in future releases.
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:::
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Here are some common use cases:
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- **Monorepo build failures**: When 3 services fail in a monorepo build, use a "diagnose failure" sub-recipe with each build URL to diagnose all failures in parallel
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- **Document summarization**: Process a CSV file with document links by running a "summarize document" sub-recipe for each link simultaneously
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- **Code analysis across repositories**: Run security, quality, and performance analysis on multiple codebases simultaneously
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## How It Works
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Parallel sub-recipe execution uses an isolated worker system that automatically manages concurrent task execution. Goose creates individual tasks for each sub-recipe instance and distributes them across up to 10 concurrent workers.
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| Scenario | Default Behavior | Override Options |
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|----------|------------------|------------------|
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| **Different sub-recipes** | Sequential | Add "in parallel" to prompt |
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| **Same sub-recipe** with different parameters | Parallel | • Set `sequential_when_repeated: true`<br />• Add "sequentially" to prompt |
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### Different Sub-Recipes
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When running different sub-recipes, Goose determines the execution mode based on:
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1. **Explicit user request** in the prompt ("in parallel", "sequentially")
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2. **Sequential execution by default**: Different sub-recipes run one after another unless explicitly requested to run in parallel
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In your prompt, you can simply mention "in parallel" in your prompt when calling different sub-recipes:
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```yaml
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prompt: |
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run the following sub-recipes in parallel:
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- use weather sub-recipe to get the weather for Sydney
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- use things-to-do sub-recipe to find activities in Sydney
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```
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### Same Sub-Recipe
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When running the same sub-recipe with different parameters, Goose determines the execution mode based on:
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1. **[Recipe-level configuration](#choosing-between-sequential-and-parallel-execution)** (`sequential_when_repeated` flag) - when set to true, this forces sequential execution
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2. **User request** in the prompt ("sequentially" to override default parallel behavior)
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3. **Parallel execution by default**: Multiple instances of the same sub-recipe run concurrently
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If your prompt implies multiple executions of the same sub-recipe, Goose will automatically create parallel instances:
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```yaml
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prompt: |
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get the weather for three biggest cities in Australia
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```
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In this example, Goose recognizes that "three biggest cities" requires running the weather sub-recipe multiple times for different cities, so it executes them in parallel.
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If you wanted to run them sequentially, you can just tell Goose:
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```yaml
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prompt: |
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get the weather for three biggest cities in Australia one at a time
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```
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### Real-Time Progress Monitoring
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When running multiple tasks in parallel from the CLI, you can track progress through a real-time dashboard that automatically appears during execution. The dashboard provides:
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- **Live progress tracking**: Monitor task completion in real-time with statistics for completed, running, failed, and pending counts
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- **Task details**: View unique task IDs, parameter sets, execution timing, output previews, and error information as tasks progress from Pending → Running → Completed/Failed
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## Examples
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### Running Different Sub-Recipes in Parallel
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This example runs the `weather` and `things-to-do` sub-recipes in parallel:
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```yaml
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# plan_trip.yaml
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version: 1.0.0
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title: Plan Your Trip
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description: Get weather forecast and find things to do for your destination
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instructions: You are a travel planning assistant that helps users prepare for their trips.
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prompt: |
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run the following sub-recipes in parallel to plan my trip:
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- use weather sub-recipe to get the weather forecast for Sydney
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- use things-to-do sub-recipe to find activities and attractions in Sydney
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sub_recipes:
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- name: weather
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path: "./sub-recipes/weather.yaml"
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values:
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city: Sydney
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- name: things-to-do
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path: "./sub-recipes/things-to-do.yaml"
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values:
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city: Sydney
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duration: "3 days"
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extensions:
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- type: builtin
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name: developer
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timeout: 300
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bundled: true
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```
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### Running the Same Sub-Recipe in Parallel (with Different Parameters)
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This example runs three instances of the `weather` sub-recipe in parallel for different cities:
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```yaml
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# multi_city_weather.yaml
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version: 1.0.0
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title: Multi-City Weather Comparison
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description: Compare weather across multiple cities for trip planning
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instructions: You are a travel weather specialist helping users compare conditions across cities.
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prompt: |
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get the weather forecast for the three biggest cities in Australia
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to help me decide where to visit
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sub_recipes:
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- name: weather
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path: "./sub-recipes/weather.yaml"
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extensions:
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- type: builtin
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name: developer
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timeout: 300
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bundled: true
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```
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**Sub-Recipes:**
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<details>
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<summary>weather</summary>
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```yaml
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# sub-recipes/weather.yaml
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version: 1.0.0
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title: Find weather
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description: Get weather data for a city
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instructions: You are a weather expert. You will be given a city and you will need to return the weather data for that city.
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prompt: |
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Get the weather forecast for {{ city }} for today and the next few days.
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parameters:
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- key: city
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input_type: string
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requirement: required
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description: city name
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extensions:
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- type: stdio
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name: weather
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cmd: uvx
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args:
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- mcp_weather@latest
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timeout: 300
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```
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</details>
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<details>
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<summary>things-to-do</summary>
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```yaml
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# sub-recipes/things-to-do.yaml
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version: 1.0.0
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title: Things to do in a city
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description: Find activities and attractions for travelers
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instructions: You are a local travel expert who knows the best activities, attractions, and experiences in cities around the world.
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prompt: |
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Suggest the best things to do in {{ city }} for a {{ duration }} trip.
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Include a mix of popular attractions, local experiences, and hidden gems.
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{% if weather_context %}
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Consider the weather conditions: {{ weather_context }}
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{% endif %}
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parameters:
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- key: city
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input_type: string
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requirement: required
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description: city name
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- key: duration
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input_type: string
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requirement: required
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description: trip duration (e.g., "2 days", "1 week")
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- key: weather_context
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input_type: string
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requirement: optional
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default: ""
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description: weather conditions to consider for activity recommendations
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```
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</details>
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## Choosing Between Execution Modes
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While parallel execution offers speed benefits, sequential execution is sometimes necessary or preferable. Here's how to decide:
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**Use Sequential When:**
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- Tasks modify shared resources
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- Order of execution matters
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- Memory or CPU constraints exist
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- Debugging complex failures in parallel mode
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**Use Parallel When:**
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- Tasks are independent
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- Faster completion is desired
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- System resources can handle concurrent executions for up to 10 parallel workers
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- Processing large datasets or multiple files
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**Recipe-Level Configuration:**
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For sub-recipes that should never run in parallel, set `sequential_when_repeated: true` to override user requests:
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```yaml
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sub_recipes:
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- name: database-migration
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path: "./sub-recipes/migrate.yaml"
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sequential_when_repeated: true # Always sequential
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```
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## Learn More
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Check out the [Goose Recipes](/docs/guides/recipes) guide for more docs, tools, and resources to help you master Goose recipes.
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