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Tasks represent discrete units of work that need to be completed by AI agents. Marvin provides several ways to execute tasks and retrieve their results.

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Creating and Running Tasks

The most convenient way to run a task is with the marvin.run() function. This function creates and runs a task in a single call, accepting all the same arguments as the Task constructor: 
import marvin

poem = marvin.run("Write a poem about AI")
print(poem)

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Running a Single Task

You can also create a task first and run it later using the run() method: 
import marvin

task = marvin.Task("Write a poem about AI")
poem = task.run()

print(poem)

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Running Multiple Tasks

To run multiple tasks at once, use the run_tasks() function: 
import marvin

task_1 = marvin.Task("Write a poem about AI")
task_2 = marvin.Task("Critique the poem", depends_on=[task_1])

results = marvin.run_tasks([task_1, task_2])
# Can also access task_1.result and task_2.result

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Task Dependencies

Tasks can depend on other tasks, ensuring they run in the correct order: 
import marvin

name_task = marvin.Task("Get the user's name", cli=True)
poem_task = marvin.Task(
    "Write a poem about the user",
    depends_on=[name_task]
)

# Running poem_task will automatically run name_task first
poem = poem_task.run()
When you run a task, Marvin will:
  1. Check if there are any dependencies
  2. Run any incomplete dependencies first
  3. Execute the task itself
  4. Return the result

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Task Results

Tasks can specify their expected result type using the result_type parameter: 
import marvin

# Get a boolean result
is_spam = marvin.run(
    "Is this email spam?",
    result_type=bool,
    context={"email": "You just won a million dollars!"}
)

# Get a list of integers
rolls = marvin.run(
    "Roll 3 dice",
    result_type=list[int],
    tools=[roll_dice]
)

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Task Status

You can check a task’s status at any time: 
import marvin

task = marvin.Task("Write a poem")

# Before running
print(task.status)  # TaskStatus.PENDING

# After running
task.run()
print(task.status)  # TaskStatus.SUCCESSFUL
Tasks have several helper methods for checking their status:
  • is_pending(): Task hasn’t started
  • is_running(): Task is currently executing
  • is_successful(): Task completed successfully
  • is_failed(): Task failed to complete
  • is_skipped(): Task was skipped
  • is_complete(): Task is done (successful, failed, or skipped)
  • is_incomplete(): Task isn’t done (pending or running)
  • is_ready(): Task can be run (dependencies are complete)

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Task Context

Tasks maintain their own context, which can include:
  • Instructions: What needs to be done
  • Context: Additional information needed for the task
  • Tools: Functions the agent can use
  • Memories: Persistent information from previous runs
  • Result type: Expected format of the output
This context helps agents understand and complete the task effectively: 
import marvin

task = marvin.Task(
    instructions="Analyze the sentiment of this text",
    context={"text": "I love this product!"},
    result_type=str,
    memories=[knowledge_base],
    tools=[analyze_text]
)