Running Tasks

Execute tasks and manage their results.

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.

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:

Code

import marvin

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

Running a Single Task

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

Code

import marvin

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

print(poem)

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

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

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]
)

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)

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]
)