Marvin is a Python framework by Prefect for producing structured outputs from LLMs and building agentic AI workflows. With 6.1K+ GitHub stars, it provides intuitive building blocks – cast, classify, extract, generate – that bridge AI unpredictability with type-safe, observable, composable software patterns. Version 3.0 introduces full agentic control flow with Tasks, Agents, and Threads.

Marvins philosophy treats AI capabilities as engineering building blocks rather than black-box magic. Each function enforces type safety via Pydantic, maintains observability, and composes naturally into larger workflows. The framework prioritizes making LLM interactions reliable and predictable while keeping the API minimal.

Core design principles:

• Type-safe results – All outputs validated through Pydantic models
• Task-centric – Discrete, observable units of work with clear objectives
• Composable – Chain tasks into threads for complex multi-step workflows
• Flexible control – Tune autonomy vs. determinism per task
• Multi-agent – Assign specialized agents to specific tasks

pip install marvin
# or with uv
uv add marvin

Configure your LLM provider:

export OPENAI_API_KEY="your-api-key"

Marvin provides high-level functions for common LLM tasks:

import marvin
from pydantic import BaseModel
 
 
# --- cast: Convert unstructured text to typed objects ---
class Location(BaseModel):
    city: str
    state: str
    country: str
 
location = marvin.cast("The Big Apple", target=Location)
# Location(city=New York, state=NY, country=US)
 
 
# --- classify: Categorize data into labels ---
sentiment = marvin.classify(
    "I absolutely love this product!",
    labels=["positive", "negative", "neutral"]
)
# "positive"
 
 
# --- extract: Pull structured data from text ---
class Contact(BaseModel):
    name: str
    email: str
 
contacts = marvin.extract(
    "Reach out to Alice at alice@example.com or Bob at bob@test.io",
    target=Contact
)
# [Contact(name=Alice, email=alice@example.com),
#  Contact(name=Bob, email=bob@test.io)]
 
 
# --- generate: Produce structured outputs ---
colors = marvin.generate(
    target=str,
    instructions="Generate 5 creative color names for a cyberpunk palette",
    n=5
)
 
 
# --- fn: AI-powered function decorator ---
@marvin.fn
def summarize(text: str, max_words: int = 50) -> str:
    "Summarize the given text in the specified number of words."
 
summary = summarize("Long article text here...", max_words=30)

Marvin v3.0 introduces a full agentic control flow engine:

+-----------------------------------------+
|              marvin.Thread              |
|  (shared context across all tasks)      |
|                                         |
|  +---------+    +-----------------+     |
|  | Task 1  |--->|    Task 2       |     |
|  | Research |    | Write Article   |     |
|  |          |    | agent=Writer    |     |
|  +---------+    +--------+--------+     |
|                          |              |
|                 +--------v--------+     |
|                 |    Task 3       |     |
|                 | Review and Edit |     |
|                 | agent=Editor    |     |
|                 +-----------------+     |
+-----------------------------------------+

import marvin
from pydantic import BaseModel
 
 
class Article(BaseModel):
    title: str
    content: str
    key_points: list[str]
 
 
# Define specialized agents
writer = marvin.Agent(
    name="Writer",
    instructions="Write clear, engaging content for a technical audience."
)
 
editor = marvin.Agent(
    name="Editor",
    instructions="Review and improve articles for clarity and accuracy."
)
 
# Orchestrate with a Thread (shared context)
with marvin.Thread() as thread:
    # Task 1: Research
    key_points = marvin.run(
        "Research the top 5 trends in AI agent frameworks for 2025",
        result_type=list[str]
    )
 
    # Task 2: Write (assigned to writer agent)
    draft = marvin.run(
        "Write a technical article using the research",
        agent=writer,
        result_type=Article,
        context={"research": key_points}
    )
 
    # Task 3: Edit (assigned to editor agent)
    final = marvin.run(
        "Review and polish the article",
        agent=editor,
        result_type=Article,
        context={"draft": draft}
    )
 
print(f"Title: {final.title}")
print(final.content)

import marvin
 
# AI-powered task decomposition
tasks = marvin.plan("Create a comprehensive blog post about AI trends")
marvin.run_tasks(tasks)
 
# Team coordination
from marvin import Team, Swarm
 
# Structured team with delegation rules
research_team = Team(
    agents=[researcher, analyst],
    instructions="Collaborate on research tasks"
)
 
# Swarm: free-form delegation among agents
creative_swarm = Swarm(agents=[writer, designer, editor])

Marvin integrates naturally with Prefect for production orchestration:

from prefect import flow
import marvin
 
@flow
def process_github_issue(issue_payload: dict):
    summary = marvin.cast(
        issue_payload["body"],
        target=str,
        instructions="Summarize this GitHub issue in one sentence"
    )
 
    category = marvin.classify(
        summary,
        labels=["bug", "feature_request", "question", "documentation"]
    )
 
    response = marvin.generate(
        target=str,
        instructions=f"Write a helpful response to this {category} issue",
        context={"summary": summary}
    )
 
    return {"summary": summary, "category": category, "response": response}

• Streaming responses – Real-time output streaming for interactive applications
• Tool support – Custom Python functions as agent tools
• Observability – Built-in tracing and progress tracking
• Multimodal – Image and audio input support
• Multiple LLM providers – OpenAI (default), Claude, and all Pydantic AI-supported models

• Marvin on GitHub
• Marvin Documentation
• Marvin on Prefect.io
• Marvin on PyPI

• ell – Lightweight prompt engineering framework
• Instructor – Structured LLM outputs with Pydantic
• LangChain – Full LLM application framework
• Prefect – Workflow orchestration platform