Master ControlFlow with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install ControlFlow via pip: run 'pip install controlflow' in your Python
9+ environment and set your OPENAI_API_KEY (or preferred LLM provider key) as an environment variable Create your first task by importing controlflow and running: result = cf.run('Summarize the key points of quantum computing') — this uses the default agent with automatic thread management Define a structured task with typed output: create a Pydantic model for your expected output, then use cf.Task(objective='Extract entities', result_type=YourModel) to get validated, schema
conforming results Build a multi
step flow: use the @cf.flow decorator on a function, define multiple cf.Task objects with dependencies, and assign specialized agents to each task using the agents parameter Connect Prefect observability: install Prefect with 'pip install prefect' and run 'prefect server start' to launch the local dashboard where all ControlFlow tasks appear automatically with execution metrics
💡 Quick Start: Follow these 4 steps in order to get up and running with ControlFlow quickly.
Explore the key features that make ControlFlow powerful for ai agent builders workflows.
ControlFlow's core innovation is treating AI operations as typed, validated tasks rather than open-ended agent conversations. Each task declares its expected output type using Python annotations and Pydantic models. When an agent completes a task, the framework automatically validates the output against the schema — catching malformed responses before they propagate downstream. This eliminates the fragile string-parsing patterns common in LangChain and similar frameworks, making AI outputs reliable enough for production data pipelines.
Unlike single-agent frameworks, ControlFlow allows multiple specialized agents to collaborate within a single flow. Developers assign agents to tasks based on capability, cost, or domain expertise — using GPT-4 for creative tasks, Claude for analysis, and smaller models for classification. Agents share context through structured threads and pass validated outputs between each other, enabling sophisticated orchestration patterns that would require significant custom code in frameworks like LangChain or AutoGPT.
Every ControlFlow task automatically appears in the Prefect dashboard with execution timelines, input/output inspection, retry logic, dependency graphs, and cost tracking. No other AI agent framework offers this level of production monitoring out of the box. Teams get immediate visibility into what agents are doing, how long each step takes, where failures occur, and how much each workflow costs in LLM API calls — without writing any instrumentation code.
ControlFlow provides a continuous dial between full agent autonomy and tight developer control. Tasks can be loosely defined ('summarize this document') or precisely constrained with specific instructions, limited tool access, output validators, and explicit dependencies. This lets teams start with controlled workflows and gradually increase agent freedom as confidence grows — critical for enterprise environments where predictability matters more than flexibility.
Complex workflows in ControlFlow define explicit task dependencies, creating directed acyclic graphs (DAGs) of AI operations. Tasks can depend on outputs from previous tasks, run in parallel when independent, and share context through the flow's thread. The framework handles execution ordering, parallel dispatch, and error propagation automatically — bringing the reliability of traditional workflow orchestration to AI agent systems.
ControlFlow supports multiple LLM providers including OpenAI, Anthropic, Google, and open-source models through a unified interface. Different agents in the same flow can use different providers, enabling cost optimization and capability matching. Switching providers for a task requires changing a single configuration parameter, not rewriting workflow logic.
No. ControlFlow was archived by Prefect in early 2025. The next-generation engine was merged into the Marvin agentic framework. New projects should use Marvin instead, which carries forward ControlFlow's task-centric design philosophy with continued development and support.
ControlFlow emphasizes structured, observable tasks with type-safe outputs, while LangChain provides a more flexible chain-based architecture. ControlFlow's tasks produce Pydantic-validated results and integrate natively with Prefect for monitoring. LangChain offers a larger ecosystem of integrations but less built-in structure for production reliability.
Yes. ControlFlow supports multiple LLM providers including OpenAI, Anthropic (Claude), Google (Gemini), and open-source models. Different agents in the same workflow can use different providers, enabling cost optimization by routing tasks to the most appropriate model.
Prefect recommends migrating to Marvin (github.com/prefecthq/marvin), which incorporates ControlFlow's next-generation engine. The core concepts — tasks, agents, flows, structured outputs — map to Marvin equivalents. Prefect provides migration guidance in the Marvin documentation.
While ControlFlow's design was production-focused, its archived status means no security patches or bug fixes are being released. For new production deployments, Marvin is the recommended alternative. Existing ControlFlow deployments should plan migration timelines based on their risk tolerance.
No. ControlFlow works with the open-source Prefect server for local observability. Prefect Cloud is optional and provides hosted monitoring, alerting, team features, and managed infrastructure. The framework itself is fully functional without any cloud dependency.
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Tutorial updated March 2026