ControlFlow is a Python framework for building structured, observable, and controllable AI agentic workflows. It targets developers seeking to integrate LLMs into applications with predictable outcomes, offering task-centric architecture, structured results, and multi-agent orchestration.

How It Works

ControlFlow breaks down complex AI processes into discrete, observable tasks. Each task can be assigned to specialized AI agents, allowing for modularity and focused execution. The framework bridges AI outputs with traditional software by enabling type-safe, validated results via Pydantic models. This approach provides flexible control over AI autonomy and integrates seamlessly with the Prefect 3.0 observability stack.

Quick Start & Requirements

• Install with: pip install controlflow
• Requires an LLM provider configuration (defaults to OpenAI, needs OPENAI_API_KEY environment variable).
• Full documentation and example projects are available.

Highlighted Details

• Task-centric architecture for manageable, observable steps.
• Structured results with type-safe, validated outputs.
• Support for specialized agents and multi-agent orchestration.
• Native integration with Prefect 3.0 for observability.

Maintenance & Community

• Community support is available via Slack.
• Links to documentation and example projects are provided.

Licensing & Compatibility

• The license is not explicitly stated in the README.

Limitations & Caveats

The README does not specify licensing details, which may impact commercial use or closed-source integration.