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Basic Information

aiFlows is an open-source Python framework that implements the Flows abstraction to simplify the design, implementation and sharing of complex workflows that combine humans, AI systems and external tools. It provides a message-based interface where Flows are independent, goal-driven building blocks that can be composed into atomic or nested composite Flows. The project emphasizes modularity, reusability and reproducibility, and supports sharing Flow configurations on a community FlowVerse repository. The library targets researchers and developers who need controlled, customizable workflows and supports Python 3.10+. Recent updates introduce a Flows engine to enable concurrent execution and peer-to-peer distributed collaboration. The repository includes tutorials, examples and demo notebooks such as FunSearch to illustrate common use cases and to accelerate experimentation.

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Features
Modular Flow abstraction with atomic and composite Flows that communicate via standardized messages. A FlowVerse repository model for sharing, downloading and extending Flows, with Flow configurations that make API-based tool Flows portable. Flows engine (v1.1.0) introducing concurrency and peer-to-peer distributed collaboration. Integration with CoLink to enable remote peer-to-peer interactions. Dedicated examples and demo notebooks (e.g., FunSearch, CodeForce demos), predefined example Flows such as ChatAtomicFlow, VisionAtomicFlow, ReAct and AutoGPT, and a developer guide and tutorials for quick starts. Easy installation via pip or editable install from source. Emphasis on reproducibility, composability and the ability to tailor models, tools and permissions for each Flow.
Use Cases
aiFlows helps researchers and practitioners build, experiment with and share complex multi-step workflows that involve AI models, tools and humans. It makes it easier to reproduce published workflows, to reuse and extend community Flows from the FlowVerse, and to study interaction patterns because Flows expose a clear, message-based interface. The framework supports concurrent executions and remote collaboration so workflows can coordinate across peers and run at scale. Quick-start guides, tutorials and example notebooks lower the barrier to running question-answering and other demo Flows. For practitioners it enables tailoring tools, models and access controls, and for researchers it facilitates systematic study and distribution of experimental setups.

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