⚠️ AttentionScint is a work in progress, with code, APIs, and documentation that may change without notice.
Though not yet production-ready, it serves as an experimental playground for those looking to explore novel ways of integrating AI-driven reasoning, memory management, and structured interactions into Python applications.
Scint is an experimental Python framework for building goal-directed, AI-driven systems. The primary aim is to provide a foundation for developing complex, context-aware systems that can reason about tasks, manage evolving states of knowledge, and interact coherently with both users and external resources. A core design goal is to bridge the gap between structured application logic and the unstructured nature of language model outputs. By encapsulating memory, event flows, and choreographed, multi-step tasks in a composable manner, Scint offers developers a more predictable and flexible way to leverage AI within their projects.
Though Scint remains in an experimental phase, its composable design opens up a broad spectrum of possibilities. You might build an autonomous document parser that uses a Traversal Interface to walk through nested structures, a specialized Trait to interpret text, and a Memory Interface to log relevant insights for later queries. Alternatively, you might design a decision-tree system where each node has an AI-driven Trait for logic processing, an Interface for broadcasting events, and a Struct-based data model that evolves as the agent learns. Across all these scenarios, the common thread is the ability to layer new abilities on top of existing objects in a low-friction manner.
Because Scint’s APIs and internals are still under development, the recommended way to begin is by exploring the code examples and core modules in the repository. Look at how Structs are defined and how Traits and Interfaces attach to them, then experiment with smaller prototypes that evolve into more complex workflows. As your needs grow, the framework’s flexible architecture should let you snap in new behaviors without refactoring large swaths of code.
Contributions that expand Scint’s capabilities or refine its existing patterns are welcome. Whether you’re interested in new Interfaces (for logging, concurrency, or specialized indexing), additional Traits (for AI reasoning or domain-specific integrations), or performance improvements, there’s ample room to shape Scint’s future. Please see the repository’s contributing guidelines for details on submitting pull requests, discussing feature ideas, and reporting issues.
By leveraging the “Struct + Trait + Interface” pattern, Scint aspires to be a powerful toolkit for those building agentic processes and AI-driven workflows. Its compositional nature grants developers the freedom to integrate capabilities layer by layer, ultimately creating systems that can reason, adapt, and manage knowledge in a more structured and dynamic fashion.