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Extensibility mechanisms that allow users to write custom Python code to define the behavior of system building blocks.
Distinct from Custom Component Extensions: Distinct from Custom Component Extensions: specifically targets the use of Python source code for backend logic rather than frontend widget integration.
Explore 8 awesome GitHub repositories matching user interface & experience · Custom Python Components. Refine with filters or upvote what's useful.
Langflow is a low-code platform for designing and deploying multi-step AI agent pipelines and large language model sequences. It provides a visual environment to map logic and data flow between components, serving as an orchestrator for managing conversations and data retrieval across multiple autonomous agents. The platform distinguishes itself through a drag-and-drop interface that allows for the construction of complex AI pipelines without extensive boilerplate code. It enables the conversion of these internal workflows into standardized tools for external connectivity via the Model Contex
Allows customization of building block behavior by writing and modifying Python source code for specific logical outcomes.
Keras is a high-level deep learning API used to design, build, and train neural networks for tasks such as computer vision, natural language processing, and time series forecasting. It provides a framework for defining model architectures and optimizing weights through a structured interface. The project is defined by a backend-agnostic design that allows the same model code to run across different compute engines. This multi-backend execution enables users to swap underlying engines to optimize for specific hardware or performance requirements. The system supports distributed model training
Enables the creation of custom layers, models, and metrics that remain compatible across different compute engines.
ModelScope is a comprehensive machine learning platform that functions as a model hub, training framework, inference engine, and cloud development environment. It provides a centralized repository for discovering, downloading, and managing pre-trained models and datasets across multiple modalities, including natural language, vision, and speech. The platform features a unified interface for multimodal model inference and a standardized framework for fine-tuning and evaluating large-scale models. It supports distributed training to scale workloads across multiple processors and provides contai
Allows the modification of specific pipeline modules to implement custom logic during model inference and training.
Uses a utility function to prepend the base path to relative links in custom components.
Provides a decorator-based mechanism to convert any Python function into a reusable workflow tool.
Qtile este un tiling window manager programabil și compositor, scris și configurat în Python. Acesta organizează ferestrele aplicațiilor în tile-uri care nu se suprapun sau în moduri flotante pentru a maximiza spațiul de pe ecran și suportă atât protocoalele de server grafic X11, cât și Wayland. Mediul este definit prin executarea scripturilor Python, permițând personalizarea programatică a tastelor rapide, a stilurilor vizuale și a comportamentelor sistemului. Această abordare permite un spațiu de lucru personalizat unde întreaga interfață utilizator și logica de layout sunt gestionate printr-o configurație bazată pe scripturi. Proiectul acoperă domenii largi de capabilități, inclusiv organizarea spațiilor de lucru virtuale, fluxuri de lucru desktop automatizate și gestionarea ferestrelor aplicațiilor pe diferite protocoale grafice. Include, de asemenea, instrumente pentru monitorizarea stării sistemului prin urmărirea alocării memoriei și profilarea performanței proceselor.
Uses Python scripts to programmatically define keybindings, visual styles, and system behaviors.
Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran
Implements a decorator pattern to simplify the registration of Python functions as agent tools.
This is a Python SDK for interacting with large language models via API. It serves as a client library to generate text, process messages, and manage conversational states, while providing a specialized interface for connecting to models hosted across different cloud infrastructure providers. The SDK includes a tool-calling framework that maps Python functions to JSON schemas, allowing models to execute external tools. It also features a built-in token counting utility to estimate input size before transmission and a server-sent events client for receiving model tokens in real time. The libr
Allows Python functions to be registered as tools via decorators, enabling the model to execute external capabilities.