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Basic Information

Agently is a Python framework designed to speed up GenAI application development by providing developer-focused runtime control of large language model outputs. The repository contains the Agently library (v4.0.0b2 at the time of the README) with installation instructions for pip and editable local installs. It exposes a concise API pattern for creating agents, declaring inputs, tool capabilities, and structured output schemas so applications can parse and stream model responses reliably. Agently abstracts away low-level differences in model parameters and output formatting while enabling engineers to retain full control over business logic and system integration. The README includes examples demonstrating agent creation, tool definitions, response consumption, streaming deltas, and instant parsing modes. The project emphasizes developer experience and provides multi-language documentation pointers and community channels recorded in the README.

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Features
The README highlights structured and streamed output control as a core feature, with a developer-centric pattern for defining agent behavior. Key APIs shown include Agently.create_agent(), chained input/info/output declarations for tool descriptions and output schemas, and response handling functions such as response.get_text(), response.get_result(), and response.get_generator() for streaming or instant parsing. Examples demonstrate declaring tools with names and kwargs, instructing the model when to call a tool, and consuming incremental deltas or instant parsed messages. The project documents pip installation with a beta version specifier, local editable install steps, multilingual docs, a WeChat group and GitHub discussion channels for community support. The README focuses on output control, parsing, and runtime integration.
Use Cases
Agently helps developers build production-ready GenAI features by turning raw model outputs into structured, streamable data that integrates with application logic. It reduces engineering overhead by abstracting model parameter differences and output formatting so teams can focus on tool integration and business rules rather than parsing nuisances. Streaming and instant parsing examples enable real-time UI updates or tool invocation decisions as the model generates content. The structured output schema and tooling pattern make it easier to call external functions reliably and to handle both textual and command-style responses. Installation via pip or editable clone is documented so developers can quickly iterate. The repository also provides community and documentation paths for support and bilingual guidance.

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