mirascope

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Basic Information

Mirascope is a developer-focused Python library that provides a unified, provider-agnostic interface for working with large language models. It is designed to simplify making calls to multiple LLM providers and to streamline common tasks such as text generation, information extraction, and building AI-driven workflows or agents. The README highlights interoperability with a range of providers and shows a minimal quickstart where a decorated function invokes an LLM and returns a typed Pydantic model. The project includes installation instructions, a short example demonstrating extraction of structured data from unstructured text, and links to tutorials and documentation. The repo is packaged for PyPI, uses semantic versioning, and is distributed under the MIT license, making it suitable for developers who need a consistent API to integrate different LLM backends into applications.

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Features
Mirascope exposes a simple, consistent API for invoking multiple LLM providers and models. It lists first-class support for OpenAI, Anthropic, Mistral, Google (Gemini/Vertex), Groq, Cohere, LiteLLM, Azure AI, and Bedrock. The library integrates with Pydantic to declare response models so outputs can be validated and typed. Examples show a decorator-based call pattern that wraps provider/model selection and response parsing. The README and badges indicate available documentation, tutorials, tests, and coverage, and the package is published to PyPI. The project emphasizes a quickstart experience, provider-agnostic calls, and developer ergonomics for building text generation, structured extraction, and higher-level agent workflows.
Use Cases
Mirascope reduces boilerplate and fragmentation when developing applications that use LLMs by providing one API surface that works across many providers. Developers can switch providers or models without rewriting call logic and can enforce output schemas using Pydantic response models, which improves reliability and makes downstream processing simpler. The example quickstart demonstrates how to extract structured data from free text with minimal code. Documentation, tutorials, and CI/test badges in the repo help teams adopt the library safely. Packaging on PyPI and an MIT license make it easy to install and include in projects, while semantic versioning aids dependency management.

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