Key features
- Functional API: APAC LLM calls as typed Python functions with IDE autocomplete
- Prompt versioning: docstring-based APAC prompt templates with variable interpolation
- Pydantic extraction: APAC structured output as typed Python objects via extract()
- Multi-provider: OpenAI/Anthropic/Gemini/Mistral/Ollama with same APAC code
- Type-safe: APAC mypy/pyright compatible for production code quality standards
- Open-source: MIT licensed for APAC commercial use and contribution
Best for
- APAC Python developers building LLM applications who prefer a functional, type-safe programming model over LangChain's chain-based approach — particularly APAC teams with strict type checking requirements or who want clean, testable LLM function code without framework abstractions.
Limitations to know
- ! Smaller APAC ecosystem than LangChain — fewer integrations and third-party extensions
- ! Structured extraction relies on LLM compliance rather than FSM guarantees (use Outlines for hard guarantees)
- ! APAC teams needing complex agentic workflows may require LangGraph or LlamaIndex alongside Mirascope
About Mirascope
Mirascope is a Python LLM SDK designed for type-safe, functional LLM development — allowing APAC developers to define LLM calls as decorated Python functions with typed inputs and outputs, automatic prompt versioning via docstrings, and structured extraction without boilerplate. APAC Python developers who want a more Pythonic alternative to LangChain's chain-based API use Mirascope to write LLM-powered applications as plain Python with type checking and IDE support.
Mirascope's `@prompt_template` decorator converts Python function docstrings into prompt templates — APAC developers write prompts as Python docstrings with f-string-style variable interpolation, and Mirascope handles message formatting for any provider. The function signature defines typed inputs, and IDE autocomplete works naturally with Mirascope prompts because they are standard Python functions.
Mirascope's structured extraction uses Pydantic models to define APAC output schemas — the `extract()` function calls the LLM and automatically deserializes the response into a typed Pydantic object. For APAC teams extracting structured data from documents (invoice fields, contact information, regulatory APAC references), Mirascope's extract pattern eliminates manual JSON parsing while providing full type safety and Pydantic validation.
Mirascope's multi-provider support uses the same function code with different provider backends — APAC teams switch between OpenAI, Anthropic, Google Gemini, Mistral, Cohere, and local Ollama by changing the `call_params` decorator argument. This enables APAC cost optimization (routing simple APAC tasks to cheaper models) and provider resilience (fallback to secondary APAC provider on primary failure) without rewriting application logic.
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