decorator-based llm call transformation with provider abstraction
Transforms Python functions into LLM API calls using the @llm.call decorator, which intercepts function execution and routes calls through a provider-agnostic call factory system. The decorator extracts function signatures, type hints, and docstrings to construct prompts, then dispatches to provider-specific implementations (OpenAI, Anthropic, Gemini, etc.) while maintaining consistent Python semantics. This approach eliminates boilerplate by treating LLM invocations as native Python function calls rather than explicit API client instantiation.
Unique: Uses a modular call factory pattern (_call_factory.py) that dispatches to provider-specific CallResponse implementations, allowing each provider (OpenAI, Anthropic, Gemini, etc.) to maintain native typing and features while exposing a unified decorator interface. This differs from frameworks that normalize all providers to a lowest-common-denominator API.
vs alternatives: Lighter and more Pythonic than LangChain's verbose chain syntax, while offering more provider flexibility than Anthropic's native SDK; maintains full access to provider-specific features without abstraction leakage.
multi-format prompt construction with template and message composition
Provides four distinct prompt definition methods (shorthand strings, Messages.{Role} builders, @prompt_template decorators, and BaseMessageParam instances) that compile into provider-native message formats. The prompt system parses function docstrings, type hints, and template variables to construct structured message arrays compatible with each provider's API. This enables flexible prompt engineering from simple strings to complex multi-turn conversations with role-based message composition.
Unique: Supports four orthogonal prompt definition methods (shorthand, Messages builder, template decorator, BaseMessageParam) that all compile to the same internal representation, allowing developers to choose the most ergonomic syntax for each use case. The system parses docstrings and type hints to auto-populate system prompts and parameter descriptions.
vs alternatives: More flexible than LangChain's PromptTemplate (supports multiple syntaxes), simpler than Anthropic's native message construction (decorator-driven), and includes built-in multimodal support that LiteLLM abstracts away.
provider-specific parameter passthrough via call_params
Allows developers to pass provider-specific parameters (e.g., OpenAI's temperature, top_p, presence_penalty; Anthropic's thinking budget; Google's safety_settings) via a call_params dictionary without losing the unified interface. The system validates and forwards these parameters to the provider's native API, enabling access to advanced features not exposed by the abstraction layer.
Unique: Implements a call_params passthrough mechanism that allows arbitrary provider-specific parameters to be forwarded to the native API without validation, enabling access to new provider features without framework updates.
vs alternatives: More flexible than frameworks that normalize all providers to a common API (allows provider-specific features), but less type-safe than frameworks with full provider-specific typing.
extensible provider integration architecture for custom providers
Provides a documented extension mechanism for adding custom LLM providers by implementing provider-specific subclasses (CallResponse, tool schema translators, streaming handlers). The architecture defines clear interfaces and protocols (_protocols.py) that custom providers must implement, enabling integration with proprietary, local, or experimental LLM services. The development guide documents the process for adding new providers.
Unique: Defines clear provider protocols (_protocols.py) and provides a development guide for adding custom providers. The modular architecture allows custom providers to inherit from base classes and override specific methods without reimplementing the entire framework.
vs alternatives: More extensible than frameworks with hardcoded provider lists, simpler than building a custom framework, and enables integration with local/proprietary LLMs that other frameworks don't support.
automatic prompt generation from function signatures and docstrings
Extracts function names, docstrings, parameter names, and type hints to automatically construct system prompts and user message templates. When a function is decorated with @llm.call, Mirascope parses the function's metadata to build a prompt that includes the function's purpose (from docstring) and parameter descriptions. This reduces boilerplate and keeps prompts in sync with code changes.
Unique: Automatically parses function docstrings, type hints, and parameter names to construct prompts without explicit prompt definition. This reduces boilerplate and keeps prompts synchronized with code changes.
vs alternatives: More automatic than manual prompt writing, reduces boilerplate compared to frameworks requiring explicit prompts, and maintains prompt-code synchronization better than external prompt files.
structured output extraction with pydantic response models
Enables automatic extraction of structured data from LLM responses by defining Pydantic models as response types. When a function is decorated with @llm.call and returns a Pydantic model type, Mirascope automatically constructs JSON schema constraints, sends them to the provider's structured output API (OpenAI JSON mode, Anthropic structured outputs, etc.), and parses the response into the model instance. This approach leverages provider-native structured output capabilities rather than post-hoc parsing, reducing hallucination and improving reliability.
Unique: Automatically generates and sends JSON schemas to providers' native structured output APIs (not post-hoc regex parsing), leveraging provider-specific optimizations like OpenAI's JSON mode and Anthropic's structured outputs. The _extract.py module handles schema generation and response parsing transparently.
vs alternatives: More reliable than LangChain's OutputParser (uses native provider APIs instead of prompt-based extraction), more ergonomic than raw Anthropic SDK (automatic schema generation), and supports more providers than specialized tools like Instructor.
tool calling with schema-based function registry and multi-provider support
Implements tool calling by converting Python functions into provider-native tool schemas (OpenAI function calling, Anthropic tool use, Google tool declarations, etc.) and managing the request-response loop. Developers define tools as decorated functions, Mirascope generates JSON schemas from type hints, sends them to the LLM, and handles tool invocation and result feedback. The system supports automatic tool execution, manual tool selection, and parallel tool calls depending on provider capabilities.
Unique: Generates tool schemas automatically from Python type hints and docstrings, then dispatches to provider-specific tool calling APIs (OpenAI functions, Anthropic tool_use, Google tool declarations). The mirascope/core/google/tool.py and similar provider modules handle provider-specific tool schema translation.
vs alternatives: More Pythonic than raw provider SDKs (automatic schema generation), more flexible than LangChain's tool abstraction (supports more providers and maintains provider-specific features), and lighter than full agent frameworks like AutoGPT.
streaming response handling with chunked token processing
Provides streaming support for real-time token-by-token or chunk-by-chunk response processing via provider-native streaming APIs. The Stream and StructuredStream classes wrap provider streaming responses, yielding CallResponseChunk objects that contain partial content, tool calls, or structured data fragments. Developers can iterate over chunks to build progressive UIs, implement early stopping, or process tokens as they arrive without waiting for full response completion.
Unique: Wraps provider-native streaming APIs (OpenAI SSE, Anthropic event streams, etc.) in a unified Stream/StructuredStream interface that yields CallResponseChunk objects. The base/stream.py and base/structured_stream.py modules handle provider-agnostic chunk accumulation and parsing.
vs alternatives: Simpler than raw provider streaming APIs (unified interface), supports structured output streaming (unlike many frameworks), and provides both sync and async iteration patterns.
+5 more capabilities