Pythonic LLM toolkit providing clean, type-safe abstractions for building agent interactions with calls, tools, structured outputs, and automatic versioning across 15+ providers.
A clean, Pythonic way to call AI models and build agents — focuses on type safety, simplicity, and giving developers full control without framework lock-in.
Mirascope is a Python library that provides clean, type-safe abstractions for LLM interactions, designed for developers who want the power of structured LLM usage without the complexity of full agent frameworks. It calls itself 'The LLM Anti-Framework' because it focuses on making common LLM patterns — prompting, tool calling, structured extraction, and multi-turn conversations — as Pythonic as possible without imposing framework-level opinions.
The core philosophy is that LLM interactions should feel like writing normal Python code. Mirascope uses decorators and Pydantic models to define prompts, tools, and expected outputs. A prompt is a decorated function (@llm.call). A tool is a decorated function with typed parameters (@llm.tool). An extraction target is a Pydantic model passed via the format parameter. There's minimal boilerplate and maximum Python idiom.
Mirascope supports all major LLM providers — OpenAI, Anthropic, Google, Mistral, Cohere, DeepSeek, and local models — through a unified interface using provider/model string format (e.g., 'openai/gpt-4o', 'anthropic/claude-sonnet-4-5'). Unlike abstraction layers that reduce everything to a lowest common denominator, Mirascope preserves provider-specific features like thinking mode support while maintaining code portability.
The library's approach to agent building is compositional. Rather than providing a monolithic agent class, Mirascope gives you building blocks: calls (LLM interactions), tools (function calling), and format models (structured output). You compose these into agent-like behaviors using standard Python control flow — the agent loop is just a while loop over tool calls with response.resume(tool_outputs).
The @ops.version() decorator provides automatic versioning, tracing, and cost tracking for every LLM call. This integrates with Langfuse and other observability tools through OpenTelemetry-compatible tracing, making production monitoring straightforward.
Type safety is a first-class concern. All inputs and outputs are typed, enabling IDE autocompletion, static analysis, and catch-at-compile-time errors. This matters enormously as agent systems grow in complexity.
For developers who find LangChain too opinionated and raw API clients too bare, Mirascope occupies an appealing middle ground. It provides just enough abstraction to eliminate boilerplate while staying close enough to the metal that you always understand what's happening.
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Mirascope is a Python-native LLM toolkit that prioritizes type safety, developer experience, and composability over framework lock-in. Its decorator-based API feels natural to Python developers, and built-in versioning/tracing makes production deployment straightforward. Best for developers who want full control over their agent logic without sacrificing type safety or observability.
Define LLM interactions as decorated Python functions using @llm.call('provider/model'). The function's return value becomes the prompt, and the decorator handles API calls, response parsing, and error handling.
Use Case:
Creating a reusable, testable librarian function that can be called like any Python function but executes an LLM query with structured tool access.
Tools defined as decorated functions with @llm.tool, using typed parameters and docstrings that auto-generate the tool schema. Pydantic validation ensures tool inputs are correct before execution.
Use Case:
Building a search tool with validated query parameters that the LLM can call, with full IDE autocompletion and type checking on both inputs and outputs.
Extract typed data from LLM responses by passing a Pydantic model to the format parameter. Mirascope handles schema generation, response parsing, and validation automatically.
Use Case:
Extracting structured product information from customer reviews with guaranteed schema compliance and automatic retry on validation failures.
The @ops.version() decorator automatically versions prompts, traces LLM calls, and tracks token usage and costs. Changes to decorated functions are detected and versioned automatically.
Use Case:
Tracking which version of a prompt performs best in production and monitoring LLM costs per function across your entire application.
Build agent behaviors using standard Python while loops: call the LLM, check for tool calls, execute tools, resume with outputs. No framework-specific agent class needed — just Python control flow.
Use Case:
Creating a custom agent with specific error handling, fallback logic, and conditional tool execution that wouldn't fit into a rigid agent framework.
Unified interface across OpenAI, Anthropic, Google, Mistral, DeepSeek, and others using provider/model strings. Supports provider-specific features like thinking mode ({"include_thoughts": True}) without losing portability.
Use Case:
Testing the same agent across Claude with thinking mode, GPT-4o, and Gemini to compare quality and cost while using provider-specific optimizations.
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Mirascope has evolved its API to use a unified @llm.call('provider/model') decorator format with thinking mode support, added @ops.version() for automatic versioning and cost tracking, expanded provider support to include DeepSeek and more OpenAI-compatible endpoints, and improved integration with Langfuse for production observability.
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