Mirascope
Provider-agnostic Python LLM library using an @llm.call decorator pattern that gives type-safe, async-ready, streaming-capable LLM calls with clean ergonomics and minimal boilerplate.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
No network surface from the library itself. Store provider API keys in env vars or a secrets manager; Pydantic models ensure no raw LLM output is passed unsanitized.
⚡ Reliability
Best When
You want clean, Pythonic LLM call abstractions that are provider-switchable, type-safe, and easy to unit test.
Avoid When
You need a batteries-included agent framework with built-in tools, memory, and orchestration — Mirascope intentionally stays minimal.
Use Cases
- • Build provider-agnostic LLM call wrappers that can switch between OpenAI, Anthropic, and Gemini by changing a single decorator argument
- • Stream LLM responses asynchronously in FastAPI or async agent loops without blocking
- • Extract structured Pydantic v2 models from LLM responses with built-in response model support
- • Compose multi-step LLM pipelines where each step is a typed Python function with full IDE support
- • Write testable LLM-calling code where mocking and unit testing are first-class concerns
Not For
- • Full agent orchestration with memory and planning — Mirascope handles LLM calls, not agent lifecycle management
- • Teams on Pydantic v1 — Pydantic v2 is a hard requirement
- • Low-code or non-Python teams who want natural language scripting rather than Python code
Interface
Authentication
LLM provider API keys passed via environment variables. Mirascope itself requires no auth.
Pricing
MIT licensed. You pay your LLM provider (OpenAI, Anthropic, Google, etc.) directly.
Agent Metadata
Known Gotchas
- ⚠ Pydantic v2 is a hard dependency — migration is required for any codebase still on Pydantic v1
- ⚠ Provider switching via decorator argument is clean but each provider has slightly different parameter support — test across providers explicitly
- ⚠ Streaming and structured response models are mutually exclusive in most providers — you cannot stream a Pydantic response_model
- ⚠ No built-in retry or exponential backoff — you must wrap calls with tenacity or similar for production resilience
- ⚠ The library intentionally has no agent memory or tool registry — building agentic loops requires composing additional libraries
Alternatives
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Scores are editorial opinions as of 2026-03-07.