Master Mirascope with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Mirascope powerful for ai agent builders workflows.
Define LLM interactions as decorated Python functions using provider/model call strings. The function's return value becomes the prompt, and the decorator handles the LLM call pattern shown in the visible content.
Creating a reusable, testable librarian function that can be called like any Python function but executes an LLM query with structured tool access.
Tools are shown as decorated functions with typed parameters and docstrings to represent the tool schema. The visible content emphasizes typed Python ergonomics for inputs and outputs.
Building a search tool with typed query parameters that the LLM can call, with IDE autocompletion and type checking on both inputs and outputs.
Extract typed data from LLM responses by passing a structured model to the format parameter. The visible content positions this as a way to keep LLM outputs aligned with Python data structures.
Extracting structured product information from customer reviews with schema-oriented parsing in a Python application.
The @ops.version() decorator is shown versioning prompts, tracing LLM calls, and tracking costs. The visible trace rows include concrete per-call example costs of $0.0024, $0.0019, and $0.0016.
Tracking which version of a prompt performs best in production and monitoring LLM costs per function across your application.
Build agent behaviors using standard Python while loops: call the LLM, check for tool calls, execute tools, and resume with outputs. No framework-specific agent class is shown in the visible example; the pattern is standard Python control flow.
Creating a custom agent with specific error handling, fallback logic, and conditional tool execution that would not fit into a rigid agent framework.
The visible homepage content shows provider examples for OpenAI, Anthropic, and Google using provider/model strings. Broader provider and model compatibility should be verified in the official documentation.
Testing the same agent design across visibly shown provider options while keeping the surrounding Python implementation consistent.
Mirascope is used to build LLM-powered applications and agent-like workflows in Python. The website example shows a librarian function that calls a provider/model string, uses a typed library tool, executes tool calls, and resumes the response loop.
No. The provided website content presents Mirascope through Python code using imports from `mirascope`, decorators such as `@llm.tool`, `@ops.version()`, and `@llm.call`, and an explicit agent loop. That makes it a developer framework rather than a no-code builder.
The homepage visibly highlights OpenAI, Anthropic, and Google in the main hero interface. The example code shows a concrete provider/model string, but teams should validate current supported models and provider options in the official documentation before relying on a specific model.
The homepage describes `@ops.version()` as providing automatic versioning, tracing, and cost tracking. Its trace example includes version, time, input/output, and cost fields, with example costs of $0.0024, $0.0019, and $0.0016.
Mirascope is more code-first and compositional than many full agent frameworks. Instead of hiding the workflow inside a large abstraction, the website shows a normal Python loop that checks `response.tool_calls`, executes tools, and resumes the response.
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Tutorial updated March 2026