{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mirascope","slug":"mirascope","name":"Mirascope","type":"framework","url":"https://github.com/Mirascope/mirascope","page_url":"https://unfragile.ai/mirascope","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mirascope__cap_0","uri":"capability://tool.use.integration.decorator.based.llm.call.transformation.with.provider.abstraction","name":"decorator-based llm call transformation with provider abstraction","description":"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.","intents":["I want to call an LLM with minimal setup code, just decorating a function","I need to switch between LLM providers without rewriting my call logic","I want type hints and IDE autocomplete for LLM parameters and responses","I need to maintain full control over provider-specific parameters while using a unified interface"],"best_for":["Python developers building LLM applications who prefer decorator patterns","Teams needing multi-provider support without vendor lock-in","Engineers who want type safety and IDE support for LLM calls"],"limitations":["Python-only — no JavaScript, Go, or other language SDKs","Decorator pattern requires understanding of Python function introspection and async/await semantics","Provider-specific parameters must be passed via call_params dict, losing some type safety for advanced options"],"requires":["Python 3.9+","API key for at least one supported LLM provider (OpenAI, Anthropic, Google, Mistral, Groq, xAI, Cohere, Azure, Bedrock, or LiteLLM)","mirascope package installed via pip"],"input_types":["Python function with type hints","String prompts (shorthand, template, or Messages format)","Structured message parameters (BaseMessageParam)"],"output_types":["CallResponse object with message content and metadata","Structured data via response models (Pydantic)","Streaming chunks for real-time processing"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_1","uri":"capability://text.generation.language.multi.format.prompt.construction.with.template.and.message.composition","name":"multi-format prompt construction with template and message composition","description":"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.","intents":["I want to write prompts as simple strings without boilerplate","I need to construct multi-turn conversations with system, user, and assistant messages","I want to use Jinja2-style templates with variable interpolation in prompts","I need to include images, documents, or other multimodal content in prompts"],"best_for":["Prompt engineers who want multiple syntactic styles for different use cases","Teams building conversational agents with complex message histories","Developers working with multimodal models (vision, document understanding)"],"limitations":["Template syntax requires understanding of Jinja2 or Python f-string conventions","Multimodal support varies by provider — not all providers support all content types (e.g., images, documents)","Message composition requires explicit role specification; no automatic role inference"],"requires":["Python 3.9+","Understanding of provider-specific message formats (OpenAI Messages API, Anthropic MessageParam, etc.)","For templates: Jinja2 library (included as dependency)"],"input_types":["Python strings (shorthand)","Messages.{Role} builder calls","String templates with {{ variable }} placeholders","BaseMessageParam instances with role, content, and optional media"],"output_types":["Provider-native message arrays (list[BaseMessageParam])","Compiled prompt strings for simple cases","Structured message objects with role, content, and metadata"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_10","uri":"capability://tool.use.integration.provider.specific.parameter.passthrough.via.call.params","name":"provider-specific parameter passthrough via call_params","description":"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.","intents":["I want to use advanced provider-specific features (e.g., OpenAI's vision_detail, Anthropic's thinking)","I need to fine-tune model behavior with provider-specific parameters","I want to access new provider features without waiting for Mirascope updates","I need to experiment with provider-specific configurations"],"best_for":["Advanced users leveraging provider-specific capabilities","Teams experimenting with new provider features","Applications requiring fine-grained control over model behavior"],"limitations":["call_params are not type-checked — invalid parameters may cause runtime errors","Provider-specific parameters are not portable across providers","Documentation for call_params must be maintained manually as providers update APIs","Using call_params reduces code portability and makes switching providers harder"],"requires":["Python 3.9+","Knowledge of provider-specific parameter names and types","Provider documentation for advanced parameters"],"input_types":["Dictionary of provider-specific parameters","Parameter names and values matching provider API"],"output_types":["Provider-native API calls with custom parameters","Responses reflecting provider-specific behavior"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_11","uri":"capability://tool.use.integration.extensible.provider.integration.architecture.for.custom.providers","name":"extensible provider integration architecture for custom providers","description":"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.","intents":["I want to integrate a custom or proprietary LLM provider","I need to add support for a new provider that Mirascope doesn't support yet","I want to use a local LLM (Ollama, LM Studio) with Mirascope","I need to wrap a legacy LLM API with Mirascope's interface"],"best_for":["Teams with custom or proprietary LLM providers","Developers integrating local LLMs (Ollama, LM Studio, vLLM)","Contributors adding new provider support to Mirascope"],"limitations":["Requires understanding of Mirascope's internal architecture and provider protocols","Custom providers must implement all required methods (call, stream, tool schema translation, etc.)","No guarantee that custom providers will be compatible with all Mirascope features","Maintenance burden falls on the custom provider implementer"],"requires":["Python 3.9+","Understanding of Mirascope's provider architecture and protocols","Knowledge of the custom provider's API","Ability to implement required CallResponse and tool schema methods"],"input_types":["Custom provider API client","Provider-specific request/response formats"],"output_types":["Custom CallResponse subclass","Provider-specific tool schema translators","Streaming handler for custom provider"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_12","uri":"capability://text.generation.language.automatic.prompt.generation.from.function.signatures.and.docstrings","name":"automatic prompt generation from function signatures and docstrings","description":"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.","intents":["I want to avoid writing prompts manually for simple LLM calls","I need prompts to stay in sync with function signatures and documentation","I want to generate prompts from docstrings without duplication","I need to automatically document LLM call behavior"],"best_for":["Developers building simple LLM calls with minimal prompt engineering","Teams maintaining large numbers of LLM calls (auto-generation reduces maintenance)","Projects where code documentation and prompts should be synchronized"],"limitations":["Auto-generated prompts are generic and may not be optimal for complex tasks","Docstring quality directly affects prompt quality — poor documentation leads to poor prompts","Complex parameter types may not translate well to natural language descriptions","Auto-generation is best for simple calls; complex prompts should be manually crafted"],"requires":["Python 3.9+","Well-documented functions with clear docstrings and type hints","Understanding of how docstrings are parsed (Google-style, NumPy-style, etc.)"],"input_types":["Python function with docstring and type hints","Function parameters with type annotations"],"output_types":["Auto-generated system prompt from function docstring","Auto-generated user message template from function parameters","Compiled prompt sent to LLM"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_2","uri":"capability://data.processing.analysis.structured.output.extraction.with.pydantic.response.models","name":"structured output extraction with pydantic response models","description":"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.","intents":["I want the LLM to return structured data (JSON) that I can immediately use in my code","I need type-safe extraction of entities, classifications, or complex objects from LLM responses","I want to enforce schema validation on LLM outputs without post-processing","I need to extract data from documents or unstructured text with guaranteed structure"],"best_for":["Data extraction pipelines requiring reliable structured outputs","Applications building knowledge graphs or entity databases from LLM responses","Teams needing type-safe LLM integrations with minimal validation overhead"],"limitations":["Requires provider support for structured outputs — not all providers support JSON mode or structured output APIs","Complex nested Pydantic models may exceed token limits due to schema size","Structured output mode may incur additional latency (provider-dependent)","Some providers (e.g., Groq, Mistral) have limited or no structured output support"],"requires":["Python 3.9+","Pydantic v1 or v2 installed","Provider that supports structured outputs (OpenAI, Anthropic, Google Gemini, Azure, Bedrock)","Function return type annotation set to a Pydantic model class"],"input_types":["Pydantic BaseModel or v2 model class","Nested Pydantic models with complex field types","Optional fields and Union types for flexible extraction"],"output_types":["Instantiated Pydantic model with validated fields","Nested model instances for complex hierarchies","Type-safe Python objects ready for downstream processing"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_3","uri":"capability://tool.use.integration.tool.calling.with.schema.based.function.registry.and.multi.provider.support","name":"tool calling with schema-based function registry and multi-provider support","description":"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.","intents":["I want the LLM to call functions in my code based on user requests","I need to build an agent that can use multiple tools to solve problems","I want automatic schema generation from Python function signatures","I need to handle tool results and feed them back to the LLM for multi-step reasoning"],"best_for":["Agentic applications requiring function calling and tool orchestration","Teams building retrieval-augmented generation (RAG) systems with tool-based document access","Developers needing multi-provider tool support without rewriting tool definitions"],"limitations":["Tool execution is not automatic — developers must implement the request-response loop or use an agent framework","Parallel tool calls are provider-dependent (OpenAI supports them, others may not)","Tool schemas are generated from type hints; complex types (Union, generic types) may not translate cleanly","No built-in tool result caching or memoization — each tool call is independent"],"requires":["Python 3.9+","Provider that supports tool calling (OpenAI, Anthropic, Google, Mistral, Groq, Cohere, Azure, Bedrock)","Tool functions must have complete type hints (no Any types)","Understanding of tool calling semantics (request-response loop, tool result feedback)"],"input_types":["Python functions with type hints and docstrings","Tool definitions via @tool decorator or Tool class","Tool parameters as function arguments with type annotations"],"output_types":["Provider-native tool schemas (OpenAI ToolParam, Anthropic ToolUseBlock, etc.)","Tool call objects with tool name and arguments","Tool results formatted for LLM feedback"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_4","uri":"capability://text.generation.language.streaming.response.handling.with.chunked.token.processing","name":"streaming response handling with chunked token processing","description":"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.","intents":["I want to display LLM responses in real-time as tokens arrive","I need to process streaming responses for low-latency applications","I want to implement early stopping or interrupt logic during streaming","I need to extract structured data from streaming responses incrementally"],"best_for":["Chat applications and conversational UIs requiring real-time token display","Low-latency applications where waiting for full response is unacceptable","Streaming data pipelines processing large LLM outputs incrementally"],"limitations":["Streaming adds complexity to error handling — partial responses may be incomplete if stream is interrupted","Structured output streaming is not supported by all providers (OpenAI, Anthropic support it; others may not)","Token counting during streaming is approximate — final token count may differ from stream-time estimates","Streaming responses cannot be retried mid-stream; must restart from the beginning"],"requires":["Python 3.9+","Provider that supports streaming (all major providers: OpenAI, Anthropic, Google, Mistral, Groq, etc.)","Async/await support or synchronous iteration over stream chunks","Understanding of streaming semantics (partial content, no backpressure)"],"input_types":["LLM call with stream=True parameter","Provider-native streaming response objects"],"output_types":["CallResponseChunk objects with partial content, finish_reason, and metadata","Iterable/async iterable of chunks for progressive processing","Accumulated full response after stream completion"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_5","uri":"capability://automation.workflow.context.and.parameter.override.management.for.dynamic.call.configuration","name":"context and parameter override management for dynamic call configuration","description":"Implements a context-based override system (_context.py, _override.py) that allows dynamic modification of LLM call parameters at runtime without changing function definitions. Developers can set global or scoped overrides for model, temperature, max_tokens, and other parameters using context managers or direct override calls. This enables A/B testing, cost optimization, and dynamic model selection without code duplication.","intents":["I want to switch models or adjust parameters at runtime based on conditions","I need to run A/B tests with different model configurations","I want to implement cost-aware model selection (use cheaper models for simple tasks)","I need to override parameters for specific calls without modifying the function"],"best_for":["Teams running multi-model experiments or A/B tests","Cost-conscious applications needing dynamic model selection","Developers building flexible LLM pipelines with runtime configuration"],"limitations":["Overrides are thread-local or async-context-local; not suitable for concurrent calls with different overrides","Override scope is implicit (context manager-based); easy to accidentally apply overrides to unintended calls","Not all parameters can be overridden — some are provider-specific and may not be supported by all models"],"requires":["Python 3.9+","Understanding of Python context managers (with statements) or async context managers","Knowledge of which parameters are overridable for each provider"],"input_types":["Override parameters (model, temperature, max_tokens, etc.)","Context manager or direct override call"],"output_types":["Modified LLM call with overridden parameters","Scoped overrides that revert after context exit"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_6","uri":"capability://data.processing.analysis.cost.tracking.and.token.counting.across.providers","name":"cost tracking and token counting across providers","description":"Automatically tracks API costs and token usage by extracting pricing information from provider responses and applying provider-specific pricing models. The system accumulates costs per call, per session, or globally, and provides cost breakdowns by model and provider. This enables budget monitoring, cost optimization, and usage analytics without manual tracking.","intents":["I want to track API costs for billing or budget monitoring","I need to understand which models or providers are most expensive","I want to implement cost-aware model selection or rate limiting","I need to generate cost reports for stakeholders"],"best_for":["Production applications requiring cost monitoring and budget control","Teams optimizing LLM spend across multiple providers","Billing systems that need to track per-user or per-feature costs"],"limitations":["Token counting is approximate for streaming responses — final counts may differ","Pricing data must be manually updated as providers change rates","Some providers (e.g., Bedrock) have complex pricing models not fully supported","Cost tracking adds minimal overhead but requires storing metadata per call"],"requires":["Python 3.9+","Provider that returns token usage in responses (all major providers do)","Knowledge of provider pricing models (input tokens, output tokens, per-model rates)"],"input_types":["LLM call responses with token usage metadata","Provider pricing configuration"],"output_types":["Cost metadata per call (input_cost, output_cost, total_cost)","Aggregated cost reports by model, provider, or time period","Token usage statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_7","uri":"capability://tool.use.integration.multi.provider.support.with.native.api.feature.parity","name":"multi-provider support with native api feature parity","description":"Maintains provider-specific implementations (OpenAI, Anthropic, Google Gemini, Mistral, Groq, xAI, Cohere, Azure, Bedrock, LiteLLM) that preserve native API features while exposing a unified interface. Each provider has its own CallResponse subclass, tool schema translator, and streaming handler, allowing developers to access provider-specific capabilities (e.g., OpenAI's vision_detail, Anthropic's thinking blocks) without losing abstraction benefits.","intents":["I want to use multiple LLM providers in the same application","I need to switch providers without rewriting my code","I want to access provider-specific features (e.g., OpenAI vision, Anthropic thinking)","I need to compare providers or implement provider fallback logic"],"best_for":["Teams evaluating or comparing multiple LLM providers","Applications requiring provider redundancy or fallback","Developers leveraging provider-specific features (vision, structured outputs, etc.)"],"limitations":["Not all features are available on all providers — feature parity is incomplete","Provider-specific parameters must be passed via call_params, reducing type safety","Switching providers may require adjusting prompts or parameters (models have different capabilities)","Some providers have rate limits, quotas, or regional restrictions"],"requires":["Python 3.9+","API keys for desired providers","Knowledge of which providers support which features"],"input_types":["Provider name (openai, anthropic, google, mistral, groq, xai, cohere, azure, bedrock, litellm)","Provider-specific call parameters"],"output_types":["Provider-native responses wrapped in unified CallResponse interface","Provider-specific metadata and features accessible via response attributes"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_8","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.tool.and.resource.discovery","name":"model context protocol (mcp) integration for tool and resource discovery","description":"Integrates with the Model Context Protocol (MCP) standard to dynamically discover and invoke tools and resources from MCP servers. Mirascope can connect to MCP servers, enumerate available tools and resources, and invoke them as if they were native Mirascope tools. This enables integration with external tool ecosystems (e.g., Anthropic's MCP servers for web search, file access, etc.) without manual schema definition.","intents":["I want to use MCP servers as tools in my LLM application","I need to integrate with external tool ecosystems (web search, file access, etc.)","I want to dynamically discover available tools from MCP servers","I need to invoke MCP resources and tools transparently from my LLM calls"],"best_for":["Applications leveraging Anthropic's MCP ecosystem","Teams building tool-rich agents with external integrations","Developers needing dynamic tool discovery and invocation"],"limitations":["MCP support is relatively new and may have incomplete feature coverage","MCP server availability and reliability depend on external services","Tool invocation latency includes MCP server communication overhead","Not all MCP servers are compatible with all Mirascope providers"],"requires":["Python 3.9+","MCP server running and accessible (local or remote)","MCP client library installed (included as optional dependency)","Understanding of MCP protocol and server capabilities"],"input_types":["MCP server connection details (URL, credentials)","Tool/resource names from MCP server"],"output_types":["Discovered tools and resources from MCP server","Tool invocation results from MCP server","Integrated tool calls in LLM responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__cap_9","uri":"capability://automation.workflow.async.await.support.for.concurrent.llm.calls.and.streaming","name":"async/await support for concurrent llm calls and streaming","description":"Provides full async/await support for all LLM operations, enabling concurrent calls, non-blocking streaming, and integration with async frameworks (FastAPI, asyncio, etc.). All core functions have async variants (async_call, async_stream, etc.), and the system uses Python's asyncio and contextvars for thread-safe concurrent execution. This allows building high-throughput applications without blocking on I/O.","intents":["I want to make multiple LLM calls concurrently without blocking","I need to integrate Mirascope with async web frameworks (FastAPI, Starlette)","I want to stream responses asynchronously in real-time applications","I need to handle concurrent tool calls and agent loops efficiently"],"best_for":["Web applications and APIs requiring concurrent LLM processing","High-throughput systems making many parallel LLM calls","Real-time applications with async streaming requirements"],"limitations":["Async code is more complex than sync code; requires understanding of asyncio and event loops","Context overrides are async-context-local; concurrent calls with different overrides require careful scoping","Some provider SDKs have incomplete async support, which may limit Mirascope's async capabilities","Debugging async code is harder than sync code; errors may be harder to trace"],"requires":["Python 3.9+","Understanding of asyncio, async/await, and event loops","Async-compatible provider SDKs (all major providers support async)"],"input_types":["Async function definitions with @llm.call decorator","Async context managers for overrides"],"output_types":["Coroutines that can be awaited or gathered","Async iterators for streaming responses","Concurrent execution results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mirascope__headline","uri":"capability://data.processing.analysis.python.toolkit.for.building.llm.applications","name":"python toolkit for building llm applications","description":"Mirascope is a Pythonic framework designed to simplify the development of Large Language Model applications by providing a unified, provider-agnostic interface that reduces boilerplate code while maintaining developer control.","intents":["best Python framework for LLM development","LLM toolkit for Python","lightweight LLM framework for multiple providers","Python library for structured LLM extraction","best practices for LLM application development"],"best_for":["developers seeking a lightweight LLM framework","teams needing multi-provider support"],"limitations":[],"requires":["Python environment"],"input_types":["Python code"],"output_types":["LLM API calls"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","API key for at least one supported LLM provider (OpenAI, Anthropic, Google, Mistral, Groq, xAI, Cohere, Azure, Bedrock, or LiteLLM)","mirascope package installed via pip","Understanding of provider-specific message formats (OpenAI Messages API, Anthropic MessageParam, etc.)","For templates: Jinja2 library (included as dependency)","Knowledge of provider-specific parameter names and types","Provider documentation for advanced parameters","Understanding of Mirascope's provider architecture and protocols","Knowledge of the custom provider's API","Ability to implement required CallResponse and tool schema methods"],"failure_modes":["Python-only — no JavaScript, Go, or other language SDKs","Decorator pattern requires understanding of Python function introspection and async/await semantics","Provider-specific parameters must be passed via call_params dict, losing some type safety for advanced options","Template syntax requires understanding of Jinja2 or Python f-string conventions","Multimodal support varies by provider — not all providers support all content types (e.g., images, documents)","Message composition requires explicit role specification; no automatic role inference","call_params are not type-checked — invalid parameters may cause runtime errors","Provider-specific parameters are not portable across providers","Documentation for call_params must be maintained manually as providers update APIs","Using call_params reduces code portability and makes switching providers harder","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mirascope","compare_url":"https://unfragile.ai/compare?artifact=mirascope"}},"signature":"CpDSF8PmqNT1oRZCxB8apKovOV40XxHDtXVrYJJQKbnKGgYGPqP6vrHd08AACgqR8feWLE7VIAC4XYHMZLCzAg==","signedAt":"2026-06-21T18:47:51.684Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mirascope","artifact":"https://unfragile.ai/mirascope","verify":"https://unfragile.ai/api/v1/verify?slug=mirascope","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}