{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mirascope--mirascope","slug":"mirascope--mirascope","name":"mirascope","type":"agent","url":"https://mirascope.com","page_url":"https://unfragile.ai/mirascope--mirascope","categories":["chatbots-assistants"],"tags":["artificial-intelligence","developer-tools","llm","llm-agent","llm-tools","python","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mirascope--mirascope__cap_0","uri":"capability://tool.use.integration.provider.agnostic.llm.call.decoration.with.unified.interface","name":"provider-agnostic llm call decoration with unified interface","description":"Transforms Python functions into LLM API calls via the @llm.call decorator, which abstracts provider-specific implementations (OpenAI, Anthropic, Gemini, Mistral, Groq, etc.) behind a consistent interface. The decorator system uses a call factory pattern that routes to provider-specific CallResponse subclasses while maintaining identical function signatures across all providers, enabling zero-friction provider switching without code changes.","intents":["I want to call an LLM API without learning each provider's SDK","I need to switch between OpenAI, Anthropic, and Gemini without refactoring","I want to use multiple LLM providers in the same codebase with consistent syntax"],"best_for":["Python developers building multi-provider LLM applications","teams evaluating different LLM providers without vendor lock-in","engineers migrating between LLM providers mid-project"],"limitations":["Abstractions add ~50-100ms latency per call due to decorator overhead and provider routing","Provider-specific features (e.g., OpenAI's vision_detail parameter) require explicit call_params overrides, not auto-mapped","Streaming responses have separate code paths per provider, not fully unified"],"requires":["Python 3.9+","API keys for at least one supported provider (OpenAI, Anthropic, Google, Mistral, Groq, xAI, Cohere, Azure, Bedrock, or LiteLLM)","mirascope package installed via pip"],"input_types":["Python function with type hints","prompt string or Messages object","optional call_params dict for provider-specific overrides"],"output_types":["CallResponse object with unified interface","provider-specific response metadata (usage, model, finish_reason)","streaming chunks for streaming calls"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_1","uri":"capability://text.generation.language.flexible.multi.format.prompt.construction.with.template.and.message.apis","name":"flexible multi-format prompt construction with template and message apis","description":"Provides four distinct prompt definition methods—shorthand (string/list), Messages API (Messages.user(), Messages.assistant()), string templates (@prompt_template decorator), and BaseMessageParam objects—allowing developers to choose the abstraction level that fits their use case. The prompt system compiles these into provider-agnostic message lists that are then converted to provider-specific formats (OpenAI's ChatCompletionMessageParam, Anthropic's MessageParam, etc.) at call time.","intents":["I want to write prompts in plain Python without learning a DSL","I need to dynamically construct multi-turn conversations with role-based messages","I want to use string templates with variable interpolation for prompt engineering"],"best_for":["prompt engineers who want to version-control prompts as Python code","developers building dynamic, context-aware prompts","teams using both simple and complex prompt patterns in the same project"],"limitations":["Shorthand method only supports single user message; multi-turn requires Messages API","String templates use Python's string.Template, not f-strings, limiting dynamic expressions","No built-in prompt validation or schema enforcement; relies on provider-side validation"],"requires":["Python 3.9+","mirascope.llm module imported","understanding of target provider's message format (for BaseMessageParam approach)"],"input_types":["Python string","list of strings","Messages.{Role} method calls","BaseMessageParam instances"],"output_types":["provider-agnostic message list","provider-specific message format (ChatCompletionMessageParam, MessageParam, etc.)"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_10","uri":"capability://tool.use.integration.provider.specific.parameter.customization.via.call.params.override","name":"provider-specific parameter customization via call_params override","description":"Allows developers to pass provider-specific parameters that are not exposed by Mirascope's unified API via the call_params argument, enabling access to advanced provider features (e.g., OpenAI's vision_detail, Anthropic's thinking budget, Gemini's safety settings) without waiting for framework updates. The call_params dict is merged with Mirascope's standard parameters and passed directly to the provider SDK.","intents":["I want to use a provider-specific feature that Mirascope doesn't expose yet","I need to pass custom parameters to the underlying provider SDK","I want to experiment with new provider features without waiting for framework support"],"best_for":["developers using cutting-edge provider features","teams needing fine-grained control over provider behavior","early adopters of new provider capabilities"],"limitations":["call_params are provider-specific; switching providers requires updating call_params","No validation of call_params; invalid parameters may fail silently or cause runtime errors","call_params bypass Mirascope's abstractions; using them reduces portability across providers"],"requires":["Python 3.9+","knowledge of provider-specific parameter names and types","provider SDK documentation"],"input_types":["dict of provider-specific parameters"],"output_types":["provider-specific response with custom parameters applied"],"categories":["tool-use-integration","provider-customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_11","uri":"capability://image.visual.multi.modal.prompt.support.with.document.and.image.handling","name":"multi-modal prompt support with document and image handling","description":"Supports multi-modal prompts via the Messages API and BaseMessageParam, enabling developers to include images, documents, and other media in prompts alongside text. The system handles provider-specific media formats (OpenAI's image_url and base64, Anthropic's source types, Gemini's inline_data) and automatically converts between formats, supporting both URL-based and base64-encoded media.","intents":["I want to include images in my LLM prompts for vision tasks","I need to pass documents or PDFs to the LLM for analysis","I want to build multi-modal applications that combine text and images"],"best_for":["developers building vision-enabled LLM applications","teams analyzing images or documents with LLMs","applications requiring multi-modal reasoning"],"limitations":["Image handling varies by provider; some providers require base64 encoding, others support URLs, with different size limits","Document support is limited; most providers only support images natively, not PDFs or other formats","Multi-modal prompts are more expensive (higher token counts); no built-in cost optimization for media"],"requires":["Python 3.9+","provider support for vision (OpenAI, Anthropic, Gemini, Mistral, Groq)","image file or URL"],"input_types":["image URL (string)","base64-encoded image (string)","image file path (string)","document file (PDF, image, etc.)"],"output_types":["provider-specific media format","LLM response analyzing the media"],"categories":["image-visual","multi-modal"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_12","uri":"capability://tool.use.integration.provider.integration.framework.for.adding.new.llm.providers","name":"provider integration framework for adding new llm providers","description":"Provides a structured framework for integrating new LLM providers by subclassing base classes (CallResponse, Stream, Tool) and implementing provider-specific logic. The framework handles common patterns (parameter mapping, response parsing, error handling) and provides extension points for provider-specific features, enabling community contributions and custom provider support.","intents":["I want to add support for a new LLM provider to Mirascope","I need to integrate a custom or private LLM API","I want to contribute a new provider integration to the project"],"best_for":["developers extending Mirascope with new providers","teams using custom or proprietary LLM APIs","contributors to the Mirascope project"],"limitations":["Provider integration requires understanding of Mirascope's internal architecture (CallResponse, Stream, Tool base classes)","No automated testing framework for new providers; requires manual testing against Mirascope's test suite","Provider-specific features may not map cleanly to Mirascope's abstractions, requiring custom handling"],"requires":["Python 3.9+","understanding of Mirascope's architecture","provider SDK or API documentation","knowledge of the provider's API format and parameters"],"input_types":["provider SDK or HTTP API","provider-specific response format"],"output_types":["Mirascope CallResponse subclass","provider-specific Stream implementation","provider-specific Tool implementation"],"categories":["tool-use-integration","extensibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_2","uri":"capability://data.processing.analysis.structured.output.extraction.with.json.mode.and.response.models","name":"structured output extraction with json mode and response models","description":"Enables automatic extraction of structured data from LLM responses via response models (Pydantic BaseModel subclasses or dataclasses) that are compiled into provider-specific JSON schemas and passed to the LLM with JSON mode enforcement. The system handles schema generation, validation, and fallback parsing, converting unstructured LLM text into strongly-typed Python objects with zero manual parsing code.","intents":["I want the LLM to return structured data (JSON) that I can use directly in my code","I need type-safe extraction of entities, classifications, or summaries from LLM responses","I want to enforce a schema on LLM outputs without writing custom parsing logic"],"best_for":["developers building data extraction pipelines","teams using LLMs for structured classification or entity recognition","applications requiring guaranteed schema compliance from LLM outputs"],"limitations":["Requires provider support for JSON mode (OpenAI, Anthropic, Gemini support it; some providers fall back to regex parsing)","Complex nested schemas may exceed token budgets due to schema size in prompts","Fallback parsing (when JSON mode fails) uses regex and may silently drop invalid fields"],"requires":["Python 3.9+","Pydantic 2.0+ or Python dataclass","provider with JSON mode support (OpenAI, Anthropic, Gemini recommended)"],"input_types":["Pydantic BaseModel subclass","Python dataclass","LLM response text"],"output_types":["strongly-typed Python object (instance of response model)","provider-specific JSON schema","validation errors if schema enforcement fails"],"categories":["data-processing-analysis","structured-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--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-specific tool schemas (OpenAI's ToolDefinition, Anthropic's ToolUseBlock, Gemini's FunctionDeclaration) via a schema registry. The system introspects function signatures, generates JSON schemas for parameters, and handles tool execution with automatic argument marshaling, supporting both synchronous and asynchronous tool functions across all major LLM providers.","intents":["I want the LLM to call my functions (tools) to retrieve data or perform actions","I need to define tools once and use them across multiple LLM providers","I want automatic argument validation and type conversion for tool calls"],"best_for":["developers building LLM agents with external tool access","teams implementing function calling across multiple providers","applications requiring structured tool execution with validation"],"limitations":["Tool schema generation relies on Python type hints; untyped functions produce generic 'object' schemas","Async tool execution requires event loop management; mixing sync/async tools can cause deadlocks","Provider tool limits (OpenAI: 128 tools, Anthropic: 10,000 tools) may require tool selection/filtering for large registries"],"requires":["Python 3.9+","Python functions with type hints","provider support for tool calling (OpenAI, Anthropic, Gemini, Mistral, Groq, Cohere)","mirascope.core.{provider}.tool module imported"],"input_types":["Python function with type hints","@tool decorator or Tool class","function docstring (used as tool description)"],"output_types":["provider-specific tool schema (ToolDefinition, ToolUseBlock, etc.)","tool call result (return value of executed function)","tool execution errors"],"categories":["tool-use-integration","function-calling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_4","uri":"capability://automation.workflow.streaming.response.handling.with.unified.chunk.interface","name":"streaming response handling with unified chunk interface","description":"Provides streaming support via the @llm.call decorator with stream=True parameter, returning a Stream object that yields CallResponseChunk instances. The streaming system handles provider-specific chunk formats (OpenAI's ChatCompletionChunk, Anthropic's ContentBlockDelta, etc.) and normalizes them into a unified CallResponseChunk interface, supporting both text streaming and structured streaming (for response models).","intents":["I want to stream LLM responses to the user in real-time for better UX","I need to process LLM output incrementally without waiting for the full response","I want to extract structured data from streaming responses"],"best_for":["developers building chat interfaces or real-time LLM applications","teams needing low-latency response handling","applications processing large LLM outputs incrementally"],"limitations":["Streaming adds ~100-200ms latency overhead per chunk due to provider API round-trips","Structured streaming (response models) requires buffering chunks until complete JSON is available, negating some latency benefits","Provider-specific streaming behaviors (e.g., Anthropic's start_turn vs content_block_start) are abstracted away, limiting fine-grained control"],"requires":["Python 3.9+","provider support for streaming (all major providers support it)","stream=True parameter in @llm.call decorator"],"input_types":["LLM call with stream=True","optional response_model for structured streaming"],"output_types":["Stream[CallResponseChunk] iterator","CallResponseChunk with text, tool_calls, or structured data","final CallResponse after stream exhaustion"],"categories":["automation-workflow","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_5","uri":"capability://planning.reasoning.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 via mirascope.llm._context that allows developers to dynamically override call parameters (model, temperature, max_tokens, etc.) at runtime without modifying function signatures. The system uses Python's contextvars for thread-safe context management, enabling per-request parameter overrides in multi-threaded or async applications.","intents":["I want to override the model or temperature for specific calls without changing function signatures","I need to use different parameters for different requests in a multi-user application","I want to A/B test different model configurations without code changes"],"best_for":["developers building production LLM services with dynamic configuration","teams running A/B tests or parameter experiments","applications requiring per-request parameter customization"],"limitations":["Context overrides are thread-local (contextvars); async context propagation requires explicit context copying in some scenarios","Override precedence (function params > context > defaults) can be confusing; no built-in logging of which override was applied","Overrides only work for parameters exposed by the provider; custom provider-specific params require call_params dict"],"requires":["Python 3.9+","mirascope.llm.context module","understanding of contextvars for async applications"],"input_types":["call parameter name (string)","override value (model name, temperature, etc.)"],"output_types":["context-modified call parameters","provider-specific parameter format"],"categories":["planning-reasoning","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_6","uri":"capability://data.processing.analysis.cost.tracking.and.token.usage.calculation.across.providers","name":"cost tracking and token usage calculation across providers","description":"Automatically tracks API costs and token usage by extracting usage metadata from provider responses (input_tokens, output_tokens, cache_tokens) and applying provider-specific pricing models. The system maintains a cost registry with per-model pricing and supports both synchronous cost calculation and async cost aggregation for batch operations.","intents":["I want to track how much my LLM API calls are costing","I need to calculate token usage across multiple providers for billing","I want to monitor cost trends and optimize expensive calls"],"best_for":["developers building cost-conscious LLM applications","teams managing LLM budgets and billing","applications requiring per-user or per-request cost attribution"],"limitations":["Pricing data is manually maintained and may lag behind provider price changes","Cache token pricing (OpenAI, Anthropic) is only calculated if provider returns cache_token counts; not all providers support this","Cost calculation is approximate; actual provider billing may differ due to rounding, discounts, or batch pricing"],"requires":["Python 3.9+","mirascope.core.base.call_response module","provider response with usage metadata (all major providers include this)"],"input_types":["CallResponse object with usage metadata","model name (string)"],"output_types":["cost (float, in USD)","token counts (input_tokens, output_tokens, cache_tokens)","cost breakdown by token type"],"categories":["data-processing-analysis","cost-tracking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_7","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.standardized.tool.interfaces","name":"model context protocol (mcp) integration for standardized tool interfaces","description":"Integrates with the Model Context Protocol (MCP) standard, allowing Mirascope agents to discover and call tools exposed via MCP servers. The integration translates between Mirascope's tool system and MCP's tool definition format, enabling interoperability with any MCP-compliant tool provider (e.g., Anthropic's MCP servers, community tools).","intents":["I want to use MCP-standard tools with my Mirascope agents","I need to integrate with Anthropic's MCP ecosystem","I want to build agents that can discover and use tools dynamically"],"best_for":["developers building agents that need access to MCP-standard tools","teams integrating with Anthropic's MCP ecosystem","applications requiring standardized tool discovery and execution"],"limitations":["MCP integration is relatively new; edge cases and provider-specific behaviors may not be fully handled","MCP server discovery and connection management is not built-in; requires external MCP client setup","Tool execution latency includes MCP server round-trip time, which may be significant for remote servers"],"requires":["Python 3.9+","MCP server running and accessible","mirascope.core.mcp module (if available)","understanding of MCP protocol"],"input_types":["MCP server connection","MCP tool definition"],"output_types":["Mirascope Tool object","tool execution result from MCP server"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_8","uri":"capability://planning.reasoning.agent.orchestration.with.multi.step.reasoning.and.tool.loops","name":"agent orchestration with multi-step reasoning and tool loops","description":"Provides an agent system that orchestrates multi-step LLM interactions with tool calling loops, enabling agents to reason about tasks, call tools, process results, and iterate until completion. The agent system handles tool execution, result processing, and conversation history management, supporting both synchronous and asynchronous agent loops with configurable stopping conditions.","intents":["I want to build an agent that can reason about tasks and call tools iteratively","I need to implement a multi-step workflow where the LLM decides what to do next","I want to create a chatbot that can use tools to answer user questions"],"best_for":["developers building autonomous agents","teams implementing multi-step LLM workflows","applications requiring iterative reasoning and tool use"],"limitations":["Agent loops can be expensive (multiple LLM calls per iteration); no built-in cost optimization or early stopping","Conversation history grows unbounded; requires manual pruning or summarization for long-running agents","Tool execution errors are not automatically recovered; requires explicit error handling in agent code"],"requires":["Python 3.9+","mirascope.core.agent module","at least one tool defined for the agent","understanding of agent loop patterns"],"input_types":["initial user message or task","list of available tools","system prompt (optional)"],"output_types":["agent response (final answer)","tool call history","reasoning trace (if logged)"],"categories":["planning-reasoning","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mirascope--mirascope__cap_9","uri":"capability://automation.workflow.async.await.support.for.non.blocking.llm.calls.and.concurrent.execution","name":"async/await support for non-blocking llm calls and concurrent execution","description":"Provides full async/await support via async versions of all core APIs (@llm.call with async functions, async streaming, async tool execution, async agents). The async system uses Python's asyncio for non-blocking I/O, enabling concurrent LLM calls and efficient resource utilization in async applications without callback hell or promise chains.","intents":["I want to make multiple LLM calls concurrently without blocking","I need to integrate Mirascope into an async web framework (FastAPI, Starlette)","I want to build high-throughput LLM applications with minimal latency"],"best_for":["developers building async web services with LLM integration","teams needing high-throughput LLM applications","applications requiring concurrent LLM calls"],"limitations":["Mixing sync and async code requires careful event loop management; can cause deadlocks if not handled correctly","Async context propagation (contextvars) requires explicit context copying in some scenarios","Provider SDKs may have different async implementations; some providers have slower async than sync"],"requires":["Python 3.9+","async/await syntax support","asyncio event loop (built-in to Python)"],"input_types":["async Python function","async tool function"],"output_types":["coroutine returning CallResponse","async iterator for streaming"],"categories":["automation-workflow","async-support"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","API keys for at least one supported provider (OpenAI, Anthropic, Google, Mistral, Groq, xAI, Cohere, Azure, Bedrock, or LiteLLM)","mirascope package installed via pip","mirascope.llm module imported","understanding of target provider's message format (for BaseMessageParam approach)","knowledge of provider-specific parameter names and types","provider SDK documentation","provider support for vision (OpenAI, Anthropic, Gemini, Mistral, Groq)","image file or URL","understanding of Mirascope's architecture"],"failure_modes":["Abstractions add ~50-100ms latency per call due to decorator overhead and provider routing","Provider-specific features (e.g., OpenAI's vision_detail parameter) require explicit call_params overrides, not auto-mapped","Streaming responses have separate code paths per provider, not fully unified","Shorthand method only supports single user message; multi-turn requires Messages API","String templates use Python's string.Template, not f-strings, limiting dynamic expressions","No built-in prompt validation or schema enforcement; relies on provider-side validation","call_params are provider-specific; switching providers requires updating call_params","No validation of call_params; invalid parameters may fail silently or cause runtime errors","call_params bypass Mirascope's abstractions; using them reduces portability across providers","Image handling varies by provider; some providers require base64 encoding, others support URLs, with different size limits","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.45524230884347816,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-04-30T18:35:19Z"},"community":{"stars":1472,"forks":116,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mirascope--mirascope","compare_url":"https://unfragile.ai/compare?artifact=mirascope--mirascope"}},"signature":"S+HKcd4Lx3IcW/4BpnICcuqOrL/B5Nu910db5qdeJKMaIfGuJXANLE3Q3NcTndQR8Fvav33t8Id/e7KnSO7zAA==","signedAt":"2026-06-20T10:51:25.055Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mirascope--mirascope","artifact":"https://unfragile.ai/mirascope--mirascope","verify":"https://unfragile.ai/api/v1/verify?slug=mirascope--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"}}