{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-microsoft--promptflow","slug":"microsoft--promptflow","name":"promptflow","type":"repo","url":"https://microsoft.github.io/promptflow/","page_url":"https://unfragile.ai/microsoft--promptflow","categories":["app-builders","deployment-infra","testing-quality"],"tags":["ai","ai-application-development","ai-applications","chatgpt","gpt","llm","prompt","prompt-engineering"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-microsoft--promptflow__cap_0","uri":"capability://automation.workflow.dag.based.flow.definition.and.execution.with.yaml.configuration","name":"dag-based flow definition and execution with yaml configuration","description":"Defines executable LLM application workflows as directed acyclic graphs (DAGs) using YAML syntax (flow.dag.yaml), where nodes represent tools, LLM calls, or custom Python code and edges define data flow between components. The execution engine parses the YAML, builds a dependency graph, and executes nodes in topological order with automatic input/output mapping and type validation. This approach enables non-programmers to compose complex workflows while maintaining deterministic execution order and enabling visual debugging.","intents":["I want to define a multi-step LLM workflow without writing complex orchestration code","I need to visualize how data flows between LLM calls, prompts, and custom logic","I want to reuse the same flow definition across local testing, CI/CD, and cloud deployment"],"best_for":["teams building production LLM applications with clear step-by-step logic","prompt engineers who need reproducible, version-controlled workflows","organizations requiring audit trails of flow execution paths"],"limitations":["DAG structure cannot express dynamic branching or loops — all paths must be known at definition time","YAML syntax becomes unwieldy for flows with >20 nodes; Flex Flows recommended for complex logic","No built-in support for conditional execution based on runtime values without custom tool wrappers"],"requires":["Python 3.9+","promptflow-core and promptflow-devkit packages installed","Valid YAML syntax and understanding of flow.dag.yaml schema"],"input_types":["YAML configuration file (flow.dag.yaml)","JSON/JSONL input data with typed fields matching flow inputs"],"output_types":["Structured JSON output with all node outputs","Execution trace with timing and intermediate values"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_1","uri":"capability://automation.workflow.flex.flow.execution.with.python.function.class.based.workflows","name":"flex flow execution with python function/class-based workflows","description":"Enables defining flows as standard Python functions or classes decorated with @flow, allowing developers to write imperative LLM application logic with full Python expressiveness including loops, conditionals, and dynamic branching. The framework wraps these functions with automatic tracing, input/output validation, and connection injection, executing them through the same runtime as DAG flows while preserving Python semantics. This approach bridges the gap between rapid prototyping and production-grade observability.","intents":["I want to write LLM workflows in pure Python without learning YAML syntax","I need dynamic control flow like loops and conditionals based on LLM outputs","I want to reuse existing Python functions and libraries in my LLM application"],"best_for":["Python developers building complex LLM agents with dynamic logic","teams migrating from pure Python scripts to production-grade LLM apps","prototyping scenarios where rapid iteration matters more than visual debugging"],"limitations":["Less visual debugging than DAG flows — execution path is implicit in Python code","Requires understanding of Python decorators and function signatures","Tracing overhead is higher than DAG flows due to function call interception"],"requires":["Python 3.9+","promptflow-core package with @flow decorator support","Understanding of Python async/await if using async flows"],"input_types":["Python function with typed parameters","Class methods decorated with @flow"],"output_types":["Return value of Python function (any type)","Execution trace with function call stack"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_10","uri":"capability://automation.workflow.flow.serving.and.rest.api.deployment.with.auto.generated.endpoints","name":"flow serving and rest api deployment with auto-generated endpoints","description":"Automatically generates REST API endpoints from flow definitions, enabling flows to be served as HTTP services without writing API code. The framework handles request/response serialization, input validation, error handling, and OpenAPI schema generation. Flows can be deployed to various platforms (local Flask, Azure App Service, Kubernetes) with the same code, and the framework provides health checks, request logging, and performance monitoring out of the box.","intents":["I want to expose my LLM flow as a REST API without writing Flask/FastAPI code","I need to deploy my flow to production with automatic scaling and monitoring","I want to generate OpenAPI documentation for my LLM application automatically"],"best_for":["teams deploying LLM applications as microservices","organizations needing quick API deployment without backend development","developers building LLM-powered chatbots or content generation services"],"limitations":["Auto-generated APIs are basic — complex routing or authentication requires custom wrappers","Request/response serialization may not handle all Python types — requires custom serializers for complex objects","No built-in rate limiting or authentication — requires external API gateway for production"],"requires":["Python 3.9+","promptflow-devkit with serving support","Flow definition (YAML or Python)","Deployment platform (local, Azure, Kubernetes, etc.)"],"input_types":["Flow definition","HTTP POST requests with JSON payload matching flow inputs"],"output_types":["HTTP 200 response with JSON payload containing flow outputs","HTTP error responses with error details","OpenAPI schema (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_11","uri":"capability://automation.workflow.azure.ml.integration.with.cloud.execution.and.workspace.management","name":"azure ml integration with cloud execution and workspace management","description":"Integrates with Azure ML workspaces to enable cloud execution of flows, automatic scaling, and integration with Azure ML's experiment tracking and model registry. Flows can be submitted to Azure ML compute clusters, with automatic environment setup, dependency management, and result tracking in the workspace. This enables seamless transition from local development to cloud-scale execution without code changes.","intents":["I want to run my flow on cloud compute for faster execution and scaling","I need to integrate my LLM application with Azure ML's experiment tracking","I want to deploy my flow as an Azure ML endpoint for production serving"],"best_for":["organizations using Azure ML for ML operations","teams needing cloud-scale execution of LLM workflows","enterprises requiring integration with existing Azure ML infrastructure"],"limitations":["Azure-specific — no support for other cloud providers (AWS, GCP) in core framework","Requires Azure ML workspace setup and configuration — adds operational complexity","Cloud execution costs scale with compute usage — requires careful resource management"],"requires":["Python 3.9+","promptflow-azure package","Azure subscription and ML workspace","Azure credentials configured locally or in CI/CD"],"input_types":["Flow definition","Input data (local or in Azure blob storage)","Compute cluster configuration"],"output_types":["Flow outputs stored in Azure ML workspace","Experiment tracking with metrics and artifacts","Deployed endpoint URL for serving"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_12","uri":"capability://automation.workflow.ci.cd.integration.with.automated.testing.and.deployment.pipelines","name":"ci/cd integration with automated testing and deployment pipelines","description":"Provides CLI commands and GitHub Actions/Azure Pipelines templates for integrating flows into CI/CD pipelines, enabling automated testing on every commit, evaluation against test datasets, and conditional deployment based on quality metrics. The framework supports running batch evaluations, comparing metrics against baselines, and blocking deployments if quality thresholds are not met. This enables continuous improvement of LLM applications with automated quality gates.","intents":["I want to automatically test my flow on every code change","I need to evaluate flow quality and block deployments if metrics drop","I want to track how flow quality changes over time as I iterate"],"best_for":["teams practicing continuous integration for LLM applications","organizations with quality requirements and need for automated gates","developers using GitHub or Azure DevOps for version control and CI/CD"],"limitations":["Evaluation in CI/CD can be expensive due to LLM API costs — requires careful test dataset sizing","Metric-based deployment gates may be too strict or too lenient — requires tuning based on domain","No built-in support for canary deployments or gradual rollouts — requires external orchestration"],"requires":["Python 3.9+","promptflow-devkit with CI/CD support","GitHub Actions or Azure Pipelines configuration","Test dataset and evaluation metrics defined"],"input_types":["Flow definition (committed to git)","Test dataset (JSONL)","Evaluation metrics and thresholds"],"output_types":["CI/CD pipeline results (pass/fail)","Evaluation metrics and comparisons","Deployment status (deployed or blocked)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_13","uri":"capability://image.visual.multimedia.processing.with.image.and.document.handling","name":"multimedia processing with image and document handling","description":"Supports processing of images and documents (PDFs, Word, etc.) as flow inputs and outputs, with automatic format conversion, resizing, and embedding generation. Flows can accept image URLs or file paths, process them through vision LLMs or custom tools, and generate outputs like descriptions, extracted text, or structured data. The framework handles file I/O, format validation, and integration with vision models.","intents":["I want to build a flow that analyzes images using vision LLMs","I need to extract text from PDFs and process it through my LLM workflow","I want to generate images or documents as outputs from my flow"],"best_for":["teams building document processing pipelines with LLMs","organizations using vision LLMs for image analysis","developers creating multimodal LLM applications"],"limitations":["Image processing is limited to LLM-based analysis — no built-in computer vision tools","Document parsing is basic — complex PDFs with tables/forms may require custom tools","File size limits depend on LLM provider — large images/documents may fail"],"requires":["Python 3.9+","promptflow-core with multimedia support","Vision LLM API access (GPT-4V, Claude 3 Vision, etc.)","Image or document files in supported formats"],"input_types":["Image URLs or file paths (PNG, JPG, GIF, WebP)","Document file paths (PDF, DOCX, TXT)","Base64-encoded images"],"output_types":["Text descriptions or extracted content","Structured data (JSON) from document analysis","Generated images (if using image generation models)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_14","uri":"capability://memory.knowledge.run.management.and.execution.history.tracking.with.result.persistence","name":"run management and execution history tracking with result persistence","description":"Automatically tracks all flow executions with metadata (inputs, outputs, duration, status, errors), persisting results to local storage or cloud backends for audit trails and debugging. The framework provides CLI commands to list, inspect, and compare runs, enabling developers to understand flow behavior over time and debug issues. Run data includes full execution traces, intermediate node outputs, and performance metrics.","intents":["I want to see the history of all my flow executions for debugging","I need to compare outputs from different flow versions on the same inputs","I want to audit what my flow did and when for compliance purposes"],"best_for":["teams requiring execution audit trails for compliance","developers debugging production issues with LLM flows","organizations tracking flow performance and quality over time"],"limitations":["Local storage can grow large with many runs — requires periodic cleanup","Run comparison is limited to same flow version — cross-version comparison requires manual work","No built-in retention policies — requires external storage management"],"requires":["Python 3.9+","promptflow-devkit with run management support","Local storage or cloud backend for run persistence"],"input_types":["Flow execution (automatic capture)"],"output_types":["Run metadata (inputs, outputs, duration, status)","Execution traces with timing","Performance metrics and error details"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_2","uri":"capability://text.generation.language.prompty.file.format.for.prompt.centric.development","name":"prompty file format for prompt-centric development","description":"Introduces a markdown-based file format (.prompty) that bundles prompt templates, LLM configuration (model, temperature, max_tokens), and Python code in a single file, enabling prompt engineers to iterate on prompts and model parameters without touching code. The format separates front-matter YAML configuration from markdown prompt content and optional Python execution logic, with built-in support for prompt variables, few-shot examples, and model-specific optimizations. This approach treats prompts as first-class artifacts with version control and testing support.","intents":["I want to iterate on prompts and model parameters without redeploying code","I need to version control prompts separately from application logic","I want to test different prompt variations and measure their impact on outputs"],"best_for":["prompt engineers and non-technical team members optimizing LLM behavior","teams using prompt management as part of their MLOps pipeline","organizations needing audit trails of prompt changes and their performance impact"],"limitations":["Limited to single-turn or simple multi-turn interactions — complex conversation management requires Flex Flows","Prompty files cannot express complex branching logic; use Flex Flows for conditional prompting","No built-in A/B testing framework — requires external evaluation harness"],"requires":["Python 3.9+","promptflow-devkit with Prompty support","VS Code extension for syntax highlighting (optional but recommended)"],"input_types":[".prompty markdown file with YAML front-matter","Input variables matching prompt template placeholders"],"output_types":["LLM response text","Token usage and cost metrics"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_3","uri":"capability://tool.use.integration.built.in.llm.tool.integration.with.multi.provider.support","name":"built-in llm tool integration with multi-provider support","description":"Provides native tool nodes for calling LLMs (OpenAI, Azure OpenAI, Anthropic, Ollama) with automatic connection management, token counting, and cost tracking. Each LLM tool accepts a prompt template, model name, and parameters, handles authentication via connection objects, and returns structured responses with token usage metadata. The framework abstracts provider-specific APIs behind a unified interface, enabling flows to switch LLM providers by changing configuration without code changes.","intents":["I want to call different LLM providers from the same flow without rewriting code","I need to track token usage and costs across multiple LLM calls","I want to manage API keys and credentials securely without hardcoding them"],"best_for":["teams evaluating multiple LLM providers and needing easy switching","organizations requiring cost tracking and token usage monitoring","enterprises with credential management policies requiring centralized connection storage"],"limitations":["Limited to request-response LLM calls; streaming responses require custom tool wrappers","Token counting is approximate for some providers and may not match actual billing","No built-in retry logic or rate limiting — requires custom tool composition"],"requires":["API key for at least one LLM provider (OpenAI, Azure OpenAI, Anthropic, or local Ollama)","promptflow-core with LLM tool support","Connection object configured with provider credentials"],"input_types":["Prompt template string with variable placeholders","Model name (e.g., 'gpt-4', 'claude-3-opus')","LLM parameters (temperature, max_tokens, top_p, etc.)"],"output_types":["LLM response text","Token usage object (prompt_tokens, completion_tokens, total_tokens)","Cost estimate based on provider pricing"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_4","uri":"capability://tool.use.integration.custom.tool.creation.and.registration.system","name":"custom tool creation and registration system","description":"Enables developers to wrap Python functions as reusable tools via decorators (@tool) with automatic input/output schema generation from type hints, enabling them to be composed into flows as first-class nodes. The framework introspects function signatures, generates JSON schemas for validation, and handles tool invocation with dependency injection for connections and context. Tools can be packaged and shared across flows, with support for async functions and complex input types (lists, dicts, file paths).","intents":["I want to wrap my existing Python functions as reusable flow components","I need to validate tool inputs and outputs against a schema","I want to share tools across multiple flows and teams"],"best_for":["developers building custom logic for LLM applications","teams creating domain-specific tool libraries for their LLM workflows","organizations standardizing on a set of reusable components"],"limitations":["Tool schema generation from type hints may not capture complex validation rules — requires manual schema override","Async tools add complexity and require understanding of Python async/await","No built-in versioning for tools — requires external package management"],"requires":["Python 3.9+","Type hints on function parameters and return values","promptflow-core with @tool decorator support"],"input_types":["Python function with typed parameters","Async or sync function"],"output_types":["Any Python type (str, dict, list, custom objects)","JSON-serializable output for flow composition"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_5","uri":"capability://data.processing.analysis.batch.execution.with.jsonl.input.processing.and.result.aggregation","name":"batch execution with jsonl input processing and result aggregation","description":"Processes large datasets by executing flows against multiple input records from JSONL files, with automatic parallelization, result aggregation, and error handling. The batch executor reads JSONL line-by-line, maps each record to flow inputs, executes the flow in parallel (configurable worker count), and writes results to output JSONL with execution metadata (duration, status, error messages). This enables evaluation and testing of flows at scale without manual iteration.","intents":["I want to test my flow against hundreds of test cases and see aggregate results","I need to evaluate LLM outputs on a dataset and measure quality metrics","I want to process a large corpus of documents through my LLM workflow efficiently"],"best_for":["teams evaluating LLM application quality on test datasets","organizations processing large document collections through LLM pipelines","developers running regression tests on flow changes"],"limitations":["Parallelization is limited by API rate limits of LLM providers — may require throttling","Memory usage scales with worker count and flow state size — large flows may require tuning","No built-in deduplication or idempotency — requires external state management for resuming failed batches"],"requires":["Python 3.9+","JSONL input file with records matching flow input schema","promptflow-devkit with batch execution support","Sufficient API quota for parallel LLM calls"],"input_types":["JSONL file (one JSON object per line)","Each line must have fields matching flow input names and types"],"output_types":["JSONL output file with flow outputs + execution metadata","Summary statistics (success rate, average duration, error counts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_6","uri":"capability://data.processing.analysis.evaluation.system.with.metric.calculation.and.result.comparison","name":"evaluation system with metric calculation and result comparison","description":"Provides a framework for defining evaluation flows that compute quality metrics (accuracy, F1, BLEU, custom metrics) on flow outputs by comparing against ground truth or using LLM-based evaluators. Evaluation flows are standard flows that accept flow outputs and reference data, returning metric scores. The framework aggregates metrics across batch runs, enables comparison between flow versions, and visualizes metric trends over time. This enables data-driven optimization of LLM applications.","intents":["I want to measure how well my LLM flow performs on a test dataset","I need to compare two versions of my flow and see which performs better","I want to track quality metrics over time as I iterate on prompts"],"best_for":["teams building production LLM applications with quality requirements","organizations using LLM outputs for critical decisions and needing quality assurance","prompt engineers optimizing prompts based on measurable metrics"],"limitations":["LLM-based evaluators are expensive and may not correlate with human judgment","Metric calculation requires ground truth labels — not suitable for open-ended generation tasks","No built-in support for human-in-the-loop evaluation — requires external annotation tools"],"requires":["Python 3.9+","promptflow-devkit with evaluation support","Ground truth data or reference outputs for comparison","LLM API access if using LLM-based evaluators"],"input_types":["Flow outputs (predictions)","Reference data (ground truth or expected outputs)","Evaluation flow definition"],"output_types":["Metric scores (numeric values)","Aggregated metrics across batch","Metric comparison between flow versions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_7","uri":"capability://memory.knowledge.distributed.tracing.with.opentelemetry.integration.and.token.counting","name":"distributed tracing with opentelemetry integration and token counting","description":"Automatically instruments flow execution with distributed tracing via OpenTelemetry, capturing all LLM API calls, tool invocations, and custom code execution as spans with timing, parameters, and outputs. The tracing system tracks token usage across all LLM calls, calculates costs, and exports traces to OpenTelemetry-compatible backends (Jaeger, DataDog, Azure Monitor). This enables observability of production LLM applications without code changes.","intents":["I want to see exactly what my LLM flow is doing at each step for debugging","I need to track token usage and costs across all LLM calls in production","I want to export execution traces to my observability platform for monitoring"],"best_for":["teams running LLM applications in production requiring observability","organizations needing cost tracking and usage monitoring","developers debugging complex multi-step LLM workflows"],"limitations":["Tracing overhead adds ~50-200ms per flow execution depending on span count","Token counting is approximate and may not match actual LLM billing exactly","Exporting large traces to remote backends may impact performance — requires batching configuration"],"requires":["Python 3.9+","promptflow-tracing package","OpenTelemetry-compatible backend (optional, local storage available)","Environment variables for trace exporter configuration"],"input_types":["Flow execution (automatic instrumentation)","LLM API calls (automatic capture)"],"output_types":["Trace spans with timing and metadata","Token usage aggregation","Cost estimates","OpenTelemetry-compatible trace format"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_8","uri":"capability://tool.use.integration.connection.management.with.secure.credential.storage.and.provider.abstraction","name":"connection management with secure credential storage and provider abstraction","description":"Provides a centralized connection management system that stores API keys, connection strings, and credentials securely (encrypted at rest), abstracting provider-specific authentication details. Connections are defined once and referenced by name in flows, enabling credential rotation without code changes and supporting multiple authentication methods (API keys, OAuth, connection strings). The system integrates with Azure Key Vault for enterprise credential management.","intents":["I want to manage API keys securely without hardcoding them in flows","I need to rotate credentials without redeploying my LLM application","I want to use different credentials for development, testing, and production"],"best_for":["enterprises with credential management policies and compliance requirements","teams deploying LLM applications across multiple environments","organizations using multiple LLM providers and needing centralized credential management"],"limitations":["Local credential storage is encrypted but not as secure as cloud vaults — Azure Key Vault recommended for production","Credential rotation requires flow restart — no hot-reload support","No built-in audit logging of credential access — requires external monitoring"],"requires":["Python 3.9+","promptflow-core with connection management support","Credentials for LLM providers or other services","Azure Key Vault (optional, for enterprise deployments)"],"input_types":["Connection configuration (provider, auth method, credentials)","Connection name reference in flows"],"output_types":["Authenticated API client or connection object","Injected into tools and LLM nodes automatically"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--promptflow__cap_9","uri":"capability://code.generation.editing.local.flow.testing.and.debugging.with.interactive.execution","name":"local flow testing and debugging with interactive execution","description":"Provides CLI and SDK tools for testing flows locally with interactive debugging, including single-step execution, breakpoints, variable inspection, and execution visualization. Developers can run flows against test inputs, inspect intermediate values at each node, and visualize the execution DAG with actual data flowing through it. The framework caches node outputs to enable rapid iteration on specific nodes without re-executing upstream dependencies.","intents":["I want to debug my flow step-by-step to see what's happening at each node","I need to test my flow locally before deploying to production","I want to iterate quickly on a specific node without re-running the entire flow"],"best_for":["developers building and debugging LLM workflows locally","teams using test-driven development for LLM applications","prompt engineers iterating on prompts and seeing immediate results"],"limitations":["Interactive debugging is slower than production execution due to instrumentation overhead","Node output caching can cause stale data if upstream nodes are modified — requires manual cache invalidation","VS Code integration is optional — CLI-only debugging is less visual"],"requires":["Python 3.9+","promptflow-devkit with local testing support","VS Code (optional, for visual debugging)","Test input data in JSON or JSONL format"],"input_types":["Flow definition (YAML or Python)","Test input data (JSON or JSONL)"],"output_types":["Flow output","Execution trace with timing","Intermediate node outputs","Error messages and stack traces"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","promptflow-core and promptflow-devkit packages installed","Valid YAML syntax and understanding of flow.dag.yaml schema","promptflow-core package with @flow decorator support","Understanding of Python async/await if using async flows","promptflow-devkit with serving support","Flow definition (YAML or Python)","Deployment platform (local, Azure, Kubernetes, etc.)","promptflow-azure package","Azure subscription and ML workspace"],"failure_modes":["DAG structure cannot express dynamic branching or loops — all paths must be known at definition time","YAML syntax becomes unwieldy for flows with >20 nodes; Flex Flows recommended for complex logic","No built-in support for conditional execution based on runtime values without custom tool wrappers","Less visual debugging than DAG flows — execution path is implicit in Python code","Requires understanding of Python decorators and function signatures","Tracing overhead is higher than DAG flows due to function call interception","Auto-generated APIs are basic — complex routing or authentication requires custom wrappers","Request/response serialization may not handle all Python types — requires custom serializers for complex objects","No built-in rate limiting or authentication — requires external API gateway for production","Azure-specific — no support for other cloud providers (AWS, GCP) in core framework","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6715459889004174,"quality":0.35,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"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":"2026-05-03T13:59:50.673Z","last_commit":"2026-05-01T03:15:20Z"},"community":{"stars":11118,"forks":1104,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--promptflow","compare_url":"https://unfragile.ai/compare?artifact=microsoft--promptflow"}},"signature":"gv6FdAgOTshsT68O7MbWi3glQJYYqRPXbZp8YTim2Zwh1tgWxBbcQxRhwD//4DrPHv6HyAD/xK1sB8sMRPr+Dg==","signedAt":"2026-06-22T01:57:25.760Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--promptflow","artifact":"https://unfragile.ai/microsoft--promptflow","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--promptflow","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"}}