{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-promptflow","slug":"pypi-promptflow","name":"promptflow","type":"framework","url":"https://github.com/microsoft/promptflow","page_url":"https://unfragile.ai/pypi-promptflow","categories":["app-builders","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-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":"Enables declarative definition of LLM application workflows using YAML (flow.dag.yaml) that specify a directed acyclic graph of nodes representing LLM calls, prompts, and custom Python functions. The execution engine parses the YAML, validates node dependencies, and executes nodes in topological order with automatic input/output mapping between connected nodes. Supports conditional branching, loops, and dynamic node instantiation through template variables.","intents":["Define complex multi-step LLM workflows without writing orchestration code","Version control and collaborate on flow definitions as YAML files","Visualize and debug flow execution paths and data transformations","Reuse flow components across different applications"],"best_for":["Teams building production LLM applications with complex orchestration requirements","Non-technical prompt engineers who need to compose LLM chains declaratively","Organizations requiring flow versioning and code review workflows"],"limitations":["YAML syntax can become verbose for deeply nested flows with many conditional branches","No built-in support for dynamic node creation at runtime based on LLM outputs","Limited debugging visibility into intermediate node states without explicit logging"],"requires":["Python 3.9+","promptflow-core package","Valid YAML syntax and understanding of flow.dag.yaml schema"],"input_types":["YAML configuration files","JSON/structured input data","Environment variables for connections"],"output_types":["Structured execution results","Flow run metadata and logs","Node-level outputs for downstream consumption"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_1","uri":"capability://code.generation.editing.flex.flow.execution.with.python.function.class.based.definitions","name":"flex flow execution with python function/class-based definitions","description":"Allows developers to define flows as Python functions or classes decorated with @flow and @tool decorators, enabling programmatic control flow with full Python expressiveness. The framework introspects function signatures to automatically extract input/output schemas, handles dependency injection of connections and tools, and executes flows with the same observability and tracing infrastructure as YAML-based DAG flows. Supports async/await patterns for concurrent execution.","intents":["Build LLM applications using familiar Python patterns without learning YAML syntax","Implement complex conditional logic and loops that are cumbersome in declarative YAML","Leverage existing Python libraries and frameworks within flow definitions","Gradually migrate from prototype scripts to production flows"],"best_for":["Python developers comfortable with decorators and function introspection","Teams with existing Python codebases integrating LLM capabilities","Rapid prototyping and experimentation workflows"],"limitations":["Less suitable for non-technical stakeholders who need to understand flow logic","Requires Python knowledge; cannot be modified by non-programmers","Async/await patterns add complexity for developers unfamiliar with concurrent Python"],"requires":["Python 3.9+","promptflow-core package","Understanding of Python decorators and function signatures"],"input_types":["Python function parameters","Structured input objects","Connection objects injected via dependency injection"],"output_types":["Python return values (any serializable type)","Structured flow outputs","Async coroutines for concurrent execution"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_10","uri":"capability://automation.workflow.cli.based.flow.operations.and.management","name":"cli-based flow operations and management","description":"Provides comprehensive command-line interface for flow operations including creation, testing, execution, and deployment. CLI commands enable developers to test flows locally, run batch evaluations, manage connections, and deploy to cloud platforms. Integrates with VS Code extension for IDE-based flow development and visualization.","intents":["Test flows locally before deployment without writing test code","Run batch evaluations from command line for CI/CD integration","Manage connections and credentials via CLI","Visualize flow execution and debug issues"],"best_for":["Developers preferring command-line workflows","CI/CD pipelines automating flow testing and deployment","Teams using VS Code for flow development"],"limitations":["CLI output can be verbose; parsing programmatically is difficult","Some advanced operations require SDK usage; CLI is not feature-complete","VS Code extension requires VS Code 1.80+; no support for other editors"],"requires":["Python 3.9+","promptflow-devkit package installed","For VS Code integration: VS Code 1.80+ with Prompt Flow extension"],"input_types":["Command-line arguments and flags","Flow definition files","Input data files"],"output_types":["Console output and logs","Run results and metrics","Deployment status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_11","uri":"capability://memory.knowledge.run.management.and.execution.history.tracking","name":"run management and execution history tracking","description":"Maintains a persistent record of all flow executions (runs) including inputs, outputs, execution time, and resource usage. Runs can be queried, compared, and visualized to understand flow behavior over time. Supports local SQLite storage for development and Azure ML backend for production, enabling run data to be accessed across environments.","intents":["Track execution history for debugging and auditing","Compare outputs across different flow versions or configurations","Analyze performance trends and identify regressions","Reproduce specific executions for investigation"],"best_for":["Teams requiring execution audit trails for compliance","Developers debugging intermittent flow issues","Organizations tracking flow performance over time"],"limitations":["Local SQLite storage has limited query capabilities; complex analysis requires exporting data","Run data includes sensitive information (prompts, outputs); requires careful access control","Storage grows quickly with high-volume execution; requires periodic cleanup","No built-in data retention policies; manual cleanup needed"],"requires":["Python 3.9+","promptflow-devkit package","For local storage: SQLite (included with Python)","For cloud storage: Azure ML workspace"],"input_types":["Flow execution context","Execution results and metadata"],"output_types":["Run records with full execution details","Run comparison reports","Performance metrics and trends"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_12","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 within flows, including image loading, resizing, format conversion, and OCR for text extraction. Integrates with vision LLM models (GPT-4V, etc.) for image understanding tasks. Handles various input formats (PNG, JPEG, PDF) and automatically manages image encoding for LLM APIs.","intents":["Process images through vision LLM models for analysis and understanding","Extract text from documents using OCR for downstream processing","Build multimodal applications combining text and image inputs","Handle image format conversion and optimization for LLM APIs"],"best_for":["Teams building vision-based LLM applications","Document processing workflows requiring text extraction","Multimodal applications combining text and image understanding"],"limitations":["OCR accuracy depends on document quality; handwritten text not reliably extracted","Image encoding for LLM APIs adds latency; large images may exceed token limits","Limited image manipulation capabilities; complex image processing requires external libraries","PDF processing requires external dependencies; not all PDF formats supported"],"requires":["Python 3.9+","promptflow-core package","For OCR: pytesseract and Tesseract OCR engine","For PDF handling: PyPDF2 or similar library"],"input_types":["Image files (PNG, JPEG, GIF, WebP)","PDF documents","Image URLs"],"output_types":["Processed images","Extracted text from OCR","Image metadata and statistics"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_13","uri":"capability://automation.workflow.azure.ml.integration.for.cloud.scale.execution.and.deployment","name":"azure ml integration for cloud-scale execution and deployment","description":"Provides deep integration with Azure ML platform enabling flows to be executed on cloud compute clusters, stored in Azure ML registries, and deployed as managed endpoints. Handles authentication, compute resource management, and integration with Azure ML monitoring and governance tools. Enables seamless transition from local development to cloud production.","intents":["Scale flow execution to cloud compute without code changes","Deploy flows as managed Azure ML endpoints with auto-scaling","Store and version flows in Azure ML model registry","Integrate flows with Azure ML pipelines and workflows"],"best_for":["Organizations using Azure cloud platform","Teams requiring enterprise-scale LLM application deployment","Applications needing integration with Azure ML governance and monitoring"],"limitations":["Requires Azure subscription and workspace setup; significant operational overhead","Azure-specific features not available in open-source promptflow-core","Cost of cloud compute can be significant for high-volume execution","Vendor lock-in to Azure ecosystem; migration to other platforms requires refactoring"],"requires":["Python 3.9+","promptflow-azure package","Azure subscription with ML workspace","Azure credentials configured locally or in CI/CD"],"input_types":["Flow definitions (YAML or Python)","Input data for batch execution","Compute configuration and resource specifications"],"output_types":["Cloud execution results","Managed endpoint URLs","Azure ML run metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_2","uri":"capability://text.generation.language.prompty.file.format.for.single.file.llm.prompt.applications","name":"prompty file format for single-file llm prompt applications","description":"Introduces a lightweight .prompty file format that bundles prompt templates, LLM configuration (model, temperature, max_tokens), and Python code in a single file for simple LLM interactions. The format uses YAML frontmatter for metadata and configuration, followed by Jinja2 template syntax for the prompt, enabling quick iteration on prompts without managing separate files. Prompty files can be executed directly via CLI or imported as flows.","intents":["Quickly prototype and iterate on single LLM prompts with configuration","Share prompt templates with version control as self-contained files","Transition from simple prompts to full flows as complexity grows","Reduce boilerplate for basic LLM interactions"],"best_for":["Prompt engineers and researchers experimenting with LLM behavior","Teams sharing prompt templates across projects","Simple use cases that don't require complex orchestration"],"limitations":["Not suitable for multi-step workflows or complex orchestration","Limited to single LLM calls; cannot compose multiple models","Jinja2 template syntax may be unfamiliar to non-technical users"],"requires":["Python 3.9+","promptflow-devkit for CLI execution","API keys for configured LLM providers"],"input_types":["Prompty file with YAML frontmatter and Jinja2 template","Input variables matching template placeholders","LLM configuration (model, temperature, etc.)"],"output_types":["LLM text response","Structured output if parsing is configured","Execution metadata and token counts"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 pre-built tool nodes for common LLM providers (OpenAI, Azure OpenAI, Anthropic, Ollama) with standardized interfaces that abstract provider-specific API differences. Tools handle authentication via connection objects, parameter validation, token counting, and response parsing. Developers can reference these tools in flows without implementing provider-specific logic, and the framework automatically manages API calls, retries, and error handling.","intents":["Use multiple LLM providers in the same flow without provider-specific code","Switch between providers (e.g., OpenAI to Azure OpenAI) by changing connection configuration","Automatically track token usage and costs across different models","Handle provider-specific response formats transparently"],"best_for":["Teams evaluating multiple LLM providers and wanting to switch easily","Organizations using Azure OpenAI or on-premises Ollama deployments","Applications requiring cost tracking and token counting"],"limitations":["Limited to officially supported providers; custom providers require custom tool implementation","Token counting accuracy depends on provider-specific tokenizers; may not match actual billing","Retry logic is basic; complex resilience patterns require custom wrappers"],"requires":["Python 3.9+","promptflow-core package","Valid API keys or connection strings for target LLM providers","Connection objects configured in promptflow connection management"],"input_types":["Prompt text or messages","Model configuration (temperature, max_tokens, etc.)","Connection reference (provider credentials)"],"output_types":["LLM text response","Token usage metadata","Structured response if parsing configured"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_4","uri":"capability://tool.use.integration.custom.tool.creation.and.registration.with.schema.validation","name":"custom tool creation and registration with schema validation","description":"Enables developers to wrap Python functions as reusable tools using @tool decorator, which automatically generates JSON schemas from function signatures for input validation and type coercion. Tools are registered in a tool registry that can be referenced in flows, supporting both synchronous and asynchronous functions. The framework validates inputs against schemas before execution and provides type hints for IDE autocomplete.","intents":["Encapsulate custom business logic as reusable components in flows","Ensure type safety and input validation for tool parameters","Share custom tools across multiple flows and teams","Enable LLM function calling with validated tool schemas"],"best_for":["Teams building domain-specific tools for LLM applications","Organizations with existing Python utility libraries to integrate","Applications requiring strict input validation and type checking"],"limitations":["Schema generation from Python signatures has limitations with complex types (nested generics, unions)","Async tools require careful error handling; exceptions may not propagate cleanly","Tool registry is process-scoped; no built-in distributed tool discovery"],"requires":["Python 3.9+","promptflow-core package","Understanding of Python decorators and type hints"],"input_types":["Python function with type-annotated parameters","Docstring for tool description","Optional custom schema overrides"],"output_types":["Registered tool object","JSON schema for input validation","Tool execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_5","uri":"capability://automation.workflow.batch.flow.execution.with.distributed.processing","name":"batch flow execution with distributed processing","description":"Supports executing flows over large datasets by processing multiple input rows in parallel, with configurable batch sizes and worker processes. The batch executor reads input data from JSONL files, distributes rows across worker processes, and aggregates results into output files. Integrates with Azure ML for distributed cloud execution, enabling horizontal scaling without code changes.","intents":["Process thousands of inputs through a flow efficiently without sequential execution","Evaluate flow performance on large datasets for quality assessment","Scale flow execution to cloud infrastructure for production workloads","Generate batch predictions or transformations at scale"],"best_for":["Teams processing large datasets through LLM flows","Evaluation and testing workflows requiring high throughput","Production systems needing to scale LLM processing to thousands of requests"],"limitations":["Batch execution requires input data in JSONL format; other formats need preprocessing","Worker process overhead makes small batches inefficient; minimum viable batch size ~100 rows","Distributed execution on Azure ML requires Azure subscription and workspace configuration","No built-in fault tolerance; failed rows are not automatically retried"],"requires":["Python 3.9+","promptflow-core and promptflow-devkit packages","Input data in JSONL format","For cloud execution: Azure ML workspace and credentials"],"input_types":["JSONL file with input rows","Flow definition (YAML or Python)","Batch configuration (batch size, worker count)"],"output_types":["JSONL file with results","Batch run metadata and statistics","Per-row execution logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_6","uri":"capability://planning.reasoning.flow.evaluation.and.quality.assessment.with.custom.metrics","name":"flow evaluation and quality assessment with custom metrics","description":"Provides a framework for defining evaluation flows that measure quality of primary flow outputs using custom metrics (accuracy, relevance, safety, etc.). Evaluation flows are themselves flows that take primary flow outputs as inputs and produce metric scores. The framework aggregates metrics across batch runs and generates quality reports, enabling systematic comparison of flow variants.","intents":["Measure and track quality metrics for LLM application outputs","Compare different prompt versions or model configurations objectively","Identify and debug quality issues in production flows","Establish quality gates for CI/CD pipelines"],"best_for":["Teams building production LLM applications requiring quality assurance","Researchers comparing prompt engineering approaches systematically","Organizations with strict quality requirements and compliance needs"],"limitations":["Metric definition requires custom flow implementation; no pre-built metrics for common use cases","Evaluation flows themselves consume LLM tokens, increasing costs","Subjective metrics (relevance, coherence) are difficult to automate; human evaluation still needed","No built-in statistical significance testing for metric comparisons"],"requires":["Python 3.9+","promptflow-core and promptflow-devkit packages","Primary flow outputs to evaluate","Custom evaluation flow definitions"],"input_types":["Primary flow outputs","Ground truth labels (optional)","Evaluation flow definitions"],"output_types":["Metric scores (numeric values)","Aggregated quality reports","Per-sample evaluation results"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_7","uri":"capability://memory.knowledge.comprehensive.flow.tracing.and.observability.with.opentelemetry.integration","name":"comprehensive flow tracing and observability with opentelemetry integration","description":"Instruments flow execution to capture detailed traces of all operations including LLM calls, tool invocations, and custom code execution. Uses OpenTelemetry standards to emit traces that can be exported to various backends (Azure Application Insights, Jaeger, etc.). Captures inputs, outputs, latency, token usage, and errors for each operation, enabling post-execution analysis and debugging.","intents":["Debug flow execution by inspecting inputs/outputs at each step","Monitor production flow performance and identify bottlenecks","Track token usage and costs across LLM calls","Correlate errors with specific flow steps for root cause analysis"],"best_for":["Teams operating LLM applications in production requiring observability","Developers debugging complex multi-step flows","Organizations needing to track LLM token usage for cost management"],"limitations":["Trace collection adds overhead (~50-200ms per flow execution depending on trace backend)","Sensitive data in prompts/outputs is captured in traces; requires careful credential management","Trace storage and retention can become expensive at scale; requires external backend","OpenTelemetry integration requires compatible backend; not all observability platforms supported"],"requires":["Python 3.9+","promptflow-tracing package","OpenTelemetry-compatible backend (optional; local in-memory storage available)","API keys for trace backend if using cloud service"],"input_types":["Flow execution context","LLM API calls and responses","Tool invocations and results"],"output_types":["OpenTelemetry traces","Structured execution logs","Performance metrics and statistics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_8","uri":"capability://safety.moderation.connection.management.with.secure.credential.storage","name":"connection management with secure credential storage","description":"Provides a centralized connection management system where API keys, connection strings, and other credentials are stored securely and referenced by name in flows. Connections are environment-specific (local, cloud) and can be managed via CLI or SDK. Supports multiple connection types (OpenAI, Azure OpenAI, SQL, custom) with provider-specific configuration validation.","intents":["Store API keys and credentials securely without embedding them in flow definitions","Switch between different API keys/environments without modifying flows","Share flows across teams without exposing credentials","Audit and rotate credentials centrally"],"best_for":["Teams managing multiple environments (dev, staging, prod) with different credentials","Organizations with security requirements for credential management","Distributed teams sharing flow definitions without exposing secrets"],"limitations":["Local connection storage uses filesystem encryption; not suitable for highly sensitive environments","Azure connection storage requires Azure ML workspace; no built-in support for other secret managers","Connection validation is provider-specific; custom connection types require custom validation logic","No built-in credential rotation or expiration management"],"requires":["Python 3.9+","promptflow-core package","For local storage: filesystem access with encryption support","For Azure storage: Azure ML workspace and credentials"],"input_types":["Connection configuration (type, credentials, endpoint)","Connection name and metadata"],"output_types":["Connection object for use in flows","Connection metadata and validation status"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-promptflow__cap_9","uri":"capability://automation.workflow.flow.serving.and.rest.api.deployment","name":"flow serving and rest api deployment","description":"Enables deployment of flows as REST APIs that can be invoked via HTTP requests. The serving layer automatically generates OpenAPI schemas from flow input/output definitions, handles request validation, and manages concurrent requests. Supports deployment to various platforms including local Flask/FastAPI servers, Azure Container Instances, and Kubernetes.","intents":["Expose flows as REST endpoints for integration with external systems","Deploy flows to production without writing API boilerplate code","Generate API documentation automatically from flow definitions","Scale flow serving across multiple instances"],"best_for":["Teams deploying LLM applications as microservices","Organizations integrating flows with existing REST-based systems","Applications requiring horizontal scaling of flow execution"],"limitations":["REST API adds latency (~50-100ms overhead per request for serialization/deserialization)","Streaming responses not supported; all outputs must be buffered before returning","Authentication/authorization must be implemented separately; no built-in API key management","Concurrent request handling depends on underlying server (Flask/FastAPI); no built-in rate limiting"],"requires":["Python 3.9+","promptflow-devkit package","Flask or FastAPI for local serving","Docker for containerized deployment","Kubernetes or cloud platform for production deployment"],"input_types":["HTTP POST requests with JSON body","Flow input parameters"],"output_types":["HTTP 200 response with JSON body","OpenAPI schema for API documentation","Error responses with detailed messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","promptflow-core package","Valid YAML syntax and understanding of flow.dag.yaml schema","Understanding of Python decorators and function signatures","promptflow-devkit package installed","For VS Code integration: VS Code 1.80+ with Prompt Flow extension","promptflow-devkit package","For local storage: SQLite (included with Python)","For cloud storage: Azure ML workspace","For OCR: pytesseract and Tesseract OCR engine"],"failure_modes":["YAML syntax can become verbose for deeply nested flows with many conditional branches","No built-in support for dynamic node creation at runtime based on LLM outputs","Limited debugging visibility into intermediate node states without explicit logging","Less suitable for non-technical stakeholders who need to understand flow logic","Requires Python knowledge; cannot be modified by non-programmers","Async/await patterns add complexity for developers unfamiliar with concurrent Python","CLI output can be verbose; parsing programmatically is difficult","Some advanced operations require SDK usage; CLI is not feature-complete","VS Code extension requires VS Code 1.80+; no support for other editors","Local SQLite storage has limited query capabilities; complex analysis requires exporting data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.49999999999999994,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:12.848Z","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=pypi-promptflow","compare_url":"https://unfragile.ai/compare?artifact=pypi-promptflow"}},"signature":"XeoTgzFpVRWsLrjxct00wAolx2He07SPq+D4Iua6DNAfNGuq0kfNGcfTVenM3hXv2v/yGLKT58jGtv3E65thAg==","signedAt":"2026-06-20T05:32:31.960Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-promptflow","artifact":"https://unfragile.ai/pypi-promptflow","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}