{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"prompt-flow","slug":"prompt-flow","name":"Prompt Flow","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow","page_url":"https://unfragile.ai/prompt-flow","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"prompt-flow__cap_0","uri":"capability://planning.reasoning.dag.based.visual.flow.composition.with.yaml.serialization","name":"dag-based visual flow composition with yaml serialization","description":"Enables users to construct directed acyclic graph (DAG) pipelines through a dual-mode editor: a visual node-and-edge canvas for drag-and-drop composition, and a YAML-based `flow.dag.yaml` file for declarative pipeline definition. The visual editor generates and synchronizes with the underlying YAML representation, allowing both graphical and text-based editing modes. Nodes represent LLM calls, tool invocations, or Python functions; edges define data flow between nodes. The extension parses the YAML DAG structure and renders it as an interactive graph in the sidebar and editor overlay.","intents":["I want to visually design a multi-step LLM application without writing boilerplate orchestration code","I need to define prompt chains where output from one LLM call feeds into the next","I want to version-control my pipeline structure as human-readable YAML","I need to quickly prototype and iterate on prompt workflows without redeploying"],"best_for":["prompt engineers building multi-step LLM applications","teams prototyping conversational AI flows","developers migrating from hardcoded prompt chains to declarative pipelines"],"limitations":["DAG structure enforces acyclic constraints — no loops or conditional branching documented","Visual editor is VS Code-only; no web-based or cloud IDE support","YAML syntax errors in flow.dag.yaml can break visual rendering; no built-in schema validation shown","No explicit support for dynamic node creation at runtime based on LLM outputs"],"requires":["VS Code 1.70+ (assumed; exact version not specified)","VS Code Python extension installed and configured","Python 3.9+ with promptflow SDK installed","Local file system access to workspace directory"],"input_types":["YAML (flow.dag.yaml definition)","Python source files (for custom nodes)","Connection configuration YAML"],"output_types":["YAML (serialized DAG structure)","Visual graph representation (rendered in editor)","Execution logs and debug output"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_1","uri":"capability://automation.workflow.local.python.environment.based.flow.execution.with.debug.mode","name":"local python environment-based flow execution with debug mode","description":"Executes DAG-based flows within a selected local Python interpreter, leveraging the VS Code Python extension to discover and manage Python environments. The extension invokes the promptflow SDK to parse the flow.dag.yaml, instantiate nodes (LLM calls, tools, Python functions), and execute the DAG sequentially or in parallel based on dependencies. Debug mode (F5) attaches a debugger to the execution context, enabling breakpoints and step-through inspection. Test execution (Shift+F5) runs predefined test cases against the flow and reports pass/fail results.","intents":["I want to test my prompt pipeline locally before deploying to production","I need to debug why a specific node in my flow is producing unexpected output","I want to run my flow against a batch of test inputs and validate correctness","I need to inspect intermediate outputs between pipeline steps"],"best_for":["individual developers iterating on prompt logic","teams validating flows in CI/CD pipelines","prompt engineers debugging multi-step LLM applications"],"limitations":["Execution is local-only; no remote or cloud execution support","No built-in distributed execution or parallelization across machines","Debug mode requires VS Code debugger attachment, which may not work in all Python environments (e.g., conda, venv edge cases)","No explicit timeout or resource limits documented; long-running flows may hang VS Code","Test execution requires pre-defined test cases in a specific format (format not documented)"],"requires":["Python 3.9+ installed and discoverable by VS Code Python extension","promptflow Python SDK installed in the selected environment","flow.dag.yaml file with valid DAG structure","Connection credentials configured for any external LLM/API calls"],"input_types":["flow.dag.yaml (pipeline definition)","Test case files (format unspecified)","Connection configuration YAML","Input data for flow parameters"],"output_types":["Execution logs (stdout/stderr)","Node output values (intermediate and final)","Test results (pass/fail with error messages)","Debug breakpoint state and variable inspection"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_10","uri":"capability://automation.workflow.run.management.with.execution.history.artifact.storage.and.visualization","name":"run management with execution history, artifact storage, and visualization","description":"Tracks all flow executions (runs) with detailed metadata including inputs, outputs, execution time, token usage, and error information. Runs are stored in a run database (local or Azure) with full artifact storage (logs, traces, intermediate results). The run dashboard visualizes execution history, enables filtering and comparison across runs, and displays detailed execution traces with node-level granularity.","intents":["Track execution history and performance metrics across multiple flow runs","Debug failed runs by examining inputs, outputs, and execution traces","Compare performance across prompt variants and model versions","Audit flow execution for compliance and troubleshooting"],"best_for":["Teams iterating on flows with need for execution history and debugging","Organizations auditing LLM application behavior for compliance","Developers comparing performance across variants and model versions"],"limitations":["Local run storage is file-based — no built-in query or filtering capabilities","Run database can grow large with high-volume execution — requires periodic cleanup","Artifact storage is not deduplicated — identical outputs across runs consume separate storage","Run comparison is limited to metrics — no support for statistical significance testing","Dashboard is web-based — requires local server or Azure ML workspace for access"],"requires":["Python 3.9+","promptflow-devkit for local run management or promptflow-azure for cloud storage","Flow execution (local or cloud)","Run database configuration (local directory or Azure ML workspace)"],"input_types":["Flow execution context (automatically captured)","Run metadata and configuration"],"output_types":["Run records with inputs, outputs, metrics, and traces","Execution logs and artifact files","Dashboard visualizations and reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_11","uri":"capability://automation.workflow.ci.cd.integration.with.automated.testing.and.metric.based.gates","name":"ci/cd integration with automated testing and metric-based gates","description":"Integrates with CI/CD pipelines (GitHub Actions, Azure Pipelines) to automatically run flows against test datasets, compute evaluation metrics, and enforce quality gates based on metric thresholds. Provides CLI commands for batch execution, evaluation, and result reporting. Supports pull request workflows where new prompt versions are tested against baselines before merging.","intents":["Automatically test prompt changes in CI/CD pipelines before deployment","Enforce quality gates based on evaluation metrics (e.g., accuracy > 0.9)","Compare new prompt versions against baseline metrics in pull requests","Prevent prompt degradation from being deployed to production"],"best_for":["Teams using GitHub or Azure DevOps with need for automated prompt testing","Organizations enforcing quality standards for LLM application changes","DevOps teams automating LLM application deployment pipelines"],"limitations":["CI/CD integration requires custom workflow configuration — no out-of-the-box templates","Metric-based gates are simple threshold checks — no support for statistical significance or trend analysis","Test dataset must be committed to repository or fetched from external storage — no built-in data management","Execution time can be long for large test datasets — may slow down CI/CD pipelines","No built-in rollback mechanism — failed gates require manual intervention"],"requires":["Python 3.9+","promptflow-devkit package","CI/CD platform (GitHub Actions, Azure Pipelines, etc.)","Test dataset (JSONL format)","Evaluation metrics and quality gate thresholds"],"input_types":["Flow definition (YAML or Python)","Test dataset (JSONL)","Evaluation metrics and gate configuration","CI/CD workflow definition"],"output_types":["Test execution results (JSONL)","Evaluation metrics and comparison reports","CI/CD status and gate pass/fail decisions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_12","uri":"capability://text.generation.language.prompty.format.for.single.file.prompt.definitions.with.metadata","name":"prompty format for single-file prompt definitions with metadata","description":"Introduces a .prompty file format that combines prompt template, model configuration, and metadata in a single YAML/JSON file. Prompty files can be executed directly or embedded in flows, enabling lightweight prompt experimentation without full flow definitions. Supports variable substitution, model selection, and hyperparameter configuration within the file.","intents":["Define and test prompts quickly without creating full flow structures","Version control prompts with associated metadata and configuration","Share prompt templates across teams with embedded configuration","Experiment with prompt variations and model settings in isolation"],"best_for":["Prompt engineers iterating on wording and configuration quickly","Teams sharing prompt templates with embedded configuration","Developers prototyping LLM interactions before building full flows"],"limitations":["Prompty files are limited to single prompts — no support for multi-step workflows","No built-in evaluation or metric computation — requires embedding in flows for testing","Variable substitution is basic — no support for complex templating logic","Prompty format is Prompt Flow-specific — limited portability to other frameworks","No built-in version control or diff tools — requires manual comparison of variants"],"requires":["Python 3.9+","promptflow-devkit package",".prompty file with prompt template and configuration","Connection to LLM provider"],"input_types":[".prompty file (YAML or JSON format)","Input variables for template substitution"],"output_types":["LLM response text","Token usage and cost metadata","Execution logs"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_13","uri":"capability://image.visual.multimedia.processing.with.image.and.document.handling","name":"multimedia processing with image and document handling","description":"Supports processing multimedia inputs (images, PDFs, documents) within flows through built-in tools for image analysis, OCR, and document parsing. Images can be passed to vision-capable LLMs (GPT-4V, Claude), and documents are automatically converted to text or embeddings. The framework handles format conversion, size optimization, and error handling transparently.","intents":["Process images with vision-capable LLMs for analysis, captioning, or classification","Extract text from PDFs and documents for downstream processing","Build multimodal LLM applications combining text and image inputs","Analyze document content using LLM-based reasoning"],"best_for":["Teams building vision-enabled LLM applications","Organizations processing document-heavy workflows with LLMs","Developers creating multimodal AI applications"],"limitations":["Image processing is limited to LLM vision APIs — no built-in image manipulation or filtering","Document parsing is basic — complex layouts and tables may not be extracted correctly","Large files are not automatically optimized — may exceed LLM context limits","No built-in OCR for non-English languages — limited multilingual support","Multimedia processing adds latency — image encoding and document parsing can be slow"],"requires":["Python 3.9+","promptflow-core package","Vision-capable LLM (GPT-4V, Claude, etc.) for image processing","Document processing libraries (optional, for advanced parsing)"],"input_types":["Image files (PNG, JPG, GIF, WebP)","PDF and document files","Base64-encoded image data"],"output_types":["Extracted text from documents","LLM analysis of images","Structured data from document parsing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_14","uri":"capability://automation.workflow.azure.ml.integration.with.managed.execution.and.workspace.integration","name":"azure ml integration with managed execution and workspace integration","description":"Integrates with Azure ML workspaces for cloud-based flow execution, enabling managed compute, auto-scaling, and enterprise features (RBAC, audit logging). Flows can be registered as Azure ML models, deployed as endpoints, and monitored with Azure's observability tools. Supports both batch execution on compute clusters and real-time serving on managed endpoints.","intents":["Deploy LLM flows to Azure ML for managed execution and auto-scaling","Integrate flows with Azure ML pipelines for enterprise workflows","Leverage Azure's RBAC and audit logging for compliance","Monitor deployed flows with Azure Monitor and Application Insights"],"best_for":["Organizations using Azure ML for ML operations and governance","Enterprise teams requiring RBAC, audit logging, and compliance features","Teams deploying LLM applications at scale with auto-scaling requirements"],"limitations":["Azure ML integration is tightly coupled to Azure ecosystem — limited portability to other cloud providers","Requires Azure ML workspace and compute resources — adds infrastructure complexity and cost","Deployment to Azure ML endpoints adds latency (~500ms-1s) compared to local execution","Monitoring and debugging require Azure Portal access — limited local visibility","Cost tracking is integrated with Azure billing — requires Azure cost management setup"],"requires":["Python 3.9+","promptflow-azure package","Azure ML workspace","Azure subscription with appropriate permissions","Compute resources (clusters for batch, endpoints for serving)"],"input_types":["Flow definition (YAML or Python)","Azure ML workspace configuration","Compute cluster or endpoint configuration"],"output_types":["Registered Azure ML model","Deployed endpoint with REST API","Execution logs and monitoring data in Azure Monitor"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_2","uri":"capability://tool.use.integration.connection.management.with.yaml.based.credential.storage","name":"connection management with yaml-based credential storage","description":"Provides a sidebar-based connection manager that abstracts credential handling for external services (LLM APIs, databases, etc.). Connections are defined as YAML files with key-value pairs for authentication details (API keys, endpoints, OAuth tokens). The extension stores connection definitions locally in the workspace, with inline YAML comments providing configuration guidance. When a flow node references a connection by name, the extension resolves the connection YAML at runtime and injects credentials into the node's execution context. The sidebar UI allows users to create, edit, and delete connections without manual YAML editing.","intents":["I want to manage API keys and credentials for multiple LLM providers (OpenAI, Azure OpenAI, etc.) without hardcoding them","I need to switch between different API endpoints (dev, staging, prod) by changing connection references","I want to share flow definitions with teammates without exposing sensitive credentials","I need to rotate API keys without modifying flow YAML files"],"best_for":["teams managing multiple LLM provider credentials","developers separating configuration from pipeline logic","organizations with credential rotation policies"],"limitations":["Credentials are stored as plaintext YAML in the local workspace; no encryption at rest","No built-in secret management integration (e.g., Azure Key Vault, HashiCorp Vault)","Connection YAML files must be manually excluded from version control (.gitignore) to prevent credential leaks","No audit logging for credential access or modification","Connection types and required fields are not documented; unclear which providers are supported"],"requires":["VS Code with Prompt Flow extension installed","Local file system write access to workspace directory","Knowledge of connection YAML schema (not fully documented)"],"input_types":["Connection name (string identifier)","Credential key-value pairs (API keys, endpoints, tokens)","Connection type (e.g., 'openai', 'azure_openai', 'custom')"],"output_types":["Connection YAML file (stored locally)","Resolved credentials injected into node execution context","Connection list in sidebar UI"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_3","uri":"capability://data.processing.analysis.built.in.flow.evaluation.and.variant.testing","name":"built-in flow evaluation and variant testing","description":"Enables users to define evaluation metrics and run variant tests against flows to measure performance and correctness. The extension supports creating evaluation flows that assess outputs from a main flow (e.g., comparing LLM-generated text against ground truth using metrics like BLEU, similarity scores, or custom Python functions). Variant testing allows users to test multiple versions of a flow (e.g., different prompts, model parameters) against the same test dataset and compare results side-by-side. Evaluation results are aggregated and displayed in a results dashboard within the extension.","intents":["I want to measure how well my prompt performs against a test dataset using standard metrics","I need to compare two different prompt versions and see which one produces better outputs","I want to validate that my flow changes don't degrade performance before committing","I need to run A/B tests on different LLM configurations (temperature, model, etc.)"],"best_for":["prompt engineers optimizing prompt quality","teams validating LLM application improvements","researchers comparing model and prompt variants"],"limitations":["Evaluation metrics and variant testing implementation details are not documented; unclear which metrics are built-in vs custom","No integration with external evaluation platforms (e.g., Weights & Biases, Hugging Face Spaces)","Results are stored locally; no cloud-based results persistence or sharing","Variant testing requires manual creation of variant flows; no automated parameter sweep or hyperparameter optimization","No statistical significance testing or confidence intervals for variant comparisons"],"requires":["flow.dag.yaml with main flow definition","Evaluation flow definition (format unspecified)","Test dataset with expected outputs (format unspecified)","Python 3.9+ with promptflow SDK"],"input_types":["Main flow definition (flow.dag.yaml)","Evaluation flow definition (YAML)","Test dataset (format unspecified; likely CSV or JSON)","Variant flow definitions (multiple flow.dag.yaml files)"],"output_types":["Evaluation metrics (numeric scores, pass/fail results)","Variant comparison results (side-by-side metric comparison)","Results dashboard (rendered in VS Code)","Evaluation logs and detailed output"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_4","uri":"capability://tool.use.integration.node.level.tool.and.llm.provider.abstraction","name":"node-level tool and llm provider abstraction","description":"Abstracts LLM provider APIs and tool integrations through a node-based system where each node encapsulates a specific operation (LLM call, tool invocation, Python function). Nodes are configured with provider-agnostic parameters (e.g., model name, temperature, max_tokens) and reference connections for credentials. The extension resolves the connection type at runtime and routes the node execution to the appropriate provider SDK (OpenAI, Azure OpenAI, Anthropic, etc.). Built-in tool nodes provide access to common operations (web search, code execution, database queries) without requiring custom Python code.","intents":["I want to swap between OpenAI and Azure OpenAI without rewriting my flow","I need to use multiple LLM providers in a single flow (e.g., GPT-4 for reasoning, Claude for summarization)","I want to call external tools (APIs, databases) from my prompt pipeline without writing integration code","I need to parameterize LLM settings (temperature, model) at the node level for easy experimentation"],"best_for":["developers building multi-provider LLM applications","teams avoiding vendor lock-in","prompt engineers experimenting with different model configurations"],"limitations":["Supported LLM providers and tool types are not fully documented","No explicit support for custom LLM providers or self-hosted models (e.g., Ollama, vLLM)","Tool node types are limited to built-in tools; custom tool integration requires Python code","Provider-specific features (e.g., function calling, vision capabilities) may not be exposed through the abstraction","No fallback or retry logic documented for provider failures"],"requires":["Connection configuration for the target LLM provider","API key or authentication credentials for the provider","promptflow SDK with provider-specific plugins installed"],"input_types":["Node type (e.g., 'llm', 'tool', 'python')","Provider name (e.g., 'openai', 'azure_openai')","Model name and parameters (temperature, max_tokens, etc.)","Connection reference (by name)"],"output_types":["LLM completion text or structured output","Tool execution results","Python function return values","Node execution logs and errors"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_5","uri":"capability://automation.workflow.sidebar.based.flow.and.connection.project.management","name":"sidebar-based flow and connection project management","description":"Provides a VS Code sidebar pivot labeled 'Prompt flow' that serves as a project hub for managing flows, connections, and dependencies. The sidebar displays a hierarchical view of flows in the workspace, quick-access buttons for common tasks (dependency installation, connection creation), and sections for browsing available connections. Right-click context menus on flows and connections enable actions like create, edit, delete, and rename. The sidebar integrates with VS Code's file explorer, allowing users to navigate and open flow files directly.","intents":["I want a centralized view of all flows and connections in my project","I need to quickly install promptflow dependencies without opening a terminal","I want to create a new flow or connection without manually creating YAML files","I need to manage multiple flows and keep track of which connections each flow uses"],"best_for":["individual developers managing small to medium prompt projects","teams organizing flows within a shared workspace","non-technical users who prefer UI-based project management over file system navigation"],"limitations":["Sidebar is VS Code-only; no web or remote IDE support","No multi-workspace or cross-project flow management","No built-in search or filtering for flows and connections in large projects","Sidebar state is not persisted across VS Code sessions (assumed)","No drag-and-drop reordering or grouping of flows by category"],"requires":["VS Code with Prompt Flow extension installed","Workspace with at least one flow.dag.yaml file"],"input_types":["Flow file paths (flow.dag.yaml)","Connection file paths (connection YAML)","User actions (right-click, button clicks)"],"output_types":["Sidebar UI with flow and connection hierarchy","Context menu actions (create, edit, delete)","File creation dialogs and templates"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_6","uri":"capability://automation.workflow.yaml.code.lens.actions.for.flow.editing.and.execution","name":"yaml code lens actions for flow editing and execution","description":"Adds inline code lens actions to flow.dag.yaml files in the VS Code editor, providing quick access to common operations without context menu navigation. Code lens actions include 'Visual editor' (opens the visual DAG editor), 'Debug' (F5 equivalent), 'Run tests' (Shift+F5 equivalent), and 'Create connection' (for connection YAML files). These actions are rendered as clickable links above the YAML content, enabling one-click access to flow operations from the text editor.","intents":["I want to quickly switch between YAML and visual editing modes without using keyboard shortcuts","I need to run or debug a flow without leaving the YAML editor","I want to create a new connection while editing a flow that references it","I need visual cues in the editor about available actions for the current file"],"best_for":["developers who prefer keyboard-free navigation","teams with mixed technical backgrounds (some prefer YAML, some prefer visual editing)","users discovering Prompt Flow features through discoverable UI elements"],"limitations":["Code lens actions are only available for flow.dag.yaml and connection YAML files; no support for other file types","Code lens rendering may be slow for very large YAML files (performance not documented)","No customization of which code lens actions are displayed","Code lens actions are not accessible via keyboard shortcuts (must use mouse or Tab navigation)"],"requires":["VS Code with Prompt Flow extension installed","flow.dag.yaml or connection YAML file open in the editor"],"input_types":["YAML file content (flow.dag.yaml or connection YAML)"],"output_types":["Clickable code lens actions rendered in the editor","Navigation to visual editor, debug session, or test execution"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_7","uri":"capability://automation.workflow.python.environment.discovery.and.sdk.dependency.management","name":"python environment discovery and sdk dependency management","description":"Integrates with the VS Code Python extension to discover installed Python interpreters and manage promptflow SDK dependencies. The extension detects available Python environments (system Python, virtual environments, conda environments) and allows users to select a target environment for flow execution. A 'Quick access' button in the sidebar triggers dependency installation, which runs `pip install promptflow promptflow-tools` in the selected environment. The extension validates that required SDKs are installed before executing flows and provides error messages if dependencies are missing.","intents":["I want to use a specific Python environment (venv, conda) for my flows without manual configuration","I need to install promptflow dependencies with a single click instead of opening a terminal","I want the extension to warn me if required SDKs are missing before I try to run a flow","I need to switch between multiple Python environments for testing flows with different package versions"],"best_for":["developers new to Prompt Flow who want minimal setup friction","teams managing multiple Python environments","users without strong command-line experience"],"limitations":["Dependency installation is limited to pip; no support for conda, poetry, or other package managers","No version pinning or lock file support; always installs the latest promptflow version","Environment discovery relies on VS Code Python extension; may miss manually-installed Python interpreters","No support for virtual environment creation; users must create environments manually","No validation of SDK version compatibility with the extension"],"requires":["VS Code Python extension installed and configured","Python 3.9+ installed in at least one discoverable location","pip package manager available in the selected Python environment"],"input_types":["Python interpreter path (discovered from VS Code Python extension)","User action (click 'Install dependencies' button)"],"output_types":["List of available Python environments (displayed in sidebar or command palette)","Installation logs (stdout/stderr from pip)","Validation status (SDK installed or missing)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_8","uri":"capability://tool.use.integration.azure.ai.integration.and.cloud.deployment.readiness","name":"azure ai integration and cloud deployment readiness","description":"Provides integration points with Azure AI services, enabling flows to be deployed to Azure AI platforms and leverage Azure-hosted LLM models. The extension supports Azure OpenAI connections, allowing flows to call Azure-hosted GPT models. While specific cloud deployment mechanisms are not documented, the architecture suggests flows can be packaged and deployed to Azure AI without significant modification. Azure integration is positioned as a primary use case in the product description, indicating native support for Azure authentication, model selection, and resource management.","intents":["I want to build flows locally and deploy them to Azure AI without rewriting code","I need to use Azure OpenAI models in my flows with proper credential management","I want to leverage Azure's enterprise features (RBAC, compliance, monitoring) for my LLM applications","I need to scale my flows to production using Azure's managed infrastructure"],"best_for":["enterprises using Azure as their cloud platform","teams requiring compliance and governance features","organizations with existing Azure subscriptions and expertise"],"limitations":["Azure deployment mechanisms are not documented; unclear how flows are packaged and deployed","No explicit support for other cloud platforms (AWS, GCP); Azure-specific","Azure authentication setup is not detailed; assumes users have Azure credentials and subscriptions","No built-in monitoring or logging integration with Azure Monitor or Application Insights","Pricing and cost management features are not documented"],"requires":["Azure subscription with AI services enabled","Azure OpenAI resource provisioned and accessible","Azure authentication credentials (service principal or user account)","Prompt Flow extension with Azure integration components installed"],"input_types":["Azure OpenAI connection configuration (endpoint, API key, model name)","Flow definition (flow.dag.yaml)","Deployment target (Azure resource group, app service, etc.)"],"output_types":["Deployed flow endpoint (URL for API access)","Azure resource configuration (app service, container, etc.)","Deployment logs and status"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__cap_9","uri":"capability://code.generation.editing.custom.python.node.execution.with.inline.code.editing","name":"custom python node execution with inline code editing","description":"Allows users to define custom Python function nodes within flows, enabling arbitrary Python code execution as part of the DAG. Custom nodes are defined in flow.dag.yaml with a reference to a Python function (e.g., `my_module.my_function`), and the extension executes the function with inputs from upstream nodes. Users can edit custom node code directly in the VS Code editor, and the extension validates Python syntax and function signatures. Custom nodes support input/output type hints, enabling type checking and IDE autocomplete for node connections.","intents":["I want to add custom logic to my flow that isn't available as a built-in node (e.g., data transformation, custom validation)","I need to integrate existing Python libraries or functions into my prompt pipeline","I want to preprocess or postprocess LLM outputs with custom Python code","I need to implement complex business logic that spans multiple nodes"],"best_for":["developers comfortable with Python programming","teams with existing Python codebases to integrate","applications requiring custom data transformation or validation"],"limitations":["Custom nodes require Python code; no visual programming for custom logic","No built-in testing framework for custom nodes; users must test manually","Type hints are optional; no enforcement of input/output types at runtime","Custom node code is stored in separate Python files; no inline code editing within flow.dag.yaml","No sandboxing or security restrictions on custom node code; arbitrary code execution is possible"],"requires":["Python 3.9+ with promptflow SDK","Python source files in the workspace with function definitions","Type hints (optional but recommended) for input/output parameters"],"input_types":["Python function reference (module.function_name)","Input parameters from upstream nodes (any Python type)","Type hints (optional)"],"output_types":["Function return value (any Python type)","Execution logs and errors","Type-checked outputs for downstream nodes"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"prompt-flow__headline","uri":"capability://tool.use.integration.visual.tool.for.building.llm.application.flows","name":"visual tool for building llm application flows","description":"Prompt Flow is a visual tool integrated into Visual Studio Code for creating and testing large language model application flows using DAG-based prompt pipelines, specifically designed for Azure AI integration.","intents":["best visual tool for LLM applications","LLM flow builder for Azure AI","create prompt pipelines in VS Code","test LLM application flows","DAG-based prompt pipeline tool"],"best_for":["developers using Azure AI","teams building LLM applications"],"limitations":["requires VSCode Python extension","no support for web development"],"requires":["Visual Studio Code","Python environment"],"input_types":["prompt flows","DAG configurations"],"output_types":["tested LLM application flows"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.70+ (assumed; exact version not specified)","VS Code Python extension installed and configured","Python 3.9+ with promptflow SDK installed","Local file system access to workspace directory","Python 3.9+ installed and discoverable by VS Code Python extension","promptflow Python SDK installed in the selected environment","flow.dag.yaml file with valid DAG structure","Connection credentials configured for any external LLM/API calls","Python 3.9+","promptflow-devkit for local run management or promptflow-azure for cloud storage"],"failure_modes":["DAG structure enforces acyclic constraints — no loops or conditional branching documented","Visual editor is VS Code-only; no web-based or cloud IDE support","YAML syntax errors in flow.dag.yaml can break visual rendering; no built-in schema validation shown","No explicit support for dynamic node creation at runtime based on LLM outputs","Execution is local-only; no remote or cloud execution support","No built-in distributed execution or parallelization across machines","Debug mode requires VS Code debugger attachment, which may not work in all Python environments (e.g., conda, venv edge cases)","No explicit timeout or resource limits documented; 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