| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 14 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Uses a modular multi-package architecture (promptflow-core, promptflow-devkit, promptflow-tracing) where the core execution engine is decoupled from development tools and observability, enabling both lightweight runtime deployments and rich IDE experiences. Implements topological sorting for dependency resolution and node-level caching to optimize re-execution of unchanged nodes.
vs alternatives: Provides tighter integration with Azure ML and enterprise deployment pipelines compared to Langchain's graph-based approach, while maintaining local-first development and testing capabilities that cloud-only solutions lack.
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.
Unique: Implements automatic schema extraction from Python function signatures using introspection, eliminating the need for separate schema definitions. Supports both synchronous and asynchronous execution with the same decorator interface, and integrates dependency injection for connections and tools without explicit parameter passing.
vs alternatives: More flexible than pure YAML DAG flows for complex logic, while maintaining the same deployment and observability infrastructure; differs from Langchain's LangGraph by providing automatic schema inference and tighter Azure integration.
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.
Unique: Provides a unified CLI interface for all flow operations (test, run, evaluate, deploy) that integrates with VS Code extension for visual flow editing and debugging. CLI commands map directly to SDK operations, enabling both interactive and scripted workflows.
vs alternatives: More comprehensive CLI than Langchain which lacks integrated flow testing commands; VS Code integration provides visual debugging not available in pure CLI tools.
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.
Unique: Implements a dual-backend run storage system where local development uses SQLite for lightweight tracking, while production deployments use Azure ML backend for scalability. Enables run comparison and visualization without external tools.
vs alternatives: More integrated run tracking than Langchain which lacks built-in execution history; local SQLite storage enables offline development unlike cloud-only solutions.
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.
Unique: Integrates image and document handling directly into flow execution model, enabling seamless processing of multimodal inputs without separate preprocessing steps. Automatically handles image encoding for different LLM vision APIs (OpenAI, Azure, etc.).
vs alternatives: More integrated multimedia support than Langchain which requires separate image processing libraries; automatic image encoding for LLM APIs reduces boilerplate.
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.
Unique: Implements a separate promptflow-azure package that extends core functionality with Azure-specific features, enabling local-first development with optional cloud deployment without forcing Azure dependency. Integrates with Azure ML compute clusters for distributed execution and managed endpoints for production serving.
vs alternatives: Tighter Azure ML integration than generic containerization approaches; enables cloud deployment without Docker/Kubernetes expertise. Supports both batch and real-time serving on Azure ML unlike tools that only support one mode.
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.
Unique: Combines prompt template, LLM configuration, and execution logic in a single human-readable file format with YAML frontmatter and Jinja2 templating, reducing file fragmentation and making prompts more portable and shareable than separate configuration files.
vs alternatives: Simpler and more self-contained than managing separate prompt files + configuration files like in Langchain, while still supporting version control and sharing; bridges the gap between ad-hoc prompt experimentation and production flows.
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.
Unique: Implements a connection-based abstraction layer where provider credentials are stored separately from flow definitions, enabling secure credential management and easy provider switching without modifying flow YAML. Integrates token counting via provider-specific tokenizers and tracks usage metrics for cost analysis.
vs alternatives: More seamless provider switching than Langchain's LLMChain which requires explicit model instantiation; tighter Azure OpenAI integration than open-source alternatives; built-in token counting and cost tracking that most frameworks lack.
+6 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs promptflow at 24/100. However, promptflow offers a free tier which may be better for getting started.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities