Prompt Flow is an open-source toolkit from Microsoft designed to cover the full lifecycle of LLM application development — from initial prototyping through evaluation, optimization, and production deployment. At its core, a flow is a DAG (directed acyclic graph) defined in a flow.dag.yaml file that chains together LLM nodes, prompt templates, Python functions, and custom tools into an executable pipeline. The VS Code extension provides a visual designer for building and editing these flows interactively, while the CLI (pf command) handles connection management, flow execution, and deployment. Flows can use OpenAI, Azure OpenAI, or other LLM providers through a configurable connection system that stores API keys securely.
Where Prompt Flow distinguishes itself from simpler prompt chaining tools is its built-in evaluation and experimentation framework. Developers can run flows against larger datasets to calculate quality metrics, compare prompt variants and hyperparameter combinations across multiple nodes, and integrate these evaluation runs into CI/CD pipelines so that prompt quality is validated before deployment — not after. The tracing system captures detailed interaction logs with LLMs, making it straightforward to debug why a particular chain of calls produced unexpected output. This evaluation-first approach aligns with LLMOps best practices where prompt engineering is treated as an iterative, measurable process rather than one-shot guesswork.
The project has around 11,000 GitHub stars and integrates deeply with Azure Machine Learning and Azure AI Studio for teams that want cloud-based collaboration, A/B deployment, and centralized flow hosting. A GenAIOps template provides a complete CI/CD pipeline structure with GitHub Actions for experimentation, evaluation, and deployment across development and production environments. Deployment targets include Azure endpoints, Docker containers, or direct code integration. While the local open-source version is fully functional, the Azure cloud version adds enterprise features like multi-user collaboration, centralized experiment tracking, and managed compute for evaluation runs at scale.
