Prompt flow is a Microsoft-developed suite of development tools written in Python that addresses the complete lifecycle of building large language model applications, from initial prototyping through testing, evaluation, and production deployment with monitoring capabilities. The project is classified across multiple domains including large language models, developer tools, prompt engineering, workflow orchestration, and low-code solutions, reflecting its broad applicability in AI-driven application development.
The core functionality enables developers to create executable flows that connect LLMs, prompts, Python code, and other tools together in a cohesive workflow. Users can debug and iterate flows with particular emphasis on tracing interactions with LLMs, evaluate flow quality and performance against larger datasets, and integrate testing and evaluation into CI/CD systems to maintain quality standards. The platform supports deployment to various serving platforms or integration directly into application codebases. An optional cloud version called Prompt flow in Azure AI is available for team collaboration.
The development experience is enhanced through multiple interfaces. A VS Code extension provides an interactive flow designer for visual development, while command-line tools enable quick initialization of flows from templates. The quick start process demonstrates creating a chatbot by initializing a flow from a chat template, establishing API connections for OpenAI or Azure OpenAI keys, and interacting with the resulting chatbot through a defined flow.dag.yaml configuration file that specifies inputs, outputs, nodes, connections, and model deployments.
The repository emphasizes a development philosophy centered on ensuring high quality from prototype to production. Documentation includes a 15-minute tutorial guiding users through prompt tuning, batch testing, and evaluation workflows. Additional learning resources include end-to-end tutorials such as building a chat application with PDF integration, demonstrating practical application development patterns.
Community engagement is actively encouraged through discussions, issue reporting, and pull request submissions. The project maintains a Contributor License Agreement requirement and has adopted the Microsoft Open Source Code of Conduct. GitGenius activity tracking shows 390 tracked issues and pull requests with a median response latency of 0.0 hours and mean latency of 310.1 hours. The most frequently applied labels are no-recent-activity with 250 occurrences, bug with 240 occurrences, and enhancement with 121 occurrences. Top contributors tracked by GitGenius include D-W- with 118 events, brynn-code with 114 events, and zhengfeiwang with 74 events. The repository shares overlapping contributors with github/gh-aw, solo-io/gloo, and microsoft/vscode.
Installation requires Python 3.9 to 3.11, and telemetry collection is enabled by default but can be disabled through configuration. The project is licensed under the MIT license and includes Microsoft's standard data collection and privacy policies for telemetry purposes.
