Prompt flow is a Microsoft-developed, MIT-licensed suite of Python development tools that streamlines the end-to-end lifecycle of LLM-based AI applications — ideation, prototyping, testing, evaluation, production deployment and monitoring — for developers and teams who need prompt-driven apps to reach production quality rather than stay in a notebook.

What it is

Prompt flow is an open-source toolkit published as the Python package promptflow, maintained in the microsoft/promptflow repository under the MIT licence. Its core abstraction is the flow: an executable artifact, described in a flow.dag.yaml file, that links LLMs, prompts, Python code and other tools together into a graph with defined inputs, outputs, nodes and connections. Around that abstraction the project provides a command-line interface (pf), a Visual Studio Code extension (prompt-flow.prompt-flow), tracing of LLM interactions, dataset-based evaluation, and deployment paths onto a serving platform of choice or directly into an application's codebase. Documentation lives at microsoft.github.io/promptflow, and the project sits in the AI and machine learning tooling ecosystem, with topics spanning llm, prompt-engineering, chatgpt and AI application development.

The concrete problem it solves is the gap between a prompt that works in a scratch script and a prompt-backed feature that can be trusted in production. Prompt engineering work typically fragments into ad-hoc Python snippets, untracked model and connection settings, and manual spot checks that never scale past a handful of examples. Prompt flow replaces that scattered workflow with a structured flow definition, reusable tool nodes, a named connection layer for model credentials, tracing for debugging iterations, and a repeatable evaluation step that can run against larger datasets and be wired into CI/CD so quality is checked on every change.

Key capabilities

• Executable flows defined in flow.dag.yaml, combining LLMs, prompts, Python code and tools into one graph with explicit inputs, outputs, nodes and connections.
• Connection management through the pf CLI, for example pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=... api_base=... --name open_ai_connection for Azure OpenAI and the equivalent openai.yaml for OpenAI.
• Model selection via the deployment_name field, which specifies either an OpenAI model or an Azure OpenAI deployment resource, with the connection named in the connection field.
• Tracing of interactions with LLMs to debug and iterate on flows.
• Evaluation of flow quality and performance against larger datasets.
• Integration of testing and evaluation into a CI/CD system to enforce flow quality gates.
• Deployment to a chosen serving platform or direct integration into an application's codebase.
• A Visual Studio Code extension and a GitHub Codespaces quickstart for setting up a development environment.

Who uses it and how

• Python teams building LLM applications, working in an environment with python>=3.9 and the packages installed locally.
• Developers using OpenAI or Azure OpenAI models, who configure credentials once as a named connection such as open_ai_connection and reference it from flow nodes.
• MLOps and platform engineers who embed flow evaluation into CI/CD pipelines so that every change to a prompt or node is measured against a dataset before release.
• Teams that want to collaborate on flows through the cloud version, Prompt flow in Azure AI, which the README describes as optional but highly recommended.
• New contributors or evaluators who want a working environment without local setup, using the GitHub Codespaces quickstart on the repository.

Getting started

The README offers two paths: open the repository in GitHub Codespaces using the quickstart button for a pre-built development environment, or install the packages locally in a Python environment with python>=3.9, then create a model connection with pf connection create before chatting with a flow. Documentation is hosted at microsoft.github.io/promptflow.

How it compares

The facts provide no list of paid products that this project replaces, and no comparable third-party tools are named in the topics or README. The only adjacent offering mentioned is the cloud version of the same project, Prompt flow in Azure AI, which the README frames as an optional collaboration layer rather than a separate product. On the evidence available, prompt flow stands alone in this registry.

When to use it — and when not to

A self-hoster must maintain a Python environment at version 3.9 or above, install and upgrade the promptflow package, and manage model credentials and connection files such as openai.yaml and azure_openai.yaml along with the API keys they contain. Teams that do not write Python, or that want a fully managed turnkey platform with no local tooling, should look elsewhere. One further caveat visible in this listing: the README excerpt is truncated and its installation snippet is incomplete, so the project's own documentation site should be treated as the authoritative setup reference.

pf flow test --flow ./my_chatbot --interactive