microsoft/promptflow
promptflow
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Usage guide
promptflow is an open-source project around ai-application-development, ai-applications, chatgpt with 11,165 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating promptflow for Python AI workflows.
- Comparing a GitHub project with 11,165 stars and current repository activity.
Pros
- promptflow has visible GitHub traction with 11,165 stars. Topics: ai, ai-application-development, ai-applications.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
promptflow should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
promptflow architecture preview
promptflow's main path starts at the entry surface, runs through promptflow core runtime, combines OpenAI, Runtime context, GitHub / Shell commands, and returns User-facing result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://microsoft.github.io/promptflow/
Runtime
promptflow core runtime
The core coordinates project logic, configuration, and AI-related execution in Python.
Python
Model
OpenAI
Model calls are likely routed through OpenAI based on README and topic signals.
OpenAI
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub / Shell commands
Tool adapters let the runtime act outside the model through GitHub / Shell commands.
GitHub, Shell commands
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Featured video
WorldofAI
PromptFlow: Create LLM Apps In SECONDS with NO Code FOR FREE! (Installation Guide)
17,549 views ยท 2023-09-08
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
promptflow depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/microsoft/promptflow.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ pip install promptflow promptflow-toolsAdoption guidance and sources
Practical use cases
Build high-quality LLM apps - from prototyping, testing to production
This is one of the documented reasons to evaluate promptflow before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate promptflow before choosing a stack.
All project comparison
Compare promptflow with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official promptflow setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented promptflow example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is promptflow?
promptflow is an open-source all project. Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
How do I install promptflow?
Start with the official README. The first detected setup step is: git clone https://github.com/microsoft/promptflow.git.
Is promptflow beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can promptflow be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does promptflow need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt promptflow?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.