# Prompt Flow

> Build, test, and ship production-quality LLM apps with linked flows

- Category: App Orchestration / App Frameworks
- Language: Python
- Licence: MIT
- Repository: https://github.com/microsoft/promptflow
- Website: https://microsoft.github.io/promptflow/
- Canonical page: https://ai-tldr.dev/tools/promptflow/

## In short

Prompt Flow is a suite of development tools from Microsoft for building LLM-based applications across the whole lifecycle, from first idea and prototype through testing, evaluation, and production deployment. It sits in the App Frameworks category of the App Orchestration tools and is open-source, written in Python and licensed MIT.

## Overview

Prompt Flow is a suite of development tools from Microsoft for building LLM-based applications across the whole lifecycle, from first idea and prototype through testing, evaluation, and production deployment. It treats an LLM app as an executable flow that links large language models, prompts, Python code, and other tools together.

The goal is to make prompt engineering easier while keeping quality high. You can trace every interaction with an LLM while debugging, run your flow against larger datasets to measure quality and performance, wire that testing into a CI/CD pipeline, and then deploy the finished flow to the serving platform you choose. It ships with a command-line interface and a VS Code extension for visual flow design.

## What it does

- Define LLM apps as executable flows that connect models, prompts, Python code, and tools in one graph (flow.dag.yaml)
- Trace and debug LLM interactions step by step while you iterate on a flow
- Evaluate flow quality and performance against larger datasets, with metrics for batch runs
- Integrate testing and evaluation into your CI/CD system to guard quality on every change
- Deploy flows to your chosen serving platform or embed them directly in your app's codebase
- Visual flow designer through the official VS Code extension, plus a pf command-line interface

## Getting started

Prompt Flow runs in a local Python environment (python 3.9 to 3.11 is recommended). Install the packages, scaffold a chat flow, connect your API key, then chat with the flow from the terminal.

1. Install the packages

   Install the core library and the bundled tools from PyPI.

```sh
pip install promptflow promptflow-tools
```

2. Scaffold a chatbot flow

   Initialize a flow from the chat template. This creates a folder named my_chatbot with the required files inside.

```sh
pf flow init --flow ./my_chatbot --type chat
```

3. Create a connection for your API key

   Set up a connection so the flow can reach your model. For an OpenAI key, use the openai.yaml file in the my_chatbot folder.

```sh
pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection
```

4. Chat with your flow

   Run the flow in interactive mode to talk to your chatbot. Press Ctrl + C to end the session.

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

## When to use it

- Prototype a chatbot or LLM app, then iterate quickly while tracing each model call to find and fix issues
- Run batch evaluations of a flow against a dataset to measure answer quality and performance before release
- Add LLM flow testing and evaluation to a CI/CD pipeline so quality is checked on every code change
- Package a finished flow and deploy it to a serving platform or embed it inside an existing application