# Prompt Flow — Build, Test & Deploy LLM Pipelines

> Prompt Flow by Microsoft provides a visual editor and CLI for building LLM application workflows with built-in evaluation, tracing, and CI/CD integration for production deployment.

## Install

Save in your project root:

# Prompt Flow — Build, Test & Deploy LLM Pipelines

## Quick Use
```bash
pip install promptflow promptflow-tools
pf flow init --flow my-flow --type chat
pf flow test --flow my-flow --inputs question="What is Prompt Flow?"
```

## Introduction
Prompt Flow is an open-source framework from Microsoft for building, evaluating, and deploying LLM-based applications. It treats each step of an LLM pipeline—prompt, API call, post-processing—as a node in a directed acyclic graph, making complex chains testable and reproducible.

## What Prompt Flow Does
- Defines LLM pipelines as DAGs with prompt nodes, Python nodes, and tool nodes
- Provides a visual editor in VS Code for designing and debugging flows
- Includes a batch evaluation system for testing flows against datasets with metrics
- Traces every node execution with inputs, outputs, and latency for debugging
- Integrates with CI/CD pipelines for automated testing before deployment

## Architecture Overview
Each flow is a YAML-defined DAG where nodes represent either LLM calls, Python functions, or tool invocations. The runtime resolves node dependencies, executes them in order, and passes outputs downstream. A tracing layer records every execution for replay and debugging. The evaluation engine runs flows in batch against labeled datasets and computes metrics like groundedness, relevance, and coherence.

## Self-Hosting & Configuration
- Install the Python SDK and optionally the VS Code extension for visual editing
- Define flows in YAML with node types, connections, and input/output mappings
- Configure LLM connections (OpenAI, Azure OpenAI, or custom endpoints) via connection objects
- Run evaluations with pf run create to batch-test flows against datasets
- Deploy finished flows as REST APIs using the built-in serving command or Docker export

## Key Features
- DAG-based flow definition makes complex LLM chains explicit and testable
- VS Code extension provides drag-and-drop visual editing with live debugging
- Built-in evaluation metrics for groundedness, coherence, fluency, and relevance
- Execution tracing captures every node's input/output for easy debugging
- Native CI/CD integration lets teams automate quality gates for LLM applications

## Comparison with Similar Tools
- **LangChain** — code-first chain building; less emphasis on visual editing and batch evaluation
- **Haystack** — pipeline-based but oriented toward search and RAG rather than general LLM workflows
- **Flowise** — visual flow builder; lighter evaluation and tracing capabilities
- **Dagster** — general data pipeline orchestrator; not LLM-specific

## FAQ
**Q: Do I need Azure to use Prompt Flow?**
A: No. The open-source SDK works fully locally with OpenAI or any compatible API endpoint.

**Q: Can I use custom Python functions as nodes?**
A: Yes. Any Python function decorated as a tool becomes a node you can wire into a flow.

**Q: How does batch evaluation work?**
A: Provide a dataset of inputs and expected outputs. Prompt Flow runs the flow against every row and computes configurable metrics.

**Q: Can I deploy flows as APIs?**
A: Yes. Use pf flow serve for local serving or export to Docker for production deployment.

## Sources
- https://github.com/microsoft/promptflow
- https://microsoft.github.io/promptflow/

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Source: https://tokrepo.com/en/workflows/eb2e9c0d-3fda-11f1-9bc6-00163e2b0d79
Author: AI Open Source