Prompt Flow — Build, Test & Deploy LLM Pipelines

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/