promptflow
Build high-quality LLM apps from prototyping to production deployment and monitoring
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Decision brief
Promptflow is a development framework catered towards creating LLM applications through all stages of development, including initial prototyping and ongoing monitoring.
Good fit when
- When you require a comprehensive environment to manage the full lifecycle of LLM applications from ideation to deployment in Python.
- For projects that benefit from integrated tools for both prompt engineering and application oversight throughout their existence.
Avoid when
- If your team prefers or requires tools that support languages other than Python, as Promptflow is exclusively focused on the Python ecosystem.
- In cases where a more fragmented toolset with specialized components for different development stages might be preferable to an integrated framework approach.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (17d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Microsoft. Display-only - separate from trust and ranking.
- Company
- Microsoft·GitHub org profile·1mo
- Employees
- 221,000·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install promptflowSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A framework for developing LLM applications from initial ideation through to deployment and ongoing oversight.
Capability facts
- Languages
- python
Source: github.language · Jul 27, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 27, 2026)
Ensure you have a python environment, `python>=3.9, <=3.11` is recommended.Source link
Tags
README
Installation
To get started quickly, you can use a pre-built development environment. Click the button below to open the repo in GitHub Codespaces, and then continue the readme!
If you want to get started in your local environment, first install the packages:
Ensure you have a python environment, python>=3.9, <=3.11 is recommended.
pip install promptflow promptflow-tools
Quick Start ⚡
Create a chatbot with prompt flow
Run the command to initiate a prompt flow from a chat template, it creates folder named my_chatbot and generates required files within it:
pf flow init --flow ./my_chatbot --type chat
Setup a connection for your API key
For OpenAI key, establish a connection by running the command, using the openai.yaml file in the my_chatbot folder, which stores your OpenAI key (override keys and name with --set to avoid yaml file changes):
pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection
For Azure OpenAI key, establish the connection by running the command, using the azure_openai.yaml file:
pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
Chat with your flow
In the my_chatbot folder, there's a flow.dag.yaml file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc
Note that in the
chatnode, we're using a connection namedopen_ai_connection(specified inconnectionfield) and thegpt-35-turbomodel (specified indeployment_namefield). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource.
Interact with your chatbot by running: (press Ctrl + C to end the session)
pf flow test --flow ./my_chatbot --interactive
Core value: ensuring "High Quality” from prototype to production
Explore our 15-minute tutorial that guides you through prompt tuning ➡ batch testing ➡ evaluation, all designed to ensure high quality ready for production.
Next Step! Continue with the Tutorial 👇 section to delve deeper into prompt flow.
License
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.
For agents
This page has a .md twin and JSON over the API.