LangChain vs Prompt flow
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building AI agents and apps powered by language models.
Open source · MIT
Open-source toolkit for building, testing, evaluating and deploying LLM apps, from prototype to production.
The short answer
Choose LangChain if you need…
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Choose Prompt flow if you need…
- Teams with existing Prompt flow applications that need to keep running until migration
- Developers who want to trace and evaluate LLM app behavior on datasets
- Engineers who want to run flow tests in CI/CD pipelines
- Developers who prefer a VS Code extension for visual flow editing
Side-by-side
| LangChain | Prompt flow | |
|---|---|---|
| Type | Framework | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 149k | 11.3k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 6 days ago |
What each one is
LangChain
LangChain is a Python framework for building applications powered by large language models (LLMs) and agents. It provides a standard interface for models, embeddings, and vector stores, plus a library of integrations with model providers and tools. Developers use it to connect LLMs to their own data and systems and to swap models without rewriting their application.
Prompt flow
Prompt flow is a set of development tools for building applications that use large language models (LLMs). You connect LLMs, prompts and Python code into a flow, then debug it, run it against larger datasets to evaluate quality, and deploy it. The README notes that Prompt flow is being retired by Microsoft on April 20, 2027, and is no longer recommended for new development.
Best for
LangChain
- Python developers building LLM-powered applications
- Teams that want to switch between model providers without rewriting code
- Developers connecting LLMs to external data sources and tools
- Prototyping LLM workflows with reusable components
Prompt flow
- Teams with existing Prompt flow applications that need to keep running until migration
- Developers who want to trace and evaluate LLM app behavior on datasets
- Engineers who want to run flow tests in CI/CD pipelines
- Developers who prefer a VS Code extension for visual flow editing
Strengths
LangChain
- Standard interface lets you swap model providers with limited code changes
- Large library of integrations with model providers, tools, and vector stores
- Open source under the MIT license with a documentation site and free courses
Prompt flow
- Covers the full cycle from prototyping through testing, evaluation and deployment in one toolkit
- Includes a VS Code extension and a command-line interface for flow development
- Released under the MIT license with an active public repository
Not ideal for
LangChain
- People who want a finished end-user app rather than a library to code against
- Developers who need low-level control over agent orchestration; the README points to LangGraph for that
- JavaScript or TypeScript projects, which the README directs to the separate LangChain.js library
Prompt flow
- Anyone starting a new project: the README says Prompt flow is no longer recommended for new development and suggests Microsoft Agent Framework instead
- Teams relying on Prompt flow runtime container images, which no longer receive security or package updates
- Non-developers, since setup uses the command line and a Python environment
Trade-offs
LangChain
- The README does not detail the API surface, so you rely on external docs for depth
- Several related products (LangGraph, Deep Agents, LangSmith) are needed for advanced or production work, which adds to the learning curve
- The README's quickstart targets a specific model string, so you must have access to that provider's API
Prompt flow
- The README states retirement on April 20, 2027, and no new-development recommendation, so long-term support is limited
- Container images are no longer updated, which is a security concern for deployed workloads
- Setup and tutorials assume familiarity with Python and LLM API keys
Getting started
Prompt flow
Install the packages
pip install promptflow promptflow-toolsFrequently asked questions
What is the difference between LangChain and Prompt flow?
LangChain: A Python framework for building AI agents and apps powered by language models. Prompt flow: Open-source toolkit for building, testing, evaluating and deploying LLM apps, from prototype to production. LangChain suits python developers building llm-powered applications, while Prompt flow suits teams with existing prompt flow applications that need to keep running until migration.
Are LangChain and Prompt flow free?
LangChain: Free and open source. Prompt flow: Free and open source.
Is LangChain or Prompt flow more popular?
LangChain has more GitHub stars (149k) than Prompt flow (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangChain and Prompt flow?
Other options people compare them with include LlamaIndex, Semantic Kernel, CrewAI, OpenUI, VoltAgent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.