Learn how Dograh and Langflow differ in their key features, development activity, technology stack and community adoption, so you can decide which of these ai agent platforms is best for you.
Last 30 days
Last commit
Repository age
Version
License
Self-hosted
Repository

Last 30 days
Last commit
Repository age
Version
License
Self-hosted
Repository

Both Dograh and Langflow have their unique strengths and serve similar purposes effectively. Consider your specific needs regarding popularity, growth, activity, maturity, licensing and features when making your decision.
Langflow significantly outpaces Dograh in community adoption with 153,709 stars compared to 5,503 stars on GitHub. This 27.9x difference suggests Langflow has a much larger and more active community. In terms of developer contributions, Langflow has 9,938 forks, indicating strong developer engagement.
Dograh is growing faster, adding 299 stars in the last 30 days (+5.7%) against adding 1,220 stars for Langflow (+0.8%). Dograh is the smaller project of the two, so it is closing the gap rather than extending a lead.
Both projects show recent activity, with Dograh last updated 2 days ago and Langflow 10 hours ago.
Langflow has been in development longer, starting 4 years ago, compared to Dograh which began 1 year ago. This 2.6-year head start suggests Langflow may have more mature features and established processes.
The projects use different licenses: Dograh is licensed under BSD-2-Clause while Langflow uses MIT. Consider the licensing requirements when choosing for your project.
Both tools serve similar use cases in AI Agent Platforms. However, they also have distinct specializations: Dograh also focuses on Chatbot Platforms while Langflow extends into Low-Code/No-Code, LLM Application Frameworks.
Both Dograh and Langflow offer self-hosting capabilities, giving you full control over your data and infrastructure.