AutoGen vs Dify
Side-by-side comparison built from DeepYard's structured catalog. Content updated Aug 21, 2026.
Direct Answer
AutoGen suits teams needing open-source access, multi agent focus, and 60,568 GitHub stars in current listing data. Dify suits teams needing freemium free tier, typescript focus, and 153,166 GitHub stars in current listing data. This summary reflects catalog metadata only for decision support, not independent testing.
What this comparison weighs
- Pricing and free-tier availability
- License model and deployment fit
- GitHub adoption and contributor depth
- Catalog tags, integrations, and supported workflows
| Metric | AutoGen | Dify |
|---|---|---|
| GitHub Stars | 60.6K | 153.2K |
| Contributors | 444 | 457 |
| Last Commit | Apr 15, 2026 | Aug 22, 2026 |
| Open Issues | 997 | 953 |
| License | open-source | open-source |
| Pricing | open-source | freemium |
| Free Tier | Yes | Yes |
| Category | frameworks | frameworks |
| Trending | No | No |
Choose AutoGen if you need
- • AutoGen is the stronger pick when Multi Agent matters because that capability is listed only on its profile.
- • AutoGen is the cleaner fit if you specifically need Microsoft workflows from the catalog tags.
Choose Dify if you need
- • Dify stands out for Typescript workflows that are not listed for AutoGen.
- • Dify is the stronger pick when Agentic Workflow matters because that capability is listed only on its profile.
- • Dify shows broader GitHub adoption with 153,166 stars versus 60,568 for AutoGen.
Meaningful differences
- • Pricing model: AutoGen is listed as open-source, while Dify is listed as freemium.
- • Workflow emphasis: AutoGen highlights Python, while Dify highlights Typescript.
- • GitHub adoption: AutoGen shows 60,568 stars versus 153,166 for Dify.
- • Contributor count: AutoGen lists 444 contributors and Dify lists 457.
Shared capabilities
- • Multi Agent
Shared Tags
Only in AutoGen
Only in Dify
Limitations and evidence
- • DeepYard compares structured public metadata; this is not an independent benchmark unless a test record is shown.
- • Signals such as stars, contributors, and last commit indicate public activity, not purchase fit or runtime quality.
- • Pricing and feature coverage reflect the stored listing snapshot and may lag vendor changes between refreshes.
About AutoGen
AutoGen is Microsoft's open-source framework for building multi-agent conversational systems where agents can converse with each other and with humans to solve complex tasks. Agents are highly customizable and can use LLMs, tools, and human input in flexible combinations. The framework supports group chats, nested conversations, and code execution sandboxes, making it well-suited for coding assistants, research automation, and enterprise agentic workflows. AutoGen Studio provides a no-code UI for prototyping agent systems visually.
View full listingAbout Dify
Dify is an open-source LLM app development platform that combines agentic AI workflow, RAG pipeline, agent capabilities, model management, and observability features. It lets you go from prototype to production quickly with a visual canvas for building complex AI workflows. Supports 100+ model providers and can be self-hosted or used via cloud.
View full listing