Dev-GPT vs OmO
Side-by-side comparison of two AI agent tools
Short answer
- Dev-GPT has had no commit in 39 months; OmO is actively maintained (9,367 commits in the last 90 days).
- OmO is growing faster: +1,005 GitHub stars in the last 30 days vs +-0 for Dev-GPT.
- Pick Dev-GPT for: your Virtual Development Team. Pick OmO for: omO: Just type "mass ulw" keyword with your prompt.
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Dev-GPTopen-source
Your Virtual Development Team
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OmOopen-source
OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.
Metrics
| Dev-GPT | OmO | |
|---|---|---|
| Stars | 1.9k | 69.8k |
| Star velocity /mo | -0.31746031746031744 | 1.0k |
| Commits (90d) | 0 | 9.4k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.1306053811660771 | 0.9105351293499632 |
Pros
- +Multi-agent AI system with specialized roles (Product Manager, Developer, DevOps) provides comprehensive development coverage
- +Simple installation and CLI interface makes it accessible to developers of all skill levels
- +Cross-platform support and integration with popular APIs (OpenAI, Google) ensures broad compatibility
Cons
- -Experimental version status indicates potential instability and incomplete features
- -Requires paid OpenAI API access, adding ongoing operational costs
- -Limited scope to microservice development only, not suitable for larger applications or different architectural patterns
Use Cases
- •Rapid prototyping of microservices for MVP development and proof-of-concept projects
- •Solo developers or small teams lacking expertise in specific areas (DevOps, architecture) who need full-stack automation
- •Learning and experimentation with microservice architecture patterns through AI-generated examples
FAQ
- Which is more popular, Dev-GPT or OmO?
- OmO has more GitHub stars (69,754 vs 1,866).
- Which is more actively developed, Dev-GPT or OmO?
- OmO had more commits in the last 90 days (9,367 vs 0).
- Should I use Dev-GPT or OmO?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.