Which is more popular, Devika or DevOpsGPT?

Devika has more GitHub stars (19,556 vs 5,967).

Which is more actively developed, Devika or DevOpsGPT?

DevOpsGPT had more commits in the last 90 days (4 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Devika has had no commit in 12 months; DevOpsGPT is actively maintained (4 commits in the last 90 days).
Devika is growing faster: +9 GitHub stars in the last 30 days vs +0 for DevOpsGPT.
Pick Devika for: devika is the first open-source implementation of an Agentic Software Engineer. Pick DevOpsGPT for: multi-agent system combining LLMs with DevOps tools to turn natural language requirements into software.

From GitHub data refreshed daily.

Devikaopen-source

Devika is the first open-source implementation of an Agentic Software Engineer. Initially started as an open-source alternative to Devin.

Multi-agent system combining LLMs with DevOps tools to turn natural language requirements into software

+Multi-LLM support with flexibility to choose from commercial providers (Claude 3, GPT-4, Gemini) or run local models via Ollama
+Comprehensive AI capabilities including planning, reasoning, web research, and multi-language code generation in a single platform
+Open-source alternative to proprietary solutions like Devin, allowing community contributions and customization

+Automated end-to-end development pipeline from natural language requirements to deployed software
+Eliminates traditional requirement documentation overhead and reduces communication costs between teams
+Multi-language support with integration capabilities for various DevOps platforms and deployment environments

-Currently in early development/experimental stage with many unimplemented and broken features
-Requires specific Python version constraints (>= 3.10 and < 3.12) which may limit compatibility
-Performance heavily dependent on chosen LLM provider, with optimal results requiring paid commercial models

-Complex setup and configuration required for integration with existing DevOps infrastructure
-Quality and accuracy heavily dependent on LLM capabilities and clarity of input requirements
-Advanced features like professional model selection and private deployment require enterprise edition

•Creating new software features from high-level requirements with minimal human guidance
•Debugging and fixing existing code issues through AI-powered analysis and solution generation
•Developing entire projects from scratch by breaking down complex objectives into manageable coding tasks

•Rapid prototyping where business stakeholders need to quickly convert ideas into working MVPs
•Internal tool development for teams wanting to automate repetitive software creation tasks
•Small to medium development projects where traditional SDLC overhead outweighs development complexity

Which is more popular, Devika or DevOpsGPT?: Devika has more GitHub stars (19,556 vs 5,967).
Which is more actively developed, Devika or DevOpsGPT?: DevOpsGPT had more commits in the last 90 days (4 vs 0).
Should I use Devika or DevOpsGPT?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.