AI-Powered CI/CD Log Analyzer for Jenkins Using Ollama + Streamlit

How I Built an Offline

Managing CI/CD pipelines is tough — and debugging failed builds can be even tougher when you're staring at thousands of lines of Jenkins logs.

So I built a tool that:
✅ Uses a local LLM (via Ollama) to analyze logs

✅ Runs entirely offline (no cloud, no API keys)

✅ Shows AI-generated error summaries and suggestions

✅ Displays output in a Streamlit dashboard

🧠 Why I Built It

I was tired of spending hours digging through Jenkins logs to find the root cause of build failures. Tools like OpenAI or Datadog are great — but they require internet, cloud access, and API costs.

So I thought: what if I could run an LLM completely offline to analyze logs, on-demand, during or after a pipeline run?

⚙️ Stack Used

🧠 Ollama – local LLM runtime
🐍 Python
📊 Streamlit – simple frontend for log visualization
⚙️ Jenkins – CI/CD system
🐳 Optional: Dockerized setup for portability

🚀 How It Works

Jenkins runs your pipeline and stores logs
The Python script reads the log file
It feeds the log to the local LLM via Ollama
The model returns an analysis:
- Errors & warnings
- Root cause summary
- Suggestions
Streamlit displays all of this in a clean UI

🔗 Want to Try It?

I’m offering the tool on Gumroad for early users:
👉 CI/CD Log Analyzer – Gumroad

You can run it entirely offline on your own machine.

💬 Open to Feedback!

If you're in DevOps, working with Jenkins, or just curious about LLM-powered automation, I’d love to hear what you think — suggestions, criticism, or feature ideas.

Happy debugging!