Azure DevOps Build AI Analyzer

Automatically explains why your pipeline failed using an LLM of your choice — including a local, self-hosted model (Ollama, LM Studio, vLLM, llama.cpp) or any OpenAI-compatible API.

When a build fails, a pipeline task reads the logs of the failed tasks, sends them to your LLM, and attaches a structured diagnosis to the build. An AI Analyzer tab on the build results page then shows the summary, likely root cause, key errors, and suggested fixes.

No separate backend, no extra ports, no inbound changes to your Azure DevOps server — everything ships inside the extension, and the LLM is only ever called from the build agent.

Quick start

1. Install this extension into your organization, then into your project.
2. Add the task to the end of your job:

- task: AIBuildAnalyzer@2
  displayName: AI Build Analyzer
  condition: failed()
  inputs:
    llmUrl: http://localhost:11434/v1   # your OpenAI-compatible endpoint
    llmModel: llama3.1

3. From the next failed run onward, open the build's AI Analyzer tab.

Use your own LLM

The task works with any OpenAI-compatible /v1/chat/completions endpoint that your build agent can reach. Set llmUrl and llmModel; optionally llmApiKey (use a secret variable), insecureTls (for self-signed HTTPS), and timeoutMs.

If the LLM is unreachable, the task falls back to a built-in offline heuristic analyzer that recognizes common CI failures.

Documentation & source

Full docs, configuration, and troubleshooting: https://github.com/jafarimohammad/azdevops-build-ai-analyzer

Licensed under MIT.