Hey everyone,
I want to share a tool I’ve been building: Result Companion — a CLI that reads your output.xml and enriches log.html with AI-generated analysis per failed test: root cause, test flow summary, and suggested fixes.
The problem it solves
You run your suite, something fails, and then you spend 20 minutes tracing keywords through the log trying to figure out why. Result Companion does that trace for you and gives you a plain-English explanation in seconds.
pip install result-companion
result-companion analyze -o output.xml
Open rc_log.html — each failed test now has an AI analysis attached.
What it supports today
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Local models via Ollama (free, private)
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GitHub Copilot — if you already have a Copilot subscription (Business, Enterprise, or Pro+), you can use models like gpt-5-mini at no extra cost
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OpenAI, Azure OpenAI, Google Gemini, Anthropic, AWS Bedrock
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Any OpenAI-compatible endpoint (Databricks, self-hosted, etc.)
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Tag-based filtering (–include, --exclude)
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Text output for CI pipelines or agent workflows (–text-report, --print-text-report)
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--overall-summary for a synthesised digest across all failures
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Fully customisable prompts — the default analyses failures, but you can swap in a security audit, performance bottleneck review, or test quality assessment just by changing the question_prompt in your config
Where it stands
It’s early — version 0.0.6, marked Beta on PyPI. The core workflow is solid, but I’m actively looking for real-world feedback: edge cases in output.xml parsing, models that behave unexpectedly, workflows that don’t fit the current config model.
Links
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PyPI: result-companion · PyPI
If you try it — even just with --dryrun to check parsing without calling any LLM — I’d love to hear what works and what doesn’t.