Quick-Start LOCI GitHub Integration
LOCI Quick-Start User Flow
The LOCI AI Agent GitHub integration enables developers and engineering teams to automate performance analysis directly within their existing GitHub CI/CD workflows. By connecting LOCI's hardware-aware optimization engine with GitHub Actions and the LOCI GitHub App, teams gain immediate visibility into the performance impact of every commit, build, and pull request.
User Flow Overview
Setup steps (1–3) are performed once per repository.
Usage steps (4–5) repeat automatically on every pull request.
Step-by-Step Guide
Step 1 — Install the LOCI GitHub App
Go to https://github.com/marketplace/loci-agentic-ai and install the app on your repository. This enables LOCI to post automated performance analysis comments directly on your pull requests. An active LOCI license is required (free plans available).
Step 2 — Add Your Credentials to GitHub
In your repository settings, add two values:
LOCI_API_KEY— as a GitHub SecretLOCI_BACKEND_URL— as a GitHub Variable
Optionally add LOCI_GITHUB_TOKEN to enable workflow summary integration. These credentials connect the LOCI Action to your licensed backend.
Step 3 — Add the LOCI Action to Your CI Workflow
Add the LOCI Action to your existing .github/workflows file. The action runs in two steps: upload (build and ship your binary after compilation) and summary (wait for analysis and attach the Agent Report to the workflow run).
This snippet is a single step — add it to the steps: list of a job in your existing .github/workflows file, after your build step.
Step 4 — Open a Pull Request
Push a branch and open a PR as normal. LOCI automatically detects the changed functions, compiles the before/after binaries, and runs hardware-aware analysis — no manual trigger needed.
Step 5 — Review the LOCI Report
LOCI posts its findings directly in the PR as a comment from the loci-review [Bot]. The report includes:
Execution timing and energy deltas per changed function
Flame graph comparison between base and target versions (when relevant)
Control-flow analysis highlighting call-depth changes
Agent Summary with optimization recommendations and a pass/fail performance check
Next Steps
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