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.

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User Flow Overview

Setup steps (1–3) are performed once per repository.

Usage steps (4–5) repeat automatically on every pull request.

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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).

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Step 2 — Add Your Credentials to GitHub

In your repository settings, add two values:

• LOCI_API_KEY — as a GitHub Secret

• LOCI_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.

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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).

name: Upload to LOCI
  uses: auroralabs-loci/loci-action@v1
  env:
          LOCI_BACKEND_URL: '${{ vars.LOCI_PROD_BACKEND_URL }}'
          LOCI_API_KEY: '${{ secrets.LOCI_PROD_API_KEY_STD }}'
  with:
    mode: upload
    project: my-project

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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.

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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

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Next Steps

• Explore the full PR Insights Report in LOCI Inspector

• Explore the full GitHub Actions reference

• View a live example PR