> For the complete documentation index, see [llms.txt](https://docs.loci-dev.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.loci-dev.net/quick-start-loci-github-integration.md).

# Quick-Start LOCI GitHub Integration

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

<figure><img src="/files/TdXcJaK7i9p1dyGxRGiq" alt=""><figcaption></figcaption></figure>

**Setup steps (1–3)** are performed once per repository.&#x20;

**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 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.

***

#### Step 3 — Add the LOCI Action to Your CI Workflow

Add the [LOCI Action](https://github.com/marketplace/actions/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).

```yaml

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
```

***

#### 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

* [Explore the full PR Insights Report in LOCI Inspector](https://app.gitbook.com/o/ZFw3dndBL81eukjWL25m/s/S2ZTr9umTUpVzGSJHbuR/~/edit/~/changes/51/loci-inspector)
* [Explore the full GitHub Actions reference](https://github.com/marketplace/actions/loci-action)
* [View a live example PR](https://github.com/auroralabs-loci/llama.cpp/pull/1361)


---

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