GitHub Integration

The Line of Code Insights (LOCI) Agentic AI is a powerful tool designed to predict performance inefficiencies and power spikes in your compiled binaries before testing or inference. It autonomously optimizes code, configurations, and serving mechanisms. By integrating the LOCI GitHub Action into your workflow, you can shift observability left, reduce resolution time, and lower costs associated with hardware and software reliability issues.

Key Capabilities

The LOCI GitHub Action allows you to:

• Upload compiled binaries for in-depth performance analysis.

• View a comprehensive Agent Report and insights directly in your workflow summary.

• Automatically receive Pull Request (PR) comments with analysis results (requires the GitHub App).

Action Modes

The action operates in two primary modes via the mode input:

Mode	Description	Required Inputs
upload	Uploads compiled binaries to the LOCI backend for analysis.	project, binaries
summary	Waits for the analysis to complete and attaches the Agent Report (in Agentic Mode) and Function Insights to the workflow run summary.	project

Prerequisites and Setup

Before using the action, ensure the following are configured in your repository:

Required Configuration

1. GitHub Variable: Add LOCI_BACKEND_URL as a GitHub Variable.

2. GitHub Secret: Add LOCI_API_KEY as a GitHub Secret.

Optional Configuration

1. LOCI GitHub App: Install the LOCI GitHub App on your repository to enable automatic PR comments with results and overall Performance Reviews.

2. Agentic Summary Token: If you are using Agentic Mode and want the summary step to display the full report in the job UI, you must provide a Personal Access Token (PAT) as a GitHub Secret (for example LOCI_GITHUB_TOKEN) and pass it to the action via an environment variable:

Workflow snippet

env:
     LOCI_GITHUB_TOKEN: ${{ secrets.LOCI_GITHUB_TOKEN }} # required only for agentic summary

Usage Examples

The action is sourced from auroralabs-loci/loci-action@v1.

Example 1: Build + Upload

This minimal example checks out code, builds a binary, and uploads it to LOCI for analysis.

.github/workflows/loci-build-upload.yml

name: LOCI Integration
on:
  pull_request:
    types: [opened, synchronize, reopened]

env:
  LOCI_PROJECT: GHDemo

jobs:
  build-and-upload:
    runs-on: ubuntu-latest
    steps:
      - name: Prepare environment
        run: |
          sudo apt-get update
          sudo apt-get install -y gcc-aarch64-linux-gnu g++-aarch64-linux-gnu

      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Build sample
        run: |
          mkdir -p samples/build/bin
          aarch64-linux-gnu-g++ -o samples/build/bin/sample_01 samples/sample_01.cpp

      - name: Upload Artifacts
        uses: auroralabs-loci/loci-action@v1
        env:
          LOCI_API_KEY: ${{ secrets.LOCI_API_KEY }}
          LOCI_BACKEND_URL: ${{ vars.LOCI_BACKEND_URL }}
        with:
          mode: upload
          project: ${{ env.LOCI_PROJECT }}
          binaries: samples/build/bin/

Example 2: Build + Upload + Summary

This example adds the summary step, which waits for the analysis to complete and embeds the report and insights directly into the workflow run summary.

.github/workflows/loci-build-upload-summary.yml

jobs:
  build-upload-and-summary:
    runs-on: ubuntu-latest
    steps:
      # ... (Your build and checkout steps here) ...

      - name: LOCI Upload
        uses: auroralabs-loci/loci-action@v1
        env:
          LOCI_API_KEY: ${{ secrets.LOCI_API_KEY }}
          LOCI_BACKEND_URL: ${{ vars.LOCI_BACKEND_URL }}
        with:
          mode: upload
          project: ${{ env.LOCI_PROJECT }}
          binaries: samples/build/bin/

      - name: LOCI Summary
        uses: auroralabs-loci/loci-action@v1
        env:
          LOCI_API_KEY: ${{ secrets.LOCI_API_KEY }}
          LOCI_BACKEND_URL: ${{ vars.LOCI_BACKEND_URL }}
          LOCI_GITHUB_TOKEN: ${{ secrets.LOCI_GITHUB_TOKEN }} # Only needed for agentic summary in job summary
        with:
          mode: summary
          project: ${{ env.LOCI_PROJECT }}
          top-n-symbols: 10 # Optional: customize the number of functions shown

Inputs

Input	Required	Default	Description
mode	Yes	—	Operation to perform: upload or summary.
project	Yes	—	Project name to tag the analysis results.
binaries	Yes (for upload)	—	Newline-separated list of binary paths, glob patterns, or a directory. Required for upload, ignored for summary.
target	No	<branch>@<shortSHA>	Project version label. Auto-resolves if not set.
base	No	PR merge base	Base version to compare against. Empty unless set or PR context is used.
top-n-symbols	Yes (for summary)	5	Number of functions shown in function insights during the summary step.

Outputs

Output	Description
target	The resolved project version name for this run.
base	The resolved base version name (may be empty).
summary	The LOCI Agent Summary Report (only available when mode: summary).

LOCI Features and Modes Overview

LOCI supports a Standard Mode and a more comprehensive Agentic Mode, which is generally enabled per company/configuration.

Feature	Standard Mode	Agentic Mode	GitHub App Required?	Where it Appears
Upload of binaries	✅ Yes	✅ Yes	❌ No	LOCI Dashboard (always)
Function Insights	✅ Yes (via summary step)	✅ Yes (via summary step)	❌ No	Workflow run summary (summary step)
LOCI Agent Summary Report	❌ Not available	✅ Yes	✅ For PR comment	Workflow run summary (summary step`) & PR comment (App)
Performance Review Check	❌ Not available	✅ Yes	✅ Yes (App only)	GitHub Checks tab (pass/fail based on loci.yml config)

Key Workflow Logic:

• The upload step is always required to send binaries to the LOCI backend.

• The summary step is optional and is only needed if you want the LOCI report and insights within the workflow run summary itself.

  • In Standard Mode, the summary step adds Function Insights only.

  • In Agentic Mode, the summary step adds the Agent Summary Report plus Function Insights.

• If the GitHub App is installed (and Agentic Mode is enabled), it automatically handles:

  • Posting the LOCI Agent Summary Report as a PR comment.

  • Enabling the Performance Review Check Run (pass/fail status based on configuration).