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How to Run an AI GitHub Productivity Agent with OE Runtime

Get a clear picture of your GitHub repository's health in seconds. OE Runtime fetches open issues and pull requests, triages by age and label, flags what is stale or unreviewed, and delivers a prioritised action list — no manual triage required.

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Step 1
Fetch Repository Activity
Get up to 20 open issues and 10 open PRs sorted by last updated with title, author, labels, and age.
Step 2
Triage & Prioritise
Flag stale items, group issues by label, find unreviewed PRs, and identify bugs with no linked PR.
Step 3
Report
Produce a repository health summary with issue breakdown, PR status, urgent items, and recommended next actions.

The Agent YAML

Create a file called agent.yaml inside a productivity-crm/ directory:

name: GitHub Assistant
description: Summarize open issues and PRs in a repository
instructions: |
  You are a GitHub assistant. Fetch repository activity, summarize open issues and pull requests,
  and identify what needs immediate attention.
  Complete all steps fully before writing your report.
steps:
  - name: Fetch Repository Activity
    content: |
      Using the GitHub connector, fetch:
      - All open issues (up to 20), sorted by most recently updated
      - All open pull requests (up to 10), sorted by most recently updated
      For each, note: title, author, labels, created date, and days open.
  - name: Triage and Prioritize
    content: |
      From the fetched issues and PRs:
      - Flag any issue or PR open for more than 14 days as stale
      - Group issues by label (bug, enhancement, question, other)
      - Identify PRs that have no reviewers assigned
      - Find any issues marked as "bug" that have no assigned PR
  - name: Report
    content: |
      Produce a repository health summary:
      - Open issues: total, breakdown by label, stale count
      - Open PRs: total, unreviewed count, stale count
      - Top 3 most urgent issues (bugs with no PR)
      - Recommended next actions
connectors:
  - connection_name: GitHub
    connection_type: github

The Config File

Create oe-config.json in the same directory:

{
  "llm": {
    "provider": "openai",
    "model": "gpt-4o",
    "apiKey": "YOUR_OPENAI_API_KEY"
  },
  "server": {
    "enabled": false,
    "port": 3333,
    "apiKey": "your-secret-api-key"
  },
  "connectors": [
    {
      "connection_name": "GitHub",
      "connection_type": "github",
      "token": "ghp_YOUR_GITHUB_PERSONAL_ACCESS_TOKEN",
      "owner": "your-org",
      "repo": "your-repo"
    }
  ]
}

Create a Personal Access Token at github.com/settings/tokens with repo scope. Replace your-org and your-repo with your organisation and repository names.

Download OE Runtime

OE Runtime — Direct Downloads

Run the Agent

Windows

# Run the agent
oe-runtime-win.exe productivity-crm/agent.yaml --config productivity-crm/oe-config.json

macOS

# Make executable (first time only)
chmod +x oe-runtime-macos

# Run the agent
oe-runtime-macos productivity-crm/agent.yaml --config productivity-crm/oe-config.json

Linux

# Make executable (first time only)
chmod +x oe-runtime-linux

# Run the agent
oe-runtime-linux productivity-crm/agent.yaml --config productivity-crm/oe-config.json

macOS Gatekeeper: On first run, macOS may block the binary. Go to System Settings → Privacy & Security → click "Allow Anyway" next to oe-runtime-macos.

Running as an API Server

Add --serve to start OE Runtime as an HTTP server on port 3333:

oe-runtime-win.exe --serve --config productivity-crm/oe-config.json

Then send a request:

curl -X POST http://localhost:3333/run \
  -H "Content-Type: application/json" \
  -d '{"yaml": "productivity-crm/agent.yaml", "params": {}}'

Or import the Postman collection (download above) to test all endpoints visually.

Use Cases

Build your own agents with OE Runtime

Download OE Runtime and run any AI agent locally or as a server — no cloud required.

Get OE Runtime →