GitHub App · Works at the merge gate

The institutional memory
AI agents were never given

Codela watches your production incidents, extracts failure patterns from APM data, and injects them as GitHub PR comments — before the same bug ships twice.

GitHub PR Review · inline comment
C
codela bot high severity

N+1 External API Call Pattern

This diff matches a pattern seen in your production environment.

// Production evidence · 14 days ago
p95 latency 180ms → 1.4s (+677%) · 12 sequential API calls per request

Fix: Use inventory.get_batch(item_ids) instead of calling get() in a loop.

The problem

22%+

of merged code is now AI-authored — and rising. So are the failure rates that come with it.

Blind spot

APM tools see every production incident in detail. AI coding agents see none of it. That knowledge stays locked in a dashboard.

2 weeks

The same AI-generated bug ships again — different service, different engineer, same pattern. Nobody remembered to tell the agent.

How it works

From production incident to PR comment

1

Deploy detected

GitHub deployment webhook fires. Codela captures the commit SHA, timestamp, and environment.

2

APM signals pulled

Pre/post snapshots fetched from Datadog, New Relic, or Grafana — latency, error rate, traces, logs.

3

Pattern extracted

Claude analyzes the APM delta and PR diff together, extracting a named failure pattern with evidence.

4

Next PR flagged

When any future PR matches the pattern, Codela posts an inline comment with the production proof and fix.

Works with your existing APM stack

🐶 Datadog
🔍 New Relic
📊 Grafana
🔥 Prometheus
📝 Loki

Features

Everything you need, nothing you don't

⚙️

GitHub App

Zero per-developer install. Works at the merge gate. Inline comments exactly where the code lives.

🔌

APM connectors

Datadog, New Relic, and Grafana out of the box. Plug in your API key and it starts watching immediately.

Day-1 value

Ships with 13 seed patterns. Automatically bootstraps from your last 90 days of deploys on first startup.

🏢

Company memory

Every incident your team has seen becomes a pattern. Private, scoped to your environment.

🌐

Industry patterns

Anonymized cross-company library so you benefit from incidents you've never had yet. Coming soon.

🧠

LLM-powered

Claude analyzes APM deltas and diffs together — not static rules. Catches novel failure patterns, not just known ones.

Get started

Up and running in minutes

1

Install the GitHub App on your repo

Codela listens for deployment events and pull request webhooks. No per-developer setup.

2

Add your APM credentials and repo to .env

GITHUB_REPO=your-org/your-repo
DATADOG_API_KEY=...
DATADOG_APP_KEY=...
3

Start the server

docker compose up

Historical bootstrap runs automatically in the background, scanning your last 90 days of deploys.