Profile AI code
before it breaks prod

Codela profiles your AI-generated code when you run it locally, flags the issues inline in your IDE, learns the patterns that break prod, and feeds them back to your AI coding agent — so it doesn't write the same bug twice.

Install for VS Code View on GitHub

VS Code · Codela profiler · users.py

Codela

users.py · 2m ago

▾CRITICAL(1)

N+1 query

line 14 · 47 queries

▾HIGH(2)

Unbounded pagination

line 88 · 12k rows

Sync blocking in async

line 104

▸MEDIUM(0)

▸LOW(0)

12def get_users_with_posts():

13users = db.query(User).all()

14for user in users:⚠ N+1 detected · 47 queries

15user.posts = db.query(Post).filter(

16Post.user_id == user.id

17).all()

18return users

N+1 Query Patternhighdetected in your test run

Your pytest run executed 47 sequential database queries — a classic N+1 load pattern. Codela knows what this looks like under production traffic.

// what this becomes in prod · users-service

p95 latency 340ms → 28s (+8,135%)

db queries/req 3 → 214

root cause: loaded posts per user inside the request handler

Fix: Use joinedload(User.posts) to load posts in a single query. This pattern is now in your team's memory — your AI agent won't write it again.

Memory layer

Your AI agent learns from every deploy

Every pattern Codela catches lives in your team's private library. Claude Code, Cursor, and Windsurf query it via MCP before they write — so the bug that broke your payments service doesn't ship in another service next week.

Claude Code · MCP tool response · codela

codela · check_codehigh1 match found

Missing Timeout on External HTTP Call

This pattern caused 2 production incidents in your environment. An external HTTP call with no timeout will hang indefinitely if the downstream service is slow or unresponsive.

// last incident · 6 days ago · payments-service

p95 latency 340ms → 28s (+8,135%)

error rate 0.1% → 14.3%

root cause: Stripe webhook endpoint unresponsive for 4 min

Fix: Set timeout=5000 on the request. Wrap in a circuit breaker so cascading failures don't take down the caller.

The problem

25%+

of merged code is now AI-authored — and nobody's profiling it before it ships.

~30%

of AI-authored commits introduced at least one production issue.

Every run

Your local tests pass. Prod latency tells a completely different story — and you find out hours later.

Days

Between the deploy and the Slack thread that traces the regression back to the AI commit that caused it.

How it works

From local test run to prevented regression

Setup — done once

Install the extension

VS Code or Cursor — 2 minutes. The profiler activates automatically when you run tests.

Add your APM credentials

Connect Datadog, New Relic, or Grafana. Codela auto-bootstraps from your last 90 days of deploys — no cold start.

(Optional) Add to your AI agent

One MCP block in Claude Code or Cursor. Your agent now queries the same memory before writing anything.

The loop — runs forever

AI writes code

Claude Code, Cursor, Copilot, or a human — Codela doesn't care who wrote it.

You run it locally

pytest, jest, vitest — your normal dev loop. Codela wraps the run with OTEL auto-instrumentation.

Codela profiles

Real runtime signals — query counts, call trees, durations — matched against your team's pattern library.

Fix before merge

Inline annotations in your IDE link to the exact prod incident that matches the pattern, with the suggested fix.

Works with your IDE, test runner, AI agent, and APM stack

VS Code

Cursor

Claude Code

pytest

jest

Datadog

New Relic

Grafana

Prometheus

Loki

Features

Everything you need, nothing you don't

IDE profiler

Wraps your pytest and jest runs with OTEL + Scalene auto-instrumentation. Real runtime signals — not LLM guesses — surface in the Codela sidebar grouped by severity, with inline line highlights and prod-incident evidence on every match.

Production pattern library

Every incident your team has seen becomes a named pattern — private to your environment. The same library powers the IDE, the MCP server, and the PR review.

APM connectors

Datadog, New Relic, and Grafana out of the box. Plug in your API key and Codela starts learning from every deploy immediately.

MCP server

Same memory, exposed to Claude Code and Cursor via MCP. Your agent queries Codela before it writes. One config line, zero per-developer setup.

PR review fallback

If a pattern slips past the IDE profiler and the agent, Codela still posts inline GitHub PR comments as a safety net before merge.

Day-1 value

Ships with seed patterns. Bootstraps from your last 90 days of deploys on first startup — no cold start, no manual seeding.

Get started

Up and running in minutes

Install the extension

VS Code or Cursor. The profiler activates automatically the next time you run your tests.

# VS Code
code --install-extension Codela.codela

# Cursor
cursor --install-extension Codela.codela

Install the GitHub App

Codela listens for your deployments and seeds the pattern library from your last 90 days of production. The same library powers the IDE profiler.

Add your APM credentials

Plug in Datadog, New Relic, or Grafana. Codela cross-references your local traces against every past prod incident.

GITHUB_REPO=your-org/your-repo
DATADOG_API_KEY=...
ANTHROPIC_API_KEY=...

(Optional) Add to your AI agent

Same memory, exposed via MCP. Claude Code and Cursor can now query Codela before they write anything.

{
  "mcpServers": {
    "codela": {
      "command": "python",
      "args": ["-m", "app.mcp_server"]
    }
  }
}

Get Started on GitHub

Built and trusted by engineers from the world's best AI and software companies and startups

Profile AI codebefore it breaks prod

Your AI agent learns from every deploy

From local test run to prevented regression

Everything you need, nothing you don't

Up and running in minutes

Profile AI code
before it breaks prod