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Zero-config observability for Python web frameworks

Project description

  Brakit Python SDK

AI writes your code. Brakit watches what it does.
Every request, query, and API call mapped to the action that triggered it. See your entire backend at a glance.
Open source · Local first · Zero config · AI-native via MCP

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MIT License PyPI version Python versions npm version PRs welcome!


Brakit Actions — endpoints grouped by user action
Every endpoint grouped by the action that triggered it — see "Sign Up" and "Load Dashboard", not 47 raw requests

Brakit Dashboard — issues surfaced automatically
Brakit catches N+1 queries, PII leaks, and slow endpoints as you develop

Claude reading Brakit findings in VS Code
Claude reads issues via MCP and fixes your code

Quick Start

The Python SDK captures queries, fetches, and requests from your FastAPI or Flask backend and forwards them to the Brakit dashboard running in your Node.js frontend.

1. Install the Node.js core (runs the dashboard)

npx brakit install

2. Install the Python SDK

pip install brakit

3. Add one import to your app

FastAPI:

import brakit  # must be before FastAPI import
from fastapi import FastAPI

app = FastAPI()

Flask:

import brakit  # must be before Flask import
from flask import Flask

app = Flask(__name__)

That's it. Start both apps normally. The Python SDK auto-detects your framework, instruments queries and fetches, and forwards everything to the Brakit dashboard at http://localhost:<port>/__brakit.

Full setup guide: brakit.ai/docs/introduction


What You Get

  • Cross-service tracing — FETCH from Next.js to FastAPI? The DB query inside FastAPI nests under the fetch automatically
  • Full server tracing — DB queries, outgoing HTTP calls, errors — zero code changes
  • Live dashboard at /__brakit — performance overview, request timeline, scatter charts
  • 8 security rules scanned against live traffic — leaked secrets, PII in responses, missing auth flags
  • N+1 query detection — same query pattern repeated 5+ times in a single request
  • AI-native via MCP — Claude Code and Cursor can query issues and verify fixes directly

Supported Frameworks

Framework Status
FastAPI Supported
Flask Supported
Django Planned

Supported Databases

Driver Status
SQLAlchemy Supported
asyncpg Supported
psycopg Planned

Supported HTTP Clients

Library Status
urllib3 Supported
httpx Supported
aiohttp Supported
requests Supported (via urllib3)

How It Works

import brakit  →  auto-detect framework  →  instrument everything
                        |
                        +-- DB queries    (SQLAlchemy, asyncpg)
                        +-- HTTP fetches  (httpx, aiohttp, urllib3)
                        +-- Errors        (global exception hooks)
                        +-- Logs          (logging module)
                              |
                              +-- Forward to Node.js dashboard via localhost

import brakit patches your framework's middleware to capture request/response pairs, hooks into database drivers and HTTP clients, and forwards telemetry to the Brakit dashboard running in your Node.js process. No proxy, no extra ports.

Production Safety

Brakit never runs in production. Multiple layers ensure it:

Layer How it blocks
should_activate() Checks NODE_ENV + 15 cloud/CI env vars
devDependency Not installed in production
safe_wrap + circuit breaker Errors in brakit code fall back silently
BRAKIT_DISABLE=true Manual kill switch

Optional Dependencies

# FastAPI support
pip install brakit[fastapi]

# Flask support
pip install brakit[flask]

# Development (tests, linting)
pip install brakit[dev]

Development

git clone https://github.com/brakit-ai/brakit.git
cd brakit/sdks/python

pip install -e ".[dev]"
pytest tests/ -v

Contributing

Brakit is early and moving fast. Areas where help would be great:

  • Database adapters — psycopg, Tortoise ORM, Django ORM
  • Framework adapters — Django, Starlette
  • HTTP client hooks — more client libraries
  • Security rules — new patterns, configurable severity

Please open an issue first for larger changes so we can discuss the approach.

License

MIT

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