Time-travel debugging for FastAPI, Starlette, Flask, and Django — Record API requests, replay with mocked dependencies.

Timetracer captures real API requests into portable cassettes and replays them with mocked dependencies (HTTP calls, database queries, Redis commands).

Record once. Replay anywhere. Debug faster.

Common use cases:

• Reproduce production bugs locally without access to external services
• Build regression tests from real traffic patterns
• Run demos offline with pre-recorded data
• Detect performance regressions between releases
• Compare behavior between different code versions

Timetracer acts as middleware that intercepts your app's external calls:

                      RECORD MODE
┌──────────┐      ┌──────────────┐      ┌──────────────┐
│  Client  │ ───► │   Your App   │ ───► │   External   │
│  (curl)  │      │ + Timetracer │      │   APIs/DB    │
└──────────┘      └──────────────┘      └──────────────┘
                         │
                         ▼
                   ┌───────────┐
                   │  Cassette │  (saves everything)
                   │   .json   │
                   └───────────┘


                      REPLAY MODE
┌──────────┐      ┌──────────────┐       ╳ External APIs
│  Client  │ ───► │   Your App   │       ╳ (not called)
│  (curl)  │      │ + Timetracer │
└──────────┘      └──────────────┘
                         ▲
                         │
                   ┌───────────┐
                   │  Cassette │  (replays from here)
                   │   .json   │
                   └───────────┘

pip install timetracer[all]

Or install only what you need:

pip install timetracer[fastapi,httpx]    # FastAPI + httpx
pip install timetracer[starlette,httpx]  # Starlette + httpx
pip install timetracer[flask,requests]   # Flask + requests
pip install timetracer[django,requests]  # Django + requests
pip install timetracer[motor]            # Motor (MongoDB async) + PyMongo (sync)

from fastapi import FastAPI
from timetracer.integrations.fastapi import auto_setup

app = auto_setup(FastAPI())

@app.get("/users/{user_id}")
async def get_user(user_id: int):
    async with httpx.AsyncClient() as client:
        return (await client.get(f"https://api.example.com/users/{user_id}")).json()

from starlette.applications import Starlette
from timetracer.integrations.starlette import auto_setup

app = auto_setup(Starlette(debug=True))

from flask import Flask
from timetracer.integrations.flask import auto_setup

app = auto_setup(Flask(__name__))

# Record mode - captures all external calls
TIMETRACER_MODE=record uvicorn app:app
curl http://localhost:8000/users/123

# Replay mode - mocks external calls from cassette
TIMETRACER_MODE=replay \
  TIMETRACER_CASSETTE=./cassettes/2026-01-16/GET__users_{user_id}__abc.json \
  uvicorn app:app

For more control over configuration:

import httpx
from fastapi import FastAPI
from timetracer import TraceConfig
from timetracer.integrations.fastapi import TimeTracerMiddleware
from timetracer.plugins import enable_httpx

app = FastAPI()

config = TraceConfig(
    mode="record",
    cassette_dir="./my-cassettes",
    errors_only=True,
)
app.add_middleware(TimeTracerMiddleware, config=config)

enable_httpx()

All settings are controlled via environment variables:

timetracer list --dir ./cassettes              # List all cassettes
timetracer show ./cassettes/GET__users.json    # Show cassette details
timetracer diff --a old.json --b new.json      # Compare two cassettes
timetracer timeline ./cassettes/GET__users.json --open  # Generate timeline
timetracer dashboard --dir ./cassettes --open  # Generate interactive dashboard
timetracer serve --dir ./cassettes --open      # Start live dashboard with replay

Browse and debug all recorded cassettes with the interactive dashboard:

# Static HTML dashboard (open in browser)
timetracer dashboard --dir ./cassettes --open

# Live dashboard with real-time replay
timetracer serve --dir ./cassettes --open

Features:

• Sortable table - Sort by time, method, status, duration
• Filters - Filter by method, status, duration, time range
• Error highlighting - Errors shown in red with warning icons
• Stack traces - View exception details and Python tracebacks
• Replay - One-click replay to see recorded request/response
• Raw JSON - Expandable view of full cassette data

Timetracer automatically protects sensitive data:

• Removes Authorization, Cookie, and Set-Cookie headers
• Masks sensitive fields like password, token, api_key in request/response bodies
• Enforces a 64KB body size limit to prevent large data captures

• Changelog - Version history and changes
• Release Notes - Detailed release information
• Why Timetracer?
• Quick Start Guide
• Configuration Reference
• Dashboard Guide
• Plugin Guide
• SQLAlchemy Integration
• Motor (MongoDB) Integration
• PyMongo (MongoDB) Integration
• Starlette Integration
• Flask Integration
• Django Integration
• pytest Plugin
• Cassette Compression
• S3 Storage
• Cassette Search
• Security Best Practices

Contributions are welcome. See CONTRIBUTING.md for guidelines.

MIT License. See LICENSE for details.