Insights overview

Insights lets you observe what your Python application does in production.

Honeybadger records common Python activity automatically, including Django and Flask requests, ORM queries, ASGI traffic, and Celery tasks. From there, you can add context and custom events from your own code, then use BadgerQL to ask questions across the whole event stream. Any field you send is queryable as soon as it arrives, with no schema to define ahead of time.

Start with automatic instrumentation

Set insights_enabled=True in your config and the package starts recording events as soon as your app boots.

Automatic instrumentation Configure what the package captures.

Python event reference See every Python event type and field.

Add a built-in dashboard

Automatic events power built-in dashboards.

Celery Task counts by status, average durations, failures and retries

Django Request durations, response status counts, slowest views and queries

Flask Request durations, response codes, and slowest views and queries by blueprint

Add application context

Context adds fields to the current thread. Once set, every event emitted from that thread carries them.

Say the app is A/B testing a new checkout flow against the control. Each checkout request sets context like this:

Set the variant on context

honeybadger.set_event_context(checkout_variant=checkout_variant)

The checkout_variant field is now on every database event for that request. You can group by it like any other field.

Database work by checkout variant

filter event_type::str == "db.query" and isNotNull(checkout_variant::str)
| stats
    count() as queries,
    avg(duration::float) as avg_ms
  by checkout_variant::str
| sort queries desc

queries	avg_ms	checkout_variant
26815	2.48	new
11873	2.21	control

The new variant ran more than twice as many queries with similar per-query time. Keep in mind that adding another A/B variant will extend any of the examples here without the need to change anything on the Honeybadger side.

Go deeper: check for possible N+1 queries

The package attaches a request_id to every event from the same request. To turn total database work into queries per request, group events by request_id first to get a per-request count, then aggregate by variant.

filter event_type::str == "db.query" and isNotNull(checkout_variant::str)
| stats count() as queries by request_id::str, checkout_variant::str
| stats
    count() as request_count,
    avg(queries) as avg_q,
    percentile(95, queries) as p95_q
  by checkout_variant
| sort p95_q desc
| only
    toHumanString(request_count) as requests,
    toHumanString(avg_q) as avg_queries,
    toHumanString(p95_q) as p95_queries,
    checkout_variant

requests	avg_queries	p95_queries	checkout_variant
631	42.18	97	new
638	18.61	31	control

The new variant runs more queries per request, and the p95 is much higher than control. That pattern often points at an N+1.

Event context Context lifecycle, temporary scopes, and precedence.

Record application events

Custom events record activity the framework cannot see at all. Django knows a checkout request ran. Only your app knows whether the payment authorized:

Send a custom payment event

honeybadger.event("payment.authorized", {
    "payment_provider": payment.provider,
    "amount": checkout.total,
    "currency": checkout.currency,
    "authorization_id": payment.authorization_id,
})

This query breaks down the amounts collected by variant and provider:

Payments by variant and provider

filter event_type::str == "payment.authorized"
| stats
    count() as authorizations,
    sum(amount::float) as authorized_amount
  by checkout_variant::str, payment_provider::str
| sort authorized_amount desc

authorizations	authorized_amount	checkout_variant	payment_provider
413	34108.00	new	stripe
218	18722.00	new	paypal
418	32167.00	control	stripe
220	13639.00	control	paypal

Go deeper: more insights, same instrumentation

Conversion rate by variant

filter event_type::str == "payment.authorized" or view::str == "checkout_create"
| stats count(event_type::str == "payment.authorized") as auth_events
  by request_id::str, checkout_variant::str
| stats
    count() as auths,
    count(auth_events > 0) as checkouts,
    checkouts / auths as conv_rate
  by checkout_variant::str
| only conv_rate, checkout_variant

conv_rate	checkout_variant
0.92	new
0.86	control

Revenue per payment provider per variant

filter event_type::str == "payment.authorized"
| stats sum(amount::float) as total
  by payment_provider::str, checkout_variant::str
| sort total desc
| only toHumanString(total) as revenue, payment_provider, checkout_variant

revenue	payment_provider	checkout_variant
34,108	stripe	new
32,167	stripe	control
18,722	paypal	new
13,639	paypal	control

Average checkout response time by variant

filter event_type::str == "django.request"
  and view::str == "checkout_create"
| stats avg(duration::float) as avg_ms by checkout_variant::str
| only toHumanString(avg_ms, "milliseconds") as avg, checkout_variant

avg	checkout_variant
142ms	new
78ms	control

Conversion rate over time, by variant

filter event_type::str == "payment.authorized" or view::str == "checkout_create"
| stats
    count(event_type::str == "payment.authorized") as auth_events,
    min(@ts) as request_ts
  by request_id::str, checkout_variant::str
| stats count(auth_events > 0) / count() as conv_rate
  by checkout_variant::str, bin(1h, request_ts) as hour
| sort hour asc

conv_rate	checkout_variant	hour
0.93	new	2026-06-26 14:00:00
0.86	control	2026-06-26 14:00:00
0.92	new	2026-06-26 15:00:00
0.86	control	2026-06-26 15:00:00
0.91	new	2026-06-26 16:00:00
0.87	control	2026-06-26 16:00:00

The new variant holds a consistent lead over control across the rollout window.

Sending custom events The full honeybadger.event API.