hello@logkit.io · Status · GitHub

Your logs know what happened.
Can you find it?

Structured logs. Clear thinking.
Stop hunting for needles in a haystack. LogKit enforces a schema at the edge so you get the context you need, exactly when you need it.

incident.log
# You see this at 3am...

[ERROR] database: connection timeout
[WARN] gateway: 502 upstream failed
# But which request? Who triggered it?

The "Before" Reality

Most production debugging is a guessing game. Here is what you are currently dealing with.

The Midnight Panic

You have the timestamp, but not the correlation. You see an error, but not the request ID that triggered it.

2023-10-24 02:15:22
[ERROR] Connection refused at port 5432

The Missing Trace

Services talk to each other, but the signal gets lost. You know a user logged in, but you can't find the specific payment request that failed.

[INFO] User alice@example.com logged in
# No trace_id attached to the request

The Schema Drift

Inconsistency kills alerting. Service A sends `error_code`, Service B sends `code`. Your regex queries break.

Service A: {"level": "error", "code": 500}
Service B: {"error": "500", "msg": "Internal"}
How LogKit Helps

Feature-to-outcome mapping for backend workflows.

Auto-capture Context

Outcomes: Zero latency debugging. The SDK injects trace IDs, user IDs, and environment variables automatically into every log call.

Typed Schema

Outcomes: Instant alerting. Enforce JSON schema at compile time. If a field is missing, the build fails.

Structured Query

Outcomes: Find the needle in 100M logs. Query by specific field values, not just text patterns.

Case Study

Nexus Payments reduced MTTR by 60%

A mid-size fintech processing 5k transactions/sec needed visibility into a distributed payment pipeline.

"Before LogKit, a failed transaction meant we had to grep logs across three different services to find the correlation. With context threading, we see the full request flow in a single timeline."

60% Faster MTTR
0 Build-time Schema Errors
Read the full case study →
LogKit case study dashboard showing timeline view

Fits your stack, not the other way around.

LogKit is a drop-in SDK for your existing microservices. No pipeline changes required.

LogKit integration diagram showing services
  • Native SDKs

    Drop-in Go, Node.js, Python, and Java SDKs that emit JSON automatically.

  • OpenTelemetry Compatible

    Seamlessly inject into your existing trace context without breaking instrumentation.

  • Real-time Shipping

    Ship logs directly to LogKit or your existing S3/ES cluster via our push gateway.

Ready to clear the noise?

Ship better software.
Debug faster. Sleep soundly.