Alert-first
Focus on the signal you actually need in chat: grouped ERROR alerts with useful context.
About PrairieLog API
The idea for the name PrairieLog comes from the sound of a prairie dog's bark. Similar to this API, it's a small animal that can make a lot of noise when it's scared. PrairieLog started out as an idea for a portfolio project that sends structured ERROR logs to Slack or Discord, over time it has evolved into a more useful smart alert notification system.
What it does
PrairieLog accepts log events from your app, then pools together related ERROR logs and INFO/DEBUG over a short window. PrairieLog then spits out a summarized alert to a Slack or Discord webhook.
Anaylsis features can be bypassed by using the API directly, but the SDKs will automatically use the analysis features.
Key Analysis features include:
- Summarizing the alert
- Confidence scoring for the analysis
- Root cause analysis of the error
- Potential remediation steps
Design principles
SDK-first
Use TypeScript, Python, or Java Logback SDKs for batching, level normalization, and required fields.
Small by design
No Redis, Elasticsearch, or log warehouse — Kafka only decouples alert aggregation and delivery from the request path; plain log relay stays simple.
Secrets and data handling
- Ingestion tokens are shown once at creation. Save yours in a server environment variable, not in browser code or git.
- Full alert webhook URLs should be treated as secrets and are not meant to be displayed after creation.
- Production apps should send logs from backend or server-controlled contexts. Browser tokens are visible to users.
- PrairieLog relays alerts; it does not provide long-term retention, search, or compliance archiving.
Who it is for
PrairieLog is for indie developers and small teams who want simple log monitoring and real-time error alerts without running observability infrastructure. If a full APM platform is overkill, PrairieLog covers the "tell me in Slack or Discord when something breaks" workflow.
Contact
PrairieLog is built and maintained by Robert Sima. For questions, feedback, or collaboration, reach out on GitHub or LinkedIn.