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Documentation
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- updated jun 2026
Technical documentation for the Maple observability platform — SDKs, OpenTelemetry guides, and the concepts that power the product.
# 01
Effect SDK
stable $npm i @maple-dev/effect-sdk
Official, opinionated wrappers around OpenTelemetry that auto-detect platform conventions. One layer() call ships traces, logs, and metrics from Node, Bun, Deno, the browser, or a Worker.
# effect sdk
# platforms
- server Set up the Effect SDK on Node.js, Bun, or Deno with environment-variable auto-detection.
- browser Set up the Effect SDK in browser environments with explicit configuration and auto-captured browser metadata.
- cloudflare workers Set up the Effect SDK on Cloudflare Workers with explicit flush() in ctx.waitUntil and in-isolate buffering.
# 02
Other languages
guidesStandard OpenTelemetry setup pointed at Maple's ingest endpoint. One guide per language — pick yours and ship traces, logs, and metrics in minutes.
# instrumentation
- node.js Instrument a Node.js application with OpenTelemetry and send traces, logs, and metrics to Maple.
- next.js Instrument a Next.js application with @vercel/otel and send traces, logs, and metrics to Maple.
- python Instrument a Python application with OpenTelemetry and send traces, logs, and metrics to Maple.
- go Instrument a Go application with OpenTelemetry and send traces, logs, and metrics to Maple.
- rust Instrument a Rust application with OpenTelemetry and send traces, logs, and metrics to Maple.
- java Instrument a Java application with OpenTelemetry and send traces, logs, and metrics to Maple.
- c# Instrument a .NET application with OpenTelemetry and send traces, logs, and metrics to Maple.
- kotlin Instrument a Kotlin JVM application (Ktor, Spring Boot) with OpenTelemetry and send traces, logs, and metrics to Maple.
# 03
Concepts
docsLanguage-agnostic explanations of how Maple works — OTLP conventions, sampling-aware throughput, and where to start.