Lanterna

Agent-first Node.js CPU, memory & experimental async profiler.
Spawns or attaches to your program, captures selected profile kinds plus timed runtime signals,
and emits a structured JSON report that humans and AI agents can act on directly.

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Lanterna is built so its output is useful to an AI agent, not just a human reader. Instead of a flamegraph, you get a categorized, correlated, and actionable LanternaReport — ready to pipe into an LLM or a CLI tool.

Why Lanterna?

Most Node.js profilers were designed for a human staring at a flamegraph. That's a problem when an AI agent is doing the investigation: a flamegraph isn't parseable, hot stacks aren't categorized, and "what should I fix first?" requires a human to interpret the visual.

Lanterna takes a different stance:

• Structured JSON, not pixels. The LanternaReport is a stable schema — hotspots, allocators, async chains, GC pauses, event-loop lag, and findings — that an agent can read, correlate, and act on directly.
• Detectors, not just data. 17 built-in detectors emit categorized findings (sync crypto, blocking I/O, deopt loops, memory growth, orphan async resources, …) with confidence and proofLevel so consumers know when to trust a hypothesis vs. require corroboration.
• CPU + memory + async in one capture. Combine kinds in a single run; cross-kind detectors like alloc-in-hot-path and hot-async-context surface the highest-priority fixes (something flamegraph tools can't represent).
• Spawn or attach. Profile a CLI, a server under load, or a live production process — same report shape, same detector surface.

Compared to other Node.js profilers

Tool	Primary output	CPU	Memory	Async	Findings / detectors	Agent-friendly
Lanterna	Structured JSON (+ text/markdown/agent renderers)	✅	✅	✅ (experimental)	✅ 17 built-in, pluggable	✅
node --prof / --cpu-prof	V8 isolate log / .cpuprofile	✅	—	—	—	⚠️ raw, post-processing required
0x	HTML flamegraph	✅	—	—	—	❌
Clinic.js (Doctor / Flame / Bubbleprof)	HTML dashboards	✅	⚠️ via Doctor	⚠️ via Bubbleprof	⚠️ heuristic recommendations	❌
Chrome DevTools (inspector)	Interactive UI	✅	✅	⚠️ stack-only	—	❌

When to reach for something else:

• You want a flamegraph for human inspection. 0x and Chrome DevTools are purpose-built for that.
• You're already on Clinic.js's Doctor diagnostics workflow. Clinic does well as a one-shot human triage.
• You need raw V8 internals (deoptimization traces, ICs, etc.). Use --prof and the V8 tooling directly.

Lanterna is the right fit when the consumer of the report is an agent or an automated pipeline that needs categorized signals, not pixels.

What you get

• Two capture modes — lanterna run to spawn & profile a command, lanterna attach to connect to a live process via the inspector.
• Three profile kinds — opt in with --kind: cpu (V8 sampling profiler, default), memory (heap allocation profile + RSS series), and async (experimental async-resource profiling). Combine kinds by repeating --kind (--kind cpu --kind memory) or using commas (--kind cpu,memory).
• Enriched LanternaReport — categorized hotspots, hot stacks, GC pauses, event-loop lag, allocator ranking, async chains, capture-integrity flags.
• 17 built-in detectors across CPU, memory, and async kinds — see the Built-in detectors section below.
• Stable JSON schema with finding confidence and proofLevel fields so consumers can distinguish direct sampled evidence from heuristics.
• Extensible — ship your own detectors and profile kinds as plugins.

60-second example

# Install
npm install -g @lanterna-profiler/cli
# or run without installing
npx -y @lanterna-profiler/cli --help

# Profile a CLI script for 30 s and read the report
lanterna run --duration 30s --output report.json -- node app.js
lanterna report report.json --format text
lanterna report report.json --format agent --output report.agent.md

# Profile a server with representative load
lanterna run \
  --duration 30s \
  --wait-for-url http://127.0.0.1:3000/health \
  --workload "npx -y autocannon http://127.0.0.1:3000" \
  --output report.json \
  -- node server.js

# Memory leak hunt with start/end heap snapshot
lanterna run --kind memory --heap-snapshot-analysis --duration 60s -- node app.js

Ctrl+C stops profiling early and still emits a final report.

Built-in detectors

Lanterna ships 17 detectors out of the box. Each emits a Finding in the report with confidence and proofLevel so consumers can distinguish direct sampled evidence from heuristics.

CPU kind (8)

ID	What it flags
sync-crypto-on-hot-path	Synchronous crypto calls (pbkdf2Sync, randomBytesSync, …) dominating CPU
blocking-io	Synchronous fs / zlib / dns calls on hot stacks
json-on-hot-path	JSON.parse / JSON.stringify dominating CPU
excessive-gc	High GC pause time relative to wall time
event-loop-stall	Long event-loop lag spikes correlated with stack samples
deopt-loop	V8 deoptimisation cycles repeatedly hit on the same function
require-in-hot-path	Dynamic require() resolved on hot stacks (cold-start surprise)
node-modules-hotspot	A third-party dependency dominating CPU

Memory kind (4)

ID	What it flags
memory-growth	Sustained heap / RSS growth over the capture window
large-allocator	A single allocator responsible for a dominant share of bytes
external-buffer-pressure	Off-heap pressure (Buffers, ArrayBuffers)
alloc-in-hot-path	Allocators that are also CPU hot stacks — double impact, top-priority fix (cross-kind: requires both cpu and memory, auto-skips otherwise)

Async kind (experimental, 5)

ID	What it flags
long-await	await expressions exceeding the wait-time threshold
orphan-async-resource	Async resources created but never resolved / destroyed
deep-async-chain	Deeply nested await chains amplifying latency
microtask-flood	Microtask queue saturation starving the event loop
hot-async-context	Async contexts dominating CPU (cross-kind: requires both cpu and async, auto-skips otherwise)

Thresholds are configurable via .lanterna.json — see docs/configuration.md. To ship your own detectors, see docs/extending/detectors.md.

Requirements

Environment	Minimum	Why
Node.js running Lanterna	>= 22	Active LTS lines (22, 24).
Node.js running the profiled program	>= 12	Needs monitorEventLoopDelay and PerformanceObserver GC entries.

The profiled target must support the V8 inspector. If the inspector cannot start, Lanterna fails fast — it never silently falls back to a weaker mode.

Packages

Package	What it is
@lanterna-profiler/cli	The lanterna binary.
@lanterna-profiler/core	Capture orchestration, profile kinds, analysis pipeline, report builder.
@lanterna-profiler/detectors	Default detector pack for CPU, memory and async kinds, plus plugin helpers.

Documentation

Start here, then dive into whichever topic you need:

• docs/getting-started.md — install, first capture, reading the output.
• docs/cli.md — full CLI reference and option groups.
• docs/configuration.md — .lanterna.json reference.
• docs/programmatic-api.md — runProfile, attachProfile, low-level capture and analysis APIs.
• docs/report-schema.md — LanternaReport shape (schema v2).
• docs/reading-a-report.md — interpretation playbook and common mistakes.
• docs/signal-quality.md — confidence, integrity flags, degradation modes.
• docs/architecture.md — capture flow and enrichment pipeline.
• docs/troubleshooting.md — symptom-keyed fixes.
• docs/performance-overhead.md — measured startup cost and steady-state overhead per kind.

Per-kind details:

• docs/kinds/cpu.md — CPU kind, hotspots, event loop, GC, deopts.
• docs/kinds/memory.md — memory kind, allocators, RSS series, heap snapshots.
• docs/kinds/async.md — async kind (experimental), instrumentation modes, attach caveats.

Extending Lanterna:

• docs/extending/detectors.md — write a finding detector.
• docs/extending/profile-kinds.md — write a brand-new profile kind.
• docs/extending/plugin-loading.md — how plugins are discovered and packaged.

Runnable examples:

• examples/ — three tiny standalone scripts that exhibit a CPU hotspot, a memory leak, and an event-loop stall, with the matching lanterna command for each.

For agents (Claude Code skill):

• skills/lanterna-profiler/SKILL.md — the agent-oriented profiling workflow.

Install the skill into an agent workspace with:

npx skills add arkerone/lanterna --skill lanterna-profiler

Repository layout

packages/
  core/       @lanterna-profiler/core       — capture orchestration, kinds, pipeline, report
  detectors/  @lanterna-profiler/detectors  — default detector pack (CPU + memory + async) and plugin helpers
  cli/        @lanterna-profiler/cli        — `lanterna` binary
docs/                                       — human documentation
skills/lanterna-profiler/                   — agent workflow for Claude Code

Dependency direction: cli → core, cli → detectors, detectors → core. core never imports detectors.

Development

npm install
npm run build       # builds all three packages
npm test            # runs every package's vitest suite

Per-package work: npm run build -w @lanterna-profiler/core, npm test -w @lanterna-profiler/cli, etc.

Tests use Vitest and cover frame classification, hotspot aggregation, detector evidence attribution, and live profiling paths — including short-lived processes and real event-loop stall correlation.

Changelog

Each package ships its own changelog, generated by Changesets:

• packages/cli/CHANGELOG.md
• packages/core/CHANGELOG.md
• packages/detectors/CHANGELOG.md

License

MIT.