agent-trace-debugger

Debug, diff, and replay AI agent traces

Record every LLM call, tool invocation, and reasoning step your agent makes. Then diff two runs side-by-side to find exactly where behavior diverged.

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Why

Building AI agents is hard. Debugging them is harder.

"It worked yesterday." -- LLM non-determinism means the same prompt can produce different tool calls, different reasoning chains, different outcomes. You need a way to see exactly what changed between two runs.
"Which call is eating my budget?" -- A single agent run can make dozens of LLM calls across multiple models. Without per-span cost and token tracking, you are flying blind.
"I can't reproduce the bug." -- Agent executions are ephemeral. Once the run is over, the intermediate steps are gone. You need a recording you can replay step by step.

Features

| | Feature | Description | |---|---|---| | :pencil2: | Trace Collector | Auto-instrument OpenAI and Anthropic SDKs, or build spans manually with the fluent SpanBuilder API | | :package: | SQLite + FTS5 Store | Persistent storage with full-text search across all span inputs and outputs | | :arrows_counterclockwise: | Step-by-Step Replay | Walk through any trace span-by-span with breadcrumb navigation | | :mag: | Trace Diff | LCS-based structural alignment, content diffs, metrics diffs, and automatic divergence detection | | :bar_chart: | Multi-Trace Compare | Compare N traces to find the cheapest, fastest, and best run | | :mag_right: | Full-Text Search | FTS5-powered search across span names, inputs, and outputs | | :chart_with_upwards_trend: | Aggregated Stats | Cost, tokens, latency, and per-model breakdown across all recorded traces | | :outbox_tray: | Export (HTML/MD/JSON) | Export traces as self-contained HTML, Markdown for GitHub, or raw JSON | | :label: | Tags | Attach arbitrary string tags to traces for grouping and filtering | | :speech_balloon: | Annotations | Attach free-text notes to any span for post-hoc commentary | | :moneybag: | Cost Alerts | Budget monitoring with warning/exceeded callbacks, checked automatically after each trace | | :wrench: | Tool Result Capture | Auto-close pending tool spans when results appear, or record results manually | | :fast_forward: | Parallel Spans | Trace concurrent tool calls with isolated async contexts via startParallelSpans() | | :electric_plug: | Generic Adapter | wrapFunction() instruments any async function without an SDK-specific adapter | | :wastebasket: | Maintenance | Prune old or errored traces with cleanup, reclaim space with vacuum | | :computer: | CLI (15 commands) | list, show, record, replay, diff, compare, search, stats, export, tag, untag, tags, annotate, cleanup, vacuum |

Quick Start

npm install agent-trace-debugger

import { Tracer, FileExporter, SpanKind } from 'agent-trace-debugger';

// 1. Create a tracer with a file exporter
const tracer = new Tracer({
  name: 'my-agent',
  exporter: new FileExporter('./traces/run.json'),
});

// 2. Start a trace
const root = tracer.startTrace('agent-run');

// 3. Record spans
const span = tracer.startSpan('think', SpanKind.REASONING);
span.setOutput('I should search the database first.');
tracer.endSpan(span);

// 4. End and export
const trace = await tracer.endTrace();

Then inspect the trace from the command line:

npx trace-debugger show <trace-id>
npx trace-debugger replay <trace-id>

Collector SDK

Tracer

The Tracer manages the lifecycle of a single trace. It creates spans, tracks parent-child relationships via AsyncLocalStorage, and exports the finished trace.

import { Tracer, StoreExporter, SQLiteTraceStore } from 'agent-trace-debugger';

const store = new SQLiteTraceStore('./traces.db');
const tracer = new Tracer({
  name: 'my-agent',
  exporter: new StoreExporter(store),
});

const root = tracer.startTrace('run-42');

// Spans are automatically parented to the current context
const llmSpan = tracer.startSpan('gpt-4o-call', SpanKind.LLM_CALL);
llmSpan.setOutput('The answer is 42.');
llmSpan.setMetadata({
  model: 'gpt-4o',
  promptTokens: 150,
  completionTokens: 12,
  cost: 0.00093,
});
tracer.endSpan(llmSpan);

const trace = await tracer.endTrace();

SpanBuilder (Fluent API)

For lower-level control, use SpanBuilder directly:

import { SpanBuilder, SpanKind } from 'agent-trace-debugger';

const activeSpan = new SpanBuilder(traceId)
  .named('search-documents')
  .ofKind(SpanKind.TOOL_CALL)
  .withInput(JSON.stringify({ query: 'quarterly revenue' }))
  .childOf(parentSpanId)
  .start();

// ... do work ...

activeSpan.setOutput(JSON.stringify(results));
activeSpan.setMetadata({ latency: 230 });
const span = activeSpan.end();

Parallel Spans

Trace concurrent work (e.g. multiple tool calls) with isolated async contexts so nested spans parent correctly:

const branches = tracer.startParallelSpans(
  ['search-docs', 'query-db', 'call-api'],
  SpanKind.TOOL_CALL,
);

await Promise.all(
  branches.map(({ span, run }) =>
    run(async () => {
      // Any startSpan() calls here will parent under this branch's span
      const result = await doWork();
      span.setOutput(result);
      tracer.endSpan(span);
    }),
  ),
);

Exporters

| Exporter | Description | |---|---| | FileExporter(path) | Writes the trace as a JSON file | | StoreExporter(store) | Saves the trace into a SQLiteTraceStore | | HtmlExporter(path) | Writes a self-contained HTML report with collapsible span tree | | MarkdownExporter(path) | Writes a structured Markdown document |

SDK Integration

OpenAI Adapter

Automatically instruments client.chat.completions.create to capture every LLM call and tool invocation as spans. Supports both sync and streaming responses.

import { Tracer, StoreExporter, OpenAIAdapter } from 'agent-trace-debugger';
import OpenAI from 'openai';

const client = new OpenAI();
const tracer = new Tracer({ name: 'openai-agent', exporter: new StoreExporter(store) });

const root = tracer.startTrace('chat-session');

// Instrument the client
const adapter = new OpenAIAdapter(root.traceId);
adapter.instrument(client);

// All calls are now traced automatically
const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'What is 2+2?' }],
});
// ^ Creates an llm_call span with model, tokens, and cost metadata.
//   If the response includes tool_calls, each one becomes a child tool_call span.

// Restore original method when done
adapter.restore(client);
await tracer.endTrace();

Anthropic Adapter

Automatically instruments client.messages.create to capture LLM calls and tool_use content blocks.

import { Tracer, StoreExporter, AnthropicAdapter } from 'agent-trace-debugger';
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic();
const tracer = new Tracer({ name: 'claude-agent', exporter: new StoreExporter(store) });

const root = tracer.startTrace('claude-session');

// Instrument the client
const adapter = new AnthropicAdapter(root.traceId);
adapter.instrument(client);

// All calls are now traced automatically
const response = await client.messages.create({
  model: 'claude-4-sonnet',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'Summarize this document.' }],
});
// ^ Creates an llm_call span with input/output tokens.
//   tool_use content blocks become child tool_call spans.

adapter.restore(client);
await tracer.endTrace();

Generic Adapter

wrapFunction() instruments any async (or sync) function without requiring an SDK-specific adapter. It creates a span around each invocation, records input/output, and attaches it to the current trace context.

import { wrapFunction, SpanKind } from 'agent-trace-debugger';
import type { SpanTracker } from 'agent-trace-debugger';

const tracker: SpanTracker = {
  traceId: root.traceId,
  onSpanEnd: (span) => finishedSpans.push(span),
};

const tracedFetch = wrapFunction(fetchDocuments, tracker, {
  name: 'fetch-documents',
  kind: SpanKind.TOOL_CALL,
  extractInput: (query) => JSON.stringify(query),
  extractOutput: (result) => JSON.stringify(result),
  extractUsage: (result) => ({ promptTokens: result.tokens }),
});

// Use it as a drop-in replacement -- spans are created automatically
const docs = await tracedFetch({ query: 'quarterly revenue' });

Streaming Support

Both adapters automatically handle streaming responses. When you pass stream: true to either SDK, the adapter accumulates chunks, reconstructs the full response, and records it as a single span.

OpenAI: Accumulates streamed content deltas and extracts token usage from the final chunk
Anthropic: Processes message_start, content_block_delta, and message_delta events to reconstruct the complete message

When streaming responses do not include exact token counts, the built-in estimateTokens fallback provides approximate usage metrics so cost tracking remains functional.

Tool Result Capture

Both adapters automatically close pending tool-call spans when tool results appear in a subsequent API call. You can also record a result explicitly:

// Manually record the output of a tool call span
adapter.recordToolResult(toolCallId, resultContent);

Auto-detection: When the next LLM call contains a tool_result block (Anthropic) or a tool role message (OpenAI) referencing a previously opened tool span, the adapter matches it by ID and closes the span with the result automatically -- no manual wiring required.

Trace Replay

Step through a recorded trace span-by-span using the ReplayEngine or the CLI.

Programmatic API

import { ReplayEngine } from 'agent-trace-debugger';

const engine = new ReplayEngine(trace);

let frame = engine.current();
console.log(frame.span.name);    // "agent-run"
console.log(frame.position);     // "1/5"
console.log(frame.breadcrumb);   // [{ spanId: "...", name: "agent-run" }]

frame = engine.next();           // advance to next span
frame = engine.prev();           // go back one span
frame = engine.jumpTo(spanId);   // jump to specific span
engine.toStart();                // return to first span
engine.toEnd();                  // jump to last span

CLI Replay

# Replay all steps sequentially
trace-debugger replay <trace-id>

# Jump to a specific step
trace-debugger replay <trace-id> --step 3

# Compact output (span name + position only, no input/output bodies)
trace-debugger replay <trace-id> --compact

Example output:

[1/5] agent-run  (reasoning)
  input:  "What is the capital of France?"
  output: "I should look this up."

[2/5] search-documents  (tool_call)
  input:  {"query":"capital of France"}
  output: {"result":"Paris"}

[3/5] gpt-4o-call  (llm_call)
  input:  "Given the search result, answer the question."
  output: "The capital of France is Paris."
...

The --budget flag on show and record prints a cost warning when the trace exceeds a threshold:

trace-debugger show <trace-id> --budget 0.10
trace-debugger record --file run.json --budget 0.05

Diff Analysis

This is the core feature. Compare two traces to understand exactly what changed and why.

The diff engine performs four analyses in a single analyze() call:

1. Structural Diff

LCS-based alignment detects added, removed, and reordered spans between two runs.

import { analyze } from 'agent-trace-debugger';

const result = analyze(traceA, traceB);

console.log(result.structuralDiff.added);     // Span[] - spans only in B
console.log(result.structuralDiff.removed);   // Span[] - spans only in A
console.log(result.structuralDiff.reordered); // [Span, Span][] - position changes

2. Content Diff

For each matched span pair, computes line-level diffs of input and output, classifying changes as minor (whitespace only) or major (substantive).

for (const diff of result.contentDiffs) {
  console.log(diff.spanName);      // "chat.completions.create"
  console.log(diff.significance);  // "major" | "minor"
  console.log(diff.inputDiff);     // unified diff string
  console.log(diff.outputDiff);    // unified diff string
}

3. Metrics Diff

Per-span and trace-level deltas for cost, latency, and token usage.

const metrics = result.metricsSummary;

console.log(metrics.costChangePercent);     // -12.5 (percent)
console.log(metrics.latencyChangePercent);  // +8.3
console.log(metrics.totalCostA);            // 0.0234
console.log(metrics.totalCostB);            // 0.0205

for (const s of metrics.perSpan) {
  console.log(s.spanName, s.costDelta, s.latencyDelta, s.tokenDelta);
}

4. Divergence Finder

Walks matched pairs chronologically to find the first point of divergence, classifying it as:

llm_nondeterminism -- same input, different output (the LLM chose differently)
input_change -- different input (caused by an upstream change)

if (result.divergencePoint) {
  console.log(result.divergencePoint.type);             // "llm_nondeterminism"
  console.log(result.divergencePoint.cause);            // human-readable explanation
  console.log(result.divergencePoint.downstreamImpact); // 3 subsequent spans affected
}

Summary

Every diff result includes a human-readable summary string:

console.log(result.summary);
// "Structural changes: 1 added, 1 removed spans. 2 span(s) have major content
//  differences. Divergence at "plan": llm_nondeterminism, affecting 3 downstream
//  span(s). Cost decreased by 12.5%."

CLI

# Full diff between two traces
trace-debugger diff <trace-id-1> <trace-id-2>

# Short summary only
trace-debugger diff <trace-id-1> <trace-id-2> --format short

Multi-Trace Compare

Compare two or more traces at once to identify the cheapest, fastest, and overall best run.

import { analyze, generateCompareSummary } from 'agent-trace-debugger';

// Generate pairwise diffs between adjacent traces
const diffs = [
  analyze(traceA, traceB),
  analyze(traceB, traceC),
];

const summary = generateCompareSummary(diffs);

console.log(summary.runs[summary.cheapest].totalCost);     // lowest cost
console.log(summary.runs[summary.fastest].totalLatency);    // lowest latency
console.log(summary.runs[summary.bestRun].score);           // best composite score
console.log(summary.text);                                  // formatted comparison table

# CLI: compare 2 or more traces
trace-debugger compare <id1> <id2> [id3 ...]

Example output:

=== Trace Comparison Summary ===

Run | Cost       | Latency (ms) | Score
--- | ---------- | ------------ | -----
  0 | $0.0234    |         2340 | 0.026740
  1 | $0.0205    |         1890 | 0.022390
  2 | $0.0310    |         3100 | 0.034100

Cheapest: Run 1 ($0.0205)
Fastest:  Run 1 (1890ms)
Most different: Run 2
Best run: Run 1 (lowest combined score)

Search & Stats

Full-Text Search

SQLite FTS5-powered search across span names, inputs, and outputs.

const store = new SQLiteTraceStore('./traces.db');

// Search all spans
const spans = store.searchSpans('quarterly revenue');

// Search within a specific trace
const spans = store.searchSpans('error', traceId);

# CLI search
trace-debugger search "quarterly revenue"
trace-debugger search "error" --kind llm_call --limit 20

Aggregated Statistics

import { aggregateStats } from 'agent-trace-debugger';

const stats = aggregateStats(store, { since: '2026-01-01', model: 'gpt-4o' });

console.log(stats.count);        // 47 traces
console.log(stats.totalCost);    // 1.23
console.log(stats.avgCost);      // 0.026
console.log(stats.totalTokens);  // 142000
console.log(stats.avgLatency);   // 2340 ms

for (const [model, data] of stats.modelBreakdown) {
  console.log(model, data.count, data.cost, data.tokens);
}

# CLI stats
trace-debugger stats
trace-debugger stats --since 2026-01-01 --model gpt-4o

Export Formats

Export any recorded trace to HTML, Markdown, or JSON for sharing and reporting.

# Export to self-contained HTML
trace-debugger export <trace-id> --format html --output report.html

# Export to Markdown (for GitHub issues/PRs)
trace-debugger export <trace-id> --format markdown --output trace.md

# Export to JSON (default)
trace-debugger export <trace-id> --format json --output trace.json

# Print to stdout (omit --output)
trace-debugger export <trace-id> --format json

| Format | Description | |---|---| | html | Self-contained HTML file with embedded CSS, collapsible span tree, and color-coded span kinds | | markdown | Structured Markdown document suited for pasting into GitHub issues or pull requests | | json | Raw JSON trace data |

Tags & Annotations

Annotations

Attach free-text notes to any span for post-hoc commentary, review, or debugging context.

// Add an annotation
store.addAnnotation(traceId, spanId, 'This retried due to rate limit', 'alice', new Date().toISOString());

// Retrieve annotations for a span
const annotations = store.getAnnotations(spanId);
// [{ text: "This retried due to rate limit", author: "alice", timestamp: "..." }]

trace-debugger annotate <trace-id> <span-id> "This span retried twice due to a rate limit."
trace-debugger annotate <trace-id> <span-id> "Looks correct" --author bob

Annotations are stored alongside the span and displayed in trace show output.

Cost Alerts

Monitor cumulative spend across traces and trigger callbacks when thresholds are crossed.

import { CostAlert, Tracer, StoreExporter, SQLiteTraceStore } from 'agent-trace-debugger';

const store = new SQLiteTraceStore('./traces.db');

const alert = new CostAlert({
  budget: 5.00,               // total budget in dollars
  warningThreshold: 0.8,      // fire onWarning at 80% of budget (default)
  onWarning: (cumulative, budget) =>
    console.warn(`Cost warning: $${cumulative.toFixed(4)} of $${budget.toFixed(2)} budget`),
  onExceeded: (cumulative, budget) =>
    console.error(`Budget exceeded: $${cumulative.toFixed(4)} > $${budget.toFixed(2)}`),
});

// Pass the alert to Tracer -- it auto-checks after every endTrace()
const tracer = new Tracer({
  name: 'my-agent',
  exporter: new StoreExporter(store),
  costAlert: alert,
});

const root = tracer.startTrace('agent-run');
// ... record spans ...
await tracer.endTrace(); // CostAlert.check() is called automatically

// Query remaining budget programmatically
console.log(alert.getCumulativeCost());   // 3.42
console.log(alert.getRemainingBudget());  // 1.58

Maintenance

Keep the trace store lean by removing traces you no longer need and optimizing the database.

# Delete traces older than 30 days
trace-debugger cleanup --older-than 30d

# Delete all traces with error status
trace-debugger cleanup --status error

# Combine filters
trace-debugger cleanup --older-than 7d --status error

# Reclaim disk space after deletions (SQLite VACUUM)
trace-debugger vacuum

Monitor database size programmatically:

const store = new SQLiteTraceStore('./traces.db');
const bytes = store.getStorageSize();
console.log(`Store is ${(bytes / 1024 / 1024).toFixed(2)} MB`);

Examples

The examples/ directory contains three ready-to-run scripts:

| Script | Description | |---|---| | basic-usage.ts | Creates a tracer, records a few spans manually, and prints the finished trace | | openai-agent.ts | Instruments an OpenAI client with OpenAIAdapter, runs a tool-use loop, and saves to SQLite | | diff-two-runs.ts | Loads two traces from the store, calls analyze(), and prints the divergence report |

Run any example:

npx tsx examples/basic-usage.ts
npx tsx examples/openai-agent.ts
npx tsx examples/diff-two-runs.ts

Supported Models

Built-in cost calculation for the following models (per 1K tokens):

| Model | Prompt | Completion | |---|---|---| | claude-4-opus | $0.0150 | $0.0750 | | claude-4-sonnet | $0.0030 | $0.0150 | | claude-3.5-sonnet | $0.0030 | $0.0150 | | claude-3.5-haiku | $0.0010 | $0.0050 | | claude-3-opus | $0.0150 | $0.0750 | | claude-3-sonnet | $0.0030 | $0.0150 | | claude-3-haiku | $0.00025 | $0.00125 | | gpt-4o | $0.0050 | $0.0150 | | gpt-4o-mini | $0.00015 | $0.00060 | | gpt-4-turbo | $0.0100 | $0.0300 | | gemini-1.5-pro | $0.0035 | $0.0105 | | gemini-1.5-flash | $0.000075 | $0.00030 | | gemini-2.0-flash | $0.00010 | $0.00040 |

Custom models work with all features except automatic cost calculation. Token estimation uses model-aware character ratios (GPT ~4 chars/token, Claude ~3.5 chars/token) with a word-based fallback, returning the higher of the two estimates.

API Reference

Schema

| Export | Description | |---|---| | Trace | Trace type: id, name, startTime, endTime, rootSpanId, metadata, spans, tags | | Span | Span type: id, traceId, parentSpanId, name, kind, status, startTime, endTime, input, output, metadata, children, annotations | | SpanKind | Enum: llm_call, tool_call, reasoning, decision, custom | | SpanStatus | Enum: ok, error | | SpanMetadata | model, promptTokens, completionTokens, cost, latency, plus arbitrary keys | | SpanAnnotation | text, author, timestamp | | TraceFilter | Filter for listTraces: since, until, model, status, name, tags, limit, offset |

Collector

| Export | Description | |---|---| | Tracer | Main tracer class. startTrace(name?, { tags? }), startSpan(name, kind), endSpan(span), endTrace(), startParallelSpans(names, kind), getContext() | | SpanBuilder | Fluent builder. named(), ofKind(), withInput(), childOf(), start() | | ActiveSpan | Live span handle. setOutput(), setMetadata(), end(), getSpan(), plus id and traceId getters | | TraceContext | Async context manager. currentSpan(), withSpan(span, fn) | | CostAlert | Budget monitor. Constructor: { budget, warningThreshold?, onWarning?, onExceeded? }. Methods: check(trace), getCumulativeCost(), getRemainingBudget() |

Adapters

| Export | Description | |---|---| | OpenAIAdapter | Instruments client.chat.completions.create. Constructor: new OpenAIAdapter(traceId). Methods: instrument(client), restore(client), recordToolResult(id, result) | | AnthropicAdapter | Instruments client.messages.create. Constructor: new AnthropicAdapter(traceId). Methods: instrument(client), restore(client), recordToolResult(id, result) | | wrapFunction(fn, tracker, options?) | Generic adapter. Wraps any function as a traced span. Options: name, kind, extractInput, extractOutput, extractUsage | | SpanTracker | Interface for wrapFunction: { traceId, onSpanEnd(span) } |

Exporters

| Export | Description | |---|---| | Exporter | Interface: export(trace, spans): Promise<void> | | FileExporter | Writes trace as JSON to a file path | | StoreExporter | Saves trace into a TraceStore | | HtmlExporter | Writes self-contained HTML with collapsible span tree | | MarkdownExporter | Writes structured Markdown document |

Store

| Export | Description | |---|---| | TraceStore | Interface: saveTrace(), getTrace(), listTraces(), searchSpans(), deleteTrace(), getStats(), close() | | SQLiteTraceStore | Full implementation with additional methods: addTag(), removeTag(), listTags(), listTagsWithCounts(), addAnnotation(), getAnnotations(), cleanup(options), vacuum(), getStorageSize() | | CleanupOptions | { olderThanDays?, status? } |

Diff

| Export | Description | |---|---| | analyze(traceA, traceB) | Returns DiffResult with structural, content, metrics diffs and divergence point | | DiffResult | { structuralDiff, contentDiffs, metricsSummary, divergencePoint?, summary } | | StructuralDiff | { matched, added, removed, reordered } | | ContentDiff | Per-span input/output diffs with significance (major or minor) | | MetricsSummary | { totalCostA, totalCostB, costChangePercent, totalLatencyA, totalLatencyB, latencyChangePercent, perSpan } | | DivergencePoint | { type, spanA, spanB, cause, downstreamImpact } | | generateCompareSummary(diffs) | Returns CompareSummary with runs, cheapest, fastest, mostDifferent, bestRun, text | | alignTraces(spansA, spansB) | LCS-based span alignment returning matched, unmatchedA, unmatchedB | | findDivergencePoint(matched) | Locate first divergence in matched span pairs |

Replay

| Export | Description | |---|---| | ReplayEngine | Constructor: new ReplayEngine(trace). Methods: current(), next(), prev(), jumpTo(spanId), toStart(), toEnd(). Property: total | | ReplayFrame | { span, position, depth, breadcrumb } | | ReplayCursor | Internal cursor tracking position state |

Stats

| Export | Description | |---|---| | aggregateStats(store, filter?) | Returns AggregatedStats: { count, totalCost, avgCost, totalTokens, avgLatency, modelBreakdown } | | StatsFilter | { since?, model? } | | ModelStats | { count, cost, tokens } |

Utilities

| Export | Description | |---|---| | calculateCost(model, promptTokens, completionTokens) | Returns cost in dollars or null for unsupported models | | getSupportedModels() | Returns array of model names with built-in pricing | | estimateTokens(text) | Character-based token estimate (~4 chars/token) | | estimateTokensByWords(text) | Word-based token estimate (~1.3 tokens/word) | | estimateTokensForModel(text, model) | Model-aware estimate returning the higher of char-based and word-based | | generateTraceId() | Generate a unique trace ID | | generateSpanId() | Generate a unique span ID |

CLI Commands

trace-debugger list       [--since <date>] [--model <model>] [--status <status>] [--limit <n>]
trace-debugger show       <id> [--budget <dollars>]
trace-debugger record     --file <path> [--budget <dollars>]
trace-debugger replay     <id> [--step <n>] [--compact]
trace-debugger diff       <id1> <id2> [--format short|full]
trace-debugger compare    <id1> <id2> [ids...] [--format short|full]
trace-debugger search     <query> [--kind <kind>] [--limit <n>]
trace-debugger stats      [--since <date>] [--model <model>]
trace-debugger export     <id> [--format json|html|markdown] [--output <path>]
trace-debugger tag        <id> <tag>
trace-debugger untag      <id> <tag>
trace-debugger tags
trace-debugger annotate   <trace-id> <span-id> "<text>" [--author <name>]
trace-debugger cleanup    [--older-than <duration>] [--status <status>]
trace-debugger vacuum

Global option: --db <path> to specify a custom database file path.

License

MIT