Observability

For maintainers. Using T3 Code? See docs/user.

T3 Code has one server-side observability model:

• pretty logs go to stdout for humans
• completed spans go to a local NDJSON trace file
• traces and metrics can also be exported over OTLP to a real backend like Grafana LGTM

The local trace file is the persisted source of truth for normal local launches. Those launches do not write a separate server log file, but SSH-managed launches also persist the remote process's stdout/stderr at ~/.t3/ssh-launch/<state>/server.log.

Where To Find Things

Logs

Logs are human-facing:

• destination: stdout
• format: Logger.consolePretty()
• normal local persistence: none
• SSH-managed launch persistence: ~/.t3/ssh-launch/<state>/server.log

If you want a log message to show up in the trace file, emit it inside an active span with Effect.log.... Logger.tracerLogger will attach it as a span event.

Traces

Completed spans are written as NDJSON records to serverTracePath. The default depends on how the server starts: production and explicitly configured homes use <home>/userdata/logs/server.trace.ndjson (so ~/.t3/userdata/... by default, or /custom/path/userdata/... with --home-dir /custom/path), a linked worktree dev run uses <worktree>/.t3/userdata/logs/server.trace.ndjson, and an implicit dev run outside a linked worktree uses ~/.t3/dev/logs/server.trace.ndjson.

Important fields common to both record types:

• type: effect-span or otlp-span
• name: span name
• traceId, spanId, parentSpanId: correlation
• durationMs: elapsed time
• attributes: structured context
• events: embedded logs and custom events

effect-span records also contain exit with Success, Failure, or Interrupted. otlp-span records instead carry OTLP resource, scope, and optional status fields.

The TraceRecord, EffectTraceRecord, and OtlpTraceRecord schemas live in packages/shared/src/observability.ts.

DPoP proof failures include the safe environment.dpop.failure_code span attribute. A time_window failure means that a signed proof was too old or too far in the future for the environment server's allowed window. It can point to a date or time problem on either device, but it can also result from a delayed request.

Metrics

Metrics are not written to a local file.

• local persistence: none
• remote export: OTLP only, when configured
• current definitions: apps/server/src/observability/Metrics.ts

If OTLP is not configured, metrics still exist in-process, but you will not have a local artifact to inspect.

Related Artifacts

Provider event NDJSON files still exist for provider runtime streams. Those are separate from the main server trace file.

Run The Server In Instrumented Mode

There are two useful modes:

• local-only: stdout + local server.trace.ndjson
• full local observability: stdout + local trace file + OTLP export to Grafana/Tempo/Prometheus

The local trace file is always on. OTLP export is opt-in.

Option 1: Local Traces Only

You do not need any extra env vars. Just run the app normally and inspect server.trace.ndjson.

Examples:

npx t3

node --run dev

node --run dev:desktop

Option 2: Run With A Local LGTM Stack

1. Start Grafana LGTM

docker run --name lgtm \
  -p 3000:3000 \
  -p 4317:4317 \
  -p 4318:4318 \
  --rm -ti \
  grafana/otel-lgtm

Then open http://localhost:3000.

Default Grafana login:

• username: admin
• password: admin

2. Export OTLP env vars

export T3CODE_OTLP_TRACES_URL=http://localhost:4318/v1/traces
export T3CODE_OTLP_METRICS_URL=http://localhost:4318/v1/metrics
export T3CODE_OTLP_SERVICE_NAME=t3-local

Optional:

export T3CODE_TRACE_MIN_LEVEL=Info
export T3CODE_TRACE_TIMING_ENABLED=true

3. Launch the app from that same shell

CLI:

npx t3

Monorepo web/server dev:

node --run dev

Monorepo desktop dev:

node --run dev:desktop

Packaged desktop app:

Launch the actual app executable from the same shell so the desktop app and embedded backend inherit T3CODE_OTLP_*.

macOS app bundle example:

T3CODE_OTLP_TRACES_URL=http://localhost:4318/v1/traces \
T3CODE_OTLP_METRICS_URL=http://localhost:4318/v1/metrics \
T3CODE_OTLP_SERVICE_NAME=t3-desktop \
"/Applications/T3 Code.app/Contents/MacOS/T3 Code"

Direct binary example:

T3CODE_OTLP_TRACES_URL=http://localhost:4318/v1/traces \
T3CODE_OTLP_METRICS_URL=http://localhost:4318/v1/metrics \
T3CODE_OTLP_SERVICE_NAME=t3-desktop \
./path/to/your/desktop-app-binary

Do not rely on launching from Finder, Spotlight, the dock, or the Start menu after setting shell env vars. Those launches usually will not pick them up.

4. Fully restart after changing env

The backend reads observability config at process start. If you change OTLP env vars, stop the app completely and start it again.

How To Use Traces And Metrics To Debug The Server

Start With The Local Trace File

The trace file is the fastest way to inspect raw span data.

Resolve the path for the launch mode once. Production and explicitly configured homes store runtime state under the base directory's userdata folder:

TRACE_FILE="${T3CODE_HOME:-$HOME/.t3}/userdata/logs/server.trace.ndjson"

A dev server started from a linked worktree defaults to that worktree's local home:

TRACE_FILE="$WORKTREE/.t3/userdata/logs/server.trace.ndjson"

Only an implicit dev run outside a linked worktree uses the shared dev directory:

TRACE_FILE="$HOME/.t3/dev/logs/server.trace.ndjson"

Tail the selected file:

tail -f "$TRACE_FILE"

Show failed spans:

jq -c 'select(.type == "effect-span" and .exit._tag != "Success") | {
  name,
  durationMs,
  exit,
  attributes
}' "$TRACE_FILE"

Show slow spans:

jq -c 'select(.durationMs > 1000) | {
  name,
  durationMs,
  traceId,
  spanId
}' "$TRACE_FILE"

Inspect embedded log events:

jq -c 'select(any(.events[]?; .attributes["effect.logLevel"] != null)) | {
  name,
  durationMs,
  events: [
    .events[]
    | select(.attributes["effect.logLevel"] != null)
    | {
        message: .name,
        level: .attributes["effect.logLevel"]
      }
  ]
}' "$TRACE_FILE"

Follow one trace:

jq -r 'select(.traceId == "TRACE_ID_HERE") | [
  .name,
  .spanId,
  (.parentSpanId // "-"),
  .durationMs
] | @tsv' "$TRACE_FILE"

Filter orchestration commands:

jq -c 'select(.attributes["orchestration.command_type"] != null) | {
  name,
  durationMs,
  commandType: .attributes["orchestration.command_type"],
  aggregateKind: .attributes["orchestration.aggregate_kind"]
}' "$TRACE_FILE"

Filter git activity:

jq -c 'select(.attributes["git.operation"] != null) | {
  name,
  durationMs,
  operation: .attributes["git.operation"],
  cwd: .attributes["git.cwd"],
  hookEvents: [
    .events[]
    | select(.name == "git.hook.started" or .name == "git.hook.finished")
  ]
}' "$TRACE_FILE"

Use Tempo When You Need A Real Trace Viewer

Tempo is better than raw NDJSON when you want to:

• search across many traces
• inspect parent/child relationships visually
• compare many slow traces
• drill into one failing request without hand-joining by traceId

Recommended flow in Grafana:

1. Open Explore.
2. Pick the Tempo data source.
3. Set the time range to something recent like Last 15 minutes.
4. Start broad. Do not begin with a very narrow query.
5. Look for spans from your configured service name, then narrow by span name or attributes.

Good first searches:

• service name such as t3-local, t3-dev, or t3-desktop
• span names like sendTurn or a Git operation such as GitVcsDriver.statusDetails.status
• Git spans whose git.operation attribute identifies the operation
• orchestration spans with attributes like orchestration.command_type

Once you know traces are arriving, narrower TraceQL queries for names such as sendTurn or Git operation names become useful.

Use Metrics To See Systemic Problems

Traces are best for one request. Metrics are best for trends.

Good metric families to watch:

• t3_rpc_request_duration
• t3_orchestration_command_duration
• t3_orchestration_command_ack_duration
• t3_provider_turn_duration
• t3_git_command_duration

Counters tell you volume and failure rate:

• t3_rpc_requests_total
• t3_orchestration_commands_total
• t3_provider_turns_total
• t3_git_commands_total

Use metrics when the question is:

• "is this always slow?"
• "did this get worse after a change?"
• "which command type is failing most often?"

Use traces when the question is:

• "what happened in this specific request?"
• "which child span caused this one slow interaction?"
• "what logs were emitted inside the failing flow?"

What The New Ack Metric Means

t3_orchestration_command_ack_duration measures:

• start: command dispatch enters the orchestration engine
• end: the first committed domain event for that command is published by the server

That is a server-side acknowledgment metric. It does not measure:

• websocket transit to the browser
• client receipt
• React render time

If you need those later, add client-side instrumentation or a dedicated server fanout metric.

Common Workflows

"Why did this request fail?"

1. Start with the local NDJSON file.
2. Find effect-span records where exit._tag != "Success".
3. Group by traceId.
4. Inspect sibling spans and span events.
5. If needed, move to Tempo for the full trace tree.

"Why is the UI feeling slow?"

1. Search for slow top-level spans in the trace file or Tempo.
2. Check child spans for sqlite, git, provider, or terminal work.
3. Look at the matching duration metrics to see whether the slowness is systemic.

"Did this command take too long to acknowledge?"

1. Check t3_orchestration_command_ack_duration by commandType.
2. If it is high, inspect the corresponding orchestration trace.
3. Look at child spans for projection, sqlite, provider, or git work.

"Are git hooks causing latency?"

1. Filter git.operation spans.
2. Inspect git.hook.started and git.hook.finished events.
3. Compare hook timing to the enclosing git span duration.

"Why do I have spans locally but nothing in Grafana?"

Usually one of these is true:

• T3CODE_OTLP_TRACES_URL was not set
• the app was launched from a different environment than the one where you exported the vars
• the app was not fully restarted after changing env
• Grafana is looking at the wrong time range or service name

If the local NDJSON file is updating, local tracing is working. The problem is almost always OTLP export configuration or process startup.

How To Think About Adding Tracing To Future Code

Prefer Boundaries Over Tiny Helpers

Good span boundaries:

• RPC methods
• orchestration command handling
• provider adapter calls
• external process calls
• persistence writes
• queue handoffs

Avoid tracing every tiny helper. Most helpers should inherit the active span rather than create a new one.

Reuse Effect.fn(...) Where It Already Exists

The codebase already uses Effect.fn("name") heavily. That should usually be your first tracing boundary.

For ad hoc work:

import { Effect } from "effect";

const runThing = Effect.gen(function* () {
  yield* Effect.annotateCurrentSpan({
    "thing.id": "abc123",
    "thing.kind": "example",
  });

  yield* Effect.logInfo("starting thing");
  return yield* doWork();
}).pipe(Effect.withSpan("thing.run"));

Put High-Cardinality Detail On Spans

Use span annotations for IDs, paths, and other detailed context:

yield *
  Effect.annotateCurrentSpan({
    "provider.thread_id": input.threadId,
    "provider.request_id": input.requestId,
    "git.cwd": input.cwd,
  });

Keep Metric Labels Low Cardinality

Good metric labels:

• operation kind
• method name
• provider kind
• aggregate kind
• outcome

Bad metric labels:

• raw thread IDs
• command IDs
• file paths
• cwd
• full prompts
• full model strings when a normalized family label would do

Detailed context belongs on spans, not metrics.

Use Logs As Span Events

Logs inside a span become part of the trace story:

yield * Effect.logInfo("starting provider turn");
yield * Effect.logDebug("waiting for approval response");

Those messages show up as span events because Logger.tracerLogger is installed.

Use The Pipeable Metrics API

withMetrics(...) is the default way to attach a counter and timer to an effect:

import { someCounter, someDuration, withMetrics } from "../observability/Metrics.ts";

const program = doWork().pipe(
  withMetrics({
    counter: someCounter,
    timer: someDuration,
    attributes: {
      operation: "work",
    },
  }),
);

Detailed API Reference