Adds an `annotations` JSONB column to task runs that captures where and
how each run was triggered.
This enables filtering and analyzing trigger origins without querying up
the run tree. Also enables making scheduling decisions based on the
trigger source, e.g., use separate affinities for scheduled runs.
Each run records:
- **triggerSource**: who initiated it (sdk, api, dashboard, cli, mcp,
schedule)
- **triggerAction**: what kind of action (trigger, replay, test)
- **rootTriggerSource**: the trigger source of the root ancestor,
propagated through the entire run
tree
- **rootScheduleId**: schedule id, in case the run tree was triggered
from a schedule
Currently the main motivation for annotations it to determine whether a
run is part of a schedule-originated tree without traversing ancestors.
### A couple of design considerations
- **Decoupled source from method**: triggerSource and triggerAction are
separate fields to avoid
combinatorial explosion (every new source × every new action)
- **Server-side first**: all annotation values are primarily determined
on the server, only a minor SDK change needed
- **Forward-compatible**: annotation fields use
`z.enum([...]).or(anyString)` so new values can be
added without breaking validation; we currently don't need an explicit
version field for annotations.
Note: `metadata` would have been a more fitting name for the db column,
as it is consistent with other tables where we store this type of
information. It is already in use to store user metadata though, so we
go with `annotations` instead.
- Full prompt management UI: list, detail, override, and version
management for AI prompts defined with `prompts.define()`
- Rich AI span inspectors for all AI SDK operations with token usage,
messages, and prompt context
- Real-time generation tracking with live polling and filtering
## Prompt management
Define prompts in your code with `prompts.define()`, then manage
versions and overrides from the dashboard without redeploying:
```typescript
import { task, prompts } from "@trigger.dev/sdk";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";
const supportPrompt = prompts.define({
id: "customer-support",
model: "gpt-4o",
variables: z.object({
customerName: z.string(),
plan: z.string(),
issue: z.string(),
}),
content: `You are a support agent for Acme SaaS.
Customer: {{customerName}} ({{plan}} plan)
Issue: {{issue}}
Respond with empathy and precision.`,
});
export const supportTask = task({
id: "handle-support",
run: async (payload) => {
const resolved = await supportPrompt.resolve({
customerName: payload.name,
plan: payload.plan,
issue: payload.issue,
});
const result = await generateText({
model: openai(resolved.model ?? "gpt-4o"),
system: resolved.text,
prompt: payload.issue,
...resolved.toAISDKTelemetry(),
});
return { response: result.text };
},
});
```
The prompts list page shows each prompt with its current version, model,
override status, and a usage sparkline over the last 24 hours.
From the prompt detail page you can:
- **Create overrides** to change the prompt template or model without
redeploying. Overrides take priority over the deployed version when
`prompt.resolve()` is called.
- **Promote** any code-deployed version to be the current version
- **Browse generations** across all versions with infinite scroll and
live polling for new results
- **Filter** by version, model, operation type, and provider
- **View metrics** (total generations, avg tokens, avg cost, latency)
broken down by version
## AI span inspectors
Every AI SDK operation now gets a custom inspector in the run trace
view:
- **`ai.generateText` / `ai.streamText`** — Shows model, token usage,
cost, the full message thread (system prompt, user message, assistant
response), and linked prompt details
- **`ai.generateObject` / `ai.streamObject`** — Same as above plus the
JSON schema and structured output
- **`ai.toolCall`** — Shows tool name, call ID, and input arguments
- **`ai.embed`** — Shows model and the text being embedded
For generation spans linked to a prompt, a "Prompt" tab shows the prompt
metadata, the input variables passed to `resolve()`, and the template
content from the prompt version.
All AI span inspectors include a compact timestamp and duration header.
## Other improvements
- Resizable panel sizes now persist across page refreshes (patched
`@window-splitter/state` to fix snapshot restoration)
- Run page panels also persist their sizes
- Fixed `<div>` inside `<p>` DOM nesting warnings in span titles and
chat messages
- Added Operations and Providers filters to the AI metrics dashboard
## Screenshots
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 14
17@2x"
src="https://github.com/user-attachments/assets/f3e59989-a2fa-4990-a9d0-3cacda431868"
/>
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 15
37@2x"
src="https://github.com/user-attachments/assets/2f2d02df-2d2b-44fb-ac6f-9153f6a6c387"
/>
<img width="3680" height="2392" alt="CleanShot 2026-03-21 at 10 15
54@2x"
src="https://github.com/user-attachments/assets/baa161e0-ef91-4fa4-a55f-986b71cccdf0"
/>
- Automatic LLM cost enrichment for AI SDK spans (streamText,
generateText, generateObject) or any other spans that use semantic
gen_ai attributes with support for 145+ models
- New AI span inspector sidebar showing model, tokens, cost, messages,
tool calls, and response text
- LLM metrics dual-write to ClickHouse `llm_metrics_v1` table for
analytics
- LLM metrics built-in dashboard (unlinked at the moment)
- Provider cost fallback — uses gateway/OpenRouter reported costs from
`providerMetadata` when registry pricing is unavailable
- Prefix-stripping for gateway/OpenRouter model names (e.g.
`mistral/mistral-large-3` matches `mistral-large-3` pricing)
- Admin dashboard for managing LLM model pricing (list, create, edit,
delete, search, test pattern matching)
- Missing models detection page — queries ClickHouse for unpriced models
with sample spans and Claude Code-ready prompts for adding pricing
- AI span seed script (`pnpm run db:seed:ai-spans`) with 51 spans across
12 provider systems for local dev testing
- UI fixes: `completionTokens`/`promptTokens` aliases,
`ai.response.object` display for generateObject, cache read/write token
breakdown
## Screenshots:
<img width="1030" height="104" alt="CleanShot 2026-03-17 at 16 48 54@2x"
src="https://github.com/user-attachments/assets/bc8fccda-e48b-4d0c-bfb1-e620064e5979"
/>
<img width="1094" height="1512" alt="CleanShot 2026-03-17 at 16 49
23@2x"
src="https://github.com/user-attachments/assets/c2424569-d07e-4d67-a436-e8250043a1ee"
/>
<img width="1074" height="1412" alt="CleanShot 2026-03-17 at 16 49
18@2x"
src="https://github.com/user-attachments/assets/22342ac4-4769-45d1-a328-a24fb9a82a50"
/>
<img width="1012" height="2292" alt="CleanShot 2026-03-17 at 16 39
01@2x"
src="https://github.com/user-attachments/assets/59e327d1-6652-4293-8be0-bb8326e5fbc5"
/>
<img width="3680" height="2392" alt="CleanShot 2026-03-15 at 08 29
38@2x"
src="https://github.com/user-attachments/assets/1f77beb8-de67-495b-b890-bcdb8d7f1fe8"
/>
---------
Co-authored-by: James Ritchie <james@trigger.dev>
## Summary
Major expansion of the MCP server (14 → 25 tools), context efficiency
optimizations, new API endpoints, and a fix for the dev CLI leaking
build directories on disk.
### New MCP tools
- **Query & analytics**: `get_query_schema`, `query`, `list_dashboards`,
`run_dashboard_query` — query your data using TRQL directly from AI
assistants
- **Profile management**: `whoami`, `list_profiles`, `switch_profile` —
see and switch CLI profiles per-project (persisted to
`.trigger/mcp.json`)
- **Dev server control**: `start_dev_server`, `stop_dev_server`,
`dev_server_status` — start/stop `trigger dev` and stream build output
- **Task introspection**: `get_task_schema` — get payload schema for a
specific task (split out from `get_current_worker` to reduce context)
### New API endpoints
- `GET /api/v1/query/schema` — discover TRQL tables and columns
(server-driven, multi-table)
- `GET /api/v1/query/dashboards` — list built-in dashboard widgets and
their queries
### New features
- **`--readonly` flag** — hides write tools (`deploy`, `trigger_task`,
`cancel_run`) so agents can't make changes
- **`read:query` JWT scope** — new authorization scope for query
endpoints, with per-table granularity (`read:query:runs`,
`read:query:llm_metrics`, etc.)
- **Paginated trace output** — `get_run_details` now paginates trace
events via cursor, caching the full trace in a temp file so subsequent
pages don't re-fetch
- **MCP tool annotations** — all tools now have
`readOnlyHint`/`destructiveHint` annotations for clients that support
them
- **Project-scoped profile persistence** — `switch_profile` saves to
`.trigger/mcp.json` (gitignored), automatically loaded on next MCP
server start
### Context optimizations
- `get_query_schema` requires a table name — returns one table's schema
instead of all tables (60-80% fewer tokens)
- `get_current_worker` no longer inlines payload schemas — use
`get_task_schema` for specific tasks
- Query results formatted as text tables instead of JSON (~50% fewer
tokens for flat data)
- `cancel_run`, `list_deploys`, `list_preview_branches` formatted as
text instead of raw `JSON.stringify()`
- Schema and dashboard API responses cached (1hr and 5min respectively)
### Bug fixes
- Fixed `search_docs` failing due to renamed upstream Mintlify tool
(`SearchTriggerDev` → `search_trigger_dev`)
- Fixed `list_deploys` failing when deployments have null
`runtime`/`runtimeVersion` fields (fixes#3139)
- Fixed `list_preview_branches` crashing due to incorrect response shape
access
- Fixed `metrics` table column documented as `value` instead of
`metric_value` in query docs
- Fixed `/api/v1/query` not accepting JWT auth (added `allowJWT: true`)
### Dev CLI build directory fix
The dev CLI was leaking `build-*` directories in `.trigger/tmp/` on
every rebuild, accumulating hundreds of MB over time (842MB observed).
Three layers of protection added:
1. **During session**: deprecated workers are pruned (capped at 2
retained) when no active runs reference them, preventing unbounded
accumulation
2. **On SIGKILL/crash**: the watchdog process now cleans up
`.trigger/tmp/` when it detects the parent CLI was killed
3. **On next startup**: existing `clearTmpDirs()` wipes any remaining
orphans
## Test plan
- [ ] `pnpm run mcp:smoke` — 17 automated smoke tests for all read-only
MCP tools
- [ ] `pnpm run mcp:test list` — verify 25 tools registered (21 in
`--readonly` mode)
- [ ] `pnpm run mcp:test --readonly list` — verify write tools hidden
- [ ] Manual: start dev server, trigger task, rebuild multiple times,
verify build dirs stay capped at 4
- [ ] Manual: SIGKILL the dev CLI, verify watchdog cleans up
`.trigger/tmp/`
- [ ] Verify new API endpoints return correct data: `GET
/api/v1/query/schema`, `GET /api/v1/query/dashboards`
🤖 Generated with [Claude Code](https://claude.com/claude-code)
## Adds 2 self serve features
### 1. self serve preview branches
- Copies the patterns of the self serve concurrency
- Self serve only available on Pro plan (otherwise you are linked to the
billing plans page)
- Global self serve branches limit: 180 (+20 for the Pro plan). It can
be overridden per Org
- You need to archive branches before reducing the number of extra
branches you're paying for
- Branches are removed immediately but remain billed until the end of
the billing cycle like extra concurrency
### 2. self serve team members
- Copies the patterns of the self serve concurrency
- Self serve only available on Pro plan (otherwise you are linked to the
billing plans page)
- Global self serve members is unlimited but can be limited with the
same env var quota and overridden per org if needed
- You need to remove team members before reducing the number of members
you pay for
- Team members are removed immediately but remain billed until the end
of the billing cycle like extra concurrency
When the dev CLI exits (e.g. ctrl+c via pnpm), runs that were
mid-execution
previously stayed stuck in EXECUTING status for up to 5 minutes until
the
heartbeat timeout fired. Now they are cancelled within seconds.
The dev CLI spawns a lightweight detached watchdog process at startup.
The
watchdog monitors the CLI process ID and, when it detects the CLI has
exited,
calls a new POST /engine/v1/dev/disconnect endpoint to cancel all
in-flight
runs immediately (skipping PENDING_CANCEL since the worker is known to
be dead).
Watchdog design:
- Fully detached (detached: true, stdio: ignore, unref()) so it survives
even when pnpm sends SIGKILL to the process tree
- Active run IDs maintained via atomic file write
(.trigger/active-runs.json)
- Single-instance guarantee via PID file (.trigger/watchdog.pid)
- Safety timeout: exits after 24 hours to prevent zombie processes
- On clean shutdown, the watchdog is killed (no disconnect needed)
Disconnect endpoint:
- Rate-limited: 5 calls/min per environment
- Capped at 500 runs per call
- Small counts (<= 25): cancelled inline with pMap concurrency 10
- Large counts: delegated to the bulk action system
- Uses finalizeRun: true to skip PENDING_CANCEL and go straight to
FINISHED
Run engine change:
- cancelRun() now respects finalizeRun when the run is in EXECUTING
status,
skipping the PENDING_CANCEL waiting state and going directly to FINISHED
### Fixes and improvements to the onboarding questions:
**This change is worth double checking @matt-aitken**
- Update to the Button.tsx file: it now takes `isLoading` that shows a
spinner in the middle of the button (replacing the button text and any
icons) and sets it to `disabled`. It does this nicely by keeping the
button width the same so there's no layout shift.
**Other fixes**
- Fixes an issue where if you type a custom option in the "What
technologies do you use" question, it doesn't check the list to see if
it matches. Now it checks the box if you've typed an option from that
list.
- When we randomize the list of onboarding question options, we now
store the position they appeared in the list
<img width="2191" height="1023" alt="CleanShot 2026-03-06 at 13 36 53"
src="https://github.com/user-attachments/assets/4eba0d1a-1528-49a3-be5b-6bde89030193"
/>
<img width="411" height="1069" alt="CleanShot 2026-03-06 at 13 37 28"
src="https://github.com/user-attachments/assets/e5f7bb9c-c894-41cc-9ca6-96b43fcf6005"
/>
Add a tabbed sidebar to the Test page for standard tasks, reusing the
ClientTabs pattern from the Query page.
- Options tab: existing sidebar content (machine, version, queue, etc.)
- AI tab: AI-powered payload generation with streaming, supports JSON
Schema, inferred schema from recent runs, and task source code lookup
via tool calling for tasks without schemas
- Schema tab: displays payload JSON Schema (from schemaTask), inferred
schema (from recent runs via @jsonhero/schema-infer), or empty state
with schemaTask docs and example code
Data layer changes:
- Surface payloadSchema and inferredPayloadSchema from TestTaskPresenter
- Add payloadSchema and fileId to WorkerDeploymentWithWorkerTasks type
- Decompress zlib-deflated source files for AI context
A top-level Errors page that aggregates errors from failed runs with
occurrences metrics.
https://github.com/user-attachments/assets/8f0ef55e-90dd-4faa-9051-59f4665181e4
Errors are “fingerprinted” so similar errors are grouped together (e.g.
has an ID in the error message).
You can view an individual error to view a timeline of when it fired,
the runs, and bulk replay them.
The global rate limiter was being applied at the FairQueue claim phase,
consuming 1 token per queue-claim-attempt rather than per item
processed.
With many small queues (each batch is its own queue), consumers burned
through tokens on empty or single-item queues, causing aggressive
throttling well below the intended items/sec limit.
Changes:
- Move rate limiter from FairQueue claim phase to BatchQueue worker
queue
consumer loop (before blockingPop), so each token = 1 item processed
- Replace the FairQueue rate limiter with a worker queue depth cap to
prevent unbounded growth that could cause visibility timeouts
- Add BATCH_QUEUE_WORKER_QUEUE_MAX_DEPTH env var (optional, disabled by
default)
Input streams enable sending typed data to executing tasks from external
callers — backends, frontends, or other tasks. This unlocks interactive
use cases like approval UIs, cancel buttons, chat interfaces, and
human-in-the-loop AI workflows where the task needs to receive data
while running.
Three consumption patterns inside a task:
* `.wait()` — Suspend the task until data arrives (process freed, most
efficient)
* `.once()` — Wait for the next message (process stays alive)
* `.on()` — Subscribe to a continuous stream of messages
One send pattern from outside:
* `.send(runId, data)` — Send typed data to a specific run's input
stream
## User-facing API
### Define a typed input stream
```ts
import { streams, task } from "@trigger.dev/sdk";
const approval = streams.input<{ approved: boolean; reviewer: string }>({ id: "approval" });
```
### Consume inside a task
```ts
export const myTask = task({
id: "my-task",
run: async () => {
// Pattern 1: Suspend until data arrives (most efficient — frees the process)
const result = await approval.wait({ timeout: "5m" });
// Pattern 2: Wait for next message (process stays alive)
const data = await approval.once().unwrap();
// Pattern 3: Subscribe to multiple messages
approval.on((data) => { /* handle each message */ });
},
});
```
### Send from outside
```ts
// From a backend (using secret API key)
await approval.send(runId, { approved: true, reviewer: "alice" });
// From a frontend (using public JWT token from trigger response)
const { send } = useInputStreamSend("approval", runId, { accessToken });
send({ approved: true, reviewer: "alice" });
```
---------
Co-authored-by: Claude <noreply@anthropic.com>
- New User onboarding questions added and stored in a new
`onboardingData` col
- Keeps the same Org creation screen and stores the data in the same
format in same DB column
- New Org onboarding questions addded and stored in a new
`onboardingData` col
https://github.com/user-attachments/assets/244e4bae-f74d-4ed4-a545-92c9b927e98b
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
There's a bug in react-window-splitter on Firefox. When trying to expand
the inspector panel in the query editor, it checks if the main panel has
space but gets an object instead of a number for the auto-sized
query-main panel. This causes the expand calculation to fail and it
snaps it back to collapsed.
I've removed this behavior for now as it's not an important feature.
Gracefully handle oversized batch items instead of aborting the stream.
When an NDJSON batch item exceeds the maximum size, the parser now emits
an error marker instead of throwing, allowing the batch to seal
normally. The oversized item becomes a pre-failed run with
`PAYLOAD_TOO_LARGE` error code, while other items in the batch process
successfully. This prevents `batchTriggerAndWait` from seeing connection
errors and retrying with exponential backoff.
Also fixes the NDJSON parser not consuming the remainder of an oversized
line split across multiple chunks, which caused "Invalid JSON" errors on
subsequent lines.
Closes #
## ✅ Checklist
- [ ] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [ ] The PR title follows the convention.
- [ ] I ran and tested the code works
---
## Testing
Verified the pricing definition displays the correct tier amount on the
plan selection page.
---
## Changelog
Fixed incorrect pricing tier for additional realtime connections from
$10/month per 100 to $10/month per 1000.
---
## Screenshots
N/A
💯https://claude.ai/code/session_015QrZZJHPWta3QCBnhX2Pff
Co-authored-by: Claude <noreply@anthropic.com>
Fix slow fair queue processing by removing spurious cooloff on
concurrency blocks and fixing a race condition where retry attempt
counts were not atomically updated during message re-queue.
Removed cooloff entirely from the batch queue
- Fix for series color assignment being out of sync with the graph
(ensures added series colors match their graph representation)
- Reload widgets when returning to screen if props changed (prevents
stale widgets after filtering and scrolling)
What changed
- Fixed some functions like dateAdd, toString, ifNotFinite
- Removed all functions that accept lambdas as they're not supported
(yet)
- Added tests for all TRQL functions that use ClickHouse
This PR implements a new run TTL system and queue size limits to prevent
unbounded queue growth which should help prevent situations where queues
enter a "death spiral" where the queue will never be able to catch up.
The main/correct way to battle this situation is to enforce a maximum
TTL on all runs (e.g. up to 14 days) where runs that have been queued
for that maximum TTL will get auto-expired, making room for newer runs
to execute. This required creating a new TTL system that can handle
higher workloads and is now deeply integrated into the RunQueue. When
runs are enqueued with a TTL, they are added to their normal queue as
well as to the TTL queue. When runs are dequeued, they are removed from
both their normal queue and the TTL queue. If runs are dequeued by the
TTL system, they are removed from their normal queue. Both these
dequeues happen automatically so there is no race condition.
The TTL expiration system is also made reliable by expiring runs via a
Redis worker, which is enqueued to atomically inside the TTL dequeue lua
script.
### Optional associated waitpoints
Additionally, this PR implements an optimization where runs that aren't
triggered with a dependent parent run will no longer create an
associated waitpoint. Associated waitpoints are then lazily created if a
dependent run wants to wait for the child run post-facto (via debounce
or idempotency), which is a rare situation but is possible. This means
fewer waitpoint creations but also fewer waitpoint completions for runs
with no dependencies.
### Environment Queue Limits
Prevents any single queue growing too large by enforcing queue size
limits at trigger time.
- Queue size checks happen at trigger time - runs are rejected if queue
would exceed limit
- Dashboard UI shows queue limits on both the Queues page and a new
Limits page
- In-memory caching for queue size checks to reduce Redis load
### Batch trigger fixes
Currently when a batch item cannot be created for whatever reason (e.g.
queue limits) the run will never get created, which means a stalled run
if using `batchTriggerAndWait`. We've updated the system to handle this
differently: now when a batch item cannot be triggered and converted
into a run, we will eventually (after retrying 8 times up to 30s) we
will create a "pre-failed" run with the error details, correctly
resolving the batchTriggerAndWait.
Without doing an expensive query we can’t tell if it’s definitely a v3
projects – like getting run counts.
So let’s just assume if the project hasn’t been upgraded to v4 (by
running dev/deploy CLI with v4) AND the project is older than the v4
release then it’s v3.
Small fixes and improvements to the logs page:
- Clicking the Run ID didn't open inspector
- Swapped the "open link in tab" icon with Runs icon
- Prevent tooltip hovering on Level info
<img width="350" height="206" alt="CleanShot 2026-02-20 at 10 00 37@2x"
src="https://github.com/user-attachments/assets/3e82f24a-c0a1-4c01-a8e9-9e06a8af982a"
/>
- Adds an end-to-end OTEL metrics pipeline: task workers collect and
export metrics via OpenTelemetry, the webapp ingests them into
ClickHouse, and they're queryable through the existing dashboard query
engine
- Workers emit process CPU/memory metrics (via
`@opentelemetry/host-metrics`) and Node.js runtime metrics (event loop
utilization, event loop delay, heap usage)
- Users can create custom metrics in their tasks via
`otel.metrics.getMeter()` from `@trigger.dev/sdk`
- Metrics are automatically tagged with run context (run ID, task slug,
machine, worker version) so they can be sliced per-run, per-task, or
per-machine
- The TSQL query engine gains metrics table support with typed attribute
columns, `prettyFormat()` for human-readable values, and per-schema time
bucket thresholds
- Includes reference tasks
(`references/hello-world/src/trigger/metrics.ts`) demonstrating
CPU-intensive, memory-ramp, bursty workload, and custom metrics patterns
## What changed
### Metrics collection (packages/core, packages/cli-v3)
- **Metrics export pipeline** — `TracingSDK` now sets up a
`MeterProvider` with a `PeriodicExportingMetricReader` that chains
through `TaskContextMetricExporter` (adds run context attributes) and
`BufferingMetricExporter` (batches exports to reduce overhead)
- **Host metrics** — Enabled `@opentelemetry/host-metrics` for process
CPU, memory, and system-level metrics
- **Node.js runtime metrics** — New `nodejsRuntimeMetrics.ts` module
using `performance.eventLoopUtilization()`, `monitorEventLoopDelay()`,
and `process.memoryUsage()` to emit 6 observable gauges
- File system and diskio metrics
- **Custom metrics** — Exposed `otel.metrics` from `@trigger.dev/sdk` so
users can create counters, histograms, and gauges in their tasks
- **Machine ID** — Stable per-worker machine identifier for grouping
metrics
- **Dev worker** — Drops `system.*` metrics to reduce noise, keeps
sending metrics between runs in warm workers
### Metrics ingestion (apps/webapp)
- **OTEL endpoint** — `otel.v1.metrics.ts` accepts OTEL metric export
requests (JSON and protobuf), converts to ClickHouse rows
- **ClickHouse schema** — `017_create_metrics_v1.sql` with 10-second
aggregation buckets, JSON attributes column, 60-day TTLs
### Query engine (internal-packages/tsql, apps/webapp)
- **Metrics query schema** — Typed columns for metric attributes
(`task_identifier`, `run_id`, `machine_name`, `worker_version`, etc.)
extracted from the JSON attributes column
- **`prettyFormat()`** — TSQL function that annotates columns with
format hints (`bytes`, `percent`, `durationSeconds`) for frontend
rendering without changing the underlying data
- **Per-schema time buckets** — Different tables can define their own
time bucket thresholds (metrics uses tighter intervals than runs)
- **AI query integration** — The AI query service knows about the
metrics table and can generate metric queries
- **Chart improvements** — Better formatting for byte values,
percentages, and durations in charts and tables
### Reference project
- **`references/hello-world/src/trigger/metrics.ts`** — 6 example tasks:
`cpu-intensive`, `memory-ramp`, `bursty-workload`, `sustained-workload`,
`concurrent-load`, `custom-metrics`
## Test plan
- [ ] Build all packages and webapp
- [ ] Start dev worker with hello-world reference project
- [ ] Run `cpu-intensive`, `memory-ramp`, and `custom-metrics` tasks
- [ ] Verify metrics in ClickHouse: `SELECT DISTINCT metric_name FROM
metrics_v1`
- [ ] Query via dashboard AI: "show me CPU utilization over time"
- [ ] Verify `prettyFormat` renders correctly in chart tooltips and
table cells
- [ ] Confirm dev worker drops `system.*` metrics but keeps `process.*`
and `nodejs.*`
Adds a region selector to the Test task page and Replay run dialog, so
users can override the region from the dashboard. Disabled with a
placeholder for dev environments.
Closes#3016
This will prevent internal logs to be added to the
task_events_search_table
Closes #<issue>
## ✅ Checklist
- [x] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [x] The PR title follows the convention.
- [x] I ran and tested the code works
---
## Testing
Ran the migration, deleted the old invalid rows and ran new tasks.
The undesired logs are not added to the table.
---
## Changelog
Updated the MATERIALIZED VIEW to also filter for `trace_id != ''`
---------
Co-authored-by: Matt Aitken <matt@mattaitken.com>
Fixes an issue introduced in #3024.
The behavior for local builds in older CLI versions relies on
`externalBuildData` to be defined to distinguish from the self-hosting
local build path, even though it doesn't actually use the token.
Summary
- Add API endpoint to run TRQL queries
- Implement SDK function for executing queries
## SDK
Added `query.execute()` which lets you query your Trigger.dev data using
TRQL (Trigger Query Language) and returns results as typed JSON rows or
CSV. It supports configurable scope (environment, project, or
organization), time filtering via `period` or `from`/`to` ranges, and a
`format` option for JSON or CSV output.
```typescript
import { query } from "@trigger.dev/sdk";
import type { QueryTable } from "@trigger.dev/sdk";
// Basic untyped query
const result = await query.execute("SELECT run_id, status FROM runs LIMIT 10");
// Type-safe query using QueryTable to pick specific columns
const typedResult = await query.execute<QueryTable<"runs", "run_id" | "status" | "triggered_at">>(
"SELECT run_id, status, triggered_at FROM runs LIMIT 10"
);
typedResult.results.forEach(row => {
console.log(row.run_id, row.status); // Fully typed
});
// Aggregation query with inline types
const stats = await query.execute<{ status: string; count: number }>(
"SELECT status, COUNT(*) as count FROM runs GROUP BY status",
{ scope: "project", period: "30d" }
);
// CSV export
const csv = await query.execute(
"SELECT run_id, status FROM runs",
{ format: "csv", period: "7d" }
);
console.log(csv.results); // Raw CSV string
```
Eric Allam
