local

Sentry for local development

Commands

sentry local serve

Start the local dev server and tail events

Options:

Option	Description
-p, --port <port>	Port to listen on (default 8969) (default: "8969")
-H, --host <host>	Hostname to bind to (default localhost) (default: "localhost")
-q, --quiet	Suppress per-envelope tail output
-f, --filter <filter>...	Only show items of this type (repeatable: error, transaction, log, ai)
-F, --format <format>	Output format: human (default) or json (NDJSON) (default: "human")

sentry local run <command...>

Run a command with the local dev server enabled

Arguments:

Argument	Description
<command...>	Command to run

Options:

Option	Description
-p, --port <port>	Port for the local server (default 8969) (default: "8969")
--host <host>	Hostname for the local server (default localhost) (default: "localhost")

All commands support --json for machine-readable output and --fields to select specific JSON fields.

sentry local runs a local development server that captures Sentry SDK envelopes from your dev stack and surfaces errors, traces, and logs in real time — right in your terminal. No authentication required.

If a server is already running on the port, the command attaches as an SSE consumer instead of starting a duplicate.

Examples

# Start the server and tail events (default)
sentry local

# Run your app with the local server auto-enabled
sentry local run -- npm run dev
sentry local run -- python manage.py runserver

# Use a custom port
sentry local --port 9000

# Only show errors and logs (filter out transactions)
sentry local -f error -f log

# Run quietly (suppress per-envelope tail output)
sentry local --quiet

sentry local run

Runs a command with SENTRY_SPOTLIGHT injected into the environment. The Sentry SDK automatically detects this variable and sends envelopes to the local server. No code changes needed.

Env vars injected into the child process:

Variable	Value
SENTRY_SPOTLIGHT	http://localhost:<port>/stream
NEXT_PUBLIC_SENTRY_SPOTLIGHT	http://localhost:<port>/stream
SENTRY_TRACES_SAMPLE_RATE	1 (unless already set)

Endpoints

Method	Path	Description
POST	/stream	Envelope ingest
POST	/api/{projectId}/envelope/	Sentry SDK ingest path
GET	/stream	Server-Sent Events feed of incoming envelopes
GET	/health	Liveness check (returns OK)

Tail output

By default, incoming envelopes are pretty-printed to the terminal:

14:32:01 [ERROR]   [SERVER]  TypeError: x is not a function [app.ts:42:5] [handleRequest]
14:32:02 [TRACE]   [BROWSER] [http.client] GET /api/users [245ms] [3 spans]
14:32:03 [INFO]    [SERVER]  User logged in [user_id=1234] [region=us]

Errors show the exception type, message, and top stack frame. Transactions show the operation, duration, and span count. Logs show the severity level, message, and custom attributes.

Use --filter / -f to narrow the output to specific event types (repeatable):

sentry local -f error -f log    # only errors and logs

Use --quiet to suppress tail output entirely if you only need the SSE stream.

Agent monitoring

sentry local shows rich output for AI agent spans when your SDK instruments with OpenTelemetry semantic attributes:

14:32:01 [TRACE]   [SERVER]  [gen_ai] chat anthropic/claude-4-sonnet [1200ms] [5 spans]
14:32:02 [TRACE]   [SERVER]  [mcp] tools/call search_files [320ms]
14:32:03 [TRACE]   [SERVER]  [db] SELECT users [postgresql] [12ms]
14:32:04 [ERROR]   [SERVER]  RateLimitError: API quota exceeded [api_client.py:42]

GenAI operations show the model name, MCP tool calls show the tool being invoked, and database queries show the system and query summary. This works automatically when your Sentry SDK is configured with AI/LLM integrations.

JSON output

Use --format json (or -F json) for machine-readable NDJSON output, one JSON object per envelope item:

sentry local --format json

{"type":"transaction","timestamp":1700000001,"op":"gen_ai","label":"chat anthropic/claude-4-sonnet","duration_ms":1200,"span_count":5,"source":"server"}
{"type":"error","timestamp":1700000002,"error_type":"RateLimitError","message":"API quota exceeded","source":"server"}
{"type":"log","timestamp":1700000003,"level":"info","message":"User logged in","attributes":{"user_id":1234},"source":"server"}

This is useful for AI coding agents and automation tools that need to consume Sentry events programmatically.