Dashboard Guide

The Bloop dashboard is a single-page application served at the root URL of your Bloop instance.

Main View

• Stats bar — Shows total errors, unresolved count, events in the last 24 hours, and buffer usage
• Trend chart — SVG area chart showing hourly event volume over the last 72 hours
• Error list — Aggregated errors with sparklines, sorted by last seen. Each row shows error type, message, count, source badge, and a 24-hour trend sparkline
• Filters — Filter by project, status (unresolved/resolved/ignored/muted), release, route, and sort order

Error Detail

Click any error row to see the detail view:

• Aggregates — Breakdown by release and environment with event counts and timestamps
• Samples — Up to 5 sample occurrences with stack traces, metadata, and source info
• Deobfuscated stacks — When source maps are uploaded, a toggle appears to switch between Original and Raw stack views
• Actions — Resolve, Ignore, Mute, or Unresolve the error
• Status timeline — Audit trail of all status changes with timestamps and who made them

Settings

Click the gear icon in the header to open the settings panel (admin only):

• Projects — Create, view, and delete projects. Each project shows its full API key with click-to-copy and expandable SDK snippets
• API Tokens — Create, list, and revoke scoped bearer tokens for programmatic access. Tokens are project-scoped with granular permissions (errors, sourcemaps, alerts)
• Source Maps — Upload .map files per project and release, view and delete uploaded maps
• Alerts — Create and manage alert rules, add notification channels (Slack, webhook, email), enable/disable and test rules
• Users — View registered users, promote/demote admin roles, and remove accounts
• Invites — Generate invite links for new users
• Data Retention — Configure global and per-project retention periods, view storage stats, and trigger manual purges

Insights Panel

When the analytics feature is enabled (--features analytics), an Insights button appears in the header. The button is hidden when analytics is not compiled in — the dashboard probes /v1/analytics/spikes on load and only shows the button if it gets a 200 response.

The Insights panel has five sub-tabs:

• Spikes — Anomalous errors detected via z-score analysis. Each row shows a horizontal bar (width = z-score severity), the error message, event count, and z-score value. Color-coded: dim for z≥2.5, coral for z≥3, red for z≥4
• Movers — Errors with the largest absolute change between the current and previous time window. Shows delta arrows, count comparison, and percentage badges
• Correlations — Pairs of errors that tend to spike together (Pearson correlation ≥0.7, requiring ≥6 data points). Useful for root cause discovery
• Releases — Impact score per release combining new fingerprints and error count deltas. Cards are color-coded: red = high impact (score >50), green = improvement (score <0), neutral otherwise
• Environments — Table showing each environment's total count, percentage bar, unique error count, and P50/P90/P99 hourly rate percentiles

Results are cached for 60 seconds (configurable via cache_ttl_secs). All queries respect the active project filter.

LLM Tracing Panel

When LLM tracing is enabled (--features llm-tracing), an LLM button appears in the header. Like Insights, it auto-detects by probing /v1/llm/overview?hours=1 on load.

Bloop is provider-agnostic — it works with any LLM you use: OpenAI (GPT-4o, o1, etc.), Anthropic (Claude), Google (Gemini), Mistral, Cohere, LLaMA, or any local/self-hosted model. You pass the model name and provider as strings when creating traces, so Bloop tracks whatever your code reports.

The LLM panel has eight sub-tabs:

Overview

Summary cards showing total traces, total spans, tokens consumed, cost in dollars, average latency, and error rate for the selected time window.

Usage

Hourly breakdown of token consumption and cost, grouped by model. Each row shows the hour bucket, model name, request count, input/output tokens, and total cost.

Latency

Latency percentiles (p50, p90, p99) by model, plus average time-to-first-token. Useful for identifying slow models or degraded performance.

Models

Per-model comparison table: total calls, tokens consumed, cost, average latency, and error rate. Sorted by cost to quickly identify the most expensive models.

Traces

Paginated list of all LLM traces with status badges, model info, token counts, cost, and latency. Click any trace to expand its full span hierarchy with parent-child relationships.

Search

Full-text search across trace names, input, and output content. Results show matching traces with highlighted context.

Prompts

Prompt version tracking table: prompt name, latest version, total traces, average latency, average tokens, cost, and error rate. Useful for comparing prompt performance across versions.

Scores

Score cards showing quality metrics attached to traces. Each card displays the score name, count, average value (with color-coded bar: red < 0.3, amber 0.3–0.7, green > 0.7), min, and max values.

Enabling LLM Tracing

# Build with LLM tracing enabled
docker build --build-arg FEATURES=llm-tracing -t bloop .

# Or enable both analytics and LLM tracing
docker build --build-arg FEATURES=analytics,llm-tracing -t bloop .

# Configure in config.toml
[llm_tracing]
enabled = true
default_content_storage = "metadata_only"

Content storage policies control what gets persisted: none (no prompts/completions), metadata_only (tokens and costs only), or full (everything). Set per-project via the Settings panel or API.

Error Lifecycle

Every error in Bloop has a status that tracks its lifecycle:

Status Transitions

All status changes are recorded in an audit trail visible in the error detail view.

• New error → unresolved (automatic)
• Resolve → resolved — marks the error as fixed
• Ignore → ignored — permanently suppresses alerts for this error
• Mute → muted — temporarily suppresses alerts
• Unresolve → unresolved — re-opens the error for attention

Regression Detection

If a resolved error receives a new occurrence, Bloop can detect the regression. The error appears again in the unresolved list with its full history preserved.

API

# Resolve
curl -X POST http://localhost:5332/v1/errors/FINGERPRINT/resolve \
  -H "Cookie: session=YOUR_SESSION_TOKEN"

# Mute
curl -X POST http://localhost:5332/v1/errors/FINGERPRINT/mute \
  -H "Cookie: session=YOUR_SESSION_TOKEN"

# Unresolve
curl -X POST http://localhost:5332/v1/errors/FINGERPRINT/unresolve \
  -H "Cookie: session=YOUR_SESSION_TOKEN"

# View status history
curl http://localhost:5332/v1/errors/FINGERPRINT/history \
  -H "Cookie: session=YOUR_SESSION_TOKEN"

Troubleshooting

Common Issues

Checking Server Health

curl http://localhost:5332/health | jq .

# Response:
# {
#   "status": "ok",
#   "db_ok": true,
#   "buffer_usage": 0.002
# }

buffer_usage shows the MPSC channel fill ratio (0.0 = empty, 1.0 = full). If consistently above 0.5, consider increasing ingest.channel_capacity or pipeline.flush_batch_size.

Debug Logging

# Enable detailed logging
RUST_LOG=bloop=debug cargo run

# Or in Docker
docker run -e RUST_LOG=bloop=debug ...

Debug logging shows every ingested event, fingerprint computation, batch write, and alert evaluation.