{
  "timestamp": 1700000000,
  "source": "ios",
  "environment": "production",
  "release": "1.2.0",
  "app_version": "1.2.0",
  "build_number": "42",
  "route_or_procedure": "/api/checkout",
  "screen": "PaymentScreen",
  "error_type": "RuntimeError",
  "message": "Connection refused at port 5432",
  "stack": "RuntimeError: Connection refused\n  at connect (db.swift:42)",
  "http_status": 500,
  "request_id": "req-abc-123",
  "user_id_hash": "sha256:a1b2c3...",
  "device_id_hash": "sha256:d4e5f6...",
  "fingerprint": "custom-fp-override",
  "metadata": {
    "os_version": "17.2",
    "device_model": "iPhone 15"
  }
}

# Create a token (requires session auth)
curl -X POST http://localhost:5332/v1/tokens \
  -H "Cookie: session=..." \
  -H "Content-Type: application/json" \
  -d '{"name":"CI deploy","project_id":"my-project","scopes":["errors:read","sourcemaps:upload"]}'

# Use the token
curl http://localhost:5332/v1/errors \
  -H "Authorization: Bearer bloop_abc123..."

# Alert when hourly LLM cost exceeds $10
curl -X POST http://localhost:5332/v1/alerts \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "LLM cost spike",
    "rule_type": "llm_cost_spike",
    "threshold_dollars": 10.0,
    "window_hours": 1,
    "project_id": "my-project"
  }'

# Alert when LLM error rate exceeds 5%
curl -X POST http://localhost:5332/v1/alerts \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "LLM error rate",
    "rule_type": "llm_error_rate",
    "threshold_percent": 5.0,
    "window_hours": 1,
    "project_id": "my-project"
  }'

# Alert when gpt-4o p90 latency exceeds 5 seconds
curl -X POST http://localhost:5332/v1/alerts \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "GPT-4o slow responses",
    "rule_type": "llm_latency",
    "threshold_ms": 5000,
    "window_hours": 1,
    "model": "gpt-4o",
    "project_id": "my-project"
  }'

curl -X POST http://localhost:5332/v1/alerts \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "High error rate",
    "rule_type": "threshold",
    "threshold": 100,
    "window_secs": 3600,
    "project_id": "my-project"
  }'

[smtp]
enabled = true
host = "smtp.example.com"
port = 587
username = "[email protected]"
password = "your-password"
from = "[email protected]"
starttls = true

curl -X POST http://localhost:5332/v1/alerts/1/channels \
  -H "Content-Type: application/json" \
  -H "Cookie: session=YOUR_SESSION_TOKEN" \
  -d '{"config": {"type": "email", "to": "[email protected]"}}'

# Create project
curl -X POST http://localhost:5332/v1/projects \
  -H "Cookie: session=..." \
  -H "Content-Type: application/json" \
  -d '{"name":"My Mobile App","slug":"my-mobile-app"}'

# List projects
curl http://localhost:5332/v1/projects -H "Cookie: session=..."

# Promote user to admin
curl -X PUT http://localhost:5332/v1/admin/users/2/role \
  -H "Cookie: session=..." \
  -H "Content-Type: application/json" \
  -d '{"role":"admin"}'

# Get current retention settings
curl http://localhost:5332/v1/admin/retention -H "Cookie: session=..."

# Update retention
curl -X PUT http://localhost:5332/v1/admin/retention \
  -H "Cookie: session=..." \
  -H "Content-Type: application/json" \
  -d '{"raw_events_days": 14}'

# Trigger immediate purge
curl -X POST http://localhost:5332/v1/admin/retention/purge -H "Cookie: session=..."

# Upload a source map
curl -X POST http://localhost:5332/v1/projects/my-app/sourcemaps \
  -H "Authorization: Bearer bloop_..." \
  -F "release=1.2.3" \
  -F "file=@dist/app.js.map"

# Get spike detection results
curl "http://localhost:5332/v1/analytics/spikes?hours=24&threshold=2.5" \
  -H "Authorization: Bearer bloop_..."

{
  "id": "trace-001",
  "session_id": "session-abc",
  "user_id": "user-123",
  "name": "chat-completion",
  "prompt_name": "summarize-v2",
  "prompt_version": "1.3.0",
  "status": "completed",
  "input": "User prompt text",
  "output": "AI response text",
  "metadata": {"key": "value"},
  "started_at": 1700000000000,
  "ended_at": 1700000001200,
  "spans": [
    {
      "id": "span-001",
      "parent_span_id": null,
      "span_type": "generation",
      "name": "gpt-4o call",
      "model": "gpt-4o",
      "provider": "openai",
      "input_tokens": 100,
      "output_tokens": 50,
      "cost": 0.0025,
      "latency_ms": 1200,
      "time_to_first_token_ms": 300,
      "status": "ok",
      "error_message": null,
      "input": "prompt text",
      "output": "completion text",
      "metadata": {}
    }
  ]
}

# Get LLM usage overview
curl "http://localhost:5332/v1/llm/overview?hours=24" \
  -H "Authorization: Bearer bloop_..."

# Get traces for a specific model
curl "http://localhost:5332/v1/llm/traces?model=gpt-4o&limit=20" \
  -H "Authorization: Bearer bloop_..."

# Point your OpenAI client at bloop
# Python
import openai
client = openai.OpenAI(
    api_key="sk-your-openai-key",
    base_url="http://localhost:5332/v1/proxy/openai"
)

# TypeScript
import OpenAI from "openai";
const client = new OpenAI({
    apiKey: "sk-your-openai-key",
    baseURL: "http://localhost:5332/v1/proxy/openai"
});

# Or via environment variable (most SDKs)
export OPENAI_BASE_URL=http://localhost:5332/v1/proxy/openai

[llm_tracing.proxy]
enabled = true
providers = ["openai"]
openai_base_url = "https://api.openai.com/v1"
capture_prompts = true
capture_completions = true
capture_streaming = true

# Check current policy
curl "http://localhost:5332/v1/llm/settings?project_id=my-app" \
  -H "Authorization: Bearer bloop_..."

# Update policy
curl -X PUT "http://localhost:5332/v1/llm/settings?project_id=my-app" \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{"content_storage": "full"}'

# Create an A/B test for prompt variants
curl -X POST "http://localhost:5332/v1/llm/experiments" \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Summarizer Prompt Test",
    "prompt_name": "summarizer",
    "description": "Testing detailed vs concise prompts",
    "variants": [
      {
        "name": "Baseline",
        "version": "v1",
        "prompt_template": "Summarize: {text}",
        "traffic_percentage": 50
      },
      {
        "name": "Detailed",
        "version": "v2",
        "prompt_template": "Provide a detailed summary in 3-5 sentences: {text}",
        "traffic_percentage": 50
      }
    ]
  }'

# Compare baseline vs treatment variant
curl "http://localhost:5332/v1/llm/experiments/experiment-123/compare?baseline=v1&treatment=v2" \
  -H "Authorization: Bearer bloop_..."

# Response includes:
# - Latency improvement %
# - Cost improvement %
# - Success rate improvement %
# - Statistical significance (p-value, confidence level)
# - Recommendation

# Search for traces by name
curl "http://localhost:5332/v1/llm/search?q=chat-completion&hours=48" \
  -H "Authorization: Bearer bloop_..."

# Search by session ID
curl "http://localhost:5332/v1/llm/search?q=session-abc&limit=20" \
  -H "Authorization: Bearer bloop_..."

{
  "traces": [
    {
      "id": "trace-001",
      "name": "chat-completion",
      "session_id": "session-abc",
      "user_id": "user-123",
      "status": "completed",
      "total_tokens": 150,
      "total_cost_micros": 2500,
      "latency_ms": 1200,
      "started_at": 1700000000000
    }
  ],
  "total": 1,
  "query": "chat-completion"
}

curl -X POST http://localhost:5332/v1/llm/traces/trace-001/scores \
  -H "Authorization: Bearer bloop_..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "quality",
    "value": 0.85,
    "comment": "Good response, accurate and well-structured"
  }'

curl "http://localhost:5332/v1/llm/traces/trace-001/scores" \
  -H "Authorization: Bearer bloop_..."

{
  "scores": [
    {
      "id": 1,
      "trace_id": "trace-001",
      "name": "quality",
      "value": 0.85,
      "comment": "Good response, accurate and well-structured",
      "created_at": "2024-01-15T10:30:00Z"
    },
    {
      "id": 2,
      "trace_id": "trace-001",
      "name": "relevance",
      "value": 0.92,
      "comment": null,
      "created_at": "2024-01-15T10:31:00Z"
    }
  ]
}

curl "http://localhost:5332/v1/llm/scores/summary?hours=48" \
  -H "Authorization: Bearer bloop_..."

{
  "scores": [
    {
      "name": "quality",
      "count": 142,
      "avg_value": 0.78,
      "min_value": 0.12,
      "max_value": 0.99
    },
    {
      "name": "relevance",
      "count": 89,
      "avg_value": 0.85,
      "min_value": 0.45,
      "max_value": 1.0
    }
  ],
  "window_hours": 48
}

{
  "id": "trace-042",
  "name": "summarize-article",
  "prompt_name": "summarize-v2",
  "prompt_version": "1.3.0",
  "session_id": "session-xyz",
  "status": "completed",
  "started_at": 1700000000000,
  "ended_at": 1700000001500,
  "spans": [...]
}

curl "http://localhost:5332/v1/llm/prompts?hours=72" \
  -H "Authorization: Bearer bloop_..."

{
  "prompts": [
    {
      "prompt_name": "summarize-v2",
      "prompt_version": "1.3.0",
      "trace_count": 482,
      "avg_latency_ms": 1350.5,
      "avg_tokens": 175.2,
      "total_cost_micros": 1205000,
      "error_rate": 0.02,
      "last_seen": "2024-01-15T14:22:00Z"
    },
    {
      "prompt_name": "summarize-v2",
      "prompt_version": "1.2.0",
      "trace_count": 1204,
      "avg_latency_ms": 1580.3,
      "avg_tokens": 210.8,
      "total_cost_micros": 3612000,
      "error_rate": 0.05,
      "last_seen": "2024-01-14T09:15:00Z"
    }
  ]
}