# Emdb Analytics

> View EmergentDB analytics and usage stats. Use when the user wants to check API usage, latency, errors, growth, or per-key stats.

- Skill: `justrach/emdb-analytics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add justrach/emdb-analytics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/justrach/emdb-analytics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: justrach (https://skillmd.com/u/justrach)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/justrach/emdb-analytics

---


# EmergentDB Analytics

Help the user retrieve analytics and usage data from their EmergentDB account.

## TypeScript SDK

```typescript
import { EmergentDB } from "emergentdb";

const db = new EmergentDB("emdb_your_api_key");

// Request stats by endpoint (last 30 days)
const endpoints = await db.analyticsEndpoints();
// [{ endpoint, requestCount, totalBytes, avgLatencyMs, p95LatencyMs, errorCount }]

// Usage by namespace (last 30 days)
const namespaces = await db.analyticsNamespaces();
// [{ namespace, requestCount, totalVectors, avgLatencyMs }]

// Latency percentiles by day (last 30 days)
const latency = await db.analyticsLatency();
// [{ date, p50, p95, p99, requestCount }]

// Error rates by day (last 30 days)
const errors = await db.analyticsErrors();
// [{ date, totalRequests, errorCount, error4xx, error5xx }]

// Per-API-key usage (last 30 days)
const keys = await db.analyticsKeys();
// [{ apiKeyId, keyName, keyPrefix, requestCount, totalBytes, avgLatencyMs, lastUsed }]

// Vector count growth (daily snapshots, last 90 days)
const growth = await db.analyticsGrowth();
// [{ date, vectorCount }]
```

## Python SDK

```python
from emergentdb import EmergentDB

db = EmergentDB("emdb_your_api_key")

endpoints  = db.analytics_endpoints()   # request stats per endpoint
namespaces = db.analytics_namespaces()  # usage per namespace
latency    = db.analytics_latency()     # p50/p95/p99 by day
errors     = db.analytics_errors()      # 4xx/5xx counts by day
keys       = db.analytics_keys()        # per-API-key stats
growth     = db.analytics_growth()      # vector count by day (90 days)
```

## Available Analytics

| Method | Data | Window |
|---|---|---|
| `analyticsEndpoints` / `analytics_endpoints` | Requests, bytes, latency per endpoint | 30 days |
| `analyticsNamespaces` / `analytics_namespaces` | Requests, vectors, latency per namespace | 30 days |
| `analyticsLatency` / `analytics_latency` | p50, p95, p99 latency by day | 30 days |
| `analyticsErrors` / `analytics_errors` | Error counts (4xx, 5xx) by day | 30 days |
| `analyticsKeys` / `analytics_keys` | Usage stats per API key | 30 days |
| `analyticsGrowth` / `analytics_growth` | Total vector count by day | 90 days |

## Error Codes

| Code | Meaning |
|------|---------|
| 401 | Missing or invalid API key |
| 429 | Rate limit exceeded |
| 500 | Server error — retry with backoff |

## Plans & Limits

| Plan | Vectors | Price |
|------|---------|-------|
| Free | 10,000 | $0/mo |
| Launch | 500,000 | $29/mo |
| Scale | 2,500,000 | $99/mo |

When helping the user, suggest the right analytics method based on what they want to understand (performance, errors, growth, etc.).