Datasets:
roanbrasil
/
devops-gitops-corpus

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
59.1k
The scores widget aggregates all your evaluation metrics. You can see how many assessments have been performed across different criteria like toxicity, relevance, and accuracy. This gives you a comprehensive view of your system's quality.
#### Time-Based Analysis
Graphs show how traces are distributed over time, helping you identify usage patterns, peak hours, and potential issues. Understanding temporal patterns is crucial for capacity planning and performance optimization.
#### Model Usage Breakdown
Detailed model usage statistics help you understand which AI models you're using most and their relative costs. You can filter by different criteria and view data from multiple perspectives to optimize your model selection.
#### Real-Time Monitoring
Live statistics update continuously, showing current system load, traffic volume, response speeds, error rates, and spending velocity. This real-time visibility enables immediate response to issues.
#### Historical Trends
Trend analysis over time reveals whether your system is improving or degrading. You can track usage growth, performance changes, quality evolution, and cost optimization effectiveness.
#### Segmentation & Filtering
Multiple data views help you analyze different user segments, geographic regions, agent performance, and usage patterns. This segmentation reveals insights that aggregate data might hide.
#### Custom Dashboards
Role-specific dashboards serve different team needs. Developers see error rates and performance bottlenecks. Product managers view user engagement and feature usage. Business stakeholders see revenue impact and ROI calculations.
#### Alerting System
Automated alerts notify you of critical issues. Performance alerts trigger on slow responses, error alerts fire on high failure rates, cost alerts warn of budget overruns, and quality alerts flag declining user satisfaction.
#### Data Export
Export capabilities let you integrate with other systems. CSV/JSON exports, API access, webhook integrations, and BI tool connections ensure your data works with your existing analytics infrastructure.
## VoltAgent + Langfuse Integration
[VoltAgent + Langfuse integration](https://voltagent.dev/docs/observability/langfuse/) is super simple. As described in our docs, you just add the `@voltagent/langfuse-exporter` package and attach it to VoltAgent's observability via a SpanProcessor helper.
### Simple Setup
```typescript
import { Agent, VoltAgent, VoltAgentObservability } from "@voltagent/core";
import { openai } from "@ai-sdk/openai";
import { createLangfuseSpanProcessor } from "@voltagent/langfuse-exporter";
// Define your agent
const agent = new Agent({
name: "my-voltagent-app",
instructions: "A helpful assistant that answers questions without using tools",
model: openai("gpt-4o"),
});
// Configure Observability with Langfuse processor
const observability = new VoltAgentObservability({
spanProcessors: [
createLangfuseSpanProcessor({
publicKey: process.env.LANGFUSE_PUBLIC_KEY,
secretKey: process.env.LANGFUSE_SECRET_KEY,
baseUrl: process.env.LANGFUSE_BASE_URL, // optional
debug: true, // optional
}),
],
// Optional: disable VoltOps remote export to avoid duplicates
voltOpsSync: { sampling: { strategy: "never" } },
});
// Start VoltAgent with custom observability
new VoltAgent({
agents: { agent },
observability,
});
```
### Automatic Tracing
Everything in your VoltAgent is automatically traced:
- Agent conversations
- Tool executions
- Multi-agent delegations
- Error handling
- Performance metrics
### Zero Configuration
:::note
You don't need to insert extra code. VoltAgent follows the OpenTelemetry standard, and Langfuse follows it as well. Plug-and-play!
:::
### How It All Works Together
import ZoomableMermaid from '@site/src/components/blog-widgets/ZoomableMermaid';
<ZoomableMermaid chart={`
%%{init: {
'theme': 'base',
'themeVariables': {
'primaryColor': '#10b981',
'primaryTextColor': '#ffffff',