> ## Documentation Index
> Fetch the complete documentation index at: https://moonshotfactory.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> AgentVista is a unified observability platform that monitors your AI agents and your infrastructure in a single place.

In 2026, production software is a hybrid of traditional services and AI agents. Most teams end up with two disconnected observability stacks: one for AI (LangSmith, Langfuse, Helicone) and one for infrastructure (Datadog, Grafana, Prometheus). When something breaks, you can't answer the questions that matter:

* Was it the prompt, the model, the tool call, or the underlying infrastructure?
* Did a database timeout cause the agent to return a fallback response?
* Is the cost spike from a model change or from a retry loop caused by a failing downstream service?

**The gap isn't AI observability or infrastructure observability — it's the correlation layer between them.**

AgentVista closes that gap. One platform, one SDK, one place to ask questions about your entire stack.

## Who AgentVista is for

AgentVista is built for solo founders and small teams (typically 2–10 people) shipping AI-native products. If AI agents are core to your product — not a side feature — and you don't have the time or budget to run two separate observability stacks, AgentVista is for you.

## What AgentVista does

<CardGroup cols={2}>
  <Card title="Traces" icon="diagram-project">
    Every agent run becomes a trace. Child spans capture LLM calls, tool invocations, and sub-agent delegation — including spans from your infrastructure services in the same waterfall.
  </Card>

  <Card title="Metrics" icon="chart-line">
    Track token usage, cost per run, latency, success rate, CPU, memory, request rate, and error rate — all in one dashboard, attributed to the same workflow.
  </Card>

  <Card title="Logs" icon="scroll">
    Ingest prompt/completion pairs, tool call inputs and outputs, and standard application logs. Click from any log line to the trace it belongs to.
  </Card>

  <Card title="Cost tracking" icon="circle-dollar-to-slot">
    See the full cost of a workflow: LLM token spend plus infrastructure compute, broken down by agent, by feature, and by customer.
  </Card>

  <Card title="Outcome signals" icon="circle-check">
    Define what success means for each agent. Track success rates over time, by agent, by model, and by prompt version. Correlate failures back to their root cause.
  </Card>

  <Card title="Alerts" icon="bell">
    Set threshold-based and composite alerts on any combination of AI metrics, infrastructure metrics, and outcome signals — delivered by email or webhook.
  </Card>
</CardGroup>

## Get started

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="/quickstart">
    Send your first trace in under 5 minutes.
  </Card>

  <Card title="Install the SDK" icon="code" href="/sdk/installation">
    Install the Python SDK and explore all tracing APIs.
  </Card>

  <Card title="API reference" icon="brackets-curly" href="/api/overview">
    Explore REST endpoints for ingestion and querying.
  </Card>

  <Card title="Traces and spans" icon="diagram-project" href="/concepts/traces-and-spans">
    Understand how AgentVista models agent runs and infrastructure calls.
  </Card>
</CardGroup>