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

# LangWatch: The Complete LLMOps Platform

> Ship AI agents 8x faster with comprehensive observability, evaluation, and prompt optimization. Open-source platform, with over 2.5k stars on GitHub.

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  <img className="block" src="https://mintcdn.com/langwatch/CYVXWbKFuujxTm5z/images/langwatch-quick-preview.gif?s=239edde2c4327f1c2312a884de3b1884" alt="LangWatch Quick Preview" data-og-width="1080" width="1080" data-og-height="614" height="614" data-path="images/langwatch-quick-preview.gif" data-optimize="true" data-opv="3" />
</Frame>

## Quick Start

Ready to start taking control of your LLM application quality? Quick start with observability or agent simulations right away:

<CardGroup cols={2}>
  <Card title="Observability Quick Start" description="Track every LLM call, tool usage, and user interaction with detailed traces, spans, and metadata." icon="chart-network" href="/integration/quick-start" arrow horizontal />

  <Card title="Agent Simulation Testing" description="Test and optimize agents with collaborative tools and A/B testing." icon="masks-theater" href="/agent-simulations/getting-started" arrow horizontal />

  <Card title="LLM Evaluation" description="Measure output quality with built-in evaluators, custom metrics, and human feedback integration." icon="square-check" href="/evaluations/overview" arrow horizontal />

  <Card title="Prompt Management" description="Version control, test, and optimize prompts with collaborative tools and A/B testing." icon="code" href="/prompt-management/overview" arrow horizontal />

  <Card title="Cost & Performance Tracking" description="Monitor token usage, costs, and performance metrics across all models and providers." icon="chart-line" href="/integration/python/tutorials/tracking-llm-costs" arrow horizontal />

  <Card title="Alerts & Automations" description="Set up alerts and automations for your LLM applications." icon="bell" href="/features/automations" arrow horizontal />
</CardGroup>

## What is LangWatch?

LangWatch is the **open-source** LLMOps platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. All platform features are natively integrated to accelerate the development workflow.

Building AI applications is hard. Developers spend weeks debugging issues, optimizing prompts, and ensuring quality. Without proper observability, you're flying blind - you don't know why your AI behaves the way it does, where it fails, or how to improve it.

LangWatch provides the missing operations platform for AI applications. Every LLM call, tool usage, and user interaction is automatically tracked with detailed traces, spans, and metadata. See the full conversation flow, identify bottlenecks, and understand exactly how your AI applications behave in production.

## For Every Role

LangWatch serves different needs across your organization, providing value to every team member working with AI applications.

### For Developers

Debug faster with detailed traces that show exactly what happened in each LLM call. Build datasets from production data, run batch evaluations, and continuously improve your AI applications with comprehensive debugging tools and performance insights.

### For Domain Experts

Easily sift through conversations, see topics being discussed, and annotate messages for improvement in a collaborative manner with the development team. Provide feedback on AI outputs and help guide quality improvements through intuitive interfaces.

### For Business Teams

Track conversation metrics, user analytics, and cost tracking with custom dashboards and reporting. Monitor AI application performance, understand user behavior, and make data-driven decisions about your AI investments.

## Where to Start?

Setting up the full process of online tracing, prompt management, production evaluations, and offline evaluations requires some time. This guide helps you figure out what's most important for your use case.

<CardGroup cols={2}>
  <Card title="Just Getting Started?" description="Start with basic tracing to understand what's happening in your LLM applications." icon="rocket" href="/integration/quick-start" arrow horizontal />

  <Card title="Already Instrumented?" description="Add prompt management and evaluation to optimize your existing setup." icon="wrench" href="/prompt-management/overview" arrow horizontal />

  <Card title="Production Ready?" description="Set up comprehensive monitoring, alerts, and cost tracking for production." icon="chart-line" href="/observability/overview" arrow horizontal />

  <Card title="Research & Development?" description="Use datasets, experiments, and evaluation tools for systematic testing." icon="flask" href="/evaluations/overview" arrow horizontal />
</CardGroup>

Ready to get started? [Sign up for free](https://app.langwatch.ai) and begin building better AI applications today.