Simulate agent scenarios, trace failures, and watch production regressions with LangWatch

Run end-to-end agent simulations, review traces, and watch production regressions when reliability work spans pre-release testing and live monitoring.

Prerequisites

LangWatch platform, your agent application traces, optional Docker for self-hosting

Installation

Use the upstream install or setup path that matches your environment:

• npx @langwatch/server
• Prefer Docker? You can still use docker compose:
• git clone https://github.com/langwatch/langwatch.git
• docker compose up -d --wait --build

Requirements and caveats from upstream:

• <a href="https://pypi.python.org/pypi/langwatch" target="_blank"><img src="https://img.shields.io/pypi/dm/langwatch?logo=python&logoColor=white&label=pypi%20langwatch&color=blue" alt="langwatch Python package on PyPi"...
• The fastest way to run LangWatch locally — only Node.js required:
• Docker Compose - Run LangWatch on your own machine.

Basic usage or getting-started notes:

• Run realistic scenarios against your full stack (tools, state, user simulator, judge) and pinpoint where your agents break, and why? down to each decision.

• Cloud ☁️

• The easiest way to get started with LangWatch.

• Source: https://github.com/langwatch/langwatch

• Extracted from upstream docs: https://raw.githubusercontent.com/langwatch/langwatch/HEAD/README.md

Documentation

• https://docs.langwatch.ai/

Source

• Agent Skill Exchange