What is LangWatch?
LangWatch helps AI engineering teams achieve a closed-loop workflow for agent development: it automatically generates test cases from requirement specifications, conducts continuous simulations mimicking real user behavior via text and voice, then performs multi-dimensional evaluations and iterative optimizations based on complete call traces, forming an automated cycle of “specification → simulation → improvement.” It supports unified operation across local development, CI pipelines, and production environments, enabling seamless integration with any agent framework without rewriting code, while offering both white-box and black-box testing capabilities.
Key features
It supports automatically converting natural-language‑described requirements into executable agent test scenarios, allowing you to write them directly in the editor and run them synchronously during local development and with every Pull Request.
Built-in LLM judges enable you to drill down layer by layer through the complete call chain—including tool invocations, skill executions, and MCP services—score individual outputs or entire dialogues, and provide reasoning behind your evaluations.
Use cases and audience
AI platform teams, AI engineers, and technical leads responsible for developing and governing AI agents
- An LLM call observability and evaluation solution tailored for AI platform teams, supporting end-to-end tracking, testing, routing, and governance—from self-developed agents to programming assistants used by engineers.
- Conduct multi-round dialogue testing by simulating real users (text and voice) to proactively identify Agent failures in the production environment.
- Drive Agent development with requirements specifications: automatically convert product requirements into test scenarios, and generate Pull Request-based fix suggestions based on failure results.
The full call chain during agent runtime—including tool invocations, skill executions, and interactions with MCP servers—alongside naturally written test scenario descriptions and real-world trace data from production environments
Searchable, comprehensive trace data (including token counts, costs, and cache statuses), multi-dimensional evaluation scores (from single outputs to entire dialogues), results of topic clustering analyses, custom metric visualizations, and automatically generated Prompt revision pull requests
Frequently asked questions
Does LangWatch support self-hosting?
Supports cloud services, self-hosted, and hybrid deployment modes; the data plane can be deployed on the user’s infrastructure, while the control plane is provided by LangWatch.
Can it evaluate multimodal content such as images?
Supports the evaluation of images and mixed-media content, offering a unified scoring interface.
How to use
- 01
Write natural-language–described test scenarios locally or in a CI environment.
- 02
Run multiple concurrent simulations to perform text-and-voice interaction testing on the Agent.
- 03
Use an LLM as a judge (JudgeAgent) to score outputs, covering dimensions such as fidelity, policy compliance, and degradation capabilities.
- 04
Automatically transform regression issues into prompt revision suggestions and deliver them for deployment via the Prompt Registry.
Official website & sources
Updated on 2026-10-30
