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EvaluationProduction Ready
langwatch
by langwatch
Platform for scenario-driven LLM and agent evaluation
TypeScript
Updated Oct 7, 2026
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What It Does
Runs large-scale LLM evaluations and simulation-based agent testing via Evaluation-Driven Development. Provides low-code pipelines to define scenarios, run batched trials, and capture structured signals from model interactions and prompts. Includes analytics and observability features for comparing models, prompts, and agent policies across datasets and runs enabled by Emergence-Aware Monitoring.
Key Benefits
As agents interact and delegate, simple benchmarks no longer reveal real-world failure modes — you need continuous, scenario-driven evaluation using the Human-in-the-Loop Pattern. That makes it easier to build agent track records and compare benchmark scores with operational reputation signals.
When to Use
Teams validating LLMs and autonomous agent behaviors with repeatable simulations and analytics before production rollout leveraging the Dynamic Task Routing Pattern.
How It's Used
- When you need to simulate multi-step agent interactions to discover delegation failure modes
- When you need repeatable benchmark runs across prompt variations and model versions
- When you need searchable interaction logs and analytics to build an agent track record
Works With
openaigptdatasets
Topics
agent-testingaianalyticsdatasetsdspyevaluationgptllmllm-opsllmops+6 more
Similar Tools
agent-playgroundagent-arena
Keywords
A2A evaluationmulti-agent trustagent track recordcontinuous agent evaluation