LangWatch — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4Open Source + CloudFounded 2023|Amsterdam, Netherlands|2-10
Visit websiteWhat is LangWatch?
LangWatch is an open-source LLMOps platform focused on testing, evaluating, and monitoring AI agents. Founded in 2023 in Amsterdam by Rogerio Chaves (CTO, ex-Booking.com, ex-Lightspeed) and Manouk Draisma (CEO), the company raised EUR 1M in pre-seed funding led by Passion Capital with participation from Volta Ventures and Antler.
LangWatch's standout differentiator is its Scenario framework — an open-source agent testing library (804 GitHub stars) that enables multi-turn, simulation-based testing of AI agents. Unlike static input/output evaluations, Scenario provides a User Simulator Agent that generates realistic conversations against your agent, with a Judge Agent evaluating pass/fail at every turn. Available in Python, TypeScript, and Go, it works with any agent framework (LangGraph, CrewAI, Pydantic AI, OpenAI, Vercel AI SDK, Google ADK).
The platform combines OpenTelemetry-native tracing, custom evaluators with real-time scoring, prompt and model management with version control, and dataset management that converts production traces into reusable test cases. LangWatch processes 900K+ daily evaluations, has 780K+ monthly package installs, and holds ISO 27001 and SOC2 certifications. It supports self-hosted deployment via Docker and Kubernetes with no feature gating.
Key Features
- ✓Multi-turn agent simulation testing
- ✓LLM evaluation with custom evaluators
- ✓OpenTelemetry-native tracing
- ✓Prompt & model management
- ✓Dataset management from production traces
Pros & Cons
Pros
- +Unique agent simulation testing via Scenario framework — enables multi-turn, stateful agent testing unmatched by LangSmith or Langfuse
- +Fully open-source with MIT license and no feature gating on self-hosted deployments
- +All-in-one platform combining tracing, evaluation, prompt management, and PII redaction
- +Framework-agnostic with OpenTelemetry-native integration across all major agent frameworks
- +Enterprise-ready with ISO 27001, SOC2, GDPR compliance, and on-prem deployment options
Cons
- -Very small team (~5 employees) with only EUR 1M pre-seed — long-term viability less proven than well-funded competitors
- -Smaller community and fewer tutorials compared to LangSmith or Langfuse ecosystems
- -Self-hosting requires Kubernetes/Helm for production with known issues on reverse proxy deployments
- -Only supports LLM-as-a-judge evaluations — no heuristic or rule-based evaluation options
LangWatch Pricing
Free trial available
Developer (Free)Free
- ✓All platform features
- ✓50K logs/month
- ✓14-day data retention
- ✓2 users
- ✓3 Scenarios + 3 Simulations
- ✓Community support
GrowthEUR 59/seat/monthmonthly
- ✓200K events included
- ✓EUR 0.0005/additional event
- ✓30-day retention
- ✓Unlimited lite-users
- ✓Unlimited evals/simulations
- ✓Private Slack/Teams support
EnterpriseCustom
- ✓On-prem/hybrid/self-hosted
- ✓Custom retention
- ✓SSO/RBAC
- ✓Audit logs
- ✓Uptime & support SLA
- ✓Forward Deployed Engineer
Common Use Cases
AI teams building and testing LLM-powered agents
- •Agent testing before deployment
- •Hallucination detection
- •Regression testing for LLM apps
- •Production monitoring
- •Prompt optimization
Using LangWatch with Respan
LangWatch and Respan serve complementary roles in the AI observability stack. LangWatch excels at pre-production agent testing and evaluation through its simulation framework, while Respan provides production-grade LLM monitoring, cost tracking, and gateway management. Together they cover the full agent lifecycle from testing to production.
- ✓Use LangWatch Scenario testing during development and Respan monitoring in production for full lifecycle coverage
- ✓Combine LangWatch agent evaluations with Respan production metrics for comprehensive quality tracking
- ✓Feed Respan production traces back into LangWatch for regression testing and evaluation
- ✓Monitor LLM costs through Respan while using LangWatch to evaluate output quality
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Last verified: March 27, 2026