Developer (Free)
Free
- All platform features
- 50K logs/month
- 14-day data retention
- 2 users
- 3 Scenarios + 3 Simulations
LangWatch: Observability, Prompts & Evals
Founded 2023|Amsterdam, Netherlands|2-10 people|Open Source + Cloud
What 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
Core capabilities this platform advertises.
- Multi-turn agent simulation testing
- LLM evaluation with custom evaluators
- OpenTelemetry-native tracing
- Prompt & model management
- Dataset management from production traces
Strengths and tradeoffs
What this tool does well, and the limitations to keep in mind.
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
Plans & pricing
What's included in each plan, and how the tiers compare.
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
Run LangWatch in production with Respan
10KFree traces/mo
500+Models
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Last verified: March 27, 2026
