Overview

Rating

10.0 / 10

Rating

Best For

AI teams building and testing LLM-powered agents

Best For

Developers building RAG applications who need specialized evaluation metrics

Product Summary

Open-source LLMOps platform for testing, evaluating, and monitoring AI agents. Differentiated by multi-turn agent simulation testing.

Product Summary

Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.

Starting Price

Free

Starting Price

Open Source

Free Trial

Yes

Free Trial

Free Version

Yes

Free Version

Website

langwatch.ai

Website

ragas.io

Key features

Core capabilities each platform advertises.

LangWatch

Multi-turn agent simulation testing
LLM evaluation with custom evaluators
OpenTelemetry-native tracing
Prompt & model management
Dataset management from production traces

Ragas

RAG-specific evaluation framework
Component-wise metrics for RAG
Synthetic test data generation
LLM-as-judge evaluators
Open-source Python library

Strengths and tradeoffs

What each tool does well, and the limitations to keep in mind.

LangWatch

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

Ragas

Pros

Specialized focus on RAG evaluation with metrics specifically designed for retrieval systems
Open-source framework providing transparency and community-driven development
Strong industry endorsement from OpenAI, LangChain, and LlamaIndex
Y Combinator backing providing credibility and potential for growth
Easy integration with popular frameworks like LangChain and LlamaIndex

Cons

LangWatch or Ragas — which should you choose?

Choose LangWatch if you wantChoose if you want

Agent testing before deployment
Hallucination detection
Regression testing for LLM apps
Production monitoring
Prompt optimization

Choose Ragas if you wantChoose if you want

Evaluating RAG pipeline quality end-to-end
Measuring retrieval precision and recall
Testing faithfulness and answer relevance
Generating synthetic evaluation datasets
Benchmarking RAG across configurations

Compare LangWatch and Ragas on your own traffic

10KFree traces/mo

1,000+Models

5 minSetup

Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 1,000+ models through one gateway.

Try Respan free

LangWatch vs Ragas (2026)

Overview

Key features

Strengths and tradeoffs

LangWatch or Ragas — which should you choose?

Compare LangWatch and Ragas on your own traffic

Other popular comparisons