Comparison

LangSmith vs Langfuse: Open Source Agent Evals & Observability

LangSmith and Langfuse: Open Source Agent Evals & Observability both land on Depends.

LangSmith

Depends

Confidence: Medium

Buy if your team runs real LLM agents in production and needs tracing, evals, and failure analysis at scale.

Depends

Confidence: Low

Buy if you ship LLM apps to production and need tracing, prompt management, and evals in one place; skip if you don't build AI features.

LangSmith versus Langfuse: Open Source Agent Evals & Observability
Compare	LangSmith	Langfuse: Open Source Agent Evals & Observability
Verdict	Depends	Depends
Best for	Teams shipping LLM agents to production	Teams shipping LLM apps to production
Who it's not for	Hobby projects or simple chatbots . heavy overkill	Teams not building LLM-powered products
Privacy	Two critical 2026 vulnerabilities disclosed: an auth bypass (CVE-2026-25750) and an SDK deserialization flaw (CVE-2026-40190).^⁹	No known public vulnerabilities found in the sources reviewed.¹⁴
Support quality	No support evidence in sources	No support-quality evidence found
Public sentiment	Users praise detailed tracing and debugging but grumble about new pricing, reliability, and ecosystem lock-in.^²	No independent user reviews surfaced in reviewed sources; all claims are vendor-published adoption and feature statements.¹³
Biggest gotcha	Reddit users report Plus tier in Europe makes you non-compliant^⁶	Free tier caps at 50k observations/month; high-volume agents outgrow it fast, costs scale with volume¹³