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Langfuse vs LangSmith
Trust Score comparison · March 2026
Signal Comparison
160k / wknpm downloads2.5M / wk
310 commitsCommits (90d)220 commits
10k ★GitHub stars3.5k ★
60 q'sStack Overflow200 q's
GrowingCommunityHigh
LangfuseLangSmith
Key Differences
| Factor | Langfuse | LangSmith |
|---|---|---|
| License | MIT | Proprietary |
| Language | TypeScript | TypeScript / Python |
| Hosted | Self-hosted | Self-hosted |
| Free tier | — | — |
| Open Source | ✓ Yes | — |
| TypeScript | ✓ | ✓ |
Pick Langfuse if…
- You need full visibility into LLM call traces, costs, and latency
- Running evals and A/B testing different prompts or models
- Self-hosting observability data for compliance or privacy
Pick LangSmith if…
- Your app is built on LangChain or LangGraph — integration is seamless
- You need prompt management and dataset-driven evaluations
- Team wants a managed cloud with minimal setup
Side-by-side Quick Start
Langfuse
import Langfuse from 'langfuse';
const langfuse = new Langfuse({ secretKey: process.env.LANGFUSE_SECRET_KEY });
const trace = langfuse.trace({ name: 'chat-completion' });
const span = trace.span({ name: 'openai-call' });
// ... make your LLM call ...
span.end({ output: responseText });
await langfuse.flushAsync();LangSmith
import { Client } from 'langsmith';
const client = new Client({ apiKey: process.env.LANGSMITH_API_KEY });
// Auto-traces when LANGCHAIN_TRACING_V2=true is set
// Or manually:
const run = await client.createRun({
name: 'my-llm-call',
run_type: 'llm',
inputs: { prompt: 'Hello' },
});Community Verdict
Based on upvoted notes🏆
Langfuse wins this comparison
Trust Score 85 vs 80 · 5-point difference
Langfuse leads on Trust Score with stronger signal data across downloads and community health. That said, the other tool is worth considering if your use case matches its specific strengths above.