LangSmith
LangChain की eval व trace प्लेटफ़ॉर्म—डेटासेट, स्कोरर, लाइव मॉनिटरिंग व मानव समीक्षा; LangChain/LangGraph से सबसे गहरा एकीकरण।
सबसे अच्छा जब
Teams already deep on LangChain / LangGraph that want traces, scoring, datasets, and replay in one loop—especially to ship a change and run 200 regressions in one click.
कम उपयुक्त जब
Minimal stacks that call APIs directly, strict OSS/air-gapped requirements, or teams that don’t use the LangChain ecosystem.
तुलना करते समय
Compare with Langfuse / Braintrust / Arize Phoenix on custom scorer depth, dataset management, and whether offline/online share one store.
जल्द चेकलिस्ट
- Verify project-level permissions and PII redaction
- Model trace sampling vs cost at your volume
- Build a 50+ example regression set before deciding
- Review self-hosting/enterprise plan requirements
खोज आधारित प्रश्न
LangSmith vs Langfuse—how to choose?
LangSmith is deepest if you already build with LangChain/LangGraph; Langfuse is open-source and self-hostable, which wins when OSS/data-locality matters. Features overlap—wire real traffic into both for a week before committing.
What metrics should an LLM eval cover?
Business Q&A needs groundedness + hallucination sampling + human scores; structured extraction needs field-level F1; agentic tasks add success rate and step count. Always pair these with P95 latency and per-call cost.
उपयोग के मामले
यह सारांश बताता है कि टूल आपकी ज़रूरत के अनुरूप है या नहीं। विकल्प ज़्यादा हों तो उपयोग आवृत्ति, बजट और डेटा गोपनीयता पहले तय करें।