For LangSmithAI Platform

Prompt Hub And Prompt Management

LangSmith · LangSmith

LLM Observability & Evaluation Platform — LangSmith (LangChain)

LangSmith evals — Prompt Hub / Prompt Management (relift v3 InfraRed)

About LangSmith

LangSmith is LangChain's LLM observability and evaluation platform: tracing, datasets, evaluators (LLM-as-judge, code, and human), experiments, prompt management, and online monitoring used by AI teams to measure and improve LLM apps in production.

Employees

~200

Industry

LLM Observability

Headquarters

San Francisco, CA

Website

www.langchain.com/langsmith

Sample tests· showing 3 of 9

#	Input	Expected behavior	Check
01	Operator pushes a new prompt 'support-triage' to the Prompt Hub with client.push_prompt.	push_prompt creates a new commit hash for the prompt. Record the returned commit hash in the deployment manifest (e.g., infra repo). Subsequent pull_prompt('support-triage:<commit_hash>') pins to that exact version. Pushing the same content twice is a no-op (same hash).	Pass / FailAi Platformhigh
02	Production deployment must pin to a specific prompt commit so a UI edit cannot silently change behavior.	Call client.pull_prompt('support-triage:<commit_hash>') at deploy time and cache the resulting prompt object in the process. Treat the commit hash as part of the deployment artifact. NEVER pull bare 'support-triage' in prod — that resolves to the latest commit and is mutable.	Pass / FailAi Platformcritical
03	Prompt is published as a runnable chain with include_model=true so the model binding lives alongside the prompt.	client.pull_prompt(name, include_model=True) returns a runnable that already binds the documented model+params. Use this only when the team wants model+prompt to version together. For ops control over model rollouts, keep model selection outside the prompt and pass include_model=False (default).	Pass / FailAi Platformmedium
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