LangSmith CLI
/install langsmith-cli
LangSmith CLI Skill
CLI: scripts/langsmith.py. Requires LANGSMITH_API_KEY in env (or ~/.zshrc).
No second API key needed — the ask command fetches and formats traces as structured context for your agent to analyze. No trace data is sent to any third-party LLM.
Commands
Tier 0 — Ask (agent Q&A over traces)
python3 scripts/langsmith.py ask "\x3Cquestion>" --project \x3Cname> [--since 24h] [--limit 50]
Fetches recent runs and prints them as structured JSON context. Your agent reads the output and answers the question — no external LLM calls, no data leaving your machine beyond the LangSmith API.
Examples:
ask "why is my chain slow this week" --project my-projectask "what do failing runs have in common" --project my-project --since 7dask "did the system prompt change on Friday affect output quality" --project my-project
Tier 1 — Situational Awareness
python3 scripts/langsmith.py runs \x3Cproject> [--since 2h] [--status error|success] [--limit 20]
python3 scripts/langsmith.py cost \x3Cproject> [--since 7d] # token spend by chain/node
python3 scripts/langsmith.py latency \x3Cproject> [--since 24h] # p50/p95/p99 per run name
Tier 2 — Before/After Comparisons
python3 scripts/langsmith.py diff \x3Cproject> --before \x3CISO_date> --after \x3CISO_date>
python3 scripts/langsmith.py prompt-diff \x3Crun_id_a> \x3Crun_id_b>
diff compares avg latency, error rate, cost, output length across two time windows.
prompt-diff shows side-by-side system prompts + outputs for two specific runs.
Tier 3 — Deep Analysis (stubs, expand as needed)
python3 scripts/langsmith.py cluster-failures \x3Cproject> [--since 7d]
python3 scripts/langsmith.py replay \x3Crun_id>
Auth Setup
export LANGSMITH_API_KEY=\x3Cyour-key>
# or add to ~/.zshrc
Test with: python3 scripts/langsmith.py runs \x3Cproject> --limit 3
Security & Data Flow
This skill makes outbound network requests only to api.smith.langchain.com (the LangSmith API). That's it.
LANGSMITH_API_KEY— sent as an HTTP header toapi.smith.langchain.comonly. Never logged or stored.- Trace data — fetched from LangSmith and printed to stdout for your agent to read. No trace data is sent to any third-party LLM or external service.
- No second API key required — the
askcommand outputs structured trace context for your existing agent to analyze, rather than making its own LLM calls. - No telemetry — the script collects no usage data.
The script is ~300 lines of pure Python with no obfuscation. Audit it at scripts/langsmith.py.
API Reference
See references/langsmith-api.md for endpoint details and run object schema.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install langsmith-cli - 安装完成后,直接呼叫该 Skill 的名称或使用
/langsmith-cli触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
LangSmith CLI 是什么?
Query and analyze LangSmith traces with natural language or structured commands. Use when the user asks about LangSmith runs, trace failures, latency, cost,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 213 次。
如何安装 LangSmith CLI?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install langsmith-cli」即可一键安装,无需额外配置。
LangSmith CLI 是免费的吗?
是的,LangSmith CLI 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
LangSmith CLI 支持哪些平台?
LangSmith CLI 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 LangSmith CLI?
由 Ralph Esber(@ralphesber)开发并维护,当前版本 v1.1.0。