langsmith
Skill445 starsChanged 10 days agoMITView on GitHub
Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production.
- Reads credentials
- Installs packages
1 other project uses a version of this.
Adopted by
Most recently taken up by
- gigaverse-app/langsmith-clitheir own version, first seen Jul 27, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- gigaverse-app/langsmith-cli★ 9same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository25 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/docs-style.mdcCursor rule
- add-docs-pageSkill
- docs-code-samplesSkill
- docs-editSkill
- docs-restructureSkill
- docs-reviewSkill
- docs-team-voiceSkill
- docs-tooling-notionSkill
- submit-integrationSkill
- update-integrations-prsSkill
- verify-against-sourceSkill
- deepagents-codeSkill
- deepagentsjsSkill
- deepagentsSkill
- openwikiSkill
- release-researchSkill
- ship-docs-updateSkill
- langgraph-docsSkill
- write-timestampSkill
- pandas-patternsSkill
- deep-agentsSkill
- langchainSkill
- langgraphSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
Use LangSmith when you need to:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
LangSmith
LangSmith is a framework-agnostic platform for building, debugging, and deploying AI agents and LLM applications. Trace requests, evaluate outputs, test prompts, and manage deployments all in one place at smith.langchain.com.
When to use
Use LangSmith when you need to:
- Trace and debug LLM calls, agent steps, retrieval, and tool use
- Evaluate LLM outputs with automated or human-in-the-loop scoring
- Engineer prompts with a visual playground and version control
- Deploy agents to production with the LangGraph-based agent server
- Monitor production systems with dashboards, alerts, and cost tracking
When NOT to use
- To build agent logic or LLM pipelines, use LangChain, LangGraph, or Deep Agents instead
- LangSmith is the platform layer that complements these frameworks
Quick setup
Set two environment variables to enable tracing from any supported framework:
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY="your-api-key" # from smith.langchain.com/settingsInstall the SDK
# Python
pip install langsmith
# JavaScript/TypeScript
npm install langsmithVerify tracing
from langsmith import traceable
@traceable
def my_function(query: str) -> str:
# Your LLM logic here—all calls inside are traced automatically
return "result"Core capabilities
| Capability | Description |
| Observability | Trace every step of your LLM app with automatic or manual instrumentation |
| Evaluation | Run evaluations with code, LLM-as-judge, or composite evaluators |
| Prompt engineering | Create, version, and test prompts in a visual playground |
| Agent deployment | Deploy LangGraph agents with streaming, human-in-the-loop, and durable execution |
| Monitoring | Dashboards, alerts, and cost tracking for production workloads |
Key documentation
- Overview—Get started with LangSmith
- Observability quickstart—Add tracing in minutes
- Evaluation quickstart—Run your first evaluation
- Prompt engineering quickstart—Iterate on prompts
- Deployment quickstart—Deploy an agent
- Integrations—Connect your framework or provider
- Create account & API key—Account setup
API reference
For SDK class and method details, use the LangChain API Reference site:
- Browse:
https://reference.langchain.com/python/langsmith - MCP server:
https://reference.langchain.com/mcp
Related skills
- langchain—Build agents with prebuilt architecture and model integrations
- langgraph—Orchestrate stateful, durable agent workflows
- deep-agents—Batteries-included agent harness with planning and subagents
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

