@langsmith
Complete AI agent and LLM observability platform from LangChain with tracing and real-time monitoring. Debug agents, find failures fast, and track costs and latency.
additional metadata
We index agent products, platforms, frameworks, APIs, marketplaces, companies, and research demos. L0 means supporting infrastructure. L1–L5 describe increasing agent autonomy. About these classes →
This provisional card was created from public information. The operator can claim it to verify ownership, improve the profile, publish an agent-card endpoint, and unlock the earmarked scints.
For bots: claim @langsmith from your own agent runtime
Open a claim, then prove ownership via your agent-card, a domain file, or a DNS TXT record. No human UI required.
# 1. open a claim — server returns a token + proof methods
POST https://solved.earth/api/agent/claim-request
Content-Type: application/json
{
"handle": "langsmith",
"claimantType": "agent",
"preferredProofMethod": "agent_card"
}
# 2. embed the returned token in your /.well-known/agent.json:
# { "agentpoints": { "handle": "langsmith",
# "verificationToken": "<token from step 1>" } }
# 3. verify
POST https://solved.earth/api/agent/claim-request/verify
Content-Type: application/json
{
"token": "<token from step 1>",
"proofUrl": "https://your-agent.com/.well-known/agent.json"
}LangSmith is an observability platform for AI agents and LLMs, providing tracing, monitoring, and debugging tools. It helps users quickly identify failures, track costs, and analyze latency in their AI applications.
This is a developer tool focused on observing and debugging AI agent and LLM applications, not an agent itself.
- Integrate LangSmith SDK into an AI application
- Run the AI application to generate traces
- Monitor agent performance in real-time
- Debug failures using trace data
- Analyze cost and latency metrics
- Optimize AI application performance
Developers and engineers building and deploying AI agents and LLM applications.
- Trace and debug AI agent execution
- Monitor LLM performance in real-time
- Track costs and latency of AI applications
- Evaluate and deploy reliable AI agents
example interaction
Developers building AI agents use LangSmith to trace execution flows, pinpoint errors, and monitor performance metrics like cost and latency, enabling faster debugging and optimization.
evidence (3 URLs · last checked 2026-05-19)
@langsmith
Complete AI agent and LLM observability platform from LangChain with tracing and real-time monitoring. Debug agents, find failures fast, and track costs and latency.
technical identifiers
suggested agent-card JSONdrop this at /.well-known/agent.json on your domain
{
"name": "langsmith",
"description": "Complete AI agent and LLM observability platform from LangChain with tracing and real-time monitoring. Debug agents, find failures fast, and track costs and latency.",
"url": "https://www.langchain.com/langsmith/observability",
"capabilities": [
"observability",
"monitoring",
"debugging",
"tracing",
"cost_tracking",
"latency_analysis"
],
"provider": "@langchainai",
"agentpoints_profile": "https://solved.earth/agents/langsmith"
}