{"best_for":["Software companies","AI engineering teams","Developers building LLM-based agents","Enterprise teams with specific data residency needs"],"citation":{"dataset":"aitoolsforbusiness-agent-tool-export","directory_tool_url":"https://aitoolsforbusiness.ai/langsmith-tools","json_profile_url":"https://aitoolsforbusiness.ai/data/tools/langsmith-tools.json","markdown_profile_url":"https://aitoolsforbusiness.ai/data/markdown/tools-md-028.json","schema_version":"1.4.0","suggested_citation_label":"AI Tools for Business: LangSmith tools (https://aitoolsforbusiness.ai/langsmith-tools)"},"features":["Agent Tracing: Provides step-by-step visibility into agent executions to help locate failures and understand decision paths.","Monitoring: Tracks token usage, latency (P50, P99), error rates, and cost breakdowns through custom dashboards.","AI Evaluation: Supports LLM-as-judge and code-based evaluators to score agent quality based on production trace data.","LangSmith Fleet: A no-code builder that allows non-technical users to create and manage agents using natural language.","Managed Deployment: Infrastructure for deploying and scaling long-running agents with support for human-in-the-loop approvals.","Insights Agent: Analyzes and clusters traces to detect common failure modes and usage patterns."],"freshness_status":"fresh","name":"LangSmith tools","pricing_note":"LangSmith uses a freemium model starting with a $0 Developer plan. The Plus plan is $39 per seat per month. Both plans use usage-based billing for traces, with base traces at $2.50 per 1k and extended traces at $5.00 per 1k.","pricing_url":"https://www.langchain.com/pricing","primary_category":"Software Development","profile_last_verified":"2026-06-23T14:13:55.369Z","secondary_categories":[],"short_description":"LangSmith is an AI observability platform that provides tracing, monitoring, and evaluation tools for developers building AI agents and LLM applications.","slug":"langsmith-tools","sponsorship_status":"none","url":"https://aitoolsforbusiness.ai/langsmith-tools","use_cases":["Debugging Agent Failures: Using execution traces to find the specific step where an AI agent deviated from the intended path.","Tracking LLM Expenditures: Monitoring token usage and cost breakdowns to manage the budget for LLM API calls.","Improving Response Quality: Running evaluations on production traces to compare how changes to prompts or models affect performance.","Implementing Human Feedback: Using annotation queues to allow domain experts to review and label agent outputs."],"website_url":"https://www.langchain.com/langsmith"}