LangSmith
AIPlatform to debug, evaluate, monitor, and optimize LLM applications with SDKs, integrations, prompt management, and observability.
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Free Available
Starting from Free
Premium plans available
LangSmith
AI
Platform to debug, evaluate, monitor, and optimize LLM applications with SDKs, integrations, prompt management, and observability.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About LangSmith
LangSmith is a platform for observability, debugging, evaluation, and monitoring of language models and intelligent agents. It provides client SDKs (Python and JavaScript), CLI tools, and server components to collect traces, store run metadata, manage prompts, run evaluations and experiments, and analyze model/agent behavior. LangSmith integrates natively with the LangChain ecosystem but is designed to work with any LLM application, enabling teams to track conversations, version and fetch prompts, run automated evaluations with datasets and judges, and self-host or configure custom endpoints for data residency and regional deployments.
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Key Features
SDKs for Python and JavaScript: Official client libraries to instrument, send, and query run traces, evaluations, and prompt metadata from LLM applications and agent chains, enabling language-agnostic integration and programmatic access to platform data.
End-to-end Tracing and Run Storage: Capture detailed step-level traces of LLM calls and agent actions (including inputs, outputs, tools used, timings, and errors) for reproducible debugging and root-cause analysis of complex flows.
Evaluation & Experimentation: Create datasets, run evaluations, and track experiments with automated scoring (including LLM-based judges) to compare prompts, models, or agent strategies over time and measure improvements.
Prompt Management and Versioning: Centralized prompt repository and APIs to list, fetch, and manage prompt templates, visibility (public/private), and versions to support prompt reuse, auditing, and A/B testing.
Conversation & Thread History: Retrieve chronological message histories and thread metadata for conversations, enabling replay, analytics, and context-aware debugging of chat-based applications.
MCP Server & Integration Components: Optional MCP server and integration layer that bridges language models, agents, and the LangSmith platform, providing endpoints for prompt retrieval, analytics integration, and workspace-scoped API keys.
Self-hosting & Custom Endpoints: Support for custom LANGSMITH_ENDPOINT configuration and self-hosted deployments to meet data residency, regulatory, or on-premises requirements.
CLI and Tooling: Command-line utilities (pip-installable) to create datasets, run evaluations, configure API keys, and interact with the LangSmith platform directly from developer workflows.
Client SDKs for Python and JavaScript for interacting with the LangSmith platform
Native integration with LangChain (Python and JS) for automatic trace collection
Trace and conversation history capture with chronological message retrieval
Evaluation pipelines and tools to run model/agent evaluations and record results
Prompt management: list, fetch, and retrieve prompts and templates
Support for self-hosting and custom API endpoints (LANGSMITH_ENDPOINT)
API key based authentication (LANGSMITH_API_KEY) and optional workspace scoping (LANGSMITH_WORKSPACE_ID)
PII removal and anonymization utilities (environment flags and custom anonymizers)
MCP server to bridge models and LangSmith for conversation tracking and analytics integration
Documentation site and cookbook with tutorials, recipes, and examples
Use Cases
Agent Step Debugging: Inspect step-level traces for multi-step agents to identify which tool call or prompt produced incorrect results and rapidly iterate fixes.
Model Evaluation Experiments: Run controlled experiments comparing model versions or prompt variants against curated datasets using automated scoring and track results over time.
Production Monitoring: Monitor live LLM applications for errors, latency spikes, or behavioral drift using run telemetry and alerting integrations to reduce downtime.
Prompt Library Management: Store, version, and fetch canonical prompts across teams to ensure consistency, enable A/B testing, and audit prompt changes in production.
Conversation Analysis and Support: Retrieve full thread histories to reproduce user issues, analyze user interactions, and improve response quality or routing logic.
Self-hosted Deployments: Deploy LangSmith endpoints in-region or on-premises for organizations requiring data residency or isolated environments while keeping LangChain integrations.
Continuous Improvement Workflows: Use the cookbook recipes and SDKs to automate feedback collection, run regular evaluations, and feed insights back into prompt/model tuning pipelines.
Debugging and tracing multi-step agent executions to find failure points
Monitoring LLM performance and behavior in production with observability dashboards
Evaluating prompts and model responses via automated evaluation pipelines
Managing and retrieving prompt templates and shared prompt libraries
Anonymizing sensitive data in traces to comply with data protection requirements
Self-hosting LangSmith in regulated or regional deployments (custom endpoint support)
Integrating with LangChain-based apps to capture telemetry and analytics
