LangSmith is an all-in-one developer platform for every step of the LLM-powered application lifecycle, whether you’re building with LangChain or not.
LangSmith is an all-in-one developer platform for every step of the LLM-powered application lifecycle, whether you’re building with LangChain or not. Debug, collaborate, test, and monitor your LLM applications. LLM-apps are powerful, but have peculiar characteristics. The non-determinism, coupled with unpredictable, natural language inputs, make for countless ways the system can fall short. Traditional engineering best practices need to be re-imagined for working with LLMs, and LangSmith supports all phases of the development lifecycle. Building LLM-powered applications requires a close partnership between developers and subject matter experts. See what’s happening with your production application, so you can take action when needed or rest assured while your chains and agents do the hard work. Many companies who don’t build with LangChain use LangSmith. You can log traces to LangSmith via the Python SDK, the TypeScript SDK, or the API. We allow customers to self-host LangSmith on our enterprise plan. We deliver the software to run on your Kubernetes cluster, and data will not leave your environment. Traces are stored in GCP us-central-1. Organizations' traces are logically separated from each other in a Clickhouse database and encrypted in transit and at rest. We will not train on your data, and you own all rights to your data.
Add your AI agent to our curated directory
Handle multi-step workflows autonomously
Example
Schedule meeting → Find time → Send invite → Confirm attendees
Save 5-10 hours/week on routine coordination tasks
Gather data from multiple sources and summarize
Example
Research competitor pricing across 5 websites, create comparison table
Reduce research time from hours to minutes
Analyze options and recommend actions
Example
Review 20 vendor proposals, score against criteria, rank top 3
Make data-driven decisions faster
AI agents combine large language models with tools, memory, and decision-making logic to autonomously complete multi-step tasks without constant human guidance.
Large language model for reasoning and decision-making
Understand tasks, plan steps, generate responses
APIs, databases, external services the agent can call
Take actions beyond text generation (search, compute, write files)
Short-term (conversation) and long-term (persistent) memory
Maintain context across interactions and learn from past actions
Decision engine for choosing next action
Plan multi-step workflows and handle errors/edge cases
Prerequisites
Steps
✓ Do
✗ Don't
Key Metrics
Optimization Tips
We compared LangSmith with three neighbors in the same category; this one had the most concrete “what it does” framing.
We piloted LangSmith for two weeks; the registry summary and category tag matched what the product actually emphasizes.
According to our evaluation, LangSmith benefits from clear positioning — fewer buzzwords than typical agent landing pages.
I recommend LangSmith for teams already running multiple AI agents; the listing helped us narrow the short list quickly.
We piloted LangSmith for two weeks; the registry summary and category tag matched what the product actually emphasizes.
Good discoverability: LangSmith shows up in the agents directory with enough detail to pre-qualify buyers.
Solid agent profile: LangSmith links out cleanly and the on-site reviews add signal beyond marketing copy.
We compared LangSmith with three neighbors in the same category; this one had the most concrete “what it does” framing.
We piloted LangSmith for two weeks; the registry summary and category tag matched what the product actually emphasizes.
We compared LangSmith with three neighbors in the same category; this one had the most concrete “what it does” framing.
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Key Considerations
