LangChain × LangSmith
LLM Orchestration & Observability
LangChain for orchestration, LangSmith for evals and observability — our production-grade AI stack.
What we build with LangChain × LangSmith
Joint offerings
Agent orchestration in LangChain
Multi-tool, multi-step agents wired to your internal systems — with explicit guardrails, retries, and tool-layer authorization.
LangSmith observability
Every agent call traced with inputs, outputs, tool invocations, and latency — with eval suites that block regressions before deploy.
LangGraph stateful workflows
Long-running, human-in-the-loop workflows modeled as graphs — persistable state, checkpoint-resumption, and audit trails.
FAQ
Frequent questions
How does LangSmith compare to Datadog or OpenTelemetry for AI observability?
LangSmith is purpose-built for LLM traces — token-level granularity, tool-call inspection, eval suites. We typically pair it with your existing Datadog/Sentry for infrastructure metrics rather than replacing them.
Do you always use LangChain, or sometimes build from raw model APIs?
Both. Simple chains often don't need LangChain's abstraction — a 30-line function is clearer. Complex multi-agent systems benefit from LangGraph's stateful workflow model with persistable checkpoints.
Can LangSmith evals catch regressions before we deploy?
Yes. We wire LangSmith evals into CI so that prompt updates, schema changes, or model upgrades that degrade quality block the deploy — same pattern as code coverage gates.