LLM traces, correlated with your whole application.

Deep LLM Tracing, Narrow Application Visibility

What happens when the problem is below the LLM layer?

• LangSmith traces the LLM layer, but production AI applications rarely stay within one framework. You might use LangChain for orchestration, a custom retrieval pipeline, and a fine-tuned model served via vLLM. LangSmith now ingests OpenTelemetry from any framework via its OTLP endpoint, so the trace data travels; what it does not provide is the application topology underneath. (source: docs.langchain.com/langsmith/trace-with-opentelemetry, verified 2026-08-18)
• No visibility into your services: LangSmith does not see the API gateway routing requests, the vector database latency, or the service topology your LLM application depends on. Its data model is runs, traces, threads, and trajectories; there is no host or service entity in it. Infrastructure charts exist only for agents you host on LangSmith Deployment. (source, verified 2026-08-18)
• Open SDK, closed platform: LangSmith's client SDK is open source (MIT) and it accepts end-to-end OpenTelemetry ingestion from any OTel-instrumented app, so instrumentation is not the lock-in point. The platform that stores and renders the data is. The differentiator is not framework coverage; it is correlating LLM traces with full application and service topology. (source, verified 2026-08-18)

Framework-Agnostic, Application-Level Observability

Works with any LLM framework. Correlates with everything else.

• Built on OpenTelemetry, not on any single LLM framework. DeepCube works with LangChain, LlamaIndex, Semantic Kernel, custom pipelines, or any combination.
• Your LLM traces live alongside your application metrics and service topology in a single 3D spatial environment.
• When your LangChain agent slows down, DeepCube shows you whether the issue is in the chain logic, the model provider, the retrieval layer, or the application underneath.

Architecture: How We Differ

Open standard ingestion vs a closed platform.

LangSmith is framework agnostic and ingests OpenTelemetry from any framework. The prompt playground, annotation queues, and dataset management make it a strong choice for teams doing active LLM development. Where it stops is the layer below the LLM: it does not build a service topology or correlate traces with application-level metrics.

DeepCube works with any LLM framework via OpenTelemetry too. The difference is correlation: it provides deep tracing for LangChain, LlamaIndex, Semantic Kernel, or custom pipelines, and ties that LLM telemetry to your full application health and 3D service topology.

AI: Tessa vs Manual Debugging

Human on the loop.

Tessa accesses your connected codebase workspace, diagnoses from 3D topology, and makes the fix. You review, you own it. When your LangChain agent produces degraded results, Tessa does not just show you the trace. It correlates the retrieval latency with the vector database performance, checks the embedding service health, and proposes a fix in your codebase.

LangSmith helps you find the problem in the trace tree. Tessa finds the problem across your entire stack and writes the fix.

Pricing: Application Observability Value

One platform vs LLM tool + APM tool.

Already Using LangSmith? Add Application Context.

No rip and replace required.

• Keep LangSmith for LLM development: Prompt playground, annotation queues, dataset management. Add DeepCube for application monitoring and cross-signal correlation.
• Complement or replace: Use DeepCube alongside LangSmith, or consolidate when ready. Your choice.
• Full-stack correlation: As your stack grows, DeepCube ties LLM telemetry to your service topology and application metrics, the layer LangSmith does not cover. (source, verified 2026-08-18)
• Exit guarantee: If DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.