Langfuse is organized around application traces and observations
Current Langfuse documentation describes tracing as the core observability primitive for prompts, responses, tool calls, latency, token usage, costs, and the relationships among them. Langfuse also connects those traces to prompt management, scores, datasets, and evaluation workflows. That is a strong fit when you own or can instrument the LLM application.
AgentSight is organized around the local run and process family
AgentSight begins with a command or agent session and records supported model activity alongside processes, files, network destinations, and resources. It can therefore add useful information when a local CLI is closed source or when an instrumented tool launches descendants that do not report back as detailed application observations.
A Langfuse tool observation and a child process are not the same object
A tool observation can preserve the tool name, inputs, outputs, timing, and application context. A child process has an executable, parent/child relationship, exit status, paths, sockets, and local resource behavior. For debugging a tool implementation, these views often need to be correlated rather than translated into one another.
Evaluation and quality workflows remain application-layer concerns
Langfuse provides evaluation concepts, scores, datasets, and experiments that AgentSight does not attempt to replace. AgentSight can help explain execution context behind an output or failure, but it is not an LLM quality-evaluation platform. If the question is whether the answer is good, use evaluation tooling; if the question is what the local run actually executed, use system profiling.
Local-first and hosted workflows create different data decisions
A local AgentSight database can contain sensitive prompts, responses, paths, headers, and network targets. Langfuse deployments have their own ingestion, hosting, and retention model. When combining them, decide which fields actually need to leave the workstation and export only the information required for the central workflow rather than copying every local system event by default.
A practical combined architecture keeps both traces
Use Langfuse as the application trace and evaluation system when it is already instrumented. Run AgentSight around selected local or CI agent tasks when you need process/file/network context, then correlate the relevant run or model requests by timestamps and stable identifiers. This preserves Langfuse semantics and AgentSight system provenance instead of flattening both into a thin comparison table.