langfuseInvestigate AI traces, observations, exceptions, latency, sessions, prompts, datasets, annotation queues, and scores through Langfuse MCP. Use when the request names Langfuse or asks to diagnose recorded AI behavior.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use Langfuse evidence to move from a broad symptom to the relevant trace, observation, session, prompt, or dataset. Start with the narrowest read-only query that can locate the event, then inspect its concrete inputs, outputs, timing, and errors.
A common investigation starts with fetch_traces(age=60) or
find_exceptions(age=1440, group_by="file"), then fetches the selected trace or
observation by ID. Use full_json_file only when the complete payload is needed;
exports can contain sensitive user data.
Treat model names, prompts, timestamps, and other values in historical traces
as recorded evidence. Do not rewrite or normalize old model identifiers to a
current name such as gpt-6-astra.
Trace discovery and inspection are read-only. Prompt creation or relabeling, dataset changes, annotation-queue changes, and deletions mutate Langfuse; perform them only when the user requested that change.
Prefer read-only server mode for diagnostic work. It disables prompt and dataset write tools:
langfuse-mcp --read-only
# or LANGFUSE_MCP_READ_ONLY=true
Never commit Langfuse credentials. Keep public key, secret key, and host in the supported environment or MCP configuration described in the setup reference. Rotate any exposed key.
Preserve exact IDs and filters in findings so another reader can reproduce the query. State when pagination, lookback limits, or compact output limits the conclusion.