Connect your traces

If your AI traces already live in an observability tool, connect it and DataFramer pulls the traces in. This is separate from server instrumentation: connecting a project pulls traces you’ve already created, while dataframer-journey auto-tags traces as your service creates them.

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Connect a project

In the DataFramer UI, under Findings, add a project and choose your provider: Once connected, DataFramer pulls traces (Langfuse) or runs (LangSmith) in. Import new traces on the Traces page pulls on demand; the count next to it says how many were imported and how many were skipped. Two things to decide up front:

• Data level is permanent. It sets what one datapoint is, and discovery and evaluations work at that level. You cannot change it after the project is created — pick a new project instead. At trace or session level a datapoint spans many observations, so very large ones are common; those are imported and analyzed in pieces rather than truncated, and only a datapoint that can’t be read that way at all is skipped at import.
• Importing traces is an AI task. It reads PDF and spreadsheet attachments on the traces it pulls with a model, so it draws on your DataFramer credits or your own model provider key, and it is blocked while AI usage is unfunded or over the plan’s spend limit.

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See your traces

Traces lists the imported traces for the time range you pick; choose the columns you want, such as model, latency, tokens or cost, with Columns. Open a trace to see everything DataFramer knows about it, and use the Review Copilot there to dig in with the full context around it.

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Route traces onward

Wherever DataFramer lists traces, select any subset and route it onward: Assign review sends the traces to a Review Queue, Add to dataset → Evaluation Dataset turns them into ground truth for judges, and Add to dataset → Generation Seed Dataset turns them into seed examples for synthetic generation.

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Journey correlation: Langfuse only, for now

If you also use the Signals SDK and want traces auto-tagged with a journey_id at creation time, that only works for Langfuse today, and only its v2 SDK, via dataframer-journey. See the Langfuse v2 caveat for details. LangSmith traces still pull in fine, they just won’t have a journey_id auto-stamped on them. If you want journey correlation on LangSmith traces, add journey_id to your run’s metadata or tags yourself before it’s pulled in.

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Next steps