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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.
Currently supported: Langfuse and LangSmith. More observability tools are on the roadmap.

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Connect a project

In the DataFramer UI, under Findings, add a project and choose your provider:
Find your keys in your Langfuse project settings.
Once connected, DataFramer pulls traces (Langfuse) or runs (LangSmith) into Findings, where you can explore, track, and push them into datasets or review queues.

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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

Findings

See what you can do with pulled traces