> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataframer.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Connect AI Observability

> Pull your existing AI traces into DataFramer from your observability tool

If your AI traces already live in an observability tool, connect it and DataFramer pulls the traces in. This is separate from [server instrumentation](/user-signals/server-instrumentation): connecting a project pulls traces you've *already* created, while `dataframer-journey` auto-tags traces *as your service creates them*.

<Note>
  **Currently supported: Langfuse and LangSmith.** More observability tools are on the roadmap.
</Note>

## Connect a project

In the DataFramer UI, under **Findings**, add a project and choose your provider:

<Tabs>
  <Tab title="Langfuse">
    | Field        | Example                                |
    | ------------ | -------------------------------------- |
    | Project Name | `Production Langfuse`                  |
    | Base URL     | `https://cloud.langfuse.com` (default) |
    | Public Key   | `pk-lf-...`                            |
    | Secret Key   | `sk-lf-...`                            |

    Find your keys in your Langfuse project settings.
  </Tab>

  <Tab title="LangSmith">
    | Field                        | Example                                                       |
    | ---------------------------- | ------------------------------------------------------------- |
    | Project Name (in DataFramer) | `Production LangSmith`                                        |
    | Endpoint URL                 | `https://api.smith.langchain.com` (default)                   |
    | API Key                      | `ls-...`                                                      |
    | LangSmith Project            | `my-project`, the project name in LangSmith to pull runs from |

    Find your API key in your LangSmith account settings.
  </Tab>
</Tabs>

Once connected, DataFramer pulls traces (Langfuse) or runs (LangSmith) into Findings, where you can explore, track, and push them into datasets or review queues.

## Journey correlation: Langfuse only, for now

If you also use the [Signals SDK](/user-signals/browser-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](/user-signals/server-instrumentation) 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.

## Next steps

<Card title="Findings" icon="magnifying-glass" href="/findings/overview">
  See what you can do with pulled traces
</Card>