Python
Connect directly to Foxglove or play back local data recorded with the Python SDK.
Install the SDK
The SDK is distributed through PyPI. You can get started with a new project or use Foxglove from a notebook.
Install foxglove-sdk
from PyPI: https://pypi.org/project/foxglove-sdk
pip install foxglove-sdk
The exact installation will depend on your package manager; here's an example using poetry.
poetry new example
poetry add foxglove-sdk
Live data
Log messages from Python
Create a new file in your project (for example, src/example/main.py
)
import foxglove
import time
foxglove.start_server()
while True:
foxglove.log("/hello", {"time": time.time()})
time.sleep(0.03)
Run the script
Run the script to start logging. Following the poetry example above, you can run the following command from the "example" directory:
poetry run python src/example/main.py
Connect
In Foxglove, select "Open connection" from the dashboard or left-hand menu.
Select "Foxglove WebSocket" in the "Open a new connection" dialog, then click "Open" to accept the default connection string:
Local data
Recording data to a file
The SDK can also log data to disk. Let's augment the example above to also produce an MCAP file which can later be opened in Foxglove:
import foxglove
import time
foxglove.start_server()
# Keep a reference to the mcap. It will automatically close the file when the program exits, but
# we could also close it manually with `mcap.close()`.
mcap = foxglove.open_mcap("example.mcap")
while True:
foxglove.log("/hello", {"time": time.time()})
time.sleep(0.03)
Viewing data from a file
To load local files for visualization, you can:
- Click "Open local file(s)…" in the dashboard or left-hand menu
- Open or drag-and-drop the files from your OS file manager (desktop only)
When opening multiple files, Foxglove will merge the data into a single playback timeline.
You can only merge multiple files of the same format.
Imported data
After importing data to Foxglove, select individual resources to visualize on the Recordings or Events pages:

Select a custom time range of data (can span multiple recordings or events) to visualize on the Timeline page:
Links and resources
- SDK documentation with a more detailed example
- Python reference
- Source code with additional examples