CozeloopExporter reports span data to the Cozeloop platform over OTLP (HTTP). Once connected, you can observe traces with Cozeloop’s trace feature or evaluate agents with its evaluation feature. Data is isolated by workspace (space).
When to use
- You want to observe your agent’s traces on Cozeloop;
- You want to analyze conversation quality and tool-usage effectiveness with Cozeloop’s evaluation capabilities;
- You already have a Cozeloop workspace and access token.
Prerequisites
Complete installation and model configuration, then set the environment variables below before starting Python. Replace placeholders with accessible resources and valid credentialsUsage
Save asapp.py and run python app.py. The example uses existing resources and flushes pending traces after one model call
app.py
CozeloopExporterConfig:
Parameters
Constructor parameters
CozeloopExporter carries its connection parameters in the config field of type CozeloopExporterConfig; when omitted, each field is read automatically from the corresponding environment variable.
Cozeloop connection config
config is a CozeloopExporterConfig; each field defaults from CozeloopConfig, with the following environment variables:
space_id is taken from the environment variable OBSERVABILITY_OPENTELEMETRY_COZELOOP_SERVICE_NAME; when unset, the exporter creates a default workspace automatically using token as the credential. After logging in to Cozeloop, the segment after space in the URL is the workspace ID.Agent attach the Cozeloop exporter automatically from an environment variable, without constructing it explicitly:
Verification and troubleshooting
Search for the run’s Trace ID in the target backend. A successful model response does not prove trace delivery. If data is missing, check the endpoint and region, credential permissions, target resource, and whethertracer.force_export() ran before the process exited