Update Agent Log
Update a Log.
Update the details of a Log with the given ID.
Path parameters
Unique identifier for Agent.
Unique identifier for the Log.
Headers
Request
List of chat messages that were used as an input to the Flow.
The output message returned by this Flow.
The inputs passed to the Flow Log.
The output of the Flow Log. Provide None to unset existing output value. Provide either this, output_message or error.
The error message of the Flow Log. Provide None to unset existing error value. Provide either this, output_message or output.
Status of the Flow Log. When a Flow Log is updated to complete, no more Logs can be added to it. Monitoring Evaluators will only run on complete Flow Logs.
Response
Successful Response
Agent that generated the Log.
Unique identifier for the Log.
List of Evaluator Logs associated with the Log. These contain Evaluator judgments on the Log.
The message returned by the provider.
Number of tokens in the prompt used to generate the output.
Number of reasoning tokens used to generate the output.
Number of tokens in the output generated by the model.
Cost in dollars associated to the tokens in the prompt.
Cost in dollars associated to the tokens in the output.
Reason the generation finished.
The messages passed to the to provider chat endpoint.
Controls how the model uses tools. The following options are supported:
'none'means the model will not call any tool and instead generates a message; this is the default when no tools are provided as part of the Prompt.'auto'means the model can decide to call one or more of the provided tools; this is the default when tools are provided as part of the Prompt.'required'means the model must call one or more of the provided tools.{'type': 'function', 'function': {name': <TOOL_NAME>}}forces the model to use the named function.
When the logged event started.
When the logged event ended.
Generated output from your model for the provided inputs. Can be None if logging an error, or if creating a parent Log with the intention to populate it later.
User defined timestamp for when the log was created.
Error message if the log is an error.
Duration of the logged event in seconds.
Captured log and debug statements.
Raw request sent to provider.
Raw response received the provider.
The inputs passed to the prompt template.
Identifies where the model was called from.
Any additional metadata to record.
Status of a Log. Set to incomplete if you intend to update and eventually complete the Log and want the File’s monitoring Evaluators to wait until you mark it as complete. If log_status is not provided, observability will pick up the Log as soon as possible. Updating this from specified to unspecified is undefined behavior.
Unique identifier for the Datapoint that this Log is derived from. This can be used by Humanloop to associate Logs to Evaluations. If provided, Humanloop will automatically associate this Log to Evaluations that require a Log for this Datapoint-Version pair.
The ID of the parent Log to nest this Log under in a Trace.
Array of Batch IDs that this Log is part of. Batches are used to group Logs together for offline Evaluations
End-user ID related to the Log.
The name of the Environment the Log is associated to.
Whether the request/response payloads will be stored on Humanloop.
This will identify a Log. If you don’t provide a Log ID, Humanloop will generate one for you.
Identifier for the Flow that the Trace belongs to.
Identifier for the Trace that the Log belongs to.
Logs nested under this Log in the Trace.