Update Monitoring
Activate and deactivate Evaluators for monitoring the Agent.
An activated Evaluator will automatically be run on all new Logs within the Agent for monitoring purposes.
Path parameters
Headers
Request
Evaluators to activate for Monitoring. These will be automatically run on new Logs.
Evaluators to deactivate. These will not be run on new Logs.
Response
Successful Response
Path of the Agent, including the name, which is used as a unique identifier.
Unique identifier for the Agent.
The model instance used, e.g. gpt-4. See supported models
List of tools that the Agent can call. These can be linked files or inline tools.
Name of the Agent.
Unique identifier for the specific Agent Version. If no query params provided, the default deployed Agent Version is returned.
The status of the Agent Version.
The number of logs that have been generated for this Agent Version
The number of logs that have been generated across all Agent Versions
Inputs associated to the Agent. Inputs correspond to any of the variables used within the Agent template.
ID of the directory that the file is in on Humanloop.
The provider model endpoint used.
The template contains the main structure and instructions for the model, including input variables for dynamic values.
For chat models, provide the template as a ChatTemplate (a list of messages), e.g. a system message, followed by a user message with an input variable. For completion models, provide a prompt template as a string.
Input variables should be specified with double curly bracket syntax: {{input_name}}.
The template language to use for rendering the template.
The company providing the underlying model service.
The maximum number of tokens to generate. Provide max_tokens=-1 to dynamically calculate the maximum number of tokens to generate given the length of the prompt
What sampling temperature to use when making a generation. Higher values means the model will be more creative.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
The string (or list of strings) after which the model will stop generating. The returned text will not contain the stop sequence.
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the generation so far.
Number between -2.0 and 2.0. Positive values penalize new tokens based on how frequently they appear in the generation so far.
Other parameter values to be passed to the provider call.
If specified, model will make a best effort to sample deterministically, but it is not guaranteed.
The format of the response. Only {"type": "json_object"} is currently supported for chat.
Guidance on how many reasoning tokens it should generate before creating a response to the prompt. OpenAI reasoning models (o1, o3-mini) expect a OpenAIReasoningEffort enum. Anthropic reasoning models expect an integer, which signifies the maximum token budget.
Additional fields to describe the Prompt. Helpful to separate Prompt versions from each other with details on how they were created or used.
The maximum number of iterations the Agent can run. This is used to limit the number of times the Agent model is called.
Unique name for the Agent version. Version names must be unique for a given Agent.
Description of the version, e.g., the changes made in this version.
Description of the Agent.
Long description of the file.
The JSON schema for the Prompt.
The list of environments the Agent Version is deployed to.
The user who created the Agent.
The user who committed the Agent Version.
The date and time the Agent Version was committed.
Evaluators that have been attached to this Agent that are used for monitoring logs.
Aggregation of Evaluator results for the Agent Version.
The raw content of the Agent. Corresponds to the .agent file.