Call Agent

Call an Agent. The Agent will run on the Humanloop runtime and return a completed Agent Log.

If the Agent requires a tool call that cannot be ran by Humanloop, execution will halt. To continue, pass the ID of the incomplete Log and the required tool call to the /agents/continue endpoint.

The agent will run for the maximum number of iterations, or until it encounters a stop condition, according to its configuration.

You can use query parameters version_id, or environment, to target an existing version of the Agent. Otherwise the default deployed version will be chosen.

Instead of targeting an existing version explicitly, you can instead pass in Agent details in the request body. A new version is created if it does not match any existing ones. This is helpful in the case where you are storing or deriving your Agent details in code.

Call an Agent. The Agent will run on the Humanloop runtime and return a completed Agent Log. If the Agent requires a tool call that cannot be ran by Humanloop, execution will halt. To continue, pass the ID of the incomplete Log and the required tool call to the /agents/continue endpoint. The agent will run for the maximum number of iterations, or until it encounters a stop condition, according to its configuration. You can use query parameters `version_id`, or `environment`, to target an existing version of the Agent. Otherwise the default deployed version will be chosen. Instead of targeting an existing version explicitly, you can instead pass in Agent details in the request body. A new version is created if it does not match any existing ones. This is helpful in the case where you are storing or deriving your Agent details in code.

Query parameters

version_idstringOptional

A specific Version ID of the Agent to log to.

environmentstringOptional

Name of the Environment identifying a deployed version to log to.

Request

This endpoint expects an object.

streamtrueRequiredDefaults to true

If true, Agent events and tokens will be sent as data-only server-sent events.

pathstringOptional

Path of the Agent, including the name. This locates the Agent in the Humanloop filesystem and is used as as a unique identifier. For example: folder/name or just name.

idstringOptional

ID for an existing Agent.

messageslist of objectsOptional

The messages passed to the to provider chat endpoint.

tool_choice"none" or "auto" or "required" or objectOptional

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.

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.

agentobject or stringOptional

The Agent configuration to use. Two formats are supported:

An object representing the details of the Agent configuration
A string representing the raw contents of a .agent file

A new Agent version will be created if the provided details do not match any existing version.

The Agent configuration to use. Two formats are supported: - An object representing the details of the Agent configuration - A string representing the raw contents of a .agent file A new Agent version will be created if the provided details do not match any existing version.

inputsmap from strings to anyOptional

The inputs passed to the prompt template.

sourcestringOptional

Identifies where the model was called from.

metadatamap from strings to anyOptional

Any additional metadata to record.

start_timedatetimeOptional

When the logged event started.

end_timedatetimeOptional

When the logged event ended.

source_datapoint_idstringOptional

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.

trace_parent_idstringOptional

The ID of the parent Log to nest this Log under in a Trace.

userstringOptional

End-user ID related to the Log.

environmentstringOptional

The name of the Environment the Log is associated to.

savebooleanOptionalDefaults to true

Whether the request/response payloads will be stored on Humanloop.

log_idstringOptional

This will identify a Log. If you don't provide a Log ID, Humanloop will generate one for you.

provider_api_keysobjectOptional

API keys required by each provider to make API calls. The API keys provided here are not stored by Humanloop. If not specified here, Humanloop will fall back to the key saved to your organization.

return_inputsbooleanOptionalDefaults to true

Whether to return the inputs in the response. If false, the response will contain an empty dictionary under inputs. This is useful for reducing the size of the response. Defaults to true.

include_trace_childrenbooleanOptionalDefaults to false

If true, populate trace_children for the returned Agent Log. Only applies when not streaming. Defaults to false.

Response

log_idstring

messagestring

typeenum

An enumeration.

created_atdatetime

payloadobject or null

1	from humanloop import Humanloop
2	client = Humanloop(api_key="YOUR_API_KEY", )
3	response = client.agents.call_stream()
4	for chunk in response.data:
5	yield chunk

Headers

Query parameters

Request

Response

Errors