Continue Agent Call | Humanloop Docs

Continue an incomplete Agent call. This endpoint allows continuing an existing incomplete Agent call, by passing the tool call requested by the Agent. The Agent will resume processing from where it left off. The messages in the request will be appended to the original messages in the Log. You do not have to provide the previous conversation history. The original log must be in an incomplete state to be continued.

Authentication

X-API-KEYstring

API Key authentication via header

Request

This endpoint expects an object.

log_idstringRequired

This identifies the Agent Log to continue.

messageslist of objectsRequired

The additional messages with which to continue the Agent Log. Often, these should start with the Tool messages with results for the previous Assistant message's tool calls.

streamfalseRequired

If true, packets will be sent as data-only server-sent events.

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.

include_trace_childrenbooleanOptionalDefaults to false

If true, populate trace_children for the returned Agent Log. Defaults to false.

Response

agentobject

Agent that generated the Log.

idstring

Unique identifier for the Log.

evaluator_logslist of objects

List of Evaluator Logs associated with the Log. These contain Evaluator judgments on the Log.

output_messageobject

The message returned by the provider.

prompt_tokensinteger

Number of tokens in the prompt used to generate the output.

reasoning_tokensinteger

Number of reasoning tokens used to generate the output.

output_tokensinteger

Number of tokens in the output generated by the model.

prompt_costdouble

Cost in dollars associated to the tokens in the prompt.

output_costdouble

Cost in dollars associated to the tokens in the output.

finish_reasonstring

Reason the generation finished.

messageslist of objects

The messages passed to the to provider chat endpoint.

tool_choice"none" or "auto" or "required" or object

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.

start_timedatetime

When the logged event started.

end_timedatetime

When the logged event ended.

outputstring

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.

created_atdatetime

User defined timestamp for when the log was created.

errorstring

Error message if the log is an error.

provider_latencydouble

Duration of the logged event in seconds.

stdoutstring

Captured log and debug statements.

provider_requestmap from strings to any

Raw request sent to provider.

provider_responsemap from strings to any

Raw response received the provider.

inputsmap from strings to any

The inputs passed to the prompt template.

sourcestring

Identifies where the model was called from.

metadatamap from strings to any

Any additional metadata to record.

log_statusenum

Status of the Agent Log. If incomplete, the Agent turn was suspended due to a tool call and can be continued by calling /agents/continue with responses to the Agent’s last message (which should contain tool calls). See the previous_agent_message field for easy access to the Agent’s last message.

Allowed values:

source_datapoint_idstring

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_idstring

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

batcheslist of strings

Array of Batch IDs that this Log is part of. Batches are used to group Logs together for offline Evaluations

userstring

End-user ID related to the Log.

environmentstring

The name of the Environment the Log is associated to.

savebooleanDefaults to true

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

log_idstring

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

trace_flow_idstring

Identifier for the Flow that the Trace belongs to.

trace_idstring

Identifier for the Trace that the Log belongs to.

trace_childrenlist of objects

Logs nested under this Log in the Trace.

previous_agent_messageobject

The Agent’s last message, which should contain tool calls. Only populated if the Log is incomplete due to a suspended Agent turn with tool calls. This is useful for continuing the Agent call by calling /agents/continue.

Errors

422

Agents Continue Call Request Unprocessable Entity Error

1	{
2	"agent": {
3	"path": "Banking/Teller Agent",
4	"id": "ag_1234567890",
5	"model": "claude-3-7-sonnet-latest",
6	"tools": [
7	{
8	"type": "file",
9	"link": {
10	"file_id": "pr_1234567890",
11	"version_id": "prv_1234567890"
12	},
13	"on_agent_call": "continue"
14	},
15	{
16	"type": "inline",
17	"json_schema": {
18	"name": "stop",
19	"description": "Call this tool when you have finished your task.",
20	"strict": true,
21	"parameters": {
22	"type": "object",
23	"properties": {
24	"output": {
25	"type": "string"
26	}
27	},
28	"additionalProperties": false,
29	"required": [
30	"output"
31	]
32	}
33	},
34	"on_agent_call": "stop"
35	}
36	],
37	"name": "Teller Agent",
38	"version_id": "agv_1234567890",
39	"created_at": "2024-05-01T12:00:00Z",
40	"updated_at": "2024-05-01T12:00:00Z",
41	"status": "committed",
42	"last_used_at": "2024-05-01T12:00:00Z",
43	"version_logs_count": 1,
44	"total_logs_count": 1,
45	"inputs": [],
46	"endpoint": "chat",
47	"template": [
48	{
49	"role": "system",
50	"content": "You are a helpful digital assistant, helping users navigate our digital banking platform."
51	}
52	],
53	"provider": "anthropic",
54	"reasoning_effort": 1024,
55	"max_iterations": 3
56	},
57	"id": "log_1234567890",
58	"evaluator_logs": []
59	}

1	from humanloop import Humanloop
2	client = Humanloop(api_key="YOUR_API_KEY", )
3	client.agents.continue_call(log_id='log_1234567890', messages=[{'role': "tool", 'content': '{"type": "checking", "balance": 5200}', 'tool_call_id': 'tc_1234567890'}], )