Agents

client.agents manages agents (“co-workers”) — configurable AI assistants with tools, personas, and their own versioned lifecycle. Once created, you chat with them through conversations and read their analytics through insights.

Creating an agent

An agent is configured with AgentSettings, which controls its model, behavior, and available tools.

from noxus_sdk.resources.assistants import AgentSettings

settings = AgentSettings(
    model=["gpt-4o-mini"],
    temperature=0.7,
    max_tokens=4000,
    tools=[],                       # required (empty list = no tools)
    extra_instructions="Be concise and friendly.",
    persona=None, tone=None,        # optional
)
agent = client.agents.create(name="Support Bot", settings=settings)

AgentSettings fields: model: list[str], temperature: float, max_tokens (default 64000), tools: list[...] (required — the discriminated tool union: web_research, kb_qa, workflow, code_execution, sandbox, …), persona, tone, extra_instructions.

CRUD & lifecycle

client.agents.list()                         # -> list[Agent]
client.agents.get(agent_id)                  # -> Agent
client.agents.update(agent_id, name=None, settings=None, preview=False)
client.agents.delete(agent_id)
client.agents.duplicate(agent_id)            # -> new Agent
client.agents.publish(agent_id)              # publish the current draft as a version
client.agents.restore(agent_id)              # restore last published
client.agents.list_versions(agent_id, page=1, page_size=10)
client.agents.get_tool_schemas()             # -> dict of available tool configs

update(..., preview=True) returns what the change would look like without saving. From an Agent object you can call .update(name, settings), .delete(), and .triggers() / .add_trigger(trigger_data) directly.

Export / import

blob = client.agents.export(agent_id, version="auto", version_id=None,
                            set_active_on_import=False)          # -> bytes
client.agents.export_preview(agent_id)                          # -> dict
client.agents.import_(blob, version="auto", mode="clone",
                      activate=False, dry_run=False)            # -> list[dict]

mode is "clone" | "version" | "replace"; dry_run=True validates only.

Triggers

Agent triggers (as opposed to workflow triggers) are managed directly from an Agent object:

agent = client.agents.get(agent_id)
agent.add_trigger(trigger_data)
agent.triggers()

For workflow triggers and deployment channels, see Deployments & Triggers.

Overview

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API Reference

Creating an agent

CRUD & lifecycle

Export / import

Triggers

​Creating an agent

​CRUD & lifecycle

​Export / import

​Triggers

Creating an agent

CRUD & lifecycle

Export / import

Triggers