Python SDK

Overview

The agentlab-sdk package is the official Python client for the AgentLab Agentic OS. It wraps the AgentLab HTTP API in a single AgentLabClient class so you can automate the platform from scripts, notebooks, or backend services instead of the dashboard. Use the SDK when you need to:

List, deploy, pause, or resume swarm agent nodes.
Trigger DAG workflow runs and approve or reject them as a human-in-the-loop operator.
Create and control agent sessions, including messaging, navigation, and screenshots.
Stream real-time Server-Sent Events (SSE) from a session.
Read SAIF compliance stats and audit logs.
Mount and unmount knowledge playbooks from the marketplace.

The SDK requires Python 3.9 or later and depends on requests, urllib3, and typing-extensions.

Installation

Install the SDK in editable mode from the sdk/python directory of the AgentLab repository:

pip install -e ./sdk/python

Client setup

Create an AgentLabClient with the base URL of your AgentLab instance and a workspace token:

from agentlab import AgentLabClient

client = AgentLabClient(
    base_url="http://localhost:3000",
    api_key="<YOUR_WORKSPACE_TOKEN>",
)

The constructor accepts three parameters:

Parameter	Default	Description
`base_url`	`http://localhost:3000`	Target AgentLab instance. Point this at your local runtime or your production deployment.
`api_key`	Empty	JWT token or API key for workspace authentication. Sent as a `Bearer` token in the `Authorization` header.
`timeout`	`30.0`	HTTP request timeout in seconds.

If you omit base_url or api_key, the client reads the AGENTLAB_BASE_URL and AGENTLAB_API_KEY environment variables. If a request fails, the client raises a RuntimeError that includes the HTTP status code and the response body.

Quickstart

from agentlab import AgentLabClient

client = AgentLabClient(
    base_url="http://localhost:3000",
    api_key="<YOUR_WORKSPACE_TOKEN>",
)

# Check SAIF compliance and 24h telemetry counters
stats = client.get_audit_stats()
print("24h Events:", stats["totalEvents24h"])
print("SAIF Compliance:", stats["saifComplianceRate"])

# List swarm agents
agents = client.list_agents()["agents"]
for agent in agents:
    print(f"{agent['name']} ({agent['status']}) - {agent['role']}")

# Pause or resume an agent node
client.toggle_agent("alpha-node-01")

# Trigger a DAG workflow run
run = client.trigger_workflow(
    workflow_id="mkt-01-lead-scraper",
    inputs={"target_industry": "Healthcare", "lead_count": 25},
)
print("Triggered Run ID:", run["runId"])

# Mount a knowledge playbook
client.mount_playbook("ops-playbook")

Swarm agents

Manage the agent nodes in your swarm:

list_agents() returns active swarm agents and telemetry counters.
toggle_agent(agent_id) pauses or resumes an agent node.
deploy_agent(payload) deploys a new autonomous swarm agent node.

Workflows and DAG runs

Trigger and supervise autonomous DAG executions:

list_workflows() lists all deployable DAG workflows.
trigger_workflow(workflow_id, inputs) starts a run and returns its runId.
list_runs() and get_run(run_id) return execution traces and detailed run state.
approve_run(run_id) and reject_run(run_id, reason) handle human-in-the-loop operator decisions.

run = client.trigger_workflow("mkt-01-lead-scraper", inputs={"lead_count": 25})
details = client.get_run(run["runId"])
client.approve_run(run["runId"])

Agent sessions

Create and control autonomous agent sessions, including browser interaction:

create_session(agent_name), list_sessions(), get_session(session_id), and delete_session(session_id) manage the session lifecycle.
message_agent(session_id, message) sends a natural language instruction.
navigate(session_id, url) directs the agent to browse a URL.
screenshot(session_id) captures a live viewport screenshot.
pause(session_id), resume(session_id), and cancel(session_id) control execution.

The AgentLabSession context manager creates a session on entry and deletes it on exit:

from agentlab import AgentLabClient, AgentLabSession

client = AgentLabClient()

with AgentLabSession(client, agent_name="Alpha-Node-01") as session:
    client.message_agent(session.session_id, "Summarize today's pipeline results")

Real-time SSE event streaming

stream_events() yields live agent reasoning steps, tool calls, and model tokens from a session as parsed Server-Sent Events. Each event is a dictionary with id, event, and data keys:

for event in client.stream_events(session_id="session_123", sanitize=True):
    print(f"[{event['event']}] -> {event['data']}")

Streaming options:

Parameter	Default	Description
`sanitize`	`True`	Redact sensitive values from streamed events.
`include_history`	`True`	Replay earlier session events before streaming live ones.
`event_types`	All	Optional list of event types to filter the stream.

Auditing and SAIF compliance

get_audit_stats() returns real-time SAIF compliance rates and 24-hour event counters.
get_audit_logs() fetches model execution traces and policy check telemetry.

Marketplace and playbooks

list_marketplace_items() fetches available playbooks, apps, and books.
mount_playbook(playbook_id) mounts a knowledge playbook into the active workspace.
unmount_playbook(playbook_id) removes it.

System health and models

health() checks system health status.
list_models() lists the active foundational LLM backbones.

API documentation

SDKs

Runs and artifacts

Endpoint examples

Playbooks

Integrations

Audit logs

Workspace snapshots

Voice agent

GTM tools

Overview

Installation

Client setup

Quickstart

Swarm agents

Workflows and DAG runs

Agent sessions

Real-time SSE event streaming

Auditing and SAIF compliance

Marketplace and playbooks

System health and models

​Overview

​Installation

​Client setup

​Quickstart

​Swarm agents

​Workflows and DAG runs

​Agent sessions

​Real-time SSE event streaming

​Auditing and SAIF compliance

​Marketplace and playbooks

​System health and models

Overview

Installation

Client setup

Quickstart

Swarm agents

Workflows and DAG runs

Agent sessions

Real-time SSE event streaming

Auditing and SAIF compliance

Marketplace and playbooks

System health and models