computer-agents 2.6.3

Official Python SDK for Computer Agents. Run agents, manage cloud computers and projects, and deploy web apps, functions, databases, auth, runtimes, and secrets.

Computer Agents Python SDK

Official Python SDK for Computer Agents, the Agentic Compute Platform.

Computer Agents gives AI agents the things a real teammate needs to finish work: persistent cloud computers, files, project plans, tasks, memory, skills, scheduled work, and deployable resources. Use this SDK from Python scripts, services, notebooks, backend jobs, and internal tools to start agents, stream their work, manage projects and computers, deploy resources, store data, and monitor usage.

What You Can Build

• Agentic product workspaces with projects, releases, tickets, reviewers, comments, and task-linked threads.
• Persistent cloud computers where agents can browse, code, run CLIs, install packages, edit files, and keep state across sessions.
• Hosted products and internal tools with Web Apps, Functions, Databases, Auth, Agent Runtimes, and Secrets.
• Automated research and operations with threads, schedules, triggers, skills, and reusable custom agents.
• Python control planes for your own apps, CI jobs, data workflows, research systems, and backend automation.

Install

pip install computer-agents

Python 3.9 or newer is required.

Authenticate

Create an API key in Computer Agents, then set:

export COMPUTER_AGENTS_API_KEY="ca_..."

from computer_agents import ComputerAgentsClient

client = ComputerAgentsClient()

You can also pass the key directly:

client = ComputerAgentsClient(api_key="ca_...")

Quick Start

Run a task and stream the agent's work:

from computer_agents import ComputerAgentsClient

client = ComputerAgentsClient()

result = client.run(
    "Create a small FastAPI service and explain how to run it.",
    on_event=lambda event: print(event["type"]),
)

print(result.content)
print(result.thread_id)

Core Concepts

Concept	What it means
Threads	Multi-turn agent sessions with messages, logs, reasoning, diffs, permission requests, feedback, and resumable state.
Computers	Persistent cloud workspaces with files, runtimes, packages, GUI access, Git, snapshots, and deployment context.
Projects	Shared workspaces for complex work: strategy, releases, tasks, comments, resources, review state, and task-linked threads.
Agents	Reusable agent profiles with model, instructions, skills, reasoning effort, and analytics.
Resources	Deployable product surfaces: Web Apps, Functions, Databases, Auth, Agent Runtimes, and Secrets.
Skills	Reusable capabilities agents can invoke, such as research, image generation, app deployment, or task management.

Persistent Computers and Threads

Create a computer, start a thread inside it, and continue later with the same files and state:

computer = client.computers.create(
    name="product-build-computer",
    internet_access=True,
)

thread = client.threads.create(computer_id=computer["id"])

client.threads.send_message(
    thread["id"],
    content="Create a Python API with a health route.",
    on_event=lambda event: print(event["type"]),
)

client.threads.send_message(
    thread["id"],
    content="Now add authentication and tests.",
)

logs = client.threads.get_logs(thread["id"])
diffs = client.threads.get_diffs(thread["id"])

Thread methods include create, list, get, send_message, cancel, resume, copy, search, get_messages, get_logs, get_status, get_diffs, list_steps, download_step_file, fork_from_step, revert_to_step, set_feedback, report_issue, and permission request approval/denial.

Projects and Tasks

Use projects when agents need the same context a human team would need: the goal, current release, backlog, comments, dependencies, resources, and review policy.

project = client.projects.create(
    "Customer Portal",
    description="Build and deploy an authenticated customer portal.",
)

release = client.tasks.create_release(
    project["id"],
    "v0.1 MVP",
)

task = client.tasks.create(
    "Deploy login and account settings",
    project_id=project["id"],
    release_id=release["id"],
    status="todo",
    priority="high",
)

client.tasks.create_comment(
    task["id"],
    body="Include password reset and session validation.",
)

client.tasks.start_thread(
    task["id"],
    environment_id=computer["id"],
)

Deployable Server Resources

Computer Agents resources let humans and agents ship software from the same workspace where the work is planned and built.

Manager	Resource kind	Typical use
client.web_apps	web_app	Dashboards, internal tools, portals, prototypes, AI apps.
client.functions	function	APIs, webhooks, jobs, data transforms, backend actions.
client.databases	Database	Collections and JSON documents for apps, functions, and agents.
client.auth	auth	Sign-up, sign-in, sessions, protected app workflows.
client.runtimes	agent_runtime	Always-on agent APIs and embedded agent services.
client.secrets	secrets	Secret vaults for API keys, tokens, credentials, and private config.
client.resources	Generic resources	Cross-kind automation when one workflow handles multiple resource types.

Create and Deploy a Function

Upload source code from a computer, create the Function, deploy it, then invoke it.

client.files.upload_file(
    computer["id"],
    path="functions/hello-world",
    filename="index.mjs",
    content="""
export default async function handler(request) {
  return Response.json({ message: 'Hello from Computer Agents Functions' });
}
""",
    content_type="text/javascript",
)

fn = client.functions.create(
    name="hello-world",
    source_type="computer",
    source_environment_id=computer["id"],
    source_path="functions/hello-world",
    runtime="nodejs22",
    auth_mode="public",
)

client.functions.deploy(fn["id"])

response = client.functions.invoke(
    fn["id"],
    method="GET",
    path="/",
)

print(response)

Resource managers support create, list, get, update, delete, deploy, list_deployments, rollback_deployment, invoke, get_analytics, get_logs, list_bindings, upsert_binding, delete_binding, file operations, and secret operations. Auth resources also support list_users, create_user, sign_up, and sign_in.

Databases and Secrets

Use databases for app state and structured output. Use Secrets for credentials that functions, web apps, and agents can read at runtime.

import os

db = client.databases.create(name="crm-data")

leads = client.databases.create_collection(
    db["id"],
    name="leads",
)

client.databases.create_document(
    db["id"],
    leads["id"],
    data={
        "company": "Acme",
        "stage": "qualified",
        "owner": "agent",
    },
)

vault = client.secrets.create(name="production-secrets")

client.secrets.create_secret(
    vault["id"],
    name="SENDGRID_API_KEY",
    value=os.environ["SENDGRID_API_KEY"],
)

client.functions.upsert_binding(
    fn["id"],
    "database",
    target_id=db["id"],
    alias="appDatabase",
)

client.functions.upsert_binding(
    fn["id"],
    "secrets",
    target_id=vault["id"],
    alias="productionSecrets",
)

Server-side runtime helpers for deployed Node Functions and server-rendered Web Apps are available from the JavaScript SDK:

import { getSecretValue } from 'computer-agents/runtime/server';

Use this Python SDK to create, bind, deploy, invoke, monitor, and operate those resources from Python services and automation.

Schedules, Triggers, and Orchestrations

client.schedules.create(
    name="Daily competitor brief",
    schedule_type="recurring",
    cron_expression="0 9 * * *",
    task="Research competitors and write a concise Markdown brief.",
    environment_id=computer["id"],
)

client.triggers.create(
    name="New lead enrichment",
    event_type="webhook",
    task="Enrich the new lead and update the CRM database.",
)

client.orchestrations.create(
    name="Research and build landing page",
    objective="Research the market, write copy, and deploy a landing page.",
)

Agents and Models

models = client.agents.list_models()

agent = client.agents.create(
    name="Senior Product Engineer",
    model="deepseek-v4-pro",
    instructions="Build carefully, test changes, and explain tradeoffs.",
    reasoning_effort="high",
)

Computer Agents supports built-in models from Anthropic, OpenAI, Gemini, DeepSeek, Kimi, and connected external models on supported plans. Use client.agents.list_models() to read the current catalog instead of hard-coding model availability.

Budget and Usage

budget = client.budget.get_status()
can_run = client.budget.can_execute()
usage = client.billing.get_stats(days=30)

print({
    "budget": budget,
    "can_run": can_run,
    "usage": usage,
})

Context Manager

with ComputerAgentsClient(api_key="ca_...") as client:
    result = client.run("Hello world")
    print(result.content)

SDK Surface

Manager	Scope
client.threads	Messages, logs, diffs, research, feedback, permission requests, and thread lifecycle.
client.computers / client.environments	Persistent cloud computers, runtimes, packages, snapshots, GUI, analytics.
client.files	Workspace files and directories.
client.git	Git status, diffs, commits, branches, clone, push.
client.projects	Project lifecycle, project files, schedules, computers, sync.
client.tasks	Tasks, comments, releases, sprints, and task-linked threads.
client.agents	Agent profiles, models, analytics.
client.web_apps, client.functions, client.auth, client.databases, client.runtimes, client.secrets	Product resources.
client.resources	Generic server resource operations.
client.skills	Custom skills.
client.schedules, client.triggers, client.orchestrations	Recurring, event-driven, and multi-agent work.
client.notifications	In-app notifications and push tokens.
client.budget / client.billing	Budget checks, checkout, usage, and transactions.

Links

• Website
• Documentation
• API Reference
• PyPI
• GitHub

License

MIT