MCP server Computer-Use Agents API

Contents

• Installation
• Quickstart: command line
• Quickstart: Python
• Command line
• Python SDK guides
• Documentation

Installation

pip install "hai-agents[cli]"   # SDK and the holo command
pip install hai-agents          # SDK only

Python 3.10 or newer is required.

Quickstart: command line

pip install "hai-agents[cli]"
holo login
holo "What's the top story on Hacker News?"

holo login signs you in with Google in the browser, or takes a key you paste with holo login --key, and stores it in ~/.config/hai/.env. Any command does the same on first use in a terminal, so this step is optional. The task then runs on a hosted agent with its own browser: its steps stream to your terminal, and its answer prints at the end. In a terminal you can follow up on the same session, or run holo alone to start an interactive session.

Quickstart: Python

Get an API key at platform.hcompany.ai/settings/api-keys and export it, or run holo login once:

export HAI_API_KEY=hk-...

Launch the built-in h/web-surfer-pro agent, which ships with its own browser, and describe the task in plain language. run_session polls until the agent finishes and returns the final answer.

from hai_agents import Client

client = Client()

result = client.run_session(
    agent="h/web-surfer-pro",
    messages="What are the top 3 stories on Hacker News right now?",
)

print(result.status)
print(result.answer)

Client() reads HAI_API_KEY from the environment, else the key holo login stored in ~/.config/hai/.env.

result is a SessionRunResult: id, status, answer, the accumulated events, and final_changes.

Command line

The cli extra installs the holo command, also available as hai.

holo "What's the top story on Hacker News?"      # run one task
holo                                            # interactive session
holo -a h/web-surfer-pro "Compare 3 laptops"    # pick an agent for one run
holo agents use h/web-surfer-pro                # change the default agent
holo -a h/holo-desktop "Open Calculator"        # drive this machine's desktop
holo --local "Summarize news.ycombinator.com"   # run the agent loop on this machine
holo sessions watch <session-id>                # follow a running session
holo mcp install                                # add the MCP server to your editor

Tasks run on h/web-surfer-flash unless you pass --agent or set a default with holo agents use. Run holo --help, or holo <command> --help, for every option.

~/.holo/agents.md joins the instructions of every agent holo runs, and ~/.holo/skills/<name>/SKILL.md joins its skills.

Interactive sessions

holo in a terminal opens a session you talk to in plain words. Each message continues the same session, with its browser and context, until you start fresh with /new.

Key	Action
type, then enter	message the agent while it works
esc	pause the agent; esc again resumes
/answer	ask the agent to answer now
/	list commands: /new, /retry, /resume, /agent, /live, /share, /status, /copy
?	all shortcuts
ctrl+c	cancel the turn; twice to exit

An agent that drives this desktop, like h/holo-desktop, uses your mouse and keyboard, so typing goes to your apps instead: press esc twice, from any app, to stop it.

Commands

Command	What it does
holo run <task>	run one task; same as holo <task>
holo chat	start an interactive session
holo sessions	list, status, watch, events, send, cancel, share sessions
holo agents	list agents, get one, use one as the default
holo skills	browse available skills
holo mcp install	let Cursor, Claude Code, Codex, and other MCP clients run agents through holo mcp; --remote adds the hosted server instead
holo local	serve a browser or desktop on this machine for a session started elsewhere
holo login, logout, whoami, doctor	manage and check your credentials and setup

Scripts and CI

Scripts never prompt. Set HAI_API_KEY, or pipe a key into holo login --key, then use --json for machine-readable output or -q for the answer alone:

holo --json run "What's the top story on Hacker News?" | jq -r .answer
holo run -q "What's the top story on Hacker News?" > answer.txt

--json prints session_id, status, answer, outcome, error, error_code, and agent_view_url. The exit code is 0 when the session completes and 1 otherwise.

Credentials resolve from --api-key, then HAI_API_KEY, then ~/.config/hai/.env.

Python SDK guides

How a session works

A session is one run of an agent against a task. It moves through a small set of states: pending, running, and then a settled state such as completed, idle, failed, timed_out, or interrupted.

You drive a session two ways. run_session creates it and blocks until it settles, which suits one-shot tasks. start_session creates it and returns a handle right away, so you can read and steer the agent while it works.

session = client.start_session(
    agent="h/web-surfer-pro",
    messages="Find the top story on Hacker News",
)

print(session.id)
result = session.wait_for_completion()
print(result.status, result.answer)

Watch and steer a running session

A handle bound to the session id exposes the full lifecycle. Read the agent's progress at three levels of detail:

session.status()
session.changes(from_index=0)
session.get()

status() is a cheap snapshot with the state, step count, and token usage. changes(from_index=0) long-polls for new events and the final answer. get() returns the full Session resource.

While the session is not in a terminal state, you can intervene:

session.send_message({"type": "user_message", "message": "Only consider the last 24 hours"})
session.pause()
session.resume()
session.force_answer()
session.cancel()

send_message redirects the agent on its next step and wakes an idle session. pause halts with state preserved until resume. force_answer makes the agent stop exploring and answer from what it has. cancel ends the session as interrupted.

Multi-turn sessions

By default a session ends as soon as the agent answers. Set idle_timeout_s to keep it open: after each answer the session goes idle and waits that long for your next message, carrying its full context and browser state across turns.

session = client.start_session(
    agent="h/web-surfer-pro",
    idle_timeout_s=600,
    messages="Find the top story on Hacker News",
)
first = session.wait_for_completion()

session.send_message({"type": "user_message", "message": "Now summarize its comments"})
second = session.wait_for_completion()

Structured output

Pass a pydantic model as answer_schema and the agent's final answer comes back as a validated instance. The model's JSON schema is sent as the agent's answer format; the raw wire value stays at result.final_changes.answer.

from pydantic import BaseModel
from hai_agents import Client

class Job(BaseModel):
    title: str
    company: str

class Jobs(BaseModel):
    jobs: list[Job]

client = Client()
result = client.run_session(
    agent="h/web-surfer-pro",
    messages="Find 3 open ML engineering roles in Paris.",
    answer_schema=Jobs,
)

for job in result.answer.jobs:
    print(job.title, "@", job.company)

A completed answer that does not match the schema raises AnswerValidationError, with the raw payload on .raw. Sessions that end without completing return their raw answer untouched.

Custom tools

Expose your own Python functions to the agent. Pass them to run_session and the polling loop runs each one when the agent calls it, then posts the result back so the session continues. Any function with typed parameters and a docstring works; the input schema is derived from the signature.

from hai_agents import Client

def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"Sunny in {city}"

client = Client()

result = client.run_session(
    agent="h/web-surfer-pro",
    messages="What should I wear in Paris today?",
    tools=[get_weather],
)

Use the @tool decorator to override the name or description:

from hai_agents import tool

@tool(name="lookup_order", description="Look up an order by its id.")
def lookup(order_id: str) -> dict:
    return {"id": order_id, "status": "shipped"}

A tool that raises is reported to the agent as a tool error rather than crashing the run. With AsyncClient, tools may be async def.

Prebuilt: one-time passwords (2FA)

hai_agents_tools ships ready-made tools. otp_tool lets the agent ask for a one-time password, verification code, or confirmation link when a login or signup step needs one. Without a handler it prompts on stdin; imap_otp_handler reads the code straight from a mailbox over IMAP (for Gmail, use an app password).

import os

from hai_agents import Client
from hai_agents_tools import imap_otp_handler, otp_tool

handler = imap_otp_handler(
    host="imap.gmail.com",
    username="agent-inbox@gmail.com",
    password=os.environ["GMAIL_APP_PASSWORD"],
)

client = Client()
result = client.run_session(
    agent="h/web-surfer-pro",
    messages="Log in to example.com and check for new notifications",
    tools=[otp_tool(handler)],
)

Like every custom tool, the handler runs entirely in your process: the IMAP credentials never leave your machine, and the agent only receives the single extracted code or link -- never mailbox contents.

Browser profiles and vaults

Start a session on a browser that already knows the user. A browser profile restores saved cookies and storage from an earlier session, and a vault lets the agent sign in to sites with secrets that never enter its context. Bind both through per-run overrides:

result = client.run_session(
    agent="h/web-surfer-pro",
    messages="Open my dashboard and report any new alerts",
    overrides={
        "agent.environments[kind=web].browser_profile_id": "<profile-id>",
        "agent.environments[kind=web].vault_id": "<vault-id>",
    },
)

Async

AsyncClient mirrors Client for asyncio. Every session method is a coroutine.

import asyncio
from hai_agents import AsyncClient

async def main():
    client = AsyncClient()
    result = await client.run_session(
        agent="h/web-surfer-pro",
        messages="What are the top 3 stories on Hacker News right now?",
    )
    print(result.answer)

asyncio.run(main())

Local agents

Client.local() and await AsyncClient.local() run the agent on this machine through a local agent runtime, started on demand or passed in with runtime=.... A desktop is this machine; a browser picks host="user_device" or host="cloud". Client() keeps using the hosted Agents API. Closing the client stops a runtime it started, unless other clients still have active sessions there.

from hai_agents import Client

with Client.local(local_options={"binary_path": "/path/to/hai-agent-runtime"}) as client:
    session = client.sessions.create_session(
        agent={
            "name": "local-example",
            "description": "Local desktop example",
            "instructions": "Answer the user's task on this desktop.",
            "environments": [{"id": "my-laptop", "kind": "desktop"}],
        },
        messages=[{"type": "user_message", "message": "Open the Calculator app."}],
        max_steps=8,
        max_time_s=120,
    )
    # Poll or steer the session here, before leaving the client context.

Inference stays hosted (HAI_API_KEY) unless you pass inference=Inference.self_hosted(url, model=...) (from hai_agents_local.runtime import Inference).

One agent drives this machine's desktop at a time: a second process asking for a desktop bridge fails fast instead of sharing the mouse. holo local stop stops every local turn on the machine; so does a double Esc once hai_agents_local.killswitch.arm_esc_listener() is armed (macOS, Windows, and X11 Linux).

Inspect and share sessions

List past sessions and create a public replay link:

page = client.sessions.list_sessions(size=10)
for summary in page.items:
    print(summary.id, summary.status)

link = client.sessions.share_session("<session-id>")
print(link.share_url)

Regions and configuration

The client targets the EU region by default; pass environment to use the US region instead:

from hai_agents import Client, HaiAgentsEnvironment

client = Client(environment=HaiAgentsEnvironment.US)

Client also accepts a custom base_url, and an api_key when you do not want to use the environment variable:

client = Client(base_url="https://agp.hcompany.ai", api_key="hk-...")

Errors

from hai_agents import AnswerValidationError, UnprocessableEntityError
from hai_agents.core import ApiError

ApiError is the base for HTTP failures and carries .status_code and .body. UnprocessableEntityError is the 422 raised when a request fails validation. AnswerValidationError is raised when a completed answer does not match answer_schema, with the unparsed value on .raw.

Webhooks

Verify the signature on an incoming webhook before trusting it:

from hai_agents import verify_webhook, WebhookVerificationError

event = verify_webhook(request_body, signature, timestamp, secret)
print(event.type, event.data)

Documentation

Guides, core concepts, and the full API reference live at hub.hcompany.ai/agents-api.

License

MIT