Computer-Use Agents API

Python SDK for H Company 's Agents API .

Documentation · Get an API key · PyPI · TypeScript SDK · H Company

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 # 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 --help, for every option.

~/.holo/agents.md joins the instructions of every agent holo runs, and ~/.holo/skills/ /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.

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

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": " ", "agent.environments[kind=web].vault_id": " ", }, )

Async

`Async