hai-agents 1.0.6

Python SDK for H Company's Computer-Use Agents: autonomous agents powered by Holo.

Python SDK for H Company's Computer-Use Agents.

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

Installation

pip install hai-agents

Add the optional command-line tools with the cli extra:

pip install "hai-agents[cli]"

Python 3.10 or newer is required. Get an API key at platform.hcompany.ai/settings/api-keys and export it:

export HAI_API_KEY=hk-...

Quickstart

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.

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

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.

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())

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)

Command line

The cli extra installs the hai command for driving agents from your terminal:

hai login
hai run "What's the top story on Hacker News?"
hai sessions list
hai sessions watch <session-id>
hai mcp install

hai login signs in through the browser and stores a key in ~/.config/hai/.env. hai mcp install adds the hai-agents MCP server to Cursor, VS Code, Claude Code, and other MCP clients. Credentials resolve from --api-key, then HAI_API_KEY, then a local .env, then ~/.config/hai/.env. Run hai --help for the full command set.

Documentation

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

License

MIT