Quickstart

pip install hud-python

HUD_API_KEY=sk-hud-...
ANTHROPIC_API_KEY=sk-ant-...  # For Claude agents
OPENAI_API_KEY=sk-...         # For OpenAI agents

import asyncio
from hud import gym
from hud.task import Task
from hud.agent import ClaudeAgent

async def main():
    # Define what the agent should do
    task = Task(
        prompt="Search for information about artificial intelligence",
        gym="hud-browser",
        setup=("goto", "google.com"),
        evaluate=("contains_text", "artificial intelligence")
    )

    # Create environment and agent
    env = await gym.make(task)
    agent = ClaudeAgent()

    # Run the task
    result = await env.run(agent)
    print(f"Evaluation: {result}")

    await env.close()

if __name__ == "__main__":
        asyncio.run(main())

env = await gym.make(task)
agent = ClaudeAgent()

# Reset the environment to get the initial observation
obs, _ = await env.reset()

for i in range(10):
    # Get the next action from the agent
    action, done = await agent.predict(obs)

    # Take the action in the environment
    obs, reward, terminated, info = await env.step(action)

    # If the episode is done, break
    if done or terminated:
        break

# Evaluate the agent's performance
result = await env.evaluate()

# View the browser session live
env = await gym.make(task)
await env.stream()  # Opens live view in notebook/browser

from browser_use import Agent, Browser, BrowserConfig
from langchain_openai import ChatOpenAI

# Get CDP URL from HUD environment
env = await gym.make(task)

# Connect browser_use to the same browser
browser = Browser(config=BrowserConfig(cdp_url=env.url))
agent = Agent(task=task.prompt, llm=ChatOpenAI(model="gpt-4o"), browser=browser)

# Run the task
result = await agent.run()

# Evaluate the agent's performance
evaluation = await env.evaluate()
print(f"Evaluation: {evaluation}")

from hud import load_taskset, run_job, ClaudeAgent

# Load a TaskSet
taskset = await load_taskset("hud-samples")  # Start here
# taskset = await load_taskset("WebVoyager")  # Web navigation  
# taskset = await load_taskset("GAIA")        # Question answering

# Run evaluation
job = await run_job(ClaudeAgent, taskset, "my-evaluation")
print(f"Job ID: {job.id}")

# View results at app.hud.so/jobs/{job.id}
analytics = await job.get_analytics()
print(f"Success rate: {analytics['success_rate']}")

from mcp_use import MCPAgent, MCPClient
from langchain_openai import ChatOpenAI
import hud
from hud import gym, Response

# Configure MCP server
config = {
    "mcpServers": {
        "airbnb": {
            "command": "npx",
            "args": ["-y", "@openbnb/mcp-server-airbnb"]
        }
    }
}

client = MCPClient.from_dict(config)
llm = ChatOpenAI(model="gpt-4o")
agent = MCPAgent(llm=llm, client=client, max_steps=5)

# MCP calls are automatically traced
with hud.trace("airbnb-search"):
    task = Task(
        prompt="Find a nice place to stay in Barcelona with good reviews",
        gym="qa", # since this agent doesn't use a browser, we use the qa environment
        evaluate=("response_includes", "airbnb")
    )
    env = await gym.make(task)
    
    # Run MCP agent (tool calls captured automatically)
    result = await agent.run(
        task.prompt
    )
    
    # Submit final answer
    await env.step(Response(text=result))
    
    # Evaluate the agent's performance
    evaluation = await env.evaluate()
    print(f"Evaluation: {evaluation}")

# View trace with MCP calls at app.hud.so/jobs/traces/{trace_id}

Task(
    prompt="What is the current population of Tokyo?",
    gym="hud-browser",
    setup=("goto", "google.com"),
    evaluate=("response_includes", "million")
)

Task(
    prompt="Search for laptops and add the first result to cart",
    gym="hud-browser",
    setup=("goto", "https://shop.example.com"),
    evaluate=("contains_text", "Added to cart")
)

Task(
    prompt="Open the Excel file and verify cell A1 contains 'Total'",
    gym="hud-browser",
    setup=("sheets_from_xlsx", "https://example.com/data.xlsx"),
    evaluate=("sheets_cell_values", {"A1": "Total"})
)

Task(
    prompt="What is the capital of France?",
    gym="qa",  # No browser environment needed if the agent doesn't interact with it
    evaluate=("response_includes", "Paris")
)

# Install the latest stable release
pip install hud-python

# Or, to get the very latest pre-release version:
pip install --pre hud-python

# .env example
HUD_API_KEY="your_hud_api_key_here"
OPENAI_API_KEY="your_openai_api_key_here"   # Only if using OpenAI models
ANTHROPIC_API_KEY="your_anthropic_api_key_here" # Only if using Anthropic models

# Clone the repository
git clone https://github.com/hud-evals/hud-sdk.git # Corrected repo URL
cd hud-sdk

# Create and activate a virtual environment (uv is recommended)
# python -m venv .venv  # Standard venv
# source .venv/bin/activate # Or .\venv\Scripts\activate on Windows

uv venv # If using uv
source .venv/bin/activate # Or .venv\Scripts\activate.bat or .venv\Scripts\Activate.ps1

# Install in editable mode with development dependencies
pip install -e ".[dev]"