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An environment is everything an agent can interact with—your APIs, services, databases, wrapped as tools. But it’s more than that: the environment also defines how agents are evaluated through scripts. When you deploy an environment, you’re creating a sandbox that agents can learn from at scale.

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Why Environments, Not API Servers?

Your production API is a single live instance with shared state—you can’t run 500 tests against it in parallel without causing chaos. Environments spin up fresh for every evaluation: isolated, deterministic, reproducible. Run thousands in parallel, each starting from the exact state you define, each generating training data. An API server is a live system you observe. An environment is a sandbox you control.

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Tools

Start with hud init to scaffold an environment—works with existing codebases or from scratch: 
hud init
Every tool is just a function. Decorate it with @env.tool() and agents can call it: 
from hud import Environment

env = Environment("my-env")

@env.tool()
async def search(query: str) -> str:
    """Search the knowledge base."""
    return db.search(query)
Got a FastAPI app? One line: 
env.connect_fastapi(app)
All your routes become tools. Run it: 
async with env() as ctx:
    tools = await ctx.list_tools()
    result = await ctx.call_tool("search", query="test")

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Scripts

To evaluate an agent, you need two things: what to tell it, and how to score what it did. Scripts capture both with two yield statements: 
@env.scenario("checkout")
async def checkout_flow(product_name: str):
    # Yield the prompt, receive the agent's final answer
    answer = yield f"Add '{product_name}' to cart and complete checkout"
    
    # Score based on environment state and/or the answer
    order_exists = await check_order_status(product_name)
    yield 1.0 if order_exists else 0.0
The agent runs between the yields. First yield sends the prompt and returns the agent’s answer. Second yield checks environment state—database rows, files, API calls—and returns a reward. Scripts live with the environment because only the environment knows how to verify what happened.

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Evals

Call the environment with a scenario name and arguments to create a task: 
task = env("checkout", product_name="Laptop")

async with hud.eval(task, group=4) as ctx:
    # Connect your agent here. Handle tool calls, run agent loop...
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": ctx.prompt}],
        tools=ctx.as_openai_chat_tools()
    )

    await ctx.submit(response.choices[0].message.content)

print(ctx.reward)
This creates a trace on hud.ai. Add variants to A/B test across models. To run evals at scale, deploy your environment.

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Mock Mode

Testing your agent loop without hitting real services? Mock mode returns fake responses based on tool schemas: 
env.mock()
env.mock_tool("search", "Mock search results") # Manual override of mock

async with hud.eval(env(), group=4) as ctx:
    tools = env.as_openai_chat_tools()
    
    response = await client.chat.completions.create(
        model="claude-sonnet-4-5",
        messages=[{"role": "user", "content": "Search for X"}],
        tools=tools
    )
    
    # Returns mock value instead of hitting real service
    result = await env.call_tool(response.choices[0].message.tool_calls[0])
Your agent code stays the same—just toggle env.mock() for local testing.