hud-python

OSS RL environment + evals toolkit

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hud-python is a Python library for creating interactive heads-up displays (HUDs) in video games. It provides a simple and flexible way to overlay information on the screen, such as player health, score, and notifications. The library is designed to be easy to use and customizable, allowing game developers to enhance the user experience by adding dynamic elements to their games. With hud-python, developers can create engaging HUDs that improve gameplay and provide important feedback to players.

README:

OSS RL environment + evals toolkit. Wrap software as environments, run benchmarks, and train with RL – locally or at scale.

Are you a startup building agents?

📅 Hop on a call or 📧 [email protected]

Highlights

🚀 MCP environment skeleton – any agent can call any environment.
⚡️ Live telemetry – inspect every tool call, observation, and reward in real time.
🗂️ Public benchmarks – OSWorld-Verified, SheetBench-50, and more.
🌱 Reinforcement learning built-in – Verifiers gym pipelines for GRPO on any environment.
🌐 Cloud browsers – AnchorBrowser, Steel, BrowserBase integrations for browser automation.
🛠️ Hot-reload dev loop – hud dev for iterating on environments without rebuilds.

We welcome contributors and feature requests – open an issue or hop on a call to discuss improvements!

Installation

# Core installation - MCP servers, telemetry, basic tools for environment design
pip install hud-python

# Agent installation - Adds AI providers, datasets
pip install "hud-python[agent]"

# CLI utilities
uv tool install hud-python
# uv tool update-shell

# From source (latest)
git clone https://github.com/hud-evals/hud-python
pip install -e "hud-python[dev]"

See docs.hud.so, or add docs to any MCP client: claude mcp add --transport http docs-hud https://docs.hud.so/mcp

Quickstart

For a tutorial that explains the agent and evaluation design, run (see quickstart docs):

uvx hud-python quickstart

Or just write your own agent loop (more examples here).

import asyncio, hud, os
from hud.settings import settings
from hud.clients import MCPClient
from hud.agents import ClaudeAgent
from hud.datasets import Task  # See docs: https://docs.hud.so/reference/tasks

async def main() -> None:
    with hud.trace("Quick Start 2048"): # All telemetry works for any MCP-based agent (see https://app.hud.so)
        task = {
            "prompt": "Reach 64 in 2048.",
            "mcp_config": {
                "hud": {
                    "url": "https://mcp.hud.so/v3/mcp",  # HUD's cloud MCP server (see https://docs.hud.so/core-concepts/architecture)
                    "headers": {
                        "Authorization": f"Bearer {settings.api_key}",  # Get your key at https://app.hud.so
                        "Mcp-Image": "hudpython/hud-text-2048:v1.2"  # Docker image from https://hub.docker.com/u/hudpython
                    }
                }
            },
            "evaluate_tool": {"name": "evaluate", "arguments": {"name": "max_number", "arguments": {"target": 64}}},
        }
        task = Task(**task)

        # 1. Define the client explicitly:
        client = MCPClient(mcp_config=task.mcp_config)
        agent = ClaudeAgent(
            mcp_client=client,
            model="claude-sonnet-4-20250514",  # requires ANTHROPIC_API_KEY
        )

        result = await agent.run(task)

        # 2. Or just:
        # result = await ClaudeAgent().run(task)

        print(f"Reward: {result.reward}")
        await client.shutdown()

asyncio.run(main())

The above example let's the agent play 2048 (See replay)

Reinforcement Learning with GRPO

This is a Qwen‑2.5‑VL‑3B agent training a policy on the 2048-basic browser environment:

Train with the new interactive hud rl flow:

# Install CLI with RL extras
uv tool install "hud-python[rl]"

# Option A: Run directly from a HuggingFace dataset
hud rl hud-evals/basic-2048

# Option B: Download first, modify, then train
hud get hud-evals/basic-2048
hud rl basic-2048.jsonl

# Optional: baseline evaluation
hud eval basic-2048.jsonl

Supports multi‑turn RL for both:

Language‑only models (e.g., Qwen/Qwen2.5-7B-Instruct)
Vision‑Language models (e.g., Qwen/Qwen2.5-VL-3B-Instruct)

By default, hud rl provisions a persistant server and trainer in the cloud, streams telemetry to app.hud.so, and lets you monitor/manage models at app.hud.so/models. Use --local to run entirely on your machines (typically 2+ GPUs: one for vLLM, the rest for training).

Any HUD MCP environment and evaluation works with our RL pipeline (including remote configurations). See the guided docs: https://docs.hud.so/train-agents/quickstart.

Benchmarking Agents

This is Claude Computer Use running on our proprietary financial analyst benchmark SheetBench-50:

See this trace on app.hud.so

This example runs the full dataset (only takes ~20 minutes) using run_evaluation.py:

python examples/run_evaluation.py hud-evals/SheetBench-50 --full --agent claude

Or in code:

import asyncio
from hud.datasets import run_dataset
from hud.agents import ClaudeAgent

results = await run_dataset(
    name="My SheetBench-50 Evaluation",
    dataset="hud-evals/SheetBench-50",      # <-- HuggingFace dataset
    agent_class=ClaudeAgent,                # <-- Your custom agent can replace this (see https://docs.hud.so/evaluate-agents/create-agents)
    agent_config={"model": "claude-sonnet-4-20250514"},
    max_concurrent=50,
    max_steps=30,
)
print(f"Average reward: {sum(r.reward for r in results) / len(results):.2f}")

Running a dataset creates a job and streams results to the app.hud.so platform for analysis and leaderboard submission.

Building Environments (MCP)

This is how you can make any environment into an interactable one in 5 steps:

Define MCP server layer using MCPServer

from hud.server import MCPServer
from hud.tools import HudComputerTool

mcp = MCPServer("My Environment")

# Add hud tools (see all tools: https://docs.hud.so/reference/tools)
mcp.add_tool(HudComputerTool())

# Or custom tools (see https://docs.hud.so/build-environments/adapting-software)
@mcp.tool("launch_app"):
def launch_app(name: str = "Gmail")
...

if __name__ == "__main__":
    mcp.run()

Write a simple Dockerfile that installs packages and runs:

CMD ["python", "-m", "hud_controller.server"]

And build the image:

hud build # runs docker build under the hood

Or run it in interactible mode

hud dev

Debug it with the CLI to see if it launches:

$ hud debug my-name/my-environment:latest

✓ Phase 1: Docker image exists
✓ Phase 2: MCP server responds to initialize 
✓ Phase 3: Tools are discoverable
✓ Phase 4: Basic tool execution works
✓ Phase 5: Parallel performance is good

Progress: [█████████████████████] 5/5 phases (100%)
✅ All phases completed successfully!

Analyze it to see if all tools appear:

$ hud analyze hudpython/hud-remote-browser:latest
⠏ ✓ Analysis complete
...
Tools
├── Regular Tools
│   ├── computer
│   │   └── Control computer with mouse, keyboard, and screenshots
...
└── Hub Tools
    ├── setup
    │   ├── navigate_to_url
    │   ├── set_cookies
    │   ├── ...
    └── evaluate
        ├── url_match
        ├── page_contains
        ├── cookie_exists
        ├── ...

📡 Telemetry Data
 Live URL  https://live.anchorbrowser.io?sessionId=abc123def456

When the tests pass, push it up to the docker registry:

hud push # needs docker login, hud api key

Now you can use mcp.hud.so to launch 100s of instances of this environment in parallel with any agent, and see everything live on app.hud.so:

from hud.agents import ClaudeAgent

result = await ClaudeAgent().run({  # See all agents: https://docs.hud.so/reference/agents
    "prompt": "Please explore this environment",
    "mcp_config": {
        "my-environment": {
            "url": "https://mcp.hud.so/v3/mcp",
            "headers": {
                "Authorization": f"Bearer {os.getenv('HUD_API_KEY')}",
                "Mcp-Image": "my-name/my-environment:latest"
            }
        }
        # "my-environment": { # or use hud run which wraps local and remote running
        #     "cmd": "hud",
        #     "args": [
        #         "run",
        #         "my-name/my-environment:latest",
        #     ]
        # }
    }
})

See the full environment design guide and common pitfalls in environments/README.md

Leaderboards & benchmarks

All leaderboards are publicly available on app.hud.so/leaderboards (see docs)

We highly suggest running 3-5 evaluations per dataset for the most consistent results across multiple jobs.

Using the run_dataset function with a HuggingFace dataset automatically assigns your job to that leaderboard page, and allows you to create a scorecard out of it:

Architecture

%%{init: {"theme": "neutral", "themeVariables": {"fontSize": "14px"}} }%%
graph LR
    subgraph "Platform"
        Dashboard["📊 app.hud.so"]
        API["🔌 mcp.hud.so"]
    end
  
    subgraph "hud"
        Agent["🤖 Agent"]
        Task["📋 Task"]
        SDK["📦 SDK"]
    end
  
    subgraph "Environments"
        LocalEnv["🖥️ Local Docker<br/>(Development)"]
        RemoteEnv["☁️ Remote Docker<br/>(100s Parallel)"]
    end
  
    subgraph "otel"
        Trace["📡 Traces & Metrics"]
    end
  
    Dataset["📚 Dataset<br/>(HuggingFace)"]
  
    AnyMCP["🔗 Any MCP Client<br/>(Cursor, Claude, Custom)"]
  
    Agent <--> SDK
    Task --> SDK
    Dataset <-.-> Task
    SDK <-->|"MCP"| LocalEnv
    SDK <-->|"MCP"| API
    API  <-->|"MCP"| RemoteEnv
    SDK  --> Trace
    Trace --> Dashboard
    AnyMCP -->|"MCP"| API

CLI reference

Command	Purpose	Docs
`hud init`	Create new environment with boilerplate.	📖
`hud dev`	Hot-reload development with Docker.	📖
`hud build`	Build image and generate lock file.	📖
`hud push`	Share environment to registry.	📖
`hud pull <target>`	Get environment from registry.	📖
`hud analyze <image>`	Discover tools, resources, and metadata.	📖
`hud debug <image>`	Five-phase health check of an environment.	📖
`hud run <image>`	Run MCP server locally or remotely.	📖

Roadmap

Merging our forks in to the main mcp, mcp_use, verifiers repositories
Helpers for building new environments (see current guide)
Integrations with every major agent framework
Evaluation environment registry
Native RL training to hud environments (see current RL support)
MCP opentelemetry standard

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Key areas:

Environment examples - Add new MCP environments
Agent implementations - Add support for new LLM providers
Tool library - Extend the built-in tool collection
RL training - Improve reinforcement learning pipelines

Thanks to all our contributors!

Citation

@software{hud2025agentevalplatform,
  author = {HUD and Jay Ram and Lorenss Martinsons and Parth Patel and Oskars Putans and Govind Pimpale and Mayank Singamreddy and Nguyen Nhat Minh},
  title  = {HUD: An Evaluation Platform for Agents},
  date   = {2025-04},
  url    = {https://github.com/hud-evals/hud-python},
  langid = {en}
}

License: HUD is released under the MIT License – see the LICENSE file for details.

For Tasks:

Click tags to check more tools for each tasks

display health status show player score notify events enhance gameplay provide feedback

For Jobs:

game developer software engineer ui/ux designer video game tester graphic designer

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