gym-mcp-client 0.1.0

A unified interface for local and remote Gymnasium environments via gym-mcp-server

Gym MCP Client

A unified Python interface for working with both local and remote Gymnasium environments via gym-mcp-server.

Goal: Write code once, seamlessly switch between local development and remote execution.

Features

• 🎮 Unified API: Same Gymnasium interface for both local and remote environments
• 🔄 Seamless Switching: Change modes with a single parameter
• 🌐 Remote Execution: Connect to gym-mcp-server instances over HTTP
• 🔧 Full Compatibility: Supports all Gymnasium environment types (Box, Discrete, MultiBinary, etc.)
• 🐍 Modern Python: Python 3.12+ with complete type hints
• 📦 Easy Setup: Managed with uv for fast dependency management
• ✅ Well Tested: 14 comprehensive tests, all passing

Installation

# Clone the repository
git clone <your-repo-url>
cd gym-mcp-client

# Install with uv (recommended)
uv sync

# Or with pip
pip install -e .

Quick Start

Local Mode

from gym_mcp_client import GymMCPClient

# Create a local environment
env = GymMCPClient("CartPole-v1", mode="local")

# Use standard Gymnasium API
observation, info = env.reset(seed=42)
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)

env.close()

Remote Mode

First, start a gym-mcp-server:

python -m gym_mcp_server --env CartPole-v1 --transport streamable-http --port 8000

Then connect to it:

from gym_mcp_client import GymMCPClient

# Create a remote environment
env = GymMCPClient(
    "CartPole-v1",
    mode="remote",
    gym_server_url="http://localhost:8000"
)

# Use the exact same API as local mode!
observation, info = env.reset(seed=42)
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)

env.close()

Context Manager

from gym_mcp_client import GymMCPClient

# Automatic cleanup
with GymMCPClient("CartPole-v1", mode="local") as env:
    observation, info = env.reset()
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)
    # env.close() called automatically

Switching Between Modes

The main benefit: write once, run anywhere!

import os
from gym_mcp_client import GymMCPClient

# Configuration from environment variables
MODE = os.getenv("GYM_MODE", "local")
SERVER_URL = os.getenv("GYM_SERVER_URL", "http://localhost:8000")

# Create environment based on mode
if MODE == "local":
    env = GymMCPClient("CartPole-v1", mode="local")
else:
    env = GymMCPClient(
        "CartPole-v1",
        mode="remote",
        gym_server_url=SERVER_URL,
        gym_server_key=os.getenv("GYM_API_KEY")
    )

# Your code works the same regardless of mode!
observation, info = env.reset()
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        observation, info = env.reset()

env.close()

Switch modes via command line:

# Run locally
export GYM_MODE=local
python your_training_script.py

# Run remotely
export GYM_MODE=remote
export GYM_SERVER_URL=http://gpu-server:8000
python your_training_script.py

API Reference

GymMCPClient

GymMCPClient(
    env_id: str,
    mode: str = "local",
    render_mode: str | None = None,
    gym_server_url: str | None = None,
    gym_server_key: str | None = None,
    **kwargs
)

Parameters:

• env_id: Gymnasium environment ID (e.g., "CartPole-v1")
• mode: Either "local" or "remote"
• render_mode: Render mode ("rgb_array", "human", etc.)
• gym_server_url: URL of gym-mcp-server (required for remote mode)
• gym_server_key: Optional API key for authentication
• **kwargs: Additional arguments passed to gym.make() in local mode

Methods:

• reset(seed=None, options=None) → (observation, info)
• step(action) → (observation, reward, terminated, truncated, info)
• render() → render_output
• close() → None

Properties:

• observation_space: The observation space
• action_space: The action space
• reward_range: The reward range
• metadata: Environment metadata

Architecture

┌─────────────────┐
│  Your Code      │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ GymMCPClient    │
└────────┬────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌────────┐ ┌──────────────┐
│ Local  │ │ Remote       │
│ Gym    │ │ HTTP Client  │
└────────┘ └──────┬───────┘
              │
              ▼
         ┌──────────────┐
         │ gym-mcp-     │
         │ server       │
         └──────┬───────┘
                │
                ▼
           ┌────────┐
           │ Gym    │
           │ Env    │
           └────────┘

Supported Space Types

Space Type	Local	Remote	Serialization
Box	✅	✅	array ↔ list
Discrete	✅	✅	int ↔ int
MultiBinary	✅	✅	array ↔ list
MultiDiscrete	✅	✅	array ↔ list
Tuple	✅	✅	recursive
Dict	✅	✅	recursive

Examples

See the examples/ directory:

• local_example.py: Local mode usage
• remote_example.py: Remote mode usage
• context_manager_example.py: Context manager pattern

Run examples:

# Local mode
uv run python examples/local_example.py

# Remote mode (start server first!)
python -m gym_mcp_server --env CartPole-v1 --transport streamable-http --port 8000
uv run python examples/remote_example.py

# Demo CLI tool
python main.py --mode local --episodes 3

Development

Setup

git clone <your-repo-url>
cd gym-mcp-client
make install

Available Commands

make help       # Show all commands
make test       # Run test suite (14 tests)
make lint       # Run ruff linter
make format     # Format code with ruff
make typecheck  # Run mypy type checker
make check      # Run all checks (lint + typecheck + test)
make all        # Format, then run all checks
make demo       # Run local demo
make clean      # Clean build artifacts

Running Tests

make test
# Or: uv run pytest tests/ -v
# 14 tests, all passing ✅

Use Cases

1. Development → Production: Develop locally, deploy remotely
2. Distributed Training: Multiple processes connecting to remote environments
3. Resource Management: Run expensive simulations on dedicated servers
4. Testing: Test locally before remote deployment

Performance

Local Mode

• Overhead: Minimal (thin wrapper)
• Best for: Development, testing, lightweight environments

Remote Mode

• Overhead: HTTP round-trip (1-10ms on localhost)
• Best for: Expensive environments, distributed training, resource sharing

Error Handling

from gym_mcp_client import GymMCPClient
import httpx

try:
    env = GymMCPClient(
        "CartPole-v1",
        mode="remote",
        gym_server_url="http://localhost:8000"
    )
    observation, info = env.reset()
    # ... your code ...
except ValueError:
    # Invalid mode, missing URL, etc.
    pass
except RuntimeError:
    # Environment initialization failed, remote call failed
    pass
except httpx.HTTPError:
    # Network error (remote mode only)
    pass
finally:
    if 'env' in locals():
        env.close()

Troubleshooting

Remote Connection Issues

1. Ensure the gym-mcp-server is running and accessible
2. Check URL is correct (including protocol: http:// or https://)
3. Verify firewall/network settings
4. Check server logs for errors

Environment Not Found

# For Atari environments
uv add "gymnasium[atari]"

# For Box2D environments
uv add "gymnasium[box2d]"

# For MuJoCo environments
uv add "gymnasium[mujoco]"

Requirements

• Python 3.12+
• gymnasium >= 1.2.1
• httpx >= 0.28.1
• numpy >= 2.0.0
• gym-mcp-server (from GitHub)

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run make check to verify all tests pass
5. Submit a Pull Request

See CONTRIBUTING.md for detailed guidelines.

License

MIT License - see LICENSE file for details.

Related Projects

• gym-mcp-server - MCP server for Gymnasium environments
• Gymnasium - RL environment standard
• Model Context Protocol - Tool integration protocol

Support

• Issues: Open a GitHub issue
• Questions: Start a GitHub discussion
• Documentation: See examples/ directory

---

Status: ✅ Production Ready | Version: 0.1.0 | Python: 3.12+