From caf3d137dcc1f428af3497b0a7070de45111a8bb Mon Sep 17 00:00:00 2001
From: luccabb <luccabazzo@gmail.com>
Date: Mon, 19 Jan 2026 20:24:54 -0800
Subject: [PATCH 1/6] [7/9] Add Late Move Reductions (LMR) and Principal
 Variation Search (PVS)

Implements two key search optimizations:

**Late Move Reductions (LMR):**
- Reduce search depth for late quiet moves (move_index >= 3)
- Only apply when: depth >= 3, not in check, move is quiet
- Quiet moves = no capture, no check, no promotion
- Simple reduction of 1 ply (more aggressive formulas tested but hurt accuracy)
- Re-search at full depth if reduced search finds promising score

**Principal Variation Search (PVS):**
- First move: search with full alpha-beta window
- Later moves: search with zero window (alpha, alpha+1)
- If zero window search beats alpha, re-search with full window
- Saves time when first move is best (which is often true with good ordering)

Both techniques work together:
- PVS assumes first move is best (good with TT/killer/MVV-LVA ordering)
- LMR reduces work on moves unlikely to be best
- Combined, they significantly reduce nodes searched

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 moonfish/engines/alpha_beta.py | 72 ++++++++++++++++++++++++++++------
 1 file changed, 61 insertions(+), 11 deletions(-)

diff --git a/moonfish/engines/alpha_beta.py b/moonfish/engines/alpha_beta.py
index c9b0df1..8926b53 100644
--- a/moonfish/engines/alpha_beta.py
+++ b/moonfish/engines/alpha_beta.py
@@ -300,22 +300,72 @@ def negamax(
             moves.remove(tt_move)
             moves.insert(0, tt_move)
 
-        for move in moves:
+        in_check = board.is_check()
+
+        for move_index, move in enumerate(moves):
             is_capture = board.is_capture(move)
+            gives_check = board.gives_check(move)
+            is_promotion = move.promotion is not None
 
             # make the move
             board.push(move)
 
-            board_score = -self.negamax(
-                board=board,
-                depth=depth - 1,
-                null_move=null_move,
-                cache=cache,
-                alpha=-beta,
-                beta=-alpha,
-                ply=ply + 1,
-                killers=killers,
-            )[0]
+            # Late Move Reductions (LMR):
+            # Reduce search depth for late quiet moves that are unlikely to be good
+            # Conditions: sufficient depth, late move, quiet (no capture/check/promotion)
+            reduction = 0
+            if (
+                depth >= 3
+                and move_index >= 3
+                and not is_capture
+                and not gives_check
+                and not is_promotion
+                and not in_check
+            ):
+                # Simple reduction: reduce by 1 ply
+                reduction = 1
+
+            # Principal Variation Search (PVS):
+            # For the first move, search with full window
+            # For subsequent moves, search with zero window first
+            if move_index == 0:
+                # First move: full window search
+                board_score = -self.negamax(
+                    board=board,
+                    depth=depth - 1,
+                    null_move=null_move,
+                    cache=cache,
+                    alpha=-beta,
+                    beta=-alpha,
+                    ply=ply + 1,
+                    killers=killers,
+                )[0]
+            else:
+                # Later moves: zero window search (with LMR reduction if applicable)
+                board_score = -self.negamax(
+                    board=board,
+                    depth=depth - 1 - reduction,
+                    null_move=null_move,
+                    cache=cache,
+                    alpha=-alpha - 1,  # Zero window
+                    beta=-alpha,
+                    ply=ply + 1,
+                    killers=killers,
+                )[0]
+
+                # If zero window search found a promising move, re-search with full window
+                if board_score > alpha and (board_score < beta or reduction > 0):
+                    board_score = -self.negamax(
+                        board=board,
+                        depth=depth - 1,  # Full depth (no reduction)
+                        null_move=null_move,
+                        cache=cache,
+                        alpha=-beta,
+                        beta=-alpha,
+                        ply=ply + 1,
+                        killers=killers,
+                    )[0]
+
             if board_score > self.config.checkmate_threshold:
                 board_score -= 1
             if board_score < -self.config.checkmate_threshold:

From 204c4396d524b1865dd9967281305e68e2c9ef14 Mon Sep 17 00:00:00 2001
From: Lucca Bertoncini <32229669+luccabb@users.noreply.github.com>
Date: Thu, 22 Jan 2026 12:23:04 -0800
Subject: [PATCH 2/6] Add OpenEnv-compatible RL environment with HuggingFace
 Space (#40)

This adds a chess reinforcement learning environment following the
OpenEnv interface pattern, with both local and HTTP client-server modes.

Features:
- ChessEnvironment class with configurable rewards, opponents, and game limits
- FastAPI server with REST endpoints (/reset, /step, /state, /engine-move)
- HTTP client for remote environment access
- Web UI for playing against the engine
- HuggingFace Spaces deployment configuration (Dockerfile, openenv.yaml)
- Example training scripts for local and remote usage

Also includes:
- mypy configuration for optional RL dependencies
- Import formatting fixes for ufmt compliance
---
 README.md                                | 113 +++
 moonfish/rl/README.md                    | 185 +++++
 moonfish/rl/__init__.py                  |  18 +
 moonfish/rl/client.py                    | 181 +++++
 moonfish/rl/examples/__init__.py         |   0
 moonfish/rl/examples/basic_usage.py      | 128 ++++
 moonfish/rl/examples/openenv_training.py | 141 ++++
 moonfish/rl/models.py                    |  82 +++
 moonfish/rl/openenv.yaml                 |   6 +
 moonfish/rl/outputs/.gitkeep             |   0
 moonfish/rl/pyproject.toml               |  21 +
 moonfish/rl/server/Dockerfile            |  49 ++
 moonfish/rl/server/__init__.py           |   5 +
 moonfish/rl/server/app.py                | 201 ++++++
 moonfish/rl/server/chess_environment.py  | 329 +++++++++
 moonfish/rl/server/static/index.html     | 278 ++++++++
 moonfish/rl/uv.lock                      | 854 +++++++++++++++++++++++
 pyproject.toml                           |  22 +-
 18 files changed, 2611 insertions(+), 2 deletions(-)
 create mode 100644 moonfish/rl/README.md
 create mode 100644 moonfish/rl/__init__.py
 create mode 100644 moonfish/rl/client.py
 create mode 100644 moonfish/rl/examples/__init__.py
 create mode 100644 moonfish/rl/examples/basic_usage.py
 create mode 100644 moonfish/rl/examples/openenv_training.py
 create mode 100644 moonfish/rl/models.py
 create mode 100644 moonfish/rl/openenv.yaml
 create mode 100644 moonfish/rl/outputs/.gitkeep
 create mode 100644 moonfish/rl/pyproject.toml
 create mode 100644 moonfish/rl/server/Dockerfile
 create mode 100644 moonfish/rl/server/__init__.py
 create mode 100644 moonfish/rl/server/app.py
 create mode 100644 moonfish/rl/server/chess_environment.py
 create mode 100644 moonfish/rl/server/static/index.html
 create mode 100644 moonfish/rl/uv.lock

diff --git a/README.md b/README.md
index 3cdb7e9..a4e3046 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,10 @@ Moonfish is a didactic Python chess engine designed to showcase parallel search
 
 The engine achieves approximately ~2000 Elo when playing against Lichess Stockfish bots (beats level 5 and loses to level 6) and includes comprehensive test suites including the Bratko-Kopec tactical test positions.
 
+## Play Online
+
+**[Play against Moonfish in your browser](https://huggingface.co/spaces/luccabb/moonfish_chess)** - No installation required!
+
 # Quickstart
 
 ## Requirements
@@ -79,6 +83,7 @@ $ curl "http://localhost:5000/?fen=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR%2
 - **UCI Protocol** - Compatible with popular chess GUIs
 - **Web API** - RESTful interface for online integration
 - **Lichess Bot** - Ready for deployment on [Lichess.org](/CONTRIBUTING.md#lichess-bot-python-bridge)
+- **RL Environment** - OpenEnv-compatible environment for reinforcement learning
 
 ## Configuration Options
 
@@ -92,6 +97,114 @@ $ curl "http://localhost:5000/?fen=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR%2
 | `--quiescence-search-depth` | Max depth of quiescence search | `3` | `1-N` |
 | `--syzygy-path` | Tablebase directory | `None` | Valid path |
 
+## Reinforcement Learning Environment
+
+Moonfish includes an [OpenEnv](https://github.com/huggingface/openenv)-compatible RL environment for training chess agents. OpenEnv is a framework for RL environments that supports both local and remote (HTTP) execution.
+
+### Installation
+
+```shell
+pip install moonfish[rl]
+```
+
+### Local Usage
+
+```python
+from moonfish.rl import ChessEnvironment, ChessAction
+
+# Create environment with moonfish as opponent
+env = ChessEnvironment(opponent="moonfish", opponent_depth=2)
+
+# Reset and play
+obs = env.reset()
+while not obs.done:
+    move = select_move(obs.legal_moves)  # Your policy here
+    obs, reward, done = env.step(ChessAction(move=move))
+
+env.close()
+```
+
+### Configuration Options
+
+```python
+from moonfish.rl import ChessEnvironment, RewardConfig
+
+# Custom reward shaping
+config = RewardConfig(
+    win=1.0,              # Reward for winning
+    loss=-1.0,            # Penalty for losing
+    draw=0.0,             # Reward for draw
+    illegal_move=-0.1,    # Penalty for illegal moves
+    use_evaluation=True,  # Enable position-based intermediate rewards
+    evaluation_scale=0.001,
+)
+
+# Environment options
+env = ChessEnvironment(
+    reward_config=config,
+    opponent="moonfish",   # "moonfish", "random", or None (self-play)
+    opponent_depth=2,      # Search depth for moonfish opponent
+    agent_color=True,      # True=White, False=Black, None=alternate
+    max_moves=500,         # Max half-moves before draw
+)
+```
+
+### OpenEnv Server Mode
+
+For distributed training or integration with OpenEnv-compatible frameworks:
+
+```shell
+# Start the server locally
+python -m uvicorn moonfish.rl.server.app:app --port 8000
+```
+
+```python
+# Connect via HTTP client
+from moonfish.rl import make_env, ChessAction
+
+client = make_env("http://localhost:8000")
+obs = client.reset()
+result = client.step(ChessAction(move="e2e4"))
+print(result.observation.fen)
+```
+
+### API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Health check |
+| `/metadata` | GET | Environment configuration |
+| `/reset` | POST | Start new game (optional: `fen`, `seed`) |
+| `/step` | POST | Make a move (`{"move": "e2e4"}`) |
+| `/state` | GET | Current game state |
+| `/engine-move` | POST | Get best move for position |
+
+### Hosted on Hugging Face
+
+A hosted version is available on Hugging Face Spaces - use it for training without running your own server:
+
+```python
+from moonfish.rl import make_env, ChessAction
+
+client = make_env("https://luccabb-moonfish-chess.hf.space")
+obs = client.reset()
+result = client.step(ChessAction(move="e2e4"))
+```
+
+**Space URL:** https://huggingface.co/spaces/luccabb/moonfish_chess
+
+### Deploy Your Own
+
+```shell
+# Install OpenEnv CLI
+pip install openenv
+
+# Clone and deploy to Hugging Face Spaces
+cd moonfish/rl
+openenv validate  # Check environment structure
+openenv push      # Deploy to HF Spaces
+```
+
 ## Contributing
 
 We welcome contributions, feel free to open PRs/Issues! Areas of interest:
diff --git a/moonfish/rl/README.md b/moonfish/rl/README.md
new file mode 100644
index 0000000..31f09d0
--- /dev/null
+++ b/moonfish/rl/README.md
@@ -0,0 +1,185 @@
+---
+title: Moonfish Chess
+emoji: ♟️
+colorFrom: gray
+colorTo: blue
+sdk: docker
+pinned: false
+license: mit
+---
+
+# Chess OpenEnv
+
+A chess environment for reinforcement learning, built on [moonfish](https://github.com/luccab/moonfish) and compatible with the [OpenEnv](https://github.com/meta-pytorch/OpenEnv) framework.
+
+## Features
+
+- **Full Chess Rules**: Legal move generation, checkmate/stalemate detection, draw conditions
+- **Position Evaluation**: PeSTO evaluation function from moonfish for reward shaping
+- **OpenEnv Compatible**: Standard `reset()`, `step()`, `state()` interface
+- **Configurable Rewards**: Win/loss/draw payoffs, illegal move penalties, evaluation-based rewards
+- **HTTP API**: FastAPI server for remote training and multi-agent setups
+- **Containerized**: Docker support for reproducible deployments
+
+## Quick Start
+
+### Local Usage (No Server)
+
+```python
+from moonfish.rl import ChessEnvironment, ChessAction
+
+# Create environment
+env = ChessEnvironment()
+
+# Start a new game
+obs = env.reset()
+print(f"Legal moves: {obs.legal_moves}")
+
+# Make a move
+action = ChessAction(move="e2e4")
+obs, reward, done = env.step(action)
+
+print(f"FEN: {obs.fen}")
+print(f"Reward: {reward}, Done: {done}")
+```
+
+### Client-Server Usage
+
+Start the server:
+
+```bash
+cd moonfish/rl
+python -m uvicorn server.app:app --host 0.0.0.0 --port 8000
+```
+
+Connect with the client:
+
+```python
+from moonfish.rl import ChessEnvClient, ChessAction
+
+client = ChessEnvClient("http://localhost:8000")
+
+obs = client.reset()
+result = client.step(ChessAction(move="e2e4"))
+print(f"Reward: {result.reward}")
+
+client.close()
+```
+
+## Data Models
+
+### ChessAction
+```python
+@dataclass
+class ChessAction:
+    move: str  # UCI format: "e2e4", "e7e8q" (promotion)
+```
+
+### ChessObservation
+```python
+@dataclass
+class ChessObservation:
+    fen: str              # Board state in FEN notation
+    legal_moves: List[str]  # Available moves in UCI format
+    is_check: bool        # Current player in check
+    done: bool            # Game over
+    reward: Optional[float]  # Terminal reward
+    result: Optional[str]    # "1-0", "0-1", "1/2-1/2"
+    metadata: Dict[str, Any]  # Evaluation, material, etc.
+```
+
+### ChessState
+```python
+@dataclass
+class ChessState:
+    episode_id: str        # Unique game identifier
+    step_count: int        # Half-moves played
+    current_player: str    # "white" or "black"
+    fen: str               # Current position
+    move_history: List[str]  # All moves in UCI format
+```
+
+## Reward Configuration
+
+```python
+from moonfish.rl import ChessEnvironment, RewardConfig
+
+config = RewardConfig(
+    win=1.0,           # Reward for winning
+    loss=-1.0,         # Penalty for losing
+    draw=0.0,          # Reward for draw
+    illegal_move=-0.1, # Penalty for illegal moves
+    use_evaluation=True,  # Enable intermediate rewards
+    evaluation_scale=0.0001,  # Scale for eval-based rewards
+)
+
+env = ChessEnvironment(reward_config=config)
+```
+
+## Docker
+
+Build and run:
+
+```bash
+docker build -t chess-openenv .
+docker run -p 8000:8000 chess-openenv
+```
+
+## Integration with RL Frameworks
+
+### With TorchRL
+
+```python
+from moonfish.rl import ChessEnvironment, ChessAction
+
+class ChessTorchRLWrapper:
+    def __init__(self):
+        self.env = ChessEnvironment()
+
+    def reset(self):
+        obs = self.env.reset()
+        return self._obs_to_tensor(obs)
+
+    def step(self, action_idx):
+        move = self._idx_to_move(action_idx)
+        obs, reward, done = self.env.step(ChessAction(move=move))
+        return self._obs_to_tensor(obs), reward, done
+```
+
+### With OpenEnv Training Loop
+
+```python
+from moonfish.rl import make_env, ChessAction
+import random
+
+client = make_env("http://localhost:8000")
+
+for episode in range(100):
+    obs = client.reset()
+    episode_reward = 0
+
+    while not obs.done:
+        # Your policy here (random for demo)
+        move = random.choice(obs.legal_moves)
+        result = client.step(ChessAction(move=move))
+        obs = result.observation
+        episode_reward += result.reward
+
+    print(f"Episode {episode}: reward={episode_reward}")
+
+client.close()
+```
+
+## API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Health check |
+| `/metadata` | GET | Environment configuration |
+| `/reset` | POST | Start new episode |
+| `/step` | POST | Execute a move |
+| `/state` | GET | Get episode metadata |
+
+## License
+
+MIT - See the moonfish repository for full license details.
diff --git a/moonfish/rl/__init__.py b/moonfish/rl/__init__.py
new file mode 100644
index 0000000..ca27f38
--- /dev/null
+++ b/moonfish/rl/__init__.py
@@ -0,0 +1,18 @@
+"""Chess OpenEnv - A chess environment for reinforcement learning."""
+
+from .client import ChessEnvClient, make_env, StepResult
+from .models import ChessAction, ChessObservation, ChessState, RewardConfig
+from .server.chess_environment import ChessEnvironment
+
+__all__ = [
+    "ChessAction",
+    "ChessObservation",
+    "ChessState",
+    "RewardConfig",
+    "ChessEnvClient",
+    "StepResult",
+    "make_env",
+    "ChessEnvironment",
+]
+
+__version__ = "1.0.0"
diff --git a/moonfish/rl/client.py b/moonfish/rl/client.py
new file mode 100644
index 0000000..c65a839
--- /dev/null
+++ b/moonfish/rl/client.py
@@ -0,0 +1,181 @@
+"""Client for the Chess OpenEnv environment."""
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import httpx
+
+from .models import ChessAction, ChessObservation, ChessState
+
+
+@dataclass
+class StepResult:
+    """Result from a step() call."""
+
+    observation: ChessObservation
+    reward: float
+    done: bool
+
+
+class ChessEnvClient:
+    """
+    HTTP client for the Chess OpenEnv environment.
+
+    Provides a simple interface to interact with a remote chess environment
+    server for reinforcement learning.
+
+    Example usage:
+        client = ChessEnvClient("http://localhost:8000")
+        obs = client.reset()
+        print(f"Legal moves: {obs.legal_moves}")
+
+        result = client.step(ChessAction(move="e2e4"))
+        print(f"Reward: {result.reward}, Done: {result.done}")
+
+        state = client.state()
+        print(f"Move count: {state.step_count}")
+
+        client.close()
+    """
+
+    def __init__(self, base_url: str = "http://localhost:8000", timeout: float = 30.0):
+        """
+        Initialize the chess environment client.
+
+        Args:
+            base_url: URL of the chess environment server
+            timeout: Request timeout in seconds
+        """
+        self.base_url = base_url.rstrip("/")
+        self._client = httpx.Client(timeout=timeout)
+
+    def reset(
+        self,
+        seed: Optional[int] = None,
+        episode_id: Optional[str] = None,
+        fen: Optional[str] = None,
+    ) -> ChessObservation:
+        """
+        Reset the environment and start a new episode.
+
+        Args:
+            seed: Random seed (optional)
+            episode_id: Unique episode identifier (optional)
+            fen: Starting position in FEN notation (optional)
+
+        Returns:
+            Initial observation of the board state
+        """
+        payload: Dict[str, Any] = {}
+        if seed is not None:
+            payload["seed"] = seed
+        if episode_id is not None:
+            payload["episode_id"] = episode_id
+        if fen is not None:
+            payload["fen"] = fen
+
+        response = self._client.post(f"{self.base_url}/reset", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        return self._parse_observation(data)
+
+    def step(self, action: ChessAction) -> StepResult:
+        """
+        Execute a move in the environment.
+
+        Args:
+            action: The chess action (move in UCI format)
+
+        Returns:
+            StepResult with observation, reward, and done flag
+        """
+        payload = {"move": action.move}
+        response = self._client.post(f"{self.base_url}/step", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        return StepResult(
+            observation=self._parse_observation(data["observation"]),
+            reward=data["reward"],
+            done=data["done"],
+        )
+
+    def state(self) -> ChessState:
+        """
+        Get the current episode state.
+
+        Returns:
+            Current episode state with metadata
+        """
+        response = self._client.get(f"{self.base_url}/state")
+        response.raise_for_status()
+        data = response.json()
+
+        return ChessState(
+            episode_id=data["episode_id"],
+            step_count=data["step_count"],
+            current_player=data["current_player"],
+            fen=data["fen"],
+            move_history=data.get("move_history", []),
+        )
+
+    def metadata(self) -> Dict[str, Any]:
+        """
+        Get environment metadata.
+
+        Returns:
+            Dictionary with environment configuration
+        """
+        response = self._client.get(f"{self.base_url}/metadata")
+        response.raise_for_status()
+        return response.json()
+
+    def health(self) -> bool:
+        """
+        Check if the server is healthy.
+
+        Returns:
+            True if server is responding
+        """
+        try:
+            response = self._client.get(f"{self.base_url}/health")
+            return response.status_code == 200
+        except Exception:
+            return False
+
+    def close(self) -> None:
+        """Close the HTTP client."""
+        self._client.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+    def _parse_observation(self, data: Dict[str, Any]) -> ChessObservation:
+        """Parse observation from JSON response."""
+        return ChessObservation(
+            fen=data["fen"],
+            legal_moves=data["legal_moves"],
+            is_check=data.get("is_check", False),
+            done=data.get("done", False),
+            reward=data.get("reward"),
+            result=data.get("result"),
+            metadata=data.get("metadata", {}),
+        )
+
+
+# Convenience function for quick usage
+def make_env(base_url: str = "http://localhost:8000") -> ChessEnvClient:
+    """
+    Create a chess environment client.
+
+    Args:
+        base_url: URL of the chess environment server
+
+    Returns:
+        ChessEnvClient instance
+    """
+    return ChessEnvClient(base_url)
diff --git a/moonfish/rl/examples/__init__.py b/moonfish/rl/examples/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/moonfish/rl/examples/basic_usage.py b/moonfish/rl/examples/basic_usage.py
new file mode 100644
index 0000000..986136f
--- /dev/null
+++ b/moonfish/rl/examples/basic_usage.py
@@ -0,0 +1,128 @@
+"""
+Basic usage example for the Chess OpenEnv environment.
+
+This example shows how to use the chess environment both locally
+(without a server) and via the HTTP client.
+"""
+
+import random
+
+from moonfish.rl import ChessAction, ChessEnvironment, RewardConfig
+
+
+def play_random_game():
+    """Play a game with random moves to demonstrate the environment."""
+    print("=== Playing a random game ===\n")
+
+    # Create environment
+    env = ChessEnvironment()
+
+    # Reset to start a new game
+    obs = env.reset(episode_id="random_game_001")
+
+    print(f"Initial position: {obs.fen}")
+    print(f"Legal moves: {len(obs.legal_moves)} available")
+    print()
+
+    move_count = 0
+    total_reward = 0.0
+
+    while not obs.done:
+        # Pick a random legal move
+        move = random.choice(obs.legal_moves)
+        action = ChessAction(move=move)
+
+        # Execute the move
+        obs, reward, done = env.step(action)
+        total_reward += reward
+        move_count += 1
+
+        if move_count <= 5 or done:
+            print(f"Move {move_count}: {move}")
+            print(f"  FEN: {obs.fen}")
+            print(f"  Check: {obs.is_check}, Reward: {reward}")
+            if move_count == 5 and not done:
+                print("  ... (continuing)")
+                print()
+
+    print(f"\nGame finished after {move_count} moves")
+    print(f"Result: {obs.result}")
+    print(f"Total reward: {total_reward}")
+
+    # Check final state
+    state = env.state
+    print(f"Episode ID: {state.episode_id}")
+    print(f"Move history: {state.move_history[:10]}...")
+
+    env.close()
+
+
+def play_specific_opening():
+    """Demonstrate playing specific moves (Italian Game opening)."""
+    print("\n=== Playing the Italian Game opening ===\n")
+
+    env = ChessEnvironment()
+    obs = env.reset()
+
+    opening_moves = ["e2e4", "e7e5", "g1f3", "b8c6", "f1c4"]
+
+    for i, move in enumerate(opening_moves):
+        action = ChessAction(move=move)
+        obs, reward, done = env.step(action)
+        print(f"{i+1}. {move} -> Check: {obs.is_check}")
+
+    print(f"\nPosition after opening: {obs.fen}")
+    print(f"Legal moves for Black: {len(obs.legal_moves)}")
+    print(f"Material: {obs.metadata.get('material', {})}")
+
+    env.close()
+
+
+def demonstrate_illegal_move():
+    """Show how illegal moves are handled."""
+    print("\n=== Handling illegal moves ===\n")
+
+    env = ChessEnvironment()
+    obs = env.reset()
+
+    # Try an illegal move
+    illegal_action = ChessAction(move="e2e5")  # Can't move pawn 3 squares
+    obs, reward, done = env.step(illegal_action)
+
+    print("Attempted illegal move: e2e5")
+    print(f"Reward: {reward}")  # Should be negative
+    print(f"Error: {obs.metadata.get('error', 'None')}")
+    print(f"Done: {done}")  # Game continues
+
+    env.close()
+
+
+def with_evaluation_rewards():
+    """Show evaluation-based intermediate rewards."""
+    print("\n=== Evaluation-based rewards ===\n")
+
+    config = RewardConfig(
+        use_evaluation=True,
+        evaluation_scale=0.0001,  # Scale down the centipawn values
+    )
+
+    env = ChessEnvironment(reward_config=config)
+    obs = env.reset()
+
+    # Play a few moves and observe evaluation changes
+    moves = ["e2e4", "d7d5", "e4d5"]  # White wins a pawn
+
+    for move in moves:
+        action = ChessAction(move=move)
+        obs, reward, done = env.step(action)
+        eval_score = obs.metadata.get("evaluation", 0)
+        print(f"Move: {move}, Reward: {reward:.4f}, Eval: {eval_score:.1f}")
+
+    env.close()
+
+
+if __name__ == "__main__":
+    play_random_game()
+    play_specific_opening()
+    demonstrate_illegal_move()
+    with_evaluation_rewards()
diff --git a/moonfish/rl/examples/openenv_training.py b/moonfish/rl/examples/openenv_training.py
new file mode 100644
index 0000000..711266d
--- /dev/null
+++ b/moonfish/rl/examples/openenv_training.py
@@ -0,0 +1,141 @@
+"""
+OpenEnv Training Example
+
+This example shows how to use the chess environment with the OpenEnv
+client-server pattern, which is useful for:
+- Distributed training across machines
+- Isolated environment execution
+- Integration with OpenEnv-compatible training frameworks
+
+Usage:
+    # Terminal 1: Start the server
+    cd moonfish/rl
+    python -m uvicorn server.app:app --host 0.0.0.0 --port 8000
+
+    # Terminal 2: Run this training script
+    python examples/openenv_training.py
+"""
+
+import random
+
+from moonfish.rl import ChessAction, make_env
+
+
+def random_policy(legal_moves: list[str]) -> str:
+    """Simple random policy for demonstration."""
+    return random.choice(legal_moves)
+
+
+def train_with_remote_env():
+    """
+    Training loop using the HTTP client (OpenEnv pattern).
+
+    This pattern is useful when:
+    - Environment runs on a different machine
+    - You need environment isolation (sandboxing)
+    - You're using OpenEnv-compatible training frameworks
+    """
+    # Connect to the environment server
+    # For local testing, start the server first:
+    #   python -m uvicorn moonfish.rl.server.app:app --port 8000
+    client = make_env("http://localhost:8000")
+
+    # Check server health
+    if not client.health():
+        print("Server not running. Start it with:")
+        print("  python -m uvicorn moonfish.rl.server.app:app --port 8000")
+        return
+
+    print("Connected to chess environment server")
+    print(f"Metadata: {client.metadata()}")
+    print()
+
+    # Training loop
+    num_episodes = 5
+
+    for episode in range(num_episodes):
+        # Reset environment
+        obs = client.reset()
+        episode_reward = 0.0
+
+        print(f"Episode {episode + 1}")
+
+        while not obs.done:
+            # Select action using policy
+            move = random_policy(obs.legal_moves)
+            action = ChessAction(move=move)
+
+            # Step environment
+            result = client.step(action)
+            obs = result.observation
+            episode_reward += result.reward
+
+            # Safety limit
+            state = client.state()
+            if state.step_count > 200:
+                print("  (truncated at 200 moves)")
+                break
+
+        print(
+            f"  Moves: {client.state().step_count}, "
+            f"Result: {obs.result or 'ongoing'}, "
+            f"Reward: {episode_reward:.2f}"
+        )
+
+    # Cleanup
+    client.close()
+    print("\nTraining complete!")
+
+
+def train_with_local_env():
+    """
+    Training loop using local environment (no server needed).
+
+    This is simpler and faster for single-machine training.
+    """
+    from moonfish.rl import ChessEnvironment
+
+    env = ChessEnvironment(opponent="random")
+
+    print("Training with local environment (random opponent)")
+    print()
+
+    num_episodes = 5
+
+    for episode in range(num_episodes):
+        obs = env.reset()
+        episode_reward = 0.0
+
+        while not obs.done:
+            move = random_policy(obs.legal_moves)
+            obs, reward, done = env.step(ChessAction(move=move))
+            episode_reward += reward
+
+            if env.state.step_count > 200:
+                break
+
+        print(
+            f"Episode {episode + 1}: "
+            f"Moves={env.state.step_count}, "
+            f"Result={obs.result or 'ongoing'}, "
+            f"Reward={episode_reward:.2f}"
+        )
+
+    env.close()
+    print("\nTraining complete!")
+
+
+if __name__ == "__main__":
+    import sys
+
+    if "--remote" in sys.argv:
+        print("=== Remote Environment (OpenEnv HTTP Client) ===\n")
+        train_with_remote_env()
+    else:
+        print("=== Local Environment ===\n")
+        train_with_local_env()
+        print("\nTo test with HTTP client, run:")
+        print(
+            "  1. Start server: python -m uvicorn moonfish.rl.server.app:app --port 8000"
+        )
+        print("  2. Run: python examples/openenv_training.py --remote")
diff --git a/moonfish/rl/models.py b/moonfish/rl/models.py
new file mode 100644
index 0000000..f735609
--- /dev/null
+++ b/moonfish/rl/models.py
@@ -0,0 +1,82 @@
+"""Data models for the Chess OpenEnv environment."""
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+
+
+@dataclass
+class ChessAction:
+    """
+    Represents a chess move action.
+
+    Attributes:
+        move: UCI format move string (e.g., "e2e4", "e7e8q" for promotion)
+    """
+
+    move: str
+
+
+@dataclass
+class ChessObservation:
+    """
+    Represents the observable state of the chess environment.
+
+    Attributes:
+        fen: Board position in FEN notation
+        legal_moves: List of legal moves in UCI format
+        is_check: Whether the current player is in check
+        done: Whether the episode has ended
+        reward: Reward value (1.0 for win, -1.0 for loss, 0.0 for draw, None otherwise)
+        result: Game result string if game is over (e.g., "1-0", "0-1", "1/2-1/2")
+        metadata: Additional information about the position
+    """
+
+    fen: str
+    legal_moves: List[str]
+    is_check: bool = False
+    done: bool = False
+    reward: Optional[float] = None
+    result: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ChessState:
+    """
+    Tracks episode metadata for the chess environment.
+
+    Attributes:
+        episode_id: Unique identifier for the current episode
+        step_count: Number of moves (half-moves) played in current episode
+        current_player: "white" or "black"
+        fen: Current position in FEN notation
+        move_history: List of moves played in UCI format
+    """
+
+    episode_id: str
+    step_count: int
+    current_player: str
+    fen: str
+    move_history: List[str] = field(default_factory=list)
+
+
+@dataclass
+class RewardConfig:
+    """
+    Configuration for reward shaping in the chess environment.
+
+    Attributes:
+        win: Reward for winning the game
+        loss: Reward for losing the game
+        draw: Reward for drawing the game
+        illegal_move: Penalty for attempting an illegal move
+        use_evaluation: Whether to include position evaluation in rewards
+        evaluation_scale: Scale factor for evaluation-based rewards
+    """
+
+    win: float = 1.0
+    loss: float = -1.0
+    draw: float = 0.0
+    illegal_move: float = -0.1
+    use_evaluation: bool = False
+    evaluation_scale: float = 0.001
diff --git a/moonfish/rl/openenv.yaml b/moonfish/rl/openenv.yaml
new file mode 100644
index 0000000..fd316a3
--- /dev/null
+++ b/moonfish/rl/openenv.yaml
@@ -0,0 +1,6 @@
+spec_version: 1
+name: moonfish_chess
+type: space
+runtime: fastapi
+app: server.app:app
+port: 7860
diff --git a/moonfish/rl/outputs/.gitkeep b/moonfish/rl/outputs/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/moonfish/rl/pyproject.toml b/moonfish/rl/pyproject.toml
new file mode 100644
index 0000000..f8310ab
--- /dev/null
+++ b/moonfish/rl/pyproject.toml
@@ -0,0 +1,21 @@
+[project]
+name = "moonfish-chess-env"
+version = "1.0.0"
+description = "Chess RL environment using moonfish engine - OpenEnv compatible"
+requires-python = ">=3.10"
+
+dependencies = [
+    "chess>=1.10.0",
+    "fastapi>=0.100.0",
+    "uvicorn[standard]>=0.23.0",
+    "httpx>=0.24.0",
+    "pydantic>=2.0.0",
+    "openenv>=0.1.0",
+]
+
+[project.scripts]
+server = "server.app:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
diff --git a/moonfish/rl/server/Dockerfile b/moonfish/rl/server/Dockerfile
new file mode 100644
index 0000000..a11292a
--- /dev/null
+++ b/moonfish/rl/server/Dockerfile
@@ -0,0 +1,49 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install moonfish from PyPI (includes chess dependency)
+RUN pip install --no-cache-dir moonfish
+
+# Install additional dependencies for the RL server
+RUN pip install --no-cache-dir \
+    fastapi>=0.100.0 \
+    "uvicorn[standard]>=0.23.0" \
+    httpx>=0.24.0 \
+    pydantic>=2.0.0
+
+# Copy the RL module into the installed moonfish package
+# First, find where moonfish is installed
+RUN MOONFISH_PATH=$(python -c "import moonfish; import os; print(os.path.dirname(moonfish.__file__))") && \
+    mkdir -p ${MOONFISH_PATH}/rl/server
+
+# Copy RL module files - we need to do this in a separate step
+COPY __init__.py /tmp/rl/__init__.py
+COPY models.py /tmp/rl/models.py
+COPY client.py /tmp/rl/client.py
+COPY server/__init__.py /tmp/rl/server/__init__.py
+COPY server/app.py /tmp/rl/server/app.py
+COPY server/chess_environment.py /tmp/rl/server/chess_environment.py
+COPY server/static /tmp/rl/server/static
+
+# Move files to moonfish package location
+RUN MOONFISH_PATH=$(python -c "import moonfish; import os; print(os.path.dirname(moonfish.__file__))") && \
+    cp -r /tmp/rl/* ${MOONFISH_PATH}/rl/ && \
+    rm -rf /tmp/rl
+
+# Download cerebellum opening book from moonfish GitHub repo (~170MB)
+RUN mkdir -p /app/opening_book && \
+    curl -L -o /app/opening_book/cerebellum.bin \
+    "https://github.com/luccabb/moonfish/raw/master/opening_book/cerebellum.bin"
+
+# Expose port - HF Spaces expects 7860
+EXPOSE 7860
+
+# Run the server on HF's expected port
+CMD ["python", "-m", "uvicorn", "moonfish.rl.server.app:app", "--host", "0.0.0.0", "--port", "7860"]
diff --git a/moonfish/rl/server/__init__.py b/moonfish/rl/server/__init__.py
new file mode 100644
index 0000000..a0638df
--- /dev/null
+++ b/moonfish/rl/server/__init__.py
@@ -0,0 +1,5 @@
+"""Chess OpenEnv server module."""
+
+from .chess_environment import ChessEnvironment
+
+__all__ = ["ChessEnvironment"]
diff --git a/moonfish/rl/server/app.py b/moonfish/rl/server/app.py
new file mode 100644
index 0000000..3fee35a
--- /dev/null
+++ b/moonfish/rl/server/app.py
@@ -0,0 +1,201 @@
+"""FastAPI server for the Chess OpenEnv environment."""
+
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import chess
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+
+from moonfish.lib import search_move
+from ..models import ChessAction
+from .chess_environment import ChessEnvironment
+
+
+# Pydantic models for API requests/responses
+class ResetRequest(BaseModel):
+    seed: Optional[int] = None
+    episode_id: Optional[str] = None
+    fen: Optional[str] = None
+
+
+class StepRequest(BaseModel):
+    move: str
+
+
+class EngineMoveRequest(BaseModel):
+    fen: str
+    depth: int = 2
+
+
+class ObservationResponse(BaseModel):
+    fen: str
+    legal_moves: list[str]
+    is_check: bool = False
+    done: bool = False
+    reward: Optional[float] = None
+    result: Optional[str] = None
+    metadata: Dict[str, Any] = {}
+
+
+class StepResponse(BaseModel):
+    observation: ObservationResponse
+    reward: float
+    done: bool
+
+
+class StateResponse(BaseModel):
+    episode_id: str
+    step_count: int
+    current_player: str
+    fen: str
+    move_history: list[str]
+
+
+# Create FastAPI app
+app = FastAPI(
+    title="Chess OpenEnv",
+    description="Chess environment for reinforcement learning using moonfish",
+    version="1.0.0",
+)
+
+# Serve static files for the web UI
+STATIC_DIR = Path(__file__).parent / "static"
+if STATIC_DIR.exists():
+    app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
+
+# Global environment instance (for single-player mode)
+# For multi-player, you'd want a session manager
+_env: Optional[ChessEnvironment] = None
+
+
+def get_env() -> ChessEnvironment:
+    """Get or create environment instance."""
+    global _env
+    if _env is None:
+        _env = ChessEnvironment()
+    return _env
+
+
+@app.get("/")
+def root():
+    """Serve the chess UI."""
+    index_path = STATIC_DIR / "index.html"
+    if index_path.exists():
+        return FileResponse(str(index_path))
+    return {"message": "Moonfish Chess API", "docs": "/docs"}
+
+
+@app.get("/web")
+def web():
+    """Serve the chess UI (for HF Spaces base_path)."""
+    index_path = STATIC_DIR / "index.html"
+    if index_path.exists():
+        return FileResponse(str(index_path))
+    return {"message": "Moonfish Chess API", "docs": "/docs"}
+
+
+@app.get("/health")
+def health():
+    """Health check endpoint."""
+    return {"status": "ok"}
+
+
+@app.post("/engine-move")
+def engine_move(request: EngineMoveRequest):
+    """Get the best move from moonfish engine for a given position."""
+    try:
+        board = chess.Board(request.fen)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=f"Invalid FEN: {e}")
+
+    if board.is_game_over():
+        raise HTTPException(status_code=400, detail="Game is already over")
+
+    depth = max(1, min(request.depth, 4))  # Clamp depth 1-4
+    move = search_move(board, depth=depth)
+
+    return {"move": move.uci(), "fen": request.fen}
+
+
+@app.get("/metadata")
+def metadata():
+    """Get environment metadata."""
+    return get_env().get_metadata()
+
+
+@app.post("/reset", response_model=ObservationResponse)
+def reset(request: ResetRequest):
+    """Reset the environment and start a new episode."""
+    env = get_env()
+    obs = env.reset(
+        seed=request.seed,
+        episode_id=request.episode_id,
+        fen=request.fen,
+    )
+    return ObservationResponse(
+        fen=obs.fen,
+        legal_moves=obs.legal_moves,
+        is_check=obs.is_check,
+        done=obs.done,
+        reward=obs.reward,
+        result=obs.result,
+        metadata=obs.metadata,
+    )
+
+
+@app.post("/step", response_model=StepResponse)
+def step(request: StepRequest):
+    """Execute a move and return the result."""
+    env = get_env()
+
+    try:
+        action = ChessAction(move=request.move)
+        obs, reward, done = env.step(action)
+    except RuntimeError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    return StepResponse(
+        observation=ObservationResponse(
+            fen=obs.fen,
+            legal_moves=obs.legal_moves,
+            is_check=obs.is_check,
+            done=obs.done,
+            reward=obs.reward,
+            result=obs.result,
+            metadata=obs.metadata,
+        ),
+        reward=reward,
+        done=done,
+    )
+
+
+@app.get("/state", response_model=StateResponse)
+def state():
+    """Get current episode state."""
+    env = get_env()
+    try:
+        s = env.state
+    except RuntimeError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+    return StateResponse(
+        episode_id=s.episode_id,
+        step_count=s.step_count,
+        current_player=s.current_player,
+        fen=s.fen,
+        move_history=s.move_history,
+    )
+
+
+def main():
+    """Entry point for running the server."""
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/moonfish/rl/server/chess_environment.py b/moonfish/rl/server/chess_environment.py
new file mode 100644
index 0000000..5423893
--- /dev/null
+++ b/moonfish/rl/server/chess_environment.py
@@ -0,0 +1,329 @@
+"""Chess environment for OpenEnv using moonfish."""
+
+import random
+import uuid
+from typing import Any, Dict, Optional, Tuple
+
+import chess
+
+from moonfish.lib import search_move
+from moonfish.psqt import board_evaluation, count_pieces, get_phase, MG_PIECE_VALUES
+from ..models import ChessAction, ChessObservation, ChessState, RewardConfig
+
+
+class ChessEnvironment:
+    """
+    Chess environment implementing the OpenEnv interface.
+
+    Uses python-chess for game logic and moonfish for position evaluation.
+    Designed for RL training where an agent plays as one color against
+    an opponent (which can be random, moonfish engine, or self-play).
+    """
+
+    def __init__(
+        self,
+        reward_config: Optional[RewardConfig] = None,
+        max_moves: int = 500,
+        agent_color: Optional[
+            bool
+        ] = None,  # None = alternate, True = White, False = Black
+        opponent: Optional[
+            str
+        ] = None,  # None = self-play, "moonfish" = moonfish engine, "random" = random
+        opponent_depth: int = 2,  # Search depth for moonfish opponent
+    ):
+        """
+        Initialize the chess environment.
+
+        Args:
+            reward_config: Configuration for reward shaping
+            max_moves: Maximum half-moves before draw (prevents infinite games)
+            agent_color: Which color the RL agent plays (None = alternates each episode)
+            opponent: Opponent type - None (self-play), "moonfish", or "random"
+            opponent_depth: Search depth when using moonfish as opponent
+        """
+        self.reward_config = reward_config or RewardConfig()
+        self.max_moves = max_moves
+        self.agent_color_setting = agent_color
+        self.opponent = opponent
+        self.opponent_depth = opponent_depth
+
+        # Will be set on reset
+        self._board: Optional[chess.Board] = None
+        self._state: Optional[ChessState] = None
+        self._agent_color: bool = chess.WHITE
+
+    def reset(
+        self,
+        seed: Optional[int] = None,
+        episode_id: Optional[str] = None,
+        fen: Optional[str] = None,
+        **kwargs,
+    ) -> ChessObservation:
+        """
+        Initialize a new chess game episode.
+
+        Args:
+            seed: Random seed (unused for now, chess is deterministic)
+            episode_id: Unique identifier for this episode
+            fen: Optional starting position in FEN notation
+
+        Returns:
+            Initial observation of the board state
+        """
+        # Create new board
+        if fen:
+            self._board = chess.Board(fen)
+        else:
+            self._board = chess.Board()
+
+        # Determine agent color
+        if self.agent_color_setting is None:
+            # Alternate each episode based on episode_id hash
+            if episode_id:
+                self._agent_color = hash(episode_id) % 2 == 0
+            else:
+                self._agent_color = chess.WHITE
+        else:
+            self._agent_color = self.agent_color_setting
+
+        # Initialize state
+        self._state = ChessState(
+            episode_id=episode_id or uuid.uuid4().hex,
+            step_count=0,
+            current_player="white" if self._board.turn else "black",
+            fen=self._board.fen(),
+            move_history=[],
+        )
+
+        # If agent plays Black and opponent is configured, opponent moves first
+        if self.opponent is not None and self._agent_color == chess.BLACK:
+            self._make_opponent_move()
+
+        return self._get_observation()
+
+    def step(
+        self, action: ChessAction, timeout_s: Optional[float] = None, **kwargs
+    ) -> Tuple[ChessObservation, float, bool]:
+        """
+        Execute a chess move and return the resulting state.
+
+        Args:
+            action: The move to make in UCI format (e.g., "e2e4")
+            timeout_s: Unused timeout parameter
+
+        Returns:
+            Tuple of (observation, reward, done)
+        """
+        if self._board is None or self._state is None:
+            raise RuntimeError("Environment not initialized. Call reset() first.")
+
+        # Parse the move
+        try:
+            move = chess.Move.from_uci(action.move)
+        except ValueError:
+            # Invalid move format
+            return self._handle_illegal_move(f"Invalid move format: {action.move}")
+
+        # Check if move is legal
+        if move not in self._board.legal_moves:
+            return self._handle_illegal_move(f"Illegal move: {action.move}")
+
+        # Execute the move
+        self._board.push(move)
+        self._state.step_count += 1
+        self._state.move_history.append(action.move)
+        self._state.current_player = "white" if self._board.turn else "black"
+        self._state.fen = self._board.fen()
+
+        # Calculate reward and check for game end
+        reward, done = self._calculate_reward_and_done()
+
+        # If game not over and opponent is configured, make opponent move
+        if not done and self.opponent is not None:
+            self._make_opponent_move()
+            # Recalculate after opponent move
+            opp_reward, done = self._calculate_reward_and_done()
+            # Opponent's reward is negative of ours (zero-sum)
+            reward += -opp_reward if done else 0
+
+        observation = self._get_observation(done=done, reward=reward if done else None)
+
+        return observation, reward, done
+
+    @property
+    def state(self) -> ChessState:
+        """Return the current episode state."""
+        if self._state is None:
+            raise RuntimeError("Environment not initialized. Call reset() first.")
+        return self._state
+
+    def close(self) -> None:
+        """Clean up resources."""
+        self._board = None
+        self._state = None
+
+    def get_metadata(self) -> Dict[str, Any]:
+        """Return environment metadata."""
+        return {
+            "name": "chess",
+            "version": "1.0.0",
+            "max_moves": self.max_moves,
+            "reward_config": {
+                "win": self.reward_config.win,
+                "loss": self.reward_config.loss,
+                "draw": self.reward_config.draw,
+                "illegal_move": self.reward_config.illegal_move,
+                "use_evaluation": self.reward_config.use_evaluation,
+                "evaluation_scale": self.reward_config.evaluation_scale,
+            },
+        }
+
+    def _get_observation(
+        self,
+        done: bool = False,
+        reward: Optional[float] = None,
+        result: Optional[str] = None,
+        error: Optional[str] = None,
+    ) -> ChessObservation:
+        """Build observation from current board state."""
+        assert self._board is not None
+
+        legal_moves = [move.uci() for move in self._board.legal_moves]
+
+        metadata: Dict[str, Any] = {}
+
+        # Add evaluation if configured
+        if self.reward_config.use_evaluation:
+            metadata["evaluation"] = board_evaluation(self._board)
+
+        # Add material count
+        metadata["material"] = self._get_material_count()
+
+        # Add game phase (0 = opening, 256 = endgame)
+        metadata["phase"] = get_phase(self._board)
+        metadata["fullmove_number"] = self._board.fullmove_number
+        metadata["halfmove_clock"] = self._board.halfmove_clock
+
+        if error:
+            metadata["error"] = error
+
+        # Determine result string if game is over
+        if done and result is None:
+            result = self._get_result_string()
+
+        return ChessObservation(
+            fen=self._board.fen(),
+            legal_moves=legal_moves,
+            is_check=self._board.is_check(),
+            done=done,
+            reward=reward,
+            result=result,
+            metadata=metadata,
+        )
+
+    def _calculate_reward_and_done(self) -> Tuple[float, bool]:
+        """Calculate reward and check if episode is done."""
+        assert self._board is not None
+
+        # Check for game end
+        if self._board.is_checkmate():
+            # The side to move is checkmated, so the previous mover won
+            winner = not self._board.turn
+            if winner == self._agent_color:
+                return self.reward_config.win, True
+            else:
+                return self.reward_config.loss, True
+
+        if self._board.is_stalemate():
+            return self.reward_config.draw, True
+
+        if self._board.is_insufficient_material():
+            return self.reward_config.draw, True
+
+        if self._board.is_fifty_moves():
+            return self.reward_config.draw, True
+
+        if self._board.is_repetition(3):
+            return self.reward_config.draw, True
+
+        # Check move limit
+        if self._state and self._state.step_count >= self.max_moves:
+            return self.reward_config.draw, True
+
+        # Game continues
+        reward = 0.0
+
+        # Optional: Add evaluation-based intermediate rewards
+        if self.reward_config.use_evaluation:
+            eval_score = board_evaluation(self._board)
+            # Normalize evaluation to agent's perspective
+            if self._board.turn != self._agent_color:
+                eval_score = -eval_score
+            reward = eval_score * self.reward_config.evaluation_scale
+
+        return reward, False
+
+    def _handle_illegal_move(
+        self, error_msg: str
+    ) -> Tuple[ChessObservation, float, bool]:
+        """Handle an illegal move attempt."""
+        observation = self._get_observation(done=False, error=error_msg)
+        return observation, self.reward_config.illegal_move, False
+
+    def _get_result_string(self) -> str:
+        """Get the game result as a string."""
+        assert self._board is not None
+
+        if self._board.is_checkmate():
+            return "1-0" if not self._board.turn else "0-1"
+        return "1/2-1/2"
+
+    def _get_material_count(self) -> Dict[str, int]:
+        """Count material for both sides using moonfish piece values."""
+        assert self._board is not None
+
+        # count_pieces returns [wp, bp, wn, bn, wb, bb, wr, br, wq, bq]
+        pieces = count_pieces(self._board)
+        wp, bp, wn, bn, wb, bb, wr, br, wq, bq = pieces
+
+        white = (
+            wp * MG_PIECE_VALUES[chess.PAWN]
+            + wn * MG_PIECE_VALUES[chess.KNIGHT]
+            + wb * MG_PIECE_VALUES[chess.BISHOP]
+            + wr * MG_PIECE_VALUES[chess.ROOK]
+            + wq * MG_PIECE_VALUES[chess.QUEEN]
+        )
+        black = (
+            bp * MG_PIECE_VALUES[chess.PAWN]
+            + bn * MG_PIECE_VALUES[chess.KNIGHT]
+            + bb * MG_PIECE_VALUES[chess.BISHOP]
+            + br * MG_PIECE_VALUES[chess.ROOK]
+            + bq * MG_PIECE_VALUES[chess.QUEEN]
+        )
+
+        return {"white": white, "black": black}
+
+    def _make_opponent_move(self) -> None:
+        """Make a move for the opponent using configured strategy."""
+        assert self._board is not None
+        assert self._state is not None
+
+        if not list(self._board.legal_moves):
+            return  # No legal moves (game should be over)
+
+        if self.opponent == "moonfish":
+            # Use moonfish engine to find best move
+            move = search_move(self._board, depth=self.opponent_depth)
+        elif self.opponent == "random":
+            # Pick a random legal move
+            move = random.choice(list(self._board.legal_moves))
+        else:
+            return  # No opponent configured
+
+        # Execute opponent's move
+        self._board.push(move)
+        self._state.step_count += 1
+        self._state.move_history.append(move.uci())
+        self._state.current_player = "white" if self._board.turn else "black"
+        self._state.fen = self._board.fen()
diff --git a/moonfish/rl/server/static/index.html b/moonfish/rl/server/static/index.html
new file mode 100644
index 0000000..62d9bbd
--- /dev/null
+++ b/moonfish/rl/server/static/index.html
@@ -0,0 +1,278 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Moonfish Chess</title>
+    <link rel="stylesheet" href="https://unpkg.com/@chrisoakman/chessboardjs@1.0.0/dist/chessboard-1.0.0.min.css">
+    <style>
+        * { box-sizing: border-box; }
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
+            color: #fff;
+            min-height: 100vh;
+            margin: 0;
+            padding: 20px;
+        }
+        .container {
+            max-width: 800px;
+            margin: 0 auto;
+        }
+        h1 {
+            text-align: center;
+            margin-bottom: 10px;
+        }
+        .subtitle {
+            text-align: center;
+            color: #888;
+            margin-bottom: 30px;
+        }
+        .subtitle a { color: #4da6ff; }
+        #board-container {
+            width: 100%;
+            max-width: 500px;
+            margin: 0 auto 20px;
+        }
+        #board { width: 100%; }
+        .info-panel {
+            background: rgba(255,255,255,0.1);
+            border-radius: 8px;
+            padding: 15px;
+            margin-bottom: 15px;
+        }
+        .status { font-size: 1.2em; margin-bottom: 10px; }
+        .controls { display: flex; gap: 10px; flex-wrap: wrap; }
+        button {
+            background: #4da6ff;
+            color: #fff;
+            border: none;
+            padding: 10px 20px;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 1em;
+        }
+        button:hover { background: #3d8bd9; }
+        button:disabled { background: #555; cursor: not-allowed; }
+        .move-history {
+            background: rgba(0,0,0,0.3);
+            border-radius: 5px;
+            padding: 10px;
+            max-height: 150px;
+            overflow-y: auto;
+            font-family: monospace;
+        }
+        .thinking { color: #ffd700; }
+        .error { color: #ff6b6b; }
+        .win { color: #4ade80; }
+        .loss { color: #f87171; }
+        .draw { color: #fbbf24; }
+        select {
+            background: #2a2a4a;
+            color: #fff;
+            border: 1px solid #555;
+            padding: 8px;
+            border-radius: 5px;
+            font-size: 1em;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>♟️ Moonfish Chess</h1>
+        <p class="subtitle">Play against the <a href="https://github.com/luccabb/moonfish">Moonfish</a> chess engine (~2000 Elo)</p>
+        
+        <div id="board-container">
+            <div id="board"></div>
+        </div>
+        
+        <div class="info-panel">
+            <div class="status" id="status">Loading...</div>
+            <div class="controls">
+                <button id="newGame">New Game</button>
+                <select id="playerColor">
+                    <option value="white">Play as White</option>
+                    <option value="black">Play as Black</option>
+                </select>
+                <select id="difficulty">
+                    <option value="1">Easy</option>
+                    <option value="2" selected>Medium</option>
+                    <option value="3">Hard</option>
+                </select>
+            </div>
+        </div>
+        
+        <div class="info-panel">
+            <strong>Move History:</strong>
+            <div class="move-history" id="moveHistory">-</div>
+        </div>
+    </div>
+
+    <script src="https://code.jquery.com/jquery-3.7.1.min.js"></script>
+    <script src="https://unpkg.com/@chrisoakman/chessboardjs@1.0.0/dist/chessboard-1.0.0.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/chess.js/0.10.3/chess.min.js"></script>
+    <script>
+        const API_BASE = window.location.origin;
+        let board = null;
+        let game = new Chess();
+        let playerColor = 'white';
+        let isThinking = false;
+
+        function updateStatus(msg, className = '') {
+            const el = document.getElementById('status');
+            el.textContent = msg;
+            el.className = 'status ' + className;
+        }
+
+        function updateMoveHistory() {
+            const moves = game.history();
+            let html = '';
+            for (let i = 0; i < moves.length; i += 2) {
+                const moveNum = Math.floor(i / 2) + 1;
+                html += moveNum + '. ' + moves[i];
+                if (moves[i + 1]) html += ' ' + moves[i + 1];
+                html += '  ';
+            }
+            document.getElementById('moveHistory').textContent = html || '-';
+        }
+
+        function onDragStart(source, piece) {
+            if (game.game_over()) return false;
+            if (isThinking) return false;
+            if ((playerColor === 'white' && piece.search(/^b/) !== -1) ||
+                (playerColor === 'black' && piece.search(/^w/) !== -1)) {
+                return false;
+            }
+            if ((game.turn() === 'w' && playerColor !== 'white') ||
+                (game.turn() === 'b' && playerColor !== 'black')) {
+                return false;
+            }
+            return true;
+        }
+
+        async function makeEngineMove() {
+            if (game.game_over()) return;
+            
+            isThinking = true;
+            updateStatus('Moonfish is thinking...', 'thinking');
+            
+            try {
+                // Get legal moves for engine
+                const moves = game.moves({ verbose: true });
+                if (moves.length === 0) return;
+                
+                // Call our API to get the best move
+                const fen = game.fen();
+                const response = await fetch(API_BASE + '/engine-move', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ 
+                        fen: fen,
+                        depth: parseInt(document.getElementById('difficulty').value)
+                    })
+                });
+                
+                if (!response.ok) throw new Error('Engine error');
+                
+                const data = await response.json();
+                const move = game.move({
+                    from: data.move.substring(0, 2),
+                    to: data.move.substring(2, 4),
+                    promotion: data.move.length > 4 ? data.move[4] : undefined
+                });
+                
+                if (move) {
+                    board.position(game.fen());
+                    updateMoveHistory();
+                }
+            } catch (err) {
+                console.error('Engine error:', err);
+                // Fallback: make a random legal move
+                const moves = game.moves({ verbose: true });
+                if (moves.length > 0) {
+                    const randomMove = moves[Math.floor(Math.random() * moves.length)];
+                    game.move(randomMove);
+                    board.position(game.fen());
+                    updateMoveHistory();
+                }
+            }
+            
+            isThinking = false;
+            checkGameOver();
+        }
+
+        function checkGameOver() {
+            if (game.in_checkmate()) {
+                const winner = game.turn() === 'w' ? 'Black' : 'White';
+                const isPlayerWin = (winner.toLowerCase() === playerColor);
+                updateStatus(winner + ' wins by checkmate!', isPlayerWin ? 'win' : 'loss');
+            } else if (game.in_stalemate()) {
+                updateStatus('Draw by stalemate', 'draw');
+            } else if (game.in_threefold_repetition()) {
+                updateStatus('Draw by repetition', 'draw');
+            } else if (game.insufficient_material()) {
+                updateStatus('Draw - insufficient material', 'draw');
+            } else if (game.in_draw()) {
+                updateStatus('Draw', 'draw');
+            } else if (game.in_check()) {
+                updateStatus(game.turn() === 'w' ? 'White is in check' : 'Black is in check');
+            } else {
+                updateStatus(game.turn() === 'w' ? 'White to move' : 'Black to move');
+            }
+        }
+
+        function onDrop(source, target) {
+            const move = game.move({
+                from: source,
+                to: target,
+                promotion: 'q'
+            });
+
+            if (move === null) return 'snapback';
+            
+            updateMoveHistory();
+            checkGameOver();
+            
+            if (!game.game_over()) {
+                setTimeout(makeEngineMove, 250);
+            }
+        }
+
+        function onSnapEnd() {
+            board.position(game.fen());
+        }
+
+        async function newGame() {
+            playerColor = document.getElementById('playerColor').value;
+            game.reset();
+            board.start();
+            board.orientation(playerColor);
+            updateMoveHistory();
+            updateStatus(playerColor === 'white' ? 'Your turn (White)' : 'Moonfish is thinking...', 
+                        playerColor === 'black' ? 'thinking' : '');
+            
+            if (playerColor === 'black') {
+                setTimeout(makeEngineMove, 500);
+            }
+        }
+
+        // Initialize
+        const config = {
+            draggable: true,
+            position: 'start',
+            onDragStart: onDragStart,
+            onDrop: onDrop,
+            onSnapEnd: onSnapEnd,
+            pieceTheme: 'https://chessboardjs.com/img/chesspieces/wikipedia/{piece}.png'
+        };
+        
+        board = Chessboard('board', config);
+        $(window).resize(() => board.resize());
+        
+        document.getElementById('newGame').addEventListener('click', newGame);
+        document.getElementById('playerColor').addEventListener('change', newGame);
+        
+        updateStatus('White to move - drag a piece to play!');
+    </script>
+</body>
+</html>
diff --git a/moonfish/rl/uv.lock b/moonfish/rl/uv.lock
new file mode 100644
index 0000000..27c8825
--- /dev/null
+++ b/moonfish/rl/uv.lock
@@ -0,0 +1,854 @@
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+resolution-markers = [
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+]
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+    { url = "https://files.pythonhosted.org/packages/7e/52/93e166a81e0305b33fe416338be92ae863563fe7bce446b0f687b9df5aea/websockets-16.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:4a1aba3340a8dca8db6eb5a7986157f52eb9e436b74813764241981ca4888f03", size = 175260, upload-time = "2026-01-10T09:23:37.409Z" },
+    { url = "https://files.pythonhosted.org/packages/56/0c/2dbf513bafd24889d33de2ff0368190a0e69f37bcfa19009ef819fe4d507/websockets-16.0-pp311-pypy311_pp73-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:f4a32d1bd841d4bcbffdcb3d2ce50c09c3909fbead375ab28d0181af89fd04da", size = 176071, upload-time = "2026-01-10T09:23:39.158Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/8f/aea9c71cc92bf9b6cc0f7f70df8f0b420636b6c96ef4feee1e16f80f75dd/websockets-16.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0298d07ee155e2e9fda5be8a9042200dd2e3bb0b8a38482156576f863a9d457c", size = 176968, upload-time = "2026-01-10T09:23:41.031Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/3f/f70e03f40ffc9a30d817eef7da1be72ee4956ba8d7255c399a01b135902a/websockets-16.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:a653aea902e0324b52f1613332ddf50b00c06fdaf7e92624fbf8c77c78fa5767", size = 178735, upload-time = "2026-01-10T09:23:42.259Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/28/258ebab549c2bf3e64d2b0217b973467394a9cea8c42f70418ca2c5d0d2e/websockets-16.0-py3-none-any.whl", hash = "sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec", size = 171598, upload-time = "2026-01-10T09:23:45.395Z" },
+]
diff --git a/pyproject.toml b/pyproject.toml
index 7e7c5d6..b3da0ea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,8 +30,16 @@ dependencies = [
 [project.scripts]
 moonfish = "moonfish.main:main"
 
+[project.optional-dependencies]
+rl = [
+  "fastapi>=0.100.0",
+  "uvicorn[standard]>=0.23.0",
+  "httpx>=0.24.0",
+  "pydantic>=2.0.0",
+]
+
 [tool.setuptools]
-packages = ["moonfish"]
+packages = ["moonfish", "moonfish.rl", "moonfish.rl.server", "moonfish.rl.examples"]
 
 [tool.setuptools_scm]
 version_scheme = "post-release"
@@ -41,7 +49,17 @@ local_scheme = "no-local-version"
 "moonfish" = ["py.typed"]
 
 [[tool.mypy.overrides]]
-module = ["parameterized.*"]   # include sub‑modules, just in case
+module = [
+    "parameterized.*",
+    "httpx",
+    "httpx.*",
+    "fastapi",
+    "fastapi.*",
+    "pydantic",
+    "pydantic.*",
+    "uvicorn",
+    "uvicorn.*",
+]
 ignore_missing_imports = true
 
 [project.urls]

From abeed319d7ddbd858c562f90f66613fdb4f20ffd Mon Sep 17 00:00:00 2001
From: Lucca Bertoncini <32229669+luccabb@users.noreply.github.com>
Date: Fri, 23 Jan 2026 19:03:09 -0800
Subject: [PATCH 3/6] Remove Elo claim and fix GitHub link (#41)

* Add OpenEnv-compatible RL environment with HuggingFace Space

This adds a chess reinforcement learning environment following the
OpenEnv interface pattern, with both local and HTTP client-server modes.

Features:
- ChessEnvironment class with configurable rewards, opponents, and game limits
- FastAPI server with REST endpoints (/reset, /step, /state, /engine-move)
- HTTP client for remote environment access
- Web UI for playing against the engine
- HuggingFace Spaces deployment configuration (Dockerfile, openenv.yaml)
- Example training scripts for local and remote usage

Also includes:
- mypy configuration for optional RL dependencies
- Import formatting fixes for ufmt compliance

* Remove Elo claim and fix GitHub link to open in new tab
---
 moonfish/rl/server/static/index.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/moonfish/rl/server/static/index.html b/moonfish/rl/server/static/index.html
index 62d9bbd..56d3903 100644
--- a/moonfish/rl/server/static/index.html
+++ b/moonfish/rl/server/static/index.html
@@ -80,7 +80,7 @@
 <body>
     <div class="container">
         <h1>♟️ Moonfish Chess</h1>
-        <p class="subtitle">Play against the <a href="https://github.com/luccabb/moonfish">Moonfish</a> chess engine (~2000 Elo)</p>
+        <p class="subtitle">Play against the <a href="https://github.com/luccabb/moonfish" target="_blank" rel="noopener">Moonfish</a> chess engine</p>
         
         <div id="board-container">
             <div id="board"></div>

From 35e82bd50dd8395a783c8c0f4a8f7329a16967ef Mon Sep 17 00:00:00 2001
From: Lucca Bertoncini <32229669+luccabb@users.noreply.github.com>
Date: Sun, 25 Jan 2026 16:47:52 -0800
Subject: [PATCH 4/6] Fix and improve lichess setup script (#42)

Fixes:
- Remove incorrect `bash .env` line (was trying to execute .env as script)
- Add `set -e` to exit on errors
- Check if brew is installed before using it
- Check if git-lfs/envsubst already installed before reinstalling
- Validate build succeeded before continuing
- Verify dist/moonfish exists before copying
- Check if lichess-bot directory exists
- Validate LICHESS_TOKEN is set after sourcing .env
- Validate token is not empty when creating .env
- Use `cp -f` instead of rm + cp

Improvements:
- Make lichess-bot directory configurable via LICHESS_BOT_DIR env var
- Add progress messages for better UX
- Provide helpful error messages with next steps

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
---
 lichess/setup_macos.sh | 81 +++++++++++++++++++++++++++++++-----------
 1 file changed, 61 insertions(+), 20 deletions(-)

diff --git a/lichess/setup_macos.sh b/lichess/setup_macos.sh
index 575bef5..0681292 100755
--- a/lichess/setup_macos.sh
+++ b/lichess/setup_macos.sh
@@ -1,15 +1,29 @@
 #!/bin/bash
 
+set -e  # Exit on error
+
+LICHESS_BOT_DIR="${LICHESS_BOT_DIR:-../lichess-bot}"
+
+# Check if brew is installed
+if ! command -v brew &> /dev/null; then
+    echo "Error: Homebrew is not installed. Please install it from https://brew.sh"
+    exit 1
+fi
+
 # Check if .env file exists
 if [ ! -f .env ]; then
     echo "No .env file found. You need to create one with your Lichess API token."
     echo "Please check .env.example for the required format."
     echo "Make sure your token has the 'bot:play' OAuth scope from https://lichess.org/account/oauth/token"
-    read -p "Would you like to create .env file now? (y/n) " create_env
+    read -p "Would you like to create .env file now? [Y/n] " create_env
     create_env=$(echo "$create_env" | tr '[:upper:]' '[:lower:]')
-    
-    if [[ "$create_env" == "y" || "$create_env" == "yes" ]]; then
+
+    if [[ -z "$create_env" || "$create_env" == "y" || "$create_env" == "yes" ]]; then
         read -p "Enter your Lichess API token (with bot:play scope): " token
+        if [ -z "$token" ]; then
+            echo "Error: Token cannot be empty."
+            exit 1
+        fi
         echo "export LICHESS_TOKEN=$token" > .env
         echo ".env file created successfully!"
     else
@@ -20,33 +34,60 @@ fi
 
 source .env
 
-# build binary
-read -p "Do you want to build a new binary? (y/n) " answer
+# Validate token is set
+if [ -z "$LICHESS_TOKEN" ]; then
+    echo "Error: LICHESS_TOKEN is not set in .env file."
+    exit 1
+fi
+
+# Build binary
+read -p "Do you want to build a new binary? [Y/n] " answer
 answer=$(echo "$answer" | tr '[:upper:]' '[:lower:]')
-if [[ "$answer" == "y" || "$answer" == "yes" ]]; then
-    make build-lichess
+if [[ -z "$answer" || "$answer" == "y" || "$answer" == "yes" ]]; then
+    echo "Building moonfish binary..."
+    if ! make build-lichess; then
+        echo "Error: Build failed."
+        exit 1
+    fi
+fi
+
+# Verify binary exists
+if [ ! -f dist/moonfish ]; then
+    echo "Error: dist/moonfish not found. Please build the binary first."
+    exit 1
 fi
 
-# fetch opening book if not already downloaded
-brew install git-lfs
+# Install git-lfs if not already installed
+if ! command -v git-lfs &> /dev/null; then
+    echo "Installing git-lfs..."
+    brew install git-lfs
+fi
 git lfs install
 git lfs pull
 
-# install gettext for envsubst
-brew install gettext
+# Install gettext for envsubst if not already installed
+if ! command -v envsubst &> /dev/null; then
+    echo "Installing gettext..."
+    brew install gettext
+fi
 
-# copy files
-if [ -f ../lichess-bot/engines/main ]; then
-    rm ../lichess-bot/engines/main
+# Check if lichess-bot directory exists
+if [ ! -d "$LICHESS_BOT_DIR" ]; then
+    echo "Error: lichess-bot directory not found at $LICHESS_BOT_DIR"
+    echo "Please clone it first:"
+    echo "  cd .. && git clone https://github.com/lichess-bot-devs/lichess-bot.git"
+    exit 1
 fi
-cp dist/moonfish ../lichess-bot/engines/main
-mkdir -p ../lichess-bot/engines/opening_book
-cp opening_book/cerebellum.bin ../lichess-bot/engines/opening_book/cerebellum.bin
-bash .env
-envsubst < lichess/config.yml > ../lichess-bot/config.yml
+
+# Copy files
+echo "Copying files to $LICHESS_BOT_DIR..."
+mkdir -p "$LICHESS_BOT_DIR/engines/opening_book"
+cp -f dist/moonfish "$LICHESS_BOT_DIR/engines/main"
+cp -f opening_book/cerebellum.bin "$LICHESS_BOT_DIR/engines/opening_book/cerebellum.bin"
+envsubst < lichess/config.yml > "$LICHESS_BOT_DIR/config.yml"
 
 echo ""
 echo "Setup complete! To start playing:"
-echo "1. cd ../lichess-bot"
+echo "1. cd $LICHESS_BOT_DIR"
 echo "2. python3 lichess-bot.py"
 echo ""

From 84f07e73cc4dd5e8b7466a44478010b6fd02c2b4 Mon Sep 17 00:00:00 2001
From: Lucca Bertoncini <32229669+luccabb@users.noreply.github.com>
Date: Tue, 27 Jan 2026 10:45:05 -0800
Subject: [PATCH 5/6] Add Stockfish benchmark CI workflow (#43)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add Stockfish benchmark CI workflow

- Runs cutechess-cli matches against Stockfish on every PR
- 20 rounds with max concurrency
- Moonfish: 60s per move, Stockfish: Skill Level 5 with 60+5 time control
- Downloads full 170MB opening book from release assets (bypasses LFS)
- Reports win/loss/draw stats in GitHub job summary
- Uploads PGN and logs as artifacts

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Parallelize Stockfish benchmark with matrix strategy

- Run 20 parallel jobs (10 chunks × 2 skill levels)
- Test against both Stockfish skill level 4 and 5
- 100 games per skill level = 200 total games for reliable signal
- Add aggregation job to combine results with summary table
- Use different random seeds per chunk for opening variety

* Add PR comment with benchmark results

- Post aggregated results as a comment on the PR
- Makes it easy to see win/loss/draw rates without navigating to CI
- Includes collapsible configuration details

* Add -repeat flag for more consistent benchmark results

- Each opening is played twice with colors reversed
- Eliminates first-move advantage variance
- Doubles games to 400 total (200 per skill level)
- More statistically reliable results between runs

* Add detailed stats to benchmark PR comment

- Show win rates by color (as White / as Black)
- Show loss reasons (timeout, checkmate, adjudication)
- Separate tables per skill level for clarity

* Fix termination parsing and correct game count

- Parse game endings from PGN move text (cutechess format)
- Track: checkmate, timeout, resignation, stalemate, repetition, 50-move
- Fix config: 200 total games (not 400)

* Simplify game endings - parse merged PGN directly

- Remove per-chunk termination tracking
- Parse game endings from merged PGN in aggregate step
- Cleaner and less error-prone

* Extract game endings dynamically from PGN text

* Filter out mates from game endings (redundant with win/loss)

* Rename to 'Non-checkmate endings'

* Add skill level 3 and skip aggregate if all jobs cancelled

- Test against Stockfish skill levels 3, 4, and 5 (300 total games)
- Only run aggregate job if at least one benchmark succeeded

* Hardcode concurrency to 10 for faster benchmarks

* Increase to 20 rounds and 20 concurrency (600 total games)

* Reduce to 5 chunks (15 total jobs, 300 games)

* Add PR reactions: eyes on start, thumbs up on complete

- React with 👀 when benchmark starts
- React with 👍 after results are posted

* Add local benchmark script

* Add skill level 1, increase to 200 games per level (800 total)

* Revert CI changes, update local script: skill level 1, 200 games/level

* Add skill level 2 to local benchmark script

* Update benchmark settings

- Local script: 100 rounds, 15 concurrency
- CI: Remove eyes reaction when adding thumbs up

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
---
 .github/workflows/benchmark.yml | 331 ++++++++++++++++++++++++++++++++
 .github/workflows/ci.yml        |  11 ++
 .github/workflows/release.yml   |   7 +-
 moonfish/helper.py              |  25 ++-
 scripts/benchmark.sh            | 131 +++++++++++++
 5 files changed, 501 insertions(+), 4 deletions(-)
 create mode 100644 .github/workflows/benchmark.yml
 create mode 100755 scripts/benchmark.sh

diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
new file mode 100644
index 0000000..8dd83b3
--- /dev/null
+++ b/.github/workflows/benchmark.yml
@@ -0,0 +1,331 @@
+name: Stockfish Benchmark
+
+on:
+  pull_request:
+    branches: [ master ]
+
+permissions:
+  contents: read
+  pull-requests: write
+
+env:
+  GIT_LFS_SKIP_SMUDGE: 1
+  MOONFISH_OPENING_BOOK: ${{ github.workspace }}/opening_book/cerebellum.bin
+
+jobs:
+  react-start:
+    runs-on: ubuntu-latest
+    if: github.event_name == 'pull_request'
+    steps:
+    - name: Add eyes reaction to PR
+      env:
+        GH_TOKEN: ${{ github.token }}
+      run: |
+        gh api repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/reactions \
+          -f content='eyes' --silent || true
+
+  benchmark:
+    runs-on: ubuntu-latest
+    needs: react-start
+    strategy:
+      fail-fast: false
+      matrix:
+        chunk: [0, 1, 2, 3, 4]  # 5 parallel jobs, 20 rounds each = 100 total per skill level
+        skill_level: [3, 4, 5]  # Test against multiple skill levels
+    env:
+      UV_SYSTEM_PYTHON: 1
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        lfs: false
+        fetch-depth: 0
+
+    - name: Ensure opening book
+      run: |
+        set -euo pipefail
+
+        if [ -f opening_book/cerebellum.bin ]; then
+          if head -1 opening_book/cerebellum.bin | grep -q "git-lfs"; then
+            echo "LFS pointer detected; downloading opening book..."
+            rm -f opening_book/cerebellum.bin
+          else
+            echo "Opening book already present."
+            exit 0
+          fi
+        fi
+
+        echo "Downloading full opening book from release..."
+        curl -L -o opening_book/cerebellum.bin "https://github.com/luccabb/moonfish/releases/download/v1.0.0/cerebellum.bin"
+
+    - name: Verify opening book
+      run: |
+        ls -lh opening_book/cerebellum.bin
+        python - <<'PY'
+        import os, sys
+        path = "opening_book/cerebellum.bin"
+        size = os.path.getsize(path)
+        print(f"opening book size: {size} bytes")
+        if size < 10_000_000:
+            print("opening book too small; likely an LFS pointer", file=sys.stderr)
+            sys.exit(1)
+        PY
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        cache-dependency-glob: "requirements.txt"
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Install dependencies
+      run: make install
+
+    - name: Validate opening book with python-chess
+      run: |
+        python - <<'PY'
+        import chess
+        import chess.polyglot
+        book_path = "opening_book/cerebellum.bin"
+        with chess.polyglot.MemoryMappedReader(book_path) as reader:
+            entry = reader.find(chess.Board())
+        print(f"book entry: {entry.move.uci()}")
+        PY
+
+    - name: Install Stockfish
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y stockfish
+
+    - name: Install cutechess-cli dependencies
+      run: |
+        sudo apt-get install -y cmake qt5-qmake qtbase5-dev qtbase5-dev-tools libqt5svg5-dev
+
+    - name: Cache cutechess-cli
+      id: cache-cutechess
+      uses: actions/cache@v4
+      with:
+        path: /usr/local/bin/cutechess-cli
+        key: cutechess-cli-1.4.0
+
+    - name: Build cutechess-cli
+      if: steps.cache-cutechess.outputs.cache-hit != 'true'
+      run: |
+        git clone --depth 1 --branch v1.4.0 https://github.com/cutechess/cutechess.git /tmp/cutechess
+        cd /tmp/cutechess
+        mkdir build && cd build
+        cmake ..
+        make -j$(nproc)
+        sudo cp cutechess-cli /usr/local/bin/
+
+    - name: Build moonfish binary
+      run: make build-lichess
+
+    - name: Run Stockfish benchmark
+      run: |
+        CHUNK=${{ matrix.chunk }}
+        SKILL=${{ matrix.skill_level }}
+        ROUNDS_PER_CHUNK=20
+        SEED=$((CHUNK * 1000 + SKILL * 100 + 42))  # Different seed per chunk/skill for opening variety
+
+        echo "Running moonfish vs Stockfish benchmark (chunk $CHUNK, skill $SKILL)..."
+        echo "Stockfish skill level: $SKILL"
+        echo "Moonfish: 60s per move, Stockfish: 60+5 time control"
+        echo "Rounds: $ROUNDS_PER_CHUNK, Concurrency: $(nproc), Seed: $SEED"
+        echo ""
+
+        cutechess-cli \
+          -engine name=moonfish cmd=./dist/moonfish dir=. proto=uci tc=inf st=60 timemargin=10000 \
+          -engine name=stockfish cmd=stockfish proto=uci option.Skill\ Level=$SKILL option.Threads=1 tc=60+5 timemargin=10000 \
+          -rounds $ROUNDS_PER_CHUNK \
+          -repeat \
+          -concurrency 20 \
+          -pgnout benchmark-skill$SKILL-chunk$CHUNK.pgn \
+          -srand $SEED \
+          -recover \
+          2>&1 | tee benchmark-skill$SKILL-chunk$CHUNK.log
+
+        echo ""
+        echo "=== Benchmark Results (Skill $SKILL, Chunk $CHUNK) ==="
+        tail -20 benchmark-skill$SKILL-chunk$CHUNK.log
+
+    - name: Parse results
+      run: |
+        CHUNK=${{ matrix.chunk }}
+        SKILL=${{ matrix.skill_level }}
+        PGN="benchmark-skill$SKILL-chunk$CHUNK.pgn"
+
+        # Extract score line from log
+        SCORE=$(grep "Score of moonfish vs stockfish:" benchmark-skill$SKILL-chunk$CHUNK.log | tail -1)
+        WINS=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\1/')
+        LOSSES=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\2/')
+        DRAWS=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\3/')
+
+        # Parse PGN for detailed stats
+        # Moonfish as White: wins/losses/draws
+        WHITE_WINS=$(grep -B5 'Result "1-0"' "$PGN" | grep -c 'White "moonfish"' || echo 0)
+        WHITE_LOSSES=$(grep -B5 'Result "0-1"' "$PGN" | grep -c 'White "moonfish"' || echo 0)
+        WHITE_DRAWS=$(grep -B5 'Result "1/2-1/2"' "$PGN" | grep -c 'White "moonfish"' || echo 0)
+
+        # Moonfish as Black: wins/losses/draws
+        BLACK_WINS=$(grep -B5 'Result "0-1"' "$PGN" | grep -c 'Black "moonfish"' || echo 0)
+        BLACK_LOSSES=$(grep -B5 'Result "1-0"' "$PGN" | grep -c 'Black "moonfish"' || echo 0)
+        BLACK_DRAWS=$(grep -B5 'Result "1/2-1/2"' "$PGN" | grep -c 'Black "moonfish"' || echo 0)
+
+        # Save detailed results
+        cat > results-skill$SKILL-chunk$CHUNK.txt << EOF
+        SKILL=$SKILL
+        WINS=$WINS
+        LOSSES=$LOSSES
+        DRAWS=$DRAWS
+        WHITE_WINS=$WHITE_WINS
+        WHITE_LOSSES=$WHITE_LOSSES
+        WHITE_DRAWS=$WHITE_DRAWS
+        BLACK_WINS=$BLACK_WINS
+        BLACK_LOSSES=$BLACK_LOSSES
+        BLACK_DRAWS=$BLACK_DRAWS
+        EOF
+
+        echo "Skill $SKILL, Chunk $CHUNK: W=$WINS L=$LOSSES D=$DRAWS (White: $WHITE_WINS-$WHITE_LOSSES-$WHITE_DRAWS, Black: $BLACK_WINS-$BLACK_LOSSES-$BLACK_DRAWS)"
+
+    - name: Upload chunk results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: benchmark-skill${{ matrix.skill_level }}-chunk${{ matrix.chunk }}
+        path: |
+          benchmark-skill${{ matrix.skill_level }}-chunk${{ matrix.chunk }}.pgn
+          benchmark-skill${{ matrix.skill_level }}-chunk${{ matrix.chunk }}.log
+          results-skill${{ matrix.skill_level }}-chunk${{ matrix.chunk }}.txt
+
+  aggregate:
+    runs-on: ubuntu-latest
+    needs: benchmark
+    if: ${{ !cancelled() && contains(join(needs.benchmark.result, ','), 'success') }}
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Download all chunk results
+      uses: actions/download-artifact@v4
+      with:
+        pattern: benchmark-skill*-chunk*
+        merge-multiple: true
+
+    - name: Merge all PGN files
+      run: |
+        for SKILL in 3 4 5; do
+          cat benchmark-skill$SKILL-chunk*.pgn > benchmark-skill$SKILL-all.pgn 2>/dev/null || echo "No PGN files for skill $SKILL"
+        done
+
+    - name: Aggregate results
+      run: |
+        echo "Aggregating results from all chunks..."
+
+        # Build comment body
+        {
+          echo "## 🔬 Stockfish Benchmark Results"
+          echo ""
+
+          for SKILL in 3 4 5; do
+            # Initialize counters
+            TOTAL_WINS=0 TOTAL_LOSSES=0 TOTAL_DRAWS=0
+            TOTAL_WHITE_WINS=0 TOTAL_WHITE_LOSSES=0 TOTAL_WHITE_DRAWS=0
+            TOTAL_BLACK_WINS=0 TOTAL_BLACK_LOSSES=0 TOTAL_BLACK_DRAWS=0
+
+            for f in results-skill$SKILL-chunk*.txt; do
+              if [ -f "$f" ]; then
+                eval "$(grep -E '^[A-Z_]+=' "$f" | sed 's/^[[:space:]]*//')"
+                TOTAL_WINS=$((TOTAL_WINS + WINS))
+                TOTAL_LOSSES=$((TOTAL_LOSSES + LOSSES))
+                TOTAL_DRAWS=$((TOTAL_DRAWS + DRAWS))
+                TOTAL_WHITE_WINS=$((TOTAL_WHITE_WINS + WHITE_WINS))
+                TOTAL_WHITE_LOSSES=$((TOTAL_WHITE_LOSSES + WHITE_LOSSES))
+                TOTAL_WHITE_DRAWS=$((TOTAL_WHITE_DRAWS + WHITE_DRAWS))
+                TOTAL_BLACK_WINS=$((TOTAL_BLACK_WINS + BLACK_WINS))
+                TOTAL_BLACK_LOSSES=$((TOTAL_BLACK_LOSSES + BLACK_LOSSES))
+                TOTAL_BLACK_DRAWS=$((TOTAL_BLACK_DRAWS + BLACK_DRAWS))
+              fi
+            done
+
+            TOTAL=$((TOTAL_WINS + TOTAL_LOSSES + TOTAL_DRAWS))
+            WHITE_TOTAL=$((TOTAL_WHITE_WINS + TOTAL_WHITE_LOSSES + TOTAL_WHITE_DRAWS))
+            BLACK_TOTAL=$((TOTAL_BLACK_WINS + TOTAL_BLACK_LOSSES + TOTAL_BLACK_DRAWS))
+
+            echo "### vs Stockfish Skill Level $SKILL"
+            echo ""
+            echo "| Metric | Wins | Losses | Draws | Total | Win % |"
+            echo "|--------|------|--------|-------|-------|-------|"
+
+            if [ "$TOTAL" -gt 0 ]; then
+              WIN_RATE=$(echo "scale=1; $TOTAL_WINS * 100 / $TOTAL" | bc)
+              echo "| **Overall** | $TOTAL_WINS | $TOTAL_LOSSES | $TOTAL_DRAWS | $TOTAL | ${WIN_RATE}% |"
+            fi
+            if [ "$WHITE_TOTAL" -gt 0 ]; then
+              WHITE_WIN_RATE=$(echo "scale=1; $TOTAL_WHITE_WINS * 100 / $WHITE_TOTAL" | bc)
+              echo "| As White | $TOTAL_WHITE_WINS | $TOTAL_WHITE_LOSSES | $TOTAL_WHITE_DRAWS | $WHITE_TOTAL | ${WHITE_WIN_RATE}% |"
+            fi
+            if [ "$BLACK_TOTAL" -gt 0 ]; then
+              BLACK_WIN_RATE=$(echo "scale=1; $TOTAL_BLACK_WINS * 100 / $BLACK_TOTAL" | bc)
+              echo "| As Black | $TOTAL_BLACK_WINS | $TOTAL_BLACK_LOSSES | $TOTAL_BLACK_DRAWS | $BLACK_TOTAL | ${BLACK_WIN_RATE}% |"
+            fi
+
+            # Parse game endings (excluding checkmates, which are covered by win/loss stats)
+            PGN="benchmark-skill$SKILL-all.pgn"
+            if [ -f "$PGN" ]; then
+              ENDINGS=$(grep -oE ', [^}]+\}' "$PGN" | sed 's/, //; s/}//' | grep -v 'mates' | sort | uniq -c | sort -rn)
+              if [ -n "$ENDINGS" ]; then
+                echo ""
+                echo "**Non-checkmate endings:**"
+                echo "$ENDINGS" | while read count ending; do
+                  echo "- $ending: $count"
+                done
+              fi
+            fi
+            echo ""
+          done
+
+          echo "<details><summary>Configuration</summary>"
+          echo ""
+          echo "- 5 chunks × 20 rounds × 3 skill levels = 300 total games"
+          echo "- Each opening played with colors reversed (-repeat) for fairness"
+          echo "- Moonfish: 60s per move"
+          echo "- Stockfish: 60+5 time control"
+          echo ""
+          echo "</details>"
+        } > pr-comment.md
+
+        # Also write to step summary
+        cat pr-comment.md >> $GITHUB_STEP_SUMMARY
+
+    - name: Comment on PR
+      if: github.event_name == 'pull_request'
+      env:
+        GH_TOKEN: ${{ github.token }}
+      run: |
+        gh pr comment ${{ github.event.pull_request.number }} --body-file pr-comment.md
+
+    - name: Update PR reaction (eyes -> thumbs up)
+      if: github.event_name == 'pull_request'
+      env:
+        GH_TOKEN: ${{ github.token }}
+      run: |
+        # Remove eyes reaction
+        REACTIONS=$(gh api repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/reactions --jq '.[] | select(.content == "eyes") | .id' || true)
+        for ID in $REACTIONS; do
+          gh api -X DELETE repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/reactions/$ID --silent || true
+        done
+        # Add thumbs up
+        gh api repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/reactions \
+          -f content='+1' --silent || true
+
+    - name: Upload aggregated results
+      uses: actions/upload-artifact@v4
+      with:
+        name: benchmark-aggregated
+        path: |
+          benchmark-skill*-all.pgn
+          results-*.txt
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 91cca29..da6b73d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -4,6 +4,9 @@ on:
   pull_request:
     branches: [ master ]
 
+env:
+  GIT_LFS_SKIP_SMUDGE: 1
+
 jobs:
   test:
     runs-on: ${{ matrix.os }}
@@ -16,6 +19,8 @@ jobs:
 
     steps:
     - uses: actions/checkout@v4
+      with:
+        lfs: false
 
     - name: Install uv
       uses: astral-sh/setup-uv@v5
@@ -55,6 +60,8 @@ jobs:
       UV_SYSTEM_PYTHON: 1
     steps:
       - uses: actions/checkout@v4
+        with:
+          lfs: false
 
       - name: Install uv
         uses: astral-sh/setup-uv@v5
@@ -81,6 +88,8 @@ jobs:
       UV_SYSTEM_PYTHON: 1
     steps:
       - uses: actions/checkout@v4
+        with:
+          lfs: false
 
       - name: Install uv
         uses: astral-sh/setup-uv@v5
@@ -107,6 +116,8 @@ jobs:
       UV_SYSTEM_PYTHON: 1
     steps:
       - uses: actions/checkout@v4
+        with:
+          lfs: false
       
       - name: Install uv
         uses: astral-sh/setup-uv@v5
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index f7f7db0..e448add 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -4,6 +4,9 @@ on:
   push:
     tags: ['v[0-9].[0-9]+.[0-9]+']
 
+env:
+  GIT_LFS_SKIP_SMUDGE: 1
+
 jobs:
   build-and-publish:
     runs-on: ubuntu-latest
@@ -14,6 +17,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          lfs: false
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
@@ -31,4 +36,4 @@ jobs:
         env:
           TWINE_USERNAME: __token__
           TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-        run: twine upload --skip-existing dist/*
\ No newline at end of file
+        run: twine upload --skip-existing dist/*
diff --git a/moonfish/helper.py b/moonfish/helper.py
index 9e1c6b7..d7fd04d 100644
--- a/moonfish/helper.py
+++ b/moonfish/helper.py
@@ -1,4 +1,6 @@
+import os
 from enum import Enum
+from pathlib import Path
 
 from chess import Board, Move, polyglot
 
@@ -46,6 +48,25 @@ def get_engine(config: Config):
     raise Exception("algorithm not supported")
 
 
+def _opening_book_path() -> str:
+    """
+    Resolve the opening book path, allowing overrides for CI or custom installs.
+    """
+    env_path = os.getenv("MOONFISH_OPENING_BOOK")
+    if env_path and Path(env_path).is_file():
+        return env_path
+
+    cwd_path = Path("opening_book/cerebellum.bin")
+    if cwd_path.is_file():
+        return str(cwd_path)
+
+    repo_path = Path(__file__).resolve().parents[1] / "opening_book" / "cerebellum.bin"
+    if repo_path.is_file():
+        return str(repo_path)
+
+    return str(cwd_path)
+
+
 def find_best_move(board: Board, engine: ChessEngine) -> Move:
     """
     Finds the best move for the given board using the given engine.
@@ -61,9 +82,7 @@ def find_best_move(board: Board, engine: ChessEngine) -> Move:
     # if it fails we search on our engine. The first (12-20) moves should be
     # available in the opening book, so our engine starts playing after that.
     try:
-        best_move = (
-            polyglot.MemoryMappedReader("opening_book/cerebellum.bin").find(board).move
-        )
+        best_move = polyglot.MemoryMappedReader(_opening_book_path()).find(board).move
     except (ValueError, OSError, AttributeError, IndexError):
         best_move = engine.search_move(board)
     return best_move
diff --git a/scripts/benchmark.sh b/scripts/benchmark.sh
new file mode 100755
index 0000000..7a01805
--- /dev/null
+++ b/scripts/benchmark.sh
@@ -0,0 +1,131 @@
+#!/bin/bash
+#
+# Local Stockfish benchmark script
+# Replicates the CI benchmark workflow for local testing
+#
+
+set -euo pipefail
+
+# Configuration
+ROUNDS=${ROUNDS:-100}  # 100 games per skill level
+CONCURRENCY=${CONCURRENCY:-15}
+SKILL_LEVELS=${SKILL_LEVELS:-"1 2 3 4 5"}
+MOONFISH_TIME=${MOONFISH_TIME:-60}  # seconds per move
+STOCKFISH_TC=${STOCKFISH_TC:-"60+5"}
+OUTPUT_DIR=${OUTPUT_DIR:-"benchmark_results"}
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+echo "=== Moonfish vs Stockfish Benchmark ==="
+echo ""
+
+# Check dependencies
+check_dependency() {
+    if ! command -v "$1" &> /dev/null; then
+        echo -e "${RED}Error: $1 is not installed${NC}"
+        echo "Please install $1 and try again"
+        exit 1
+    fi
+}
+
+echo "Checking dependencies..."
+check_dependency cutechess-cli
+check_dependency stockfish
+
+# Check for moonfish binary
+MOONFISH_BIN="./dist/moonfish"
+if [ ! -f "$MOONFISH_BIN" ]; then
+    echo -e "${YELLOW}Moonfish binary not found at $MOONFISH_BIN${NC}"
+    echo "Building moonfish..."
+    make build-lichess
+fi
+
+if [ ! -f "$MOONFISH_BIN" ]; then
+    echo -e "${RED}Error: Could not find or build moonfish binary${NC}"
+    exit 1
+fi
+
+# Create output directory
+mkdir -p "$OUTPUT_DIR"
+
+echo ""
+echo "Configuration:"
+echo "  Rounds per skill level: $ROUNDS"
+echo "  Concurrency: $CONCURRENCY"
+echo "  Skill levels: $SKILL_LEVELS"
+echo "  Moonfish time: ${MOONFISH_TIME}s per move"
+echo "  Stockfish time control: $STOCKFISH_TC"
+echo "  Output directory: $OUTPUT_DIR"
+echo ""
+
+# Run benchmarks for each skill level
+for SKILL in $SKILL_LEVELS; do
+    echo -e "${YELLOW}=== Running benchmark vs Stockfish Skill Level $SKILL ===${NC}"
+
+    PGN_FILE="$OUTPUT_DIR/benchmark-skill$SKILL.pgn"
+    LOG_FILE="$OUTPUT_DIR/benchmark-skill$SKILL.log"
+
+    cutechess-cli \
+        -engine name=moonfish cmd="$MOONFISH_BIN" dir=. proto=uci tc=inf st="$MOONFISH_TIME" timemargin=10000 \
+        -engine name=stockfish cmd=stockfish proto=uci option.Skill\ Level="$SKILL" option.Threads=1 tc="$STOCKFISH_TC" timemargin=10000 \
+        -rounds "$ROUNDS" \
+        -repeat \
+        -concurrency "$CONCURRENCY" \
+        -pgnout "$PGN_FILE" \
+        -recover \
+        2>&1 | tee "$LOG_FILE"
+
+    echo ""
+done
+
+# Parse and display results
+echo ""
+echo -e "${GREEN}=== RESULTS ===${NC}"
+echo ""
+
+for SKILL in $SKILL_LEVELS; do
+    LOG_FILE="$OUTPUT_DIR/benchmark-skill$SKILL.log"
+    PGN_FILE="$OUTPUT_DIR/benchmark-skill$SKILL.pgn"
+
+    if [ -f "$LOG_FILE" ]; then
+        echo "### vs Stockfish Skill Level $SKILL"
+
+        # Extract score
+        SCORE=$(grep "Score of moonfish vs stockfish:" "$LOG_FILE" | tail -1)
+        if [ -n "$SCORE" ]; then
+            echo "$SCORE"
+        fi
+
+        # Parse win/loss/draw
+        WINS=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\1/' || echo 0)
+        LOSSES=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\2/' || echo 0)
+        DRAWS=$(echo "$SCORE" | sed -E 's/.*: ([0-9]+) - ([0-9]+) - ([0-9]+).*/\3/' || echo 0)
+        TOTAL=$((WINS + LOSSES + DRAWS))
+
+        if [ "$TOTAL" -gt 0 ]; then
+            WIN_RATE=$(echo "scale=1; $WINS * 100 / $TOTAL" | bc)
+            echo "Win rate: ${WIN_RATE}%"
+        fi
+
+        # Non-checkmate endings
+        if [ -f "$PGN_FILE" ]; then
+            ENDINGS=$(grep -oE ', [^}]+\}' "$PGN_FILE" | sed 's/, //; s/}//' | grep -v 'mates' | sort | uniq -c | sort -rn 2>/dev/null || true)
+            if [ -n "$ENDINGS" ]; then
+                echo "Non-checkmate endings:"
+                echo "$ENDINGS" | while read count ending; do
+                    echo "  - $ending: $count"
+                done
+            fi
+        fi
+
+        echo ""
+    fi
+done
+
+echo "Results saved to $OUTPUT_DIR/"
+echo "  - PGN files: benchmark-skill*.pgn"
+echo "  - Log files: benchmark-skill*.log"

From b437392e6a8262babf4d012bb233af2eaf065f7c Mon Sep 17 00:00:00 2001
From: Lucca Bertoncini <32229669+luccabb@users.noreply.github.com>
Date: Tue, 27 Jan 2026 11:18:49 -0800
Subject: [PATCH 6/6] Only run benchmarks when engine code changes (#44)

* Only run benchmarks when engine code changes

* Remove lichess from path filter (not engine code)

* Run benchmarks on PRs to any branch, not just master
---
 .github/workflows/benchmark.yml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index 8dd83b3..79315b6 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -2,7 +2,12 @@ name: Stockfish Benchmark
 
 on:
   pull_request:
-    branches: [ master ]
+    paths:
+      # Only run benchmarks when engine code changes
+      - 'moonfish/**'
+      - 'opening_book/**'
+      - 'pyproject.toml'
+      - 'requirements.txt'
 
 permissions:
   contents: read