Add Claude Code and Codex as the solution generator

skydiscover/cli.py

@@ -34,6 +34,7 @@
     "shinkaevolve",
     "gepa",
     "gepa_native",
+    "claude_code",
 ]
 
 

skydiscover/config.py

@@ -461,6 +461,19 @@ class OpenEvolveNativeDatabaseConfig(DatabaseConfig):
     random_seed: Optional[int] = 42
 
 
+@dataclass
+class ClaudeCodeConfig(DatabaseConfig):
+    """Configuration for the Claude Code baseline.
+
+    Claude Code runs autonomously inside a Docker container, iterating on
+    the solution using the evaluator directly.  max_turns maps to the
+    --max-turns flag passed to the claude CLI.
+    """
+
+    max_turns: int = 50
+    docker_image: str = "skydiscover-claude-code:latest"
+
+
 @dataclass
 class GEPANativeDatabaseConfig(DatabaseConfig):
     """Configuration for GEPA Native search database.
@@ -491,6 +504,7 @@ class GEPANativeDatabaseConfig(DatabaseConfig):
     "adaevolve": AdaEvolveDatabaseConfig,
     "openevolve_native": OpenEvolveNativeDatabaseConfig,
     "gepa_native": GEPANativeDatabaseConfig,
+    "claude_code": ClaudeCodeConfig,
 }
 
 

skydiscover/search/claude_code/controller.py

@@ -0,0 +1,296 @@
+"""Claude Code baseline controller.
+
+Runs Claude Code CLI inside a Docker container as a single-agent baseline,
+letting it iterate on the solution using the evaluator directly.  The
+framework's standard evaluator scores the final result.
+
+Docker is always required: --dangerously-skip-permissions needs isolation.
+
+For Docker evaluators, the container runs in --privileged DinD mode so
+Claude Code has its own isolated Docker daemon (no host socket mount).
+For Python evaluators, the container runs in simple --user mode.
+"""
+
+import asyncio
+import logging
+import os
+import shlex
+import shutil
+import subprocess
+import tempfile
+import uuid
+from pathlib import Path
+from typing import Callable, Optional
+
+from skydiscover.evaluation import create_evaluator
+from skydiscover.search.base_database import Program
+from skydiscover.search.default_discovery_controller import (
+    DiscoveryController,
+    DiscoveryControllerInput,
+)
+
+logger = logging.getLogger(__name__)
+
+_RUNNER_IMAGE_DIR = Path(__file__).parent / "runner_image"
+
+
+class ClaudeCodeController(DiscoveryController):
+    """Discovery controller that delegates iteration to Claude Code CLI."""
+
+    def __init__(self, controller_input: DiscoveryControllerInput):
+        # Skip the parent __init__ which creates LLMPools we don't need.
+        self.config = controller_input.config
+        self.evaluation_file = controller_input.evaluation_file
+        self.database = controller_input.database
+        self.file_suffix = controller_input.file_suffix
+        self.output_dir = controller_input.output_dir
+
+        self.config.evaluator.evaluation_file = self.evaluation_file
+        self.config.evaluator.file_suffix = self.file_suffix
+        self.config.evaluator.is_image_mode = self.config.language == "image"
+
+        self.evaluator = create_evaluator(self.config.evaluator)
+
+        self.monitor_callback = None
+        self.feedback_reader = None
+        self.early_stopping_triggered = False
+        self.shutdown_event = None
+
+    def _ensure_image_built(self, image_name: str) -> None:
+        result = subprocess.run(
+            ["docker", "image", "inspect", image_name],
+            capture_output=True,
+        )
+        if result.returncode != 0:
+            logger.info(f"Building Claude Code runner image '{image_name}'...")
+            subprocess.run(
+                ["docker", "build", "-t", image_name, str(_RUNNER_IMAGE_DIR)],
+                check=True,
+            )
+
+    def _save_evaluator_image(self, workspace: Path, image_tag: str) -> None:
+        """Export the evaluator Docker image so the DinD daemon can load it."""
+        tar_path = workspace / ".evaluator-image.tar"
+        logger.info(f"Saving evaluator image '{image_tag}' for DinD...")
+        subprocess.run(
+            ["docker", "save", "-o", str(tar_path), image_tag],
+            check=True,
+        )
+
+    def _write_eval_script(
+        self, workspace: Path, eval_type: str, evaluator_image: str = "", timeout: int = 360
+    ) -> None:
+        """Write run_eval.sh that Claude Code can call to score a solution."""
+        if eval_type == "python":
+            script = (
+                "#!/bin/bash\nset -euo pipefail\n"
+                f"timeout {timeout} python3 /workspace/evaluator.py \"$1\"\n"
+            )
+        else:
+            # Docker evaluator: the entrypoint starts a persistent evaluator
+            # container and writes its ID to .evaluator-container-id. We use
+            # docker exec (like ContainerizedEvaluator) to inject the program
+            # via stdin and run evaluate.sh — no new container per eval call.
+            script = (
+                "#!/bin/bash\n"
+                "set -euo pipefail\n"
+                "PROGRAM_PATH=\"$1\"\n"
+                "MODE=\"${2:-train}\"\n"
+                "EXT=\"${PROGRAM_PATH##*.}\"\n"
+                "CID=$(cat /workspace/.evaluator-container-id)\n"
+                "CANDIDATE=\"/tmp/candidate_$$.${EXT}\"\n"
+                "docker exec -i \"$CID\" tee \"$CANDIDATE\" < \"$PROGRAM_PATH\" > /dev/null\n"
+                f"timeout {timeout} docker exec \"$CID\" /benchmark/evaluate.sh \"$MODE\" \"$CANDIDATE\"\n"
+                "docker exec \"$CID\" rm -f \"$CANDIDATE\"\n"
+            )
+        script_path = workspace / "run_eval.sh"
+        script_path.write_text(script)
+        script_path.chmod(0o755)
+
+    def _write_task_md(self, workspace: Path, suffix: str, max_turns: int = 0) -> None:
+        system_msg = getattr(self.config.context_builder, "system_message", "") or ""
+        eval_timeout = self.config.evaluator.timeout
+        content = (
+            "# SkyDiscover: Optimization Task\n\n"
+            f"You are an AI assistant iteratively improving a program to maximize its evaluation score. "
+            f"You have **{max_turns} turns** total — be mindful of this budget and make sure to fully "
+            f"implement and test your ideas before you run out of turns.\n\n"
+            "## Current solution\n\n"
+            f"`/workspace/solution{suffix}` — read it, understand it, modify it freely.\n\n"
+            "## How to evaluate\n\n"
+            "```bash\n"
+            f"bash /workspace/run_eval.sh /workspace/solution{suffix}\n"
+            "```\n\n"
+            "Output is JSON. The `combined_score` field is what you want to maximize "
+            f"(higher is better). The evaluator has a **{eval_timeout}s timeout** — "
+            f"if your solution takes longer than that, it will be killed and score zero.\n\n"
+            "## Task description\n\n"
+            f"{system_msg}\n\n"
+            "## Instructions\n\n"
+            "- Run the evaluator once to confirm the baseline score, then **start making changes immediately**.\n"
+            "- Don't spend more than 1-2 turns analyzing — get to trying improvements fast.\n"
+            "- After each change, evaluate and decide whether to keep or revert it.\n"
+            f"- Always keep `/workspace/solution{suffix}` set to your best solution so far.\n"
+            "- Aim to try several distinct approaches within your turn budget.\n"
+        )
+        (workspace / "TASK.md").write_text(content)
+
+    async def run_discovery(
+        self,
+        start_iteration: int,
+        max_iterations: int,
+        checkpoint_callback: Optional[Callable] = None,
+        **kwargs,
+    ) -> Optional[Program]:
+        db_config = self.database.config
+        image_name = getattr(db_config, "docker_image", "skydiscover-claude-code:latest")
+        max_turns = max_iterations
+
+        model = self.config.llm.models[0].name if self.config.llm.models else None
+        _CLAUDE_MODEL_PREFIXES = ("claude-", "sonnet", "opus", "haiku")
+        if model and not any(model.startswith(p) for p in _CLAUDE_MODEL_PREFIXES):
+            raise ValueError(
+                f"claude_code only supports Claude models, got: {model!r}. "
+                f"Set llm.models[0].name to a claude-* model or alias (sonnet, opus, haiku)."
+            )
+
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, self._ensure_image_built, image_name)
+
+        initial = self.database.get_best_program()
+        initial_code = initial.solution if initial else ""
+
+        tmp_base = os.path.expanduser("~/.tmp")
+        os.makedirs(tmp_base, exist_ok=True)
+        workspace = Path(tempfile.mkdtemp(dir=tmp_base))
+
+        try:
+            suffix = self.file_suffix
+            solution_path = workspace / f"solution{suffix}"
+            solution_path.write_text(initial_code)
+
+            eval_path = Path(self.evaluation_file)
+            is_docker_eval = eval_path.is_dir()
+
+            if is_docker_eval:
+                evaluator_image = self.evaluator.image_tag
+                eval_timeout = self.config.evaluator.timeout
+                self._write_eval_script(
+                    workspace, "docker", evaluator_image=evaluator_image, timeout=eval_timeout
+                )
+                # Export the evaluator image for the DinD daemon to load.
+                await loop.run_in_executor(
+                    None, self._save_evaluator_image, workspace, evaluator_image
+                )
+            else:
+                shutil.copy(eval_path, workspace / "evaluator.py")
+                eval_timeout = self.config.evaluator.timeout
+                self._write_eval_script(workspace, "python", timeout=eval_timeout)
+                req = eval_path.parent / "requirements.txt"
+                if req.exists():
+                    shutil.copy(req, workspace / "requirements.txt")
+
+            self._write_task_md(workspace, suffix, max_turns=max_turns)
+            task_content = (workspace / "TASK.md").read_text()
+
+            api_key = os.environ.get("ANTHROPIC_API_KEY", "")
+            log_path = workspace / "claude.log"
+
+            pip_step = (
+                "pip install -q --no-warn-script-location -r /workspace/requirements.txt"
+                " >/dev/null 2>&1 && "
+                if (workspace / "requirements.txt").exists() else ""
+            )
+            model_flag = f" --model {shlex.quote(model)}" if model else ""
+            bash_cmd = (
+                f"{pip_step}"
+                f"claude -p {shlex.quote(task_content)}"
+                f" --max-turns {max_turns}"
+                f" --dangerously-skip-permissions"
+                f" --output-format stream-json"
+                f" --verbose"
+                f"{model_flag}"
+            )
+
+            if is_docker_eval:
+                # DinD mode: --privileged gives the container its own Docker
+                # daemon. The entrypoint starts dockerd, loads the evaluator
+                # image, then drops to a non-root user before running claude.
+                # Write bash_cmd to a script file so it doesn't get mangled
+                # by su -c argument expansion in the entrypoint.
+                run_script = workspace / ".run.sh"
+                run_script.write_text(f"#!/bin/bash\n{bash_cmd}\n")
+                run_script.chmod(0o755)
+                cmd = [
+                    "docker", "run", "--rm",
+                    "--privileged",
+                    "-e", "DIND=1",
+                    "-e", f"ANTHROPIC_API_KEY={api_key}",
+                    "-v", f"{workspace}:/workspace",
+                    "-w", "/workspace",
+                    image_name,
+                    "/workspace/.run.sh",
+                ]
+            else:
+                # Simple mode: no Docker needed inside the container.
+                cmd = [
+                    "docker", "run", "--rm",
+                    "--user", f"{os.getuid()}:{os.getgid()}",
+                    "-e", "HOME=/workspace",
+                    "-e", f"ANTHROPIC_API_KEY={api_key}",
+                    "-v", f"{workspace}:/workspace",
+                    "-w", "/workspace",
+                    "--entrypoint", "bash",
+                    image_name,
+                    "-c", bash_cmd,
+                ]
+
+            logger.info(
+                f"Starting Claude Code container (--max-turns {max_turns})\n"
+                f"  Monitor progress: tail -f {log_path} | cclean -t"
+                f"  (https://github.com/ariel-frischer/claude-clean)"
+            )
+
+            def _run_with_turn_limit() -> None:
+                with open(log_path, "w") as log_file:
+                    proc = subprocess.Popen(
+                        cmd, stdout=subprocess.PIPE, stderr=log_file
+                    )
+                    turn_count = 0
+                    for raw_line in proc.stdout:
+                        log_file.write(raw_line.decode("utf-8", errors="replace"))
+                        log_file.flush()
+                        if b'"type":"user"' in raw_line or b'"type": "user"' in raw_line:
+                            turn_count += 1
+                            logger.info(f"Claude Code turn {turn_count}/{max_turns}")
+                            if turn_count >= max_turns:
+                                logger.warning(
+                                    f"Reached max turns ({max_turns}), killing Claude Code process"
+                                )
+                                proc.kill()
+                                break
+                    proc.wait()
+
+            await loop.run_in_executor(None, _run_with_turn_limit)
+
+            final_code = solution_path.read_text()
+            program_id = str(uuid.uuid4())
+            eval_result = await self.evaluator.evaluate_program(final_code, program_id)
+
+            program = Program(
+                id=program_id,
+                solution=final_code,
+                language=self.config.language or "python",
+                metrics=eval_result.metrics,
+                iteration_found=0,
+                parent_id=initial.id if initial else None,
+                other_context_ids=[],
+                metadata={"claude_code_max_turns": max_turns},
+                artifacts=eval_result.artifacts,
+            )
+            self.database.add(program, iteration=0)
+
+        finally:
+            shutil.rmtree(workspace, ignore_errors=True)
+
+        return self.database.get_best_program()

skydiscover/search/claude_code/database.py

@@ -0,0 +1,26 @@
+"""Minimal database for the Claude Code baseline.
+
+Claude Code handles its own internal iteration loop, so this database
+just stores whatever the controller adds (typically one final result).
+
+# TODO: Consider adding a boilerplate no-op database option to the framework
+# so single-shot methods don't need to implement their own pass-through.
+"""
+
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+
+class ClaudeCodeDatabase(ProgramDatabase):
+    def add(self, program: Program, iteration=None, **kwargs) -> str:
+        self.programs[program.id] = program
+        if iteration is not None:
+            self.last_iteration = max(self.last_iteration, iteration)
+        if self.config.db_path:
+            self._save_program(program)
+        self._update_best_program(program)
+        return program.id
+
+    def sample(self, num_context_programs=4, **kwargs):
+        # Not called by ClaudeCodeController, but required by the interface.
+        best = self.get_best_program()
+        return best, []

skydiscover/search/claude_code/runner_image/Dockerfile

@@ -0,0 +1,27 @@
+FROM ubuntu:22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y \
+    python3 python3-pip \
+    docker.io \
+    iptables \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Node.js 20
+RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
+    && apt-get install -y nodejs \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN npm install -g @anthropic-ai/claude-code
+
+# Non-root user for running Claude Code (it refuses --dangerously-skip-permissions
+# as root). Added to the docker group so it can talk to dockerd in DinD mode.
+RUN useradd -m -s /bin/bash -G docker claude
+
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+WORKDIR /workspace
+ENTRYPOINT ["/entrypoint.sh"]