"""Harbor agent wrapper for Zed's eval-cli binary. Usage:    # Build eval-cli locally first:    cargo build --release -p eval_cli     # Run via Harbor with a local binary:    harbor run -d "dataset@version" \        --agent-import-path zed_eval.agent:ZedAgent \        --ae binary_path=/path/to/target/release/eval-cli \        --agent-model anthropic/claude-sonnet-4-6-latest     # Or with a download URL (for CI):    harbor run -d "dataset@version" \        --agent-import-path zed_eval.agent:ZedAgent \        --ae download_url=https://example.com/eval-cli \        --agent-model anthropic/claude-sonnet-4-6-latest""" import jsonimport osimport shlexfrom pathlib import Path from harbor.agents.installed.base import BaseInstalledAgent, ExecInputfrom harbor.environments.base import BaseEnvironmentfrom harbor.models.agent.context import AgentContext  class ZedAgent(BaseInstalledAgent):    """Runs Zed's headless AI agent (eval-cli) to solve tasks.     The eval-cli binary boots a headless GPUI application and uses the same    NativeAgent + AcpThread pipeline as the production Zed editor, driving    the full agentic loop (tool calls, subagents, retries) without a GUI.    """     def __init__(        self,        logs_dir: Path,        binary_path: str | None = None,        download_url: str | None = None,        *args,        **kwargs,    ):        super().__init__(logs_dir, *args, **kwargs)        self._binary_path = binary_path        self._download_url = download_url or os.environ.get("EVAL_CLI_DOWNLOAD_URL")     @staticmethod    def name() -> str:        return "zed"     @property    def _install_agent_template_path(self) -> Path:        return Path(__file__).parent / "install.sh.j2"     async def setup(self, environment: BaseEnvironment) -> None:        await environment.exec(command="mkdir -p /installed-agent")         if self._binary_path:            binary = Path(self._binary_path)            if not binary.exists():                raise FileNotFoundError(                    f"eval-cli binary not found at {binary}. "                    "Build it with: cargo build --release -p eval_cli"                )            await environment.upload_file(                source_path=binary,                target_path="/usr/local/bin/eval-cli",            )            await environment.exec(command="chmod +x /usr/local/bin/eval-cli")         await super().setup(environment)     @property    def _template_variables(self) -> dict[str, str]:        variables = super()._template_variables        if self._binary_path:            variables["binary_uploaded"] = "true"        if self._download_url:            variables["download_url"] = self._download_url        return variables     def populate_context_post_run(self, context: AgentContext) -> None:        result_data = None        for json_file in self.logs_dir.rglob("result.json"):            try:                result_data = json.loads(json_file.read_text())                break            except (json.JSONDecodeError, OSError):                continue         if result_data is None:            self.logger.warning("Could not find or parse result.json from eval-cli")            return         if result_data.get("input_tokens") is not None:            context.n_input_tokens = result_data["input_tokens"]        if result_data.get("output_tokens") is not None:            context.n_output_tokens = result_data["output_tokens"]        if result_data.get("cache_read_input_tokens") is not None:            context.n_cache_tokens = result_data["cache_read_input_tokens"]         context.metadata = {            "status": result_data.get("status"),            "duration_secs": result_data.get("duration_secs"),            "model": result_data.get("model"),        }     def _get_api_env(self) -> dict[str, str]:        env: dict[str, str] = {}        if not self.model_name or "/" not in self.model_name:            return env         provider = self.model_name.split("/", 1)[0]        provider_env_map = {            "anthropic": "ANTHROPIC_API_KEY",            "openai": "OPENAI_API_KEY",            "google": "GEMINI_API_KEY",            "gemini": "GEMINI_API_KEY",            "deepseek": "DEEPSEEK_API_KEY",            "mistral": "MISTRAL_API_KEY",        }         env_var = provider_env_map.get(provider)        if env_var:            api_key = os.environ.get(env_var, "")            if api_key:                env[env_var] = api_key         return env     def create_run_agent_commands(self, instruction: str) -> list[ExecInput]:        escaped_instruction = shlex.quote(instruction)        env = self._get_api_env()         parts = ["eval-cli", "--workdir /testbed", "--output-dir /logs/agent"]         if self.model_name:            parts.append(f"--model {self.model_name}")         timeout = self._extra_env.get("EVAL_CLI_TIMEOUT")        if timeout:            parts.append(f"--timeout {timeout}")         parts.append(f"--instruction {escaped_instruction}")         eval_cli_command = " ".join(parts) + " 2>&1 | stdbuf -oL tee /logs/agent/eval-cli.txt"         patch_command = (            "cd /testbed && "            "git add -A && "            "git diff --cached HEAD > /logs/agent/patch.diff && "            "echo \"Patch size: $(wc -c < /logs/agent/patch.diff) bytes\""        )         return [            ExecInput(command=eval_cli_command, env=env),            ExecInput(command=patch_command),        ]