diff --git a/main.py b/main.py
new file mode 100644
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+++ b/main.py
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+"""CLI 入口 — Composition Root：构建适配器，注入 Runner，调度执行。
+
+三层配置合并（YAML > .env > CLI）由 load_config 完成。
+适配器参数通过 InfraSettings(BaseSettings) 从 .env 加载。
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+from pathlib import Path
+from typing import NamedTuple
+
+from dotenv import load_dotenv
+from loguru import logger
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class InfraSettings(BaseSettings):
+    """工程配置（少变/敏感），从 .env 加载。"""
+
+    model_config = SettingsConfigDict(env_file=".env", extra="ignore")
+
+    search_llm_model: str = ""
+    search_llm_base_url: str = ""
+    search_llm_api_key: str = ""
+
+    vl_llm_model: str = ""
+    vl_llm_base_url: str = ""
+    vl_llm_api_key: str = ""
+
+    evolve_llm_model: str = ""
+    evolve_llm_base_url: str = ""
+    evolve_llm_api_key: str = ""
+
+    embed_api_key: str = ""
+    embed_api_url: str = ""
+
+    monkey_ocr_urls: str = ""
+    redis_url: str = ""
+    redis_cache_ttl: int = 86400
+
+    llm_timeout: float = 120.0
+    llm_max_retries: int = 3
+    llm_retry_base_delay: float = 2.0
+    llm_retry_max_delay: float = 30.0
+    llm_circuit_breaker_threshold: int = 48
+    llm_circuit_breaker_cooldown: float = 60.0
+    llm_ttft_timeout: float = 30.0
+    llm_inter_token_timeout: float = 15.0
+
+
+class _Adapters(NamedTuple):
+    """全套适配器实例。"""
+
+    llm: object
+    evolve_llm: object
+    vlm: object
+    telemetry: object
+    embed: object
+    ocr: object
+
+
+def _build_adapters(settings: InfraSettings, embed_cfg: dict) -> _Adapters:
+    """从 InfraSettings 构建全套适配器。
+
+    参数:
+        settings: 工程配置。
+        embed_cfg: 嵌入配置字典（来自 YAML embed 段）。
+
+    返回:
+        _Adapters 命名元组。
+    """
+    from adapters.breaker import CircuitBreaker
+    from adapters.embedding import LocalEmbeddingProvider
+    from adapters.llm import GovernedLLMClient
+    from adapters.telemetry import SQLiteTelemetryRecorder
+    from adapters.vlm import GovernedVLMClient
+
+    breaker = CircuitBreaker(
+        fail_threshold=max(settings.llm_circuit_breaker_threshold, 1),
+        cooldown_s=settings.llm_circuit_breaker_cooldown,
+    )
+
+    cache = None
+    if settings.redis_url:
+        try:
+            from adapters.redis_cache import RedisResponseCache
+
+            cache = RedisResponseCache(redis_url=settings.redis_url, ttl=settings.redis_cache_ttl)
+        except Exception:
+            logger.warning("Redis 缓存不可用，降级为无缓存模式")
+
+    telemetry_db = Path("logs/telemetry.db")
+    telemetry_db.parent.mkdir(parents=True, exist_ok=True)
+    telemetry = SQLiteTelemetryRecorder(telemetry_db)
+
+    def _make_llm(model: str, base_url: str, api_key: str, *, thinking: bool) -> GovernedLLMClient:
+        """构建单个 GovernedLLMClient 实例。"""
+        return GovernedLLMClient(
+            model=model,
+            base_url=base_url,
+            api_key=api_key,
+            provider=model.split("-")[0] if model else "unknown",
+            thinking=thinking,
+            breaker=breaker,
+            cache=cache,
+            telemetry=telemetry,
+            timeout_s=settings.llm_timeout,
+            ttft_timeout_s=settings.llm_ttft_timeout,
+            inter_token_timeout_s=settings.llm_inter_token_timeout,
+            max_retries=settings.llm_max_retries,
+            retry_base_delay_s=settings.llm_retry_base_delay,
+            retry_max_delay_s=settings.llm_retry_max_delay,
+        )
+
+    llm = _make_llm(
+        settings.search_llm_model,
+        settings.search_llm_base_url,
+        settings.search_llm_api_key,
+        thinking=True,
+    )
+    evolve_llm = llm
+
+    vl_llm = _make_llm(
+        settings.vl_llm_model,
+        settings.vl_llm_base_url,
+        settings.vl_llm_api_key,
+        thinking=False,
+    )
+    vlm = GovernedVLMClient(governed_llm=vl_llm)
+
+    backend = embed_cfg.get("backend", "local")
+    if backend == "local":
+        embed = LocalEmbeddingProvider(
+            model_name=embed_cfg.get("model_name", "BAAI/bge-base-zh-v1.5"),
+            embed_dim=embed_cfg.get("embed_dim", 768),
+            device=embed_cfg.get("device", "cpu"),
+        )
+    else:
+        from adapters.embedding import RemoteEmbeddingProvider
+
+        embed = RemoteEmbeddingProvider(
+            model_name=embed_cfg.get("model_name", ""),
+            embed_dim=embed_cfg.get("embed_dim", 768),
+            api_key=settings.embed_api_key,
+            api_url=settings.embed_api_url,
+        )
+
+    ocr = None
+    if settings.monkey_ocr_urls:
+        from adapters.ocr import MonkeyOCRClient
+
+        urls = [u.strip() for u in settings.monkey_ocr_urls.split(",") if u.strip()]
+        if urls:
+            ocr = MonkeyOCRClient(urls=urls)
+
+    return _Adapters(
+        llm=llm,
+        evolve_llm=evolve_llm,
+        vlm=vlm,
+        telemetry=telemetry,
+        embed=embed,
+        ocr=ocr,
+    )
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    """构建 CLI 参数解析器。所有参数 default=None，未传入时使用 YAML 默认值。"""
+    parser = argparse.ArgumentParser(description="Video-Tree-TRM5 实验运行器")
+    parser.add_argument("--config", type=Path, default=Path("config/default.yaml"))
+    parser.add_argument("--workspace-dir", type=Path, dest="workspace_dir")
+    parser.add_argument("--store-dir", type=Path, dest="store_dir")
+    parser.add_argument(
+        "--mode",
+        choices=["infer", "train", "diagnose", "evolve", "eval", "promote"],
+    )
+    parser.add_argument("--run-id", type=str, dest="run_id")
+    parser.add_argument("--concurrency", type=int)
+    parser.add_argument("--max-steps", type=int, dest="max_steps")
+    parser.add_argument(
+        "--skill-mode",
+        choices=["auto", "manual", "none"],
+        dest="skill_mode",
+    )
+    parser.add_argument("--n-samples", type=int, dest="n_samples")
+    parser.add_argument("--questions", type=str)
+    parser.add_argument("--skills-version", type=str, dest="skills_version")
+    parser.add_argument("--prompts-version", type=str, dest="prompts_version")
+    parser.add_argument("--task-types", nargs="+", dest="task_types")
+    parser.add_argument("--resume", action="store_true", dest="resume")
+    parser.add_argument("--fresh", action="store_true", dest="fresh")
+    parser.add_argument("--seed", type=str, dest="seed")
+    parser.add_argument("--epochs", type=int)
+    return parser
+
+
+def _log_result(result: object) -> None:
+    """输出推理结果摘要。"""
+    logger.info("=" * 60)
+    logger.info("运行 ID: {}", result.run_id)
+    logger.info(
+        "总体准确率: {:.2%} ({}/{})",
+        result.accuracy,
+        result.correct,
+        result.total,
+    )
+    logger.info("平均步数: {:.1f}", result.steps_mean)
+    logger.info(
+        "Token 用量: prompt={}, completion={}",
+        result.token_usage["prompt_tokens"],
+        result.token_usage["completion_tokens"],
+    )
+    if result.per_task_type:
+        logger.info("--- 按任务类型 ---")
+        for task_type, stats in sorted(result.per_task_type.items()):
+            logger.info(
+                "  {}: {:.2%} ({}/{})",
+                task_type,
+                stats["accuracy"],
+                stats["correct"],
+                stats["total"],
+            )
+    logger.info(
+        "停止原因: {}",
+        ", ".join(f"{k}={v}" for k, v in result.stop_reason_counts.items()),
+    )
+    logger.info("=" * 60)
+
+
+def main() -> None:
+    """入口函数。"""
+    load_dotenv()
+    parser = _build_parser()
+    args = parser.parse_args()
+
+    import yaml
+
+    with open(args.config, encoding="utf-8") as f:
+        raw_yaml = yaml.safe_load(f)
+
+    from app.harness.config import load_config
+
+    cli_overrides = {k: v for k, v in vars(args).items() if k not in ("config", "task_types")}
+    config = load_config(args.config, cli_overrides)
+    logger.info("配置加载完成: mode={}, workspace={}", config.mode, config.workspace_dir)
+
+    settings = InfraSettings()
+    embed_cfg = raw_yaml.get("embed", {})
+    adapters = _build_adapters(settings, embed_cfg)
+
+    from app.harness.deps_router import InferenceDepsRouter
+    from app.harness.runner import Runner
+
+    router = InferenceDepsRouter(
+        store_dir=Path(config.store_dir),
+        embed_provider=adapters.embed,
+        llm=adapters.llm,
+        vlm=adapters.vlm,
+        ocr=adapters.ocr,
+        default_prompts_dir=Path(config.store_dir) / "prompts" / config.prompts_version,
+        default_skills_dir=Path(config.store_dir) / "skills" / config.skills_version,
+        skill_mode=config.skill_mode,
+        verify_vision=True,
+        anchor=True,
+        assemble_mode="default",
+    )
+
+    runner = Runner(
+        config,
+        llm=adapters.llm,
+        evolve_llm=adapters.evolve_llm,
+        vlm=adapters.vlm,
+        telemetry=adapters.telemetry,
+        tool_dispatch_factory=router.create_dispatch,
+        prompt_builder_factory=router.create_prompt_builder,
+    )
+
+    if config.mode == "infer":
+        task_types = getattr(args, "task_types", None)
+        result = asyncio.run(runner.infer(task_types=task_types))
+        _log_result(result)
+    else:
+        raise SystemExit(f"模式 {config.mode!r} 尚未实现")
+
+
+if __name__ == "__main__":
+    main()