feat: lite deep researcher implementation

src/config/__init__.py

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+from .tools import TAVILY_MAX_RESULTS
+from .loader import load_yaml_config
+
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Team configuration
+TEAM_MEMBER_CONFIGRATIONS = {
+    "researcher": {
+        "name": "researcher",
+        "desc": (
+            "Responsible for searching and collecting relevant information, understanding user needs and conducting research analysis"
+        ),
+        "desc_for_llm": (
+            "Uses search engines and web crawlers to gather information from the internet. "
+            "Outputs a Markdown report summarizing findings. Researcher can not do math or programming."
+        ),
+        "is_optional": False,
+    },
+    "coder": {
+        "name": "coder",
+        "desc": (
+            "Responsible for code implementation, debugging and optimization, handling technical programming tasks"
+        ),
+        "desc_for_llm": (
+            "Executes Python or Bash commands, performs mathematical calculations, and outputs a Markdown report. "
+            "Must be used for all mathematical computations."
+        ),
+        "is_optional": True,
+    },
+}
+
+TEAM_MEMBERS = list(TEAM_MEMBER_CONFIGRATIONS.keys())
+
+__all__ = [
+    # Other configurations
+    "TEAM_MEMBERS",
+    "TEAM_MEMBER_CONFIGRATIONS",
+    "TAVILY_MAX_RESULTS",
+]

src/config/agents.py

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+from typing import Literal
+
+# Define available LLM types
+LLMType = Literal["basic", "reasoning", "vision"]
+
+# Define agent-LLM mapping
+AGENT_LLM_MAP: dict[str, LLMType] = {
+    "coordinator": "basic",  # 协调默认使用basic llm
+    "planner": "basic",  # 计划默认使用basic llm
+    "researcher": "basic",  # 简单搜索任务使用basic llm
+    "coder": "basic",  # 编程任务使用basic llm
+    "reporter": "basic",  # 报告使用basic llm
+}

src/config/configuration.py

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+import os
+from dataclasses import dataclass, fields
+from typing import Any, Optional
+
+from langchain_core.runnables import RunnableConfig
+
+
+@dataclass(kw_only=True)
+class Configuration:
+    """The configurable fields."""
+
+    max_plan_iterations: int = 2  # Maximum number of plan iterations
+    max_step_num: int = 5  # Maximum number of steps in a plan
+
+    @classmethod
+    def from_runnable_config(
+        cls, config: Optional[RunnableConfig] = None
+    ) -> "Configuration":
+        """Create a Configuration instance from a RunnableConfig."""
+        configurable = (
+            config["configurable"] if config and "configurable" in config else {}
+        )
+        values: dict[str, Any] = {
+            f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
+            for f in fields(cls)
+            if f.init
+        }
+        return cls(**{k: v for k, v in values.items() if v})

src/config/loader.py

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+import os
+import yaml
+from typing import Dict, Any
+
+
+def replace_env_vars(value: str) -> str:
+    """Replace environment variables in string values."""
+    if not isinstance(value, str):
+        return value
+    if value.startswith("$"):
+        env_var = value[1:]
+        return os.getenv(env_var, value)
+    return value
+
+
+def process_dict(config: Dict[str, Any]) -> Dict[str, Any]:
+    """Recursively process dictionary to replace environment variables."""
+    result = {}
+    for key, value in config.items():
+        if isinstance(value, dict):
+            result[key] = process_dict(value)
+        elif isinstance(value, str):
+            result[key] = replace_env_vars(value)
+        else:
+            result[key] = value
+    return result
+
+
+_config_cache: Dict[str, Dict[str, Any]] = {}
+
+
+def load_yaml_config(file_path: str) -> Dict[str, Any]:
+    """Load and process YAML configuration file."""
+    # 如果文件不存在，返回{}
+    if not os.path.exists(file_path):
+        return {}
+
+    # 检查缓存中是否已存在配置
+    if file_path in _config_cache:
+        return _config_cache[file_path]
+
+    # 如果缓存中不存在，则加载并处理配置
+    with open(file_path, "r") as f:
+        config = yaml.safe_load(f)
+    processed_config = process_dict(config)
+
+    # 将处理后的配置存入缓存
+    _config_cache[file_path] = processed_config
+    return processed_config

src/config/tools.py

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+# Tool configuration
+TAVILY_MAX_RESULTS = 3