feat: lite deep researcher implementation

src/agents/__init__.py

@@ -0,0 +1,3 @@
+from .agents import research_agent, coder_agent
+
+__all__ = ["research_agent", "coder_agent"]

src/agents/agents.py

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+from langgraph.prebuilt import create_react_agent
+
+from src.prompts import apply_prompt_template
+from src.tools import (
+    bash_tool,
+    crawl_tool,
+    python_repl_tool,
+    tavily_tool,
+)
+
+from src.llms.llm import get_llm_by_type
+from src.config.agents import AGENT_LLM_MAP
+
+
+# Create agents using configured LLM types
+def create_agent(agent_name: str, agent_type: str, tools: list, prompt_template: str):
+    """Factory function to create agents with consistent configuration."""
+    return create_react_agent(
+        name=agent_name,
+        model=get_llm_by_type(AGENT_LLM_MAP[agent_type]),
+        tools=tools,
+        prompt=lambda state: apply_prompt_template(prompt_template, state),
+    )
+
+
+# Create agents using the factory function
+research_agent = create_agent(
+    "researcher", "researcher", [tavily_tool, crawl_tool], "researcher"
+)
+coder_agent = create_agent("coder", "coder", [python_repl_tool, bash_tool], "coder")