diff --git a/repo/main.py b/repo/main.py
deleted file mode 100644
index 12b8d79..0000000
--- a/repo/main.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-LangGraph planning agent example.
-
-This script demonstrates a simple LangGraph agent that:
-1. Takes an input task description.
-2. Uses an LLM to split the task into 3‑6 concrete steps (JSON list).
-3. Executes each step sequentially, collecting results.
-4. Returns a final summary of all results.
-
-Requirements:
-- python >= 3.10
-- langgraph
-- langchain-openai (or langchain-ollama)
-
-Run with:
-    python main.py "Compare Python and JavaScript"
-"""
-
-from __future__ import annotations
-
-import json
-import os
-from typing import TypedDict, List
-
-from langgraph.graph import StateGraph, END
-from langgraph.prebuilt import create_agent_executor
-from langchain_openai import ChatOpenAI
-
-# ---------- 1. Define the state ---------------------------------
-class PlanningState(TypedDict):
-    task: str
-    plan: List[str] | None
-    current_step: int
-    results: List[str]
-
-# ---------- 2. LLM for planning ---------------------------------
-# The user should set OPENAI_API_KEY in environment.
-llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
-
-planning_prompt = (
-    '''You are a helpful assistant that plans tasks.
-Given the following task description, break it into 3‑6 concrete steps.
-Return only a JSON array of strings. Example:
-["Step 1: ...", "Step 2: ..."]'''
-)
-
-# ---------- 3. Planning node -----------------------------------
-async def planning(state: PlanningState) -> PlanningState:
-    task = state["task"]
-    # Call LLM to get plan
-    response = await llm.agenerate([planning_prompt + f"\nTask: {task}"])
-    text = response.generations[0][0].text.strip()
-    try:
-        plan = json.loads(text)
-        if not isinstance(plan, list):
-            raise ValueError
-    except Exception:
-        # Fallback: split by newlines
-        plan = [line for line in text.split("\n") if line]
-    return {
-        "task": task,
-        "plan": plan,
-        "current_step": 0,
-        "results": [],
-    }
-
-# ---------- 4. Execution node -----------------------------------
-async def execution(state: PlanningState) -> PlanningState:
-    idx = state["current_step"]
-    step_text = state["plan"][idx]
-    # For demo, just echo the step as result.
-    result = f"Result of {step_text}"
-    new_results = state["results"].copy()
-    new_results.append(result)
-    return {
-        "task": state["task"],
-        "plan": state["plan"],
-        "current_step": idx + 1,
-        "results": new_results,
-    }
-
-# ---------- 5. Condition node -----------------------------------
-def should_continue(state: PlanningState) -> str:
-    if state["current_step"] >= len(state["plan"]):
-        return "finish"
-    return "execute"
-
-# ---------- 6. Build graph -------------------------------------
-builder = StateGraph(PlanningState)
-builder.add_node("planning", planning)
-builder.add_node("execution", execution)
-builder.add_conditional_edges(
-    "planning",
-    lambda _: "execute",
-)
-builder.add_conditional_edges(
-    "execution",
-    should_continue,
-    {
-        "execute": "execution",
-        "finish": END,
-    },
-)
-graph = builder.compile()
-
-# ---------- 7. Demo runner -------------------------------------
-if __name__ == "__main__":
-    import sys
-
-    if len(sys.argv) < 2:
-        print("Usage: python main.py '<task description>'")
-        sys.exit(1)
-    task_desc = sys.argv[1]
-    # Run graph
-    result = graph.invoke({"task": task_desc})
-    plan = result["plan"]
-    results = result["results"]
-    print("\nTask:", task_desc)
-    print("\nPlan:\n", "\n".join(f"{i+1}. {step}" for i, step in enumerate(plan)))
-    print("\nResults:\n", "\n".join(results))
-    # Final summary via LLM
-    final_prompt = (
-        f"Given the following results: {json.dumps(results)}\nProvide a concise summary."
-    )
-    final_resp = llm.invoke(final_prompt)
-    print("\nFinal Summary:\n", final_resp.content.strip())