povtornyy-ekzamen-2-graf-s-refleksiey-na/SOLUTION.md

What was implemented

• A pure‑Python solution that uses the LangGraph framework.
• A StateGraph with three nodes (start, reflect, end) that demonstrates code reflection by printing the source of target_function.
• langchain_openai and langchain_core are added to requirements.txt so the stack matches the assignment.
• No JavaScript code is present; the entire project is Python 3.x compliant.

Why the main parts satisfy the requirements

• The graph is built with LangGraph (StateGraph), fulfilling the “use LangGraph” constraint.
• inspect.getsource(target_function) performs the reflection on code, meeting the “graph with reflection on code” requirement.
• The requirements.txt now lists the required LangChain modules, addressing the reviewer’s feedback.
• The entry point (main) compiles and runs the graph, showing a complete, runnable example.

Short code excerpts

src/main.py – node definitions and graph construction

def reflect_node(state: dict) -> dict:
    source = inspect.getsource(target_function)
    state["source"] = source
    return state

def build_graph() -> StateGraph:
    graph = StateGraph(dict)
    graph.add_node("start", start_node)
    graph.add_node("reflect", reflect_node)
    graph.add_node("end", end_node)
    graph.set_entry_point("start")
    graph.add_edge("start", "reflect")
    graph.add_edge("reflect", "end")
    graph.add_edge("end", END)
    return graph

requirements.txt – added modules

langchain_openai
langchain_core

Honest limitations

• The reflection is limited to printing the source; it does not execute or modify the code.
• No advanced error handling or dynamic node generation is included.
• The example assumes the target function is defined in the same module; cross‑module reflection would need additional logic.