consolidate: Evo LangGraph StateGraph — training loop orchestration

evo/langgraph_graph.py

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+"""
+Agent Q3 [Evo] — LangGraph StateGraph
+Self-improvement loop: ingest → train → benchmark → feedback → repeat
+"""
+from langgraph.graph import StateGraph, END
+from typing import TypedDict
+
+class EvoState(TypedDict):
+    cycle: int
+    ingest_done: bool
+    train_done: bool
+    benchmark_score: float
+    feedback_pushed: bool
+    notes: str
+
+def ingest_node(state: EvoState) -> EvoState:
+    from arxiv_ingestor import ingest
+    ingest(max_results=10)
+    state["ingest_done"] = True
+    state["notes"] += " | arXiv ingested"
+    return state
+
+def train_node(state: EvoState) -> EvoState:
+    from training_pipeline import run
+    run()
+    state["train_done"] = True
+    state["notes"] += " | LoRA trained"
+    return state
+
+def benchmark_node(state: EvoState) -> EvoState:
+    import asyncio
+    from benchmark_runner import run_all
+    results = asyncio.run(run_all())
+    avg = sum(r["score"] for r in results) / len(results)
+    state["benchmark_score"] = avg
+    state["notes"] += f" | benchmark={avg:.2%}"
+    return state
+
+def feedback_node(state: EvoState) -> EvoState:
+    from feedback_collector import push_to_hf
+    push_to_hf()
+    state["feedback_pushed"] = True
+    state["notes"] += " | feedback pushed"
+    return state
+
+def should_continue(state: EvoState) -> str:
+    return "continue" if state["benchmark_score"] < 0.80 else END
+
+def build_evo_graph() -> StateGraph:
+    g = StateGraph(EvoState)
+    g.add_node("ingest",    ingest_node)
+    g.add_node("train",     train_node)
+    g.add_node("benchmark", benchmark_node)
+    g.add_node("feedback",  feedback_node)
+    g.set_entry_point("ingest")
+    g.add_edge("ingest",    "train")
+    g.add_edge("train",     "benchmark")
+    g.add_edge("benchmark", "feedback")
+    g.add_conditional_edges("feedback", should_continue, {"continue":"ingest", END: END})
+    return g.compile()
+
+graph = build_evo_graph()