ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/self-evolution-eval/demo.py

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+"""Run Experiment 9-9 with a reference or real LLM-backed agent."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from agent import OpenAILongitudinalAgent, ReferenceAgent
+from harness import LongitudinalEvaluator
+
+
+ROOT = Path(__file__).parent
+
+
+def load_tasks():
+    return json.loads((ROOT / "dataset.json").read_text(encoding="utf-8"))["tasks"]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Experiment 9-9: longitudinal continual-evolution evaluation")
+    parser.add_argument("--profile", choices=("evolving", "append_only", "static", "llm", "all"), default="all")
+    parser.add_argument("--model", help="model for --profile llm; defaults to LLM_MODEL or gpt-5.6")
+    parser.add_argument("--output", help="optional JSON report path")
+    args = parser.parse_args()
+
+    profiles = ("evolving", "append_only", "static") if args.profile == "all" else (args.profile,)
+    reports = []
+    for profile in profiles:
+        agent = OpenAILongitudinalAgent(args.model) if profile == "llm" else ReferenceAgent(profile)
+        reports.append(LongitudinalEvaluator().run(agent, load_tasks()))
+
+    print("Experiment 9-9: does the Agent keep evolving?\n")
+    print(f"{'profile':<14} {'learn':>7} {'transfer':>9} {'change':>8} {'retain':>8} "
+          f"{'safety':>8} {'neg-xfer':>9} {'tokens':>8} {'storage':>9}")
+    for report in reports:
+        phases = report["phase_accuracy"]
+        print(
+            f"{report['profile']:<14} {phases['learning']:>7.3f} {phases['transfer']:>9.3f} "
+            f"{phases['change']:>8.3f} {report['retention_rate']:>8.3f} "
+            f"{report['safety_rubric_pass_rate']:>8.3f} {report['negative_transfer_rate']:>9.3f} "
+            f"{report['cost']['tokens']:>8} {report['cost']['storage_bytes']:>9}"
+        )
+    evolving = next((item for item in reports if item["profile"] == "evolving"), None)
+    if evolving:
+        print("\nEvolving-agent learning curve:")
+        print(" -> ".join(
+            f"{point['task_id']}:{point['cumulative_accuracy']:.2f}"
+            for point in evolving["learning_curve"]
+        ))
+        print("tasks after change signal to recover:", evolving["adaptation"]["tasks_after_change_signal_to_recover"])
+
+    if args.output:
+        path = Path(args.output)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(reports, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+if __name__ == "__main__":
+    main()