chore: import upstream snapshot with attribution

2026-07-13 12:39:17 +08:00
commit 4ed4e9ff99
1368 changed files with 334957 additions and 0 deletions
@@ -0,0 +1,86 @@
+"""
+Example demonstrating OpenAI responses.compact session functionality.
+
+This example shows how to use OpenAIResponsesCompactionSession to automatically
+compact conversation history when it grows too large, reducing token usage
+while preserving context.
+"""
+
+import asyncio
+
+from agents import Agent, OpenAIResponsesCompactionSession, Runner, SQLiteSession
+
+
+async def main():
+    # Create an underlying session for storage
+    underlying = SQLiteSession(":memory:")
+
+    # Wrap with compaction session - will automatically compact when threshold hit
+    session = OpenAIResponsesCompactionSession(
+        session_id="demo-session",
+        underlying_session=underlying,
+        model="gpt-4.1",
+        # Custom compaction trigger (default is 10 candidates)
+        should_trigger_compaction=lambda ctx: len(ctx["compaction_candidate_items"]) >= 4,
+    )
+
+    agent = Agent(
+        name="Assistant",
+        instructions="Reply concisely. Keep answers to 1-2 sentences.",
+    )
+
+    print("=== Compaction Session Example ===\n")
+
+    prompts = [
+        "What is the tallest mountain in the world?",
+        "How tall is it in feet?",
+        "When was it first climbed?",
+        "Who was on that expedition?",
+        "What country is the mountain in?",
+    ]
+
+    for i, prompt in enumerate(prompts, 1):
+        print(f"Turn {i}:")
+        print(f"User: {prompt}")
+        result = await Runner.run(agent, prompt, session=session)
+        print(f"Assistant: {result.final_output}\n")
+
+    # Show session state after automatic compaction (if triggered)
+    items = await session.get_items()
+    print("=== Session State (Auto Compaction) ===")
+    print(f"Total items: {len(items)}")
+    for item in items:
+        # Some inputs are stored as easy messages (only `role` and `content`).
+        item_type = item.get("type") or ("message" if "role" in item else "unknown")
+        if item_type == "compaction":
+            print("  - compaction (encrypted content)")
+        elif item_type == "message":
+            role = item.get("role", "unknown")
+            print(f"  - message ({role})")
+        else:
+            print(f"  - {item_type}")
+    print()
+
+    # Manual compaction after inspecting the auto-compacted state.
+    print("=== Manual Compaction ===")
+    await session.run_compaction({"force": True})
+    print("Done")
+    print()
+
+    # Show final session state after manual compaction
+    items = await session.get_items()
+    print("=== Session State (Manual Compaction) ===")
+    print(f"Total items: {len(items)}")
+    for item in items:
+        item_type = item.get("type") or ("message" if "role" in item else "unknown")
+        if item_type == "compaction":
+            print("  - compaction (encrypted content)")
+        elif item_type == "message":
+            role = item.get("role", "unknown")
+            print(f"  - message ({role})")
+        else:
+            print(f"  - {item_type}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())