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 +1,117 @@
+import argparse
+import asyncio
+import base64
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from zipfile import ZIP_DEFLATED, ZipFile
+
+from openai.types.responses import ResponseFunctionShellToolCall
+from openai.types.responses.response_container_reference import ResponseContainerReference
+
+from agents import Agent, Runner, ShellTool, ShellToolInlineSkill, trace
+from agents.items import ModelResponse
+
+SKILL_NAME = "csv-workbench"
+SKILL_DIR = Path(__file__).resolve().parent / "skills" / SKILL_NAME
+
+
+def build_skill_zip_bundle() -> bytes:
+    with TemporaryDirectory(prefix="agents-inline-skill-") as temp_dir:
+        zip_path = Path(temp_dir) / f"{SKILL_NAME}.zip"
+        with ZipFile(zip_path, "w", compression=ZIP_DEFLATED) as archive:
+            for path in sorted(SKILL_DIR.rglob("*")):
+                if path.is_file():
+                    archive.write(path, f"{SKILL_NAME}/{path.relative_to(SKILL_DIR)}")
+        return zip_path.read_bytes()
+
+
+def build_inline_skill() -> ShellToolInlineSkill:
+    bundle = build_skill_zip_bundle()
+    return {
+        "type": "inline",
+        "name": SKILL_NAME,
+        "description": "Analyze CSV files in /mnt/data and return concise numeric summaries.",
+        "source": {
+            "type": "base64",
+            "media_type": "application/zip",
+            "data": base64.b64encode(bundle).decode("ascii"),
+        },
+    }
+
+
+def extract_container_id(raw_responses: list[ModelResponse]) -> str | None:
+    for response in raw_responses:
+        for item in response.output:
+            if isinstance(item, ResponseFunctionShellToolCall) and isinstance(
+                item.environment, ResponseContainerReference
+            ):
+                return item.environment.container_id
+
+    return None
+
+
+async def main(model: str) -> None:
+    inline_skill = build_inline_skill()
+
+    with trace("container_shell_inline_skill_example"):
+        agent1 = Agent(
+            name="Container Shell Agent (Inline Skill)",
+            model=model,
+            instructions="Use the available container skill to answer user requests.",
+            tools=[
+                ShellTool(
+                    environment={
+                        "type": "container_auto",
+                        "network_policy": {"type": "disabled"},
+                        "skills": [inline_skill],
+                    }
+                )
+            ],
+        )
+
+        result1 = await Runner.run(
+            agent1,
+            (
+                "Use the csv-workbench skill. Create /mnt/data/orders.csv with columns "
+                "id,region,amount,status and at least 6 rows. Then report total amount by "
+                "region and count failed orders."
+            ),
+        )
+        print(f"Agent: {result1.final_output}")
+
+        container_id = extract_container_id(result1.raw_responses)
+        if not container_id:
+            raise RuntimeError("Container ID was not returned in shell call output.")
+
+        print(f"[info] Reusing container_id={container_id}")
+
+        agent2 = Agent(
+            name="Container Reference Shell Agent",
+            model=model,
+            instructions="Reuse the existing shell container and answer concisely.",
+            tools=[
+                ShellTool(
+                    environment={
+                        "type": "container_reference",
+                        "container_id": container_id,
+                    }
+                )
+            ],
+        )
+
+        result2 = await Runner.run(
+            agent2,
+            "Run `ls -la /mnt/data`, then summarize in one sentence.",
+        )
+        print(f"Agent (container reuse): {result2.final_output}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        default="gpt-5.6-sol",
+        help="Model name to use.",
+    )
+    args = parser.parse_args()
+    asyncio.run(main(args.model))