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 +1,112 @@
+import argparse
+import asyncio
+import os
+
+from openai.types.responses import ResponseFunctionShellToolCall
+from openai.types.responses.response_container_reference import ResponseContainerReference
+
+from agents import Agent, Runner, ShellTool, ShellToolSkillReference, trace
+from agents.items import ModelResponse
+
+SHELL_SKILL_ID_ENV = "OPENAI_SHELL_SKILL_ID"
+SHELL_SKILL_VERSION_ENV = "OPENAI_SHELL_SKILL_VERSION"
+DEFAULT_SKILL_REFERENCE: ShellToolSkillReference = {
+    "type": "skill_reference",
+    "skill_id": "skill_698bbe879adc81918725cbc69dcae7960bc5613dadaed377",
+    "version": "1",
+}
+
+
+def resolve_skill_reference() -> ShellToolSkillReference:
+    skill_id = os.environ.get(SHELL_SKILL_ID_ENV)
+    if not skill_id:
+        return DEFAULT_SKILL_REFERENCE
+
+    reference: ShellToolSkillReference = {"type": "skill_reference", "skill_id": skill_id}
+    skill_version = os.environ.get(SHELL_SKILL_VERSION_ENV)
+    if skill_version:
+        reference["version"] = skill_version
+    return reference
+
+
+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:
+    skill_reference = resolve_skill_reference()
+    print(
+        "[info] Using skill reference:",
+        skill_reference["skill_id"],
+        f"(version {skill_reference.get('version', 'default')})",
+    )
+
+    with trace("container_shell_skill_reference_example"):
+        agent1 = Agent(
+            name="Container Shell Agent (Skill Reference)",
+            model=model,
+            instructions="Use the available container skill to answer user requests.",
+            tools=[
+                ShellTool(
+                    environment={
+                        "type": "container_auto",
+                        "network_policy": {"type": "disabled"},
+                        "skills": [skill_reference],
+                    }
+                )
+            ],
+        )
+
+        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))