Python: Support skill scripts execution (#4558)

* support skill scripts execution * fix mixed line endings * address comments and fix syntax issues * use few try/except instead of one * change samples * validate either script path or script resource is set not both * fix: separate LLM args from runtime kwargs in skill script execution * address pr review comments * address PR review comments * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * 1. Fixing the caching bug where parameters_schema would re-inspect on every call when the result was None 2. Updating the arguments tool description to be more generic (not CLI-specific) * fix failing tests * address pr review comments * address pr review comments * allow resource function returning any instead of sting * address PR review comments --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-03-11 18:28:30 +00:00
parent 2f8fd5f82f
commit 23ebfbc937
27 changed files with 2994 additions and 528 deletions
@@ -0,0 +1,124 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from textwrap import dedent
+
+from agent_framework import Agent, Skill, SkillsProvider
+from agent_framework.azure import AzureOpenAIResponsesClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+"""
+Skill Script Approval — Require human approval before executing skill scripts
+
+This sample demonstrates how to use ``require_script_approval=True`` on
+:class:`SkillsProvider` so that every call to ``run_skill_script`` is
+gated by a human-in-the-loop approval step.
+
+How it works:
+1. A code-defined skill with a script is registered via SkillsProvider.
+2. ``require_script_approval=True`` causes the agent to pause and return
+   approval requests in ``result.user_input_requests`` instead of executing
+   scripts immediately.
+3. The application inspects each request and calls
+   ``request.to_function_approval_response(approved=True|False)`` to approve
+   or reject.
+4. The approval response is sent back via ``agent.run(approval_response, session=session)``
+   and the agent continues — executing the script if approved, or receiving
+   an error if rejected.
+
+Prerequisites:
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME (defaults to "gpt-4o-mini").
+"""
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Define a code skill with a script that performs a sensitive operation
+deployment_skill = Skill(
+    name="deployment",
+    description="Tools for deploying application versions to production",
+    content=dedent("""\
+        Use this skill when the user asks to deploy an application.
+
+        1. Run the deploy script with the version and environment parameters.
+    """),
+)
+
+
+@deployment_skill.script
+def deploy(version: str, environment: str = "staging") -> str:
+    """Deploy the application to the specified environment."""
+    return f"Deployed version {version} to {environment}"
+
+
+async def main() -> None:
+    """Run the skill script approval demo."""
+    endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+    deployment = os.environ.get("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME", "gpt-4o-mini")
+
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=endpoint,
+        deployment_name=deployment,
+        credential=AzureCliCredential(),
+    )
+
+    # Create the skills provider with script approval enabled
+    skills_provider = SkillsProvider(
+        skills=[deployment_skill],
+        require_script_approval=True,
+    )
+
+    async with Agent(
+        client=client,
+        instructions="You are a deployment assistant. Use the deployment skill to deploy applications.",
+        context_providers=[skills_provider],
+    ) as agent:
+        session = agent.create_session()
+
+        print("Starting agent with skill script approval enabled...")
+        print("-" * 60)
+
+        # Step 1: Send the user request — the agent will try to call the script
+        query = "Deploy the latest application version 2.5.0 to the production environment"
+        print(f"User: {query}")
+        result = await agent.run(query, session=session)
+
+        # Step 2: Handle approval requests (with sessions, context is
+        # maintained automatically — just send the approval response)
+        while result.user_input_requests:
+            for request in result.user_input_requests:
+                print(f"\nApproval needed:")
+                print(f"  Function: {request.function_call.name}")  # type: ignore[union-attr]
+                print(f"  Arguments: {request.function_call.arguments}")  # type: ignore[union-attr]
+
+                # In a real application, prompt the user here
+                approved = True  # Change to False to see rejection
+                print(f"  Decision: {'Approved' if approved else 'Rejected'}")
+
+                # Send the approval response — session preserves conversation history
+                approval_response = request.to_function_approval_response(approved=approved)
+                result = await agent.run(approval_response, session=session)
+
+        print(f"\nAgent: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+"""
+Sample output:
+
+Starting agent with skill script approval enabled...
+------------------------------------------------------------
+User: Deploy version 2.5.0 to production
+
+Approval needed:
+  Function: run_skill_script
+  Arguments: {"skill_name": "deployment", "script_name": "deploy", ...}
+  Decision: Approved
+
+Agent: Successfully deployed version 2.5.0 to production.
+"""