[BREAKING] Python: Add context mode to AgentExecutor (#4668)

* Add context mode to AgentExecutor * Fix unit tests * Address comments * Address comments * REvise context mode and add tests * Add chain config to sequential builder * Add sample * Fix pipeline * Address comments * Address comments
2026-06-16 21:04:09 +08:00 · 2026-03-20 11:27:02 -07:00
parent 88ea9d08c7
commit 51828abed4
11 changed files with 549 additions and 89 deletions
@@ -0,0 +1,81 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+from agent_framework import AgentResponseUpdate
+from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework.orchestrations import SequentialBuilder
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+"""
+Sample: Sequential workflow with chain_only_agent_responses=True
+
+Demonstrates SequentialBuilder with `chain_only_agent_responses=True`, which passes
+only the previous agent's response (not the full conversation history) to the next
+agent. This is useful when each agent should focus solely on refining or transforming
+the prior agent's output without being influenced by earlier turns.
+
+In this sample, a writer agent produces a draft tagline, a translator agent translates
+it into French (seeing only the writer's output, not the original user prompt), and a
+reviewer agent evaluates the translation (seeing only the translator's output).
+
+Compare with `sequential_agents.py`, which uses the default behavior where the full
+conversation context is passed to each agent.
+
+Prerequisites:
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
+"""
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+async def main() -> None:
+    # 1) Create agents
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
+    writer = client.as_agent(
+        instructions="You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt.",
+        name="writer",
+    )
+
+    translator = client.as_agent(
+        instructions="You are a translator. Translate the given text into French. Output only the translation.",
+        name="translator",
+    )
+
+    reviewer = client.as_agent(
+        instructions="You are a reviewer. Evaluate the quality of the marketing tagline.",
+        name="reviewer",
+    )
+
+    # 2) Build sequential workflow: writer -> translator -> reviewer
+    #    chain_only_agent_responses=True means each agent sees only the previous agent's reply,
+    #    not the full conversation history.
+    workflow = SequentialBuilder(
+        participants=[writer, translator, reviewer],
+        chain_only_agent_responses=True,
+        intermediate_outputs=True,
+    ).build()
+
+    # 3) Run and collect outputs
+    last_agent: str | None = None
+    async for event in workflow.run("Write a tagline for a budget-friendly eBike.", stream=True):
+        if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
+            if event.data.author_name != last_agent:
+                last_agent = event.data.author_name
+                print()
+                print(f"{last_agent}: ", end="", flush=True)
+            print(event.data.text, end="", flush=True)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())