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Python: Fix tool normalization and provider sample consolidation (#3953)

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* Fix tool normalization and provider samples

- restore callable/single-tool normalization paths and unset tool-choice behavior\n- consolidate and expand chat/provider samples (OpenAI/Azure/Anthropic/Ollama/Bedrock)\n- migrate Bedrock lazy import surface to agent_framework.amazon and move provider samples

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* small fix in sample

* Finalize provider, samples, and core cleanup

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix CopilotTool passthrough in agent

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix link

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-02-16 16:30:38 +00:00
committed by GitHub
co-authored by Copilot
parent ed113f941c
commit aab621f5eb
99 changed files with 1190 additions and 969 deletions
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python/samples/02-agents/context_providers/simple_context_provider.py
+55 -64
View File
@@ -1,11 +1,13 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from contextlib import suppress
from typing import Any
from agent_framework import Agent, AgentSession, BaseContextProvider, SessionContext, SupportsChatGetResponse
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
from pydantic import BaseModel
@@ -15,19 +17,13 @@ class UserInfo(BaseModel):
class UserInfoMemory(BaseContextProvider):
def __init__(self, client: SupportsChatGetResponse, user_info: UserInfo | None = None, **kwargs: Any):
def __init__(self, source_id: str = "user-info-memory", *, client: SupportsChatGetResponse, **kwargs: Any):
"""Create the memory.
If you pass in kwargs, they will be attempted to be used to create a UserInfo object.
"""
super().__init__("user-info-memory")
super().__init__(source_id)
self._chat_client = client
if user_info:
self.user_info = user_info
elif kwargs:
self.user_info = UserInfo.model_validate(kwargs)
else:
self.user_info = UserInfo()
async def after_run(
self,
@@ -38,12 +34,15 @@ class UserInfoMemory(BaseContextProvider):
state: dict[str, Any],
) -> None:
"""Extract user information from messages after each agent call."""
request_messages = context.get_messages()
# ensure you get all the messages you want to parse from, including the input in this case.
request_messages = context.get_messages(include_input=True, include_response=True)
# Check if we need to extract user info from user messages
user_messages = [msg for msg in request_messages if hasattr(msg, "role") and msg.role == "user"] # type: ignore
if (self.user_info.name is None or self.user_info.age is None) and user_messages:
try:
if (
state[self.source_id]["user_info"].name is None or state[self.source_id]["user_info"].age is None
) and user_messages:
with suppress(Exception):
# Use the chat client to extract structured information
result = await self._chat_client.get_response(
messages=request_messages, # type: ignore
@@ -53,17 +52,12 @@ class UserInfoMemory(BaseContextProvider):
)
# Update user info with extracted data
try:
with suppress(Exception):
extracted = result.value
if self.user_info.name is None and extracted.name:
self.user_info.name = extracted.name
if self.user_info.age is None and extracted.age:
self.user_info.age = extracted.age
except Exception:
pass # Failed to extract, continue without updating
except Exception:
pass # Failed to extract, continue without updating
if state[self.source_id]["user_info"].name is None and extracted.name:
state[self.source_id]["user_info"].name = extracted.name
if state[self.source_id]["user_info"].age is None and extracted.age:
state[self.source_id]["user_info"].age = extracted.age
async def before_run(
self,
@@ -74,55 +68,52 @@ class UserInfoMemory(BaseContextProvider):
state: dict[str, Any],
) -> None:
"""Provide user information context before each agent call."""
instructions: list[str] = []
if state.setdefault(self.source_id, None) is None:
state[self.source_id] = {"user_info": UserInfo()}
if self.user_info.name is None:
instructions.append(
"Ask the user for their name and politely decline to answer any questions until they provide it."
)
else:
instructions.append(f"The user's name is {self.user_info.name}.")
if self.user_info.age is None:
instructions.append(
"Ask the user for their age and politely decline to answer any questions until they provide it."
)
else:
instructions.append(f"The user's age is {self.user_info.age}.")
# Add context with additional instructions
context.extend_instructions(self.source_id, " ".join(instructions))
def serialize(self) -> str:
"""Serialize the user info for session persistence."""
return self.user_info.model_dump_json()
context.extend_instructions(
self.source_id,
"Ask the user for their name and politely decline to answer any questions until they provide it."
if state[self.source_id]["user_info"].name is None
else f"The user's name is {state[self.source_id]['user_info'].name}.",
)
context.extend_instructions(
self.source_id,
"Ask the user for their age and politely decline to answer any questions until they provide it."
if state[self.source_id]["user_info"].age is None
else f"The user's age is {state[self.source_id]['user_info'].age}.",
)
async def main():
async with AzureCliCredential() as credential:
client = AzureAIClient(credential=credential)
client = AzureOpenAIResponsesClient(
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
credential=AzureCliCredential(),
)
# Create the memory provider
memory_provider = UserInfoMemory(client)
context_name = "user-info-memory"
# Create the agent with memory
async with Agent(
client=client,
instructions="You are a friendly assistant. Always address the user by their name.",
context_providers=[memory_provider],
) as agent:
# Create a new session for the conversation
session = agent.create_session()
# Create the memory provider
memory_provider = UserInfoMemory(context_name, client=client)
print(await agent.run("Hello, what is the square root of 9?", session=session))
print(await agent.run("My name is Ruaidhrí", session=session))
print(await agent.run("I am 20 years old", session=session))
# Create the agent with memory
async with Agent(
client=client,
instructions="You are a friendly assistant. Always address the user by their name.",
context_providers=[memory_provider],
) as agent:
# Create a new session for the conversation
session = agent.create_session()
# Access the memory component and inspect the memories
if memory_provider:
print()
print(f"MEMORY - User Name: {memory_provider.user_info.name}")
print(f"MEMORY - User Age: {memory_provider.user_info.age}")
for msg in ["Hello, what is the square root of 9?", "My name is Ruaidhrí", "I am 20 years old"]:
print(f"User: {msg}")
print(f"Assistant: {await agent.run(msg, session=session)}")
# Access the memory component and inspect the memories
print()
print(f"MEMORY - User Name: {session.state[context_name]['user_info'].name}")
print(f"MEMORY - User Age: {session.state[context_name]['user_info'].age}")
if __name__ == "__main__":