agent/agent-framework
1
0
Fork 0
mirror of https://github.com/microsoft/agent-framework.git synced 2026-06-16 21:04:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

Python: [BREAKING] update to v1.0.0 (#5062)

Browse Source 
* updates to final deprecated pieces and versions

* fix mypy

* fix readme links
This commit is contained in:
Eduard van Valkenburg
2026-04-02 17:26:30 +02:00
committed by GitHub
Unverified
parent 5f06b68535
commit 3446eb8d5d
171 changed files with 2580 additions and 2392 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/02-agents/chat_client/built_in_chat_clients.py
+1 -1
View File
@@ -88,7 +88,7 @@ async def main(client_name: ClientName = "openai_chat") -> None:
"""Run a basic prompt using a selected built-in client."""
client = get_client(client_name)
message = Message("user", text="What's the weather in Amsterdam and in Paris?")
message = Message("user", contents=["What's the weather in Amsterdam and in Paris?"])
stream = os.getenv("STREAM", "false").lower() == "true"
print(f"Client: {client_name}")
print(f"User: {message.text}")
python/samples/02-agents/chat_client/chat_response_cancellation.py
+1 -1
View File
@@ -31,7 +31,7 @@ async def main() -> None:
try:
task = asyncio.create_task(
client.get_response(messages=[Message(role="user", text="Tell me a fantasy story.")])
client.get_response(messages=[Message(role="user", contents=["Tell me a fantasy story."])])
)
await asyncio.sleep(1)
task.cancel()
python/samples/02-agents/chat_client/custom_chat_client.py
+2 -2
View File
@@ -98,7 +98,7 @@ class EchoingChatClient(BaseChatClient[OptionsT]):
response_text = f"{response_text} {suffix}"
stream_delay_seconds = float(options.get("stream_delay_seconds", 0.05))
response_message = Message(role="assistant", text=response_text)
response_message = Message(role="assistant", contents=[response_text])
response = ChatResponse(
messages=[response_message],
@@ -150,7 +150,7 @@ async def main() -> None:
# Use the chat client directly
print("Using chat client directly:")
direct_response = await echo_client.get_response(
[Message(role="user", text="Hello, custom chat client!")],
[Message(role="user", contents=["Hello, custom chat client!"])],
options={
"uppercase": True,
"suffix": "(CUSTOM OPTIONS)",
python/samples/02-agents/compaction/advanced.py
+9 -7
View File
@@ -35,25 +35,27 @@ class BudgetSummaryClient:
**kwargs: Any,
) -> ChatResponse:
summary_text = f"Budget summary generated from {len(messages)} prompt messages."
return ChatResponse(messages=[Message(role="assistant", text=summary_text)])
return ChatResponse(messages=[Message(role="assistant", contents=[summary_text])])
def _build_long_history() -> list[Message]:
history = [Message(role="system", text="You are a migration copilot.")]
history = [Message(role="system", contents=["You are a migration copilot."])]
for i in range(1, 8):
history.append(
Message(
role="user",
text=f"Iteration {i}: capture migration requirements and edge cases.",
contents=[f"Iteration {i}: capture migration requirements and edge cases."],
)
)
history.append(
Message(
role="assistant",
text=(
f"Iteration {i}: detailed plan with dependencies, rollback guidance, and testing details. "
"This sentence is intentionally long to create token pressure."
),
contents=[
(
f"Iteration {i}: detailed plan with dependencies, rollback guidance, and testing details. "
"This sentence is intentionally long to create token pressure."
)
],
)
)
return history
python/samples/02-agents/compaction/agent_client_overrides.py
+5 -5
View File
@@ -57,17 +57,17 @@ class InspectingChatClient(BaseChatClient[Any]):
self.last_messages = list(messages)
async def _get_response() -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text="done")])
return ChatResponse(messages=[Message(role="assistant", contents=["done"])])
return _get_response()
def _build_messages() -> list[Message]:
return [
Message(role="user", text="Collect the deployment requirements."),
Message(role="assistant", text="I will gather the constraints first."),
Message(role="user", text="Summarize the rollout risks."),
Message(role="assistant", text="The main risks are drift, downtime, and rollback gaps."),
Message(role="user", contents=["Collect the deployment requirements."]),
Message(role="assistant", contents=["I will gather the constraints first."]),
Message(role="user", contents=["Summarize the rollout risks."]),
Message(role="assistant", contents=["The main risks are drift, downtime, and rollback gaps."]),
]
python/samples/02-agents/compaction/basics.py
+7 -7
View File
@@ -51,15 +51,15 @@ class LocalSummaryClient:
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text=f"Summary for {len(messages)} messages.")])
return ChatResponse(messages=[Message(role="assistant", contents=[f"Summary for {len(messages)} messages."])])
async def main() -> None:
# 1. Build one baseline history and print it once.
messages = [
Message(role="system", text="You are a helpful assistant."),
Message(role="user", text="Plan a data migration."),
Message(role="assistant", text="I will gather requirements."),
Message(role="system", contents=["You are a helpful assistant."]),
Message(role="user", contents=["Plan a data migration."]),
Message(role="assistant", contents=["I will gather requirements."]),
Message(
role="assistant",
contents=[
@@ -79,9 +79,9 @@ async def main() -> None:
)
],
),
Message(role="assistant", text="I found three core tables."),
Message(role="user", text="Estimate effort and risks."),
Message(role="assistant", text="Primary risk is schema drift."),
Message(role="assistant", contents=["I found three core tables."]),
Message(role="user", contents=["Estimate effort and risks."]),
Message(role="assistant", contents=["Primary risk is schema drift."]),
]
print("\n--- Before compaction ---")
print(f"Message count: {len(messages)}")
python/samples/02-agents/compaction/custom.py
+5 -5
View File
@@ -50,11 +50,11 @@ class KeepLastUserTurnStrategy:
def _messages() -> list[Message]:
return [
Message(role="system", text="You are concise."),
Message(role="user", text="first request"),
Message(role="assistant", text="first response"),
Message(role="user", text="second request"),
Message(role="assistant", text="second response"),
Message(role="system", contents=["You are concise."]),
Message(role="user", contents=["first request"]),
Message(role="assistant", contents=["first response"]),
Message(role="user", contents=["second request"]),
Message(role="assistant", contents=["second response"]),
]
python/samples/02-agents/compaction/tiktoken_tokenizer.py
+17 -13
View File
@@ -45,30 +45,34 @@ class TiktokenTokenizer(TokenizerProtocol):
def _build_messages() -> list[Message]:
return [
Message(role="system", text="You are a migration assistant."),
Message(role="system", contents=["You are a migration assistant."]),
Message(
role="user",
text="List all migration risks and include detailed mitigations for each risk category.",
contents=["List all migration risks and include detailed mitigations for each risk category."],
),
Message(
role="assistant",
text=(
"Primary risks include schema drift, missing foreign key constraints, "
"and data quality regressions. Mitigations include staged validation, "
"shadow writes, and replay-based verification."
),
contents=[
(
"Primary risks include schema drift, missing foreign key constraints, "
"and data quality regressions. Mitigations include staged validation, "
"shadow writes, and replay-based verification."
)
],
),
Message(
role="user",
text=("Now provide a detailed checklist with owners, rollback gates, and validation criteria."),
contents=[("Now provide a detailed checklist with owners, rollback gates, and validation criteria.")],
),
Message(
role="assistant",
text=(
"Checklist: baseline snapshots, migration dry-run, production "
"canary, progressive deployment, automated integrity checks, and "
"post-migration reconciliation."
),
contents=[
(
"Checklist: baseline snapshots, migration dry-run, production "
"canary, progressive deployment, automated integrity checks, and "
"post-migration reconciliation."
)
],
),
]
python/samples/02-agents/context_providers/README.md
+1 -1
View File
@@ -6,7 +6,7 @@ These samples demonstrate how to use context providers to enrich agent conversat
| File / Folder | Description |
|---------------|-------------|
| [`simple_context_provider.py`](simple_context_provider.py) | Implement a custom context provider by extending `BaseContextProvider` to extract and inject structured user information across turns. |
| [`simple_context_provider.py`](simple_context_provider.py) | Implement a custom context provider by extending `ContextProvider` to extract and inject structured user information across turns. |
| [`azure_ai_foundry_memory.py`](azure_ai_foundry_memory.py) | Use `FoundryMemoryProvider` to add semantic memory — automatically retrieves, searches, and stores memories via Azure AI Foundry. |
| [`azure_ai_search/`](azure_ai_search/) | Retrieval Augmented Generation (RAG) with Azure AI Search in semantic and agentic modes. See its own [README](azure_ai_search/README.md). |
| [`mem0/`](mem0/) | Memory-powered context using the Mem0 integration (open-source and managed). See its own [README](mem0/README.md). |
python/samples/02-agents/conversations/README.md
+1 -1
View File
@@ -7,7 +7,7 @@ These samples demonstrate different approaches to managing conversation history
| File | Description |
|------|-------------|
| [`suspend_resume_session.py`](suspend_resume_session.py) | Suspend and resume conversation sessions, comparing service-managed sessions (Azure AI Foundry) with in-memory sessions (OpenAI). |
| [`custom_history_provider.py`](custom_history_provider.py) | Implement a custom history provider by extending `BaseHistoryProvider`, enabling conversation persistence in your preferred storage backend. |
| [`custom_history_provider.py`](custom_history_provider.py) | Implement a custom history provider by extending `HistoryProvider`, enabling conversation persistence in your preferred storage backend. |
| [`cosmos_history_provider.py`](cosmos_history_provider.py) | Use Azure Cosmos DB as a history provider for durable conversation storage with `CosmosHistoryProvider`. |
| [`redis_history_provider.py`](redis_history_provider.py) | Use Redis as a history provider for persistent conversation history storage across sessions. |
python/samples/02-agents/declarative/README.md
+2 -2
View File
@@ -43,7 +43,7 @@ Shows how to create an agent that can search and retrieve information from Micro
- Uses Azure CLI credentials for authentication
- Leverages MCP to access Microsoft documentation tools
**Requirements**: `pip install agent-framework-foundry --pre`
**Requirements**: `pip install agent-framework-foundry`
**Key concepts**: Azure AI Foundry integration, MCP server usage, async patterns, resource management
@@ -53,7 +53,7 @@ Shows how to create an agent using an inline YAML string rather than a file.
- Uses Azure AI Foundry v2 Client with instructions.
**Requirements**: `pip install agent-framework-foundry --pre`
**Requirements**: `pip install agent-framework-foundry`
**Key concepts**: Inline YAML definition.
python/samples/02-agents/declarative/azure_openai_responses_agent.py
+7 -1
View File
@@ -14,7 +14,13 @@ async def main():
"""Create an agent from a declarative yaml specification and run it."""
# get the path
current_path = Path(__file__).parent
yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "azure" / "AzureOpenAIResponses.yaml"
yaml_path = (
current_path.parent.parent.parent.parent
/ "declarative-agents"
/ "agent-samples"
/ "azure"
/ "AzureOpenAIResponses.yaml"
)
# load the yaml from the path
with yaml_path.open("r") as f:
yaml_str = f.read()
python/samples/02-agents/declarative/get_weather_agent.py
+7 -1
View File
@@ -22,7 +22,13 @@ async def main():
"""Create an agent from a declarative yaml specification and run it."""
# get the path
current_path = Path(__file__).parent
yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "chatclient" / "GetWeather.yaml"
yaml_path = (
current_path.parent.parent.parent.parent
/ "declarative-agents"
/ "agent-samples"
/ "chatclient"
/ "GetWeather.yaml"
)
# load the yaml from the path
with yaml_path.open("r") as f:
yaml_str = f.read()
python/samples/02-agents/declarative/microsoft_learn_agent.py
+7 -1
View File
@@ -28,7 +28,13 @@ async def main():
"""Create an agent from a declarative yaml specification and run it."""
# get the path
current_path = Path(__file__).parent
yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "foundry" / "MicrosoftLearnAgent.yaml"
yaml_path = (
current_path.parent.parent.parent.parent
/ "declarative-agents"
/ "agent-samples"
/ "foundry"
/ "MicrosoftLearnAgent.yaml"
)
# create the agent from the yaml
async with (
AzureCliCredential() as credential,
python/samples/02-agents/declarative/openai_agent.py
+7 -1
View File
@@ -13,7 +13,13 @@ async def main():
"""Create an agent from a declarative yaml specification and run it."""
# get the path
current_path = Path(__file__).parent
yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "openai" / "OpenAIResponses.yaml"
yaml_path = (
current_path.parent.parent.parent.parent
/ "declarative-agents"
/ "agent-samples"
/ "openai"
/ "OpenAIResponses.yaml"
)
# create the agent from the yaml
agent = AgentFactory(safe_mode=False).create_agent_from_yaml_path(yaml_path)
# use the agent
python/samples/02-agents/devui/agent_weather/agent.py
+2 -2
View File
@@ -68,7 +68,7 @@ async def security_filter_middleware(
role="assistant",
)
response = ChatResponse(messages=[Message(role="assistant", text=error_message)])
response = ChatResponse(messages=[Message(role="assistant", contents=[error_message])])
context.result = ResponseStream(blocked_stream(), finalizer=lambda _, r=response: r)
else:
# Non-streaming mode: return complete response
@@ -76,7 +76,7 @@ async def security_filter_middleware(
messages=[
Message(
role="assistant",
text=error_message,
contents=[error_message],
)
]
)
python/samples/02-agents/middleware/chat_middleware.py
+7 -3
View File
@@ -127,9 +127,13 @@ async def security_and_override_middleware(
messages=[
Message(
role="assistant",
text="I cannot process requests containing sensitive information. "
"Please rephrase your question without including passwords, secrets, or other "
"sensitive data.",
contents=[
(
"I cannot process requests containing sensitive information. "
"Please rephrase your question without including passwords, secrets, or other "
"sensitive data."
)
],
)
]
)
python/samples/02-agents/middleware/middleware_termination.py
+6 -4
View File
@@ -71,10 +71,12 @@ class PreTerminationMiddleware(AgentMiddleware):
messages=[
Message(
role="assistant",
text=(
f"Sorry, I cannot process requests containing '{blocked_word}'. "
"Please rephrase your question."
),
contents=[
(
f"Sorry, I cannot process requests containing '{blocked_word}'. "
"Please rephrase your question."
)
],
)
]
)
python/samples/02-agents/middleware/override_result_with_middleware.py
+4 -4
View File
@@ -86,7 +86,7 @@ async def weather_override_middleware(context: ChatContext, call_next: Callable[
# For non-streaming: just replace with a new message
current_text = context.result.text if isinstance(context.result, ChatResponse) else ""
custom_message = f"Weather Advisory: [0] {''.join(chunks)} Original message was: {current_text}"
context.result = ChatResponse(messages=[Message(role="assistant", text=custom_message)])
context.result = ChatResponse(messages=[Message(role="assistant", contents=[custom_message])])
async def validate_weather_middleware(context: ChatContext, call_next: Callable[[], Awaitable[None]]) -> None:
@@ -111,7 +111,7 @@ async def validate_weather_middleware(context: ChatContext, call_next: Callable[
context.result = ResponseStream(_validated_stream(), finalizer=ChatResponse.from_updates)
elif isinstance(context.result, ChatResponse):
context.result.messages.append(Message(role="assistant", text=validation_note))
context.result.messages.append(Message(role="assistant", contents=[validation_note]))
async def agent_cleanup_middleware(context: AgentContext, call_next: Callable[[], Awaitable[None]]) -> None:
@@ -153,7 +153,7 @@ async def agent_cleanup_middleware(context: AgentContext, call_next: Callable[[]
cleaned_messages.append(
Message(
role=message.role,
text=text,
contents=[text],
author_name=message.author_name,
message_id=message.message_id,
additional_properties=message.additional_properties,
@@ -166,7 +166,7 @@ async def agent_cleanup_middleware(context: AgentContext, call_next: Callable[[]
if not found_validation:
raise RuntimeError("Expected validation note not found in agent response.")
cleaned_messages.append(Message(role="assistant", text=" Agent: OK"))
cleaned_messages.append(Message(role="assistant", contents=[" Agent: OK"]))
response.messages = cleaned_messages
return response
python/samples/02-agents/observability/advanced_manual_setup_console_output.py
+1 -1
View File
@@ -121,7 +121,7 @@ async def run_chat_client() -> None:
print(f"User: {message}")
print("Assistant: ", end="")
async for chunk in client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
stream=True,
options={"tools": [get_weather]},
):
python/samples/02-agents/observability/advanced_zero_code.py
+2 -2
View File
@@ -92,7 +92,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
if stream:
print("Assistant: ", end="")
async for chunk in client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
stream=True,
options={"tools": [get_weather]},
):
@@ -101,7 +101,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
print("")
else:
response = await client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
options={"tools": [get_weather]},
)
print(f"Assistant: {response}")
python/samples/02-agents/observability/configure_otel_providers_with_env_var.py
+2 -2
View File
@@ -80,7 +80,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
if stream:
print("Assistant: ", end="")
async for chunk in client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
stream=True,
options={"tools": [get_weather]},
):
@@ -89,7 +89,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
print("")
else:
response = await client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
options={"tools": [get_weather]},
)
print(f"Assistant: {response}")
python/samples/02-agents/observability/configure_otel_providers_with_parameters.py
+2 -2
View File
@@ -84,7 +84,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
if stream:
print("Assistant: ", end="")
async for chunk in client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
stream=True,
options={"tools": [get_weather]},
):
@@ -93,7 +93,7 @@ async def run_chat_client(client: "SupportsChatGetResponse", stream: bool = Fals
print("")
else:
response = await client.get_response(
[Message(role="user", text=message)],
[Message(role="user", contents=[message])],
options={"tools": [get_weather]},
)
print(f"Assistant: {response}")
python/samples/02-agents/providers/anthropic/anthropic_advanced.py
+11 -2
View File
@@ -1,8 +1,9 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import Agent
from agent_framework.anthropic import AnthropicChatOptions, AnthropicClient
from agent_framework.anthropic import AnthropicClient
from dotenv import load_dotenv
# Load environment variables from .env file
@@ -15,12 +16,20 @@ This sample demonstrates using Anthropic with:
- Setting up an Anthropic-based agent with hosted tools.
- Using the `thinking` feature.
- Displaying both thinking and usage information during streaming responses.
Environment variables:
ANTHROPIC_API_KEY — Your Anthropic API key
ANTHROPIC_CHAT_MODEL — The Anthropic model to use (e.g., "claude-sonnet-4-6")
"""
async def main() -> None:
"""Example of streaming response (get results as they are generated)."""
client = AnthropicClient[AnthropicChatOptions]()
client = AnthropicClient(
api_key=os.getenv("ANTHROPIC_API_KEY"),
model=os.getenv("ANTHROPIC_CHAT_MODEL"),
)
# Create MCP tool configuration using instance method
mcp_tool = client.get_mcp_tool(
python/samples/02-agents/providers/azure/openai_client_with_session.py
+4 -4
View File
@@ -76,19 +76,19 @@ async def example_with_session_persistence_in_memory() -> None:
# First conversation
query1 = "What's the weather like in Tokyo?"
print(f"User: {query1}")
result1 = await agent.run(query1, session=session, store=False)
result1 = await agent.run(query1, session=session, options={"store": False})
print(f"Agent: {result1.text}")
# Second conversation using the same session - maintains context
query2 = "How about London?"
print(f"\nUser: {query2}")
result2 = await agent.run(query2, session=session, store=False)
result2 = await agent.run(query2, session=session, options={"store": False})
print(f"Agent: {result2.text}")
# Third conversation - agent should remember both previous cities
query3 = "Which of the cities I asked about has better weather?"
print(f"\nUser: {query3}")
result3 = await agent.run(query3, session=session, store=False)
result3 = await agent.run(query3, session=session, options={"store": False})
print(f"Agent: {result3.text}")
print("Note: The agent remembers context from previous messages in the same session.\n")
@@ -114,7 +114,7 @@ async def example_with_existing_session_id() -> None:
query1 = "What's the weather in Paris?"
print(f"User: {query1}")
result1 = await agent.run(query1, session=session)
result1 = await agent.run(query1, session=session, options={"store": False})
print(f"Agent: {result1.text}")
# The session ID is set after the first response
python/samples/02-agents/providers/foundry/README.md
-1
View File
@@ -9,7 +9,6 @@ This folder contains Azure AI Foundry and Foundry Local samples for Agent Framew
| [`foundry_agent_basic.py`](foundry_agent_basic.py) | Foundry Agent basic example |
| [`foundry_agent_custom_client.py`](foundry_agent_custom_client.py) | Foundry Agent custom client configuration |
| [`foundry_agent_hosted.py`](foundry_agent_hosted.py) | Foundry Agent for hosted agents |
| [`foundry_agent_with_env_vars.py`](foundry_agent_with_env_vars.py) | Foundry Agent using environment variables |
| [`foundry_agent_with_function_tools.py`](foundry_agent_with_function_tools.py) | Foundry Agent with local function tools |
## FoundryChatClient Samples
python/samples/02-agents/providers/foundry/foundry_agent_custom_client.py
+12 -10
View File
@@ -1,11 +1,13 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import Agent
from agent_framework.foundry import FoundryAgent, RawFoundryAgentChatClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
load_dotenv()
"""
Foundry Agent — Custom client configuration
@@ -25,9 +27,9 @@ Environment variables:
async def main() -> None:
# Option 1: Default — full middleware on both agent and client
agent = FoundryAgent(
project_endpoint="https://your-project.services.ai.azure.com",
agent_name="my-agent",
agent_version="1.0",
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
agent_name=os.getenv("FOUNDRY_AGENT_NAME"),
agent_version=os.getenv("FOUNDRY_AGENT_VERSION"),
credential=AzureCliCredential(),
)
result = await agent.run("Hello from the default setup!")
@@ -35,9 +37,9 @@ async def main() -> None:
# Option 2: Raw client — no client-level middleware (agent middleware still active)
agent_raw_client = FoundryAgent(
project_endpoint="https://your-project.services.ai.azure.com",
agent_name="my-agent",
agent_version="1.0",
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
agent_name=os.getenv("FOUNDRY_AGENT_NAME"),
agent_version=os.getenv("FOUNDRY_AGENT_VERSION"),
credential=AzureCliCredential(),
client_type=RawFoundryAgentChatClient,
)
@@ -47,9 +49,9 @@ async def main() -> None:
# Option 3: Composition — use Agent(client=...) directly
# this will not run the checks that the `FoundryAgent` does on things like tools.
client = RawFoundryAgentChatClient(
project_endpoint="https://your-project.services.ai.azure.com",
agent_name="my-agent",
agent_version="1.0",
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
agent_name=os.getenv("FOUNDRY_AGENT_NAME"),
agent_version=os.getenv("FOUNDRY_AGENT_VERSION"),
credential=AzureCliCredential(),
)
agent_composed = Agent(client=client)
python/samples/02-agents/providers/foundry/foundry_agent_hosted.py
+6 -2
View File
@@ -1,9 +1,13 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework.foundry import FoundryAgent
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
load_dotenv()
"""
Foundry Agent — Connect to a HostedAgent (no version needed)
@@ -20,8 +24,8 @@ Environment variables:
async def main() -> None:
# HostedAgents don't need agent_version
agent = FoundryAgent(
project_endpoint="https://your-project.services.ai.azure.com",
agent_name="my-hosted-agent",
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
agent_name=os.getenv("FOUNDRY_AGENT_NAME"),
credential=AzureCliCredential(),
)
python/samples/02-agents/providers/foundry/foundry_agent_with_env_vars.py
-40
View File
@@ -1,40 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework.foundry import FoundryAgent
from azure.identity import AzureCliCredential
"""
Foundry Agent with Environment Variables
This sample shows the recommended pattern for advanced samples that use
environment variables for configuration.
Environment variables:
FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint
FOUNDRY_AGENT_NAME — Name of the agent in Foundry
FOUNDRY_AGENT_VERSION — Version of the agent (optional, for PromptAgents)
"""
async def main() -> None:
agent = FoundryAgent(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
agent_name=os.environ["FOUNDRY_AGENT_NAME"],
agent_version=os.environ.get("FOUNDRY_AGENT_VERSION"),
credential=AzureCliCredential(),
)
session = agent.create_session()
result = await agent.run("Hello! My name is Alice.", session=session)
print(f"Agent: {result}\n")
result = await agent.run("What's my name?", session=session)
print(f"Agent: {result}")
if __name__ == "__main__":
asyncio.run(main())
python/samples/02-agents/providers/foundry/foundry_agent_with_function_tools.py
+8 -8
View File
@@ -1,13 +1,14 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from typing import Annotated
from agent_framework import tool
from agent_framework.foundry import FoundryAgent
from azure.identity import AzureCliCredential
from pydantic import Field
from dotenv import load_dotenv
load_dotenv()
"""
Foundry Agent with Local Function Tools
@@ -25,9 +26,8 @@ Environment variables:
"""
@tool(approval_mode="never_require")
def get_weather(
location: Annotated[str, Field(description="The city to get weather for.")],
location: Annotated[str, "The city to get weather for."],
) -> str:
"""Get the current weather for a location."""
return f"The weather in {location} is sunny, 22°C."
@@ -35,11 +35,11 @@ def get_weather(
async def main() -> None:
agent = FoundryAgent(
project_endpoint="https://your-project.services.ai.azure.com",
agent_name="my-weather-agent",
agent_version="1.0",
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
agent_name=os.getenv("FOUNDRY_AGENT_NAME"),
agent_version=os.getenv("FOUNDRY_AGENT_VERSION"),
credential=AzureCliCredential(),
tools=[get_weather],
tools=get_weather,
)
result = await agent.run("What's the weather in Paris?")
python/samples/02-agents/providers/foundry/foundry_chat_client_code_interpreter_files.py
+24 -31
View File
@@ -8,9 +8,8 @@ from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
from openai import AsyncAzureOpenAI
from openai import AsyncOpenAI
# Load environment variables from .env file
load_dotenv()
"""
@@ -18,12 +17,16 @@ Foundry Chat Client with Code Interpreter and Files Example
This sample demonstrates using get_code_interpreter_tool() with Responses on Foundry
for Python code execution and data analysis with uploaded files.
Environment variables:
FOUNDRY_PROJECT_ENDPOINT — Foundry project endpoint
FOUNDRY_MODEL — Foundry model to use (e.g. "gpt-4o-mini")
"""
# Helper functions
async def create_sample_file_and_upload(openai_client: AsyncAzureOpenAI) -> tuple[str, str]:
async def create_sample_file_and_upload(openai_client: AsyncOpenAI) -> tuple[str, str]:
"""Create a sample CSV file and upload it for Foundry code interpreter use."""
csv_data = """name,department,salary,years_experience
Alice Johnson,Engineering,95000,5
@@ -51,7 +54,7 @@ Frank Wilson,Engineering,88000,6
return temp_file_path, uploaded_file.id
async def cleanup_files(openai_client: AsyncAzureOpenAI, temp_file_path: str, file_id: str) -> None:
async def cleanup_files(openai_client: AsyncOpenAI, temp_file_path: str, file_id: str) -> None:
"""Clean up both local temporary file and uploaded file."""
# Clean up: delete the uploaded file
await openai_client.files.delete(file_id)
@@ -65,39 +68,29 @@ async def cleanup_files(openai_client: AsyncAzureOpenAI, temp_file_path: str, fi
async def main() -> None:
print("=== Foundry Chat Client with Code Interpreter and File Upload ===")
# Initialize the underlying OpenAI client for file operations
credential = AzureCliCredential()
async def get_token():
token = credential.get_token("https://cognitiveservices.azure.com/.default")
return token.token
openai_client = AsyncAzureOpenAI(
azure_ad_token_provider=get_token,
api_version="2024-05-01-preview",
# Create the FoundryChatClient
client = FoundryChatClient(
project_endpoint=os.getenv("FOUNDRY_PROJECT_ENDPOINT"),
model=os.getenv("FOUNDRY_MODEL"),
credential=AzureCliCredential(),
)
# use the openai client from the foundry client to upload files for the code interpreter tool
openai_client = client.project_client.get_openai_client()
temp_file_path, file_id = await create_sample_file_and_upload(openai_client)
# Create agent using FoundryChatClient
client = FoundryChatClient(credential=credential)
# Create code interpreter tool with file access
code_interpreter_tool = client.get_code_interpreter_tool(file_ids=[file_id])
# Create agent with code interpreter tool with file access
agent = Agent(
client=client,
instructions="You are a helpful assistant that can analyze data files using Python code.",
tools=[code_interpreter_tool],
tools=FoundryChatClient.get_code_interpreter_tool(file_ids=[file_id]),
)
# Test the code interpreter with the uploaded file
query = "Analyze the employee data in the uploaded CSV file. Calculate average salary by department."
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result.text}")
await cleanup_files(openai_client, temp_file_path, file_id)
try:
# Test the code interpreter with the uploaded file
query = "Analyze the employee data in the uploaded CSV file. Calculate average salary by department."
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result.text}")
finally:
await cleanup_files(openai_client, temp_file_path, file_id)
if __name__ == "__main__":
python/samples/02-agents/providers/ollama/ollama_chat_client.py
+1 -1
View File
@@ -35,7 +35,7 @@ def get_time():
async def main() -> None:
client = OllamaChatClient()
message = "What time is it? Use a tool call"
messages = [Message(role="user", text=message)]
messages = [Message(role="user", contents=[message])]
stream = False
print(f"User: {message}")
if stream:
python/samples/02-agents/response_stream.py
+2 -2
View File
@@ -256,7 +256,7 @@ async def main() -> None:
"""Result hook that wraps the response text in quotes."""
if response.text:
return ChatResponse(
messages=[Message(text=f'"{response.text}"', role="assistant")],
messages=[Message(contents=[f'"{response.text}"'], role="assistant")],
additional_properties=response.additional_properties,
)
return response
@@ -293,7 +293,7 @@ async def main() -> None:
# In real code, this would create an AgentResponse
text = "".join(u.text or "" for u in updates)
return ChatResponse(
messages=[Message(text=f"[AGENT FINAL] {text}", role="assistant")],
messages=[Message(contents=[f"[AGENT FINAL] {text}"], role="assistant")],
additional_properties={"layer": "agent"},
)
python/samples/02-agents/typed_options.py
+2 -2
View File
@@ -42,7 +42,7 @@ async def demo_anthropic_chat_client() -> None:
# Standard options work great:
response = await client.get_response(
[Message("user", text="What is the capital of France?")],
[Message("user", contents=["What is the capital of France?"])],
options={
"temperature": 1, # Must be 1 when thinking is enabled
"max_tokens": 2048,
@@ -116,7 +116,7 @@ async def demo_openai_chat_client_reasoning_models() -> None:
# With specific options, you get full IDE autocomplete!
# Try typing `client.get_response("Hello", options={` and see the suggestions
response = await client.get_response(
[Message("user", text="What is 2 + 2?")],
[Message("user", contents=["What is 2 + 2?"])],
options={
"max_tokens": 100,
"allow_multiple_tool_calls": True,