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] updated structure and samples (#875)

Browse Source 
* updated structure and samples

* updated names and removed cross tests

* updated projects etc

* updated tests

* updated test

* test fixes

* removed devui for now

* updated all-tests task

* removed old style configs

* remove coverage from tests

* updated to unit tests with all-tests

* updated foundry everywhere

* fix azure ai tests

* fix merge tests

* fix mypy
This commit is contained in:
Eduard van Valkenburg
2025-09-25 09:02:53 +02:00
committed by GitHub
Unverified
parent 366a7f7d47
commit 9355329dfd
169 changed files with 1159 additions and 1761 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/getting_started/workflow/orchestration/concurrent_agents.py
+4 -4
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import Any
from agent_framework import ChatMessage, ConcurrentBuilder
from agent_framework.azure import AzureChatClient
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
"""
@@ -21,14 +21,14 @@ Demonstrates:
- Workflow completion when idle with no pending work
Prerequisites:
- Azure OpenAI access configured for AzureChatClient (use az login + env vars)
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
- Familiarity with Workflow events (AgentRunEvent, WorkflowOutputEvent)
"""
async def main() -> None:
# 1) Create three domain agents using AzureChatClient
chat_client = AzureChatClient(credential=AzureCliCredential())
# 1) Create three domain agents using AzureOpenAIChatClient
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
researcher = chat_client.create_agent(
instructions=(
python/samples/getting_started/workflow/orchestration/concurrent_custom_agent_executors.py
+7 -7
View File
@@ -13,7 +13,7 @@ from agent_framework import (
WorkflowContext,
handler,
)
from agent_framework.azure import AzureChatClient
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
"""
@@ -25,21 +25,21 @@ and emit AgentExecutorResponse outputs, which allows reuse of the high-level
ConcurrentBuilder API and the default aggregator.
Demonstrates:
- Executors that create their ChatAgent in __init__ (via AzureChatClient)
- Executors that create their ChatAgent in __init__ (via AzureOpenAIChatClient)
- A @handler that converts AgentExecutorRequest -> AgentExecutorResponse
- ConcurrentBuilder().participants([...]) to build fan-out/fan-in
- Default aggregator returning list[ChatMessage] (one user + one assistant per agent)
- Workflow completion when all participants become idle
Prerequisites:
- Azure OpenAI configured for AzureChatClient (az login + required env vars)
- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
"""
class ResearcherExec(Executor):
agent: ChatAgent
def __init__(self, chat_client: AzureChatClient, id: str = "researcher"):
def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "researcher"):
agent = chat_client.create_agent(
instructions=(
"You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
@@ -59,7 +59,7 @@ class ResearcherExec(Executor):
class MarketerExec(Executor):
agent: ChatAgent
def __init__(self, chat_client: AzureChatClient, id: str = "marketer"):
def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "marketer"):
agent = chat_client.create_agent(
instructions=(
"You're a creative marketing strategist. Craft compelling value propositions and target messaging"
@@ -79,7 +79,7 @@ class MarketerExec(Executor):
class LegalExec(Executor):
agent: ChatAgent
def __init__(self, chat_client: AzureChatClient, id: str = "legal"):
def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "legal"):
agent = chat_client.create_agent(
instructions=(
"You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
@@ -97,7 +97,7 @@ class LegalExec(Executor):
async def main() -> None:
chat_client = AzureChatClient(credential=AzureCliCredential())
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
researcher = ResearcherExec(chat_client)
marketer = MarketerExec(chat_client)
python/samples/getting_started/workflow/orchestration/concurrent_custom_aggregator.py
+4 -4
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import Any
from agent_framework import ChatMessage, ConcurrentBuilder, Role
from agent_framework.azure import AzureChatClient
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
"""
@@ -12,7 +12,7 @@ Sample: Concurrent Orchestration with Custom Aggregator
Build a concurrent workflow with ConcurrentBuilder that fans out one prompt to
multiple domain agents and fans in their responses. Override the default
aggregator with a custom async callback that uses AzureChatClient.get_response()
aggregator with a custom async callback that uses AzureOpenAIChatClient.get_response()
to synthesize a concise, consolidated summary from the experts' outputs.
The workflow completes when all participants become idle.
@@ -23,12 +23,12 @@ Demonstrates:
- Workflow output yielded with the synthesized summary string
Prerequisites:
- Azure OpenAI configured for AzureChatClient (az login + required env vars)
- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
"""
async def main() -> None:
chat_client = AzureChatClient(credential=AzureCliCredential())
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
researcher = chat_client.create_agent(
instructions=(
python/samples/getting_started/workflow/orchestration/sequential_agents.py
+3 -3
View File
@@ -4,7 +4,7 @@ import asyncio
from typing import cast
from agent_framework import ChatMessage, Role, SequentialBuilder, WorkflowOutputEvent
from agent_framework.azure import AzureChatClient
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
"""
@@ -23,13 +23,13 @@ Note on internal adapters:
You can safely ignore them when focusing on agent progress.
Prerequisites:
- Azure OpenAI access configured for AzureChatClient (use az login + env vars)
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
"""
async def main() -> None:
# 1) Create agents
chat_client = AzureChatClient(credential=AzureCliCredential())
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
writer = chat_client.create_agent(
instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
python/samples/getting_started/workflow/orchestration/sequential_custom_executors.py
+3 -3
View File
@@ -13,7 +13,7 @@ from agent_framework import (
WorkflowContext,
handler,
)
from agent_framework.azure import AzureChatClient
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
"""
@@ -35,7 +35,7 @@ Note on internal adapters:
for completion—similar to concurrent's dispatcher/aggregator.
Prerequisites:
- Azure OpenAI access configured for AzureChatClient (use az login + env vars)
- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
"""
@@ -53,7 +53,7 @@ class Summarizer(Executor):
async def main() -> None:
# 1) Create a content agent
chat_client = AzureChatClient(credential=AzureCliCredential())
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
content = chat_client.create_agent(
instructions="Produce a concise paragraph answering the user's request.",
name="content",