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* restructure: Python samples into progressive 01-05 layout - 01-get-started/: 6 numbered steps (hello agent → hosting) - 02-agents/: all agent concept samples (tools, middleware, providers, etc.) - 03-workflows/: ALL existing workflow samples preserved as-is - 04-hosting/: azure-functions, durabletask, a2a - 05-end-to-end/: demos, evaluation, hosted agents - Old files moved to _to_delete/ for review - Added AGENTS.md with structure documentation - autogen-migration/ and semantic-kernel-migration/ preserved at root * fix: switch to AzureOpenAI Foundry, fix CI failures - Switch all 01-get-started samples to AzureOpenAIResponsesClient with Azure AI Foundry project endpoint (AZURE_AI_PROJECT_ENDPOINT + AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME + AzureCliCredential) - Add _to_delete/ and 05-end-to-end/ to pyrightconfig.samples.json excludes - Fix test paths in packages/ that referenced old getting_started/ dirs: durabletask conftest + streaming test, azurefunctions conftest, devui conftest + capture_messages + openai_sdk_integration - Fix workflow_as_agent_human_in_the_loop.py import (sibling import) - Update hosting READMEs and tool comment paths - Replace root README.md with new structure overview - Update AGENTS.md to document Azure OpenAI Foundry as default provider * cleanup: remove _to_delete folder, copy resource files to active dirs All files in _to_delete/ were either: - Exact duplicates of files in the new structure (240 files) - Same file with only comment path updates (100 files) - One import-fix diff (workflow_as_agent_human_in_the_loop.py) - One superseded minimal_sample.py Resource files (sample.pdf, countries.json, employees.pdf, weather.json) copied to 02-agents/sample_assets/ and 02-agents/resources/ since active samples reference them. * fix: address PR review comments, centralize resources, remove root duplicates - Fix type annotation in 04_memory.py (string union -> proper types) - Fix old sample paths in observability files - Fix grammar/spelling in observability samples - Move sample_assets/ and resources/ to shared/ folder - Remove 8 duplicate observability files from 02-agents root - Update resource path references in multimodal_input and provider samples * fix: update broken links from old getting_started paths to new structure - Update relative paths in READMEs: getting_started/ → 01-get-started/, 02-agents/, 03-workflows/, 04-hosting/, 05-end-to-end/ - Fix absolute GitHub URLs in package READMEs - Fix broken link in ollama package README * fix: convert absolute GitHub URLs to relative paths for link checker Absolute URLs to python/samples/ on main branch 404 until PR merges. Converted to relative paths that linkspector can verify locally. * fix: update link for handoff sample moved to orchestrations/ * fix: update chatkit-integration README path from demos/ to 05-end-to-end/ * fix: update broken links in orchestrations README to match flat directory structure
162 lines
5.1 KiB
Python
162 lines
5.1 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""
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MCP Tool via YAML Declaration
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This sample demonstrates how to create agents with MCP (Model Context Protocol)
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tools using YAML declarations and the declarative AgentFactory.
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Key Features Demonstrated:
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1. Loading agent definitions from YAML using AgentFactory
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2. Configuring MCP tools with different authentication methods:
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- API key authentication (OpenAI.Responses provider)
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- Azure AI Foundry connection references (AzureAI.ProjectProvider)
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Authentication Options:
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- OpenAI.Responses: Supports inline API key auth via headers
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- AzureAI.ProjectProvider: Uses Foundry connections for secure credential storage
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(no secrets passed in API calls - connection name references pre-configured auth)
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Prerequisites:
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- `pip install agent-framework-openai agent-framework-declarative --pre`
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- For OpenAI example: Set OPENAI_API_KEY and GITHUB_PAT environment variables
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- For Azure AI example: Set up a Foundry connection in your Azure AI project
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"""
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import asyncio
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from agent_framework.declarative import AgentFactory
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Example 1: OpenAI.Responses with API key authentication
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# Uses inline API key - suitable for OpenAI provider which supports headers
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YAML_OPENAI_WITH_API_KEY = """
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kind: Prompt
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name: GitHubAgent
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displayName: GitHub Assistant
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description: An agent that can interact with GitHub using the MCP protocol
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instructions: |
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You are a helpful assistant that can interact with GitHub.
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You can search for repositories, read file contents, and check issues.
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Always be clear about what operations you're performing.
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model:
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id: gpt-4o
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provider: OpenAI.Responses # Uses OpenAI's Responses API (requires OPENAI_API_KEY env var)
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tools:
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- kind: mcp
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name: github-mcp
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description: GitHub MCP tool for repository operations
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url: https://api.githubcopilot.com/mcp/
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connection:
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kind: key
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apiKey: =Env.GITHUB_PAT # PowerFx syntax to read from environment variable
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approvalMode: never
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allowedTools:
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- get_file_contents
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- get_me
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- search_repositories
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- search_code
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- list_issues
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"""
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# Example 2: Azure AI with Foundry connection reference
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# No secrets in YAML - references a pre-configured Foundry connection by name
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# The connection stores credentials securely in Azure AI Foundry
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YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION = """
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kind: Prompt
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name: GitHubAgent
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displayName: GitHub Assistant
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description: An agent that can interact with GitHub using the MCP protocol
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instructions: |
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You are a helpful assistant that can interact with GitHub.
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You can search for repositories, read file contents, and check issues.
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Always be clear about what operations you're performing.
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model:
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id: gpt-4o
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provider: AzureAI.ProjectProvider
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tools:
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- kind: mcp
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name: github-mcp
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description: GitHub MCP tool for repository operations
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url: https://api.githubcopilot.com/mcp/
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connection:
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kind: remote
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authenticationMode: oauth
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name: github-mcp-oauth-connection # References a Foundry connection
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approvalMode: never
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allowedTools:
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- get_file_contents
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- get_me
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- search_repositories
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- search_code
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- list_issues
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"""
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async def run_openai_example():
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"""Run the OpenAI.Responses example with API key auth."""
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print("=" * 60)
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print("Example 1: OpenAI.Responses with API Key Authentication")
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print("=" * 60)
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factory = AgentFactory(
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safe_mode=False, # Allow PowerFx env var resolution (=Env.VAR_NAME)
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)
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print("\nCreating agent from YAML definition...")
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agent = factory.create_agent_from_yaml(YAML_OPENAI_WITH_API_KEY)
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async with agent:
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query = "What is my GitHub username?"
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print(f"\nUser: {query}")
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response = await agent.run(query)
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print(f"\nAgent: {response.text}")
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async def run_azure_ai_example():
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"""Run the Azure AI example with Foundry connection.
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Prerequisites:
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1. Create a Foundry connection named 'github-mcp-oauth-connection' in your
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Azure AI project with OAuth credentials for GitHub
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2. Set PROJECT_ENDPOINT environment variable to your Azure AI project endpoint
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"""
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print("=" * 60)
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print("Example 2: Azure AI with Foundry Connection Reference")
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print("=" * 60)
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from azure.identity import DefaultAzureCredential
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factory = AgentFactory(client_kwargs={"credential": DefaultAzureCredential()})
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print("\nCreating agent from YAML definition...")
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# Use async method for provider-based agent creation
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agent = await factory.create_agent_from_yaml_async(YAML_AZURE_AI_WITH_FOUNDRY_CONNECTION)
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async with agent:
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query = "What is my GitHub username?"
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print(f"\nUser: {query}")
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response = await agent.run(query)
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print(f"\nAgent: {response.text}")
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async def main():
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"""Run the MCP tool examples."""
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# Run the OpenAI example
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await run_openai_example()
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# Run the Azure AI example (uncomment to run)
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# Requires: Foundry connection set up and PROJECT_ENDPOINT env var
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# await run_azure_ai_example()
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if __name__ == "__main__":
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asyncio.run(main())
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