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Python: Azure AI client based on new azure-ai-projects package (#1910)

Browse Source 
* Added changes (#1909)

* Python: [Feature Branch] Renamed Azure AI agent and small fixes (#1919)

* Renaming

* Small fixes

* Update python/packages/core/agent_framework/openai/_shared.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

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* Small fix

* Python: [Feature Branch] Added use_latest_version parameter to AzureAIClient (#1959)

* Added use_latest_version parameter to AzureAIClient

* Added unit tests

* Update python/samples/getting_started/agents/azure_ai/azure_ai_use_latest_version.py

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* Update python/packages/azure-ai/agent_framework_azure_ai/_client.py

Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>

---------

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* Python: [Feature Branch] Structured Outputs and more examples for AzureAIClient (#1987)

* Small updates

* Added support for structured outputs

* Added code interpreter example

* More examples and fixes

* Added more examples and README

* Small fix

* Addressed PR feedback

* Removed optional ID from FunctionResultContent (#2011)

* Added hosted MCP support (#2018)

* Python: [Feature Branch] Fixed "store" parameter handling (#2069)

* Fixed store parameter handling

* Small fix

* Python: [Feature Branch] Added more examples and fixes for Azure AI agent (#2077)

* Updated azure-ai-projects package version

* Added an example of hosted MCP with approval required

* Updated code interpreter example

* Added file search example

* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py

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* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py

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* Small fix

---------

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* Added handling for conversation_id (#2098)

* Merge from main

* Revert "Merge from main"

This reverts commit b8206a85d7.

* Python: [Feature Branch] Merge from main to Azure AI branch (#2111)

* Do not build DevUI assets during .NET project build (#2010)

* .NET: Add unit tests for declarative executor SetMultipleVariables (#2016)

* Add unit tests for create conversation executor

* Update indentation and comment typo.

* Added unit tests for declarative executor SetMultipleVariablesExecutor

* Updated comments and syntactic sugar

* Python: DevUI: Use metadata.entity_id instead of model field (#1984)

* DevUI: Use metadata.entity_id for agent/workflow name instead of model field

* OpenAI Responses: add explicit request validation

* Review feedback

* .NET: DevUI - Do not automatically add/map OpenAI services/endpoints (#2014)

* Don't add OpenAIResponses as part of Dev UI

You should be able to add and remove Dev UI without impacting your other production endpoints.

* Remove `AddDevUI()` and do not map OpenAI endpoints from `MapDevUI()`

* Fix comment wording

* Revise documentation

---------

Co-authored-by: Daniel Roth <daroth@microsoft.com>

* Python: DevUI: Add OpenAI Responses API proxy support  + HIL for Workflows (#1737)

* DevUI: Add OpenAI Responses API proxy support with enhanced UI features

This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.

Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text

Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration

* update ui, settings modal and workflow input form, add register cleanup hooks.

* add workflow HIL support, user mode, other fixes

* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas

Implement  HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.

Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response

Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints

Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form

Testing:
- Add  tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample

This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.

* improve HIL support, improve workflow execution view

* ui updates

* ui updates

* improve HIL for workflows, add auth and view modes

* update workflow

* security improvements , ui fixes

* fix mypy error

* update loading spinner in ui

---------

Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>

* .NET: Remove launchSettings.json from .gitignore in dotnet/samples (#2006)

* Remove launchSettings.json from .gitignore in dotnet/samples

* Update dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Properties/launchSettings.json

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* Update dotnet/samples/AGUIClientServer/AGUIServer/Properties/launchSettings.json

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---------

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* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021)

Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>

* Add Microsoft Agent Framework logo to assets (#2007)

* Updated package versions (#2027)

* DevUI: Prevent line breaks within words in the agent view (#2024)

Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>

* .NET [AG-UI]: Adds support for shared state. (#1996)

* Product changes

* Tests

* Dojo project

* Cleanups

* Python: Fix underlying tool choice bug and all for return to previous Handoff subagent (#2037)

* Fix tool_choice override bug and add enable_return_to_previous support

* Add unit test for handoff checkpointing

* Handle tools when we have them

* added missing chatAgent params (#2044)

* .NET: fix ChatCompletions Tools serialization (#2043)

* fix serialization in chat completions on tools

* nit

* .NET: assign AgentCard's URL to mapped-endpoint if not defined explicitly (#2047)

* fix serialization in chat completions on tools

* nit

* write e2e test for agent card resolve + adjust behavior

* nit

* Version 1.0.0-preview.251110.1 (#2048)

* .NET: Remove moved OpenAPI sample and point to SK one. (#1997)

* Remove moved OpenAPI sample and point to SK one.

* Update dotnet/samples/GettingStarted/Agents/README.md

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---------

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* Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.2 to 4.0.4.6 (#2031)

---
updated-dependencies:
- dependency-name: AWSSDK.Extensions.Bedrock.MEAI
  dependency-version: 4.0.4.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* .NET: Separate all memory and rag samples into their own folders (#2000)

* Separate all memory and rag samples into their own folders

* Fix broken link.

* Python: .Net: Dotnet devui compatibility fixes (#2026)

* DevUI: Add OpenAI Responses API proxy support with enhanced UI features

This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.

Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text

Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration

* update ui, settings modal and workflow input form, add register cleanup hooks.

* add workflow HIL support, user mode, other fixes

* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas

Implement  HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.

Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response

Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints

Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form

Testing:
- Add  tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample

This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.

* improve HIL support, improve workflow execution view

* ui updates

* ui updates

* improve HIL for workflows, add auth and view modes

* update workflow

* security improvements , ui fixes

* fix mypy error

* update loading spinner in ui

* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format

* Phase 1: Add /meta endpoint and fix workflow event naming for .NET DevUI compatibility

* additional fixes for .NET DevUI workflow visualization item ID tracking

**Problem:**
.NET DevUI was generating different item IDs for ExecutorInvokedEvent and
ExecutorCompletedEvent, causing only the first executor to highlight in the
workflow graph. Long executor names and error messages also broke UI layout.

**Changes:**
- Add ExecutorActionItemResource to match Python DevUI implementation
- Track item IDs per executor using dictionary in AgentRunResponseUpdateExtensions
- Reuse same item ID across invoked/completed/failed events for proper pairing
- Add truncateText() utility to workflow-utils.ts
- Truncate executor names to 35 chars in execution timeline
- Truncate error messages to 150 chars in workflow graph nodes

** Details:**
- ExecutorActionItemResource registered with JSON source generation context
- Dictionary cleaned up after executor completion/failure to prevent memory leaks
- Frontend item tracking by unique item.id supports multiple executor runs
- All changes follow existing codebase patterns and conventions

Tested with review-workflow showing correct executor highlighting and state
transitions for sequential and concurrent executors.

* format fixes, remove cors tests

* remove unecessary attributes

---------

Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Reuben Bond <reuben.bond@gmail.com>

* DevUI: support having both an agent and a workflow with the same id in discovery (#2023)

* Python: Fix Model ID attribute not showing up in `invoke_agent` span (#2061)

* Best effort to surface the model id to invoke agent span

* Fix tests

* Fix tests

* Version 1.0.0-preview.251107.2 (#2065)

* Version 1.0.0-preview.251110.2 (#2067)

* Update README.md to change Grafana links to Azure portal links for dashboard access (#1983)

* .NET - Enable build & test on branch `feature-foundry-agents` (#2068)

* Tests good, mkay

* Update .github/workflows/dotnet-build-and-test.yml

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* Enable feature build pipelines

---------

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Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com>

* Python: Add concrete AGUIChatClient (#2072)

* Add concrete AGUIChatClient

* Update logging docstrings and conventions

* PR feedback

* Updates to support client-side tool calls

* .NET: Move catalog samples to the HostedAgents folder (#2090)

* move catalog samples to the HostedAgents folder

* move the catalog samples' projects to the HostedAgents folder

* Bump OpenTelemetry.Instrumentation.Runtime from 1.12.0 to 1.13.0 (#1856)

---
updated-dependencies:
- dependency-name: OpenTelemetry.Instrumentation.Runtime
  dependency-version: 1.13.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* .NET: Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 (#1962)

* Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0

---
updated-dependencies:
- dependency-name: Microsoft.SemanticKernel.Agents.Abstractions
  dependency-version: 1.67.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* .NET: Bump all Microsoft.SemanticKernel packages from 1.66.* to 1.67.* (#1969)

* Initial plan

* Update all Microsoft.SemanticKernel packages to 1.67.*

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* Remove unrelated changes to package-lock.json and yarn.lock

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* .NET: fix: WorkflowAsAgent Sample (#1787)

* fix: WorkflowAsAgent Sample

* Also makes ChatForwardingExecutor public

* feat: Expand ChatForwardingExecutor handled types

Make ChatForwardingExecutor match the input types of ChatProtocolExecutor.

* fix: Update for the new AgentRunResponseUpdate merge logic

AIAgent always sends out List<ChatMessage> now.

* Updated (#2076)

* Bump vite in /python/samples/demos/chatkit-integration/frontend (#1918)

Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 7.1.9 to 7.1.12.
- [Release notes](https://github.com/vitejs/vite/releases)
- [Changelog](https://github.com/vitejs/vite/blob/v7.1.12/packages/vite/CHANGELOG.md)
- [Commits](https://github.com/vitejs/vite/commits/v7.1.12/packages/vite)

---
updated-dependencies:
- dependency-name: vite
  dependency-version: 7.1.12
  dependency-type: direct:development
...

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* Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857)

---
updated-dependencies:
- dependency-name: Roslynator.Analyzers
  dependency-version: 4.14.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* Bump MishaKav/pytest-coverage-comment from 1.1.57 to 1.1.59 (#2034)

Bumps [MishaKav/pytest-coverage-comment](https://github.com/mishakav/pytest-coverage-comment) from 1.1.57 to 1.1.59.
- [Release notes](https://github.com/mishakav/pytest-coverage-comment/releases)
- [Changelog](https://github.com/MishaKav/pytest-coverage-comment/blob/main/CHANGELOG.md)
- [Commits](https://github.com/mishakav/pytest-coverage-comment/compare/v1.1.57...v1.1.59)

---
updated-dependencies:
- dependency-name: MishaKav/pytest-coverage-comment
  dependency-version: 1.1.59
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* Python: Handle agent user input request in AgentExecutor (#2022)

* Handle agent user input request in AgentExecutor

* fix test

* Address comments

* Fix tests

* Fix tests

* Address comments

* Address comments

* Python: OpenAI Responses Image Generation Stream Support, Sample and Unit Tests (#1853)

* support for image gen streaming

* small fixes

* fixes

* added comment

* Python: Fix MCP Tool Parameter Descriptions Not Propagated to LLMs (#1978)

* mcp tool description fix

* small fix

* .NET: Allow extending agent run options via additional properties (#1872)

* Allow extending agent run options via additional properties

This mirrors the M.E.AI model in ChatOptions.AdditionalProperties which is very useful when building functionality pipelines.

Fixes https://github.com/microsoft/agent-framework/issues/1815

* Expand XML documentation

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* Add AdditionalProperties tests to AgentRunOptions

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* Python: Use the last entry in the task history to avoid empty responses (#2101)

* Use the last entry in the task history to avoid empty responses

* History only contains Messages

* Updated package versions (#2104)

---------

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* Updated azure-ai-projects package version and small fixes (#2139)

* Python: [Feature Branch] Resolve CI issues (#2143)

* Small documentation and code fixes

* Small fix in documentation

* Addressed PR feedback

* Added AI Search example

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This commit is contained in:
Dmytro Struk
2025-11-12 15:20:53 -08:00
committed by GitHub
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parent 4bffe1ebc8
commit edb367a2b9
42 changed files with 2538 additions and 488 deletions
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python/samples/README.md
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@@ -21,20 +21,20 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
| File | Description |
|------|-------------|
| [`getting_started/agents/azure_ai/azure_ai_basic.py`](./getting_started/agents/azure_ai/azure_ai_basic.py) | Azure AI Agent Basic Example |
| [`getting_started/agents/azure_ai/azure_ai_with_azure_ai_search.py`](./getting_started/agents/azure_ai/azure_ai_with_azure_ai_search.py) | Azure AI Agent with Azure AI Search Example |
| [`getting_started/agents/azure_ai/azure_ai_with_bing_grounding.py`](./getting_started/agents/azure_ai/azure_ai_with_bing_grounding.py) | Azure AI agent with Bing Grounding search for real-time web information |
| [`getting_started/agents/azure_ai/azure_ai_with_code_interpreter.py`](./getting_started/agents/azure_ai/azure_ai_with_code_interpreter.py) | Azure AI Agent with Code Interpreter Example |
| [`getting_started/agents/azure_ai/azure_ai_with_existing_agent.py`](./getting_started/agents/azure_ai/azure_ai_with_existing_agent.py) | Azure AI Agent with Existing Agent Example |
| [`getting_started/agents/azure_ai/azure_ai_with_existing_thread.py`](./getting_started/agents/azure_ai/azure_ai_with_existing_thread.py) | Azure AI Agent with Existing Thread Example |
| [`getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py`](./getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py) | Azure AI Agent with Explicit Settings Example |
| [`getting_started/agents/azure_ai/azure_ai_with_file_search.py`](./getting_started/agents/azure_ai/azure_ai_with_file_search.py) | Azure AI agent with File Search capabilities |
| [`getting_started/agents/azure_ai/azure_ai_with_function_tools.py`](./getting_started/agents/azure_ai/azure_ai_with_function_tools.py) | Azure AI Agent with Function Tools Example |
| [`getting_started/agents/azure_ai/azure_ai_with_hosted_mcp.py`](./getting_started/agents/azure_ai/azure_ai_with_hosted_mcp.py) | Azure AI Agent with Hosted MCP Example |
| [`getting_started/agents/azure_ai/azure_ai_with_local_mcp.py`](./getting_started/agents/azure_ai/azure_ai_with_local_mcp.py) | Azure AI Agent with Local MCP Example |
| [`getting_started/agents/azure_ai/azure_ai_with_multiple_tools.py`](./getting_started/agents/azure_ai/azure_ai_with_multiple_tools.py) | Azure AI Agent with Multiple Tools Example |
| [`getting_started/agents/azure_ai/azure_ai_with_openapi_tools.py`](./getting_started/agents/azure_ai/azure_ai_with_openapi_tools.py) | Azure AI agent with OpenAPI tools |
| [`getting_started/agents/azure_ai/azure_ai_with_thread.py`](./getting_started/agents/azure_ai/azure_ai_with_thread.py) | Azure AI Agent with Thread Management Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_basic.py`](./getting_started/agents/azure_ai/azure_ai_basic.py) | Azure AI Agent Basic Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py) | Azure AI Agent with Azure AI Search Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding.py) | Azure AI agent with Bing Grounding search for real-time web information |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter.py) | Azure AI Agent with Code Interpreter Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_existing_agent.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_existing_agent.py) | Azure AI Agent with Existing Agent Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py) | Azure AI Agent with Existing Thread Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py) | Azure AI Agent with Explicit Settings Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_file_search.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_file_search.py) | Azure AI agent with File Search capabilities |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_function_tools.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_function_tools.py) | Azure AI Agent with Function Tools Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_hosted_mcp.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_hosted_mcp.py) | Azure AI Agent with Hosted MCP Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_local_mcp.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_local_mcp.py) | Azure AI Agent with Local MCP Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_multiple_tools.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_multiple_tools.py) | Azure AI Agent with Multiple Tools Example |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_openapi_tools.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_openapi_tools.py) | Azure AI agent with OpenAPI tools |
| [`getting_started/agents/azure_ai_agent/azure_ai_with_thread.py`](./getting_started/agents/azure_ai_agent/azure_ai_with_thread.py) | Azure AI Agent with Thread Management Example |
### Azure OpenAI
python/samples/getting_started/agents/azure_ai/README.md
+35 -30
View File
@@ -1,24 +1,21 @@
# Azure AI Agent Examples
This folder contains examples demonstrating different ways to create and use agents with the Azure AI chat client from the `agent_framework.azure` package.
This folder contains examples demonstrating different ways to create and use agents with the Azure AI client from the `agent_framework.azure` package.
## Examples
| File | Description |
|------|-------------|
| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAIAgentClient`. It automatically handles all configuration using environment variables. |
| [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to find real-time information from the web. Demonstrates web search capabilities with proper source citations and comprehensive error handling. |
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents. |
| [`azure_ai_with_existing_thread.py`](azure_ai_with_existing_thread.py) | Shows how to work with a pre-existing thread by providing the thread ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created threads. |
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIAgentClient` settings, including project endpoint, model deployment, credentials, and agent name. |
| [`azure_ai_with_azure_ai_search.py`](azure_ai_with_azure_ai_search.py) | Demonstrates how to use Azure AI Search with Azure AI agents to search through indexed data. Shows how to configure search parameters, query types, and integrate with existing search indexes. |
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Demonstrates how to use the HostedFileSearchTool with Azure AI agents to search through uploaded documents. Shows file upload, vector store creation, and querying document content. Includes both streaming and non-streaming examples. |
| [`azure_ai_with_function_tools.py`](azure_ai_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate Azure AI agents with hosted Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates remote MCP server connections and tool discovery. |
| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
| [`azure_ai_with_multiple_tools.py`](azure_ai_with_multiple_tools.py) | Demonstrates how to use multiple tools together with Azure AI agents, including web search, MCP servers, and function tools. Shows coordinated multi-tool interactions and approval workflows. |
| [`azure_ai_with_openapi_tools.py`](azure_ai_with_openapi_tools.py) | Demonstrates how to use OpenAPI tools with Azure AI agents to integrate external REST APIs. Shows OpenAPI specification loading, anonymous authentication, thread context management, and coordinated multi-API conversations using weather and countries APIs. |
| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `AzureAIClient`. Demonstrates both streaming and non-streaming responses with function tools. Shows automatic agent creation and basic weather functionality. |
| [`azure_ai_use_latest_version.py`](azure_ai_use_latest_version.py) | Demonstrates how to reuse the latest version of an existing agent instead of creating a new agent version on each instantiation using the `use_latest_version=True` parameter. |
| [`azure_ai_with_azure_ai_search.py`](azure_ai_with_azure_ai_search.py) | Shows how to use Azure AI Search with Azure AI agents to search through indexed data and answer user questions with proper citations. Requires an Azure AI Search connection and index configured in your Azure AI project. |
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
| [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIClient` settings, including project endpoint, model deployment, and credentials rather than relying on environment variable defaults. |
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
| [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
## Environment Variables
@@ -28,27 +25,18 @@ Before running the examples, you need to set up your environment variables. You
### Option 1: Using a .env file (Recommended)
1. Copy the `.env.example` file from the `python` directory to create a `.env` file:
```bash
cp ../../.env.example ../../.env
cp ../../../../.env.example ../../../../.env
```
2. Edit the `.env` file and add your values:
```
```env
AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
```
3. For samples using Bing Grounding search (like `azure_ai_with_bing_grounding.py` and `azure_ai_with_multiple_tools.py`), you'll also need:
```
BING_CONNECTION_ID="your-bing-connection-id"
```
To get your Bing connection details:
- Go to [Azure AI Foundry portal](https://ai.azure.com)
- Navigate to your project's "Connected resources" section
- Add a new connection for "Grounding with Bing Search"
- Copy the ID
### Option 2: Using environment variables directly
Set the environment variables in your shell:
@@ -56,7 +44,6 @@ Set the environment variables in your shell:
```bash
export AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
export BING_CONNECTION_ID="your-bing-connection-id"
```
### Required Variables
@@ -64,6 +51,24 @@ export BING_CONNECTION_ID="your-bing-connection-id"
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint (required for all examples)
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment (required for all examples)
### Optional Variables
## Authentication
- `BING_CONNECTION_ID`: Your Bing connection ID (required for `azure_ai_with_bing_grounding.py` and `azure_ai_with_multiple_tools.py`)
All examples use `AzureCliCredential` for authentication by default. Before running the examples:
1. Install the Azure CLI
2. Run `az login` to authenticate with your Azure account
3. Ensure you have appropriate permissions to the Azure AI project
Alternatively, you can replace `AzureCliCredential` with other authentication options like `DefaultAzureCredential` or environment-based credentials.
## Running the Examples
Each example can be run independently. Navigate to this directory and run any example:
```bash
python azure_ai_basic.py
python azure_ai_with_code_interpreter.py
# ... etc
```
The examples demonstrate various patterns for working with Azure AI agents, from basic usage to advanced scenarios like thread management and structured outputs.
python/samples/getting_started/agents/azure_ai/azure_ai_basic.py
+10 -12
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@@ -4,15 +4,15 @@ import asyncio
from random import randint
from typing import Annotated
from agent_framework.azure import AzureAIAgentClient
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent Basic Example
This sample demonstrates basic usage of AzureAIAgentClient to create agents with automatic
lifecycle management. Shows both streaming and non-streaming responses with function tools.
This sample demonstrates basic usage of AzureAIClient.
Shows both streaming and non-streaming responses with function tools.
"""
@@ -28,14 +28,13 @@ async def non_streaming_example() -> None:
"""Example of non-streaming response (get the complete result at once)."""
print("=== Non-streaming Response Example ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential).create_agent(
name="WeatherAgent",
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
@@ -50,19 +49,18 @@ async def streaming_example() -> None:
"""Example of streaming response (get results as they are generated)."""
print("=== Streaming Response Example ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential).create_agent(
name="WeatherAgent",
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
query = "What's the weather like in Portland?"
query = "What's the weather like in Tokyo?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
python/samples/getting_started/agents/azure_ai/azure_ai_use_latest_version.py
+67
View File
@@ -0,0 +1,67 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randint
from typing import Annotated
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent Latest Version Example
This sample demonstrates how to reuse the latest version of an existing agent
instead of creating a new agent version on each instantiation. The first call creates a new agent,
while subsequent calls with `use_latest_version=True` reuse the latest agent version.
"""
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def main() -> None:
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with AzureCliCredential() as credential:
async with (
AzureAIClient(
async_credential=credential,
).create_agent(
name="MyWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# First query will create a new agent
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
# Create a new agent instance
async with (
AzureAIClient(
async_credential=credential,
# This parameter will allow to re-use latest agent version
# instead of creating a new one
use_latest_version=True,
).create_agent(
name="MyWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
query = "What's the weather like in Tokyo?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_azure_ai_search.py
+22 -93
View File
@@ -1,120 +1,49 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import ChatAgent, CitationAnnotation
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.aio import AgentsClient
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import ConnectionType
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Azure AI Search Example
This sample demonstrates how to create an Azure AI agent that uses Azure AI Search
to search through indexed hotel data and answer user questions about hotels.
This sample demonstrates usage of AzureAIClient with Azure AI Search
to search through indexed data and answer user questions about it.
Prerequisites:
1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables
1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables.
2. Ensure you have an Azure AI Search connection configured in your Azure AI project
3. The search index "hotels-sample-index" should exist in your Azure AI Search service
(you can create this using the Azure portal with sample hotel data)
NOTE: To ensure consistent search tool usage:
- Include explicit instructions for the agent to use the search tool
- Mention the search requirement in your queries
- Use `tool_choice="required"` to force tool usage
More info on `query type` can be found here:
https://learn.microsoft.com/en-us/python/api/azure-ai-agents/azure.ai.agents.models.aisearchindexresource?view=azure-python-preview
and set AI_SEARCH_PROJECT_CONNECTION_ID and AI_SEARCH_INDEX_NAME environment variable.
"""
async def main() -> None:
"""Main function demonstrating Azure AI agent with raw Azure AI Search tool."""
print("=== Azure AI Agent with Raw Azure AI Search Tool ===")
# Create the client and manually create an agent with Azure AI Search tool
async with (
AzureCliCredential() as credential,
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
):
ai_search_conn_id = ""
async for connection in project_client.connections.list():
if connection.type == ConnectionType.AZURE_AI_SEARCH:
ai_search_conn_id = connection.id
break
# 1. Create Azure AI agent with the search tool
azure_ai_agent = await project_client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="HotelSearchAgent",
instructions=(
"You are a helpful agent that searches hotel information using Azure AI Search. "
"Always use the search tool and index to find hotel data and provide accurate information."
),
tools=[{"type": "azure_ai_search"}],
tool_resources={
AzureAIClient(async_credential=credential).create_agent(
name="MySearchAgent",
instructions="""You are a helpful assistant. You must always provide citations for
answers using the tool and render them as: `[message_idx:search_idx†source]`.""",
tools={
"type": "azure_ai_search",
"azure_ai_search": {
"indexes": [
{
"index_connection_id": ai_search_conn_id,
"index_name": "hotels-sample-index",
"query_type": "vector",
"project_connection_id": os.environ["AI_SEARCH_PROJECT_CONNECTION_ID"],
"index_name": os.environ["AI_SEARCH_INDEX_NAME"],
# For query_type=vector, ensure your index has a field with vectorized data.
"query_type": "simple",
}
]
}
},
},
)
# 2. Create chat client with the existing agent
chat_client = AzureAIAgentClient(agents_client=agents_client, agent_id=azure_ai_agent.id)
try:
async with ChatAgent(
chat_client=chat_client,
# Additional instructions for this specific conversation
instructions=("You are a helpful agent that uses the search tool and index to find hotel information."),
) as agent:
print("This agent uses raw Azure AI Search tool to search hotel data.\n")
# 3. Simulate conversation with the agent
user_input = (
"Use Azure AI search knowledge tool to find detailed information about a winter hotel."
" Use the search tool and index." # You can modify prompt to force tool usage
)
print(f"User: {user_input}")
print("Agent: ", end="", flush=True)
# Stream the response and collect citations
citations: list[CitationAnnotation] = []
async for chunk in agent.run_stream(user_input):
if chunk.text:
print(chunk.text, end="", flush=True)
# Collect citations from Azure AI Search responses
for content in getattr(chunk, "contents", []):
annotations = getattr(content, "annotations", [])
if annotations:
citations.extend(annotations)
print()
# Display collected citations
if citations:
print("\n\nCitations:")
for i, citation in enumerate(citations, 1):
print(f"[{i}] Reference: {citation.url}")
print("\n" + "=" * 50 + "\n")
print("Hotel search conversation completed!")
finally:
# Clean up the agent manually
await project_client.agents.delete_agent(azure_ai_agent.id)
) as agent,
):
query = "Tell me about insurance options"
print(f"User: {query}")
result = await agent.run(query)
print(f"Result: {result}\n")
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_code_interpreter.py
+33 -37
View File
@@ -2,57 +2,53 @@
import asyncio
from agent_framework import AgentRunResponse, ChatResponseUpdate, HostedCodeInterpreterTool
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.models import (
RunStepDeltaCodeInterpreterDetailItemObject,
)
from agent_framework import ChatResponse, HostedCodeInterpreterTool
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from openai.types.responses.response import Response as OpenAIResponse
from openai.types.responses.response_code_interpreter_tool_call import ResponseCodeInterpreterToolCall
"""
Azure AI Agent with Code Interpreter Example
Azure AI Agent Code Interpreter Example
This sample demonstrates using HostedCodeInterpreterTool with Azure AI Agents
This sample demonstrates using HostedCodeInterpreterTool with AzureAIClient
for Python code execution and mathematical problem solving.
"""
def print_code_interpreter_inputs(response: AgentRunResponse) -> None:
"""Helper method to access code interpreter data."""
print("\nCode Interpreter Inputs during the run:")
if response.raw_representation is None:
return
for chunk in response.raw_representation:
if isinstance(chunk, ChatResponseUpdate) and isinstance(
chunk.raw_representation, RunStepDeltaCodeInterpreterDetailItemObject
):
print(chunk.raw_representation.input, end="")
print("\n")
async def main() -> None:
"""Example showing how to use the HostedCodeInterpreterTool with Azure AI."""
print("=== Azure AI Agent with Code Interpreter Example ===")
"""Example showing how to use the HostedCodeInterpreterTool with AzureAIClient."""
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential) as chat_client,
):
agent = chat_client.create_agent(
name="CodingAgent",
instructions=("You are a helpful assistant that can write and execute Python code to solve problems."),
AzureAIClient(async_credential=credential).create_agent(
instructions="You are a helpful assistant that can write and execute Python code to solve problems.",
tools=HostedCodeInterpreterTool(),
)
query = "Generate the factorial of 100 using python code, show the code and execute it."
) as agent,
):
query = "Use code to get the factorial of 100?"
print(f"User: {query}")
response = await AgentRunResponse.from_agent_response_generator(agent.run_stream(query))
print(f"Agent: {response}")
# To review the code interpreter outputs, you can access
# them from the response raw_representations, just uncomment the next line:
# print_code_interpreter_inputs(response)
result = await agent.run(query)
print(f"Result: {result}\n")
if (
isinstance(result.raw_representation, ChatResponse)
and isinstance(result.raw_representation.raw_representation, OpenAIResponse)
and len(result.raw_representation.raw_representation.output) > 0
):
# Find the first ResponseCodeInterpreterToolCall item
code_interpreter_item = next(
(
item
for item in result.raw_representation.raw_representation.output
if isinstance(item, ResponseCodeInterpreterToolCall)
),
None,
)
if code_interpreter_item is not None:
generated_code = code_interpreter_item.code
print(f"Generated code:\n{generated_code}")
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_agent.py
+25 -20
View File
@@ -4,55 +4,60 @@ import asyncio
import os
from agent_framework import ChatAgent
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.aio import AgentsClient
from agent_framework.azure import AzureAIClient
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Existing Agent Example
This sample demonstrates working with pre-existing Azure AI Agents by providing
agent IDs, showing agent reuse patterns for production scenarios.
agent name and version, showing agent reuse patterns for production scenarios.
"""
async def main() -> None:
print("=== Azure AI Chat Client with Existing Agent ===")
# Create the client
async with (
AzureCliCredential() as credential,
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
):
azure_ai_agent = await project_client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
# Create remote agent with default instructions
# These instructions will persist on created agent for every run.
instructions="End each response with [END].",
azure_ai_agent = await project_client.agents.create_version(
agent_name="MyNewTestAgent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
# Setting specific requirements to verify that this agent is used.
instructions="End each response with [END].",
),
)
chat_client = AzureAIAgentClient(agents_client=agents_client, agent_id=azure_ai_agent.id)
chat_client = AzureAIClient(
project_client=project_client,
agent_name=azure_ai_agent.name,
# Property agent_version is required for existing agents.
# If this property is not configured, the client will try to create a new agent using
# provided agent_name.
# It's also possible to leave agent_version empty but set use_latest_version=True.
# This will pull latest available agent version and use that version for operations.
agent_version=azure_ai_agent.version,
)
try:
async with ChatAgent(
chat_client=chat_client,
# Instructions here are applicable only to this ChatAgent instance
# These instructions will be combined with instructions on existing remote agent.
# The final instructions during the execution will look like:
# "'End each response with [END]. Respond with 'Hello World' only'"
instructions="Respond with 'Hello World' only",
) as agent:
query = "How are you?"
print(f"User: {query}")
result = await agent.run(query)
# Based on local and remote instructions, the result will be
# 'Hello World [END]'.
# Response that indicates that previously created agent was used:
# "I'm here and ready to help you! How can I assist you today? [END]"
print(f"Agent: {result}\n")
finally:
# Clean up the agent manually
await project_client.agents.delete_agent(azure_ai_agent.id)
await project_client.agents.delete_version(
agent_name=azure_ai_agent.name, agent_version=azure_ai_agent.version
)
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_conversation.py
+98
View File
@@ -0,0 +1,98 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from random import randint
from typing import Annotated
from agent_framework.azure import AzureAIClient
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent Existing Conversation Example
This sample demonstrates usage of AzureAIClient with existing conversation created on service side.
"""
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def example_with_client() -> None:
"""Example shows how to specify existing conversation ID when initializing Azure AI Client."""
print("=== Azure AI Agent With Existing Conversation and Client ===")
async with (
AzureCliCredential() as credential,
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
):
# Create a conversation using OpenAI client
openai_client = await project_client.get_openai_client()
conversation = await openai_client.conversations.create()
conversation_id = conversation.id
print(f"Conversation ID: {conversation_id}")
async with AzureAIClient(
project_client=project_client,
# Specify conversation ID on client level
conversation_id=conversation_id,
).create_agent(
name="BasicAgent",
instructions="You are a helpful agent.",
tools=get_weather,
) as agent:
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result.text}\n")
query = "What was my last question?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result.text}\n")
async def example_with_thread() -> None:
"""This example shows how to specify existing conversation ID with AgentThread."""
print("=== Azure AI Agent With Existing Conversation and Thread ===")
async with (
AzureCliCredential() as credential,
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
AzureAIClient(project_client=project_client).create_agent(
name="BasicAgent",
instructions="You are a helpful agent.",
tools=get_weather,
) as agent,
):
# Create a conversation using OpenAI client
openai_client = await project_client.get_openai_client()
conversation = await openai_client.conversations.create()
conversation_id = conversation.id
print(f"Conversation ID: {conversation_id}")
# Create a thread with the existing ID
thread = agent.get_new_thread(service_thread_id=conversation_id)
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query, thread=thread)
print(f"Agent: {result.text}\n")
query = "What was my last question?"
print(f"User: {query}")
result = await agent.run(query, thread=thread)
print(f"Agent: {result.text}\n")
async def main() -> None:
await example_with_client()
await example_with_thread()
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py
+7 -9
View File
@@ -6,7 +6,7 @@ from random import randint
from typing import Annotated
from agent_framework import ChatAgent
from agent_framework.azure import AzureAIAgentClient
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
@@ -27,28 +27,26 @@ def get_weather(
async def main() -> None:
print("=== Azure AI Chat Client with Explicit Settings ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(
chat_client=AzureAIClient(
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
model_deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
async_credential=credential,
agent_name="WeatherAgent",
should_cleanup_agent=True, # Set to False if you want to disable automatic agent cleanup
),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
result = await agent.run("What's the weather like in New York?")
print(f"Result: {result}\n")
query = "What's the weather like in New York?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py
+42 -59
View File
@@ -1,10 +1,12 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from pathlib import Path
from agent_framework import ChatAgent, HostedFileSearchTool, HostedVectorStoreContent
from agent_framework_azure_ai import AzureAIAgentClient
from agent_framework.azure import AzureAIClient
from azure.ai.agents.aio import AgentsClient
from azure.ai.agents.models import FileInfo, VectorStore
from azure.identity.aio import AzureCliCredential
@@ -24,69 +26,50 @@ USER_INPUTS = [
async def main() -> None:
"""Main function demonstrating Azure AI agent with file search capabilities."""
client = AzureAIAgentClient(async_credential=AzureCliCredential())
file: FileInfo | None = None
vector_store: VectorStore | None = None
try:
# 1. Upload file and create vector store
pdf_file_path = Path(__file__).parent.parent / "resources" / "employees.pdf"
print(f"Uploading file from: {pdf_file_path}")
file = await client.project_client.agents.files.upload_and_poll(
file_path=str(pdf_file_path), purpose="assistants"
)
print(f"Uploaded file, file ID: {file.id}")
vector_store = await client.project_client.agents.vector_stores.create_and_poll(
file_ids=[file.id], name="my_vectorstore"
)
print(f"Created vector store, vector store ID: {vector_store.id}")
# 2. Create file search tool with uploaded resources
file_search_tool = HostedFileSearchTool(inputs=[HostedVectorStoreContent(vector_store_id=vector_store.id)])
# 3. Create an agent with file search capabilities
# The tool_resources are automatically extracted from HostedFileSearchTool
async with ChatAgent(
chat_client=client,
name="EmployeeSearchAgent",
instructions=(
"You are a helpful assistant that can search through uploaded employee files "
"to answer questions about employees."
),
tools=file_search_tool,
) as agent:
# 4. Simulate conversation with the agent
for user_input in USER_INPUTS:
print(f"# User: '{user_input}'")
response = await agent.run(user_input)
print(f"# Agent: {response.text}")
# 5. Cleanup: Delete the vector store and file
try:
if vector_store:
await client.project_client.agents.vector_stores.delete(vector_store.id)
if file:
await client.project_client.agents.files.delete(file.id)
except Exception:
# Ignore cleanup errors to avoid masking issues
pass
finally:
# 6. Cleanup: Delete the vector store and file in case of eariler failure to prevent orphaned resources.
# Refreshing the client is required since chat agent closes it
client = AzureAIAgentClient(async_credential=AzureCliCredential())
async with (
AzureCliCredential() as credential,
AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
AzureAIClient(async_credential=credential) as client,
):
try:
if vector_store:
await client.project_client.agents.vector_stores.delete(vector_store.id)
if file:
await client.project_client.agents.files.delete(file.id)
except Exception:
# Ignore cleanup errors to avoid masking issues
pass
# 1. Upload file and create vector store
pdf_file_path = Path(__file__).parent.parent / "resources" / "employees.pdf"
print(f"Uploading file from: {pdf_file_path}")
file = await agents_client.files.upload_and_poll(file_path=str(pdf_file_path), purpose="assistants")
print(f"Uploaded file, file ID: {file.id}")
vector_store = await agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
print(f"Created vector store, vector store ID: {vector_store.id}")
# 2. Create file search tool with uploaded resources
file_search_tool = HostedFileSearchTool(inputs=[HostedVectorStoreContent(vector_store_id=vector_store.id)])
# 3. Create an agent with file search capabilities
# The tool_resources are automatically extracted from HostedFileSearchTool
async with ChatAgent(
chat_client=client,
name="EmployeeSearchAgent",
instructions=(
"You are a helpful assistant that can search through uploaded employee files "
"to answer questions about employees."
),
tools=file_search_tool,
) as agent:
# 4. Simulate conversation with the agent
for user_input in USER_INPUTS:
print(f"# User: '{user_input}'")
response = await agent.run(user_input)
print(f"# Agent: {response.text}")
finally:
await client.close()
# 5. Cleanup: Delete the vector store and file in case of earlier failure to prevent orphaned resources.
if vector_store:
await agents_client.vector_stores.delete(vector_store.id)
if file:
await agents_client.files.delete(file.id)
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_hosted_mcp.py
+75 -28
View File
@@ -3,23 +3,42 @@
import asyncio
from typing import Any
from agent_framework import AgentProtocol, AgentThread, HostedMCPTool
from agent_framework.azure import AzureAIAgentClient
from agent_framework import AgentProtocol, AgentRunResponse, AgentThread, ChatMessage, HostedMCPTool
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Hosted MCP Example
This sample demonstrates integration of Azure AI Agents with hosted Model Context Protocol (MCP)
servers, including user approval workflows for function call security.
This sample demonstrates integrating hosted Model Context Protocol (MCP) tools with Azure AI Agent.
"""
async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", thread: "AgentThread"):
"""Here we let the thread deal with the previous responses, and we just rerun with the approval."""
from agent_framework import ChatMessage
async def handle_approvals_without_thread(query: str, agent: "AgentProtocol") -> AgentRunResponse:
"""When we don't have a thread, we need to ensure we return with the input, approval request and approval."""
result = await agent.run(query, thread=thread, store=True)
result = await agent.run(query, store=False)
while len(result.user_input_requests) > 0:
new_inputs: list[Any] = [query]
for user_input_needed in result.user_input_requests:
print(
f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
f" with arguments: {user_input_needed.function_call.arguments}"
)
new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
user_approval = input("Approve function call? (y/n): ")
new_inputs.append(
ChatMessage(role="user", contents=[user_input_needed.create_response(user_approval.lower() == "y")])
)
result = await agent.run(new_inputs, store=False)
return result
async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", thread: "AgentThread") -> AgentRunResponse:
"""Here we let the thread deal with the previous responses, and we just rerun with the approval."""
result = await agent.run(query, thread=thread)
while len(result.user_input_requests) > 0:
new_input: list[Any] = []
for user_input_needed in result.user_input_requests:
@@ -34,36 +53,64 @@ async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", threa
contents=[user_input_needed.create_response(user_approval.lower() == "y")],
)
)
result = await agent.run(new_input, thread=thread, store=True)
result = await agent.run(new_input, thread=thread)
return result
async def main() -> None:
"""Example showing Hosted MCP tools for a Azure AI Agent."""
async def run_hosted_mcp_without_approval() -> None:
"""Example showing MCP Tools without approval."""
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential) as chat_client,
):
agent = chat_client.create_agent(
name="DocsAgent",
instructions="You are a helpful assistant that can help with microsoft documentation questions.",
AzureAIClient(async_credential=credential).create_agent(
name="MyLearnDocsAgent",
instructions="You are a helpful assistant that can help with Microsoft documentation questions.",
tools=HostedMCPTool(
name="Microsoft Learn MCP",
url="https://learn.microsoft.com/api/mcp",
approval_mode="never_require",
),
)
) as agent,
):
query = "How to create an Azure storage account using az cli?"
print(f"User: {query}")
result = await handle_approvals_without_thread(query, agent)
print(f"{agent.name}: {result}\n")
async def run_hosted_mcp_with_approval_and_thread() -> None:
"""Example showing MCP Tools with approvals using a thread."""
print("=== MCP with approvals and with thread ===")
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIClient(async_credential=credential).create_agent(
name="MyApiSpecsAgent",
instructions="You are a helpful agent that can use MCP tools to assist users.",
tools=HostedMCPTool(
name="api-specs",
url="https://gitmcp.io/Azure/azure-rest-api-specs",
approval_mode="always_require",
),
) as agent,
):
thread = agent.get_new_thread()
# First query
query1 = "How to create an Azure storage account using az cli?"
print(f"User: {query1}")
result1 = await handle_approvals_with_thread(query1, agent, thread)
print(f"{agent.name}: {result1}\n")
print("\n=======================================\n")
# Second query
query2 = "What is Microsoft Agent Framework?"
print(f"User: {query2}")
result2 = await handle_approvals_with_thread(query2, agent, thread)
print(f"{agent.name}: {result2}\n")
query = "Please summarize the Azure REST API specifications Readme"
print(f"User: {query}")
result = await handle_approvals_with_thread(query, agent, thread)
print(f"{agent.name}: {result}\n")
async def main() -> None:
print("=== Azure AI Agent with Hosted MCP Tools Example ===\n")
await run_hosted_mcp_without_approval()
await run_hosted_mcp_with_approval_and_thread()
if __name__ == "__main__":
python/samples/getting_started/agents/azure_ai/azure_ai_with_response_format.py
+54
View File
@@ -0,0 +1,54 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from pydantic import BaseModel, ConfigDict
"""
Azure AI Agent Response Format Example
This sample demonstrates basic usage of AzureAIClient with response format,
also known as structured outputs.
"""
class ReleaseBrief(BaseModel):
feature: str
benefit: str
launch_date: str
model_config = ConfigDict(extra="forbid")
async def main() -> None:
"""Example of using response_format property."""
# Since no Agent ID is provided, the agent will be automatically created.
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIClient(async_credential=credential).create_agent(
name="ProductMarketerAgent",
instructions="Return launch briefs as structured JSON.",
) as agent,
):
query = "Draft a launch brief for the Contoso Note app."
print(f"User: {query}")
result = await agent.run(
query,
# Specify type to use as response
response_format=ReleaseBrief,
)
if isinstance(result.value, ReleaseBrief):
release_brief = result.value
print("Agent:")
print(f"Feature: {release_brief.feature}")
print(f"Benefit: {release_brief.benefit}")
print(f"Launch date: {release_brief.launch_date}")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_thread.py
+63 -67
View File
@@ -4,16 +4,15 @@ import asyncio
from random import randint
from typing import Annotated
from agent_framework import AgentThread, ChatAgent
from agent_framework.azure import AzureAIAgentClient
from agent_framework.azure import AzureAIClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent with Thread Management Example
This sample demonstrates thread management with Azure AI Agents, comparing
automatic thread creation with explicit thread management for persistent context.
This sample demonstrates thread management with Azure AI Agent, showing
persistent conversation capabilities using service-managed threads as well as storing messages in-memory.
"""
@@ -26,44 +25,42 @@ def get_weather(
async def example_with_automatic_thread_creation() -> None:
"""Example showing automatic thread creation (service-managed thread)."""
"""Example showing automatic thread creation."""
print("=== Automatic Thread Creation Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# First conversation - no thread provided, will be created automatically
first_query = "What's the weather like in Seattle?"
print(f"User: {first_query}")
first_result = await agent.run(first_query)
print(f"Agent: {first_result.text}")
query1 = "What's the weather like in Seattle?"
print(f"User: {query1}")
result1 = await agent.run(query1)
print(f"Agent: {result1.text}")
# Second conversation - still no thread provided, will create another new thread
second_query = "What was the last city I asked about?"
print(f"\nUser: {second_query}")
second_result = await agent.run(second_query)
print(f"Agent: {second_result.text}")
query2 = "What was the last city I asked about?"
print(f"\nUser: {query2}")
result2 = await agent.run(query2)
print(f"Agent: {result2.text}")
print("Note: Each call creates a separate thread, so the agent doesn't remember previous context.\n")
async def example_with_thread_persistence() -> None:
"""Example showing thread persistence across multiple conversations."""
print("=== Thread Persistence Example ===")
print("Using the same thread across multiple conversations to maintain context.\n")
async def example_with_thread_persistence_in_memory() -> None:
"""
Example showing thread persistence across multiple conversations.
In this example, messages are stored in-memory.
"""
print("=== Thread Persistence Example (In-Memory) ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
@@ -72,81 +69,80 @@ async def example_with_thread_persistence() -> None:
thread = agent.get_new_thread()
# First conversation
first_query = "What's the weather like in Tokyo?"
print(f"User: {first_query}")
first_result = await agent.run(first_query, thread=thread)
print(f"Agent: {first_result.text}")
query1 = "What's the weather like in Tokyo?"
print(f"User: {query1}")
result1 = await agent.run(query1, thread=thread, store=False)
print(f"Agent: {result1.text}")
# Second conversation using the same thread - maintains context
second_query = "How about London?"
print(f"\nUser: {second_query}")
second_result = await agent.run(second_query, thread=thread)
print(f"Agent: {second_result.text}")
query2 = "How about London?"
print(f"\nUser: {query2}")
result2 = await agent.run(query2, thread=thread, store=False)
print(f"Agent: {result2.text}")
# Third conversation - agent should remember both previous cities
third_query = "Which of the cities I asked about has better weather?"
print(f"\nUser: {third_query}")
third_result = await agent.run(third_query, thread=thread)
print(f"Agent: {third_result.text}")
query3 = "Which of the cities I asked about has better weather?"
print(f"\nUser: {query3}")
result3 = await agent.run(query3, thread=thread, store=False)
print(f"Agent: {result3.text}")
print("Note: The agent remembers context from previous messages in the same thread.\n")
async def example_with_existing_thread_id() -> None:
"""Example showing how to work with an existing thread ID from the service."""
"""
Example showing how to work with an existing thread ID from the service.
In this example, messages are stored on the server.
"""
print("=== Existing Thread ID Example ===")
print("Using a specific thread ID to continue an existing conversation.\n")
# First, create a conversation and capture the thread ID
existing_thread_id = None
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Start a conversation and get the thread ID
thread = agent.get_new_thread()
first_query = "What's the weather in Paris?"
print(f"User: {first_query}")
first_result = await agent.run(first_query, thread=thread)
print(f"Agent: {first_result.text}")
query1 = "What's the weather in Paris?"
print(f"User: {query1}")
result1 = await agent.run(query1, thread=thread)
print(f"Agent: {result1.text}")
# The thread ID is set after the first response
existing_thread_id = thread.service_thread_id
print(f"Thread ID: {existing_thread_id}")
if existing_thread_id:
print("\n--- Continuing with the same thread ID in a new agent instance ---")
if existing_thread_id:
print("\n--- Continuing with the same thread ID in a new agent instance ---")
# Create a new agent instance but use the existing thread ID
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(thread_id=existing_thread_id, async_credential=credential),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Create a thread with the existing ID
thread = AgentThread(service_thread_id=existing_thread_id)
async with (
AzureAIClient(async_credential=credential).create_agent(
name="BasicWeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Create a thread with the existing ID
thread = agent.get_new_thread(service_thread_id=existing_thread_id)
second_query = "What was the last city I asked about?"
print(f"User: {second_query}")
second_result = await agent.run(second_query, thread=thread)
print(f"Agent: {second_result.text}")
print("Note: The agent continues the conversation from the previous thread.\n")
query2 = "What was the last city I asked about?"
print(f"User: {query2}")
result2 = await agent.run(query2, thread=thread)
print(f"Agent: {result2.text}")
print("Note: The agent continues the conversation from the previous thread by using thread ID.\n")
async def main() -> None:
print("=== Azure AI Chat Client Agent Thread Management Examples ===\n")
print("=== Azure AI Agent Thread Management Examples ===\n")
await example_with_automatic_thread_creation()
await example_with_thread_persistence()
await example_with_thread_persistence_in_memory()
await example_with_existing_thread_id()
python/samples/getting_started/agents/azure_ai_agent/README.md
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# Azure AI Agent Examples
This folder contains examples demonstrating different ways to create and use agents with the Azure AI chat client from the `agent_framework.azure` package.
## Examples
| File | Description |
|------|-------------|
| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAIAgentClient`. It automatically handles all configuration using environment variables. |
| [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to find real-time information from the web. Demonstrates web search capabilities with proper source citations and comprehensive error handling. |
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents. |
| [`azure_ai_with_existing_thread.py`](azure_ai_with_existing_thread.py) | Shows how to work with a pre-existing thread by providing the thread ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created threads. |
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIAgentClient` settings, including project endpoint, model deployment, credentials, and agent name. |
| [`azure_ai_with_azure_ai_search.py`](azure_ai_with_azure_ai_search.py) | Demonstrates how to use Azure AI Search with Azure AI agents to search through indexed data. Shows how to configure search parameters, query types, and integrate with existing search indexes. |
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Demonstrates how to use the HostedFileSearchTool with Azure AI agents to search through uploaded documents. Shows file upload, vector store creation, and querying document content. Includes both streaming and non-streaming examples. |
| [`azure_ai_with_function_tools.py`](azure_ai_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate Azure AI agents with hosted Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates remote MCP server connections and tool discovery. |
| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
| [`azure_ai_with_multiple_tools.py`](azure_ai_with_multiple_tools.py) | Demonstrates how to use multiple tools together with Azure AI agents, including web search, MCP servers, and function tools. Shows coordinated multi-tool interactions and approval workflows. |
| [`azure_ai_with_openapi_tools.py`](azure_ai_with_openapi_tools.py) | Demonstrates how to use OpenAPI tools with Azure AI agents to integrate external REST APIs. Shows OpenAPI specification loading, anonymous authentication, thread context management, and coordinated multi-API conversations using weather and countries APIs. |
| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
## Environment Variables
Before running the examples, you need to set up your environment variables. You can do this in one of two ways:
### Option 1: Using a .env file (Recommended)
1. Copy the `.env.example` file from the `python` directory to create a `.env` file:
```bash
cp ../../.env.example ../../.env
```
2. Edit the `.env` file and add your values:
```
AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
```
3. For samples using Bing Grounding search (like `azure_ai_with_bing_grounding.py` and `azure_ai_with_multiple_tools.py`), you'll also need:
```
BING_CONNECTION_ID="your-bing-connection-id"
```
To get your Bing connection details:
- Go to [Azure AI Foundry portal](https://ai.azure.com)
- Navigate to your project's "Connected resources" section
- Add a new connection for "Grounding with Bing Search"
- Copy the ID
### Option 2: Using environment variables directly
Set the environment variables in your shell:
```bash
export AZURE_AI_PROJECT_ENDPOINT="your-project-endpoint"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="your-model-deployment-name"
export BING_CONNECTION_ID="your-bing-connection-id"
```
### Required Variables
- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint (required for all examples)
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment (required for all examples)
### Optional Variables
- `BING_CONNECTION_ID`: Your Bing connection ID (required for `azure_ai_with_bing_grounding.py` and `azure_ai_with_multiple_tools.py`)
python/samples/getting_started/agents/azure_ai_agent/azure_ai_basic.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randint
from typing import Annotated
from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent Basic Example
This sample demonstrates basic usage of AzureAIAgentClient to create agents with automatic
lifecycle management. Shows both streaming and non-streaming responses with function tools.
"""
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def non_streaming_example() -> None:
"""Example of non-streaming response (get the complete result at once)."""
print("=== Non-streaming Response Example ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential).create_agent(
name="WeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
query = "What's the weather like in Seattle?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
async def streaming_example() -> None:
"""Example of streaming response (get results as they are generated)."""
print("=== Streaming Response Example ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential).create_agent(
name="WeatherAgent",
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
query = "What's the weather like in Portland?"
print(f"User: {query}")
print("Agent: ", end="", flush=True)
async for chunk in agent.run_stream(query):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
async def main() -> None:
print("=== Basic Azure AI Chat Client Agent Example ===")
await non_streaming_example()
await streaming_example()
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_azure_ai_search.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import ChatAgent, CitationAnnotation
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.aio import AgentsClient
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import ConnectionType
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Azure AI Search Example
This sample demonstrates how to create an Azure AI agent that uses Azure AI Search
to search through indexed hotel data and answer user questions about hotels.
Prerequisites:
1. Set AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME environment variables
2. Ensure you have an Azure AI Search connection configured in your Azure AI project
3. The search index "hotels-sample-index" should exist in your Azure AI Search service
(you can create this using the Azure portal with sample hotel data)
NOTE: To ensure consistent search tool usage:
- Include explicit instructions for the agent to use the search tool
- Mention the search requirement in your queries
- Use `tool_choice="required"` to force tool usage
More info on `query type` can be found here:
https://learn.microsoft.com/en-us/python/api/azure-ai-agents/azure.ai.agents.models.aisearchindexresource?view=azure-python-preview
"""
async def main() -> None:
"""Main function demonstrating Azure AI agent with raw Azure AI Search tool."""
print("=== Azure AI Agent with Raw Azure AI Search Tool ===")
# Create the client and manually create an agent with Azure AI Search tool
async with (
AzureCliCredential() as credential,
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
):
ai_search_conn_id = ""
async for connection in project_client.connections.list():
if connection.type == ConnectionType.AZURE_AI_SEARCH:
ai_search_conn_id = connection.id
break
# 1. Create Azure AI agent with the search tool
azure_ai_agent = await agents_client.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="HotelSearchAgent",
instructions=(
"You are a helpful agent that searches hotel information using Azure AI Search. "
"Always use the search tool and index to find hotel data and provide accurate information."
),
tools=[{"type": "azure_ai_search"}],
tool_resources={
"azure_ai_search": {
"indexes": [
{
"index_connection_id": ai_search_conn_id,
"index_name": "hotels-sample-index",
"query_type": "vector",
}
]
}
},
)
# 2. Create chat client with the existing agent
chat_client = AzureAIAgentClient(agents_client=agents_client, agent_id=azure_ai_agent.id)
try:
async with ChatAgent(
chat_client=chat_client,
# Additional instructions for this specific conversation
instructions=("You are a helpful agent that uses the search tool and index to find hotel information."),
) as agent:
print("This agent uses raw Azure AI Search tool to search hotel data.\n")
# 3. Simulate conversation with the agent
user_input = (
"Use Azure AI search knowledge tool to find detailed information about a winter hotel."
" Use the search tool and index." # You can modify prompt to force tool usage
)
print(f"User: {user_input}")
print("Agent: ", end="", flush=True)
# Stream the response and collect citations
citations: list[CitationAnnotation] = []
async for chunk in agent.run_stream(user_input):
if chunk.text:
print(chunk.text, end="", flush=True)
# Collect citations from Azure AI Search responses
for content in getattr(chunk, "contents", []):
annotations = getattr(content, "annotations", [])
if annotations:
citations.extend(annotations)
print()
# Display collected citations
if citations:
print("\n\nCitations:")
for i, citation in enumerate(citations, 1):
print(f"[{i}] Reference: {citation.url}")
print("\n" + "=" * 50 + "\n")
print("Hotel search conversation completed!")
finally:
# Clean up the agent manually
await agents_client.delete_agent(azure_ai_agent.id)
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_bing_grounding.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_bing_grounding.py
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python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from agent_framework import AgentRunResponse, ChatResponseUpdate, HostedCodeInterpreterTool
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.models import (
RunStepDeltaCodeInterpreterDetailItemObject,
)
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Code Interpreter Example
This sample demonstrates using HostedCodeInterpreterTool with Azure AI Agents
for Python code execution and mathematical problem solving.
"""
def print_code_interpreter_inputs(response: AgentRunResponse) -> None:
"""Helper method to access code interpreter data."""
print("\nCode Interpreter Inputs during the run:")
if response.raw_representation is None:
return
for chunk in response.raw_representation:
if isinstance(chunk, ChatResponseUpdate) and isinstance(
chunk.raw_representation, RunStepDeltaCodeInterpreterDetailItemObject
):
print(chunk.raw_representation.input, end="")
print("\n")
async def main() -> None:
"""Example showing how to use the HostedCodeInterpreterTool with Azure AI."""
print("=== Azure AI Agent with Code Interpreter Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential) as chat_client,
):
agent = chat_client.create_agent(
name="CodingAgent",
instructions=("You are a helpful assistant that can write and execute Python code to solve problems."),
tools=HostedCodeInterpreterTool(),
)
query = "Generate the factorial of 100 using python code, show the code and execute it."
print(f"User: {query}")
response = await AgentRunResponse.from_agent_response_generator(agent.run_stream(query))
print(f"Agent: {response}")
# To review the code interpreter outputs, you can access
# them from the response raw_representations, just uncomment the next line:
# print_code_interpreter_inputs(response)
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_existing_agent.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import ChatAgent
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.aio import AgentsClient
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Existing Agent Example
This sample demonstrates working with pre-existing Azure AI Agents by providing
agent IDs, showing agent reuse patterns for production scenarios.
"""
async def main() -> None:
print("=== Azure AI Chat Client with Existing Agent ===")
# Create the client
async with (
AzureCliCredential() as credential,
AgentsClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as agents_client,
):
azure_ai_agent = await agents_client.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
# Create remote agent with default instructions
# These instructions will persist on created agent for every run.
instructions="End each response with [END].",
)
chat_client = AzureAIAgentClient(agents_client=agents_client, agent_id=azure_ai_agent.id)
try:
async with ChatAgent(
chat_client=chat_client,
# Instructions here are applicable only to this ChatAgent instance
# These instructions will be combined with instructions on existing remote agent.
# The final instructions during the execution will look like:
# "'End each response with [END]. Respond with 'Hello World' only'"
instructions="Respond with 'Hello World' only",
) as agent:
query = "How are you?"
print(f"User: {query}")
result = await agent.run(query)
# Based on local and remote instructions, the result will be
# 'Hello World [END]'.
print(f"Agent: {result}\n")
finally:
# Clean up the agent manually
await agents_client.delete_agent(azure_ai_agent.id)
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_thread.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py
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python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from random import randint
from typing import Annotated
from agent_framework import ChatAgent
from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent with Explicit Settings Example
This sample demonstrates creating Azure AI Agents with explicit configuration
settings rather than relying on environment variable defaults.
"""
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def main() -> None:
print("=== Azure AI Chat Client with Explicit Settings ===")
# Since no Agent ID is provided, the agent will be automatically created
# and deleted after getting a response
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(
project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
model_deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
async_credential=credential,
agent_name="WeatherAgent",
should_cleanup_agent=True, # Set to False if you want to disable automatic agent cleanup
),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
result = await agent.run("What's the weather like in New York?")
print(f"Result: {result}\n")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_file_search.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from pathlib import Path
from agent_framework import ChatAgent, HostedFileSearchTool, HostedVectorStoreContent
from agent_framework.azure import AzureAIAgentClient
from azure.ai.agents.models import FileInfo, VectorStore
from azure.identity.aio import AzureCliCredential
"""
The following sample demonstrates how to create a simple, Azure AI agent that
uses a file search tool to answer user questions.
"""
# Simulate a conversation with the agent
USER_INPUTS = [
"Who is the youngest employee?",
"Who works in sales?",
"I have a customer request, who can help me?",
]
async def main() -> None:
"""Main function demonstrating Azure AI agent with file search capabilities."""
client = AzureAIAgentClient(async_credential=AzureCliCredential())
file: FileInfo | None = None
vector_store: VectorStore | None = None
try:
# 1. Upload file and create vector store
pdf_file_path = Path(__file__).parent.parent / "resources" / "employees.pdf"
print(f"Uploading file from: {pdf_file_path}")
file = await client.agents_client.files.upload_and_poll(file_path=str(pdf_file_path), purpose="assistants")
print(f"Uploaded file, file ID: {file.id}")
vector_store = await client.agents_client.vector_stores.create_and_poll(
file_ids=[file.id], name="my_vectorstore"
)
print(f"Created vector store, vector store ID: {vector_store.id}")
# 2. Create file search tool with uploaded resources
file_search_tool = HostedFileSearchTool(inputs=[HostedVectorStoreContent(vector_store_id=vector_store.id)])
# 3. Create an agent with file search capabilities
# The tool_resources are automatically extracted from HostedFileSearchTool
async with ChatAgent(
chat_client=client,
name="EmployeeSearchAgent",
instructions=(
"You are a helpful assistant that can search through uploaded employee files "
"to answer questions about employees."
),
tools=file_search_tool,
) as agent:
# 4. Simulate conversation with the agent
for user_input in USER_INPUTS:
print(f"# User: '{user_input}'")
response = await agent.run(user_input)
print(f"# Agent: {response.text}")
# 5. Cleanup: Delete the vector store and file
try:
if vector_store:
await client.agents_client.vector_stores.delete(vector_store.id)
if file:
await client.agents_client.files.delete(file.id)
except Exception:
# Ignore cleanup errors to avoid masking issues
pass
finally:
# 6. Cleanup: Delete the vector store and file in case of earlier failure to prevent orphaned resources.
# Refreshing the client is required since chat agent closes it
client = AzureAIAgentClient(async_credential=AzureCliCredential())
try:
if vector_store:
await client.agents_client.vector_stores.delete(vector_store.id)
if file:
await client.agents_client.files.delete(file.id)
except Exception:
# Ignore cleanup errors to avoid masking issues
pass
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_function_tools.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_function_tools.py
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python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_hosted_mcp.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Any
from agent_framework import AgentProtocol, AgentRunResponse, AgentThread, HostedMCPTool
from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Hosted MCP Example
This sample demonstrates integration of Azure AI Agents with hosted Model Context Protocol (MCP)
servers, including user approval workflows for function call security.
"""
async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", thread: "AgentThread") -> AgentRunResponse:
"""Here we let the thread deal with the previous responses, and we just rerun with the approval."""
from agent_framework import ChatMessage
result = await agent.run(query, thread=thread, store=True)
while len(result.user_input_requests) > 0:
new_input: list[Any] = []
for user_input_needed in result.user_input_requests:
print(
f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
f" with arguments: {user_input_needed.function_call.arguments}"
)
user_approval = input("Approve function call? (y/n): ")
new_input.append(
ChatMessage(
role="user",
contents=[user_input_needed.create_response(user_approval.lower() == "y")],
)
)
result = await agent.run(new_input, thread=thread, store=True)
return result
async def main() -> None:
"""Example showing Hosted MCP tools for a Azure AI Agent."""
async with (
AzureCliCredential() as credential,
AzureAIAgentClient(async_credential=credential) as chat_client,
):
agent = chat_client.create_agent(
name="DocsAgent",
instructions="You are a helpful assistant that can help with microsoft documentation questions.",
tools=HostedMCPTool(
name="Microsoft Learn MCP",
url="https://learn.microsoft.com/api/mcp",
),
)
thread = agent.get_new_thread()
# First query
query1 = "How to create an Azure storage account using az cli?"
print(f"User: {query1}")
result1 = await handle_approvals_with_thread(query1, agent, thread)
print(f"{agent.name}: {result1}\n")
print("\n=======================================\n")
# Second query
query2 = "What is Microsoft Agent Framework?"
print(f"User: {query2}")
result2 = await handle_approvals_with_thread(query2, agent, thread)
print(f"{agent.name}: {result2}\n")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_local_mcp.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_local_mcp.py
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python/samples/getting_started/agents/azure_ai/azure_ai_with_multiple_tools.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_multiple_tools.py
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python/samples/getting_started/agents/azure_ai/azure_ai_with_openapi_tools.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_openapi_tools.py
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python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_thread.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randint
from typing import Annotated
from agent_framework import AgentThread, ChatAgent
from agent_framework.azure import AzureAIAgentClient
from azure.identity.aio import AzureCliCredential
from pydantic import Field
"""
Azure AI Agent with Thread Management Example
This sample demonstrates thread management with Azure AI Agents, comparing
automatic thread creation with explicit thread management for persistent context.
"""
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
async def example_with_automatic_thread_creation() -> None:
"""Example showing automatic thread creation (service-managed thread)."""
print("=== Automatic Thread Creation Example ===")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# First conversation - no thread provided, will be created automatically
first_query = "What's the weather like in Seattle?"
print(f"User: {first_query}")
first_result = await agent.run(first_query)
print(f"Agent: {first_result.text}")
# Second conversation - still no thread provided, will create another new thread
second_query = "What was the last city I asked about?"
print(f"\nUser: {second_query}")
second_result = await agent.run(second_query)
print(f"Agent: {second_result.text}")
print("Note: Each call creates a separate thread, so the agent doesn't remember previous context.\n")
async def example_with_thread_persistence() -> None:
"""Example showing thread persistence across multiple conversations."""
print("=== Thread Persistence Example ===")
print("Using the same thread across multiple conversations to maintain context.\n")
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Create a new thread that will be reused
thread = agent.get_new_thread()
# First conversation
first_query = "What's the weather like in Tokyo?"
print(f"User: {first_query}")
first_result = await agent.run(first_query, thread=thread)
print(f"Agent: {first_result.text}")
# Second conversation using the same thread - maintains context
second_query = "How about London?"
print(f"\nUser: {second_query}")
second_result = await agent.run(second_query, thread=thread)
print(f"Agent: {second_result.text}")
# Third conversation - agent should remember both previous cities
third_query = "Which of the cities I asked about has better weather?"
print(f"\nUser: {third_query}")
third_result = await agent.run(third_query, thread=thread)
print(f"Agent: {third_result.text}")
print("Note: The agent remembers context from previous messages in the same thread.\n")
async def example_with_existing_thread_id() -> None:
"""Example showing how to work with an existing thread ID from the service."""
print("=== Existing Thread ID Example ===")
print("Using a specific thread ID to continue an existing conversation.\n")
# First, create a conversation and capture the thread ID
existing_thread_id = None
# For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(async_credential=credential),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Start a conversation and get the thread ID
thread = agent.get_new_thread()
first_query = "What's the weather in Paris?"
print(f"User: {first_query}")
first_result = await agent.run(first_query, thread=thread)
print(f"Agent: {first_result.text}")
# The thread ID is set after the first response
existing_thread_id = thread.service_thread_id
print(f"Thread ID: {existing_thread_id}")
if existing_thread_id:
print("\n--- Continuing with the same thread ID in a new agent instance ---")
# Create a new agent instance but use the existing thread ID
async with (
AzureCliCredential() as credential,
ChatAgent(
chat_client=AzureAIAgentClient(thread_id=existing_thread_id, async_credential=credential),
instructions="You are a helpful weather agent.",
tools=get_weather,
) as agent,
):
# Create a thread with the existing ID
thread = AgentThread(service_thread_id=existing_thread_id)
second_query = "What was the last city I asked about?"
print(f"User: {second_query}")
second_result = await agent.run(second_query, thread=thread)
print(f"Agent: {second_result.text}")
print("Note: The agent continues the conversation from the previous thread.\n")
async def main() -> None:
print("=== Azure AI Chat Client Agent Thread Management Examples ===\n")
await example_with_automatic_thread_creation()
await example_with_thread_persistence()
await example_with_existing_thread_id()
if __name__ == "__main__":
asyncio.run(main())