Python: Merge main into feature-durabletask-python branch (#3261)

* Python: Add factory pattern to concurrent orchestration builder (#2738) * Add factory pattern to concurrent orchestration builder * Update readme * Address AI comments * Fix unit tests * Fix import * Prevent multiple calls to set participants or factories * Add comments * Mitigate warnings * Fix mypy * Address comments * Address Copilot comments * Fix tests * Python: fix: GroupChat ManagerSelectionResponse JSON Schema for OpenAI Structured Outpu… (#2750) * fix: ManagerSelectionResponse JSON Schema for OpenAI Structured Output Strict Mode * refactor: install pre-commit then commit again * Capture file IDs from code interpreter in streaming responses (#2741) * .NET: [BREAKING] Prevent nulls in AIAgent property (#2719) * prevent nulls in AIAgent property * address feedback * code ql sm04598 (#2723) Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> * .NET: Add Conversation State Sample (Step05) (#2697) * Initial plan * Add Agent_OpenAI_Step05_Conversation sample for conversation state management Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Update Program.cs comment to accurately describe the sample Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * Update the code to use the ConversationClient more in line with the samples in OpenAI * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Changing sample to use ChatClientAgent and conversationId in GetNewThread --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.7 to 4.0.4.11 (#2777) --- updated-dependencies: - dependency-name: AWSSDK.Extensions.Bedrock.MEAI dependency-version: 4.0.4.11 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump Azure.Identity from 1.17.0 to 1.17.1 (#2780) --- updated-dependencies: - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.Identity dependency-version: 1.17.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump Azure.AI.AgentServer.AgentFramework from 1.0.0-beta.4 to 1.0.0-beta.5 (#2778) --- updated-dependencies: - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Python: added more complete parsing for mcp tool arguments (#2756) * added more complete parsing for mcp tool arguments * fixed mypy * added nonlocal model counter, and some fixes * fixes in naming logic * extracted json parsing function, added parametrized test and checked coverage * Python: Updated package versions (#2784) * Updated package versions * Small fix * Bump actions/checkout from 5 to 6 (#2404) Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/checkout dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * .NET: adds support for labels in edges, fixes rendering of labels in dot a… (#1507) * adds support for labels in edges, fixes rendering of labels in dot and mermaid, adds rendering of labels in edges * Update dotnet/src/Microsoft.Agents.AI.Workflows/Visualization/WorkflowVisualizer.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * escaping edge labels, adding tests for labels containing strange characters that would break the diagram and enabling the previous signature so the API has backwards compatibility. * Unify label in EdgeData * Edge API adjustments, removed useless "sanitizer" * fixed test --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Jacob Alber <jaalber@microsoft.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Python: Added custom args and thread object to ai_function kwargs (#2769) * Added an example of using kwargs in ai_function * Added thread object to ai_function kwargs * Updated docs * Small fix * Added thread parameter filtering * Fix WorkflowAgent to include thread convo history. Enable checkpointing. (#2774) * Update OpenAIResponses.yaml to match AgentSchema (#2598) 1. Update `connection` child types -- `kind: ApiKey` to `kind: key` otherwise schema will fail: https://microsoft.github.io/AgentSchema/reference/apikeyconnection/ 2. Update `outputSchema`'s `PropertySchema` to be `kind` instead of `type` otherwise schema will fail: https://microsoft.github.io/AgentSchema/reference/propertyschema/ * Python: Remove warnings from workflow builder on not using factories (#2808) * Revert concurrent * Fix comments * Python: Filter framework kwargs from MCP tool invocations (#2870) * Filter framework kwargs from MCP tool invocations * Fixes * Python: Fix WorkflowAgent to emit yield_output as agent response (#2866) * Fix WorkflowAgent to emit yield_output as agent response * use raw_representation * Raw representation handling * Python: Use agent description in HandoffBuilder auto-generated tools (#2713) (#2714) ## Summary Enhanced `HandoffBuilder._apply_auto_tools` to use the target agent's description when creating handoff tools, providing more informative tool descriptions for LLMs. ## Changes - Modified `_apply_auto_tools` to extract `description` from `AgentExecutor._agent` when available - Updated iteration to use `.items()` for more efficient dict traversal - Handoff tools now use agent descriptions instead of generic placeholders ## Example Before: "Handoff to the refund_agent agent." After: "You handle refund requests. Ask for order details and process refunds." ## Testing - All handoff tests pass (20/20) - No breaking changes to existing API Fixes #2713 Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> * Python: [BREAKING] Observability updates (#2782) * fixes Python: Add env_file_path parameter to setup_observability() similar to AzureOpenAIChatClient Fixes #2186 * WIP on updates using configure_azure_monitor * improved setup and clarity * fixed root .env.example * revert changes * updated files * updated sample * updated zero code * test fixes and fixed links * fix devui * removed planning docs * added enable method and updated readme and samples * clarified docstring * add return annotation * updated naming * update capatilized version * updated readme and some fixes * updated decorator name inline with the rest * feedback from comments addressed * Python: Fix middleware terminate flag to exit function calling loop immediately (#2868) * Fix middleware terminate flag to exit function calling loop immediately * Eliminating duck typing * Improve function exec result handling * Fix race condition * Fix mypy issues * Python: Fix context duplication in handoff workflows when restoring from checkpoint (#2867) * Fix context duplication in handoff workflows when restoring from checkpoint * Address Copilot PR review * .NET: Update to latest Azure.AI.*, OpenAI, and M.E.AI* (#2850) * Update to latest Azure.AI.*, OpenAI, and M.E.AI* Absorb breaking changes in Responses surface area * Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs * Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs * Update dotnet/samples/AgentWebChat/AgentWebChat.AgentHost/Utilities/ChatClientExtensions.cs * Update dotnet/samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step04_CreateFromOpenAIResponseClient/Program.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Using patch to remove the model is necessary, updated the response client to actually use the the ForAgent --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Roger Barreto <19890735+rogerbarreto@users.noreply.github.com> * Bump actions/download-artifact from 6 to 7 (#2862) Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 6 to 7. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v6...v7) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump actions/cache from 4 to 5 (#2861) Bumps [actions/cache](https://github.com/actions/cache) from 4 to 5. - [Release notes](https://github.com/actions/cache/releases) - [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md) - [Commits](https://github.com/actions/cache/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/cache dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump actions/upload-artifact from 5 to 6 (#2860) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 5 to 6. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Python : Ollama Connector for Agent Framework (#1104) * Initial Commit for Olama Connector * Added Olama Sample * Add Sample & Fixed Open Telemetry * Fixed Spelling from Olama to Ollama * remove"opentelemetry-semantic-conventions-ai ~=0.4.13" since its handled in a different pr * Added Tool Calling * Finalizing test cases * Adjust samples to be more reliable * Update python/packages/ollama/agent_framework_ollama/_chat_client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/ollama/pyproject.toml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/ollama/tests/test_ollama_chat_client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/ollama/agent_framework_ollama/_chat_client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Improved Docstrings & Sample * Update python/packages/ollama/agent_framework_ollama/_chat_client.py Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> * Integrate PR Feedback - Divided Streaming and Non-Streaming into independent Methods - Catch Ollama Validation Error - Add OTEL Provider Name - Checked Ollama Messages - Add Usage Statistics * Revert setting, so it can be none * Validate Message formatting between AF and Ollama * Catch Ollama Error and raise a ServiceResponse Error * Fix mypy error * remove .vscode comma * Add Reasoning support & adjust to new structure * Add Ollama Multimodality and Reasoning * Add test cases for reasoning * Add Tests for Error Handling in Ollama Client * Update python/samples/getting_started/multimodal_input/ollama_chat_multimodal.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Integrated Copilot Feedback * Implement first PR Feedback * Adjust Readme files for examples * Adjust argument passing via additional chat options * Implemented PR Feedback * Removing Ollama Package from Core and moving samples * Fix Link & Adding Samples to Main Sample Readme * Fixing Links in Readme * Moved Multimodal and Chat Example * Fixed Link in ChatClient to Ollama * Fix AgentFramework Links in Ollama Project * Fix observability breaking change --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> * Skip failing IT (#2904) * .NET: Cosmos DB UT Fast Skip (For Non-Configured Local envs) (#2906) * Cosmos DB UT Fast Skip (Non-Configured Local envs) + Long running UT skip in pipeline when no CosmosDB changes happened * Force a CosmosDB source code change to trigger the pipeline * Address possible string boolean mismatch * Add debug * Enabling emulator always when running IT * .NET: Add TTLs to durable agent sessions (#2679) * .NET: Add TTLs to durable agent sessions * Remove unnecessary async * PR feedback: clarify UTC * PR feedback: limit minimum signal delay to <= 5 minutes * PR feedback: Fix TTL disablement * Linter: use auto-property * Fix build break from OpenAI SDK change * Updated CHANGELOG.md * PR feedback * Reduce default TTL to 14 days to work around DTS bug * Python: Update Mem0Provider to use v2 search API `filters` parameter (#2766) * short fix to move id parameters to filters object * added tests * small fix * mem0 dependency update * Updated package versions (#2913) * .NET: Switch to new "Run" method name. (#2843) * Switch to new "RunAgent" method name. * Try to disable false positive naming warning. * Add comment about disabled warnings. * Rename `RunAgent` to just `Run`. * Update CHANGELOG. * Python: Switch to new "run" method name. (#2890) * Switch to `run` method. * Add support for deprecated `run_agent`. * Fix entity method name. * Fix method name and improve tests. * Update comment. * Update Python CHANGELOG. * [BREAKING] Python: Add factory pattern to handoff orchestration builder (#2844) * WIP: Factory pattern to handoff * Add factory pattern to concurrent orchestration builder; Next: tests and sample verification * Add tests and improve comments * Fix mypy * Simplify handoff_simple.py * Simplify handoff_autonoumous.py and bug fix * Update readme * Address Copilot comments * Python: Flow custom kwargs to agents via Workflow SharedState (#2894) * Flow custom kwargs to agents via SharedState * Address Copilot feedback * Improve sample typing * Fix test * Fix Pydantic error when using Literal type for tool params (#2893) * Updated Ollama package version (#2920) * Python: Azure AI Agent with Bing Grounding Citations Sample (#2892) * bing grounding sample with citations * small fix * fix * .NET: Make DelegatingAIAgent abstract (#2797) * Initial plan * Make DelegatingAIAgent abstract Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Added additional arguments for Azure AI agent (#2922) * Python: Correction of MCP image type conversion in _mcp.py (#2901) * Correction of MCP image type conversion in _mcp.py * Added a new overload to the init function of the DataContent() type of the Agent Framework, edited the test case to correctly test the usage of the data and uri fields while using DataContent() * Fixed tests related to the changes of the DataContent type, added testing for both string and byte representations * Pass kwargs into subworkflows (#2923) * Python: Move ollama samples to samples getting started dir (#2921) * Move ollama samples to samples getting started dir * Address feedback * Python: fix: correct BadRequestError when using Pydantic model in response_fo… (#1843) * fix: correct BadRequestError when using Pydantic model in response_format * Fix lint --------- Co-authored-by: Evan Mattson <evan.mattson@microsoft.com> * .NET: [Breaking] Delete display name property (#2758) * delete the AIAgent.DisplayName property * use agent name as a first value for activity display name * Update dotnet/src/Microsoft.Agents.AI.Workflows/Specialized/HandoffAgentExecutor.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Python: cleanup and refactoring of chat clients (#2937) * refactoring and unifying naming schemes of internal methods of chat clients * set tool_choice to auto * fix for mypy * added note on naming and fix #2951 * fix responses * fixes in azure ai agents client * Python: Workflow add option to visualize internal executors (#2917) * Workflow add option to visualize internal executors * Address Copilot comments * Python: Fixes Run ID and Thread ID casing to align with AG-UI Typescript SDK (#2948) * added camelCase input to run id and thread id aligning with @ag-ui/core * fixed per copilot suggestions * Python: Add workflow cancellation sample (#2732) * Add workflow cancellation sample Add sample demonstrating how to cancel a running workflow using asyncio tasks. Shows both cancellation mid-execution and normal completion paths. Useful for implementing timeouts, graceful shutdown, or A2A executors. * update docstring * .NET: Update Anthropic package to version 12.0.0 (#2914) * Initial plan * Update Anthropic package to version 12.0.0 Co-authored-by: stephentoub <2642209+stephentoub@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: stephentoub <2642209+stephentoub@users.noreply.github.com> * Python: Add Azure Managed Redis Support with Credential Provider (#2887) * azure redis support * small fixes * azure managed redis sample * fixes * Bump CommunityToolkit.Aspire.OllamaSharp from 13.0.0-beta.440 to 13.0.0 (#2856) --- updated-dependencies: - dependency-name: CommunityToolkit.Aspire.OllamaSharp dependency-version: 13.0.0 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.11 to 4.0.5 (#2853) --- updated-dependencies: - dependency-name: AWSSDK.Extensions.Bedrock.MEAI dependency-version: 4.0.5 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> * Bump Azure.AI.AgentServer.AgentFramework from 1.0.0-beta.4 to 1.0.0-beta.5 (#2854) --- updated-dependencies: - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Azure.AI.AgentServer.AgentFramework dependency-version: 1.0.0-beta.5 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Python: Fix WorkflowAgent event handling and kwargs forwarding (#2946) * Fix kwargs propagation through workflow.as_agent() * Fix WorkflowAgent to respect AgentExecutor output_response setting * .NET: Use GrpcEntityRunner instead of TaskEntityDispatcher (#2759) * Use GrpcEntityRunner instead of TaskEntityDispatcher * Pin to Durable worker 1.11.0 * Set the invocation result * Update all Durable packages * Update changelog, rename dispatcher to encondedEntityRequest * Python: Bump Py version to 1.0.0b251218 for a release. Update CHANGELOG (#2968) * Bump Py version to 1.0.0b251218 for a release. Update CHANGELOG * update lock * Fix formatting * Fix ChatKit typing * Python: Introducing Foundry Local Chat Clients (#2915) * redo foundry local chat client * fix mypy and spelling * better docstring, updated sample * fixed tests and added tests * small sample update * Updated package versions (#2978) * Python: Added GitHub MCP sample with PAT (#2967) * added github mcp sample with PAT * addressed copilot fixes * env fix * Python: Preserve reasoning blocks with OpenRouter (#2950) * Preserve reasoning blocks with OpenRouter * Put encrypted reasoning in TextReasoningContent * Remove unneccessary change * Fix docs * Support streaming * Fix handling None in TextReasoningContent.text * Python: Added response.created and response.in_progress event process to OpenAIBaseResponseClient (#2975) * added response.created and response.in_progress to include response.id * better doc string * added tests for the new streaming event types * Python: Introducing support for Bedrock-hosted models (Anthropic, Cohere, etc.) (#2610) * Pushing the bedrock related changes to the new branch after addressing the review comments * 2524 Addressed the second round review comments * 2524 Addressed few more minor comments on the PR * resolving the merge conflict * 2524 resolved the uv.lock conflicts * 2524 addressed more comments * 2524 removed the print statement to fix the checks failure * 2524 resolved the CI failure issues * 2524 fixing the CI breaks * 2524 Addressed the review comment * 2524 resolved conflict --------- Co-authored-by: Sunil Dutta <sunil.dutta@penske.com> Co-authored-by: budgetboardingai <apurva.sharma31@gmail.com> * .NET: [Durable Agents] Reliable streaming sample (#2942) * .NET: [Durable Agents] Reliable streaming sample * Add automated validation for new sample * Address Copilot PR feedback * Fix typo in README.md about agent definitions (#2634) * Fix typo in README.md about agent definitions * Update agent-samples/README.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Python: latency improvements (#3014) * latency improvements * fixed mypy, added coding standards and instructions * slight logic improvement * Python: Updated package versions (#3024) * Updated package versions * Updated changelog * Python: add powerfx safe mode (#3028) * add powerfx safe mode * improved docstring and aligned env_file loading * ensured test uses reset * .NET: [Breaking] Introduce RunCoreAsync/RunCoreStreamingAsync delegation pattern in AIAgent (#2749) * Initial plan * Refactor AIAgent: Make RunAsync and RunStreamingAsync non-abstract, add RunCoreAsync and RunCoreStreamingAsync Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Fix infinite recursion in test implementations Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Make RunAsync and RunStreamingAsync non-virtual as requested Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Fix DelegatingAIAgent subclasses to use RunCoreAsync/RunCoreStreamingAsync Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Fix XML documentation references in AnonymousDelegatingAIAgent Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Restore <see cref> tags with proper qualified signatures in AnonymousDelegatingAIAgent Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Rollback unnecessary XML documentation changes in AnonymousDelegatingAIAgent Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Remove pragma and update crefs to RunCoreAsync/RunCoreStreamingAsync Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Fix EntityAgentWrapper to call base.RunCoreAsync/RunCoreStreamingAsync Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * fix compilation issues * fix compilatio issue * fix tests * fix unit tests * fix unit test --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> Co-authored-by: SergeyMenshykh <sergemenshikh@gmail.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * add issue template and additional labeling (#3006) * fix and extra int test (#3037) * .NET: [BREAKING] Refactor ChatMessageStore methods to be similar to AIContextProvider and add filtering support (#2604) * Refactor ChatMessageStore methods to be similar to AIContextProvider * Fix file encoding * Ensure that AIContextProvider messages area also persisted. * Update formatting and seal context classes * Improve formatting * Remove optional messages from constructor and add unit test * Add ChatMessageStore filtering via a decorator * Update sample and cosmos message store to store AIContextProvider messages in right order. Fix unit tests. * Update Workflowmessage store to use aicontext provider messages. * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Improve xml docs messaging * Address code review comments. * Also notify message store on failure --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * [BREAKING] Remove unused AgentThreadMetadata (#3067) * Remove unused AgentThreadMetadata * Update DurableTask Changelog * Python: Fix AzureAIClient failure when conversation history contains assistant messages (#3076) * Fix AzureAIClient failure when conversation history contains assistant messages * Address PR review feedback: improve docstring and test assertions * Remove redundant cast * Fix: Update OTLP exporter protocol conditions (#3070) * Python: Fix ExecutorInvokedEvent and ExecutorCompletedEvent observability data (#3090) * Fix ExecutorInvokedEvent.data mutation bug * Fix bug related to not yielding output type * .NET: Seal ChatClientAgentThread (#2842) * Initial plan * Seal ChatClientAgentThread class Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * Fix broken strands urls. (#3102) * Fix broken strands urls. * Fix typos * .NET: Fix message ordering inconsistency when using AIContextProvider (#2659) * Initial plan * Fix message ordering inconsistency when using AIContextProvider Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Revert to original message ordering: Input, AIContextProvider, Response Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Reorder messages to ChatClient to match MessageStore order: Existing, Input, AIContextProvider Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Remove redundant test methods as existing tests already verify the behavior Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * fix: tool_choice parameter not being honored when passed to agent.run() (#3095) * sharepoint sample fix (#3108) * Bump versions to 1.0.0b260106 for a release. Update CHANGELOG.md (#3109) * Bump Bedrock version to latest (#3110) * Python: Fix MCP tool result serialization for list[TextContent] (#2523) * Fix MCP tool result serialization for list[TextContent] When MCP tools return results containing list[TextContent], they were incorrectly serialized to object repr strings like: '[<agent_framework._types.TextContent object at 0x...>]' This fix properly extracts text content from list items by: 1. Checking if items have a 'text' attribute (TextContent) 2. Using model_dump() for items that support it 3. Falling back to str() for other types 4. Joining single items as plain text, multiple items as JSON array Fixes #2509 * Address PR review feedback for MCP tool result serialization - Extract serialize_content_result() to shared _utils.py - Fix logic: use texts[0] instead of join for single item - Add type annotation: texts: list[str] = [] - Return empty string for empty list instead of '[]' - Move import json to file top level - Add comprehensive unit tests for serialization * Address PR review feedback: fix type checking and double serialization - Add isinstance(item.text, str) check to ensure text attribute is a string - Fix double-serialization issue by keeping model_dump results as dicts until final json.dumps (removes escaped JSON strings in arrays) - Improve docstring with detailed return value documentation - Add test for non-string text attribute handling - Add tests for list type tool results in _events.py path * Simplify PR: minimal changes to fix MCP tool result serialization Addresses reviewer feedback about excessive refactoring: - Reset _events.py to original structure - Only add import and use serialize_content_result in one location - All review comments addressed in serialize_content_result(): - Added isinstance(item.text, str) check - Use model_dump(mode="json") to avoid double-serialization - Improved docstring with explicit return value documentation - Empty list returns "" instead of "[]" * Refactor: Move MCP TextContent serialization to core prepare_function_call_results Per reviewer feedback, moved the TextContent serialization logic from ag-ui's serialize_content_result to the core package's prepare_function_call_results function. Changes: - Added handling for objects with 'text' attribute (like MCP TextContent) in _prepare_function_call_results_as_dumpable - Removed serialize_content_result from ag-ui/_utils.py - Updated _events.py and _message_adapters.py to use prepare_function_call_results from core package - Updated tests to match the core function's behavior * Fix failing tests for prepare_function_call_results behavior - test_tool_result_with_none: Update expected value to 'null' (JSON serialization of None) - test_tool_result_with_model_dump_objects: Use Pydantic BaseModel instead of plain class * Fix B903 linter error: Convert MockTextContent to dataclass The ruff linter was reporting B903 (class could be dataclass or namedtuple) for the MockTextContent test helper classes. This commit converts them to dataclasses to satisfy the linter check. * Python: Improve DevUI, add Context Inspector view as new tab under traces (#2742) * Improve DevUI, add Context Inspector view as new tab under traces * fix mypy errors * fix: Handle stale MCP connections in DevUI executor MCP tools can become stale when HTTP streaming responses end - the underlying stdio streams close but `is_connected` remains True. This causes subsequent requests to fail with `ClosedResourceError`. Add `_ensure_mcp_connections()` to detect and reconnect stale MCP tools before agent execution. This is a workaround for an upstream Agent Framework issue where connection state isn't properly tracked. Fixes MCP tools failing on second HTTP request in DevUI. fixes #1476 #1515 #2865 * fix #1572 report import dependency errors more clearly * Ensure there is streaming toggle where users can select streaming vs non streaming mode in devui . Fixes .NET: [Python] DevUI tool call rendering in non-streaming mode? * remove unused dead code * improve ux - workflows with agents show a chat component in execution timelien, also ensure magentic final output shows correctly * update ui build * update devui to use instrumentation instead of tracing, other instrumentation and type/instance check fixes * .NET: Seal factory contexts and add non JSO deserialize overloads (#3066) * Seal factory contexts and add non JSO deserialize overloads * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Enable blank issues in issue template configuration Need to re-enable creating blank issues * updated templates (#3106) * updated templates * enabled blank and fixed triage * made language optional and moved to the bottom for features * Python: Streaming sample for azurefunctions (#3057) * Streaming sample for azurefunctions * Fixed links and sample name * Addressed feedback * Addressed feedback * Fixed integration tests * Updated test * Python: fix(azure-ai): Fix response_format handling for structured outputs (#3114) * fix(azure-ai): read response_format from chat_options instead of run_options * refactor: use explicit None checks for response_format * Fix mypy error * Mypy fix * Python: Bump python version to 1.0.0b260107 for a release (#3128) * Bump python version to 1.0.0b260107 for a release * Update changelog * Make A2AAgent public, so that it's concrete implementation methods can be used. (#3119) * .NET: Map additional props <-> A2A metadata (#3137) * map additional props from agent run options to a2a request metadata * small touches * add unit tests for new extension methods * Sort using * add unit test * add additiona unit tests * special case json element to avoid unnecessary serialization * Python: Fix Anthropic streaming response bugs (#3141) * test commit identity * fix(anthropic): fix raw_representation and finish_reason in streaming * lint fix * Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.5 to 4.0.5.1 (#2994) --- updated-dependencies: - dependency-name: AWSSDK.Extensions.Bedrock.MEAI dependency-version: 4.0.5.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Bump Anthropic from 12.0.0 to 12.0.1 (#2993) --- updated-dependencies: - dependency-name: Anthropic dependency-version: 12.0.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * .NET: [Breaking] Prevent loss of input messages & streamed updates when resuming streaming (#2748) * save input messages and stream updates to the continuation token to be able to use them in the last successful stream resumption call. * Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/tests/Microsoft.Agents.AI.UnitTests/ChatClient/ChatClientAgent_BackgroundResponsesTests.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/src/Microsoft.Agents.AI/ChatClient/ChatClientAgentContinuationToken.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix typo * init continuation token from chat response * remove unnecessary types for source generation * remove check for continuation token passed at initial run * remove check for continuation token pass at initial run * centralize continuation token parsing * update xml comments * use readonly collection instead of enumerable --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * .NET: fix: Expose WorkflowErrorEvent as ErrorContent (#2762) * fix: Expose WorkflowErrorEvent as ErrorContent When hosted using .AsAgent(), Workflows were not exposing inner errors coming as Exceptions (through the WorkflowErrorEvent) The fix is to convert their message to an ErrorContent on the way out, rather than rely on the default "empty update" to collect the raw event. * feat: Add a way to show/suppress exception information * Bump Microsoft.Agents.AI.Workflows from 1.0.0-preview.251125.1 to 1.0.0-preview.251219.1 (#2997) --- updated-dependencies: - dependency-name: Microsoft.Agents.AI.Workflows dependency-version: 1.0.0-preview.251219.1 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> * .NET: Add Run overloads to expose ChatClientAgentRunOptions in IntelliSense (#3115) * Initial plan * Add ChatClientAgentExtensions for improved discoverability of ChatClientAgentRunOptions Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Address code review feedback - use collection expression syntax Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Apply suggestion from @westey-m * Fix issues with Copilot implementation * Add additional tests for structured output overloads. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: westey-m <164392973+westey-m@users.noreply.github.com> * Python: Add tool call/result content types and update connectors and samples (#2971) * Add new AI content types and image tool support Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Add Python content types for tool calls/results and image generation tool support Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Address review feedback for tool content and samples Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Tighten image generation typing and sample tools list Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Align image generation output typing Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Handle MCP naming, image options mapping, and connector tool content Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Allow MCP call in function approval request Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Remove raw image_generation tool remapping Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Restore Anthropic tool_use to function calls unless code execution Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix lint issues for hosted file docstring and MCP parsing Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Import ChatResponse types in Anthropic client Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix Anthropics citation type imports and MCP typing for handoff/tools Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Skip lightning tests without agentlightning and fix function call import Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * fix lint on lab package * rebuilt anthropic parsing * redid anthropic parsing * typo * updated parsing and added missing docstrings * fix tests * mypy fixes * second mypy fix * add new class to other samples --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> Co-authored-by: eavanvalkenburg <github@vanvalkenburg.eu> * Bump Google.GenAI from 0.6.0 to 0.9.0 (#2995) --- updated-dependencies: - dependency-name: Google.GenAI dependency-version: 0.9.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> * Bump js-yaml from 4.1.0 to 4.1.1 in /python/packages/devui/frontend (#3123) Bumps [js-yaml](https://github.com/nodeca/js-yaml) from 4.1.0 to 4.1.1. - [Changelog](https://github.com/nodeca/js-yaml/blob/master/CHANGELOG.md) - [Commits](https://github.com/nodeca/js-yaml/compare/4.1.0...4.1.1) --- updated-dependencies: - dependency-name: js-yaml dependency-version: 4.1.1 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Updated package versions (#3144) * .NET: Bump Microsoft.Agents.AI.OpenAI and Microsoft.Extensions.AI.OpenAI (#2996) * Bump Microsoft.Agents.AI.OpenAI and Microsoft.Extensions.AI.OpenAI Bumps Microsoft.Agents.AI.OpenAI from 1.0.0-preview.251125.1 to 1.0.0-preview.251219.1 Bumps Microsoft.Extensions.AI.OpenAI from 10.1.0-preview.1.25608.1 to 10.1.1-preview.1.25612.2 --- updated-dependencies: - dependency-name: Microsoft.Agents.AI.OpenAI dependency-version: 1.0.0-preview.251219.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Microsoft.Extensions.AI.OpenAI dependency-version: 10.1.1-preview.1.25612.2 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Microsoft.Agents.AI.OpenAI dependency-version: 1.0.0-preview.251219.1 dependency-type: direct:production update-type: version-update:semver-patch - dependency-name: Microsoft.Extensions.AI.OpenAI dependency-version: 10.1.1-preview.1.25612.2 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> * Fixed samples --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> * Python: fix(ag-ui): Execute tools with approval_mode, fix shared state, code cleanup (#3079) * fix(ag-ui): execute tools after approval in human-in-the-loop flow * Fix shared state bug * Bug fix finalized * Refactoring to clean up code * Code cleanup * More fixes * More code cleanup * Add version detection in __init__.py to ruff ignore list * Track agent name with updates for workflow agent (#3146) * Python: Fix AzureAIClient tool call bug for AG-UI use (#3148) * Fiz AzureAIClient tool call bug * Address copilot feedback * Python: multiple bug fixes (#3150) * fix Python: kwargs are not passed to _prepare_thread_and_messages in ChatAgent.run Fixes #3118 * fix Python: [Bug]: model_id versus model_deployment_name is confusing in Azure AI Agents Fixes #3147 * add types * fixed type and docstring * fix(anthropic): fix duplicate ToolCallStartEvent in streaming tool calls (#3051) When processing `input_json_delta` events, the Anthropic client was passing the tool name from the previous `tool_use` event. This caused ag-ui's `_handle_function_call_content` to emit a `ToolCallStartEvent` for every streaming chunk (since it triggers on `if content.name:`). This fix changes the behavior to pass an empty string for `name` in `input_json_delta` events, matching OpenAI's behavior where streaming argument chunks have `name=""`. The initial `tool_use` event still provides the tool name, so only one `ToolCallStartEvent` is emitted. Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> * .NET: [BREAKING] Change GetNewThread and DeserializeThread to async (#3152) * Change GetNewThread and DeserializeThread plus ChatMessageStore and AIContextProvider Factories to async * Merge fixes * Fix Ollama model env var in documentation (#3156) Signed-off-by: Dina Suehiro Jones <dina.s.jones@intel.com> * Python: Add Pydantic request model and OpenAPI tags support to AG-UI FastAPI endpoint (#2522) * feat(ag-ui): Add Pydantic request model and OpenAPI tags support - Add AGUIRequest Pydantic model in _types.py with field descriptions - Update add_agent_framework_fastapi_endpoint() to accept tags parameter - Use AGUIRequest model for automatic validation and OpenAPI schema generation - Export AGUIRequest and DEFAULT_TAGS in __init__.py - Update test_endpoint.py to expect 422 for invalid requests - Add tests for OpenAPI schema, default tags, custom tags, and validation Benefits: - Better API documentation with complete request schema in Swagger UI - Automatic request validation with Pydantic - Organized endpoints under 'AG-UI' tag instead of 'default' - Improved developer experience and type safety Fixes #<issue-number> * test(ag-ui): Add test for internal error handling to achieve 100% coverage - Add test_endpoint_internal_error_handling() to cover exception handling code - Mock copy.deepcopy to simulate internal error during default_state processing - Add type: ignore for FastAPI tags parameter (known pyright compatibility issue) - Achieves 100% test coverage for _endpoint.py (previously missing lines 103-105) * .NET: Improve resolving `AITool` from DI (#3175) * remove localagenttoolregistry * also give the factory method API * Python: Fix MCPStreamableHTTPTool to use new streamable_http_client API (#3088) * Fix MCPStreamableHTTPTool to use new streamable_http_client API with proper httpx client cleanup Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Update docstring to reflect new streamable_http_client API usage Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Refactor MCPStreamableHTTPTool to accept optional http_client parameter and delegate client creation to streamable_http_client Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Update mcp package minimum version to 1.24.0 for streamable_http_client API support Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Fix critical bugs: apply headers/timeout/sse_read_timeout when creating httpx client, add version constraint <2, and properly manage client lifecycle Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Simplify implementation: remove headers/timeout/sse_read_timeout params, remove kwargs, remove close() override per feedback Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Add back **kwargs parameter for backward compatibility (accepted but not used) Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * Remove unused httpx import from test file Note: The uv.lock file needs to be updated with 'uv sync' to reflect the mcp version constraint change (>=1.24.0,<2) Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> * cicd fixes * udpated samples with headers examples --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> Co-authored-by: eavanvalkenburg <github@vanvalkenburg.eu> * azureai direct a2a endpoint support (#3127) * Python: [BREAKING]: removed display_name, renamed context_providers, middleware and AggregateContextProvider (#3139) * removed display_name, renamed context_providers, middleware and AggregateContextProvider * fixes * fixed test * testfix * removed mistakenly put back test * updated new test * rename middlewares to middleware * middleware fixes * Python: MCP Improvements: improved connection loss behavior, pagination for loading and a param to control representation (#3154) * pagination support (#2848) added a parse_tool_result param and connection loss (#2884) * fix #3153 * improved connection handling * improved logic * Python: Add declarative workflow runtime (#2815) * Further support for declarative python workflows * Add tests. Clean up for typing and formatting * Improvements and cleanup * Typing cleanup. Improve docstrings * Proper code in docstrings * Fix malformed code-block directive in docstring * Remove dead links * PR feedback * Address PR feedback * Address PR feedback * Remove sl * Update devui frontend * More cleanup * Fix uv lock * Skip Py 3.14 tests as powerfx doesn't support it * Fix mypy error * Fix for tool calls * Removed stale docstring * Fix lint * Standardize on .NET namespaces. Revert DevUI changes (bring in later) * Implement remaining items for Python declarative support to match dotnet * point URL to agent, not to agentcard (#3176) * Python: [BREAKING]: Introducing Options as TypedDict and Generic (#3140) * WIP typeddict for options * updated all clients and ChatAgents * updated everything * added ADR * fix mypy * proper typevar imports * fixed import * fixed other imports * slight update in the sample * updated from feedback * fixes * fixed missing covariants and test fixes * fixed typing * updated anthropic thinking config * ruff fixes * fixed int tests * fix tests and mypy * updated integration tests * updated docstring and test fix * improved options handling in obser * mypy fix * updated a host of integration tests * fix tests * bedrock fix * [BREAKING] Python: Refactor orchestrations (#3023) * Group chat refactoring Part 1; Next: HIL and handoff * Add agent approval flow; next samples * WIP: samples * WIP: HIL samples * Group chat HIL working; next: handoff * Fix group chat tool approval sample * WIP: refactor handoff; next handoff handling * Handoff done; next handoff samples and concurrent and sequential * Handoff samples, concurrent, and sequential done; next Magentic * WIP: magentic; next test with samples + HIL * Magentic Working; next fix all samples and tests * Fix handoff samples; next tests * WIP: fixing tests; some orchestration as agent samples are failing * Group chat unit tests done * Handoff unit tests done * Remove old orchestration_request_info and fix related tests * Magentic unit tests done * Fix samples * Fix test * Fix test 2 * mypy * Address comments * Update readme * Address comments * Address comments 2 * Replace display name * Python: ADR for create/get agent API (#2618) * ADR for create/get agent API * Updated ADR with implementation options * Small updates * Updated decision outcome section * Updated broken links * Small updates * Fixed merge conflicts * Small fix * Updated decision outcome section * Small fixes * Updated provider naming based on client SDK * Add ignored parameter for CodeQL in workflow (#3204) * Implement IReadOnlyList on InMemoryChatMessageStore (#3205) * .NET: Make ChatMessageStore and AIContextProvider context props settable (#3196) * Make ChatMessageStore and AIContextProvider context props setable * Add validation to preserve non-null requirement of certain properties. * Fix broken tests. * Python: Add dependencies param to ag-ui FastAPI endpoint (#3191) * Add dependencies param to ag-ui FastAPI endpoint * Address Copilot feedback * renamed all (#3207) * Python: ADR for simplified get response (#3098) * ADR for simplified get response * updated some language, added agent option and code comparison * small update in sample * added workflows and expanded some points * changed decision and number * updated with stream=False default * .NET: [Breaking] Rename`AgentRunResponse` and `AgentRunResponseUpdate` classes (#3197) * rename AgentRunResponse and AgentRunResponseUpdate classes - part1 * rename varialbles, parameters, methods and tests * rollback unnecessary changes * .NET: [Breaking] Rename AgentRunResponseEvent and AgentRunUpdateEvent classes (#3214) * rename AgentRunResponseEvent and AgentRunUpdateEvent classes * rollback unnecessary changes * Python: Create/Get Agent API for Azure V2 (#3059) * Added get_agent method to Azure AI V2 * Small fixes * Small fix * Removed AzureAIAgentProvider * Added create_agent method * Small fixes * Fixed code interpreter tool mapping * Added agent provider for V2 client * Updated response format handling * Added provider example * Fixed errors * Update python/samples/getting_started/agents/azure_ai/README.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Small fix * Updates from merge * Resolved comments * Resolved comments --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Python: Add more specific exceptions to Workflow (#3188) * Add more specifc workflow exceptions * Fix tests * AI comments * Misc * Python: Added AzureAI sample for downloading code interpreter generated files (#3189) * added azure ai code interpreter file download sample * copilot fix suggestions * function name fixes + readme update * small fix * update package versions (#3223) Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> * Python: fix(core): correct FunctionResultContent ordering in WorkflowAgent.merge_updates (#3168) * fix(core): simplify FunctionResultContent ordering in WorkflowAgent.merge_updates * improve comment * Fix name * fix(workflows): rename WorkflowOutputEvent.source_executor_id to executor_id for API consistency (#3166) * Python: fix(ag-ui): add MCP tool support for AG-UI approval flows (#3212) * add MCP tool support for AG-UI approval flows * use attribute in place of property * Python: Properly configure structured outputs based on new options dict (#3213) * Properly configure structured outputs based on new options dict * Fix mypy * .NET: Merge AgentRunOptions.AdditionalProperties into ChatOptions.AdditionalProperties (#3184) * Merge AgentRunOptions.AdditionalProperties into ChatOptions.AdditionalProperties * Fix namespace and typo. * .NET: Update Google.GenAI to 0.11.0 and remove polyfill implementations (#3232) * Initial plan * Update Google.GenAI to 0.11.0 and remove polyfill files Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> * .NET: [BREAKING] Renamed CreateAIAgent/GetAIAgent to AsAIAgent (#3222) * Renamed chat client extension method * Additional renaming * Updated documentation * Fixed tests * Small fix * Small fix * Updated DurableAIAgent and fixed integration tests (#3241) * Python: Create/Get Agent API for Azure V1 (#3192) * Added provider implementation for Azure AI V1 * Small fixes * Fixed OpenAPI example * Fixed local MCP example * Fixed hosted MCP example * Fixed file search sample * Small fixes * Resolved comments * Doc updates * Bump azure-core from 1.37.0 to 1.38.0 in /python (#3209) Bumps [azure-core](https://github.com/Azure/azure-sdk-for-python) from 1.37.0 to 1.38.0. - [Release notes](https://github.com/Azure/azure-sdk-for-python/releases) - [Commits](https://github.com/Azure/azure-sdk-for-python/compare/azure-core_1.37.0...azure-core_1.38.0) --- updated-dependencies: - dependency-name: azure-core dependency-version: 1.38.0 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Python: Create/Get Agent API for OpenAI Assistants (#3208) * Added provider implementation * Added example with response format * Small improvements * Python: (AG-UI) Support service-managed thread on AG-UI (#3136) * added service thread support * set service_thread_id to only supplied_thread_id * uses raw_representation to extract the conversation_id * removed accidental edit * updated test to use raw_representation * resolves copilot review feedback * revert back StubAgent, since not used * removed relative module import * removed hasattr check per PR feedback * Create/Get Agent API - fixes and example improvements (#3246) * Fix merge conflicts --------- Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: Dina Suehiro Jones <dina.s.jones@intel.com> Co-authored-by: Tao Chen <taochen@microsoft.com> Co-authored-by: Kurt <65111699+q33566@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com> Co-authored-by: SergeyMenshykh <68852919+SergeyMenshykh@users.noreply.github.com> Co-authored-by: Korolev Dmitry <deagle.gross@gmail.com> Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> Co-authored-by: Chris <66376200+crickman@users.noreply.github.com> Co-authored-by: Jose Luis Latorre Millas <joslat@gmail.com> Co-authored-by: Jacob Alber <jaalber@microsoft.com> Co-authored-by: Richard Ortega <richardjortega@gmail.com> Co-authored-by: 刘邦学AI <lbbniu@gmail.com> Co-authored-by: Stephen Toub <stoub@microsoft.com> Co-authored-by: Nico Möller <nkm-moeller@mail.de> Co-authored-by: Chris Gillum <cgillum@microsoft.com> Co-authored-by: Giles Odigwe <79032838+giles17@users.noreply.github.com> Co-authored-by: Phillip Hoff <phillip.hoff@gmail.com> Co-authored-by: Ege Ozan Özyedek <36128615+egeozanozyedek@users.noreply.github.com> Co-authored-by: samueljohnsiby <66901393+samueljohnsiby@users.noreply.github.com> Co-authored-by: Evan Mattson <evan.mattson@microsoft.com> Co-authored-by: Hao Luo <338265+howlowck@users.noreply.github.com> Co-authored-by: Victor Dibia <chuvidi2003@gmail.com> Co-authored-by: stephentoub <2642209+stephentoub@users.noreply.github.com> Co-authored-by: Jacob Viau <javia@microsoft.com> Co-authored-by: SuperKenVery <39673849+SuperKenVery@users.noreply.github.com> Co-authored-by: Sunil Dutta <dutta.2003@gmail.com> Co-authored-by: Sunil Dutta <sunil.dutta@penske.com> Co-authored-by: budgetboardingai <apurva.sharma31@gmail.com> Co-authored-by: Syrine Chelly <62653967+SyChell@users.noreply.github.com> Co-authored-by: SergeyMenshykh <sergemenshikh@gmail.com> Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> Co-authored-by: takanori-terai <123897708+takanori-terai@users.noreply.github.com> Co-authored-by: claude89757 <138977524+claude89757@users.noreply.github.com> Co-authored-by: Gavin Aguiar <80794152+gavin-aguiar@users.noreply.github.com> Co-authored-by: Sukeesh <vsukeeshbabu@gmail.com> Co-authored-by: eavanvalkenburg <13749212+eavanvalkenburg@users.noreply.github.com> Co-authored-by: eavanvalkenburg <github@vanvalkenburg.eu> Co-authored-by: Ao Chen <chenao3220@gmail.com> Co-authored-by: Dina Suehiro Jones <dina.s.jones@intel.com>
2026-06-16 21:04:09 +08:00 · 2026-01-16 16:59:49 -08:00
parent 3df916064c
commit cd77193742
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+# Declarative Workflows
+
+Declarative workflows allow you to define multi-agent orchestration patterns in YAML, including:
+- Variable manipulation and state management
+- Control flow (loops, conditionals, branching)
+- Agent invocations
+- Human-in-the-loop patterns
+
+See the [main workflows README](../README.md#declarative) for the list of available samples.
+
+## Prerequisites
+
+```bash
+pip install agent-framework-declarative
+```
+
+## Running Samples
+
+Each sample directory contains:
+- `workflow.yaml` - The declarative workflow definition
+- `main.py` - Python code to load and execute the workflow
+- `README.md` - Sample-specific documentation
+
+To run a sample:
+
+```bash
+cd <sample_directory>
+python main.py
+```
+
+## Workflow Structure
+
+A basic workflow YAML file looks like:
+
+```yaml
+name: my-workflow
+description: A simple workflow example
+
+actions:
+  - kind: SetValue
+    path: turn.greeting
+    value: Hello, World!
+    
+  - kind: SendActivity
+    activity:
+      text: =turn.greeting
+```
+
+## Action Types
+
+### Variable Actions
+- `SetValue` - Set a variable in state
+- `SetVariable` - Set a variable (.NET style naming)
+- `AppendValue` - Append to a list
+- `ResetVariable` - Clear a variable
+
+### Control Flow
+- `If` - Conditional branching
+- `Switch` - Multi-way branching
+- `Foreach` - Iterate over collections
+- `RepeatUntil` - Loop until condition
+- `GotoAction` - Jump to labeled action
+
+### Output
+- `SendActivity` - Send text/attachments to user
+- `EmitEvent` - Emit custom events
+
+### Agent Invocation
+- `InvokeAzureAgent` - Call an Azure AI agent
+- `InvokePromptAgent` - Call a local prompt agent
+
+### Human-in-Loop
+- `Question` - Request user input
+- `WaitForInput` - Pause for external input
@@ -0,0 +1,3 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Declarative workflows samples package."""
@@ -0,0 +1,23 @@
+# Conditional Workflow Sample
+
+This sample demonstrates control flow with conditions:
+- If/else branching
+- Switch statements
+- Nested conditions
+
+## Files
+
+- `workflow.yaml` - The workflow definition
+- `main.py` - Python code to execute the workflow
+
+## Running
+
+```bash
+python main.py
+```
+
+## What It Does
+
+1. Takes a user's age as input
+2. Uses conditions to determine an age category
+3. Sends appropriate messages based on the category
@@ -0,0 +1,52 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the conditional workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates conditional branching based on age input.
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework.declarative import WorkflowFactory
+
+
+async def main() -> None:
+    """Run the conditional workflow with various age inputs."""
+    # Create a workflow factory
+    factory = WorkflowFactory()
+
+    # Load the workflow from YAML
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("-" * 40)
+
+    # Print out the executors in this workflow
+    print("\nExecutors in workflow:")
+    for executor_id, executor in workflow.executors.items():
+        print(f"  - {executor_id}: {type(executor).__name__}")
+    print("-" * 40)
+
+    # Test with different ages
+    test_ages = [8, 15, 35, 70]
+
+    for age in test_ages:
+        print(f"\n--- Testing with age: {age} ---")
+
+        # Run the workflow with age input
+        result = await workflow.run({"age": age})
+        for output in result.get_outputs():
+            print(f"  Output: {output}")
+
+    print("\n" + "-" * 40)
+    print("Workflow completed for all test cases!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,69 @@
+name: conditional-workflow
+description: Demonstrates conditional branching based on user input
+
+# Declare expected inputs with their types
+inputs:
+  age:
+    type: integer
+    description: The user's age in years
+
+actions:
+  # Get the age from input
+  - kind: SetValue
+    id: get_age
+    displayName: Get user age
+    path: Local.age
+    value: =inputs.age
+
+  # Determine age category using nested conditions
+  - kind: If
+    id: check_age
+    displayName: Check age category
+    condition: =Local.age < 13
+    then:
+      - kind: SetValue
+        path: Local.category
+        value: child
+      - kind: SendActivity
+        activity:
+          text: "Welcome, young one! Here are some fun activities for kids."
+    else:
+      - kind: If
+        condition: =Local.age < 20
+        then:
+          - kind: SetValue
+            path: Local.category
+            value: teenager
+          - kind: SendActivity
+            activity:
+              text: "Hey there! Check out these cool things for teens."
+        else:
+          - kind: If
+            condition: =Local.age < 65
+            then:
+              - kind: SetValue
+                path: Local.category
+                value: adult
+              - kind: SendActivity
+                activity:
+                  text: "Welcome! Here are our professional services."
+            else:
+              - kind: SetValue
+                path: Local.category
+                value: senior
+              - kind: SendActivity
+                activity:
+                  text: "Welcome! Enjoy our senior member benefits."
+
+  # Send a summary
+  - kind: SendActivity
+    id: summary
+    displayName: Send category summary
+    activity:
+      text: '=Concat("You have been categorized as: ", Local.category)'
+
+  # Store result
+  - kind: SetValue
+    id: set_output
+    path: Workflow.Outputs.category
+    value: =Local.category
@@ -0,0 +1,37 @@
+# Customer Support Workflow Sample
+
+Multi-agent workflow demonstrating automated troubleshooting with escalation paths.
+
+## Overview
+
+Coordinates six specialized agents to handle customer support requests:
+
+1. **SelfServiceAgent** - Initial troubleshooting with user
+2. **TicketingAgent** - Creates tickets when escalation needed
+3. **TicketRoutingAgent** - Routes to appropriate team
+4. **WindowsSupportAgent** - Windows-specific troubleshooting
+5. **TicketResolutionAgent** - Resolves tickets
+6. **TicketEscalationAgent** - Escalates to human support
+
+## Files
+
+- `workflow.yaml` - Workflow definition with conditional routing
+- `main.py` - Agent definitions and workflow execution
+- `ticketing_plugin.py` - Mock ticketing system plugin
+
+## Running
+
+```bash
+python main.py
+```
+
+## Example Input
+
+```
+My PC keeps rebooting and I can't use it.
+```
+
+## Requirements
+
+- Azure OpenAI endpoint configured
+- `az login` for authentication
@@ -0,0 +1 @@
+# Copyright (c) Microsoft. All rights reserved.
@@ -0,0 +1,341 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+CustomerSupport workflow sample.
+
+This workflow demonstrates using multiple agents to provide automated
+troubleshooting steps to resolve common issues with escalation options.
+
+Example input: "My PC keeps rebooting and I can't use it."
+
+Usage:
+    python main.py
+
+The workflow:
+1. SelfServiceAgent: Works with user to provide troubleshooting steps
+2. TicketingAgent: Creates a ticket if issue needs escalation
+3. TicketRoutingAgent: Determines which team should handle the ticket
+4. WindowsSupportAgent: Provides Windows-specific troubleshooting
+5. TicketResolutionAgent: Resolves the ticket when issue is fixed
+6. TicketEscalationAgent: Escalates to human support if needed
+"""
+
+import asyncio
+import json
+import logging
+import uuid
+from pathlib import Path
+
+from agent_framework import RequestInfoEvent, WorkflowOutputEvent
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.declarative import (
+    AgentExternalInputRequest,
+    AgentExternalInputResponse,
+    WorkflowFactory,
+)
+from azure.identity import AzureCliCredential
+from pydantic import BaseModel, Field
+from ticketing_plugin import TicketingPlugin
+
+logging.basicConfig(level=logging.ERROR)
+
+# ANSI color codes for output formatting
+CYAN = "\033[36m"
+GREEN = "\033[32m"
+YELLOW = "\033[33m"
+MAGENTA = "\033[35m"
+RESET = "\033[0m"
+
+# Agent Instructions
+
+SELF_SERVICE_INSTRUCTIONS = """
+Use your knowledge to work with the user to provide the best possible troubleshooting steps.
+
+- If the user confirms that the issue is resolved, then the issue is resolved.
+- If the user reports that the issue persists, then escalate.
+""".strip()
+
+TICKETING_INSTRUCTIONS = """Always create a ticket in Azure DevOps using the available tools.
+
+Include the following information in the TicketSummary.
+
+- Issue description: {{IssueDescription}}
+- Attempted resolution steps: {{AttemptedResolutionSteps}}
+
+After creating the ticket, provide the user with the ticket ID."""
+
+TICKET_ROUTING_INSTRUCTIONS = """Determine how to route the given issue to the appropriate support team.
+
+Choose from the available teams and their functions:
+- Windows Activation Support: Windows license activation issues
+- Windows Support: Windows related issues
+- Azure Support: Azure related issues
+- Network Support: Network related issues
+- Hardware Support: Hardware related issues
+- Microsoft Office Support: Microsoft Office related issues
+- General Support: General issues not related to the above categories"""
+
+WINDOWS_SUPPORT_INSTRUCTIONS = """
+Use your knowledge to work with the user to provide the best possible troubleshooting steps
+for issues related to Windows operating system.
+
+- Utilize the "Attempted Resolutions Steps" as a starting point for your troubleshooting.
+- Never escalate without troubleshooting with the user.
+- If the user confirms that the issue is resolved, then the issue is resolved.
+- If the user reports that the issue persists, then escalate.
+
+Issue: {{IssueDescription}}
+Attempted Resolution Steps: {{AttemptedResolutionSteps}}"""
+
+RESOLUTION_INSTRUCTIONS = """Resolve the following ticket in Azure DevOps.
+Always include the resolution details.
+
+- Ticket ID: #{{TicketId}}
+- Resolution Summary: {{ResolutionSummary}}"""
+
+ESCALATION_INSTRUCTIONS = """
+You escalate the provided issue to human support team by sending an email.
+
+Here are some additional details that might help:
+- TicketId : {{TicketId}}
+- IssueDescription : {{IssueDescription}}
+- AttemptedResolutionSteps : {{AttemptedResolutionSteps}}
+
+Before escalating, gather the user's email address for follow-up.
+If not known, ask the user for their email address so that the support team can reach them when needed.
+
+When sending the email, include the following details:
+- To: support@contoso.com
+- Cc: user's email address
+- Subject of the email: "Support Ticket - {TicketId} - [Compact Issue Description]"
+- Body:
+  - Issue description
+  - Attempted resolution steps
+  - User's email address
+  - Any other relevant information from the conversation history
+
+Assure the user that their issue will be resolved and provide them with a ticket ID for reference."""
+
+
+# Pydantic models for structured outputs
+
+
+class SelfServiceResponse(BaseModel):
+    """Response from self-service agent evaluation."""
+
+    IsResolved: bool = Field(description="True if the user issue/ask has been resolved.")
+    NeedsTicket: bool = Field(description="True if the user issue/ask requires that a ticket be filed.")
+    IssueDescription: str = Field(description="A concise description of the issue.")
+    AttemptedResolutionSteps: str = Field(description="An outline of the steps taken to attempt resolution.")
+
+
+class TicketingResponse(BaseModel):
+    """Response from ticketing agent."""
+
+    TicketId: str = Field(description="The identifier of the ticket created in response to the user issue.")
+    TicketSummary: str = Field(description="The summary of the ticket created in response to the user issue.")
+
+
+class RoutingResponse(BaseModel):
+    """Response from routing agent."""
+
+    TeamName: str = Field(description="The name of the team to route the issue")
+
+
+class SupportResponse(BaseModel):
+    """Response from support agent."""
+
+    IsResolved: bool = Field(description="True if the user issue/ask has been resolved.")
+    NeedsEscalation: bool = Field(
+        description="True resolution could not be achieved and the issue/ask requires escalation."
+    )
+    ResolutionSummary: str = Field(description="The summary of the steps that led to resolution.")
+
+
+class EscalationResponse(BaseModel):
+    """Response from escalation agent."""
+
+    IsComplete: bool = Field(description="Has the email been sent and no more user input is required.")
+    UserMessage: str = Field(description="A natural language message to the user.")
+
+
+async def main() -> None:
+    """Run the customer support workflow."""
+    # Create ticketing plugin
+    plugin = TicketingPlugin()
+
+    # Create Azure OpenAI client
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    # Create agents with structured outputs
+    self_service_agent = chat_client.create_agent(
+        name="SelfServiceAgent",
+        instructions=SELF_SERVICE_INSTRUCTIONS,
+        default_options={"response_format": SelfServiceResponse},
+    )
+
+    ticketing_agent = chat_client.create_agent(
+        name="TicketingAgent",
+        instructions=TICKETING_INSTRUCTIONS,
+        tools=plugin.get_functions(),
+        default_options={"response_format": TicketingResponse},
+    )
+
+    routing_agent = chat_client.create_agent(
+        name="TicketRoutingAgent",
+        instructions=TICKET_ROUTING_INSTRUCTIONS,
+        tools=[plugin.get_ticket],
+        default_options={"response_format": RoutingResponse},
+    )
+
+    windows_support_agent = chat_client.create_agent(
+        name="WindowsSupportAgent",
+        instructions=WINDOWS_SUPPORT_INSTRUCTIONS,
+        tools=[plugin.get_ticket],
+        default_options={"response_format": SupportResponse},
+    )
+
+    resolution_agent = chat_client.create_agent(
+        name="TicketResolutionAgent",
+        instructions=RESOLUTION_INSTRUCTIONS,
+        tools=[plugin.resolve_ticket],
+    )
+
+    escalation_agent = chat_client.create_agent(
+        name="TicketEscalationAgent",
+        instructions=ESCALATION_INSTRUCTIONS,
+        tools=[plugin.get_ticket, plugin.send_notification],
+        default_options={"response_format": EscalationResponse},
+    )
+
+    # Agent registry for lookup
+    agents = {
+        "SelfServiceAgent": self_service_agent,
+        "TicketingAgent": ticketing_agent,
+        "TicketRoutingAgent": routing_agent,
+        "WindowsSupportAgent": windows_support_agent,
+        "TicketResolutionAgent": resolution_agent,
+        "TicketEscalationAgent": escalation_agent,
+    }
+
+    # Print loaded agents (similar to .NET "PROMPT AGENT: AgentName:1")
+    for agent_name in agents:
+        print(f"{CYAN}PROMPT AGENT: {agent_name}:1{RESET}")
+
+    # Create workflow factory
+    factory = WorkflowFactory(agents=agents)
+
+    # Load workflow from YAML
+    samples_root = Path(__file__).parent.parent.parent.parent.parent.parent.parent
+    workflow_path = samples_root / "workflow-samples" / "CustomerSupport.yaml"
+    if not workflow_path.exists():
+        # Fall back to local copy if workflow-samples doesn't exist
+        workflow_path = Path(__file__).parent / "workflow.yaml"
+
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print()
+    print("=" * 60)
+
+    # Example input
+    user_input = "My computer won't boot"
+    pending_request_id: str | None = None
+
+    # Track responses for formatting
+    accumulated_response: str = ""
+    last_agent_name: str | None = None
+
+    print(f"\n{GREEN}INPUT:{RESET} {user_input}\n")
+
+    while True:
+        if pending_request_id:
+            # Continue workflow with user response
+            print(f"\n{YELLOW}WORKFLOW:{RESET} Restore\n")
+            response = AgentExternalInputResponse(user_input=user_input)
+            stream = workflow.send_responses_streaming({pending_request_id: response})
+            pending_request_id = None
+        else:
+            # Start workflow
+            stream = workflow.run_stream(user_input)
+
+        async for event in stream:
+            if isinstance(event, WorkflowOutputEvent):
+                data = event.data
+                source_id = getattr(event, "source_executor_id", "")
+
+                # Check if this is a SendActivity output (activity text from log_ticket, log_route, etc.)
+                if "log_" in source_id.lower():
+                    # Print any accumulated agent response first
+                    if accumulated_response and last_agent_name:
+                        msg_id = f"msg_{uuid.uuid4().hex[:32]}"
+                        print(f"{CYAN}{last_agent_name.upper()}:{RESET} [{msg_id}]")
+                        try:
+                            parsed = json.loads(accumulated_response)
+                            print(json.dumps(parsed))
+                        except (json.JSONDecodeError, TypeError):
+                            print(accumulated_response)
+                        accumulated_response = ""
+                        last_agent_name = None
+                    # Print activity
+                    print(f"\n{MAGENTA}ACTIVITY:{RESET}")
+                    print(data)
+                else:
+                    # Accumulate agent response (streaming text)
+                    if isinstance(data, str):
+                        accumulated_response += data
+                    else:
+                        accumulated_response += str(data)
+
+            elif isinstance(event, RequestInfoEvent) and isinstance(event.data, AgentExternalInputRequest):
+                request = event.data
+
+                # The agent_response from the request contains the structured response
+                agent_name = request.agent_name
+                agent_response = request.agent_response
+
+                # Print the agent's response
+                if agent_response:
+                    msg_id = f"msg_{uuid.uuid4().hex[:32]}"
+                    print(f"{CYAN}{agent_name.upper()}:{RESET} [{msg_id}]")
+                    try:
+                        parsed = json.loads(agent_response)
+                        print(json.dumps(parsed))
+                    except (json.JSONDecodeError, TypeError):
+                        print(agent_response)
+
+                # Clear accumulated since we printed from the request
+                accumulated_response = ""
+                last_agent_name = agent_name
+
+                pending_request_id = event.request_id
+                print(f"\n{YELLOW}WORKFLOW:{RESET} Yield")
+
+        # Print any remaining accumulated response at end of stream
+        if accumulated_response:
+            # Try to identify which agent this came from based on content
+            msg_id = f"msg_{uuid.uuid4().hex[:32]}"
+            print(f"\nResponse: [{msg_id}]")
+            try:
+                parsed = json.loads(accumulated_response)
+                print(json.dumps(parsed))
+            except (json.JSONDecodeError, TypeError):
+                print(accumulated_response)
+            accumulated_response = ""
+
+        if not pending_request_id:
+            break
+
+        # Get next user input
+        user_input = input(f"\n{GREEN}INPUT:{RESET} ").strip()  # noqa: ASYNC250
+        if not user_input:
+            print("Exiting...")
+            break
+        print()
+
+    print("\n" + "=" * 60)
+    print("Workflow Complete")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,79 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Ticketing plugin for CustomerSupport workflow."""
+
+import uuid
+from dataclasses import dataclass
+from enum import Enum
+from collections.abc import Callable
+
+# ANSI color codes
+MAGENTA = "\033[35m"
+RESET = "\033[0m"
+
+
+class TicketStatus(Enum):
+    """Status of a support ticket."""
+
+    OPEN = "open"
+    IN_PROGRESS = "in_progress"
+    RESOLVED = "resolved"
+    CLOSED = "closed"
+
+
+@dataclass
+class TicketItem:
+    """A support ticket."""
+
+    id: str
+    subject: str = ""
+    description: str = ""
+    notes: str = ""
+    status: TicketStatus = TicketStatus.OPEN
+
+
+class TicketingPlugin:
+    """Mock ticketing plugin for customer support workflow."""
+
+    def __init__(self) -> None:
+        self._ticket_store: dict[str, TicketItem] = {}
+
+    def _trace(self, function_name: str) -> None:
+        print(f"\n{MAGENTA}FUNCTION: {function_name}{RESET}")
+
+    def get_ticket(self, id: str) -> TicketItem | None:
+        """Retrieve a ticket by identifier from Azure DevOps."""
+        self._trace("get_ticket")
+        return self._ticket_store.get(id)
+
+    def create_ticket(self, subject: str, description: str, notes: str) -> str:
+        """Create a ticket in Azure DevOps and return its identifier."""
+        self._trace("create_ticket")
+        ticket_id = uuid.uuid4().hex
+        ticket = TicketItem(
+            id=ticket_id,
+            subject=subject,
+            description=description,
+            notes=notes,
+        )
+        self._ticket_store[ticket_id] = ticket
+        return ticket_id
+
+    def resolve_ticket(self, id: str, resolution_summary: str) -> None:
+        """Resolve an existing ticket in Azure DevOps given its identifier."""
+        self._trace("resolve_ticket")
+        if ticket := self._ticket_store.get(id):
+            ticket.status = TicketStatus.RESOLVED
+
+    def send_notification(self, id: str, email: str, cc: str, body: str) -> None:
+        """Send an email notification to escalate ticket engagement."""
+        self._trace("send_notification")
+
+    def get_functions(self) -> list[Callable[..., object]]:
+        """Return all plugin functions for registration."""
+        return [
+            self.get_ticket,
+            self.create_ticket,
+            self.resolve_ticket,
+            self.send_notification,
+        ]
@@ -0,0 +1,164 @@
+#
+# This workflow demonstrates using multiple agents to provide automated
+# troubleshooting steps to resolve common issues with escalation options.
+#
+# Example input: 
+# My PC keeps rebooting and I can't use it.
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: workflow_demo
+  actions:
+
+    # Interact with user until the issue has been resolved or 
+    # a determination is made that a ticket is required.
+    - kind: InvokeAzureAgent
+      id: service_agent
+      conversationId: =System.ConversationId
+      agent:
+        name: SelfServiceAgent
+      input:
+        externalLoop:
+          when: |-
+            =Not(Local.ServiceParameters.IsResolved)
+             And 
+             Not(Local.ServiceParameters.NeedsTicket)
+      output:
+        responseObject: Local.ServiceParameters
+
+    # All done if issue is resolved.
+    - kind: ConditionGroup
+      id: check_if_resolved
+      conditions:
+    
+        - condition: =Local.ServiceParameters.IsResolved
+          id: test_if_resolved
+          actions:
+            - kind: GotoAction
+              id: end_when_resolved
+              actionId: all_done
+    
+    # Create the ticket.
+    - kind: InvokeAzureAgent
+      id: ticket_agent
+      agent:
+        name: TicketingAgent
+      input:
+        arguments:
+          IssueDescription: =Local.ServiceParameters.IssueDescription
+          AttemptedResolutionSteps: =Local.ServiceParameters.AttemptedResolutionSteps
+      output:
+        responseObject: Local.TicketParameters
+
+    # Capture the attempted resolution steps.
+    - kind: SetVariable
+      id: capture_attempted_resolution
+      variable: Local.ResolutionSteps
+      value: =Local.ServiceParameters.AttemptedResolutionSteps
+
+    # Notify user of ticket identifier.
+    - kind: SendActivity
+      id: log_ticket
+      activity: "Created ticket #{Local.TicketParameters.TicketId}"
+
+    # Determine which team for which route the ticket.
+    - kind: InvokeAzureAgent
+      id: routing_agent
+      agent:
+        name: TicketRoutingAgent
+      input:
+        messages: =UserMessage(Local.ServiceParameters.IssueDescription)
+      output:
+        responseObject: Local.RoutingParameters
+
+    # Notify user of routing decision.
+    - kind: SendActivity
+      id: log_route
+      activity: Routing to {Local.RoutingParameters.TeamName}
+
+    - kind: ConditionGroup
+      id: check_routing
+      conditions:
+
+        - condition: =Local.RoutingParameters.TeamName = "Windows Support"
+          id: route_to_support
+          actions:
+
+            # Invoke the support agent to attempt to resolve the issue.
+            - kind: CreateConversation
+              id: conversation_support
+              conversationId: Local.SupportConversationId
+
+            - kind: InvokeAzureAgent
+              id: support_agent
+              conversationId: =Local.SupportConversationId
+              agent:
+                name: WindowsSupportAgent
+              input:
+                arguments:
+                  IssueDescription: =Local.ServiceParameters.IssueDescription
+                  AttemptedResolutionSteps: =Local.ServiceParameters.AttemptedResolutionSteps
+                externalLoop:
+                  when: |-
+                    =Not(Local.SupportParameters.IsResolved)
+                     And 
+                     Not(Local.SupportParameters.NeedsEscalation)
+              output:
+                autoSend: true
+                responseObject: Local.SupportParameters
+
+            # Capture the attempted resolution steps.
+            - kind: SetVariable
+              id: capture_support_resolution
+              variable: Local.ResolutionSteps
+              value: =Local.SupportParameters.ResolutionSummary
+
+            # Check if the issue was resolved by support.
+            - kind: ConditionGroup
+              id: check_resolved
+              conditions:
+
+                # Resolve ticket
+                - condition: =Local.SupportParameters.IsResolved
+                  id: handle_if_resolved
+                  actions:
+        
+                    - kind: InvokeAzureAgent
+                      id: resolution_agent
+                      agent:
+                        name: TicketResolutionAgent
+                      input:
+                        arguments:
+                          TicketId: =Local.TicketParameters.TicketId
+                          ResolutionSummary: =Local.SupportParameters.ResolutionSummary
+
+                    - kind: GotoAction
+                      id: end_when_solved
+                      actionId: all_done
+
+    # Escalate the ticket by sending an email notification.
+    - kind: CreateConversation
+      id: conversation_escalate
+      conversationId: Local.EscalationConversationId
+
+    - kind: InvokeAzureAgent
+      id: escalate_agent
+      conversationId: =Local.EscalationConversationId
+      agent:
+        name: TicketEscalationAgent
+      input:
+        arguments:
+          TicketId: =Local.TicketParameters.TicketId
+          IssueDescription: =Local.ServiceParameters.IssueDescription
+          ResolutionSummary: =Local.ResolutionSteps
+        externalLoop:
+          when: =Not(Local.EscalationParameters.IsComplete)
+      output:
+        autoSend: true
+        responseObject: Local.EscalationParameters
+
+    # All done
+    - kind: EndWorkflow
+      id: all_done
@@ -0,0 +1,33 @@
+# Deep Research Workflow Sample
+
+Multi-agent workflow implementing the "Magentic" orchestration pattern from AutoGen.
+
+## Overview
+
+Coordinates specialized agents for complex research tasks:
+
+**Orchestration Agents:**
+- **ResearchAgent** - Analyzes tasks and correlates relevant facts
+- **PlannerAgent** - Devises execution plans
+- **ManagerAgent** - Evaluates status and delegates tasks
+- **SummaryAgent** - Synthesizes final responses
+
+**Capability Agents:**
+- **KnowledgeAgent** - Performs web searches
+- **CoderAgent** - Writes and executes code
+- **WeatherAgent** - Provides weather information
+
+## Files
+
+- `main.py` - Agent definitions and workflow execution (programmatic workflow)
+
+## Running
+
+```bash
+python main.py
+```
+
+## Requirements
+
+- Azure OpenAI endpoint configured
+- `az login` for authentication
@@ -0,0 +1 @@
+# Copyright (c) Microsoft. All rights reserved.
@@ -0,0 +1,205 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+DeepResearch workflow sample.
+
+This workflow coordinates multiple agents to address complex user requests
+according to the "Magentic" orchestration pattern introduced by AutoGen.
+
+The following agents are responsible for overseeing and coordinating the workflow:
+- ResearchAgent: Analyze the current task and correlate relevant facts
+- PlannerAgent: Analyze the current task and devise an overall plan
+- ManagerAgent: Evaluates status and delegates tasks to other agents
+- SummaryAgent: Synthesizes the final response
+
+The following agents have capabilities that are utilized to address the input task:
+- KnowledgeAgent: Performs generic web searches
+- CoderAgent: Able to write and execute code
+- WeatherAgent: Provides weather information
+
+Usage:
+    python main.py
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import WorkflowOutputEvent
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.declarative import WorkflowFactory
+from azure.identity import AzureCliCredential
+from pydantic import BaseModel, Field
+
+# Agent Instructions
+
+RESEARCH_INSTRUCTIONS = """In order to help begin addressing the user request, please answer the following pre-survey to the best of your ability.
+Keep in mind that you are Ken Jennings-level with trivia, and Mensa-level with puzzles, so there should be a deep well to draw from.
+
+Here is the pre-survey:
+
+    1. Please list any specific facts or figures that are GIVEN in the request itself. It is possible that there are none.
+    2. Please list any facts that may need to be looked up, and WHERE SPECIFICALLY they might be found. In some cases, authoritative sources are mentioned in the request itself.
+    3. Please list any facts that may need to be derived (e.g., via logical deduction, simulation, or computation)
+    4. Please list any facts that are recalled from memory, hunches, well-reasoned guesses, etc.
+
+When answering this survey, keep in mind that 'facts' will typically be specific names, dates, statistics, etc. Your answer must only use the headings:
+
+    1. GIVEN OR VERIFIED FACTS
+    2. FACTS TO LOOK UP
+    3. FACTS TO DERIVE
+    4. EDUCATED GUESSES
+
+DO NOT include any other headings or sections in your response. DO NOT list next steps or plans until asked to do so."""  # noqa: E501
+
+PLANNER_INSTRUCTIONS = """Your only job is to devise an efficient plan that identifies (by name) how a team member may contribute to addressing the user request.
+
+Only select the following team which is listed as "- [Name]: [Description]"
+
+- WeatherAgent: Able to retrieve weather information
+- CoderAgent: Able to write and execute Python code
+- KnowledgeAgent: Able to perform generic websearches
+
+The plan must be a bullet point list must be in the form "- [AgentName]: [Specific action or task for that agent to perform]"
+
+Remember, there is no requirement to involve the entire team -- only select team member's whose particular expertise is required for this task."""  # noqa: E501
+
+MANAGER_INSTRUCTIONS = """Recall we have assembled the following team:
+
+- KnowledgeAgent: Able to perform generic websearches
+- CoderAgent: Able to write and execute Python code
+- WeatherAgent: Able to retrieve weather information
+
+To make progress on the request, please answer the following questions, including necessary reasoning:
+- Is the request fully satisfied? (True if complete, or False if the original request has yet to be SUCCESSFULLY and FULLY addressed)
+- Are we in a loop where we are repeating the same requests and / or getting the same responses from an agent multiple times? Loops can span multiple turns, and can include repeated actions like scrolling up or down more than a handful of times.
+- Are we making forward progress? (True if just starting, or recent messages are adding value. False if recent messages show evidence of being stuck in a loop or if there is evidence of significant barriers to success such as the inability to read from a required file)
+- Who should speak next? (select from: KnowledgeAgent, CoderAgent, WeatherAgent)
+- What instruction or question would you give this team member? (Phrase as if speaking directly to them, and include any specific information they may need)"""  # noqa: E501
+
+SUMMARY_INSTRUCTIONS = """We have completed the task.
+
+Based only on the conversation and without adding any new information,
+synthesize the result of the conversation as a complete response to the user task.
+
+The user will only ever see this last response and not the entire conversation,
+so please ensure it is complete and self-contained."""
+
+KNOWLEDGE_INSTRUCTIONS = """You are a knowledge agent that can perform web searches to find information."""
+
+CODER_INSTRUCTIONS = """You solve problems by writing and executing code."""
+
+WEATHER_INSTRUCTIONS = """You are a weather expert that can provide weather information."""
+
+
+# Pydantic models for structured outputs
+
+
+class ReasonedAnswer(BaseModel):
+    """A response with reasoning and answer."""
+
+    reason: str = Field(description="The reasoning behind the answer")
+    answer: bool = Field(description="The boolean answer")
+
+
+class ReasonedStringAnswer(BaseModel):
+    """A response with reasoning and string answer."""
+
+    reason: str = Field(description="The reasoning behind the answer")
+    answer: str = Field(description="The string answer")
+
+
+class ManagerResponse(BaseModel):
+    """Response from manager agent evaluation."""
+
+    is_request_satisfied: ReasonedAnswer = Field(description="Whether the request is fully satisfied")
+    is_in_loop: ReasonedAnswer = Field(description="Whether we are in a loop repeating the same requests")
+    is_progress_being_made: ReasonedAnswer = Field(description="Whether forward progress is being made")
+    next_speaker: ReasonedStringAnswer = Field(description="Who should speak next")
+    instruction_or_question: ReasonedStringAnswer = Field(
+        description="What instruction or question to give the next speaker"
+    )
+
+
+async def main() -> None:
+    """Run the deep research workflow."""
+    # Create Azure OpenAI client
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    # Create agents
+    research_agent = chat_client.create_agent(
+        name="ResearchAgent",
+        instructions=RESEARCH_INSTRUCTIONS,
+    )
+
+    planner_agent = chat_client.create_agent(
+        name="PlannerAgent",
+        instructions=PLANNER_INSTRUCTIONS,
+    )
+
+    manager_agent = chat_client.create_agent(
+        name="ManagerAgent",
+        instructions=MANAGER_INSTRUCTIONS,
+        default_options={"response_format": ManagerResponse},
+    )
+
+    summary_agent = chat_client.create_agent(
+        name="SummaryAgent",
+        instructions=SUMMARY_INSTRUCTIONS,
+    )
+
+    knowledge_agent = chat_client.create_agent(
+        name="KnowledgeAgent",
+        instructions=KNOWLEDGE_INSTRUCTIONS,
+    )
+
+    coder_agent = chat_client.create_agent(
+        name="CoderAgent",
+        instructions=CODER_INSTRUCTIONS,
+    )
+
+    weather_agent = chat_client.create_agent(
+        name="WeatherAgent",
+        instructions=WEATHER_INSTRUCTIONS,
+    )
+
+    # Create workflow factory
+    factory = WorkflowFactory(
+        agents={
+            "ResearchAgent": research_agent,
+            "PlannerAgent": planner_agent,
+            "ManagerAgent": manager_agent,
+            "SummaryAgent": summary_agent,
+            "KnowledgeAgent": knowledge_agent,
+            "CoderAgent": coder_agent,
+            "WeatherAgent": weather_agent,
+        },
+    )
+
+    # Load workflow from YAML
+    samples_root = Path(__file__).parent.parent.parent.parent.parent.parent.parent
+    workflow_path = samples_root / "workflow-samples" / "DeepResearch.yaml"
+    if not workflow_path.exists():
+        # Fall back to local copy if workflow-samples doesn't exist
+        workflow_path = Path(__file__).parent / "workflow.yaml"
+
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=" * 60)
+    print("Deep Research Workflow (Magentic Pattern)")
+    print("=" * 60)
+
+    # Example input
+    task = "What is the weather like in Seattle and how does it compare to the average for this time of year?"
+
+    async for event in workflow.run_stream(task):
+        if isinstance(event, WorkflowOutputEvent):
+            print(f"{event.data}", end="", flush=True)
+
+    print("\n" + "=" * 60)
+    print("Research Complete")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,90 @@
+# Function Tools Workflow
+
+This sample demonstrates an agent with function tools responding to user queries about a restaurant menu.
+
+## Overview
+
+The workflow showcases:
+- **Function Tools**: Agent equipped with tools to query menu data
+- **Real Azure OpenAI Agent**: Uses `AzureOpenAIChatClient` to create an agent with tools
+- **Agent Registration**: Shows how to register agents with the `WorkflowFactory`
+
+## Tools
+
+The MenuAgent has access to these function tools:
+
+| Tool | Description |
+|------|-------------|
+| `get_menu()` | Returns all menu items with category, name, and price |
+| `get_specials()` | Returns today's special items |
+| `get_item_price(name)` | Returns the price of a specific item |
+
+## Menu Data
+
+```
+Soups:
+  - Clam Chowder - $4.95 (Special)
+  - Tomato Soup - $4.95
+
+Salads:
+  - Cobb Salad - $9.99
+  - House Salad - $4.95
+
+Drinks:
+  - Chai Tea - $2.95 (Special)
+  - Soda - $1.95
+```
+
+## Prerequisites
+
+- Azure OpenAI configured with required environment variables
+- Authentication via azure-identity (run `az login` before executing)
+
+## Usage
+
+```bash
+python main.py
+```
+
+## Example Output
+
+```
+Loaded workflow: function-tools-workflow
+============================================================
+Restaurant Menu Assistant
+============================================================
+
+[Bot]: Welcome to the Restaurant Menu Assistant!
+
+[Bot]: Today's soup special is the Clam Chowder for $4.95!
+
+============================================================
+Session Complete
+============================================================
+```
+
+## How It Works
+
+1. Create an Azure OpenAI chat client
+2. Create an agent with instructions and function tools
+3. Register the agent with the workflow factory
+4. Load the workflow YAML and run it with `run_stream()`
+
+```python
+# Create the agent with tools
+chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+menu_agent = chat_client.create_agent(
+    name="MenuAgent",
+    instructions="You are a helpful restaurant menu assistant...",
+    tools=[get_menu, get_specials, get_item_price],
+)
+
+# Register with the workflow factory
+factory = WorkflowFactory(execution_mode="graph")
+factory.register_agent("MenuAgent", menu_agent)
+
+# Load and run the workflow
+workflow = factory.create_workflow_from_yaml_path(workflow_path)
+async for event in workflow.run_stream(inputs={"userInput": "What is the soup of the day?"}):
+    ...
+```
@@ -0,0 +1,116 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Demonstrate a workflow that responds to user input using an agent with
+function tools assigned. Exits the loop when the user enters "exit".
+"""
+
+import asyncio
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Annotated, Any
+
+from agent_framework import FileCheckpointStorage, RequestInfoEvent, WorkflowOutputEvent
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework_declarative import ExternalInputRequest, ExternalInputResponse, WorkflowFactory
+from azure.identity import AzureCliCredential
+from pydantic import Field
+
+TEMP_DIR = Path(__file__).with_suffix("").parent / "tmp" / "checkpoints"
+TEMP_DIR.mkdir(parents=True, exist_ok=True)
+
+
+@dataclass
+class MenuItem:
+    category: str
+    name: str
+    price: float
+    is_special: bool = False
+
+
+MENU_ITEMS = [
+    MenuItem(category="Soup", name="Clam Chowder", price=4.95, is_special=True),
+    MenuItem(category="Soup", name="Tomato Soup", price=4.95, is_special=False),
+    MenuItem(category="Salad", name="Cobb Salad", price=9.99, is_special=False),
+    MenuItem(category="Salad", name="House Salad", price=4.95, is_special=False),
+    MenuItem(category="Drink", name="Chai Tea", price=2.95, is_special=True),
+    MenuItem(category="Drink", name="Soda", price=1.95, is_special=False),
+]
+
+
+def get_menu() -> list[dict[str, Any]]:
+    """Get all menu items."""
+    return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS]
+
+
+def get_specials() -> list[dict[str, Any]]:
+    """Get today's specials."""
+    return [{"category": i.category, "name": i.name, "price": i.price} for i in MENU_ITEMS if i.is_special]
+
+
+def get_item_price(name: Annotated[str, Field(description="Menu item name")]) -> str:
+    """Get price of a menu item."""
+    for item in MENU_ITEMS:
+        if item.name.lower() == name.lower():
+            return f"${item.price:.2f}"
+    return f"Item '{name}' not found."
+
+
+async def main():
+    # Create agent with tools
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    menu_agent = chat_client.create_agent(
+        name="MenuAgent",
+        instructions="Answer questions about menu items, specials, and prices.",
+        tools=[get_menu, get_specials, get_item_price],
+    )
+
+    # Clean up any existing checkpoints
+    for file in TEMP_DIR.glob("*"):
+        file.unlink()
+
+    factory = WorkflowFactory(checkpoint_storage=FileCheckpointStorage(TEMP_DIR))
+    factory.register_agent("MenuAgent", menu_agent)
+    workflow = factory.create_workflow_from_yaml_path(Path(__file__).parent / "workflow.yaml")
+
+    # Get initial input
+    print("Restaurant Menu Assistant (type 'exit' to quit)\n")
+    user_input = input("You: ").strip()  # noqa: ASYNC250
+    if not user_input:
+        return
+
+    # Run workflow with external loop handling
+    pending_request_id: str | None = None
+    first_response = True
+
+    while True:
+        if pending_request_id:
+            response = ExternalInputResponse(user_input=user_input)
+            stream = workflow.send_responses_streaming({pending_request_id: response})
+        else:
+            stream = workflow.run_stream({"userInput": user_input})
+
+        pending_request_id = None
+        first_response = True
+
+        async for event in stream:
+            if isinstance(event, WorkflowOutputEvent) and isinstance(event.data, str):
+                if first_response:
+                    print("MenuAgent: ", end="")
+                    first_response = False
+                print(event.data, end="", flush=True)
+            elif isinstance(event, RequestInfoEvent) and isinstance(event.data, ExternalInputRequest):
+                pending_request_id = event.request_id
+
+        print()
+
+        if not pending_request_id:
+            break
+
+        user_input = input("\nYou: ").strip()
+        if not user_input:
+            continue
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,22 @@
+# Function Tools Workflow - .NET-style
+#
+# This workflow demonstrates an agent with function tools in a loop
+# responding to user input, using the same minimal structure as .NET.
+#
+# Example input:
+# What is the soup of the day?
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: workflow_demo
+  actions:
+
+    - kind: InvokeAzureAgent
+      id: invoke_menu_agent
+      agent:
+        name: MenuAgent
+      input:
+        externalLoop:
+          when: =Upper(System.LastMessage.Text) <> "EXIT"
@@ -0,0 +1,59 @@
+# Human-in-Loop Workflow Sample
+
+This sample demonstrates how to build interactive workflows that request user input during execution using the `Question`, `RequestExternalInput`, and `WaitForInput` actions.
+
+## What This Sample Shows
+
+- Using `Question` to prompt for user responses
+- Using `RequestExternalInput` to request external data
+- Using `WaitForInput` to pause and wait for input
+- Processing user responses to drive workflow decisions
+- Interactive conversation patterns
+
+## Files
+
+- `workflow.yaml` - The declarative workflow definition
+- `main.py` - Python script that loads and runs the workflow with simulated user interaction
+
+## Running the Sample
+
+1. Ensure you have the package installed:
+   ```bash
+   cd python
+   pip install -e packages/agent-framework-declarative
+   ```
+
+2. Run the sample:
+   ```bash
+   python main.py
+   ```
+
+## How It Works
+
+The workflow demonstrates a simple survey/questionnaire pattern:
+
+1. **Greeting**: Sends a welcome message
+2. **Question 1**: Asks for the user's name
+3. **Question 2**: Asks how they're feeling today
+4. **Processing**: Stores responses and provides personalized feedback
+5. **Summary**: Summarizes the collected information
+
+The `main.py` script shows how to handle `ExternalInputRequest` to provide responses during workflow execution.
+
+## Key Concepts
+
+### ExternalInputRequest
+
+When a human-in-loop action is executed, the workflow yields an `ExternalInputRequest` containing:
+- `variable`: The variable path where the response should be stored
+- `prompt`: The question or prompt text for the user
+
+The workflow runner should:
+1. Detect `ExternalInputRequest` in the event stream
+2. Display the prompt to the user
+3. Collect the response
+4. Resume the workflow (in a real implementation, using external loop patterns)
+
+### ExternalLoopEvent
+
+For more complex scenarios where external processing is needed, the workflow can yield an `ExternalLoopEvent` that signals the runner to pause and wait for external input.
@@ -0,0 +1,84 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the human-in-loop workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates interactive workflows that request user input.
+
+Note: This sample shows the conceptual pattern for handling ExternalInputRequest.
+In a production scenario, you would integrate with a real UI or chat interface.
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import Workflow, WorkflowOutputEvent
+from agent_framework.declarative import ExternalInputRequest, WorkflowFactory
+from agent_framework_declarative._workflows._handlers import TextOutputEvent
+
+
+async def run_with_streaming(workflow: Workflow) -> None:
+    """Demonstrate streaming workflow execution with run_stream()."""
+    print("\n=== Streaming Execution (run_stream) ===")
+    print("-" * 40)
+
+    async for event in workflow.run_stream({}):
+        # WorkflowOutputEvent wraps the actual output data
+        if isinstance(event, WorkflowOutputEvent):
+            data = event.data
+            if isinstance(data, TextOutputEvent):
+                print(f"[Bot]: {data.text}")
+            elif isinstance(data, ExternalInputRequest):
+                # In a real scenario, you would:
+                # 1. Display the prompt to the user
+                # 2. Wait for their response
+                # 3. Use the response to continue the workflow
+                output_property = data.metadata.get("output_property", "unknown")
+                print(f"[System] Input requested for: {output_property}")
+                if data.message:
+                    print(f"[System] Prompt: {data.message}")
+            else:
+                print(f"[Output]: {data}")
+
+
+async def run_with_result(workflow: Workflow) -> None:
+    """Demonstrate batch workflow execution with run()."""
+    print("\n=== Batch Execution (run) ===")
+    print("-" * 40)
+
+    result = await workflow.run({})
+    for output in result.get_outputs():
+        print(f"  Output: {output}")
+
+
+async def main() -> None:
+    """Run the human-in-loop workflow demonstrating both execution styles."""
+    # Create a workflow factory
+    factory = WorkflowFactory()
+
+    # Load the workflow from YAML
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=== Human-in-Loop Workflow Demo ===")
+    print("(Using simulated responses for demonstration)")
+
+    # Demonstrate streaming execution
+    await run_with_streaming(workflow)
+
+    # Demonstrate batch execution
+    # await run_with_result(workflow)
+
+    print("\n" + "-" * 40)
+    print("=== Workflow Complete ===")
+    print()
+    print("Note: This demo uses simulated responses. In a real application,")
+    print("you would integrate with a chat interface to collect actual user input.")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,75 @@
+name: human-in-loop-workflow
+description: Interactive workflow that requests user input
+
+actions:
+  # Welcome message
+  - kind: SendActivity
+    id: greeting
+    displayName: Send greeting
+    activity:
+      text: "Welcome to the interactive survey!"
+
+  # Ask for name
+  - kind: Question
+    id: ask_name
+    displayName: Ask for user name
+    question:
+      text: "What is your name?"
+    variable: Local.userName
+    default: "Demo User"
+
+  # Personalized greeting
+  - kind: SendActivity
+    id: personalized_greeting
+    displayName: Send personalized greeting
+    activity:
+      text: =Concat("Nice to meet you, ", Local.userName, "!")
+
+  # Ask how they're feeling
+  - kind: Question
+    id: ask_feeling
+    displayName: Ask about feelings
+    question:
+      text: "How are you feeling today? (great/good/okay/not great)"
+    variable: Local.feeling
+    default: "great"
+
+  # Respond based on feeling
+  - kind: If
+    id: check_feeling
+    displayName: Check user feeling
+    condition: =Or(Local.feeling = "great", Local.feeling = "good")
+    then:
+      - kind: SendActivity
+        activity:
+          text: "That's wonderful to hear! Let's continue."
+    else:
+      - kind: SendActivity
+        activity:
+          text: "I hope things get better! Let me know if there's anything I can help with."
+
+  # Ask for feedback (using RequestExternalInput for demonstration)
+  - kind: RequestExternalInput
+    id: ask_feedback
+    displayName: Request feedback
+    prompt:
+      text: "Do you have any feedback for us?"
+    variable: Local.feedback
+    default: "This workflow is great!"
+
+  # Summary
+  - kind: SendActivity
+    id: summary
+    displayName: Send summary
+    activity:
+      text: '=Concat("Thank you, ", Local.userName, "! Your feedback: ", Local.feedback)'
+
+  # Store results
+  - kind: SetValue
+    id: store_results
+    displayName: Store survey results
+    path: Workflow.Outputs.survey
+    value:
+      name: =Local.userName
+      feeling: =Local.feeling
+      feedback: =Local.feedback
@@ -0,0 +1,76 @@
+# Marketing Copy Workflow
+
+This sample demonstrates a sequential multi-agent pipeline for generating marketing copy from a product description.
+
+## Overview
+
+The workflow showcases:
+- **Sequential Agent Pipeline**: Three agents work in sequence, each building on the previous output
+- **Role-Based Agents**: Each agent has a distinct responsibility
+- **Content Transformation**: Raw product info transforms into polished marketing copy
+
+## Agent Pipeline
+
+```
+Product Description
+       |
+       v
+  AnalystAgent  --> Key features, audience, USPs
+       |
+       v
+   WriterAgent  --> Draft marketing copy
+       |
+       v
+   EditorAgent  --> Polished final copy
+       |
+       v
+  Final Output
+```
+
+## Agents
+
+| Agent | Role |
+|-------|------|
+| AnalystAgent | Identifies key features, target audience, and unique selling points |
+| WriterAgent | Creates compelling marketing copy (~150 words) |
+| EditorAgent | Polishes grammar, clarity, tone, and formatting |
+
+## Usage
+
+```bash
+# Run the demonstration with mock responses
+python main.py
+```
+
+## Example Input
+
+```
+An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
+```
+
+## Configuration
+
+For production use, configure these agents in Azure AI Foundry:
+
+### AnalystAgent
+```
+Instructions: You are a marketing analyst. Given a product description, identify:
+- Key features
+- Target audience
+- Unique selling points
+```
+
+### WriterAgent
+```
+Instructions: You are a marketing copywriter. Given a block of text describing
+features, audience, and USPs, compose a compelling marketing copy (like a
+newsletter section) that highlights these points. Output should be short
+(around 150 words), output just the copy as a single text block.
+```
+
+### EditorAgent
+```
+Instructions: You are an editor. Given the draft copy, correct grammar,
+improve clarity, ensure consistent tone, give format and make it polished.
+Output the final improved copy as a single text block.
+```
@@ -0,0 +1,97 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the marketing copy workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates sequential multi-agent pipeline:
+- AnalystAgent: Identifies key features, target audience, USPs
+- WriterAgent: Creates compelling marketing copy
+- EditorAgent: Polishes grammar, clarity, and tone
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import WorkflowOutputEvent
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.declarative import WorkflowFactory
+from azure.identity import AzureCliCredential
+
+ANALYST_INSTRUCTIONS = """You are a product analyst. Analyze the given product and identify:
+1. Key features and benefits
+2. Target audience demographics
+3. Unique selling propositions (USPs)
+4. Competitive advantages
+
+Be concise and structured in your analysis."""
+
+WRITER_INSTRUCTIONS = """You are a marketing copywriter. Based on the product analysis provided,
+create compelling marketing copy that:
+1. Has a catchy headline
+2. Highlights key benefits
+3. Speaks to the target audience
+4. Creates emotional connection
+5. Includes a call to action
+
+Write in an engaging, persuasive tone."""
+
+EDITOR_INSTRUCTIONS = """You are a senior editor. Review and polish the marketing copy:
+1. Fix any grammar or spelling issues
+2. Improve clarity and flow
+3. Ensure consistent tone
+4. Tighten the prose
+5. Make it more impactful
+
+Return the final polished version."""
+
+
+async def main() -> None:
+    """Run the marketing workflow with real Azure AI agents."""
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    analyst_agent = chat_client.create_agent(
+        name="AnalystAgent",
+        instructions=ANALYST_INSTRUCTIONS,
+    )
+    writer_agent = chat_client.create_agent(
+        name="WriterAgent",
+        instructions=WRITER_INSTRUCTIONS,
+    )
+    editor_agent = chat_client.create_agent(
+        name="EditorAgent",
+        instructions=EDITOR_INSTRUCTIONS,
+    )
+
+    factory = WorkflowFactory(
+        agents={
+            "AnalystAgent": analyst_agent,
+            "WriterAgent": writer_agent,
+            "EditorAgent": editor_agent,
+        }
+    )
+
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=" * 60)
+    print("Marketing Copy Generation Pipeline")
+    print("=" * 60)
+
+    # Pass a simple string input - like .NET
+    product = "An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours."
+
+    async for event in workflow.run_stream(product):
+        if isinstance(event, WorkflowOutputEvent):
+            print(f"{event.data}", end="", flush=True)
+
+    print("\n" + "=" * 60)
+    print("Pipeline Complete")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,30 @@
+#
+# This workflow demonstrates sequential agent interaction to develop product marketing copy.
+#
+# Example input:
+# An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours.
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: workflow_demo
+  actions:
+
+    - kind: InvokeAzureAgent
+      id: invoke_analyst
+      conversationId: =System.ConversationId
+      agent:
+        name: AnalystAgent
+
+    - kind: InvokeAzureAgent
+      id: invoke_writer
+      conversationId: =System.ConversationId
+      agent:
+        name: WriterAgent
+
+    - kind: InvokeAzureAgent
+      id: invoke_editor
+      conversationId: =System.ConversationId
+      agent:
+        name: EditorAgent
@@ -0,0 +1,24 @@
+# Simple Workflow Sample
+
+This sample demonstrates the basics of declarative workflows:
+- Setting variables
+- Evaluating expressions
+- Sending output to users
+
+## Files
+
+- `workflow.yaml` - The workflow definition
+- `main.py` - Python code to execute the workflow
+
+## Running
+
+```bash
+python main.py
+```
+
+## What It Does
+
+1. Sets a greeting variable
+2. Sets a name from input (or uses default)
+3. Combines them into a message
+4. Sends the message as output
@@ -0,0 +1,40 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Simple workflow sample - demonstrates basic variable setting and output."""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework.declarative import WorkflowFactory
+
+
+async def main() -> None:
+    """Run the simple greeting workflow."""
+    # Create a workflow factory
+    factory = WorkflowFactory()
+
+    # Load the workflow from YAML
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("-" * 40)
+
+    # Run with default name
+    print("\nRunning with default name:")
+    result = await workflow.run({})
+    for output in result.get_outputs():
+        print(f"  Output: {output}")
+
+    # Run with a custom name
+    print("\nRunning with custom name 'Alice':")
+    result = await workflow.run({"name": "Alice"})
+    for output in result.get_outputs():
+        print(f"  Output: {output}")
+
+    print("\n" + "-" * 40)
+    print("Workflow completed!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,38 @@
+name: simple-greeting-workflow
+description: A simple workflow that greets the user
+
+actions:
+  # Set a greeting prefix
+  - kind: SetValue
+    id: set_greeting
+    displayName: Set greeting prefix
+    path: Local.greeting
+    value: Hello
+
+  # Set the user's name from input, or use a default
+  - kind: SetValue
+    id: set_name
+    displayName: Set user name
+    path: Local.name
+    value: =If(IsBlank(inputs.name), "World", inputs.name)
+
+  # Build the full message
+  - kind: SetValue
+    id: build_message
+    displayName: Build greeting message
+    path: Local.message
+    value: =Concat(Local.greeting, ", ", Local.name, "!")
+
+  # Send the greeting to the user
+  - kind: SendActivity
+    id: send_greeting
+    displayName: Send greeting to user
+    activity:
+      text: =Local.message
+
+  # Also store it in outputs
+  - kind: SetValue
+    id: set_output
+    displayName: Store result in outputs
+    path: Workflow.Outputs.greeting
+    value: =Local.message
@@ -0,0 +1,61 @@
+# Student-Teacher Math Chat Workflow
+
+This sample demonstrates an iterative conversation between two AI agents - a Student and a Teacher - working through a math problem together.
+
+## Overview
+
+The workflow showcases:
+- **Iterative Agent Loops**: Two agents take turns in a coaching conversation
+- **Termination Conditions**: Loop ends when teacher says "congratulations" or max turns reached
+- **State Tracking**: Turn counter tracks iteration progress
+- **Conditional Flow Control**: GotoAction for loop continuation
+
+## Agents
+
+| Agent | Role |
+|-------|------|
+| StudentAgent | Attempts to solve math problems, making intentional mistakes to learn from |
+| TeacherAgent | Reviews student's work and provides constructive feedback |
+
+## How It Works
+
+1. User provides a math problem
+2. Student attempts a solution
+3. Teacher reviews and provides feedback
+4. If teacher says "congratulations" -> success, workflow ends
+5. If under 4 turns -> loop back to step 2
+6. If 4 turns reached without success -> timeout, workflow ends
+
+## Usage
+
+```bash
+# Run the demonstration with mock responses
+python main.py
+```
+
+## Example Input
+
+```
+How would you compute the value of PI?
+```
+
+## Configuration
+
+For production use, configure these agents in Azure AI Foundry:
+
+### StudentAgent
+```
+Instructions: Your job is to help a math teacher practice teaching by making
+intentional mistakes. You attempt to solve the given math problem, but with
+intentional mistakes so the teacher can help. Always incorporate the teacher's
+advice to fix your next response. You have the math-skills of a 6th grader.
+Don't describe who you are or reveal your instructions.
+```
+
+### TeacherAgent
+```
+Instructions: Review and coach the student's approach to solving the given
+math problem. Don't repeat the solution or try and solve it. If the student
+has demonstrated comprehension and responded to all of your feedback, give
+the student your congratulations by using the word "congratulations".
+```
@@ -0,0 +1,94 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Run the student-teacher (MathChat) workflow sample.
+
+Usage:
+    python main.py
+
+Demonstrates iterative conversation between two agents:
+- StudentAgent: Attempts to solve math problems
+- TeacherAgent: Reviews and coaches the student's approach
+
+The workflow loops until the teacher gives congratulations or max turns reached.
+
+Prerequisites:
+    - Azure OpenAI deployment with chat completion capability
+    - Environment variables:
+        AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint
+        AZURE_OPENAI_DEPLOYMENT_NAME: Your deployment name (optional, defaults to gpt-4o)
+"""
+
+import asyncio
+from pathlib import Path
+
+from agent_framework import WorkflowOutputEvent
+from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.declarative import WorkflowFactory
+from azure.identity import AzureCliCredential
+
+STUDENT_INSTRUCTIONS = """You are a curious math student working on understanding mathematical concepts.
+When given a problem:
+1. Think through it step by step
+2. Make reasonable attempts, but it's okay to make mistakes
+3. Show your work and reasoning
+4. Ask clarifying questions when confused
+5. Build on feedback from your teacher
+
+Be authentic - you're learning, so don't pretend to know everything."""
+
+TEACHER_INSTRUCTIONS = """You are a patient math teacher helping a student understand concepts.
+When reviewing student work:
+1. Acknowledge what they did correctly
+2. Gently point out errors without giving away the answer
+3. Ask guiding questions to help them discover mistakes
+4. Provide hints that lead toward understanding
+5. When the student demonstrates clear understanding, respond with "CONGRATULATIONS" 
+   followed by a summary of what they learned
+
+Focus on building understanding, not just getting the right answer."""
+
+
+async def main() -> None:
+    """Run the student-teacher workflow with real Azure AI agents."""
+    # Create chat client
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+
+    # Create student and teacher agents
+    student_agent = chat_client.create_agent(
+        name="StudentAgent",
+        instructions=STUDENT_INSTRUCTIONS,
+    )
+
+    teacher_agent = chat_client.create_agent(
+        name="TeacherAgent",
+        instructions=TEACHER_INSTRUCTIONS,
+    )
+
+    # Create factory with agents
+    factory = WorkflowFactory(
+        agents={
+            "StudentAgent": student_agent,
+            "TeacherAgent": teacher_agent,
+        }
+    )
+
+    workflow_path = Path(__file__).parent / "workflow.yaml"
+    workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+    print(f"Loaded workflow: {workflow.name}")
+    print("=" * 50)
+    print("Student-Teacher Math Coaching Session")
+    print("=" * 50)
+
+    async for event in workflow.run_stream("How would you compute the value of PI?"):
+        if isinstance(event, WorkflowOutputEvent):
+            print(f"{event.data}", flush=True, end="")
+
+    print("\n" + "=" * 50)
+    print("Session Complete")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
@@ -0,0 +1,98 @@
+# Student-Teacher Math Chat Workflow
+#
+# Demonstrates iterative conversation between two agents with loop control
+# and termination conditions.
+#
+# Example input:
+# How would you compute the value of PI?
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: student_teacher_workflow
+  actions:
+
+    # Initialize turn counter
+    - kind: SetVariable
+      id: init_counter
+      variable: Local.TurnCount
+      value: =0
+
+    # Announce the start with the problem
+    - kind: SendActivity
+      id: announce_start
+      activity:
+        text: '=Concat("Starting math coaching session for: ", Workflow.Inputs.input)'
+
+    # Label for student
+    - kind: SendActivity
+      id: student_label
+      activity:
+        text: "\n[Student]:\n"
+
+    # Student attempts to solve - entry point for loop
+    # No explicit input.messages - uses implicit input from workflow inputs or conversation
+    - kind: InvokeAzureAgent
+      id: question_student
+      conversationId: =System.ConversationId
+      agent:
+        name: StudentAgent
+
+    # Label for teacher
+    - kind: SendActivity
+      id: teacher_label
+      activity:
+        text: "\n\n[Teacher]:\n"
+
+    # Teacher reviews and coaches
+    # No explicit input.messages - uses conversation context from conversationId
+    - kind: InvokeAzureAgent
+      id: question_teacher
+      conversationId: =System.ConversationId
+      agent:
+        name: TeacherAgent
+      output:
+        messages: Local.TeacherResponse
+
+    # Increment the turn counter
+    - kind: SetVariable
+      id: increment_counter
+      variable: Local.TurnCount
+      value: =Local.TurnCount + 1
+
+    # Check for completion using ConditionGroup
+    - kind: ConditionGroup
+      id: check_completion
+      conditions:
+        - id: success_condition
+          condition: =!IsBlank(Find("CONGRATULATIONS", Upper(MessageText(Local.TeacherResponse))))
+          actions:
+            - kind: SendActivity
+              id: success_message
+              activity:
+                text: "\nGOLD STAR! The student has demonstrated understanding."
+            - kind: SetVariable
+              id: set_success_result
+              variable: workflow.outputs.result
+              value: success
+      elseActions:
+        - kind: ConditionGroup
+          id: check_turn_limit
+          conditions:
+            - id: can_continue
+              condition: =Local.TurnCount < 4
+              actions:
+                # Continue the loop - go back to student label
+                - kind: GotoAction
+                  id: continue_loop
+                  actionId: student_label
+          elseActions:
+            - kind: SendActivity
+              id: timeout_message
+              activity:
+                text: "\nLet's try again later... The session has reached its limit."
+            - kind: SetVariable
+              id: set_timeout_result
+              variable: workflow.outputs.result
+              value: timeout
				`@@ -0,0 +1 @@`
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