mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Python: [Durabletask] Update feature-durabletask-python branch with main (#3068)
* 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> * Remove from feature branch * Remove ollama changes --------- Signed-off-by: dependabot[bot] <support@github.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>
This commit is contained in:
Laveesh Rohra
committed by
GitHub
Unverified
GitHub
parent
a02527f00a
commit
a5b36dc379
@@ -99,11 +99,15 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
|
||||
|
||||
### Ollama
|
||||
|
||||
The recommended way to use Ollama is via the native `OllamaChatClient` from the `agent-framework-ollama` package.
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| [`getting_started/agents/ollama/ollama_with_openai_chat_client.py`](./getting_started/agents/ollama/ollama_with_openai_chat_client.py) | Ollama with OpenAI Chat Client Example |
|
||||
| [`packages/ollama/getting_started/ollama_agent_basic.py`](../packages/ollama/getting_started/ollama_agent_basic.py) | (Experimental) Ollama Agent with native Ollama Chat Client |
|
||||
| [`packages/ollama/getting_started/ollama_agent_reasoning.py`](../packages/ollama/getting_started/ollama_agent_reasoning.py) | (Experimental) Ollama Reasoning Agent with native Ollama Chat Client |
|
||||
| [`getting_started/agents/ollama/ollama_agent_basic.py`](./getting_started/agents/ollama/ollama_agent_basic.py) | Basic Ollama Agent with native Ollama Chat Client |
|
||||
| [`getting_started/agents/ollama/ollama_agent_reasoning.py`](./getting_started/agents/ollama/ollama_agent_reasoning.py) | Ollama Agent with reasoning capabilities |
|
||||
| [`getting_started/agents/ollama/ollama_chat_client.py`](./getting_started/agents/ollama/ollama_chat_client.py) | Direct usage of Ollama Chat Client |
|
||||
| [`getting_started/agents/ollama/ollama_chat_multimodal.py`](./getting_started/agents/ollama/ollama_chat_multimodal.py) | Ollama Chat Client with multimodal (image) input |
|
||||
| [`getting_started/agents/ollama/ollama_with_openai_chat_client.py`](./getting_started/agents/ollama/ollama_with_openai_chat_client.py) | Alternative: Ollama via OpenAI Chat Client |
|
||||
|
||||
### OpenAI
|
||||
|
||||
@@ -149,7 +153,7 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
|
||||
| [`getting_started/chat_client/openai_assistants_client.py`](./getting_started/chat_client/openai_assistants_client.py) | OpenAI Assistants Client Direct Usage Example |
|
||||
| [`getting_started/chat_client/openai_chat_client.py`](./getting_started/chat_client/openai_chat_client.py) | OpenAI Chat Client Direct Usage Example |
|
||||
| [`getting_started/chat_client/openai_responses_client.py`](./getting_started/chat_client/openai_responses_client.py) | OpenAI Responses Client Direct Usage Example |
|
||||
| [`packages/ollama/getting_started/ollama_chat_client.py`](../packages/ollama/getting_started/ollama_chat_client.py) | (Experimental) Ollama Chat Client with native Ollama Chat Client |
|
||||
|
||||
|
||||
|
||||
## Context Providers
|
||||
@@ -225,7 +229,7 @@ This directory contains samples demonstrating the capabilities of Microsoft Agen
|
||||
| [`getting_started/multimodal_input/azure_chat_multimodal.py`](./getting_started/multimodal_input/azure_chat_multimodal.py) | Azure OpenAI Chat with multimodal (image) input example |
|
||||
| [`getting_started/multimodal_input/azure_responses_multimodal.py`](./getting_started/multimodal_input/azure_responses_multimodal.py) | Azure OpenAI Responses with multimodal (image) input example |
|
||||
| [`getting_started/multimodal_input/openai_chat_multimodal.py`](./getting_started/multimodal_input/openai_chat_multimodal.py) | OpenAI Chat with multimodal (image) input example |
|
||||
| [`packages/ollama/getting_started/ollama_chat_multimodal.py`](../packages/ollama/getting_started/ollama_chat_multimodal.py) | (Experimental) Ollama Chat with multimodal native Ollama Chat Client |
|
||||
|
||||
|
||||
|
||||
## Azure Functions
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
"""This sample has moved to python/packages/bedrock/samples/bedrock_sample.py."""
|
||||
@@ -8,20 +8,41 @@ This folder contains examples demonstrating how to use Ollama models with the Ag
|
||||
2. **Start Ollama**: Ensure Ollama is running on your local machine
|
||||
3. **Pull a model**: Run `ollama pull mistral` (or any other model you prefer)
|
||||
- For function calling examples, use models that support tool calling like `mistral` or `qwen2.5`
|
||||
- For reasoning examples, use models that support reasoning like `qwen2.5:8b`
|
||||
- For reasoning examples, use models that support reasoning like `qwen3:8b`
|
||||
- For multimodal examples, use models like `gemma3:4b`
|
||||
|
||||
> **Note**: Not all models support all features. Function calling and reasoning capabilities depend on the specific model you're using.
|
||||
> **Note**: Not all models support all features. Function calling, reasoning, and multimodal capabilities depend on the specific model you're using.
|
||||
|
||||
## Recommended Approach
|
||||
|
||||
The recommended way to use Ollama with Agent Framework is via the native `OllamaChatClient` from the `agent-framework-ollama` package. This provides full support for Ollama-specific features like reasoning mode.
|
||||
|
||||
Alternatively, you can use the `OpenAIChatClient` configured to point to your local Ollama server, which may be useful if you're already familiar with the OpenAI client interface.
|
||||
|
||||
## Examples
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| [`ollama_with_openai_chat_client.py`](ollama_with_openai_chat_client.py) | Demonstrates how to configure OpenAI Chat Client to use local Ollama models. Shows both streaming and non-streaming responses with tool calling capabilities. |
|
||||
| [`ollama_agent_basic.py`](ollama_agent_basic.py) | Basic Ollama agent with tool calling using native Ollama Chat Client. Shows both streaming and non-streaming responses. |
|
||||
| [`ollama_agent_reasoning.py`](ollama_agent_reasoning.py) | Ollama agent with reasoning capabilities using native Ollama Chat Client. Shows how to enable thinking/reasoning mode. |
|
||||
| [`ollama_chat_client.py`](ollama_chat_client.py) | Direct usage of the native Ollama Chat Client with tool calling. |
|
||||
| [`ollama_chat_multimodal.py`](ollama_chat_multimodal.py) | Ollama Chat Client with multimodal (image) input capabilities. |
|
||||
| [`ollama_with_openai_chat_client.py`](ollama_with_openai_chat_client.py) | Alternative approach using OpenAI Chat Client configured to use local Ollama models. |
|
||||
|
||||
## Configuration
|
||||
|
||||
The examples use environment variables for configuration. Set the appropriate variables based on which example you're running:
|
||||
|
||||
### For Native Ollama Examples
|
||||
|
||||
Set the following environment variables:
|
||||
|
||||
- `OLLAMA_HOST`: The base URL for your Ollama server (optional, defaults to `http://localhost:11434`)
|
||||
- Example: `export OLLAMA_HOST="http://localhost:11434"`
|
||||
|
||||
- `OLLAMA_CHAT_MODEL_ID`: The model name to use
|
||||
- Example: `export OLLAMA_CHAT_MODEL_ID="qwen2.5:8b"`
|
||||
- Must be a model you have pulled with Ollama
|
||||
|
||||
### For OpenAI Client with Ollama (`ollama_with_openai_chat_client.py`)
|
||||
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
|
||||
from agent_framework.ollama import OllamaChatClient
|
||||
|
||||
"""
|
||||
Ollama Agent Basic Example
|
||||
|
||||
This sample demonstrates implementing a Ollama agent with basic tool usage.
|
||||
|
||||
Ensure to install Ollama and have a model running locally before running the sample
|
||||
Not all Models support function calling, to test function calling try llama3.2 or qwen3:4b
|
||||
Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
|
||||
https://ollama.com/
|
||||
|
||||
"""
|
||||
|
||||
|
||||
def get_time(location: str) -> str:
|
||||
"""Get the current time."""
|
||||
return f"The current time in {location} is {datetime.now().strftime('%I:%M %p')}."
|
||||
|
||||
|
||||
async def non_streaming_example() -> None:
|
||||
"""Example of non-streaming response (get the complete result at once)."""
|
||||
print("=== Non-streaming Response Example ===")
|
||||
|
||||
agent = OllamaChatClient().create_agent(
|
||||
name="TimeAgent",
|
||||
instructions="You are a helpful time agent answer in one sentence.",
|
||||
tools=get_time,
|
||||
)
|
||||
|
||||
query = "What time is it in Seattle? Use a tool call"
|
||||
print(f"User: {query}")
|
||||
result = await agent.run(query)
|
||||
print(f"Result: {result}\n")
|
||||
|
||||
|
||||
async def streaming_example() -> None:
|
||||
"""Example of streaming response (get results as they are generated)."""
|
||||
print("=== Streaming Response Example ===")
|
||||
|
||||
agent = OllamaChatClient().create_agent(
|
||||
name="TimeAgent",
|
||||
instructions="You are a helpful time agent answer in one sentence.",
|
||||
tools=get_time,
|
||||
)
|
||||
query = "What time is it in San Francisco? Use a tool call"
|
||||
print(f"User: {query}")
|
||||
print("Agent: ", end="", flush=True)
|
||||
async for chunk in agent.run_stream(query):
|
||||
if chunk.text:
|
||||
print(chunk.text, end="", flush=True)
|
||||
print("\n")
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
print("=== Basic Ollama Chat Client Agent Example ===")
|
||||
|
||||
await non_streaming_example()
|
||||
await streaming_example()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,45 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
|
||||
from agent_framework import TextReasoningContent
|
||||
from agent_framework.ollama import OllamaChatClient
|
||||
|
||||
"""
|
||||
Ollama Agent Reasoning Example
|
||||
|
||||
This sample demonstrates implementing a Ollama agent with reasoning.
|
||||
|
||||
Ensure to install Ollama and have a model running locally before running the sample
|
||||
Not all Models support reasoning, to test reasoning try qwen3:8b
|
||||
Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
|
||||
https://ollama.com/
|
||||
|
||||
"""
|
||||
|
||||
|
||||
async def reasoning_example() -> None:
|
||||
print("=== Response Reasoning Example ===")
|
||||
|
||||
agent = OllamaChatClient().create_agent(
|
||||
name="TimeAgent",
|
||||
instructions="You are a helpful agent answer in one sentence.",
|
||||
additional_chat_options={"think": True}, # Enable Reasoning on agent level
|
||||
)
|
||||
query = "Hey what is 3+4? Can you explain how you got to that answer?"
|
||||
print(f"User: {query}")
|
||||
# Enable Reasoning on per request level
|
||||
result = await agent.run(query)
|
||||
reasoning = "".join((c.text or "") for c in result.messages[-1].contents if isinstance(c, TextReasoningContent))
|
||||
print(f"Reasoning: {reasoning}")
|
||||
print(f"Answer: {result}\n")
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
print("=== Basic Ollama Chat Client Agent Reasoning ===")
|
||||
|
||||
await reasoning_example()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,43 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
|
||||
from agent_framework.ollama import OllamaChatClient
|
||||
|
||||
"""
|
||||
Ollama Chat Client Example
|
||||
|
||||
This sample demonstrates using the native Ollama Chat Client directly.
|
||||
|
||||
Ensure to install Ollama and have a model running locally before running the sample.
|
||||
Not all Models support function calling, to test function calling try llama3.2
|
||||
Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
|
||||
https://ollama.com/
|
||||
|
||||
"""
|
||||
|
||||
|
||||
def get_time():
|
||||
"""Get the current time."""
|
||||
return f"The current time is {datetime.now().strftime('%I:%M %p')}."
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
client = OllamaChatClient()
|
||||
message = "What time is it? Use a tool call"
|
||||
stream = False
|
||||
print(f"User: {message}")
|
||||
if stream:
|
||||
print("Assistant: ", end="")
|
||||
async for chunk in client.get_streaming_response(message, tools=get_time):
|
||||
if str(chunk):
|
||||
print(str(chunk), end="")
|
||||
print("")
|
||||
else:
|
||||
response = await client.get_response(message, tools=get_time)
|
||||
print(f"Assistant: {response}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,53 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
|
||||
from agent_framework import ChatMessage, DataContent, Role, TextContent
|
||||
from agent_framework.ollama import OllamaChatClient
|
||||
|
||||
"""
|
||||
Ollama Agent Multimodal Example
|
||||
|
||||
This sample demonstrates implementing a Ollama agent with multimodal input capabilities.
|
||||
|
||||
Ensure to install Ollama and have a model running locally before running the sample
|
||||
Not all Models support multimodal input, to test multimodal input try gemma3:4b
|
||||
Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
|
||||
https://ollama.com/
|
||||
|
||||
"""
|
||||
|
||||
|
||||
def create_sample_image() -> str:
|
||||
"""Create a simple 1x1 pixel PNG image for testing."""
|
||||
# This is a tiny red pixel in PNG format
|
||||
png_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
|
||||
return f"data:image/png;base64,{png_data}"
|
||||
|
||||
|
||||
async def test_image() -> None:
|
||||
"""Test image analysis with Ollama."""
|
||||
|
||||
client = OllamaChatClient()
|
||||
|
||||
image_uri = create_sample_image()
|
||||
|
||||
message = ChatMessage(
|
||||
role=Role.USER,
|
||||
contents=[
|
||||
TextContent(text="What's in this image?"),
|
||||
DataContent(uri=image_uri, media_type="image/png"),
|
||||
],
|
||||
)
|
||||
|
||||
response = await client.get_response(message)
|
||||
print(f"Image Response: {response}")
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
print("=== Testing Ollama Multimodal ===")
|
||||
await test_image()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -8,8 +8,11 @@ This folder contains an example demonstrating how to use the Redis context provi
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| [`azure_redis_conversation.py`](azure_redis_conversation.py) | Demonstrates conversation persistence with RedisChatMessageStore and Azure Redis with Azure AD (Entra ID) authentication using credential provider. |
|
||||
| [`redis_basics.py`](redis_basics.py) | Shows standalone provider usage and agent integration. Demonstrates writing messages to Redis, retrieving context via full‑text or hybrid vector search, and persisting preferences across threads. Also includes a simple tool example whose outputs are remembered. |
|
||||
| [`redis_threads.py`](redis_threads.py) | Demonstrates thread scoping. Includes: (1) global thread scope with a fixed `thread_id` shared across operations; (2) per‑operation thread scope where `scope_to_per_operation_thread_id=True` binds memory to a single thread for the provider’s lifetime; and (3) multiple agents with isolated memory via different `agent_id` values. |
|
||||
| [`redis_conversation.py`](redis_conversation.py) | Simple example showing conversation persistence with RedisChatMessageStore using traditional connection string authentication. |
|
||||
| [`redis_threads.py`](redis_threads.py) | Demonstrates thread scoping. Includes: (1) global thread scope with a fixed `thread_id` shared across operations; (2) per‑operation thread scope where `scope_to_per_operation_thread_id=True` binds memory to a single thread for the provider's lifetime; and (3) multiple agents with isolated memory via different `agent_id` values. |
|
||||
|
||||
|
||||
## Prerequisites
|
||||
|
||||
|
||||
@@ -0,0 +1,123 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
"""Azure Managed Redis Chat Message Store with Azure AD Authentication
|
||||
|
||||
This example demonstrates how to use Azure Managed Redis with Azure AD authentication
|
||||
to persist conversational details using RedisChatMessageStore.
|
||||
|
||||
Requirements:
|
||||
- Azure Managed Redis instance with Azure AD authentication enabled
|
||||
- Azure credentials configured (az login or managed identity)
|
||||
- agent-framework-redis: pip install agent-framework-redis
|
||||
- azure-identity: pip install azure-identity
|
||||
|
||||
Environment Variables:
|
||||
- AZURE_REDIS_HOST: Your Azure Managed Redis host (e.g., myredis.redis.cache.windows.net)
|
||||
- OPENAI_API_KEY: Your OpenAI API key
|
||||
- OPENAI_CHAT_MODEL_ID: OpenAI model (e.g., gpt-4o-mini)
|
||||
- AZURE_USER_OBJECT_ID: Your Azure AD User Object ID for authentication
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from agent_framework.openai import OpenAIChatClient
|
||||
from agent_framework.redis import RedisChatMessageStore
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
from redis.credentials import CredentialProvider
|
||||
|
||||
|
||||
class AzureCredentialProvider(CredentialProvider):
|
||||
"""Credential provider for Azure AD authentication with Redis Enterprise."""
|
||||
|
||||
def __init__(self, azure_credential: AzureCliCredential, user_object_id: str):
|
||||
self.azure_credential = azure_credential
|
||||
self.user_object_id = user_object_id
|
||||
|
||||
async def get_credentials_async(self) -> tuple[str] | tuple[str, str]:
|
||||
"""Get Azure AD token for Redis authentication.
|
||||
|
||||
Returns (username, token) where username is the Azure user's Object ID.
|
||||
"""
|
||||
token = await self.azure_credential.get_token("https://redis.azure.com/.default")
|
||||
return (self.user_object_id, token.token)
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
redis_host = os.environ.get("AZURE_REDIS_HOST")
|
||||
if not redis_host:
|
||||
print("ERROR: Set AZURE_REDIS_HOST environment variable")
|
||||
return
|
||||
|
||||
# For Azure Redis with Entra ID, username must be your Object ID
|
||||
user_object_id = os.environ.get("AZURE_USER_OBJECT_ID")
|
||||
if not user_object_id:
|
||||
print("ERROR: Set AZURE_USER_OBJECT_ID environment variable")
|
||||
print("Get your Object ID from the Azure Portal")
|
||||
return
|
||||
|
||||
# Create Azure CLI credential provider (uses 'az login' credentials)
|
||||
azure_credential = AzureCliCredential()
|
||||
credential_provider = AzureCredentialProvider(azure_credential, user_object_id)
|
||||
|
||||
thread_id = "azure_test_thread"
|
||||
|
||||
# Factory for creating Azure Redis chat message store
|
||||
chat_message_store_factory = lambda: RedisChatMessageStore(
|
||||
credential_provider=credential_provider,
|
||||
host=redis_host,
|
||||
port=10000,
|
||||
ssl=True,
|
||||
thread_id=thread_id,
|
||||
key_prefix="chat_messages",
|
||||
max_messages=100,
|
||||
)
|
||||
|
||||
# Create chat client
|
||||
client = OpenAIChatClient()
|
||||
|
||||
# Create agent with Azure Redis store
|
||||
agent = client.create_agent(
|
||||
name="AzureRedisAssistant",
|
||||
instructions="You are a helpful assistant.",
|
||||
chat_message_store_factory=chat_message_store_factory,
|
||||
)
|
||||
|
||||
# Conversation
|
||||
query = "Remember that I enjoy gumbo"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
# Ask the agent to recall the stored preference; it should retrieve from memory
|
||||
query = "What do I enjoy?"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
query = "What did I say to you just now?"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
query = "Remember that I have a meeting at 3pm tomorrow"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
query = "Tulips are red"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
query = "What was the first thing I said to you this conversation?"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
# Cleanup
|
||||
await azure_credential.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -91,7 +91,7 @@ async def main() -> None:
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
query = "Remember that anyone who does not clean shrimp will be eaten by a shark"
|
||||
query = "Remember that I have a meeting at 3pm tomorro"
|
||||
result = await agent.run(query)
|
||||
print("User: ", query)
|
||||
print("Agent: ", result)
|
||||
|
||||
@@ -12,8 +12,12 @@ The Model Context Protocol (MCP) is an open standard for connecting AI agents to
|
||||
|--------|------|-------------|
|
||||
| **Agent as MCP Server** | [`agent_as_mcp_server.py`](agent_as_mcp_server.py) | Shows how to expose an Agent Framework agent as an MCP server that other AI applications can connect to |
|
||||
| **API Key Authentication** | [`mcp_api_key_auth.py`](mcp_api_key_auth.py) | Demonstrates API key authentication with MCP servers |
|
||||
| **GitHub Integration with PAT** | [`mcp_github_pat.py`](mcp_github_pat.py) | Demonstrates connecting to GitHub's MCP server using Personal Access Token (PAT) authentication |
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- `OPENAI_API_KEY` environment variable
|
||||
- `OPENAI_RESPONSES_MODEL_ID` environment variable
|
||||
|
||||
For `mcp_github_pat.py`:
|
||||
- `GITHUB_PAT` - Your GitHub Personal Access Token (create at https://github.com/settings/tokens)
|
||||
|
||||
@@ -0,0 +1,81 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from agent_framework import ChatAgent, HostedMCPTool
|
||||
from agent_framework.openai import OpenAIResponsesClient
|
||||
from dotenv import load_dotenv
|
||||
|
||||
"""
|
||||
MCP GitHub Integration with Personal Access Token (PAT)
|
||||
|
||||
This example demonstrates how to connect to GitHub's remote MCP server using a Personal Access
|
||||
Token (PAT) for authentication. The agent can use GitHub operations like searching repositories,
|
||||
reading files, creating issues, and more depending on how you scope your token.
|
||||
|
||||
Prerequisites:
|
||||
1. A GitHub Personal Access Token with appropriate scopes
|
||||
- Create one at: https://github.com/settings/tokens
|
||||
- For read-only operations, you can use more restrictive scopes
|
||||
2. Environment variables:
|
||||
- GITHUB_PAT: Your GitHub Personal Access Token (required)
|
||||
- OPENAI_API_KEY: Your OpenAI API key (required)
|
||||
- OPENAI_RESPONSES_MODEL_ID: Your OpenAI model ID (required)
|
||||
"""
|
||||
|
||||
|
||||
async def github_mcp_example() -> None:
|
||||
"""Example of using GitHub MCP server with PAT authentication."""
|
||||
# 1. Load environment variables from .env file if present
|
||||
load_dotenv()
|
||||
|
||||
# 2. Get configuration from environment
|
||||
github_pat = os.getenv("GITHUB_PAT")
|
||||
if not github_pat:
|
||||
raise ValueError(
|
||||
"GITHUB_PAT environment variable must be set. Create a token at https://github.com/settings/tokens"
|
||||
)
|
||||
|
||||
# 3. Create authentication headers with GitHub PAT
|
||||
auth_headers = {
|
||||
"Authorization": f"Bearer {github_pat}",
|
||||
}
|
||||
|
||||
# 4. Create MCP tool with authentication
|
||||
# HostedMCPTool manages the connection to the MCP server and makes its tools available
|
||||
# Set approval_mode="never_require" to allow the MCP tool to execute without approval
|
||||
github_mcp_tool = HostedMCPTool(
|
||||
name="GitHub",
|
||||
description="Tool for interacting with GitHub.",
|
||||
url="https://api.githubcopilot.com/mcp/",
|
||||
headers=auth_headers,
|
||||
approval_mode="never_require",
|
||||
)
|
||||
|
||||
# 5. Create agent with the GitHub MCP tool
|
||||
async with ChatAgent(
|
||||
chat_client=OpenAIResponsesClient(),
|
||||
name="GitHubAgent",
|
||||
instructions=(
|
||||
"You are a helpful assistant that can help users interact with GitHub. "
|
||||
"You can search for repositories, read file contents, check issues, and more. "
|
||||
"Always be clear about what operations you're performing."
|
||||
),
|
||||
tools=github_mcp_tool,
|
||||
) as agent:
|
||||
# Example 1: Get authenticated user information
|
||||
query1 = "What is my GitHub username and tell me about my account?"
|
||||
print(f"\nUser: {query1}")
|
||||
result1 = await agent.run(query1)
|
||||
print(f"Agent: {result1.text}")
|
||||
|
||||
# Example 2: List my repositories
|
||||
query2 = "List all the repositories I own on GitHub"
|
||||
print(f"\nUser: {query2}")
|
||||
result2 = await agent.run(query2)
|
||||
print(f"Agent: {result2.text}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(github_mcp_example())
|
||||
@@ -45,6 +45,7 @@ Once comfortable with these, explore the rest of the samples below.
|
||||
| Workflow as Agent (Reflection Pattern) | [agents/workflow_as_agent_reflection_pattern.py](./agents/workflow_as_agent_reflection_pattern.py) | Wrap a workflow so it can behave like an agent (reflection pattern) |
|
||||
| Workflow as Agent + HITL | [agents/workflow_as_agent_human_in_the_loop.py](./agents/workflow_as_agent_human_in_the_loop.py) | Extend workflow-as-agent with human-in-the-loop capability |
|
||||
| Workflow as Agent with Thread | [agents/workflow_as_agent_with_thread.py](./agents/workflow_as_agent_with_thread.py) | Use AgentThread to maintain conversation history across workflow-as-agent invocations |
|
||||
| Workflow as Agent kwargs | [agents/workflow_as_agent_kwargs.py](./agents/workflow_as_agent_kwargs.py) | Pass custom context (data, user tokens) via kwargs through workflow.as_agent() to @ai_function tools |
|
||||
| Handoff Workflow as Agent | [agents/handoff_workflow_as_agent.py](./agents/handoff_workflow_as_agent.py) | Use a HandoffBuilder workflow as an agent with HITL via FunctionCallContent/FunctionResultContent |
|
||||
|
||||
### checkpoint
|
||||
@@ -64,6 +65,7 @@ Once comfortable with these, explore the rest of the samples below.
|
||||
| Sub-Workflow (Basics) | [composition/sub_workflow_basics.py](./composition/sub_workflow_basics.py) | Wrap a workflow as an executor and orchestrate sub-workflows |
|
||||
| Sub-Workflow: Request Interception | [composition/sub_workflow_request_interception.py](./composition/sub_workflow_request_interception.py) | Intercept and forward sub-workflow requests using @handler for SubWorkflowRequestMessage |
|
||||
| Sub-Workflow: Parallel Requests | [composition/sub_workflow_parallel_requests.py](./composition/sub_workflow_parallel_requests.py) | Multiple specialized interceptors handling different request types from same sub-workflow |
|
||||
| Sub-Workflow: kwargs Propagation | [composition/sub_workflow_kwargs.py](./composition/sub_workflow_kwargs.py) | Pass custom context (user tokens, config) from parent workflow through to sub-workflow agents |
|
||||
|
||||
### control-flow
|
||||
|
||||
@@ -75,6 +77,7 @@ Once comfortable with these, explore the rest of the samples below.
|
||||
| Switch-Case Edge Group | [control-flow/switch_case_edge_group.py](./control-flow/switch_case_edge_group.py) | Switch-case branching using classifier outputs |
|
||||
| Multi-Selection Edge Group | [control-flow/multi_selection_edge_group.py](./control-flow/multi_selection_edge_group.py) | Select one or many targets dynamically (subset fan-out) |
|
||||
| Simple Loop | [control-flow/simple_loop.py](./control-flow/simple_loop.py) | Feedback loop where an agent judges ABOVE/BELOW/MATCHED |
|
||||
| Workflow Cancellation | [control-flow/workflow_cancellation.py](./control-flow/workflow_cancellation.py) | Cancel a running workflow using asyncio tasks |
|
||||
|
||||
### human-in-the-loop
|
||||
|
||||
|
||||
@@ -0,0 +1,140 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Annotated, Any
|
||||
|
||||
from agent_framework import SequentialBuilder, ai_function
|
||||
from agent_framework.openai import OpenAIChatClient
|
||||
from pydantic import Field
|
||||
|
||||
"""
|
||||
Sample: Workflow as Agent with kwargs Propagation to @ai_function Tools
|
||||
|
||||
This sample demonstrates how to flow custom context (skill data, user tokens, etc.)
|
||||
through a workflow exposed via .as_agent() to @ai_function tools using the **kwargs pattern.
|
||||
|
||||
Key Concepts:
|
||||
- Build a workflow using SequentialBuilder (or any builder pattern)
|
||||
- Expose the workflow as a reusable agent via workflow.as_agent()
|
||||
- Pass custom context as kwargs when invoking workflow_agent.run() or run_stream()
|
||||
- kwargs are stored in SharedState and propagated to all agent invocations
|
||||
- @ai_function tools receive kwargs via **kwargs parameter
|
||||
|
||||
When to use workflow.as_agent():
|
||||
- To treat an entire workflow orchestration as a single agent
|
||||
- To compose workflows into higher-level orchestrations
|
||||
- To maintain a consistent agent interface for callers
|
||||
|
||||
Prerequisites:
|
||||
- OpenAI environment variables configured
|
||||
"""
|
||||
|
||||
|
||||
# Define tools that accept custom context via **kwargs
|
||||
@ai_function
|
||||
def get_user_data(
|
||||
query: Annotated[str, Field(description="What user data to retrieve")],
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Retrieve user-specific data based on the authenticated context."""
|
||||
user_token = kwargs.get("user_token", {})
|
||||
user_name = user_token.get("user_name", "anonymous")
|
||||
access_level = user_token.get("access_level", "none")
|
||||
|
||||
print(f"\n[get_user_data] Received kwargs keys: {list(kwargs.keys())}")
|
||||
print(f"[get_user_data] User: {user_name}")
|
||||
print(f"[get_user_data] Access level: {access_level}")
|
||||
|
||||
return f"Retrieved data for user {user_name} with {access_level} access: {query}"
|
||||
|
||||
|
||||
@ai_function
|
||||
def call_api(
|
||||
endpoint_name: Annotated[str, Field(description="Name of the API endpoint to call")],
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call an API using the configured endpoints from custom_data."""
|
||||
custom_data = kwargs.get("custom_data", {})
|
||||
api_config = custom_data.get("api_config", {})
|
||||
|
||||
base_url = api_config.get("base_url", "unknown")
|
||||
endpoints = api_config.get("endpoints", {})
|
||||
|
||||
print(f"\n[call_api] Received kwargs keys: {list(kwargs.keys())}")
|
||||
print(f"[call_api] Base URL: {base_url}")
|
||||
print(f"[call_api] Available endpoints: {list(endpoints.keys())}")
|
||||
|
||||
if endpoint_name in endpoints:
|
||||
return f"Called {base_url}{endpoints[endpoint_name]} successfully"
|
||||
return f"Endpoint '{endpoint_name}' not found in configuration"
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
print("=" * 70)
|
||||
print("Workflow as Agent kwargs Flow Demo")
|
||||
print("=" * 70)
|
||||
|
||||
# Create chat client
|
||||
chat_client = OpenAIChatClient()
|
||||
|
||||
# Create agent with tools that use kwargs
|
||||
agent = chat_client.create_agent(
|
||||
name="assistant",
|
||||
instructions=(
|
||||
"You are a helpful assistant. Use the available tools to help users. "
|
||||
"When asked about user data, use get_user_data. "
|
||||
"When asked to call an API, use call_api."
|
||||
),
|
||||
tools=[get_user_data, call_api],
|
||||
)
|
||||
|
||||
# Build a sequential workflow
|
||||
workflow = SequentialBuilder().participants([agent]).build()
|
||||
|
||||
# Expose the workflow as an agent using .as_agent()
|
||||
workflow_agent = workflow.as_agent(name="WorkflowAgent")
|
||||
|
||||
# Define custom context that will flow to ai_functions via kwargs
|
||||
custom_data = {
|
||||
"api_config": {
|
||||
"base_url": "https://api.example.com",
|
||||
"endpoints": {
|
||||
"users": "/v1/users",
|
||||
"orders": "/v1/orders",
|
||||
"products": "/v1/products",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
user_token = {
|
||||
"user_name": "bob@contoso.com",
|
||||
"access_level": "admin",
|
||||
}
|
||||
|
||||
print("\nCustom Data being passed:")
|
||||
print(json.dumps(custom_data, indent=2))
|
||||
print(f"\nUser: {user_token['user_name']}")
|
||||
print("\n" + "-" * 70)
|
||||
print("Workflow Agent Execution (watch for [tool_name] logs showing kwargs received):")
|
||||
print("-" * 70)
|
||||
|
||||
# Run workflow agent with kwargs - these will flow through to ai_functions
|
||||
# Note: kwargs are passed to workflow_agent.run_stream() just like workflow.run_stream()
|
||||
print("\n===== Streaming Response =====")
|
||||
async for update in workflow_agent.run_stream(
|
||||
"Please get my user data and then call the users API endpoint.",
|
||||
custom_data=custom_data,
|
||||
user_token=user_token,
|
||||
):
|
||||
if update.text:
|
||||
print(update.text, end="", flush=True)
|
||||
print()
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("Sample Complete")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,143 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Annotated, Any
|
||||
|
||||
from agent_framework import (
|
||||
ChatMessage,
|
||||
SequentialBuilder,
|
||||
WorkflowExecutor,
|
||||
WorkflowOutputEvent,
|
||||
ai_function,
|
||||
)
|
||||
from agent_framework.openai import OpenAIChatClient
|
||||
|
||||
"""
|
||||
Sample: Sub-Workflow kwargs Propagation
|
||||
|
||||
This sample demonstrates how custom context (kwargs) flows from a parent workflow
|
||||
through to agents in sub-workflows. When you pass kwargs to the parent workflow's
|
||||
run_stream() or run(), they automatically propagate to nested sub-workflows.
|
||||
|
||||
Key Concepts:
|
||||
- kwargs passed to parent workflow.run_stream() propagate to sub-workflows
|
||||
- Sub-workflow agents receive the same kwargs as the parent workflow
|
||||
- Works with nested WorkflowExecutor compositions at any depth
|
||||
- Useful for passing authentication tokens, configuration, or request context
|
||||
|
||||
Prerequisites:
|
||||
- OpenAI environment variables configured
|
||||
"""
|
||||
|
||||
|
||||
# Define tools that access custom context via **kwargs
|
||||
@ai_function
|
||||
def get_authenticated_data(
|
||||
resource: Annotated[str, "The resource to fetch"],
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Fetch data using the authenticated user context from kwargs."""
|
||||
user_token = kwargs.get("user_token", {})
|
||||
user_name = user_token.get("user_name", "anonymous")
|
||||
access_level = user_token.get("access_level", "none")
|
||||
|
||||
print(f"\n[get_authenticated_data] kwargs keys: {list(kwargs.keys())}")
|
||||
print(f"[get_authenticated_data] User: {user_name}, Access: {access_level}")
|
||||
|
||||
return f"Fetched '{resource}' for user {user_name} ({access_level} access)"
|
||||
|
||||
|
||||
@ai_function
|
||||
def call_configured_service(
|
||||
service_name: Annotated[str, "Name of the service to call"],
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call a service using configuration from kwargs."""
|
||||
config = kwargs.get("service_config", {})
|
||||
services = config.get("services", {})
|
||||
|
||||
print(f"\n[call_configured_service] kwargs keys: {list(kwargs.keys())}")
|
||||
print(f"[call_configured_service] Available services: {list(services.keys())}")
|
||||
|
||||
if service_name in services:
|
||||
endpoint = services[service_name]
|
||||
return f"Called service '{service_name}' at {endpoint}"
|
||||
return f"Service '{service_name}' not found in configuration"
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
print("=" * 70)
|
||||
print("Sub-Workflow kwargs Propagation Demo")
|
||||
print("=" * 70)
|
||||
|
||||
# Create chat client
|
||||
chat_client = OpenAIChatClient()
|
||||
|
||||
# Create an agent with tools that use kwargs
|
||||
inner_agent = chat_client.create_agent(
|
||||
name="data_agent",
|
||||
instructions=(
|
||||
"You are a data access agent. Use the available tools to help users. "
|
||||
"When asked to fetch data, use get_authenticated_data. "
|
||||
"When asked to call a service, use call_configured_service."
|
||||
),
|
||||
tools=[get_authenticated_data, call_configured_service],
|
||||
)
|
||||
|
||||
# Build the inner (sub) workflow with the agent
|
||||
inner_workflow = SequentialBuilder().participants([inner_agent]).build()
|
||||
|
||||
# Wrap the inner workflow in a WorkflowExecutor to use it as a sub-workflow
|
||||
subworkflow_executor = WorkflowExecutor(
|
||||
workflow=inner_workflow,
|
||||
id="data_subworkflow",
|
||||
)
|
||||
|
||||
# Build the outer (parent) workflow containing the sub-workflow
|
||||
outer_workflow = SequentialBuilder().participants([subworkflow_executor]).build()
|
||||
|
||||
# Define custom context that will flow through to the sub-workflow's agent
|
||||
user_token = {
|
||||
"user_name": "alice@contoso.com",
|
||||
"access_level": "admin",
|
||||
"session_id": "sess_12345",
|
||||
}
|
||||
|
||||
service_config = {
|
||||
"services": {
|
||||
"users": "https://api.example.com/v1/users",
|
||||
"orders": "https://api.example.com/v1/orders",
|
||||
"inventory": "https://api.example.com/v1/inventory",
|
||||
},
|
||||
"timeout": 30,
|
||||
}
|
||||
|
||||
print("\nContext being passed to parent workflow:")
|
||||
print(f" user_token: {json.dumps(user_token, indent=4)}")
|
||||
print(f" service_config: {json.dumps(service_config, indent=4)}")
|
||||
print("\n" + "-" * 70)
|
||||
print("Workflow Execution (kwargs flow: parent -> sub-workflow -> agent -> tool):")
|
||||
print("-" * 70)
|
||||
|
||||
# Run the OUTER workflow with kwargs
|
||||
# These kwargs will automatically propagate to the inner sub-workflow
|
||||
async for event in outer_workflow.run_stream(
|
||||
"Please fetch my profile data and then call the users service.",
|
||||
user_token=user_token,
|
||||
service_config=service_config,
|
||||
):
|
||||
if isinstance(event, WorkflowOutputEvent):
|
||||
output_data = event.data
|
||||
if isinstance(output_data, list):
|
||||
for item in output_data: # type: ignore
|
||||
if isinstance(item, ChatMessage) and item.text:
|
||||
print(f"\n[Final Answer]: {item.text}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("Sample Complete - kwargs successfully flowed through sub-workflow!")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -0,0 +1,103 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import asyncio
|
||||
|
||||
from agent_framework import WorkflowBuilder, WorkflowContext, executor
|
||||
from typing_extensions import Never
|
||||
|
||||
"""
|
||||
Sample: Workflow Cancellation
|
||||
|
||||
A three-step workflow where each step takes 2 seconds. We cancel it after 3 seconds
|
||||
to demonstrate mid-execution cancellation using asyncio tasks.
|
||||
|
||||
Purpose:
|
||||
Show how to cancel a running workflow by wrapping it in an asyncio.Task. This pattern
|
||||
works with both workflow.run() and workflow.run_stream(). Useful for implementing
|
||||
timeouts, graceful shutdown, or A2A executors that need cancellation support.
|
||||
|
||||
Prerequisites:
|
||||
- No external services required.
|
||||
"""
|
||||
|
||||
|
||||
@executor(id="step1")
|
||||
async def step1(text: str, ctx: WorkflowContext[str]) -> None:
|
||||
"""First step - simulates 2 seconds of work."""
|
||||
print("[Step1] Starting...")
|
||||
await asyncio.sleep(2)
|
||||
print("[Step1] Done")
|
||||
await ctx.send_message(text.upper())
|
||||
|
||||
|
||||
@executor(id="step2")
|
||||
async def step2(text: str, ctx: WorkflowContext[str]) -> None:
|
||||
"""Second step - simulates 2 seconds of work."""
|
||||
print("[Step2] Starting...")
|
||||
await asyncio.sleep(2)
|
||||
print("[Step2] Done")
|
||||
await ctx.send_message(text + "!")
|
||||
|
||||
|
||||
@executor(id="step3")
|
||||
async def step3(text: str, ctx: WorkflowContext[Never, str]) -> None:
|
||||
"""Final step - simulates 2 seconds of work."""
|
||||
print("[Step3] Starting...")
|
||||
await asyncio.sleep(2)
|
||||
print("[Step3] Done")
|
||||
await ctx.yield_output(f"Result: {text}")
|
||||
|
||||
|
||||
def build_workflow():
|
||||
"""Build a simple 3-step sequential workflow (~6 seconds total)."""
|
||||
return (
|
||||
WorkflowBuilder()
|
||||
.register_executor(lambda: step1, name="step1")
|
||||
.register_executor(lambda: step2, name="step2")
|
||||
.register_executor(lambda: step3, name="step3")
|
||||
.add_edge("step1", "step2")
|
||||
.add_edge("step2", "step3")
|
||||
.set_start_executor("step1")
|
||||
.build()
|
||||
)
|
||||
|
||||
|
||||
async def run_with_cancellation() -> None:
|
||||
"""Cancel the workflow after 3 seconds (mid-execution during Step2)."""
|
||||
print("=== Run with cancellation ===\n")
|
||||
workflow = build_workflow()
|
||||
|
||||
# Wrap workflow.run() in a task to enable cancellation
|
||||
task = asyncio.create_task(workflow.run("hello world"))
|
||||
|
||||
# Wait 3 seconds (Step1 completes, Step2 is mid-execution), then cancel
|
||||
await asyncio.sleep(3)
|
||||
print("\n--- Cancelling workflow ---\n")
|
||||
task.cancel()
|
||||
|
||||
try:
|
||||
await task
|
||||
except asyncio.CancelledError:
|
||||
print("Workflow was cancelled")
|
||||
|
||||
|
||||
async def run_to_completion() -> None:
|
||||
"""Let the workflow run to completion and get the result."""
|
||||
print("=== Run to completion ===\n")
|
||||
workflow = build_workflow()
|
||||
|
||||
# Run without cancellation - await the result directly
|
||||
result = await workflow.run("hello world")
|
||||
|
||||
print(f"\nWorkflow completed with output: {result.get_outputs()}")
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
"""Demonstrate both cancellation and completion scenarios."""
|
||||
await run_with_cancellation()
|
||||
print("\n")
|
||||
await run_to_completion()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
+4
-15
@@ -6,14 +6,12 @@ from dataclasses import dataclass
|
||||
from agent_framework import (
|
||||
AgentExecutorRequest,
|
||||
AgentExecutorResponse,
|
||||
AgentRunEvent,
|
||||
ChatAgent,
|
||||
ChatMessage,
|
||||
Executor,
|
||||
Role,
|
||||
WorkflowBuilder,
|
||||
WorkflowContext,
|
||||
WorkflowOutputEvent,
|
||||
WorkflowViz,
|
||||
handler,
|
||||
)
|
||||
@@ -124,7 +122,7 @@ def create_legal_agent() -> ChatAgent:
|
||||
async def main() -> None:
|
||||
"""Build and run the concurrent workflow with visualization."""
|
||||
|
||||
# 1) Build a simple fan-out/fan-in workflow
|
||||
# Build a simple fan-out/fan-in workflow
|
||||
workflow = (
|
||||
WorkflowBuilder()
|
||||
.register_agent(create_researcher_agent, name="researcher")
|
||||
@@ -138,31 +136,22 @@ async def main() -> None:
|
||||
.build()
|
||||
)
|
||||
|
||||
# 1.5) Generate workflow visualization
|
||||
# Generate workflow visualization
|
||||
print("Generating workflow visualization...")
|
||||
viz = WorkflowViz(workflow)
|
||||
# Print out the mermaid string.
|
||||
print("Mermaid string: \n=======")
|
||||
print(viz.to_mermaid())
|
||||
print("=======")
|
||||
# Print out the DiGraph string.
|
||||
# Print out the DiGraph string with internal executors.
|
||||
print("DiGraph string: \n=======")
|
||||
print(viz.to_digraph())
|
||||
print(viz.to_digraph(include_internal_executors=True))
|
||||
print("=======")
|
||||
|
||||
# Export the DiGraph visualization as SVG.
|
||||
svg_file = viz.export(format="svg")
|
||||
print(f"SVG file saved to: {svg_file}")
|
||||
|
||||
# 2) Run with a single prompt
|
||||
async for event in workflow.run_stream("We are launching a new budget-friendly electric bike for urban commuters."):
|
||||
if isinstance(event, AgentRunEvent):
|
||||
# Show which agent ran and what step completed.
|
||||
print(event)
|
||||
elif isinstance(event, WorkflowOutputEvent):
|
||||
print("===== Final Aggregated Output =====")
|
||||
print(event.data)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
||||
Reference in New Issue
Block a user