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.NET: Python: Merge main into feature-durabletask-python branch (#3160)
* 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 * Revert to match main * revert file to main --------- 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> 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>
This commit is contained in:
Laveesh Rohra
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parent
e3eff65a6b
commit
1e36ba33c4
@@ -102,12 +102,32 @@ agents/
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└── .env # Optional: shared environment variables
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```
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## Viewing Telemetry (Otel Traces) in DevUI
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### Importing from External Modules
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Agent Framework emits OpenTelemetry (Otel) traces for various operations. You can view these traces in DevUI by enabling tracing when starting the server.
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If your agents import tools or utilities from sibling directories (e.g., `from tools.helpers import my_tool`), you must set `PYTHONPATH` to include the parent directory:
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```bash
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devui ./agents --tracing framework
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# Project structure:
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# backend/
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# ├── agents/
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# │ └── my_agent/
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# │ └── agent.py # contains: from tools.helpers import my_tool
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# └── tools/
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# └── helpers.py
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# Run from project root with PYTHONPATH
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cd backend
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PYTHONPATH=. devui ./agents --port 8080
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```
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Without `PYTHONPATH`, Python cannot find modules in sibling directories and DevUI will report an import error.
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## Viewing Telemetry (Otel Traces) in DevUI
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Agent Framework emits OpenTelemetry (Otel) traces for various operations. You can view these traces in DevUI by enabling instrumentation when starting the server.
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```bash
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devui ./agents --instrumentation
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```
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## OpenAI-Compatible API
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@@ -196,11 +216,12 @@ Options:
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--port, -p Port (default: 8080)
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--host Host (default: 127.0.0.1)
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--headless API only, no UI
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--config YAML config file
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--tracing none|framework|workflow|all
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--no-open Don't automatically open browser
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--instrumentation Enable OpenTelemetry instrumentation
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--reload Enable auto-reload
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--mode developer|user (default: developer)
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--auth Enable Bearer token authentication
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--auth-token Custom authentication token
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```
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### UI Modes
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@@ -94,7 +94,7 @@ def serve(
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auto_open: bool = False,
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cors_origins: list[str] | None = None,
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ui_enabled: bool = True,
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tracing_enabled: bool = False,
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instrumentation_enabled: bool = False,
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mode: str = "developer",
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auth_enabled: bool = False,
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auth_token: str | None = None,
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@@ -109,7 +109,7 @@ def serve(
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auto_open: Whether to automatically open browser
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cors_origins: List of allowed CORS origins
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ui_enabled: Whether to enable the UI
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tracing_enabled: Whether to enable OpenTelemetry tracing
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instrumentation_enabled: Whether to enable OpenTelemetry instrumentation
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mode: Server mode - 'developer' (full access, verbose errors) or 'user' (restricted APIs, generic errors)
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auth_enabled: Whether to enable Bearer token authentication
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auth_token: Custom authentication token (auto-generated if not provided with auth_enabled=True)
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@@ -172,22 +172,12 @@ def serve(
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os.environ["AUTH_REQUIRED"] = "true"
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os.environ["DEVUI_AUTH_TOKEN"] = auth_token
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# Configure tracing environment variables if enabled
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if tracing_enabled:
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import os
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# Enable instrumentation if requested
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if instrumentation_enabled:
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from agent_framework.observability import enable_instrumentation
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# Only set if not already configured by user
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if not os.environ.get("ENABLE_INSTRUMENTATION"):
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os.environ["ENABLE_INSTRUMENTATION"] = "true"
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logger.info("Set ENABLE_INSTRUMENTATION=true for tracing")
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if not os.environ.get("ENABLE_SENSITIVE_DATA"):
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os.environ["ENABLE_SENSITIVE_DATA"] = "true"
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logger.info("Set ENABLE_SENSITIVE_DATA=true for tracing")
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if not os.environ.get("OTLP_ENDPOINT"):
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os.environ["OTLP_ENDPOINT"] = "http://localhost:4317"
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logger.info("Set OTLP_ENDPOINT=http://localhost:4317 for tracing")
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enable_instrumentation(enable_sensitive_data=True)
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logger.info("Enabled Agent Framework instrumentation with sensitive data")
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# Create server with direct parameters
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server = DevServer(
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@@ -28,7 +28,7 @@ Examples:
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devui ./agents # Scan specific directory
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devui --port 8000 # Custom port
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devui --headless # API only, no UI
|
||||
devui --tracing # Enable OpenTelemetry tracing
|
||||
devui --instrumentation # Enable OpenTelemetry instrumentation
|
||||
""",
|
||||
)
|
||||
|
||||
@@ -53,7 +53,7 @@ Examples:
|
||||
|
||||
parser.add_argument("--reload", action="store_true", help="Enable auto-reload for development")
|
||||
|
||||
parser.add_argument("--tracing", action="store_true", help="Enable OpenTelemetry tracing for Agent Framework")
|
||||
parser.add_argument("--instrumentation", action="store_true", help="Enable OpenTelemetry instrumentation")
|
||||
|
||||
parser.add_argument(
|
||||
"--mode",
|
||||
@@ -182,7 +182,7 @@ def main() -> None:
|
||||
host=args.host,
|
||||
auto_open=not args.no_open,
|
||||
ui_enabled=ui_enabled,
|
||||
tracing_enabled=args.tracing,
|
||||
instrumentation_enabled=args.instrumentation,
|
||||
mode=mode,
|
||||
auth_enabled=args.auth,
|
||||
auth_token=args.auth_token, # Pass through explicit token only
|
||||
|
||||
@@ -176,6 +176,31 @@ class ConversationStore(ABC):
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def add_trace(self, conversation_id: str, trace_event: dict[str, Any]) -> None:
|
||||
"""Add a trace event to the conversation for context inspection.
|
||||
|
||||
Traces capture execution metadata like token usage, timing, and LLM context
|
||||
that isn't stored in the AgentThread but is useful for debugging.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
trace_event: Trace event data (from ResponseTraceEvent.data)
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_traces(self, conversation_id: str) -> list[dict[str, Any]]:
|
||||
"""Get all trace events for a conversation.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
List of trace event dicts, or empty list if not found
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class InMemoryConversationStore(ConversationStore):
|
||||
"""In-memory conversation storage wrapping AgentThread.
|
||||
@@ -215,6 +240,7 @@ class InMemoryConversationStore(ConversationStore):
|
||||
"metadata": metadata or {},
|
||||
"created_at": created_at,
|
||||
"items": [],
|
||||
"traces": [], # Trace events for context inspection (token usage, timing, etc.)
|
||||
}
|
||||
|
||||
# Initialize item index for this conversation
|
||||
@@ -407,10 +433,20 @@ class InMemoryConversationStore(ConversationStore):
|
||||
elif content_type == "function_result":
|
||||
# Function result - create separate ConversationItem
|
||||
call_id = getattr(content, "call_id", None)
|
||||
# Output is stored in additional_properties
|
||||
output = ""
|
||||
if hasattr(content, "additional_properties"):
|
||||
output = content.additional_properties.get("output", "")
|
||||
# Output is stored in the 'result' field of FunctionResultContent
|
||||
result_value = getattr(content, "result", None)
|
||||
# Convert result to string (it could be dict, list, or other types)
|
||||
if result_value is None:
|
||||
output = ""
|
||||
elif isinstance(result_value, str):
|
||||
output = result_value
|
||||
else:
|
||||
import json
|
||||
|
||||
try:
|
||||
output = json.dumps(result_value)
|
||||
except (TypeError, ValueError):
|
||||
output = str(result_value)
|
||||
|
||||
if call_id:
|
||||
function_results.append(
|
||||
@@ -556,6 +592,34 @@ class InMemoryConversationStore(ConversationStore):
|
||||
conv_data = self._conversations.get(conversation_id)
|
||||
return conv_data["thread"] if conv_data else None
|
||||
|
||||
def add_trace(self, conversation_id: str, trace_event: dict[str, Any]) -> None:
|
||||
"""Add a trace event to the conversation for context inspection.
|
||||
|
||||
Traces capture execution metadata like token usage, timing, and LLM context
|
||||
that isn't stored in the AgentThread but is useful for debugging.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
trace_event: Trace event data (from ResponseTraceEvent.data)
|
||||
"""
|
||||
conv_data = self._conversations.get(conversation_id)
|
||||
if conv_data:
|
||||
traces = conv_data.get("traces", [])
|
||||
traces.append(trace_event)
|
||||
conv_data["traces"] = traces
|
||||
|
||||
def get_traces(self, conversation_id: str) -> list[dict[str, Any]]:
|
||||
"""Get all trace events for a conversation.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
List of trace event dicts, or empty list if not found
|
||||
"""
|
||||
conv_data = self._conversations.get(conversation_id)
|
||||
return conv_data.get("traces", []) if conv_data else []
|
||||
|
||||
async def list_conversations_by_metadata(self, metadata_filter: dict[str, str]) -> list[Conversation]:
|
||||
"""Filter conversations by metadata (e.g., agent_id)."""
|
||||
results = []
|
||||
|
||||
@@ -666,7 +666,16 @@ class EntityDiscovery:
|
||||
logger.debug(f"Successfully imported {pattern}")
|
||||
return module, None
|
||||
|
||||
except ModuleNotFoundError:
|
||||
except ModuleNotFoundError as e:
|
||||
# Distinguish between "module pattern doesn't exist" vs "module has import errors"
|
||||
# If the missing module is the pattern itself, it's just not found (try next pattern)
|
||||
# If the missing module is something else (a dependency), capture the error
|
||||
missing_module = getattr(e, "name", None)
|
||||
if missing_module and missing_module != pattern and not pattern.endswith(f".{missing_module}"):
|
||||
# The module exists but has an import error (missing dependency)
|
||||
logger.warning(f"Error importing {pattern}: {e}")
|
||||
return None, e
|
||||
# The module pattern itself doesn't exist - this is expected, try next pattern
|
||||
logger.debug(f"Import pattern {pattern} not found")
|
||||
return None, None
|
||||
except Exception as e:
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
@@ -45,8 +44,8 @@ class AgentFrameworkExecutor:
|
||||
"""
|
||||
self.entity_discovery = entity_discovery
|
||||
self.message_mapper = message_mapper
|
||||
self._setup_tracing_provider()
|
||||
self._setup_agent_framework_tracing()
|
||||
self._setup_instrumentation_provider()
|
||||
self._setup_agent_framework_instrumentation()
|
||||
|
||||
# Use provided conversation store or default to in-memory
|
||||
self.conversation_store = conversation_store or InMemoryConversationStore()
|
||||
@@ -56,7 +55,7 @@ class AgentFrameworkExecutor:
|
||||
|
||||
self.checkpoint_manager = CheckpointConversationManager(self.conversation_store)
|
||||
|
||||
def _setup_tracing_provider(self) -> None:
|
||||
def _setup_instrumentation_provider(self) -> None:
|
||||
"""Set up our own TracerProvider so we can add processors."""
|
||||
try:
|
||||
from opentelemetry import trace
|
||||
@@ -71,7 +70,7 @@ class AgentFrameworkExecutor:
|
||||
})
|
||||
provider = TracerProvider(resource=resource)
|
||||
trace.set_tracer_provider(provider)
|
||||
logger.info("Set up TracerProvider for server tracing")
|
||||
logger.info("Set up TracerProvider for instrumentation")
|
||||
else:
|
||||
logger.debug("TracerProvider already exists")
|
||||
|
||||
@@ -80,25 +79,86 @@ class AgentFrameworkExecutor:
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to setup TracerProvider: {e}")
|
||||
|
||||
def _setup_agent_framework_tracing(self) -> None:
|
||||
"""Set up Agent Framework's built-in tracing."""
|
||||
# Configure Agent Framework tracing only if ENABLE_INSTRUMENTATION is set
|
||||
if os.environ.get("ENABLE_INSTRUMENTATION"):
|
||||
try:
|
||||
from agent_framework.observability import OBSERVABILITY_SETTINGS, configure_otel_providers
|
||||
def _setup_agent_framework_instrumentation(self) -> None:
|
||||
"""Set up Agent Framework's built-in instrumentation."""
|
||||
try:
|
||||
from agent_framework.observability import OBSERVABILITY_SETTINGS, configure_otel_providers
|
||||
|
||||
# Only configure if not already executed
|
||||
# Configure if instrumentation is enabled (via enable_instrumentation() or env var)
|
||||
if OBSERVABILITY_SETTINGS.ENABLED:
|
||||
# Only configure providers if not already executed
|
||||
if not OBSERVABILITY_SETTINGS._executed_setup:
|
||||
# Run the configure_otel_providers
|
||||
# This ensures OTLP exporters are created even if env vars were set late
|
||||
configure_otel_providers(enable_sensitive_data=True)
|
||||
# Call configure_otel_providers to set up exporters.
|
||||
# If OTEL_EXPORTER_OTLP_ENDPOINT is set, exporters will be created automatically.
|
||||
# If not set, no exporters are created (no console spam), but DevUI's
|
||||
# TracerProvider from _setup_instrumentation_provider() remains active for local capture.
|
||||
configure_otel_providers(enable_sensitive_data=OBSERVABILITY_SETTINGS.SENSITIVE_DATA_ENABLED)
|
||||
logger.info("Enabled Agent Framework observability")
|
||||
else:
|
||||
logger.debug("Agent Framework observability already configured")
|
||||
else:
|
||||
logger.debug("Instrumentation not enabled, skipping observability setup")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to enable Agent Framework observability: {e}")
|
||||
|
||||
async def _ensure_mcp_connections(self, agent: Any) -> None:
|
||||
"""Ensure MCP tool connections are healthy before agent execution.
|
||||
|
||||
This is a workaround for an Agent Framework bug where MCP tool connections
|
||||
can become stale (underlying streams closed) but is_connected remains True.
|
||||
This happens when HTTP streaming responses end and GeneratorExit propagates.
|
||||
|
||||
This method detects stale connections and reconnects them. It's designed to
|
||||
be a no-op once the Agent Framework fixes this issue upstream.
|
||||
|
||||
Args:
|
||||
agent: Agent object that may have MCP tools
|
||||
"""
|
||||
if not hasattr(agent, "_local_mcp_tools"):
|
||||
return
|
||||
|
||||
for mcp_tool in agent._local_mcp_tools:
|
||||
if not getattr(mcp_tool, "is_connected", False):
|
||||
continue
|
||||
|
||||
tool_name = getattr(mcp_tool, "name", "unknown")
|
||||
|
||||
try:
|
||||
# Check if underlying write stream is closed
|
||||
session = getattr(mcp_tool, "session", None)
|
||||
if session is None:
|
||||
continue
|
||||
|
||||
write_stream = getattr(session, "_write_stream", None)
|
||||
if write_stream is None:
|
||||
continue
|
||||
|
||||
# Detect stale connection: is_connected=True but stream is closed
|
||||
is_closed = getattr(write_stream, "_closed", False)
|
||||
if not is_closed:
|
||||
continue # Connection is healthy
|
||||
|
||||
# Stale connection detected - reconnect
|
||||
logger.warning(f"MCP tool '{tool_name}' has stale connection (stream closed), reconnecting...")
|
||||
|
||||
# Clean up old connection
|
||||
try:
|
||||
if hasattr(mcp_tool, "close"):
|
||||
await mcp_tool.close()
|
||||
except Exception as close_err:
|
||||
logger.debug(f"Error closing stale MCP tool '{tool_name}': {close_err}")
|
||||
# Force reset state
|
||||
mcp_tool.is_connected = False
|
||||
mcp_tool.session = None
|
||||
|
||||
# Reconnect
|
||||
if hasattr(mcp_tool, "connect"):
|
||||
await mcp_tool.connect()
|
||||
logger.info(f"MCP tool '{tool_name}' reconnected successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to enable Agent Framework observability: {e}")
|
||||
else:
|
||||
logger.debug("ENABLE_INSTRUMENTATION not set, skipping observability setup")
|
||||
# If detection fails, log and continue - let it fail naturally during execution
|
||||
logger.debug(f"Error checking MCP tool '{tool_name}' connection: {e}")
|
||||
|
||||
async def discover_entities(self) -> list[EntityInfo]:
|
||||
"""Discover all available entities.
|
||||
@@ -192,11 +252,11 @@ class AgentFrameworkExecutor:
|
||||
|
||||
logger.info(f"Executing {entity_info.type}: {entity_id}")
|
||||
|
||||
# Extract session_id from request for trace context
|
||||
session_id = getattr(request.extra_body, "session_id", None) if request.extra_body else None
|
||||
# Extract response_id from request for trace context (added by _server.py)
|
||||
response_id = request.extra_body.get("response_id") if request.extra_body else None
|
||||
|
||||
# Use simplified trace capture
|
||||
with capture_traces(session_id=session_id, entity_id=entity_id) as trace_collector:
|
||||
with capture_traces(response_id=response_id, entity_id=entity_id) as trace_collector:
|
||||
if entity_info.type == "agent":
|
||||
async for event in self._execute_agent(entity_obj, request, trace_collector):
|
||||
yield event
|
||||
@@ -260,6 +320,12 @@ class AgentFrameworkExecutor:
|
||||
logger.debug(f"Executing agent with text input: {user_message[:100]}...")
|
||||
else:
|
||||
logger.debug(f"Executing agent with multimodal ChatMessage: {type(user_message)}")
|
||||
|
||||
# Workaround for MCP tool stale connection bug (GitHub issue pending)
|
||||
# When HTTP streaming ends, GeneratorExit can close MCP stdio streams
|
||||
# but is_connected stays True. Detect and reconnect before execution.
|
||||
await self._ensure_mcp_connections(agent)
|
||||
|
||||
# Check if agent supports streaming
|
||||
if hasattr(agent, "run_stream") and callable(agent.run_stream):
|
||||
# Use Agent Framework's native streaming with optional thread
|
||||
|
||||
@@ -12,6 +12,7 @@ from datetime import datetime
|
||||
from typing import Any, Union
|
||||
from uuid import uuid4
|
||||
|
||||
from agent_framework import ChatMessage, TextContent
|
||||
from openai.types.responses import (
|
||||
Response,
|
||||
ResponseContentPartAddedEvent,
|
||||
@@ -225,27 +226,128 @@ class MessageMapper:
|
||||
Final aggregated OpenAI response
|
||||
"""
|
||||
try:
|
||||
# Extract text content from events
|
||||
content_parts = []
|
||||
# Collect output items in order
|
||||
output_items: list[Any] = []
|
||||
|
||||
# Track text content parts per message (keyed by item_id)
|
||||
text_parts_by_message: dict[str, list[str]] = {}
|
||||
|
||||
# Track function calls (keyed by call_id) to accumulate arguments
|
||||
function_calls: dict[str, dict[str, Any]] = {}
|
||||
|
||||
# Track function results (keyed by call_id)
|
||||
function_results: dict[str, dict[str, Any]] = {}
|
||||
|
||||
for event in events:
|
||||
# Extract delta text from ResponseTextDeltaEvent
|
||||
if hasattr(event, "delta") and hasattr(event, "type") and event.type == "response.output_text.delta":
|
||||
content_parts.append(event.delta)
|
||||
event_type = getattr(event, "type", None)
|
||||
|
||||
# Combine content
|
||||
full_content = "".join(content_parts)
|
||||
# Handle text deltas - accumulate text per message
|
||||
if event_type == "response.output_text.delta":
|
||||
item_id = getattr(event, "item_id", "default")
|
||||
if item_id not in text_parts_by_message:
|
||||
text_parts_by_message[item_id] = []
|
||||
text_parts_by_message[item_id].append(event.delta)
|
||||
|
||||
# Create proper OpenAI Response
|
||||
response_output_text = ResponseOutputText(type="output_text", text=full_content, annotations=[])
|
||||
# Handle output_item.added events (function_call, message, etc.)
|
||||
elif event_type == "response.output_item.added":
|
||||
item = getattr(event, "item", None)
|
||||
if item:
|
||||
# Handle both object and dict formats
|
||||
item_type = item.get("type") if isinstance(item, dict) else getattr(item, "type", None)
|
||||
|
||||
response_output_message = ResponseOutputMessage(
|
||||
type="message",
|
||||
role="assistant",
|
||||
content=[response_output_text],
|
||||
id=f"msg_{uuid.uuid4().hex[:8]}",
|
||||
status="completed",
|
||||
)
|
||||
# Track function calls to accumulate their arguments
|
||||
if item_type == "function_call":
|
||||
# Handle both object and dict formats
|
||||
if isinstance(item, dict):
|
||||
call_id = item.get("call_id") or item.get("id")
|
||||
if call_id:
|
||||
function_calls[call_id] = {
|
||||
"id": item.get("id", call_id),
|
||||
"call_id": call_id,
|
||||
"name": item.get("name", ""),
|
||||
"arguments": item.get("arguments", ""),
|
||||
"type": "function_call",
|
||||
"status": item.get("status", "completed"),
|
||||
}
|
||||
else:
|
||||
call_id = getattr(item, "call_id", None) or getattr(item, "id", None)
|
||||
if call_id:
|
||||
function_calls[call_id] = {
|
||||
"id": getattr(item, "id", call_id),
|
||||
"call_id": call_id,
|
||||
"name": getattr(item, "name", ""),
|
||||
"arguments": getattr(item, "arguments", ""),
|
||||
"type": "function_call",
|
||||
"status": getattr(item, "status", "completed"),
|
||||
}
|
||||
|
||||
# Other output items (message, etc.) - track for later
|
||||
elif item_type == "message":
|
||||
# Messages will be built from text_parts_by_message
|
||||
pass
|
||||
|
||||
# Handle function call arguments delta - accumulate arguments
|
||||
elif event_type == "response.function_call_arguments.delta":
|
||||
item_id = getattr(event, "item_id", None)
|
||||
delta = getattr(event, "delta", "")
|
||||
# item_id for function calls is the call_id
|
||||
if item_id and item_id in function_calls:
|
||||
function_calls[item_id]["arguments"] += delta
|
||||
|
||||
# Handle function result complete events
|
||||
elif event_type == "response.function_result.complete":
|
||||
call_id = getattr(event, "call_id", None)
|
||||
if call_id:
|
||||
function_results[call_id] = {
|
||||
"type": "function_call_output",
|
||||
"call_id": call_id,
|
||||
"output": getattr(event, "output", ""),
|
||||
"status": getattr(event, "status", "completed"),
|
||||
}
|
||||
|
||||
# Build output array in order: function_calls, then final message
|
||||
|
||||
# Add function call items
|
||||
for _call_id, fc_data in function_calls.items():
|
||||
output_items.append(ResponseFunctionToolCall(**fc_data))
|
||||
|
||||
# Note: function_call_output items are NOT added to output array
|
||||
# In OpenAI's Responses API, function results are user inputs, not assistant outputs
|
||||
# The function_results dict is kept for potential future use or debugging
|
||||
# but we don't include them in the Response output
|
||||
_ = function_results # Acknowledge but don't use
|
||||
|
||||
# Build final text message from accumulated deltas
|
||||
# Combine all text parts (usually there's just one message)
|
||||
all_text_parts = []
|
||||
for _item_id, parts in text_parts_by_message.items():
|
||||
all_text_parts.extend(parts)
|
||||
|
||||
full_content = "".join(all_text_parts)
|
||||
|
||||
# Only add message if there's text content
|
||||
if full_content:
|
||||
response_output_text = ResponseOutputText(type="output_text", text=full_content, annotations=[])
|
||||
response_output_message = ResponseOutputMessage(
|
||||
type="message",
|
||||
role="assistant",
|
||||
content=[response_output_text],
|
||||
id=f"msg_{uuid.uuid4().hex[:8]}",
|
||||
status="completed",
|
||||
)
|
||||
output_items.append(response_output_message)
|
||||
|
||||
# If no output items at all, create an empty message
|
||||
if not output_items:
|
||||
response_output_text = ResponseOutputText(type="output_text", text="", annotations=[])
|
||||
response_output_message = ResponseOutputMessage(
|
||||
type="message",
|
||||
role="assistant",
|
||||
content=[response_output_text],
|
||||
id=f"msg_{uuid.uuid4().hex[:8]}",
|
||||
status="completed",
|
||||
)
|
||||
output_items.append(response_output_message)
|
||||
|
||||
# Get usage from accumulator (OpenAI standard)
|
||||
request_id = str(id(request))
|
||||
@@ -278,7 +380,7 @@ class MessageMapper:
|
||||
object="response",
|
||||
created_at=datetime.now().timestamp(),
|
||||
model=request.model or "devui",
|
||||
output=[response_output_message],
|
||||
output=output_items,
|
||||
usage=usage,
|
||||
parallel_tool_calls=False,
|
||||
tool_choice="none",
|
||||
@@ -501,7 +603,7 @@ class MessageMapper:
|
||||
return events
|
||||
|
||||
# Check if we're streaming text content
|
||||
has_text_content = any(content.__class__.__name__ == "TextContent" for content in update.contents)
|
||||
has_text_content = any(isinstance(content, TextContent) for content in update.contents)
|
||||
|
||||
# Check if we're in an executor context with an existing item
|
||||
executor_id = context.get("current_executor_id")
|
||||
@@ -791,17 +893,35 @@ class MessageMapper:
|
||||
|
||||
# Extract text from output data based on type
|
||||
text = None
|
||||
if hasattr(output_data, "__class__") and output_data.__class__.__name__ == "ChatMessage":
|
||||
if isinstance(output_data, ChatMessage):
|
||||
# Handle ChatMessage (from Magentic and AgentExecutor with output_response=True)
|
||||
text = getattr(output_data, "text", None)
|
||||
if not text:
|
||||
# Fallback to string representation
|
||||
text = str(output_data)
|
||||
elif isinstance(output_data, list):
|
||||
# Handle list of ChatMessage objects (from Magentic yield_output([final_answer]))
|
||||
text_parts = []
|
||||
for item in output_data:
|
||||
if isinstance(item, ChatMessage):
|
||||
item_text = getattr(item, "text", None)
|
||||
if item_text:
|
||||
text_parts.append(item_text)
|
||||
else:
|
||||
text_parts.append(str(item))
|
||||
elif isinstance(item, str):
|
||||
text_parts.append(item)
|
||||
else:
|
||||
try:
|
||||
text_parts.append(json.dumps(item, indent=2))
|
||||
except (TypeError, ValueError):
|
||||
text_parts.append(str(item))
|
||||
text = "\n".join(text_parts) if text_parts else str(output_data)
|
||||
elif isinstance(output_data, str):
|
||||
# String output
|
||||
text = output_data
|
||||
else:
|
||||
# Object/dict/list → JSON string
|
||||
# Object/dict → JSON string
|
||||
try:
|
||||
text = json.dumps(output_data, indent=2)
|
||||
except (TypeError, ValueError):
|
||||
@@ -1081,275 +1201,6 @@ class MessageMapper:
|
||||
|
||||
return [trace_event]
|
||||
|
||||
# Handle Magentic-specific events
|
||||
if event_class == "MagenticAgentDeltaEvent":
|
||||
agent_id = getattr(event, "agent_id", "unknown_agent")
|
||||
text = getattr(event, "text", None)
|
||||
|
||||
if text:
|
||||
# Check if we're inside an executor - route to executor's item
|
||||
# This prevents duplicate timeline entries (executor + inner agent)
|
||||
current_executor_id = context.get("current_executor_id")
|
||||
executor_item_key = f"exec_item_{current_executor_id}" if current_executor_id else None
|
||||
|
||||
if executor_item_key and executor_item_key in context:
|
||||
# Route delta to the executor's item instead of creating a new message item
|
||||
item_id = context[executor_item_key]
|
||||
|
||||
# Emit text delta event routed to the executor's item
|
||||
return [
|
||||
ResponseTextDeltaEvent(
|
||||
type="response.output_text.delta",
|
||||
output_index=context.get("output_index", 0),
|
||||
content_index=0,
|
||||
item_id=item_id,
|
||||
delta=text,
|
||||
logprobs=[],
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
]
|
||||
|
||||
# Fallback: No executor context - create separate message item (original behavior)
|
||||
# This handles cases where MagenticAgentDeltaEvent is emitted outside an executor
|
||||
events = []
|
||||
|
||||
# Track Magentic agent messages separately from regular messages
|
||||
# Use timestamp to ensure uniqueness for multiple runs of same agent
|
||||
magentic_key = f"magentic_message_{agent_id}"
|
||||
|
||||
# Check if this is the first delta from this agent (need to create message container)
|
||||
if magentic_key not in context:
|
||||
# Create a unique message ID for this agent's streaming session
|
||||
message_id = f"msg_{agent_id}_{uuid4().hex[:8]}"
|
||||
context[magentic_key] = message_id
|
||||
context["output_index"] = context.get("output_index", -1) + 1
|
||||
|
||||
# Import required types for creating message containers
|
||||
from openai.types.responses import ResponseOutputMessage, ResponseOutputText
|
||||
from openai.types.responses.response_content_part_added_event import (
|
||||
ResponseContentPartAddedEvent,
|
||||
)
|
||||
from openai.types.responses.response_output_item_added_event import ResponseOutputItemAddedEvent
|
||||
|
||||
# Emit message output item (container for the agent's message)
|
||||
# This matches what _convert_agent_update does for regular agents
|
||||
events.append(
|
||||
ResponseOutputItemAddedEvent(
|
||||
type="response.output_item.added",
|
||||
output_index=context["output_index"],
|
||||
sequence_number=self._next_sequence(context),
|
||||
item=ResponseOutputMessage(
|
||||
type="message",
|
||||
id=message_id,
|
||||
role="assistant",
|
||||
content=[],
|
||||
status="in_progress",
|
||||
# Add metadata to identify this as a Magentic agent message
|
||||
metadata={"agent_id": agent_id, "source": "magentic"}, # type: ignore[call-arg]
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
# Add content part for text (establishes the text container)
|
||||
events.append(
|
||||
ResponseContentPartAddedEvent(
|
||||
type="response.content_part.added",
|
||||
output_index=context["output_index"],
|
||||
content_index=0,
|
||||
item_id=message_id,
|
||||
sequence_number=self._next_sequence(context),
|
||||
part=ResponseOutputText(type="output_text", text="", annotations=[]),
|
||||
)
|
||||
)
|
||||
|
||||
# Get the message ID for this agent
|
||||
message_id = context[magentic_key]
|
||||
|
||||
# Emit text delta event using the message ID (matches regular agent behavior)
|
||||
events.append(
|
||||
ResponseTextDeltaEvent(
|
||||
type="response.output_text.delta",
|
||||
output_index=context["output_index"],
|
||||
content_index=0, # Always 0 for single text content
|
||||
item_id=message_id,
|
||||
delta=text,
|
||||
logprobs=[],
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
)
|
||||
return events
|
||||
|
||||
# Handle function calls from Magentic agents
|
||||
if getattr(event, "function_call_id", None) and getattr(event, "function_call_name", None):
|
||||
# Handle function call initiation
|
||||
function_call_id = getattr(event, "function_call_id", None)
|
||||
function_call_name = getattr(event, "function_call_name", None)
|
||||
function_call_arguments = getattr(event, "function_call_arguments", None)
|
||||
|
||||
# Track function call for accumulating arguments
|
||||
context["active_function_calls"][function_call_id] = {
|
||||
"item_id": function_call_id,
|
||||
"name": function_call_name,
|
||||
"arguments_chunks": [],
|
||||
}
|
||||
|
||||
# Emit function call output item
|
||||
return [
|
||||
ResponseOutputItemAddedEvent(
|
||||
type="response.output_item.added",
|
||||
item=ResponseFunctionToolCall(
|
||||
id=function_call_id,
|
||||
call_id=function_call_id,
|
||||
name=function_call_name,
|
||||
arguments=json.dumps(function_call_arguments) if function_call_arguments else "",
|
||||
type="function_call",
|
||||
status="in_progress",
|
||||
),
|
||||
output_index=context["output_index"],
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
]
|
||||
|
||||
# For other non-text deltas, emit as trace for debugging
|
||||
return [
|
||||
ResponseTraceEventComplete(
|
||||
type="response.trace.completed",
|
||||
data={
|
||||
"trace_type": "magentic_delta",
|
||||
"agent_id": agent_id,
|
||||
"function_call_id": getattr(event, "function_call_id", None),
|
||||
"function_call_name": getattr(event, "function_call_name", None),
|
||||
"function_result_id": getattr(event, "function_result_id", None),
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
},
|
||||
span_id=f"magentic_delta_{uuid4().hex[:8]}",
|
||||
item_id=context["item_id"],
|
||||
output_index=context.get("output_index", 0),
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
]
|
||||
|
||||
if event_class == "MagenticAgentMessageEvent":
|
||||
agent_id = getattr(event, "agent_id", "unknown_agent")
|
||||
message = getattr(event, "message", None)
|
||||
|
||||
# Check if we're inside an executor - if so, deltas were already routed there
|
||||
# We don't need to emit a separate message completion event
|
||||
current_executor_id = context.get("current_executor_id")
|
||||
executor_item_key = f"exec_item_{current_executor_id}" if current_executor_id else None
|
||||
|
||||
if executor_item_key and executor_item_key in context:
|
||||
# Deltas were routed to executor item - no separate message item to complete
|
||||
# The executor's output_item.done will mark completion
|
||||
logger.debug(
|
||||
f"MagenticAgentMessageEvent from {agent_id} - "
|
||||
f"deltas routed to executor {current_executor_id}, skipping"
|
||||
)
|
||||
return []
|
||||
|
||||
# Fallback: Handle case where we created a separate message item (no executor context)
|
||||
magentic_key = f"magentic_message_{agent_id}"
|
||||
|
||||
# Check if we were streaming for this agent
|
||||
if magentic_key in context:
|
||||
# Mark the streaming message as complete
|
||||
message_id = context[magentic_key]
|
||||
|
||||
# Import required types
|
||||
from openai.types.responses import ResponseOutputMessage
|
||||
from openai.types.responses.response_output_item_done_event import ResponseOutputItemDoneEvent
|
||||
|
||||
# Extract text from ChatMessage for the completed message
|
||||
text = None
|
||||
if message and hasattr(message, "text"):
|
||||
text = message.text
|
||||
|
||||
# Emit output_item.done to mark message as complete
|
||||
events = [
|
||||
ResponseOutputItemDoneEvent(
|
||||
type="response.output_item.done",
|
||||
output_index=context["output_index"],
|
||||
sequence_number=self._next_sequence(context),
|
||||
item=ResponseOutputMessage(
|
||||
type="message",
|
||||
id=message_id,
|
||||
role="assistant",
|
||||
content=[], # Content already streamed via deltas
|
||||
status="completed",
|
||||
metadata={"agent_id": agent_id, "source": "magentic"}, # type: ignore[call-arg]
|
||||
),
|
||||
)
|
||||
]
|
||||
|
||||
# Clean up context for this agent
|
||||
del context[magentic_key]
|
||||
|
||||
logger.debug(f"MagenticAgentMessageEvent from {agent_id} marked streaming message as complete")
|
||||
return events
|
||||
# No streaming occurred, create a complete message (shouldn't happen normally)
|
||||
# Extract text from ChatMessage
|
||||
text = None
|
||||
if message and hasattr(message, "text"):
|
||||
text = message.text
|
||||
|
||||
if text:
|
||||
# Emit as output item for this agent
|
||||
from openai.types.responses import ResponseOutputMessage, ResponseOutputText
|
||||
from openai.types.responses.response_output_item_added_event import ResponseOutputItemAddedEvent
|
||||
|
||||
context["output_index"] = context.get("output_index", -1) + 1
|
||||
|
||||
text_content = ResponseOutputText(type="output_text", text=text, annotations=[])
|
||||
|
||||
output_message = ResponseOutputMessage(
|
||||
type="message",
|
||||
id=f"msg_{agent_id}_{uuid4().hex[:8]}",
|
||||
role="assistant",
|
||||
content=[text_content],
|
||||
status="completed",
|
||||
metadata={"agent_id": agent_id, "source": "magentic"}, # type: ignore[call-arg]
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"MagenticAgentMessageEvent from {agent_id} converted to output_item.added (non-streaming)"
|
||||
)
|
||||
return [
|
||||
ResponseOutputItemAddedEvent(
|
||||
type="response.output_item.added",
|
||||
item=output_message,
|
||||
output_index=context["output_index"],
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
]
|
||||
|
||||
if event_class == "MagenticOrchestratorMessageEvent":
|
||||
orchestrator_id = getattr(event, "orchestrator_id", "orchestrator")
|
||||
message = getattr(event, "message", None)
|
||||
kind = getattr(event, "kind", "unknown")
|
||||
|
||||
# Extract text from ChatMessage
|
||||
text = None
|
||||
if message and hasattr(message, "text"):
|
||||
text = message.text
|
||||
|
||||
# Emit as trace event for orchestrator messages (typically task ledger, instructions)
|
||||
return [
|
||||
ResponseTraceEventComplete(
|
||||
type="response.trace.completed",
|
||||
data={
|
||||
"trace_type": "magentic_orchestrator",
|
||||
"orchestrator_id": orchestrator_id,
|
||||
"kind": kind,
|
||||
"text": text or "",
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
},
|
||||
span_id=f"magentic_orch_{uuid4().hex[:8]}",
|
||||
item_id=context["item_id"],
|
||||
output_index=context.get("output_index", 0),
|
||||
sequence_number=self._next_sequence(context),
|
||||
)
|
||||
]
|
||||
|
||||
# For unknown/legacy events, still emit as workflow event for backward compatibility
|
||||
# Get event data and serialize if it's a SerializationMixin
|
||||
raw_event_data = getattr(event, "data", None)
|
||||
|
||||
@@ -407,7 +407,7 @@ class DevServer:
|
||||
framework="agent_framework",
|
||||
runtime="python", # Python DevUI backend
|
||||
capabilities={
|
||||
"tracing": os.getenv("ENABLE_INSTRUMENTATION") == "true",
|
||||
"instrumentation": os.getenv("ENABLE_INSTRUMENTATION") == "true",
|
||||
"openai_proxy": openai_executor.is_configured,
|
||||
"deployment": True, # Deployment feature is available
|
||||
},
|
||||
@@ -748,6 +748,11 @@ class DevServer:
|
||||
response_id = f"resp_{uuid.uuid4().hex[:8]}"
|
||||
logger.info(f"[CANCELLATION] Creating response {response_id} for entity {entity_id}")
|
||||
|
||||
# Inject response_id into extra_body for trace context
|
||||
if request.extra_body is None:
|
||||
request.extra_body = {}
|
||||
request.extra_body["response_id"] = response_id
|
||||
|
||||
return StreamingResponse(
|
||||
self._stream_with_cancellation(executor, request, response_id),
|
||||
media_type="text/event-stream",
|
||||
@@ -1000,10 +1005,16 @@ class DevServer:
|
||||
logger.warning(f"Unexpected item type: {type(item)}, converting to dict")
|
||||
serialized_items.append(dict(item))
|
||||
|
||||
# Get stored traces for context inspection (DevUI extension)
|
||||
traces = executor.conversation_store.get_traces(conversation_id)
|
||||
|
||||
return {
|
||||
"object": "list",
|
||||
"data": serialized_items,
|
||||
"has_more": has_more,
|
||||
"metadata": {
|
||||
"traces": traces, # Trace events for token usage, timing, LLM context
|
||||
},
|
||||
}
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e)) from e
|
||||
@@ -1080,10 +1091,22 @@ class DevServer:
|
||||
# Collect events for final response.completed event
|
||||
events = []
|
||||
|
||||
# Get conversation_id for trace storage
|
||||
conversation_id = request._get_conversation_id()
|
||||
|
||||
# Stream all events
|
||||
async for event in executor.execute_streaming(request):
|
||||
events.append(event)
|
||||
|
||||
# Store trace events for context inspection (persisted with conversation)
|
||||
if conversation_id and hasattr(event, "type") and event.type == "response.trace.completed":
|
||||
try:
|
||||
trace_data = event.data if hasattr(event, "data") else None
|
||||
if trace_data:
|
||||
executor.conversation_store.add_trace(conversation_id, trace_data)
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to store trace event: {e}")
|
||||
|
||||
# IMPORTANT: Check model_dump_json FIRST because to_json() can have newlines (pretty-printing)
|
||||
# which breaks SSE format. model_dump_json() returns single-line JSON.
|
||||
if hasattr(event, "model_dump_json"):
|
||||
|
||||
@@ -18,14 +18,14 @@ logger = logging.getLogger(__name__)
|
||||
class SimpleTraceCollector(SpanExporter):
|
||||
"""Simple trace collector that captures spans for direct yielding."""
|
||||
|
||||
def __init__(self, session_id: str | None = None, entity_id: str | None = None) -> None:
|
||||
def __init__(self, response_id: str | None = None, entity_id: str | None = None) -> None:
|
||||
"""Initialize trace collector.
|
||||
|
||||
Args:
|
||||
session_id: Session identifier for context
|
||||
response_id: Response identifier for grouping traces by turn
|
||||
entity_id: Entity identifier for context
|
||||
"""
|
||||
self.session_id = session_id
|
||||
self.response_id = response_id
|
||||
self.entity_id = entity_id
|
||||
self.collected_events: list[ResponseTraceEvent] = []
|
||||
|
||||
@@ -93,7 +93,7 @@ class SimpleTraceCollector(SpanExporter):
|
||||
"duration_ms": duration_ms,
|
||||
"attributes": dict(span.attributes) if span.attributes else {},
|
||||
"status": str(span.status.status_code) if hasattr(span, "status") else "OK",
|
||||
"session_id": self.session_id,
|
||||
"response_id": self.response_id,
|
||||
"entity_id": self.entity_id,
|
||||
}
|
||||
|
||||
@@ -121,18 +121,18 @@ class SimpleTraceCollector(SpanExporter):
|
||||
|
||||
@contextmanager
|
||||
def capture_traces(
|
||||
session_id: str | None = None, entity_id: str | None = None
|
||||
response_id: str | None = None, entity_id: str | None = None
|
||||
) -> Generator[SimpleTraceCollector, None, None]:
|
||||
"""Context manager to capture traces during execution.
|
||||
|
||||
Args:
|
||||
session_id: Session identifier for context
|
||||
response_id: Response identifier for grouping traces by turn
|
||||
entity_id: Entity identifier for context
|
||||
|
||||
Yields:
|
||||
SimpleTraceCollector instance to get trace events from
|
||||
"""
|
||||
collector = SimpleTraceCollector(session_id, entity_id)
|
||||
collector = SimpleTraceCollector(response_id, entity_id)
|
||||
|
||||
try:
|
||||
from opentelemetry import trace
|
||||
@@ -146,7 +146,7 @@ def capture_traces(
|
||||
# Check if this is a real TracerProvider (not the default NoOpTracerProvider)
|
||||
if isinstance(provider, TracerProvider):
|
||||
provider.add_span_processor(processor)
|
||||
logger.debug(f"Added trace collector to TracerProvider for session: {session_id}, entity: {entity_id}")
|
||||
logger.debug(f"Added trace collector to TracerProvider for response: {response_id}, entity: {entity_id}")
|
||||
|
||||
try:
|
||||
yield collector
|
||||
|
||||
@@ -390,7 +390,7 @@ class MetaResponse(BaseModel):
|
||||
"""Backend runtime/language - 'python' or 'dotnet' for deployment guides and feature availability."""
|
||||
|
||||
capabilities: dict[str, bool] = {}
|
||||
"""Server capabilities (e.g., tracing, openai_proxy)."""
|
||||
"""Server capabilities (e.g., instrumentation, openai_proxy)."""
|
||||
|
||||
auth_required: bool = False
|
||||
"""Whether the server requires Bearer token authentication."""
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -19,5 +19,13 @@ export default tseslint.config([
|
||||
ecmaVersion: 2020,
|
||||
globals: globals.browser,
|
||||
},
|
||||
rules: {
|
||||
// Allow exporting constants alongside components in specific patterns
|
||||
// This is common for shadcn/ui components (buttonVariants) and form utilities
|
||||
'react-refresh/only-export-components': [
|
||||
'warn',
|
||||
{ allowConstantExport: true }
|
||||
],
|
||||
},
|
||||
},
|
||||
])
|
||||
|
||||
+5344
File diff suppressed because it is too large
Load Diff
@@ -19,6 +19,7 @@
|
||||
"@radix-ui/react-slot": "^1.2.3",
|
||||
"@radix-ui/react-switch": "^1.2.6",
|
||||
"@radix-ui/react-tabs": "^1.1.13",
|
||||
"@radix-ui/react-tooltip": "^1.2.8",
|
||||
"@tailwindcss/vite": "^4.1.12",
|
||||
"@xyflow/react": "^12.8.4",
|
||||
"class-variance-authority": "^0.7.1",
|
||||
|
||||
@@ -270,6 +270,7 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
const conversationUsage = useDevUIStore((state) => state.conversationUsage);
|
||||
const pendingApprovals = useDevUIStore((state) => state.pendingApprovals);
|
||||
const oaiMode = useDevUIStore((state) => state.oaiMode);
|
||||
const streamingEnabled = useDevUIStore((state) => state.streamingEnabled);
|
||||
|
||||
// Get conversation actions from Zustand (only the ones we actually use)
|
||||
const setCurrentConversation = useDevUIStore((state) => state.setCurrentConversation);
|
||||
@@ -570,6 +571,7 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
let allItems: unknown[] = [];
|
||||
let hasMore = true;
|
||||
let after: string | undefined = undefined;
|
||||
let storedTraces: unknown[] = [];
|
||||
|
||||
while (hasMore) {
|
||||
const result = await apiClient.listConversationItems(
|
||||
@@ -578,7 +580,12 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
);
|
||||
allItems = allItems.concat(result.data);
|
||||
hasMore = result.has_more;
|
||||
|
||||
|
||||
// Capture traces from metadata (only need from one response, they accumulate)
|
||||
if (result.metadata?.traces && result.metadata.traces.length > 0) {
|
||||
storedTraces = result.metadata.traces;
|
||||
}
|
||||
|
||||
// Get the last item's ID for pagination
|
||||
if (hasMore && result.data.length > 0) {
|
||||
const lastItem = result.data[result.data.length - 1] as { id?: string };
|
||||
@@ -590,6 +597,21 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
setChatItems(allItems as import("@/types/openai").ConversationItem[]);
|
||||
setIsStreaming(false);
|
||||
|
||||
// Restore stored traces as debug events for context inspection
|
||||
if (storedTraces.length > 0) {
|
||||
// Clear any previous debug events first
|
||||
onDebugEvent("clear");
|
||||
for (const trace of storedTraces) {
|
||||
// Convert stored trace back to ResponseTraceComplete event format
|
||||
const traceEvent: ExtendedResponseStreamEvent = {
|
||||
type: "response.trace.completed",
|
||||
data: trace as Record<string, unknown>,
|
||||
sequence_number: 0, // Not used for display
|
||||
};
|
||||
onDebugEvent(traceEvent);
|
||||
}
|
||||
}
|
||||
|
||||
// Check for incomplete stream and resume if needed
|
||||
const state = loadStreamingState(mostRecent.id);
|
||||
|
||||
@@ -724,6 +746,9 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
useDevUIStore.setState({ conversationUsage: { total_tokens: 0, message_count: 0 } });
|
||||
accumulatedTextRef.current = "";
|
||||
|
||||
// Clear debug panel for fresh conversation
|
||||
onDebugEvent("clear");
|
||||
|
||||
// Update localStorage cache with new conversation
|
||||
const cachedKey = `devui_convs_${selectedAgent.id}`;
|
||||
const updated = [newConversation, ...availableConversations];
|
||||
@@ -736,7 +761,7 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
type: "conversation_creation_error",
|
||||
});
|
||||
}
|
||||
}, [selectedAgent, setCurrentConversation, setAvailableConversations, setChatItems, setIsStreaming]);
|
||||
}, [selectedAgent, onDebugEvent, setCurrentConversation, setAvailableConversations, setChatItems, setIsStreaming]);
|
||||
|
||||
// Handle conversation deletion
|
||||
const handleDeleteConversation = useCallback(
|
||||
@@ -843,6 +868,7 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
let allItems: unknown[] = [];
|
||||
let hasMore = true;
|
||||
let after: string | undefined = undefined;
|
||||
let storedTraces: unknown[] = [];
|
||||
|
||||
while (hasMore) {
|
||||
const result = await apiClient.listConversationItems(conversationId, {
|
||||
@@ -851,7 +877,12 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
});
|
||||
allItems = allItems.concat(result.data);
|
||||
hasMore = result.has_more;
|
||||
|
||||
|
||||
// Capture traces from metadata (only need from one response, they accumulate)
|
||||
if (result.metadata?.traces && result.metadata.traces.length > 0) {
|
||||
storedTraces = result.metadata.traces;
|
||||
}
|
||||
|
||||
// Get the last item's ID for pagination
|
||||
if (hasMore && result.data.length > 0) {
|
||||
const lastItem = result.data[result.data.length - 1] as { id?: string };
|
||||
@@ -865,6 +896,19 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
setChatItems(items);
|
||||
setIsStreaming(false);
|
||||
|
||||
// Restore stored traces as debug events for context inspection
|
||||
if (storedTraces.length > 0) {
|
||||
for (const trace of storedTraces) {
|
||||
// Convert stored trace back to ResponseTraceComplete event format
|
||||
const traceEvent: ExtendedResponseStreamEvent = {
|
||||
type: "response.trace.completed",
|
||||
data: trace as Record<string, unknown>,
|
||||
sequence_number: 0, // Not used for display
|
||||
};
|
||||
onDebugEvent(traceEvent);
|
||||
}
|
||||
}
|
||||
|
||||
// Calculate usage from loaded items
|
||||
useDevUIStore.setState({
|
||||
conversationUsage: {
|
||||
@@ -1249,13 +1293,15 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
|
||||
// Handle function calls as separate conversation items
|
||||
if (item.type === "function_call") {
|
||||
// Type assertion for function call - narrows from union type
|
||||
const funcCall = item as import("@/types/openai").ResponseFunctionToolCall;
|
||||
const functionCallItem: import("@/types/openai").ConversationFunctionCall = {
|
||||
id: item.id || `call-${Date.now()}`,
|
||||
id: funcCall.id || `call-${Date.now()}`,
|
||||
type: "function_call",
|
||||
name: item.name,
|
||||
arguments: item.arguments || "",
|
||||
call_id: item.call_id,
|
||||
status: (item.status === "failed" || item.status === "cancelled" ? "incomplete" : item.status) || "in_progress",
|
||||
name: funcCall.name,
|
||||
arguments: funcCall.arguments || "",
|
||||
call_id: funcCall.call_id,
|
||||
status: funcCall.status || "in_progress",
|
||||
created_at: Math.floor(Date.now() / 1000),
|
||||
};
|
||||
|
||||
@@ -1414,6 +1460,209 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
[selectedAgent, currentConversation, onDebugEvent, setChatItems, setIsStreaming, setCurrentConversation, setAvailableConversations, setPendingApprovals, updateConversationUsage, createAbortSignal, resetCancelling]
|
||||
);
|
||||
|
||||
// Handle non-streaming message sending
|
||||
const handleSendMessageSync = useCallback(
|
||||
async (request: RunAgentRequest) => {
|
||||
if (!selectedAgent) return;
|
||||
|
||||
// Check if this is a function approval response (internal, don't show in chat)
|
||||
const isApprovalResponse = request.input.some(
|
||||
(inputItem) =>
|
||||
inputItem.type === "message" &&
|
||||
Array.isArray(inputItem.content) &&
|
||||
inputItem.content.some((c) => c.type === "function_approval_response")
|
||||
);
|
||||
|
||||
// Extract content from OpenAI format to create ConversationMessage
|
||||
const messageContent: import("@/types/openai").MessageContent[] = [];
|
||||
|
||||
// Parse OpenAI ResponseInputParam to extract content
|
||||
for (const inputItem of request.input) {
|
||||
if (inputItem.type === "message" && Array.isArray(inputItem.content)) {
|
||||
for (const contentItem of inputItem.content) {
|
||||
if (contentItem.type === "input_text") {
|
||||
messageContent.push({
|
||||
type: "text",
|
||||
text: contentItem.text,
|
||||
});
|
||||
} else if (contentItem.type === "input_image") {
|
||||
messageContent.push({
|
||||
type: "input_image",
|
||||
image_url: contentItem.image_url || "",
|
||||
detail: "auto",
|
||||
});
|
||||
} else if (contentItem.type === "input_file") {
|
||||
const fileItem = contentItem as import("@/types/agent-framework").ResponseInputFileParam;
|
||||
messageContent.push({
|
||||
type: "input_file",
|
||||
file_data: fileItem.file_data,
|
||||
filename: fileItem.filename,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Capture timestamp once for both user and assistant messages
|
||||
const messageTimestamp = Math.floor(Date.now() / 1000); // Unix seconds
|
||||
|
||||
// Only add user message to UI if it's not an approval response (internal messages)
|
||||
if (!isApprovalResponse && messageContent.length > 0) {
|
||||
const userMessage: import("@/types/openai").ConversationMessage = {
|
||||
id: `user-${Date.now()}`,
|
||||
type: "message",
|
||||
role: "user",
|
||||
content: messageContent,
|
||||
status: "completed",
|
||||
created_at: messageTimestamp,
|
||||
};
|
||||
|
||||
setChatItems([...useDevUIStore.getState().chatItems, userMessage]);
|
||||
}
|
||||
|
||||
// Show loading state (but not streaming indicator)
|
||||
setIsSubmitting(true);
|
||||
|
||||
try {
|
||||
// If no conversation selected, create one automatically
|
||||
let conversationToUse = currentConversation;
|
||||
if (!conversationToUse) {
|
||||
try {
|
||||
conversationToUse = await apiClient.createConversation({
|
||||
agent_id: selectedAgent.id,
|
||||
});
|
||||
setCurrentConversation(conversationToUse);
|
||||
setAvailableConversations([conversationToUse, ...useDevUIStore.getState().availableConversations]);
|
||||
setConversationError(null);
|
||||
} catch (error) {
|
||||
const errorMessage = error instanceof Error ? error.message : "Failed to create conversation";
|
||||
setConversationError({
|
||||
message: errorMessage,
|
||||
type: "conversation_creation_error",
|
||||
});
|
||||
setIsSubmitting(false);
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// Call non-streaming API
|
||||
const response = await apiClient.runAgentSync(selectedAgent.id, {
|
||||
input: request.input,
|
||||
conversation_id: conversationToUse?.id,
|
||||
});
|
||||
|
||||
// Extract content from response output
|
||||
const assistantContent: import("@/types/openai").MessageContent[] = [];
|
||||
const toolCalls: import("@/types/openai").ConversationFunctionCall[] = [];
|
||||
const toolResults: import("@/types/openai").ConversationFunctionCallOutput[] = [];
|
||||
|
||||
if (response.output) {
|
||||
for (const outputItem of response.output) {
|
||||
if (outputItem.type === "message") {
|
||||
// Extract message content
|
||||
const msgItem = outputItem as import("@/types/openai").ResponseOutputMessage;
|
||||
if (msgItem.content) {
|
||||
for (const content of msgItem.content) {
|
||||
if (content.type === "output_text") {
|
||||
assistantContent.push({
|
||||
type: "text",
|
||||
text: (content as { text: string }).text,
|
||||
} as import("@/types/openai").MessageTextContent);
|
||||
} else if (content.type === "output_image") {
|
||||
assistantContent.push(content as unknown as import("@/types/openai").MessageOutputImage);
|
||||
} else if (content.type === "output_file") {
|
||||
assistantContent.push(content as unknown as import("@/types/openai").MessageOutputFile);
|
||||
} else if (content.type === "output_data") {
|
||||
assistantContent.push(content as unknown as import("@/types/openai").MessageOutputData);
|
||||
}
|
||||
}
|
||||
}
|
||||
} else if (outputItem.type === "function_call") {
|
||||
const funcCall = outputItem as unknown as import("@/types/openai").ResponseFunctionToolCall;
|
||||
toolCalls.push({
|
||||
id: funcCall.id || `call-${Date.now()}`,
|
||||
type: "function_call",
|
||||
name: funcCall.name,
|
||||
arguments: funcCall.arguments || "",
|
||||
call_id: funcCall.call_id,
|
||||
status: funcCall.status || "completed",
|
||||
created_at: messageTimestamp,
|
||||
});
|
||||
} else if (outputItem.type === "function_call_output") {
|
||||
const resultItem = outputItem as unknown as { call_id: string; output: string };
|
||||
toolResults.push({
|
||||
id: `result-${Date.now()}`,
|
||||
type: "function_call_output",
|
||||
call_id: resultItem.call_id,
|
||||
output: resultItem.output,
|
||||
status: "completed",
|
||||
created_at: messageTimestamp,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Create assistant message with all content
|
||||
const assistantMessage: import("@/types/openai").ConversationMessage = {
|
||||
id: `assistant-${Date.now()}`,
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: assistantContent,
|
||||
status: "completed",
|
||||
created_at: messageTimestamp,
|
||||
usage: response.usage ? {
|
||||
input_tokens: response.usage.input_tokens,
|
||||
output_tokens: response.usage.output_tokens,
|
||||
total_tokens: response.usage.total_tokens,
|
||||
} : undefined,
|
||||
};
|
||||
|
||||
// Add all items to chat
|
||||
const currentItems = useDevUIStore.getState().chatItems;
|
||||
const newItems: import("@/types/openai").ConversationItem[] = [
|
||||
...currentItems,
|
||||
assistantMessage,
|
||||
...toolCalls,
|
||||
...toolResults,
|
||||
];
|
||||
setChatItems(newItems);
|
||||
|
||||
// Update conversation-level usage stats
|
||||
if (response.usage) {
|
||||
updateConversationUsage(response.usage.total_tokens);
|
||||
}
|
||||
|
||||
// Send debug event with response completed
|
||||
onDebugEvent({
|
||||
type: "response.completed",
|
||||
response: response,
|
||||
sequence_number: 0,
|
||||
} as ExtendedResponseStreamEvent);
|
||||
|
||||
} catch (error) {
|
||||
// Show error message
|
||||
const errorMessage = error instanceof Error ? error.message : "Failed to get response";
|
||||
const assistantMessage: import("@/types/openai").ConversationMessage = {
|
||||
id: `assistant-${Date.now()}`,
|
||||
type: "message",
|
||||
role: "assistant",
|
||||
content: [{
|
||||
type: "text",
|
||||
text: `Error: ${errorMessage}`,
|
||||
} as import("@/types/openai").MessageTextContent],
|
||||
status: "incomplete",
|
||||
created_at: messageTimestamp,
|
||||
};
|
||||
|
||||
const currentItems = useDevUIStore.getState().chatItems;
|
||||
setChatItems([...currentItems, assistantMessage]);
|
||||
} finally {
|
||||
setIsSubmitting(false);
|
||||
}
|
||||
},
|
||||
[selectedAgent, currentConversation, onDebugEvent, setChatItems, setCurrentConversation, setAvailableConversations, updateConversationUsage, setIsSubmitting]
|
||||
);
|
||||
|
||||
|
||||
// Handle message submission from ChatMessageInput
|
||||
const handleChatInputSubmit = async (content: import("@/types/agent-framework").ResponseInputContent[]) => {
|
||||
@@ -1435,11 +1684,17 @@ export function AgentView({ selectedAgent, onDebugEvent }: AgentViewProps) {
|
||||
},
|
||||
];
|
||||
|
||||
// Use pure OpenAI format
|
||||
await handleSendMessage({
|
||||
const request = {
|
||||
input: openaiInput,
|
||||
conversation_id: currentConversation?.id,
|
||||
});
|
||||
};
|
||||
|
||||
// Use streaming or non-streaming based on setting
|
||||
if (streamingEnabled) {
|
||||
await handleSendMessage(request);
|
||||
} else {
|
||||
await handleSendMessageSync(request);
|
||||
}
|
||||
} finally {
|
||||
setIsSubmitting(false);
|
||||
}
|
||||
|
||||
@@ -0,0 +1,949 @@
|
||||
/**
|
||||
* ContextInspector - Token usage visualization and context analysis
|
||||
*
|
||||
* Features:
|
||||
* - Stacked bar chart showing input/output tokens per turn
|
||||
* - Composition view showing what fills the context (system, user, assistant, tools)
|
||||
* - Per-turn vs cumulative modes
|
||||
* - Summary statistics (total, average, peak)
|
||||
* - Pure CSS visualization (no external charting library)
|
||||
*/
|
||||
|
||||
import { useState, useMemo } from "react";
|
||||
import { useDevUIStore } from "@/stores/devuiStore";
|
||||
import {
|
||||
BarChart3,
|
||||
Layers,
|
||||
Info,
|
||||
ChevronDown,
|
||||
ChevronRight,
|
||||
} from "lucide-react";
|
||||
import { Badge } from "@/components/ui/badge";
|
||||
import { Checkbox } from "@/components/ui/checkbox";
|
||||
import { ScrollArea } from "@/components/ui/scroll-area";
|
||||
import {
|
||||
Tooltip,
|
||||
TooltipContent,
|
||||
TooltipProvider,
|
||||
TooltipTrigger,
|
||||
} from "@/components/ui/tooltip";
|
||||
import type { ExtendedResponseStreamEvent } from "@/types";
|
||||
import {
|
||||
TraceAttributes,
|
||||
type TypedTraceAttributes,
|
||||
type TraceMessage,
|
||||
parseTraceMessages,
|
||||
isTextPart,
|
||||
isToolCallPart,
|
||||
isToolResultPart,
|
||||
} from "@/types/openai";
|
||||
|
||||
// Trace data interface matching debug-panel types
|
||||
interface TraceEventData {
|
||||
operation_name?: string;
|
||||
duration_ms?: number;
|
||||
status?: string;
|
||||
attributes?: TypedTraceAttributes;
|
||||
span_id?: string;
|
||||
trace_id?: string;
|
||||
parent_span_id?: string | null;
|
||||
start_time?: number;
|
||||
end_time?: number;
|
||||
entity_id?: string;
|
||||
response_id?: string | null;
|
||||
}
|
||||
|
||||
// Context composition breakdown
|
||||
interface ContextComposition {
|
||||
system: number; // character count
|
||||
user: number;
|
||||
assistant: number;
|
||||
toolCalls: number; // function definitions + arguments
|
||||
toolResults: number; // function outputs
|
||||
total: number;
|
||||
}
|
||||
|
||||
// Turn data extracted from traces
|
||||
interface TurnData {
|
||||
response_id: string;
|
||||
timestamp: number;
|
||||
input_tokens: number;
|
||||
output_tokens: number;
|
||||
total_tokens: number;
|
||||
model?: string;
|
||||
entity_id?: string;
|
||||
duration_ms: number;
|
||||
composition: ContextComposition;
|
||||
}
|
||||
|
||||
// Props for the component
|
||||
interface ContextInspectorProps {
|
||||
events: ExtendedResponseStreamEvent[];
|
||||
}
|
||||
|
||||
// Parse message content to extract composition using typed TraceMessage format
|
||||
function parseComposition(messagesJson: string | unknown): ContextComposition {
|
||||
const composition: ContextComposition = {
|
||||
system: 0,
|
||||
user: 0,
|
||||
assistant: 0,
|
||||
toolCalls: 0,
|
||||
toolResults: 0,
|
||||
total: 0,
|
||||
};
|
||||
|
||||
try {
|
||||
// Use the typed parser for string input
|
||||
let messages: TraceMessage[];
|
||||
|
||||
if (typeof messagesJson === "string") {
|
||||
messages = parseTraceMessages(messagesJson);
|
||||
} else if (Array.isArray(messagesJson)) {
|
||||
messages = messagesJson as TraceMessage[];
|
||||
} else {
|
||||
return composition;
|
||||
}
|
||||
|
||||
for (const message of messages) {
|
||||
if (!message || typeof message !== "object") continue;
|
||||
|
||||
const role = message.role;
|
||||
const parts = message.parts;
|
||||
|
||||
// Calculate character count for this message
|
||||
let charCount = 0;
|
||||
|
||||
// Handle parts array (Agent Framework format)
|
||||
// Using type guards for type-safe access to part properties
|
||||
if (Array.isArray(parts)) {
|
||||
for (const part of parts) {
|
||||
if (!part || typeof part !== "object") continue;
|
||||
|
||||
if (isTextPart(part)) {
|
||||
// Text content can be in either 'content' or 'text' field
|
||||
const text = part.content || part.text || "";
|
||||
charCount += text.length;
|
||||
} else if (isToolCallPart(part)) {
|
||||
// Tool call includes name and arguments
|
||||
const name = part.name || "";
|
||||
const args = part.arguments || "";
|
||||
composition.toolCalls += name.length + args.length;
|
||||
} else if (isToolResultPart(part)) {
|
||||
// Tool result - check both 'result' and 'response' fields
|
||||
const result = part.result || part.response || "";
|
||||
composition.toolResults += result.length;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Categorize by role
|
||||
if (role === "system") {
|
||||
composition.system += charCount;
|
||||
} else if (role === "user") {
|
||||
composition.user += charCount;
|
||||
} else if (role === "assistant") {
|
||||
composition.assistant += charCount;
|
||||
} else if (role === "tool") {
|
||||
composition.toolResults += charCount;
|
||||
}
|
||||
}
|
||||
|
||||
composition.total =
|
||||
composition.system +
|
||||
composition.user +
|
||||
composition.assistant +
|
||||
composition.toolCalls +
|
||||
composition.toolResults;
|
||||
|
||||
} catch {
|
||||
// Parsing failed, return empty composition
|
||||
}
|
||||
|
||||
return composition;
|
||||
}
|
||||
|
||||
// Extract turn data from trace events
|
||||
function extractTurnData(events: ExtendedResponseStreamEvent[]): TurnData[] {
|
||||
const traceEvents = events.filter(e => e.type === "response.trace.completed");
|
||||
|
||||
// Group by response_id
|
||||
const byResponseId = new Map<string, TraceEventData[]>();
|
||||
|
||||
for (const event of traceEvents) {
|
||||
if (!("data" in event)) continue;
|
||||
const data = event.data as TraceEventData;
|
||||
const responseId = data.response_id || "unknown";
|
||||
|
||||
if (!byResponseId.has(responseId)) {
|
||||
byResponseId.set(responseId, []);
|
||||
}
|
||||
byResponseId.get(responseId)!.push(data);
|
||||
}
|
||||
|
||||
const turns: TurnData[] = [];
|
||||
|
||||
for (const [responseId, traces] of byResponseId) {
|
||||
let inputTokens = 0;
|
||||
let outputTokens = 0;
|
||||
let model: string | undefined;
|
||||
let timestamp = Date.now() / 1000;
|
||||
let entity_id: string | undefined;
|
||||
let totalDuration = 0;
|
||||
let composition: ContextComposition = {
|
||||
system: 0, user: 0, assistant: 0, toolCalls: 0, toolResults: 0, total: 0
|
||||
};
|
||||
|
||||
for (const trace of traces) {
|
||||
const attrs = trace.attributes || {};
|
||||
|
||||
// Get token counts using typed attribute keys
|
||||
const traceInput = attrs[TraceAttributes.INPUT_TOKENS];
|
||||
const traceOutput = attrs[TraceAttributes.OUTPUT_TOKENS];
|
||||
|
||||
if (traceInput !== undefined) {
|
||||
inputTokens += Number(traceInput);
|
||||
}
|
||||
if (traceOutput !== undefined) {
|
||||
outputTokens += Number(traceOutput);
|
||||
}
|
||||
|
||||
// Get model using typed attribute key
|
||||
if (attrs[TraceAttributes.MODEL]) {
|
||||
model = String(attrs[TraceAttributes.MODEL]);
|
||||
}
|
||||
|
||||
// Get timestamp
|
||||
if (trace.start_time && trace.start_time < timestamp) {
|
||||
timestamp = trace.start_time;
|
||||
}
|
||||
|
||||
// Get entity_id
|
||||
if (trace.entity_id) {
|
||||
entity_id = trace.entity_id;
|
||||
}
|
||||
|
||||
// Sum durations
|
||||
if (trace.duration_ms) {
|
||||
totalDuration += Number(trace.duration_ms);
|
||||
}
|
||||
|
||||
// Parse composition from input messages using typed attribute key
|
||||
const inputMessages = attrs[TraceAttributes.INPUT_MESSAGES];
|
||||
if (inputMessages && composition.total === 0) {
|
||||
composition = parseComposition(inputMessages);
|
||||
}
|
||||
|
||||
// Also check for system instructions using typed attribute key
|
||||
const systemInstructions = attrs[TraceAttributes.SYSTEM_INSTRUCTIONS];
|
||||
if (systemInstructions && typeof systemInstructions === "string" && composition.system === 0) {
|
||||
composition.system = systemInstructions.length;
|
||||
composition.total += systemInstructions.length;
|
||||
}
|
||||
}
|
||||
|
||||
// Only include turns that have token data
|
||||
if (inputTokens > 0 || outputTokens > 0) {
|
||||
turns.push({
|
||||
response_id: responseId,
|
||||
timestamp,
|
||||
input_tokens: inputTokens,
|
||||
output_tokens: outputTokens,
|
||||
total_tokens: inputTokens + outputTokens,
|
||||
model,
|
||||
entity_id,
|
||||
duration_ms: totalDuration,
|
||||
composition,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by timestamp (oldest first)
|
||||
turns.sort((a, b) => a.timestamp - b.timestamp);
|
||||
|
||||
return turns;
|
||||
}
|
||||
|
||||
// Calculate summary stats
|
||||
function calculateStats(turns: TurnData[]) {
|
||||
if (turns.length === 0) {
|
||||
return {
|
||||
totalInput: 0,
|
||||
totalOutput: 0,
|
||||
totalTokens: 0,
|
||||
avgInput: 0,
|
||||
avgOutput: 0,
|
||||
avgTotal: 0,
|
||||
peakInput: 0,
|
||||
peakOutput: 0,
|
||||
peakTotal: 0,
|
||||
turnCount: 0,
|
||||
};
|
||||
}
|
||||
|
||||
const totalInput = turns.reduce((sum, t) => sum + t.input_tokens, 0);
|
||||
const totalOutput = turns.reduce((sum, t) => sum + t.output_tokens, 0);
|
||||
const totalTokens = totalInput + totalOutput;
|
||||
|
||||
const peakInput = Math.max(...turns.map(t => t.input_tokens));
|
||||
const peakOutput = Math.max(...turns.map(t => t.output_tokens));
|
||||
const peakTotal = Math.max(...turns.map(t => t.total_tokens));
|
||||
|
||||
return {
|
||||
totalInput,
|
||||
totalOutput,
|
||||
totalTokens,
|
||||
avgInput: Math.round(totalInput / turns.length),
|
||||
avgOutput: Math.round(totalOutput / turns.length),
|
||||
avgTotal: Math.round(totalTokens / turns.length),
|
||||
peakInput,
|
||||
peakOutput,
|
||||
peakTotal,
|
||||
turnCount: turns.length,
|
||||
};
|
||||
}
|
||||
|
||||
// Aggregate composition across all turns
|
||||
function aggregateComposition(turns: TurnData[]): ContextComposition {
|
||||
return turns.reduce(
|
||||
(acc, turn) => ({
|
||||
system: acc.system + turn.composition.system,
|
||||
user: acc.user + turn.composition.user,
|
||||
assistant: acc.assistant + turn.composition.assistant,
|
||||
toolCalls: acc.toolCalls + turn.composition.toolCalls,
|
||||
toolResults: acc.toolResults + turn.composition.toolResults,
|
||||
total: acc.total + turn.composition.total,
|
||||
}),
|
||||
{ system: 0, user: 0, assistant: 0, toolCalls: 0, toolResults: 0, total: 0 }
|
||||
);
|
||||
}
|
||||
|
||||
// Format large numbers with K suffix
|
||||
function formatTokenCount(n: number): string {
|
||||
if (n >= 1000) {
|
||||
return `${(n / 1000).toFixed(1)}k`;
|
||||
}
|
||||
return String(n);
|
||||
}
|
||||
|
||||
// Color constants - single source of truth for all visualizations
|
||||
const SEGMENT_COLORS = {
|
||||
// Token segments
|
||||
input: "bg-blue-500 dark:bg-blue-600",
|
||||
output: "bg-emerald-500 dark:bg-emerald-600",
|
||||
// Composition segments
|
||||
system: "bg-purple-500 dark:bg-purple-600",
|
||||
user: "bg-blue-500 dark:bg-blue-600",
|
||||
assistant: "bg-emerald-500 dark:bg-emerald-600",
|
||||
toolCalls: "bg-amber-500 dark:bg-amber-600",
|
||||
toolResults: "bg-orange-500 dark:bg-orange-600",
|
||||
} as const;
|
||||
|
||||
// Segment definition for the unified bar component
|
||||
interface BarSegment {
|
||||
key: string;
|
||||
value: number;
|
||||
color: string;
|
||||
label: string;
|
||||
}
|
||||
|
||||
// Unified segmented bar component with tooltips
|
||||
// Replaces both TokenBar and CompositionBar for consistency and maintainability
|
||||
function SegmentedBar({
|
||||
segments,
|
||||
maxValue,
|
||||
height = 20,
|
||||
renderLabel,
|
||||
}: {
|
||||
segments: BarSegment[];
|
||||
maxValue: number;
|
||||
height?: number;
|
||||
renderLabel?: (total: number, segments: BarSegment[]) => React.ReactNode;
|
||||
}) {
|
||||
const total = segments.reduce((sum, s) => sum + s.value, 0);
|
||||
|
||||
if (total === 0) {
|
||||
return (
|
||||
<div className="flex items-center gap-2 w-full">
|
||||
<div
|
||||
className="rounded bg-muted/30 flex-1"
|
||||
style={{ height: `${height}px` }}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// When maxValue is 0, use full width (100%) - focus on ratios within the bar
|
||||
// When maxValue > 0, scale relative to max - focus on size comparison
|
||||
const widthPercent = maxValue > 0 ? (total / maxValue) * 100 : 100;
|
||||
|
||||
// Pre-compute segment metadata for tooltips
|
||||
const segmentsWithMeta = segments
|
||||
.filter(s => s.value > 0)
|
||||
.map(seg => ({
|
||||
...seg,
|
||||
percent: Math.round((seg.value / total) * 100),
|
||||
}));
|
||||
|
||||
return (
|
||||
<div className="flex items-center gap-2 w-full">
|
||||
<div
|
||||
className="relative rounded overflow-hidden bg-muted/30 flex-1"
|
||||
style={{ height: `${height}px` }}
|
||||
>
|
||||
<TooltipProvider delayDuration={150}>
|
||||
<div
|
||||
className="h-full flex transition-all duration-300"
|
||||
style={{ width: `${widthPercent}%` }}
|
||||
>
|
||||
{segmentsWithMeta.map((seg) => (
|
||||
<Tooltip key={seg.key}>
|
||||
<TooltipTrigger asChild>
|
||||
<div
|
||||
className={`h-full ${seg.color} transition-all duration-150 hover:brightness-110 hover:scale-y-[1.15] origin-bottom cursor-default`}
|
||||
style={{ width: `${(seg.value / total) * 100}%` }}
|
||||
/>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent side="top" className="text-xs">
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2 h-2 rounded-sm ${seg.color} flex-shrink-0`} />
|
||||
<span className="font-medium">{seg.label}</span>
|
||||
<span className="opacity-80">{formatTokenCount(seg.value)} ({seg.percent}%)</span>
|
||||
</div>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
))}
|
||||
</div>
|
||||
</TooltipProvider>
|
||||
</div>
|
||||
|
||||
{renderLabel?.(total, segments)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// Helper to create token segments (input/output)
|
||||
function createTokenSegments(input: number, output: number): BarSegment[] {
|
||||
return [
|
||||
{ key: "input", value: input, color: SEGMENT_COLORS.input, label: "Input" },
|
||||
{ key: "output", value: output, color: SEGMENT_COLORS.output, label: "Output" },
|
||||
];
|
||||
}
|
||||
|
||||
// Helper to create composition segments
|
||||
function createCompositionSegments(composition: ContextComposition): BarSegment[] {
|
||||
return [
|
||||
{ key: "system", value: composition.system, color: SEGMENT_COLORS.system, label: "System" },
|
||||
{ key: "user", value: composition.user, color: SEGMENT_COLORS.user, label: "User" },
|
||||
{ key: "assistant", value: composition.assistant, color: SEGMENT_COLORS.assistant, label: "Assistant" },
|
||||
{ key: "toolCalls", value: composition.toolCalls, color: SEGMENT_COLORS.toolCalls, label: "Tool Calls" },
|
||||
{ key: "toolResults", value: composition.toolResults, color: SEGMENT_COLORS.toolResults, label: "Tool Results" },
|
||||
];
|
||||
}
|
||||
|
||||
// Composition breakdown list
|
||||
function CompositionBreakdown({
|
||||
composition,
|
||||
className = "",
|
||||
}: {
|
||||
composition: ContextComposition;
|
||||
className?: string;
|
||||
}) {
|
||||
const { system, user, assistant, toolCalls, toolResults, total } = composition;
|
||||
|
||||
if (total === 0) {
|
||||
return (
|
||||
<div className={`text-xs text-muted-foreground ${className}`}>
|
||||
No composition data available
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
const items = [
|
||||
{ label: "System", value: system, color: SEGMENT_COLORS.system },
|
||||
{ label: "User", value: user, color: SEGMENT_COLORS.user },
|
||||
{ label: "Assistant", value: assistant, color: SEGMENT_COLORS.assistant },
|
||||
{ label: "Tool Calls", value: toolCalls, color: SEGMENT_COLORS.toolCalls },
|
||||
{ label: "Tool Results", value: toolResults, color: SEGMENT_COLORS.toolResults },
|
||||
].filter(item => item.value > 0);
|
||||
|
||||
return (
|
||||
<div className={`space-y-1.5 ${className}`}>
|
||||
{items.map((item) => {
|
||||
const percent = Math.round((item.value / total) * 100);
|
||||
return (
|
||||
<div key={item.label} className="flex items-center gap-2 text-xs">
|
||||
<div className={`w-2 h-2 rounded-sm ${item.color}`} />
|
||||
<span className="text-muted-foreground w-20">{item.label}</span>
|
||||
<div className="flex-1 h-3 bg-muted/30 rounded overflow-hidden">
|
||||
<div
|
||||
className={`h-full ${item.color} transition-all duration-300`}
|
||||
style={{ width: `${percent}%` }}
|
||||
/>
|
||||
</div>
|
||||
<span className="font-mono w-10 text-right text-muted-foreground">
|
||||
{percent}%
|
||||
</span>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// Turn row component
|
||||
function TurnRow({
|
||||
turn,
|
||||
index,
|
||||
maxValue,
|
||||
maxCompositionValue,
|
||||
cumulativeInput,
|
||||
cumulativeOutput,
|
||||
cumulativeComposition,
|
||||
showCumulative,
|
||||
viewMode,
|
||||
}: {
|
||||
turn: TurnData;
|
||||
index: number;
|
||||
maxValue: number;
|
||||
maxCompositionValue: number;
|
||||
cumulativeInput: number;
|
||||
cumulativeOutput: number;
|
||||
cumulativeComposition: ContextComposition;
|
||||
showCumulative: boolean;
|
||||
viewMode: "tokens" | "composition";
|
||||
}) {
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
|
||||
const displayInput = showCumulative ? cumulativeInput : turn.input_tokens;
|
||||
const displayOutput = showCumulative ? cumulativeOutput : turn.output_tokens;
|
||||
const displayComposition = showCumulative ? cumulativeComposition : turn.composition;
|
||||
|
||||
const timestamp = new Date(turn.timestamp * 1000).toLocaleTimeString([], {
|
||||
hour: "2-digit",
|
||||
minute: "2-digit",
|
||||
second: "2-digit",
|
||||
});
|
||||
|
||||
return (
|
||||
<div className="border-b border-muted/50 last:border-0">
|
||||
<div
|
||||
className="flex items-center gap-3 py-2 px-2 hover:bg-muted/30 cursor-pointer transition-colors"
|
||||
onClick={() => setIsExpanded(!isExpanded)}
|
||||
>
|
||||
{/* Turn number */}
|
||||
<div className="w-6 h-6 rounded-full bg-muted flex items-center justify-center text-xs font-medium flex-shrink-0">
|
||||
{index + 1}
|
||||
</div>
|
||||
|
||||
{/* Bar */}
|
||||
<div className="flex-1 min-w-0">
|
||||
{viewMode === "tokens" ? (
|
||||
<SegmentedBar
|
||||
segments={createTokenSegments(displayInput, displayOutput)}
|
||||
maxValue={maxValue}
|
||||
height={20}
|
||||
renderLabel={(_, segs) => (
|
||||
<div className="flex items-center gap-1 text-xs font-mono text-muted-foreground min-w-[80px] justify-end">
|
||||
<span className="text-blue-600 dark:text-blue-400">↑{formatTokenCount(segs[0]?.value || 0)}</span>
|
||||
<span>/</span>
|
||||
<span className="text-emerald-600 dark:text-emerald-400">↓{formatTokenCount(segs[1]?.value || 0)}</span>
|
||||
</div>
|
||||
)}
|
||||
/>
|
||||
) : (
|
||||
<SegmentedBar
|
||||
segments={createCompositionSegments(displayComposition)}
|
||||
maxValue={maxCompositionValue}
|
||||
height={20}
|
||||
renderLabel={(total) => (
|
||||
<div className="text-xs font-mono text-muted-foreground min-w-[50px] text-right">
|
||||
{formatTokenCount(Math.round(total / 4))}~
|
||||
</div>
|
||||
)}
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Expand icon */}
|
||||
<div className="text-muted-foreground flex-shrink-0">
|
||||
{isExpanded ? (
|
||||
<ChevronDown className="h-4 w-4" />
|
||||
) : (
|
||||
<ChevronRight className="h-4 w-4" />
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Expanded details */}
|
||||
{isExpanded && (
|
||||
<div className="pb-3">
|
||||
{/* Connector line */}
|
||||
<div className="flex items-start gap-3 px-2">
|
||||
<div className="w-6 flex justify-center flex-shrink-0">
|
||||
<div className="w-px h-full bg-muted" />
|
||||
</div>
|
||||
<div className="flex-1 min-w-0">
|
||||
{/* L-connector and composition */}
|
||||
<div className="flex items-start gap-2">
|
||||
<div className="text-muted-foreground text-xs mt-1">└─</div>
|
||||
<div className="flex-1 space-y-3">
|
||||
{/* Basic info */}
|
||||
<div className="grid grid-cols-2 gap-x-4 gap-y-1 text-xs text-muted-foreground">
|
||||
<div>Time: <span className="font-mono text-foreground">{timestamp}</span></div>
|
||||
<div>Duration: <span className="font-mono text-foreground">{turn.duration_ms.toFixed(0)}ms</span></div>
|
||||
{turn.model && (
|
||||
<div>Model: <span className="font-mono text-foreground">{turn.model}</span></div>
|
||||
)}
|
||||
{turn.entity_id && (
|
||||
<div>Entity: <span className="font-mono text-foreground">{turn.entity_id}</span></div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Token counts - shown in tokens mode */}
|
||||
{viewMode === "tokens" && (
|
||||
<div className="flex gap-4 text-xs">
|
||||
<div>
|
||||
<span className="text-blue-600 dark:text-blue-400">Input:</span>{" "}
|
||||
<span className="font-mono">{turn.input_tokens.toLocaleString()}</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-emerald-600 dark:text-emerald-400">Output:</span>{" "}
|
||||
<span className="font-mono">{turn.output_tokens.toLocaleString()}</span>
|
||||
</div>
|
||||
<div>
|
||||
<span className="text-muted-foreground">Total:</span>{" "}
|
||||
<span className="font-mono">{turn.total_tokens.toLocaleString()}</span>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Composition breakdown - shown in composition mode */}
|
||||
{viewMode === "composition" && turn.composition.total > 0 && (
|
||||
<div>
|
||||
<div className="text-xs text-muted-foreground mb-2 flex items-center gap-1">
|
||||
<Info className="h-3 w-3" />
|
||||
Context Composition (estimated from ~{formatTokenCount(Math.round(turn.composition.total / 4))} tokens)
|
||||
</div>
|
||||
<CompositionBreakdown composition={turn.composition} />
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// Summary stats card
|
||||
function StatCard({
|
||||
label,
|
||||
value,
|
||||
icon: Icon,
|
||||
color = "default",
|
||||
}: {
|
||||
label: string;
|
||||
value: string | number;
|
||||
icon: typeof BarChart3;
|
||||
color?: "default" | "blue" | "green";
|
||||
}) {
|
||||
const colorClass = {
|
||||
default: "text-muted-foreground",
|
||||
blue: "text-blue-600 dark:text-blue-400",
|
||||
green: "text-emerald-600 dark:text-emerald-400",
|
||||
}[color];
|
||||
|
||||
return (
|
||||
<div className="flex items-center gap-2 p-2 bg-muted/30 rounded">
|
||||
<Icon className={`h-4 w-4 ${colorClass}`} />
|
||||
<div className="flex-1 min-w-0">
|
||||
<div className="text-xs text-muted-foreground truncate">{label}</div>
|
||||
<div className="font-mono text-sm font-medium">{value}</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// Main component
|
||||
export function ContextInspector({ events }: ContextInspectorProps) {
|
||||
// Use persisted store state instead of local useState
|
||||
const viewMode = useDevUIStore((state) => state.contextInspectorViewMode);
|
||||
const setViewMode = useDevUIStore((state) => state.setContextInspectorViewMode);
|
||||
const showCumulative = useDevUIStore((state) => state.contextInspectorCumulative);
|
||||
const setShowCumulative = useDevUIStore((state) => state.setContextInspectorCumulative);
|
||||
|
||||
// Extract turn data from traces
|
||||
const turns = useMemo(() => extractTurnData(events), [events]);
|
||||
|
||||
// Calculate stats
|
||||
const stats = useMemo(() => calculateStats(turns), [turns]);
|
||||
|
||||
// Aggregate composition
|
||||
const totalComposition = useMemo(() => aggregateComposition(turns), [turns]);
|
||||
|
||||
// Calculate max value for bar scaling (tokens)
|
||||
// In non-cumulative mode, use 0 to signal full-width bars (focus on ratios)
|
||||
// In cumulative mode, scale relative to total (focus on growth)
|
||||
const maxValue = useMemo(() => {
|
||||
if (turns.length === 0) return 0;
|
||||
|
||||
if (showCumulative) {
|
||||
return stats.totalTokens;
|
||||
} else {
|
||||
// Return 0 to signal "use full width" - each bar shows its own ratio
|
||||
return 0;
|
||||
}
|
||||
}, [turns, showCumulative, stats.totalTokens]);
|
||||
|
||||
// Calculate max value for composition bar scaling
|
||||
// Same logic: full-width in non-cumulative, scaled in cumulative
|
||||
const maxCompositionValue = useMemo(() => {
|
||||
if (turns.length === 0) return 0;
|
||||
|
||||
if (showCumulative) {
|
||||
return totalComposition.total;
|
||||
} else {
|
||||
// Return 0 to signal "use full width"
|
||||
return 0;
|
||||
}
|
||||
}, [turns, showCumulative, totalComposition.total]);
|
||||
|
||||
// Calculate cumulative values for tokens and composition
|
||||
const cumulativeData = useMemo(() => {
|
||||
let cumInput = 0;
|
||||
let cumOutput = 0;
|
||||
let cumComposition: ContextComposition = {
|
||||
system: 0, user: 0, assistant: 0, toolCalls: 0, toolResults: 0, total: 0
|
||||
};
|
||||
|
||||
return turns.map(t => {
|
||||
cumInput += t.input_tokens;
|
||||
cumOutput += t.output_tokens;
|
||||
cumComposition = {
|
||||
system: cumComposition.system + t.composition.system,
|
||||
user: cumComposition.user + t.composition.user,
|
||||
assistant: cumComposition.assistant + t.composition.assistant,
|
||||
toolCalls: cumComposition.toolCalls + t.composition.toolCalls,
|
||||
toolResults: cumComposition.toolResults + t.composition.toolResults,
|
||||
total: cumComposition.total + t.composition.total,
|
||||
};
|
||||
return {
|
||||
input: cumInput,
|
||||
output: cumOutput,
|
||||
composition: { ...cumComposition }
|
||||
};
|
||||
});
|
||||
}, [turns]);
|
||||
|
||||
// No data state
|
||||
if (turns.length === 0) {
|
||||
return (
|
||||
<div className="flex flex-col items-center text-center p-6 pt-9">
|
||||
<BarChart3 className="h-8 w-8 text-muted-foreground mb-3" />
|
||||
<div className="text-sm font-medium mb-1">No Data</div>
|
||||
<div className="text-xs text-muted-foreground max-w-[200px]">
|
||||
Run{" "}
|
||||
<span className="font-mono bg-accent/10 px-1 rounded">
|
||||
devui --instrumentation
|
||||
</span>{" "}
|
||||
and start a conversation.
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="h-full flex flex-col">
|
||||
{/* Header */}
|
||||
<div className="p-3 border-b flex-shrink-0 space-y-2">
|
||||
{/* Title row */}
|
||||
<div className="flex items-center justify-between gap-2">
|
||||
<div className="flex items-center gap-2">
|
||||
<BarChart3 className="h-4 w-4" />
|
||||
<span className="font-medium text-sm">Context Inspector</span>
|
||||
<Badge variant="outline" className="text-xs">
|
||||
{turns.length} turn{turns.length !== 1 ? "s" : ""}
|
||||
</Badge>
|
||||
</div>
|
||||
|
||||
{/* Cumulative checkbox */}
|
||||
<label className="flex items-center gap-1.5 text-xs text-muted-foreground cursor-pointer">
|
||||
<Checkbox
|
||||
checked={showCumulative}
|
||||
onCheckedChange={(checked) => setShowCumulative(checked === true)}
|
||||
className="h-3.5 w-3.5"
|
||||
/>
|
||||
<span>Cumulative</span>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
{/* View mode segmented control */}
|
||||
<div className="flex items-center bg-muted rounded-md p-1">
|
||||
<button
|
||||
onClick={() => setViewMode("tokens")}
|
||||
className={`flex-1 px-3 py-1.5 text-xs rounded transition-colors ${
|
||||
viewMode === "tokens"
|
||||
? "bg-background shadow-sm font-medium"
|
||||
: "text-muted-foreground hover:text-foreground"
|
||||
}`}
|
||||
>
|
||||
Tokens
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setViewMode("composition")}
|
||||
className={`flex-1 px-3 py-1.5 text-xs rounded transition-colors ${
|
||||
viewMode === "composition"
|
||||
? "bg-background shadow-sm font-medium"
|
||||
: "text-muted-foreground hover:text-foreground"
|
||||
}`}
|
||||
>
|
||||
Composition
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* View mode description */}
|
||||
<div className="text-xs text-muted-foreground">
|
||||
{viewMode === "tokens"
|
||||
? "Token usage per turn"
|
||||
: "Context breakdown by message type (chars)"}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<ScrollArea className="flex-1">
|
||||
<div className="p-3 space-y-4">
|
||||
{/* Legend */}
|
||||
<div className="flex items-center gap-4 text-xs px-1 flex-wrap">
|
||||
{viewMode === "tokens" ? (
|
||||
<>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-3 h-3 rounded ${SEGMENT_COLORS.input}`} />
|
||||
<span className="text-muted-foreground">Input (↑)</span>
|
||||
</div>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-3 h-3 rounded ${SEGMENT_COLORS.output}`} />
|
||||
<span className="text-muted-foreground">Output (↓)</span>
|
||||
</div>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2.5 h-2.5 rounded-sm ${SEGMENT_COLORS.system}`} />
|
||||
<span className="text-muted-foreground">System</span>
|
||||
</div>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2.5 h-2.5 rounded-sm ${SEGMENT_COLORS.user}`} />
|
||||
<span className="text-muted-foreground">User</span>
|
||||
</div>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2.5 h-2.5 rounded-sm ${SEGMENT_COLORS.assistant}`} />
|
||||
<span className="text-muted-foreground">Assistant</span>
|
||||
</div>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2.5 h-2.5 rounded-sm ${SEGMENT_COLORS.toolCalls}`} />
|
||||
<span className="text-muted-foreground">Tools</span>
|
||||
</div>
|
||||
<div className="flex items-center gap-1.5">
|
||||
<div className={`w-2.5 h-2.5 rounded-sm ${SEGMENT_COLORS.toolResults}`} />
|
||||
<span className="text-muted-foreground">Results</span>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
<div className="flex-1" />
|
||||
<div className="flex items-center gap-1 text-muted-foreground">
|
||||
<Info className="h-3 w-3" />
|
||||
<span>Click for details</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Turn bars */}
|
||||
<div className="border rounded-lg overflow-hidden">
|
||||
{turns.map((turn, index) => (
|
||||
<TurnRow
|
||||
key={turn.response_id}
|
||||
turn={turn}
|
||||
index={index}
|
||||
maxValue={maxValue}
|
||||
maxCompositionValue={maxCompositionValue}
|
||||
cumulativeInput={cumulativeData[index]?.input || 0}
|
||||
cumulativeOutput={cumulativeData[index]?.output || 0}
|
||||
cumulativeComposition={cumulativeData[index]?.composition || turn.composition}
|
||||
showCumulative={showCumulative}
|
||||
viewMode={viewMode}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
|
||||
{/* Session summary */}
|
||||
<div className="border rounded-lg overflow-hidden">
|
||||
<div className="p-3 bg-muted/30 border-b">
|
||||
<span className="text-xs font-medium">Session Summary</span>
|
||||
</div>
|
||||
|
||||
<div className="p-3 space-y-3">
|
||||
{/* Token summary cards */}
|
||||
<div className="grid grid-cols-3 gap-2">
|
||||
<StatCard
|
||||
label="Total Tokens"
|
||||
value={formatTokenCount(stats.totalTokens)}
|
||||
icon={Layers}
|
||||
/>
|
||||
<StatCard
|
||||
label="Input"
|
||||
value={formatTokenCount(stats.totalInput)}
|
||||
icon={BarChart3}
|
||||
color="blue"
|
||||
/>
|
||||
<StatCard
|
||||
label="Output"
|
||||
value={formatTokenCount(stats.totalOutput)}
|
||||
icon={BarChart3}
|
||||
color="green"
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Per-turn statistics (only for multi-turn sessions) */}
|
||||
{turns.length > 1 && (
|
||||
<div className="grid grid-cols-2 gap-x-4 gap-y-1 text-xs pt-2 border-t border-muted/50">
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Avg per turn:</span>
|
||||
<span className="font-mono">{formatTokenCount(stats.avgTotal)}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Peak turn:</span>
|
||||
<span className="font-mono">{formatTokenCount(stats.peakTotal)}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Avg input:</span>
|
||||
<span className="font-mono text-blue-600 dark:text-blue-400">{formatTokenCount(stats.avgInput)}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Avg output:</span>
|
||||
<span className="font-mono text-emerald-600 dark:text-emerald-400">{formatTokenCount(stats.avgOutput)}</span>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Total composition */}
|
||||
{totalComposition.total > 0 && (
|
||||
<div className="pt-3 border-t border-muted/50">
|
||||
<div className="flex items-start gap-2">
|
||||
<div className="text-muted-foreground text-xs mt-0.5">└─</div>
|
||||
<div className="flex-1">
|
||||
<div className="text-xs text-muted-foreground mb-2 flex items-center gap-1">
|
||||
<Info className="h-3 w-3" />
|
||||
Total Composition (all turns)
|
||||
</div>
|
||||
<CompositionBreakdown composition={totalComposition} />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</ScrollArea>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
+13
-6
@@ -133,10 +133,10 @@ function useBase64ToBlobUrl(data: string | undefined, mimeType: string): string
|
||||
function FileContentRenderer({ content, className }: ContentRendererProps) {
|
||||
const [isExpanded, setIsExpanded] = useState(true);
|
||||
|
||||
if (content.type !== "input_file" && content.type !== "output_file") return null;
|
||||
|
||||
const fileUrl = content.file_url || content.file_data;
|
||||
const filename = content.filename || "file";
|
||||
// Determine file properties (must be before hooks for conditional logic)
|
||||
const isFileContent = content.type === "input_file" || content.type === "output_file";
|
||||
const fileUrl = isFileContent ? (content.file_url || content.file_data) : undefined;
|
||||
const filename = isFileContent ? (content.filename || "file") : undefined;
|
||||
|
||||
// Determine file type from filename or data URI
|
||||
const isPdf = filename?.toLowerCase().endsWith(".pdf") || fileUrl?.includes("application/pdf");
|
||||
@@ -144,9 +144,13 @@ function FileContentRenderer({ content, className }: ContentRendererProps) {
|
||||
|
||||
// Convert base64 to blob URL for PDFs (better browser compatibility)
|
||||
// Use file_data (raw base64) if available, otherwise try file_url
|
||||
const pdfData = isPdf ? (content.file_data || content.file_url) : undefined;
|
||||
// Hook must be called unconditionally - pass undefined if not a PDF
|
||||
const pdfData = (isFileContent && isPdf) ? (content.file_data || content.file_url) : undefined;
|
||||
const pdfBlobUrl = useBase64ToBlobUrl(pdfData, 'application/pdf');
|
||||
|
||||
// Early return after all hooks
|
||||
if (!isFileContent) return null;
|
||||
|
||||
// Use blob URL if available, otherwise fall back to original URL
|
||||
const effectivePdfUrl = pdfBlobUrl || fileUrl;
|
||||
|
||||
@@ -299,9 +303,12 @@ function DataContentRenderer({ content, className }: ContentRendererProps) {
|
||||
|
||||
// Function approval request renderer - compact version
|
||||
function FunctionApprovalRequestRenderer({ content, className }: ContentRendererProps) {
|
||||
// Hooks must be called unconditionally
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
|
||||
// Early return after hooks
|
||||
if (content.type !== "function_approval_request") return null;
|
||||
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
const { status, function_call } = content;
|
||||
|
||||
// Status styling - compact
|
||||
|
||||
+5
-5
@@ -23,7 +23,7 @@ import {
|
||||
} from "lucide-react";
|
||||
import { cn } from "@/lib/utils";
|
||||
import { apiClient } from "@/services/api";
|
||||
import type { CheckpointItem, WorkflowSession } from "@/types";
|
||||
import type { CheckpointItem, WorkflowSession, FullCheckpoint, PendingRequestInfoEvent } from "@/types";
|
||||
|
||||
interface CheckpointInfoModalProps {
|
||||
session: WorkflowSession | null;
|
||||
@@ -39,7 +39,7 @@ export function CheckpointInfoModal({
|
||||
onOpenChange,
|
||||
}: CheckpointInfoModalProps) {
|
||||
const [selectedCheckpointId, setSelectedCheckpointId] = useState<string | null>(null);
|
||||
const [fullCheckpoint, setFullCheckpoint] = useState<any>(null);
|
||||
const [fullCheckpoint, setFullCheckpoint] = useState<FullCheckpoint | null>(null);
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [jsonExpanded, setJsonExpanded] = useState(true);
|
||||
|
||||
@@ -68,7 +68,7 @@ export function CheckpointInfoModal({
|
||||
session.conversation_id,
|
||||
`checkpoint_${selectedCheckpointId}`
|
||||
);
|
||||
setFullCheckpoint((item as CheckpointItem).metadata?.full_checkpoint);
|
||||
setFullCheckpoint((item as CheckpointItem).metadata?.full_checkpoint ?? null);
|
||||
} catch (error) {
|
||||
console.error("Failed to load checkpoint:", error);
|
||||
setFullCheckpoint(null);
|
||||
@@ -276,7 +276,7 @@ export function CheckpointInfoModal({
|
||||
)}
|
||||
|
||||
{/* Messages */}
|
||||
{messageExecutors.length > 0 && (
|
||||
{messageExecutors.length > 0 && fullCheckpoint && (
|
||||
<div>
|
||||
<div className="text-sm font-medium mb-3 flex items-center gap-2">
|
||||
<MessageSquare className="h-4 w-4" />
|
||||
@@ -311,7 +311,7 @@ export function CheckpointInfoModal({
|
||||
</div>
|
||||
<div className="space-y-2">
|
||||
{Object.entries(fullCheckpoint.pending_request_info_events).map(
|
||||
([reqId, reqData]: [string, any]) => (
|
||||
([reqId, reqData]: [string, PendingRequestInfoEvent]) => (
|
||||
<div
|
||||
key={reqId}
|
||||
className="bg-muted/50 border border-border p-3 rounded-lg"
|
||||
|
||||
+63
-35
@@ -9,6 +9,8 @@ import { Button } from "@/components/ui/button";
|
||||
import { Badge } from "@/components/ui/badge";
|
||||
import { HilTimelineItem } from "./hil-timeline-item";
|
||||
import { RunWorkflowButton } from "./run-workflow-button";
|
||||
import { ChatMessageInput } from "@/components/ui/chat-message-input";
|
||||
import { isChatMessageSchema } from "@/utils/workflow-utils";
|
||||
import {
|
||||
Loader2,
|
||||
CheckCircle,
|
||||
@@ -21,6 +23,7 @@ import {
|
||||
Square,
|
||||
} from "lucide-react";
|
||||
import type { ExtendedResponseStreamEvent, JSONSchemaProperty } from "@/types";
|
||||
import type { ResponseInputContent } from "@/types/agent-framework";
|
||||
import type { ExecutorState } from "./executor-node";
|
||||
import { truncateText } from "@/utils/workflow-utils";
|
||||
|
||||
@@ -262,8 +265,8 @@ export function ExecutionTimeline({
|
||||
const item = (event as import("@/types/openai").ResponseOutputItemAddedEvent).item;
|
||||
|
||||
// Handle both executor_action items AND message items from Magentic agents
|
||||
if (item && item.type === "executor_action" && item.executor_id && item.id) {
|
||||
const executorId = item.executor_id;
|
||||
if (item && item.type === "executor_action" && "executor_id" in item && item.id) {
|
||||
const executorId = String(item.executor_id);
|
||||
const itemId = item.id;
|
||||
const runNumber = (runCount.get(executorId) || 0) + 1;
|
||||
runCount.set(executorId, runNumber);
|
||||
@@ -277,22 +280,25 @@ export function ExecutionTimeline({
|
||||
timestamp: uiTimestamp,
|
||||
runNumber,
|
||||
});
|
||||
} else if (item && item.type === "message" && item.metadata?.agent_id && item.metadata?.source === "magentic" && item.id) {
|
||||
} else if (item && item.type === "message" && "metadata" in item && item.id) {
|
||||
// Handle message items from Magentic agents
|
||||
const executorId = item.metadata.agent_id;
|
||||
const itemId = item.id;
|
||||
const runNumber = (runCount.get(executorId) || 0) + 1;
|
||||
runCount.set(executorId, runNumber);
|
||||
const metadata = item.metadata as { agent_id?: string; source?: string } | undefined;
|
||||
if (metadata?.agent_id && metadata?.source === "magentic") {
|
||||
const executorId = metadata.agent_id;
|
||||
const itemId = item.id;
|
||||
const runNumber = (runCount.get(executorId) || 0) + 1;
|
||||
runCount.set(executorId, runNumber);
|
||||
|
||||
runs.push({
|
||||
executorId,
|
||||
executorName: truncateText(executorId, 35),
|
||||
itemId,
|
||||
state: "running",
|
||||
output: itemOutputs[itemId] || "",
|
||||
timestamp: uiTimestamp,
|
||||
runNumber,
|
||||
});
|
||||
runs.push({
|
||||
executorId,
|
||||
executorName: truncateText(executorId, 35),
|
||||
itemId,
|
||||
state: "running",
|
||||
output: itemOutputs[itemId] || "",
|
||||
timestamp: uiTimestamp,
|
||||
runNumber,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -301,7 +307,7 @@ export function ExecutionTimeline({
|
||||
const item = (event as import("@/types/openai").ResponseOutputItemDoneEvent).item;
|
||||
|
||||
// Handle both executor_action items AND message items from Magentic agents
|
||||
if (item && item.type === "executor_action" && item.executor_id && item.id) {
|
||||
if (item && item.type === "executor_action" && "executor_id" in item && item.id) {
|
||||
const itemId = item.id;
|
||||
// Find the run by ITEM ID (not executor ID!) to handle multiple runs correctly
|
||||
const existingRun = runs.find((r) => r.itemId === itemId);
|
||||
@@ -315,18 +321,21 @@ export function ExecutionTimeline({
|
||||
: "completed";
|
||||
// Use item-specific output, not executor-wide output
|
||||
existingRun.output = itemOutputs[itemId] || "";
|
||||
if (item.status === "failed" && item.error) {
|
||||
existingRun.error = item.error;
|
||||
if (item.status === "failed" && "error" in item && item.error) {
|
||||
existingRun.error = String(item.error);
|
||||
}
|
||||
}
|
||||
} else if (item && item.type === "message" && item.metadata?.agent_id && item.metadata?.source === "magentic" && item.id) {
|
||||
} else if (item && item.type === "message" && "metadata" in item && item.id) {
|
||||
// Handle message completion from Magentic agents
|
||||
const itemId = item.id;
|
||||
const existingRun = runs.find((r) => r.itemId === itemId);
|
||||
const metadata = item.metadata as { agent_id?: string; source?: string } | undefined;
|
||||
if (metadata?.agent_id && metadata?.source === "magentic") {
|
||||
const itemId = item.id;
|
||||
const existingRun = runs.find((r) => r.itemId === itemId);
|
||||
|
||||
if (existingRun) {
|
||||
existingRun.state = item.status === "completed" ? "completed" : "failed";
|
||||
existingRun.output = itemOutputs[itemId] || "";
|
||||
if (existingRun) {
|
||||
existingRun.state = item.status === "completed" ? "completed" : "failed";
|
||||
existingRun.output = itemOutputs[itemId] || "";
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -625,16 +634,35 @@ export function ExecutionTimeline({
|
||||
{/* Bottom Control Bar - Sticky (hidden when HIL is active) */}
|
||||
{(onRun || onCancel) && pendingHilRequests.length === 0 && (
|
||||
<div className="border-t p-3 bg-background flex-shrink-0">
|
||||
<RunWorkflowButton
|
||||
inputSchema={inputSchema}
|
||||
onRun={onRun || (() => {})}
|
||||
onCancel={onCancel}
|
||||
isSubmitting={workflowState === "running"}
|
||||
isCancelling={isCancelling}
|
||||
workflowState={workflowState}
|
||||
checkpoints={checkpoints}
|
||||
showCheckpoints={false}
|
||||
/>
|
||||
{inputSchema && isChatMessageSchema(inputSchema) ? (
|
||||
<ChatMessageInput
|
||||
onSubmit={async (content: ResponseInputContent[]) => {
|
||||
// Wrap in OpenAI message format (same as run-workflow-button modal)
|
||||
const openaiInput = [
|
||||
{ type: "message", role: "user", content },
|
||||
];
|
||||
onRun?.(openaiInput as unknown as Record<string, unknown>);
|
||||
}}
|
||||
isSubmitting={workflowState === "running"}
|
||||
isStreaming={workflowState === "running"}
|
||||
onCancel={onCancel}
|
||||
isCancelling={isCancelling}
|
||||
placeholder="Message workflow..."
|
||||
showFileUpload={true}
|
||||
entityName="workflow"
|
||||
/>
|
||||
) : (
|
||||
<RunWorkflowButton
|
||||
inputSchema={inputSchema}
|
||||
onRun={onRun || (() => {})}
|
||||
onCancel={onCancel}
|
||||
isSubmitting={workflowState === "running"}
|
||||
isCancelling={isCancelling}
|
||||
workflowState={workflowState}
|
||||
checkpoints={checkpoints}
|
||||
showCheckpoints={false}
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
|
||||
@@ -1,9 +1,11 @@
|
||||
import { memo } from "react";
|
||||
import { memo, useState } from "react";
|
||||
import { Handle, Position, type NodeProps } from "@xyflow/react";
|
||||
import {
|
||||
Workflow,
|
||||
Home,
|
||||
Loader2,
|
||||
ChevronRight,
|
||||
ChevronDown,
|
||||
} from "lucide-react";
|
||||
import { cn } from "@/lib/utils";
|
||||
import { truncateText } from "@/utils/workflow-utils";
|
||||
@@ -70,8 +72,9 @@ const getExecutorStateConfig = (state: ExecutorState) => {
|
||||
export const ExecutorNode = memo(({ data, selected }: NodeProps) => {
|
||||
const nodeData = data as ExecutorNodeData;
|
||||
const config = getExecutorStateConfig(nodeData.state);
|
||||
const [isOutputExpanded, setIsOutputExpanded] = useState(false);
|
||||
|
||||
const hasData = nodeData.inputData || nodeData.outputData || nodeData.error;
|
||||
const hasOutput = nodeData.outputData || nodeData.error;
|
||||
const isRunning = nodeData.state === "running";
|
||||
const shouldAnimate = isRunning && (nodeData.isStreaming ?? true); // Default to true for backwards compatibility
|
||||
|
||||
@@ -80,19 +83,13 @@ export const ExecutorNode = memo(({ data, selected }: NodeProps) => {
|
||||
const targetPosition = isVertical ? Position.Top : Position.Left;
|
||||
const sourcePosition = isVertical ? Position.Bottom : Position.Right;
|
||||
|
||||
// Helper to safely render data with full details
|
||||
// Helper to render output/error details when expanded
|
||||
const renderDataDetails = () => {
|
||||
const details = [];
|
||||
|
||||
if (nodeData.error && typeof nodeData.error === "string") {
|
||||
// Truncate error to first 150 characters for node display
|
||||
const truncatedError = truncateText(nodeData.error, 150);
|
||||
details.push(
|
||||
<div key="error" className="mb-2">
|
||||
<div className="text-xs font-medium text-red-600 dark:text-red-400 mb-1">Error:</div>
|
||||
<div className="text-xs text-red-600 dark:text-red-400 bg-red-50 dark:bg-red-950/20 p-2 rounded border border-red-200 dark:border-red-800 break-words">
|
||||
{truncatedError}
|
||||
</div>
|
||||
const truncatedError = truncateText(nodeData.error, 200);
|
||||
return (
|
||||
<div className="text-xs text-red-600 dark:text-red-400 bg-red-50 dark:bg-red-950/20 p-2 rounded border border-red-200 dark:border-red-800 break-words max-h-32 overflow-auto">
|
||||
{truncatedError}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -103,53 +100,21 @@ export const ExecutorNode = memo(({ data, selected }: NodeProps) => {
|
||||
typeof nodeData.outputData === "string"
|
||||
? nodeData.outputData
|
||||
: JSON.stringify(nodeData.outputData, null, 2);
|
||||
details.push(
|
||||
<div key="output" className="mb-2">
|
||||
<div className="text-xs font-medium text-green-600 dark:text-green-400 mb-1">Output:</div>
|
||||
<div className="text-xs text-gray-700 dark:text-gray-300 bg-green-50 dark:bg-green-950/20 p-2 rounded border border-green-200 dark:border-green-800 max-h-20 overflow-auto">
|
||||
<pre className="whitespace-pre-wrap font-mono">{outputStr}</pre>
|
||||
</div>
|
||||
return (
|
||||
<div className="text-xs text-gray-700 dark:text-gray-300 bg-muted/50 p-2 rounded border max-h-32 overflow-auto">
|
||||
<pre className="whitespace-pre-wrap font-mono">{outputStr}</pre>
|
||||
</div>
|
||||
);
|
||||
} catch {
|
||||
details.push(
|
||||
<div key="output" className="mb-2">
|
||||
<div className="text-xs font-medium text-green-600 dark:text-green-400 mb-1">Output:</div>
|
||||
<div className="text-xs text-gray-600 dark:text-gray-400 bg-green-50 dark:bg-green-950/20 p-2 rounded border border-green-200 dark:border-green-800">
|
||||
[Unable to display output data]
|
||||
</div>
|
||||
return (
|
||||
<div className="text-xs text-gray-600 dark:text-gray-400 bg-muted/50 p-2 rounded border">
|
||||
[Unable to display output]
|
||||
</div>
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
if (nodeData.inputData) {
|
||||
try {
|
||||
const inputStr =
|
||||
typeof nodeData.inputData === "string"
|
||||
? nodeData.inputData
|
||||
: JSON.stringify(nodeData.inputData, null, 2);
|
||||
details.push(
|
||||
<div key="input" className="mb-2">
|
||||
<div className="text-xs font-medium text-blue-600 dark:text-blue-400 mb-1">Input:</div>
|
||||
<div className="text-xs text-gray-700 dark:text-gray-300 bg-blue-50 dark:bg-blue-950/20 p-2 rounded border border-blue-200 dark:border-blue-800 max-h-20 overflow-auto">
|
||||
<pre className="whitespace-pre-wrap font-mono">{inputStr}</pre>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
} catch {
|
||||
details.push(
|
||||
<div key="input" className="mb-2">
|
||||
<div className="text-xs font-medium text-blue-600 dark:text-blue-400 mb-1">Input:</div>
|
||||
<div className="text-xs text-gray-600 dark:text-gray-400 bg-blue-50 dark:bg-blue-950/20 p-2 rounded border border-blue-200 dark:border-blue-800">
|
||||
[Unable to display input data]
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
return details.length > 0 ? details : null;
|
||||
return null;
|
||||
};
|
||||
|
||||
return (
|
||||
@@ -218,10 +183,28 @@ export const ExecutorNode = memo(({ data, selected }: NodeProps) => {
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Data details */}
|
||||
{hasData && (
|
||||
<div className="mt-3">
|
||||
{renderDataDetails()}
|
||||
{/* Collapsible output section */}
|
||||
{hasOutput && (
|
||||
<div className="mt-2 border-t border-border/50 pt-2">
|
||||
<button
|
||||
onClick={(e) => {
|
||||
e.stopPropagation();
|
||||
setIsOutputExpanded(!isOutputExpanded);
|
||||
}}
|
||||
className="flex items-center gap-1 text-xs text-muted-foreground hover:text-foreground transition-colors w-full"
|
||||
>
|
||||
{isOutputExpanded ? (
|
||||
<ChevronDown className="w-3 h-3" />
|
||||
) : (
|
||||
<ChevronRight className="w-3 h-3" />
|
||||
)}
|
||||
<span>{nodeData.error ? "Show error" : "Show output"}</span>
|
||||
</button>
|
||||
{isOutputExpanded && (
|
||||
<div className="mt-2">
|
||||
{renderDataDetails()}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
|
||||
+116
-17
@@ -110,6 +110,7 @@ export function WorkflowView({
|
||||
const removeSession = useDevUIStore((state) => state.removeSession);
|
||||
const addToast = useDevUIStore((state) => state.addToast);
|
||||
const runtime = useDevUIStore((state) => state.runtime);
|
||||
const streamingEnabled = useDevUIStore((state) => state.streamingEnabled);
|
||||
|
||||
// View options state
|
||||
const [viewOptions, setViewOptions] = useState(() => {
|
||||
@@ -304,8 +305,9 @@ export function WorkflowView({
|
||||
{ limit: 100 }
|
||||
);
|
||||
const checkpointItems = response.data.filter(
|
||||
(item: any) => item.type === "checkpoint"
|
||||
) as CheckpointItem[];
|
||||
(item): item is CheckpointItem =>
|
||||
typeof item === "object" && item !== null && "type" in item && (item as { type: string }).type === "checkpoint"
|
||||
);
|
||||
setSessionCheckpoints(checkpointItems);
|
||||
} catch (error) {
|
||||
console.error(`Failed to load checkpoints for session ${currentSession.conversation_id}:`, error);
|
||||
@@ -453,9 +455,9 @@ export function WorkflowView({
|
||||
| import("@/types/openai").ResponseOutputItemAddedEvent
|
||||
| import("@/types/openai").ResponseOutputItemDoneEvent
|
||||
).item;
|
||||
if (item && item.type === "executor_action" && item.executor_id) {
|
||||
if (item && item.type === "executor_action" && "executor_id" in item && item.executor_id) {
|
||||
history.push({
|
||||
executorId: item.executor_id,
|
||||
executorId: String(item.executor_id),
|
||||
message:
|
||||
event.type === "response.output_item.added"
|
||||
? "Executor started"
|
||||
@@ -624,7 +626,8 @@ export function WorkflowView({
|
||||
if (
|
||||
item &&
|
||||
item.type === "message" &&
|
||||
item.metadata?.source === "magentic" &&
|
||||
"metadata" in item &&
|
||||
(item.metadata as { source?: string } | undefined)?.source === "magentic" &&
|
||||
item.id
|
||||
) {
|
||||
// Track this message ID as the current streaming target for Magentic agents
|
||||
@@ -639,19 +642,21 @@ export function WorkflowView({
|
||||
if (
|
||||
item &&
|
||||
item.type === "message" &&
|
||||
!item.metadata?.source &&
|
||||
item.content
|
||||
(!("metadata" in item) || !(item.metadata as { source?: string } | undefined)?.source) &&
|
||||
"content" in item &&
|
||||
Array.isArray(item.content)
|
||||
) {
|
||||
// Extract text from message content
|
||||
for (const content of item.content) {
|
||||
for (const content of item.content as Array<{ type: string; text?: string }>) {
|
||||
if (content.type === "output_text" && content.text) {
|
||||
const text = content.text; // Capture for closure
|
||||
// Append to workflow result (support multiple yield_output calls)
|
||||
setWorkflowResult((prev) => {
|
||||
if (prev && prev.length > 0) {
|
||||
// If there's existing output, add separator
|
||||
return prev + "\n\n" + content.text;
|
||||
return prev + "\n\n" + text;
|
||||
}
|
||||
return content.text;
|
||||
return text;
|
||||
});
|
||||
|
||||
// Try to parse as JSON for structured metadata
|
||||
@@ -820,6 +825,99 @@ export function WorkflowView({
|
||||
]
|
||||
);
|
||||
|
||||
// Handle non-streaming workflow data sending
|
||||
const handleSendWorkflowDataSync = useCallback(
|
||||
async (inputData: Record<string, unknown>, checkpointId?: string) => {
|
||||
if (!selectedWorkflow || selectedWorkflow.type !== "workflow") return;
|
||||
|
||||
setIsStreaming(false); // Not actually streaming
|
||||
setWasCancelled(false);
|
||||
setOpenAIEvents([]);
|
||||
setWorkflowResult("");
|
||||
itemOutputs.current = {};
|
||||
currentStreamingItemId.current = null;
|
||||
workflowMetadata.current = null;
|
||||
setPendingHilRequests([]);
|
||||
setHilResponses({});
|
||||
onDebugEvent("clear");
|
||||
|
||||
try {
|
||||
const response = await apiClient.runWorkflowSync(selectedWorkflow.id, {
|
||||
input_data: inputData,
|
||||
conversation_id: currentSession?.conversation_id || undefined,
|
||||
checkpoint_id: checkpointId,
|
||||
});
|
||||
|
||||
// Extract workflow result from response output
|
||||
if (response.output) {
|
||||
for (const outputItem of response.output) {
|
||||
if (outputItem.type === "message" && "content" in outputItem && Array.isArray(outputItem.content)) {
|
||||
for (const content of outputItem.content as Array<{ type: string; text?: string }>) {
|
||||
if (content.type === "output_text" && content.text) {
|
||||
setWorkflowResult((prev) => {
|
||||
if (prev && prev.length > 0) {
|
||||
return prev + "\n\n" + content.text;
|
||||
}
|
||||
return content.text || "";
|
||||
});
|
||||
|
||||
// Try to parse as JSON for structured metadata
|
||||
try {
|
||||
const parsed = JSON.parse(content.text || "");
|
||||
if (typeof parsed === "object" && parsed !== null) {
|
||||
workflowMetadata.current = parsed;
|
||||
}
|
||||
} catch {
|
||||
// Not JSON, keep as text
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Create a synthetic completion event for the timeline
|
||||
const completedEvent = {
|
||||
type: "response.completed",
|
||||
response: response,
|
||||
sequence_number: 0,
|
||||
} as ExtendedResponseStreamEvent;
|
||||
setOpenAIEvents([completedEvent]);
|
||||
onDebugEvent(completedEvent);
|
||||
|
||||
// Refetch checkpoints after completion
|
||||
await loadCheckpoints();
|
||||
} catch (error) {
|
||||
console.error("Workflow execution error:", error);
|
||||
|
||||
// Create a synthetic error event for the timeline
|
||||
const errorMessage = error instanceof Error ? error.message : "Workflow execution failed";
|
||||
const errorEvent: ExtendedResponseStreamEvent = {
|
||||
type: "response.failed",
|
||||
response: {
|
||||
error: { message: errorMessage },
|
||||
},
|
||||
sequence_number: 0,
|
||||
} as ExtendedResponseStreamEvent;
|
||||
setOpenAIEvents([errorEvent]);
|
||||
onDebugEvent(errorEvent);
|
||||
}
|
||||
},
|
||||
[selectedWorkflow, currentSession, onDebugEvent, loadCheckpoints]
|
||||
);
|
||||
|
||||
// Wrapper to choose between streaming and non-streaming
|
||||
const handleWorkflowRun = useCallback(
|
||||
async (inputData: Record<string, unknown>, checkpointId?: string) => {
|
||||
if (streamingEnabled) {
|
||||
await handleSendWorkflowData(inputData, checkpointId);
|
||||
} else {
|
||||
await handleSendWorkflowDataSync(inputData, checkpointId);
|
||||
}
|
||||
},
|
||||
[streamingEnabled, handleSendWorkflowData, handleSendWorkflowDataSync]
|
||||
);
|
||||
|
||||
// Check if all HIL responses are valid
|
||||
const areAllHilResponsesValid = useCallback(() => {
|
||||
// Check each pending request has a valid response
|
||||
@@ -979,22 +1077,23 @@ export function WorkflowView({
|
||||
}
|
||||
|
||||
// Handle workflow output messages
|
||||
if (item && item.type === "message" && item.content) {
|
||||
if (item && item.type === "message" && "content" in item && Array.isArray(item.content)) {
|
||||
// Extract text from message content
|
||||
for (const content of item.content) {
|
||||
for (const content of item.content as Array<{ type: string; text?: string }>) {
|
||||
if (content.type === "output_text" && content.text) {
|
||||
const text = content.text; // Capture for closure
|
||||
// Append to workflow result (support multiple yield_output calls)
|
||||
setWorkflowResult((prev) => {
|
||||
if (prev && prev.length > 0) {
|
||||
// If there's existing output, add separator
|
||||
return prev + "\n\n" + content.text;
|
||||
return prev + "\n\n" + text;
|
||||
}
|
||||
return content.text;
|
||||
return text;
|
||||
});
|
||||
|
||||
// Try to parse as JSON for structured metadata
|
||||
try {
|
||||
const parsed = JSON.parse(content.text);
|
||||
const parsed = JSON.parse(text);
|
||||
if (typeof parsed === "object" && parsed !== null) {
|
||||
workflowMetadata.current = parsed;
|
||||
}
|
||||
@@ -1296,7 +1395,7 @@ export function WorkflowView({
|
||||
{timelineMinimized && (
|
||||
<RunWorkflowButton
|
||||
inputSchema={workflowInfo.input_schema}
|
||||
onRun={handleSendWorkflowData}
|
||||
onRun={handleWorkflowRun}
|
||||
onCancel={handleCancel}
|
||||
isSubmitting={isStreaming}
|
||||
isCancelling={isCancelling}
|
||||
@@ -1481,7 +1580,7 @@ export function WorkflowView({
|
||||
inputSchema={workflowInfo?.input_schema}
|
||||
onRun={(data, checkpointId) => {
|
||||
// Use the form data from timeline
|
||||
handleSendWorkflowData(data, checkpointId);
|
||||
handleWorkflowRun(data, checkpointId);
|
||||
}}
|
||||
onCancel={handleCancel}
|
||||
isCancelling={isCancelling}
|
||||
|
||||
@@ -3,7 +3,8 @@
|
||||
* Features: Real-time event streaming, trace visualization, tool call details
|
||||
*/
|
||||
|
||||
import { useRef, useState } from "react";
|
||||
import { useRef, useState, useMemo } from "react";
|
||||
import { useDevUIStore } from "@/stores/devuiStore";
|
||||
import { ScrollArea } from "@/components/ui/scroll-area";
|
||||
import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs";
|
||||
import { Badge } from "@/components/ui/badge";
|
||||
@@ -19,8 +20,9 @@ import {
|
||||
MessageSquare,
|
||||
ChevronRight,
|
||||
ChevronDown,
|
||||
Info,
|
||||
BarChart3,
|
||||
} from "lucide-react";
|
||||
import { ContextInspector } from "@/components/features/agent/context-inspector";
|
||||
import type { ExtendedResponseStreamEvent } from "@/types";
|
||||
|
||||
// Simple visual separator component
|
||||
@@ -88,7 +90,23 @@ interface TraceEventData extends EventDataBase {
|
||||
start_time?: number;
|
||||
end_time?: number;
|
||||
entity_id?: string;
|
||||
session_id?: string | null;
|
||||
response_id?: string | null;
|
||||
}
|
||||
|
||||
// Helper type for trace hierarchy
|
||||
interface TraceNode {
|
||||
event: ExtendedResponseStreamEvent;
|
||||
data: TraceEventData;
|
||||
children: TraceNode[];
|
||||
}
|
||||
|
||||
// Helper type for grouped traces by response
|
||||
interface TraceGroup {
|
||||
response_id: string;
|
||||
timestamp: number;
|
||||
traces: TraceNode[];
|
||||
totalDuration: number;
|
||||
entity_id?: string;
|
||||
}
|
||||
|
||||
interface DebugPanelProps {
|
||||
@@ -149,20 +167,22 @@ function processEventsForDisplay(
|
||||
const item = outputEvent.item;
|
||||
|
||||
// If it's a function call item, extract metadata
|
||||
if (item.type === "function_call" && item.call_id && item.name) {
|
||||
const callId = item.call_id;
|
||||
if (item.type === "function_call") {
|
||||
// Type assertion for function call
|
||||
const funcCall = item as import("@/types").ResponseFunctionToolCall;
|
||||
const callId = funcCall.call_id;
|
||||
|
||||
// Initialize function call tracking with REAL function name from backend!
|
||||
functionCalls.set(callId, {
|
||||
name: item.name, // ← REAL NAME! (not "unknown")
|
||||
name: funcCall.name, // ← REAL NAME! (not "unknown")
|
||||
arguments: "",
|
||||
callId: callId,
|
||||
itemId: item.id, // Track item_id for delta matching
|
||||
itemId: funcCall.id, // Track item_id for delta matching
|
||||
timestamp: new Date().toISOString(),
|
||||
});
|
||||
|
||||
// Also track in callIdToName map for result pairing
|
||||
callIdToName.set(callId, item.name);
|
||||
callIdToName.set(callId, funcCall.name);
|
||||
}
|
||||
|
||||
// Pass through the event for display
|
||||
@@ -955,27 +975,7 @@ function EventExpandedContent({
|
||||
</span>
|
||||
<div className="mt-1 max-h-32 overflow-auto">
|
||||
<pre className="text-xs bg-background border rounded p-2 whitespace-pre-wrap break-all">
|
||||
{(() => {
|
||||
try {
|
||||
// Try to pretty-print JSON, and unescape string values that contain JSON
|
||||
const attrs = { ...data.attributes };
|
||||
Object.keys(attrs).forEach((key) => {
|
||||
if (
|
||||
typeof attrs[key] === "string" &&
|
||||
attrs[key].startsWith("[")
|
||||
) {
|
||||
try {
|
||||
attrs[key] = JSON.parse(attrs[key]);
|
||||
} catch {
|
||||
// Keep original if parsing fails
|
||||
}
|
||||
}
|
||||
});
|
||||
return JSON.stringify(attrs, null, 2);
|
||||
} catch {
|
||||
return JSON.stringify(data.attributes, null, 2);
|
||||
}
|
||||
})()}
|
||||
{formatTraceAttributes(data.attributes)}
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1149,257 +1149,418 @@ function EventsTab({
|
||||
);
|
||||
}
|
||||
|
||||
function TracesTab({ events }: { events: ExtendedResponseStreamEvent[] }) {
|
||||
// ONLY show actual trace events - handle both event type formats
|
||||
const traceEvents = events.filter(
|
||||
(e) =>
|
||||
e.type === "response.trace.completed" ||
|
||||
e.type === "response.trace.completed"
|
||||
);
|
||||
// Build hierarchical trace structure from flat trace events
|
||||
function buildTraceHierarchy(traceEvents: ExtendedResponseStreamEvent[]): TraceGroup[] {
|
||||
// Group by response_id first
|
||||
const groupedByResponse = new Map<string, ExtendedResponseStreamEvent[]>();
|
||||
|
||||
// Add separators between message rounds
|
||||
const tracesWithSeparators = addSeparatorsToEvents(traceEvents);
|
||||
for (const event of traceEvents) {
|
||||
if (!("data" in event)) continue;
|
||||
const data = event.data as TraceEventData;
|
||||
const responseId = data.response_id || "unknown";
|
||||
|
||||
// Reverse to show latest traces at the top
|
||||
const reversedTraceEvents = [...tracesWithSeparators].reverse();
|
||||
if (!groupedByResponse.has(responseId)) {
|
||||
groupedByResponse.set(responseId, []);
|
||||
}
|
||||
groupedByResponse.get(responseId)!.push(event);
|
||||
}
|
||||
|
||||
// Convert each group to hierarchical structure
|
||||
const groups: TraceGroup[] = [];
|
||||
|
||||
for (const [responseId, events] of groupedByResponse) {
|
||||
// Build tree from parent_span_id relationships
|
||||
const nodeMap = new Map<string, TraceNode>();
|
||||
const rootNodes: TraceNode[] = [];
|
||||
|
||||
// First pass: create all nodes
|
||||
for (const event of events) {
|
||||
if (!("data" in event)) continue;
|
||||
const data = (event as { data: TraceEventData }).data;
|
||||
const spanId = data.span_id || `span_${Math.random()}`;
|
||||
nodeMap.set(spanId, {
|
||||
event,
|
||||
data,
|
||||
children: [],
|
||||
});
|
||||
}
|
||||
|
||||
// Second pass: build parent-child relationships
|
||||
for (const event of events) {
|
||||
if (!("data" in event)) continue;
|
||||
const data = (event as { data: TraceEventData }).data;
|
||||
const spanId = data.span_id || "";
|
||||
const parentSpanId = data.parent_span_id;
|
||||
const node = nodeMap.get(spanId);
|
||||
|
||||
if (!node) continue;
|
||||
|
||||
if (parentSpanId && nodeMap.has(parentSpanId)) {
|
||||
// Has a parent in this group
|
||||
nodeMap.get(parentSpanId)!.children.push(node);
|
||||
} else {
|
||||
// Root node (no parent or parent not in this group)
|
||||
rootNodes.push(node);
|
||||
}
|
||||
}
|
||||
|
||||
// Sort root nodes by start_time (earliest first)
|
||||
rootNodes.sort((a, b) => (a.data.start_time || 0) - (b.data.start_time || 0));
|
||||
|
||||
// Sort children recursively by start_time
|
||||
const sortChildren = (node: TraceNode) => {
|
||||
node.children.sort((a, b) => (a.data.start_time || 0) - (b.data.start_time || 0));
|
||||
node.children.forEach(sortChildren);
|
||||
};
|
||||
rootNodes.forEach(sortChildren);
|
||||
|
||||
// Calculate group metadata
|
||||
const firstEvent = events[0];
|
||||
const firstData = firstEvent && "data" in firstEvent ? (firstEvent.data as TraceEventData) : null;
|
||||
const timestamp = Math.min(...events.map(e => {
|
||||
const d = "data" in e ? (e.data as TraceEventData) : null;
|
||||
return d?.start_time || Date.now() / 1000;
|
||||
}));
|
||||
const totalDuration = events.reduce((sum, e) => {
|
||||
const d = "data" in e ? (e.data as TraceEventData) : null;
|
||||
return sum + (d?.duration_ms || 0);
|
||||
}, 0);
|
||||
|
||||
groups.push({
|
||||
response_id: responseId,
|
||||
timestamp,
|
||||
traces: rootNodes,
|
||||
totalDuration,
|
||||
entity_id: firstData?.entity_id,
|
||||
});
|
||||
}
|
||||
|
||||
// Sort groups by timestamp (newest first)
|
||||
groups.sort((a, b) => b.timestamp - a.timestamp);
|
||||
|
||||
return groups;
|
||||
}
|
||||
|
||||
// Recursively parse escaped JSON strings at any depth
|
||||
function parseEscapedJson(value: unknown): unknown {
|
||||
if (typeof value === "string") {
|
||||
// Try to parse JSON strings (arrays or objects)
|
||||
const trimmed = value.trim();
|
||||
if (trimmed.startsWith("[") || trimmed.startsWith("{")) {
|
||||
try {
|
||||
const parsed = JSON.parse(value);
|
||||
// Recursively process the parsed result
|
||||
return parseEscapedJson(parsed);
|
||||
} catch {
|
||||
return value;
|
||||
}
|
||||
}
|
||||
return value;
|
||||
}
|
||||
|
||||
if (Array.isArray(value)) {
|
||||
return value.map(parseEscapedJson);
|
||||
}
|
||||
|
||||
if (value !== null && typeof value === "object") {
|
||||
const result: Record<string, unknown> = {};
|
||||
for (const [k, v] of Object.entries(value)) {
|
||||
result[k] = parseEscapedJson(v);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
return value;
|
||||
}
|
||||
|
||||
// Format trace attributes by parsing escaped JSON strings for better readability
|
||||
function formatTraceAttributes(attributes: Record<string, unknown>): string {
|
||||
try {
|
||||
const formatted = parseEscapedJson(attributes);
|
||||
return JSON.stringify(formatted, null, 2);
|
||||
} catch {
|
||||
return JSON.stringify(attributes, null, 2);
|
||||
}
|
||||
}
|
||||
|
||||
// Get operation type badge color
|
||||
function getOperationColor(operationName: string): string {
|
||||
if (operationName.includes("invoke_agent") || operationName.includes("Agent")) {
|
||||
return "bg-purple-100 dark:bg-purple-900 text-purple-800 dark:text-purple-200";
|
||||
}
|
||||
if (operationName.includes("chat") || operationName.includes("Chat")) {
|
||||
return "bg-blue-100 dark:bg-blue-900 text-blue-800 dark:text-blue-200";
|
||||
}
|
||||
if (operationName.includes("tool") || operationName.includes("execute")) {
|
||||
return "bg-green-100 dark:bg-green-900 text-green-800 dark:text-green-200";
|
||||
}
|
||||
return "bg-orange-100 dark:bg-orange-900 text-orange-800 dark:text-orange-200";
|
||||
}
|
||||
|
||||
// Recursive component for rendering trace tree nodes
|
||||
function TraceTreeNode({ node, depth = 0 }: { node: TraceNode; depth?: number }) {
|
||||
const [isExpanded, setIsExpanded] = useState(depth < 2); // Auto-expand first 2 levels
|
||||
const [showDetails, setShowDetails] = useState(false);
|
||||
|
||||
const { data } = node;
|
||||
const operationName = data.operation_name || "Unknown";
|
||||
const duration = data.duration_ms ? `${Number(data.duration_ms).toFixed(1)}ms` : "";
|
||||
const hasChildren = node.children.length > 0;
|
||||
|
||||
// Extract token usage from attributes if available
|
||||
const inputTokens = data.attributes?.["gen_ai.usage.input_tokens"];
|
||||
const outputTokens = data.attributes?.["gen_ai.usage.output_tokens"];
|
||||
const hasTokens = inputTokens !== undefined || outputTokens !== undefined;
|
||||
|
||||
return (
|
||||
<div className="h-full flex flex-col">
|
||||
<div className="flex items-center gap-2 p-3 border-b">
|
||||
<Search className="h-4 w-4" />
|
||||
<span className="font-medium">Traces</span>
|
||||
<Badge variant="outline">{traceEvents.length}</Badge>
|
||||
<div className="relative">
|
||||
{/* Vertical line for tree structure */}
|
||||
{depth > 0 && (
|
||||
<div
|
||||
className="absolute left-0 top-0 bottom-0 border-l-2 border-muted"
|
||||
style={{ marginLeft: `${(depth - 1) * 16 + 8}px` }}
|
||||
/>
|
||||
)}
|
||||
|
||||
<div
|
||||
className="flex items-center gap-2 py-1.5 hover:bg-muted/50 rounded transition-colors"
|
||||
style={{ paddingLeft: `${depth * 16}px` }}
|
||||
>
|
||||
{/* Expand/collapse for children OR details */}
|
||||
<button
|
||||
onClick={() => hasChildren ? setIsExpanded(!isExpanded) : setShowDetails(!showDetails)}
|
||||
className="w-4 h-4 flex items-center justify-center text-muted-foreground hover:text-foreground"
|
||||
>
|
||||
{hasChildren ? (
|
||||
isExpanded ? <ChevronDown className="h-3 w-3" /> : <ChevronRight className="h-3 w-3" />
|
||||
) : (
|
||||
showDetails ? <ChevronDown className="h-3 w-3" /> : <ChevronRight className="h-3 w-3" />
|
||||
)}
|
||||
</button>
|
||||
|
||||
{/* Operation badge */}
|
||||
<span className={`text-xs px-1.5 py-0.5 rounded font-medium ${getOperationColor(operationName)}`}>
|
||||
{operationName.replace("ChatAgent.", "").replace("invoke_agent ", "")}
|
||||
</span>
|
||||
|
||||
{/* Duration */}
|
||||
{duration && (
|
||||
<span className="text-xs text-muted-foreground font-mono">
|
||||
{duration}
|
||||
</span>
|
||||
)}
|
||||
|
||||
{/* Token usage */}
|
||||
{hasTokens && (
|
||||
<span className="text-xs text-muted-foreground font-mono">
|
||||
{inputTokens !== undefined && <span>↑{String(inputTokens)}</span>}
|
||||
{inputTokens !== undefined && outputTokens !== undefined && <span className="mx-0.5">/</span>}
|
||||
{outputTokens !== undefined && <span>↓{String(outputTokens)}</span>}
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<ScrollArea className="flex-1">
|
||||
<div className="p-3">
|
||||
{traceEvents.length === 0 ? (
|
||||
<div className="text-center text-muted-foreground text-sm py-8">
|
||||
No trace data available.
|
||||
<br />
|
||||
{events && events.length > 0 && (
|
||||
<div className="mt-3 text-xs border rounded p-2">
|
||||
{" "}
|
||||
<Info className="inline h-4 w-4 mr-1 " />
|
||||
You may have to set the environment variable{" "}
|
||||
<span className="font-mono bg-accent/10 px-1 rounded">
|
||||
ENABLE_INSTRUMENTATION=true
|
||||
</span>{" "}
|
||||
or restart devui with the tracing flag{" "}
|
||||
<div className="font-mono bg-accent/10 px-1 rounded">
|
||||
devui --tracing
|
||||
</div>
|
||||
to enable tracing.
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
) : (
|
||||
<div className="space-y-3">
|
||||
{reversedTraceEvents.map((event, index) => {
|
||||
if ('type' in event && event.type === "separator") {
|
||||
return <MessageSeparator key={(event as { type: "separator"; id: string }).id} />;
|
||||
}
|
||||
return <TraceEventItem key={index} event={event as ExtendedResponseStreamEvent} />;
|
||||
})}
|
||||
</div>
|
||||
)}
|
||||
{/* Details panel */}
|
||||
{showDetails && !hasChildren && (
|
||||
<div
|
||||
className="ml-4 mt-1 mb-2 p-2 bg-muted/30 rounded border text-xs"
|
||||
style={{ marginLeft: `${depth * 16 + 20}px` }}
|
||||
>
|
||||
<div className="space-y-1">
|
||||
{data.span_id && (
|
||||
<div className="flex gap-2">
|
||||
<span className="text-muted-foreground w-20">Span ID:</span>
|
||||
<span className="font-mono text-xs break-all">{data.span_id}</span>
|
||||
</div>
|
||||
)}
|
||||
{data.trace_id && (
|
||||
<div className="flex gap-2">
|
||||
<span className="text-muted-foreground w-20">Trace ID:</span>
|
||||
<span className="font-mono text-xs break-all">{data.trace_id}</span>
|
||||
</div>
|
||||
)}
|
||||
{data.status && (
|
||||
<div className="flex gap-2">
|
||||
<span className="text-muted-foreground w-20">Status:</span>
|
||||
<span className={`px-1.5 py-0.5 rounded text-xs ${
|
||||
data.status === "StatusCode.UNSET" || data.status === "OK"
|
||||
? "bg-green-100 dark:bg-green-900 text-green-800 dark:text-green-200"
|
||||
: "bg-red-100 dark:bg-red-900 text-red-800 dark:text-red-200"
|
||||
}`}>
|
||||
{data.status}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.attributes && Object.keys(data.attributes).length > 0 && (
|
||||
<div className="mt-2">
|
||||
<span className="text-muted-foreground block mb-1">Attributes:</span>
|
||||
<pre className="text-xs bg-background border rounded p-2 overflow-auto max-h-32 whitespace-pre-wrap break-all">
|
||||
{formatTraceAttributes(data.attributes)}
|
||||
</pre>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</ScrollArea>
|
||||
)}
|
||||
|
||||
{/* Children */}
|
||||
{hasChildren && isExpanded && (
|
||||
<div>
|
||||
{node.children.map((child, idx) => (
|
||||
<TraceTreeNode key={child.data.span_id || idx} node={child} depth={depth + 1} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
function TraceEventItem({ event }: { event: ExtendedResponseStreamEvent }) {
|
||||
const [isExpanded, setIsExpanded] = useState(false);
|
||||
// Component for a single trace group (one response/turn)
|
||||
function TraceGroupItem({ group }: { group: TraceGroup }) {
|
||||
const [isExpanded, setIsExpanded] = useState(true);
|
||||
|
||||
if (
|
||||
(event.type !== "response.trace.completed" &&
|
||||
event.type !== "response.trace.completed") ||
|
||||
!("data" in event)
|
||||
) {
|
||||
return (
|
||||
<div className="border rounded p-3 text-red-600 dark:text-red-400 text-xs">
|
||||
Error: Expected trace event but got {event.type}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
const data = event.data as TraceEventData;
|
||||
|
||||
// Use stored UI timestamp first, then trace timestamps, then fallback to current time
|
||||
let timestamp: string;
|
||||
if ('_uiTimestamp' in event && typeof event._uiTimestamp === 'number') {
|
||||
// Use stored UI timestamp from when event was received
|
||||
timestamp = new Date(event._uiTimestamp * 1000).toLocaleTimeString();
|
||||
} else if (data.end_time) {
|
||||
timestamp = new Date(data.end_time * 1000).toLocaleTimeString();
|
||||
} else if (data.start_time) {
|
||||
timestamp = new Date(data.start_time * 1000).toLocaleTimeString();
|
||||
} else if (data.timestamp) {
|
||||
timestamp = new Date(data.timestamp).toLocaleTimeString();
|
||||
} else {
|
||||
timestamp = new Date().toLocaleTimeString();
|
||||
}
|
||||
|
||||
const operationName = data.operation_name || "Unknown Operation";
|
||||
const duration = data.duration_ms
|
||||
? `${Number(data.duration_ms).toFixed(1)}ms`
|
||||
: "";
|
||||
const entityId = data.entity_id || "";
|
||||
const timestamp = new Date(group.timestamp * 1000).toLocaleTimeString();
|
||||
const duration = group.totalDuration > 0 ? `${group.totalDuration.toFixed(0)}ms` : "";
|
||||
const spanCount = group.traces.reduce((count, node) => {
|
||||
const countNode = (n: TraceNode): number => 1 + n.children.reduce((c, child) => c + countNode(child), 0);
|
||||
return count + countNode(node);
|
||||
}, 0);
|
||||
|
||||
return (
|
||||
<div className="border-l-2 border-muted pl-3 py-2 hover:bg-muted/50 transition-colors">
|
||||
<div className="flex items-center gap-2 text-xs text-muted-foreground mb-1">
|
||||
<Search className="h-3 w-3 text-orange-600 dark:text-orange-400" />
|
||||
<span className="font-mono">{timestamp}</span>
|
||||
<Badge variant="outline" className="text-xs py-0">
|
||||
trace
|
||||
</Badge>
|
||||
<div className="border rounded-lg overflow-hidden">
|
||||
{/* Group header */}
|
||||
<div
|
||||
className="flex items-center gap-2 p-2 bg-muted/50 cursor-pointer hover:bg-muted/70 transition-colors"
|
||||
onClick={() => setIsExpanded(!isExpanded)}
|
||||
>
|
||||
<div className="text-muted-foreground">
|
||||
{isExpanded ? <ChevronDown className="h-4 w-4" /> : <ChevronRight className="h-4 w-4" />}
|
||||
</div>
|
||||
|
||||
<span className="font-mono text-xs text-muted-foreground">{timestamp}</span>
|
||||
|
||||
{group.entity_id && (
|
||||
<Badge variant="outline" className="text-xs py-0">
|
||||
{group.entity_id.replace("agent_", "").replace("workflow_", "")}
|
||||
</Badge>
|
||||
)}
|
||||
|
||||
<div className="flex-1" />
|
||||
|
||||
{duration && (
|
||||
<Badge variant="secondary" className="text-xs py-0">
|
||||
{duration}
|
||||
</Badge>
|
||||
)}
|
||||
|
||||
<span className="text-xs text-muted-foreground">
|
||||
{spanCount} span{spanCount !== 1 ? "s" : ""}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="text-sm">
|
||||
<div
|
||||
className="flex items-center gap-2 cursor-pointer"
|
||||
onClick={() => setIsExpanded(!isExpanded)}
|
||||
>
|
||||
<div className="text-muted-foreground">
|
||||
{isExpanded ? (
|
||||
<ChevronDown className="h-3 w-3" />
|
||||
) : (
|
||||
<ChevronRight className="h-3 w-3" />
|
||||
)}
|
||||
</div>
|
||||
<div className="text-muted-foreground flex-1 break-all">
|
||||
<span className="font-medium">{operationName}</span>
|
||||
{entityId && <span className="ml-2 text-xs">({entityId})</span>}
|
||||
</div>
|
||||
{/* Group content - trace tree */}
|
||||
{isExpanded && (
|
||||
<div className="p-2 border-t">
|
||||
{group.traces.map((node, idx) => (
|
||||
<TraceTreeNode key={node.data.span_id || idx} node={node} depth={0} />
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
{/* Expandable content */}
|
||||
{isExpanded && (
|
||||
<div className="mt-2 ml-5 p-3 bg-muted/30 rounded border">
|
||||
<div className="space-y-2">
|
||||
function TracesTab({ events }: { events: ExtendedResponseStreamEvent[] }) {
|
||||
// Use persisted store state instead of local useState
|
||||
const subTab = useDevUIStore((state) => state.debugTraceSubTab);
|
||||
const setSubTab = useDevUIStore((state) => state.setDebugTraceSubTab);
|
||||
|
||||
// ONLY show actual trace events
|
||||
const traceEvents = events.filter(
|
||||
(e) => e.type === "response.trace.completed"
|
||||
);
|
||||
|
||||
// Build hierarchical structure grouped by response_id
|
||||
const traceGroups = buildTraceHierarchy(traceEvents);
|
||||
|
||||
return (
|
||||
<div className="h-full flex flex-col">
|
||||
{/* Sub-tab header */}
|
||||
<div className="flex items-center gap-2 p-3 border-b">
|
||||
<Search className="h-4 w-4" />
|
||||
<span className="font-medium">Traces</span>
|
||||
<Badge variant="outline">{traceEvents.length}</Badge>
|
||||
|
||||
{/* Sub-tab toggle */}
|
||||
<div className="flex-1" />
|
||||
<div className="flex items-center bg-muted rounded-md p-1 min-w-0">
|
||||
<button
|
||||
onClick={() => setSubTab("spans")}
|
||||
className={`px-3 py-1.5 text-xs rounded transition-colors truncate ${
|
||||
subTab === "spans"
|
||||
? "bg-background shadow-sm font-medium"
|
||||
: "text-muted-foreground hover:text-foreground"
|
||||
}`}
|
||||
>
|
||||
OTel Spans
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setSubTab("context")}
|
||||
className={`px-3 py-1.5 text-xs rounded transition-colors flex items-center gap-1.5 min-w-0 ${
|
||||
subTab === "context"
|
||||
? "bg-background shadow-sm font-medium"
|
||||
: "text-muted-foreground hover:text-foreground"
|
||||
}`}
|
||||
>
|
||||
<BarChart3 className="h-3.5 w-3.5 flex-shrink-0" />
|
||||
<span className="truncate">Context Inspector</span>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Sub-tab content */}
|
||||
{subTab === "spans" ? (
|
||||
<div className="flex-1 flex flex-col min-h-0">
|
||||
{/* OTel Spans header - only show when we have data */}
|
||||
{traceEvents.length > 0 && (
|
||||
<div className="p-3 border-b flex-shrink-0">
|
||||
<div className="flex items-center gap-2">
|
||||
<Search className="h-4 w-4 text-orange-500" />
|
||||
<span className="font-semibold text-sm">Trace Details</span>
|
||||
</div>
|
||||
<div className="grid grid-cols-1 gap-2 text-xs">
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Operation:
|
||||
</span>
|
||||
<span className="ml-2 font-mono bg-orange-100 dark:bg-orange-900 px-2 py-1 rounded">
|
||||
{operationName}
|
||||
</span>
|
||||
</div>
|
||||
{data.span_id && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Span ID:
|
||||
</span>
|
||||
<span className="ml-2 font-mono text-xs break-all">
|
||||
{data.span_id}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.trace_id && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Trace ID:
|
||||
</span>
|
||||
<span className="ml-2 font-mono text-xs break-all">
|
||||
{data.trace_id}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.parent_span_id && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Parent Span:
|
||||
</span>
|
||||
<span className="ml-2 font-mono text-xs break-all">
|
||||
{data.parent_span_id}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.duration_ms && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Duration:
|
||||
</span>
|
||||
<span className="ml-2 font-mono text-xs">
|
||||
{Number(data.duration_ms).toFixed(2)}ms
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.status && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Status:
|
||||
</span>
|
||||
<span
|
||||
className={`ml-2 px-2 py-1 rounded text-xs font-medium ${data.status === "StatusCode.UNSET" ||
|
||||
data.status === "OK"
|
||||
? "bg-green-100 dark:bg-green-900 text-green-800 dark:text-green-200"
|
||||
: "bg-red-100 dark:bg-red-900 text-red-800 dark:text-red-200"
|
||||
}`}
|
||||
>
|
||||
{data.status || "unknown"}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.entity_id && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Entity:
|
||||
</span>
|
||||
<span className="ml-2 font-mono text-xs break-all">
|
||||
{data.entity_id}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
{data.attributes && Object.keys(data.attributes).length > 0 && (
|
||||
<div>
|
||||
<span className="font-medium text-muted-foreground">
|
||||
Attributes:
|
||||
</span>
|
||||
<div className="mt-1 max-h-32 overflow-auto">
|
||||
<pre className="text-xs bg-background border rounded p-2 whitespace-pre-wrap max-w-full break-all">
|
||||
{(() => {
|
||||
try {
|
||||
// Try to pretty-print JSON, and unescape string values that contain JSON
|
||||
const attrs = { ...data.attributes };
|
||||
Object.keys(attrs).forEach((key) => {
|
||||
if (
|
||||
typeof attrs[key] === "string" &&
|
||||
attrs[key].startsWith("[")
|
||||
) {
|
||||
try {
|
||||
attrs[key] = JSON.parse(attrs[key]);
|
||||
} catch {
|
||||
// Keep original if parsing fails
|
||||
}
|
||||
}
|
||||
});
|
||||
return JSON.stringify(attrs, null, 2);
|
||||
} catch {
|
||||
return JSON.stringify(data.attributes, null, 2);
|
||||
}
|
||||
})()}
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
<Search className="h-4 w-4" />
|
||||
<span className="font-medium text-sm">OTel Spans</span>
|
||||
<Badge variant="outline" className="text-xs">
|
||||
{traceGroups.length} turn{traceGroups.length !== 1 ? "s" : ""}
|
||||
</Badge>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{traceEvents.length === 0 ? (
|
||||
<div className="flex flex-col items-center text-center p-6 pt-9">
|
||||
<BarChart3 className="h-8 w-8 text-muted-foreground mb-3" />
|
||||
<div className="text-sm font-medium mb-1">No Data</div>
|
||||
<div className="text-xs text-muted-foreground max-w-[200px]">
|
||||
Run{" "}
|
||||
<span className="font-mono bg-accent/10 px-1 rounded">
|
||||
devui --instrumentation
|
||||
</span>{" "}
|
||||
and start a conversation.
|
||||
</div>
|
||||
</div>
|
||||
) : (
|
||||
<ScrollArea className="flex-1">
|
||||
<div className="p-3">
|
||||
<div className="space-y-3">
|
||||
{traceGroups.map((group) => (
|
||||
<TraceGroupItem key={group.response_id} group={group} />
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
</ScrollArea>
|
||||
)}
|
||||
</div>
|
||||
) : (
|
||||
<ContextInspector events={events} />
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1590,19 +1751,48 @@ export function DebugPanel({
|
||||
isStreaming = false,
|
||||
onMinimize,
|
||||
}: DebugPanelProps) {
|
||||
// Use persisted store state for active tab
|
||||
const activeTab = useDevUIStore((state) => state.debugPanelTab);
|
||||
const setActiveTab = useDevUIStore((state) => state.setDebugPanelTab);
|
||||
|
||||
// Compute counts once for tab badges (memoized to avoid perf hits)
|
||||
const counts = useMemo(() => {
|
||||
const processedEvents = processEventsForDisplay(events);
|
||||
const eventsCount = processedEvents.length;
|
||||
const tracesCount = events.filter(e => e.type === "response.trace.completed").length;
|
||||
const toolsCount = processedEvents.filter(e => e.type === "response.function_call.complete").length
|
||||
+ events.filter(e => getFunctionResultFromEvent(e) !== null).length;
|
||||
return { eventsCount, tracesCount, toolsCount };
|
||||
}, [events]);
|
||||
|
||||
return (
|
||||
<div className="flex-1 border-l flex flex-col min-h-0">
|
||||
<Tabs defaultValue="events" className="flex-1 flex flex-col min-h-0">
|
||||
<Tabs value={activeTab} onValueChange={(v) => setActiveTab(v as "events" | "traces" | "tools")} className="flex-1 flex flex-col min-h-0">
|
||||
<div className="px-3 pt-3 flex items-center gap-2 flex-shrink-0">
|
||||
<TabsList className="flex-1">
|
||||
<TabsTrigger value="events" className="flex-1">
|
||||
<TabsTrigger value="events" className="flex-1 gap-1.5">
|
||||
Events
|
||||
{counts.eventsCount > 0 && (
|
||||
<span className="text-[10px] bg-muted-foreground/20 text-muted-foreground px-1.5 py-0.5 rounded-full min-w-[1.25rem] text-center">
|
||||
{counts.eventsCount}
|
||||
</span>
|
||||
)}
|
||||
</TabsTrigger>
|
||||
<TabsTrigger value="traces" className="flex-1">
|
||||
<TabsTrigger value="traces" className="flex-1 gap-1.5">
|
||||
Traces
|
||||
{counts.tracesCount > 0 && (
|
||||
<span className="text-[10px] bg-muted-foreground/20 text-muted-foreground px-1.5 py-0.5 rounded-full min-w-[1.25rem] text-center">
|
||||
{counts.tracesCount}
|
||||
</span>
|
||||
)}
|
||||
</TabsTrigger>
|
||||
<TabsTrigger value="tools" className="flex-1">
|
||||
<TabsTrigger value="tools" className="flex-1 gap-1.5">
|
||||
Tools
|
||||
{counts.toolsCount > 0 && (
|
||||
<span className="text-[10px] bg-muted-foreground/20 text-muted-foreground px-1.5 py-0.5 rounded-full min-w-[1.25rem] text-center">
|
||||
{counts.toolsCount}
|
||||
</span>
|
||||
)}
|
||||
</TabsTrigger>
|
||||
</TabsList>
|
||||
{onMinimize && (
|
||||
|
||||
@@ -209,7 +209,7 @@ export function DeploymentModal({
|
||||
setCopiedTemplate(null);
|
||||
timeoutRef.current = null;
|
||||
}, 2000);
|
||||
} catch (err) {
|
||||
} catch {
|
||||
// Reset state on error - clipboard write failed
|
||||
setCopiedTemplate(null);
|
||||
}
|
||||
@@ -248,7 +248,7 @@ services:
|
||||
- AZURE_OPENAI_API_KEY=\${AZURE_OPENAI_API_KEY}
|
||||
- AZURE_OPENAI_ENDPOINT=\${AZURE_OPENAI_ENDPOINT}
|
||||
- AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=\${AZURE_OPENAI_CHAT_DEPLOYMENT_NAME}
|
||||
# Optional: Enable tracing
|
||||
# Optional: Enable instrumentation
|
||||
- ENABLE_INSTRUMENTATION=\${ENABLE_INSTRUMENTATION:-false}
|
||||
ports:
|
||||
- "8080:8080"
|
||||
|
||||
@@ -41,8 +41,8 @@ export function SettingsModal({
|
||||
}: SettingsModalProps) {
|
||||
const [activeTab, setActiveTab] = useState<Tab>("general");
|
||||
|
||||
// OpenAI proxy mode, Azure deployment, auth status, server capabilities, and version from store
|
||||
const { oaiMode, setOAIMode, azureDeploymentEnabled, setAzureDeploymentEnabled, authRequired, serverCapabilities, serverVersion, runtime, uiMode } = useDevUIStore();
|
||||
// OpenAI proxy mode, Azure deployment, auth status, server capabilities, streaming, and version from store
|
||||
const { oaiMode, setOAIMode, azureDeploymentEnabled, setAzureDeploymentEnabled, authRequired, serverCapabilities, serverVersion, runtime, uiMode, streamingEnabled, setStreamingEnabled } = useDevUIStore();
|
||||
|
||||
// Get current backend URL from localStorage or default
|
||||
const defaultUrl = import.meta.env.VITE_API_BASE_URL !== undefined ? import.meta.env.VITE_API_BASE_URL : "";
|
||||
@@ -353,6 +353,37 @@ export function SettingsModal({
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Streaming Mode Setting */}
|
||||
<div className="space-y-3 border-t pt-6">
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="space-y-0.5">
|
||||
<Label className="text-sm font-medium">
|
||||
Streaming Mode
|
||||
</Label>
|
||||
<p className="text-xs text-muted-foreground">
|
||||
Stream responses token-by-token as they're generated
|
||||
</p>
|
||||
</div>
|
||||
<Switch
|
||||
checked={streamingEnabled}
|
||||
onCheckedChange={setStreamingEnabled}
|
||||
/>
|
||||
</div>
|
||||
{!streamingEnabled && (
|
||||
<div className="flex items-start gap-2 text-xs text-amber-600 dark:text-amber-400 bg-amber-500/10 p-3 rounded">
|
||||
<Info className="h-3.5 w-3.5 flex-shrink-0 mt-0.5" />
|
||||
<div>
|
||||
<p className="font-medium">Non-streaming mode limitations:</p>
|
||||
<ul className="mt-1 space-y-0.5 list-disc list-inside text-amber-600/80 dark:text-amber-400/80">
|
||||
<li>Tool calls won't display in real-time</li>
|
||||
<li>No typing indicator during generation</li>
|
||||
<li>Response appears all at once when complete</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
@@ -628,11 +659,11 @@ export function SettingsModal({
|
||||
<div className="space-y-2 pt-2">
|
||||
<p className="text-xs font-medium text-muted-foreground uppercase tracking-wide">Capabilities</p>
|
||||
<div className="space-y-1 text-sm">
|
||||
{serverCapabilities?.tracing !== undefined && (
|
||||
{serverCapabilities?.instrumentation !== undefined && (
|
||||
<div className="flex justify-between items-center">
|
||||
<span className="text-muted-foreground">Tracing:</span>
|
||||
<span className={`text-xs px-2 py-0.5 rounded-full ${serverCapabilities.tracing ? 'bg-green-500/10 text-green-600 dark:text-green-400' : 'bg-muted text-muted-foreground'}`}>
|
||||
{serverCapabilities.tracing ? 'Enabled' : 'Disabled'}
|
||||
<span className="text-muted-foreground">Instrumentation:</span>
|
||||
<span className={`text-xs px-2 py-0.5 rounded-full ${serverCapabilities.instrumentation ? 'bg-green-500/10 text-green-600 dark:text-green-400' : 'bg-muted text-muted-foreground'}`}>
|
||||
{serverCapabilities.instrumentation ? 'Enabled' : 'Disabled'}
|
||||
</span>
|
||||
</div>
|
||||
)}
|
||||
|
||||
@@ -0,0 +1,30 @@
|
||||
import * as React from "react";
|
||||
import * as TooltipPrimitive from "@radix-ui/react-tooltip";
|
||||
|
||||
import { cn } from "@/lib/utils";
|
||||
|
||||
const TooltipProvider = TooltipPrimitive.Provider;
|
||||
|
||||
const Tooltip = TooltipPrimitive.Root;
|
||||
|
||||
const TooltipTrigger = TooltipPrimitive.Trigger;
|
||||
|
||||
const TooltipContent = React.forwardRef<
|
||||
React.ElementRef<typeof TooltipPrimitive.Content>,
|
||||
React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
|
||||
>(({ className, sideOffset = 4, ...props }, ref) => (
|
||||
<TooltipPrimitive.Portal>
|
||||
<TooltipPrimitive.Content
|
||||
ref={ref}
|
||||
sideOffset={sideOffset}
|
||||
className={cn(
|
||||
"z-50 overflow-hidden rounded-md bg-primary px-3 py-1.5 text-xs text-primary-foreground animate-in fade-in-0 zoom-in-95 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
|
||||
className
|
||||
)}
|
||||
{...props}
|
||||
/>
|
||||
</TooltipPrimitive.Portal>
|
||||
));
|
||||
TooltipContent.displayName = TooltipPrimitive.Content.displayName;
|
||||
|
||||
export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider };
|
||||
@@ -398,7 +398,11 @@ class ApiClient {
|
||||
async listConversationItems(
|
||||
conversationId: string,
|
||||
options?: { limit?: number; after?: string; order?: "asc" | "desc" }
|
||||
): Promise<{ data: unknown[]; has_more: boolean }> {
|
||||
): Promise<{
|
||||
data: unknown[];
|
||||
has_more: boolean;
|
||||
metadata?: { traces?: unknown[] };
|
||||
}> {
|
||||
const params = new URLSearchParams();
|
||||
if (options?.limit) params.set("limit", options.limit.toString());
|
||||
if (options?.after) params.set("after", options.after);
|
||||
@@ -409,7 +413,11 @@ class ApiClient {
|
||||
queryString ? `?${queryString}` : ""
|
||||
}`;
|
||||
|
||||
return this.request<{ data: unknown[]; has_more: boolean }>(url);
|
||||
return this.request<{
|
||||
data: unknown[];
|
||||
has_more: boolean;
|
||||
metadata?: { traces?: unknown[] };
|
||||
}>(url);
|
||||
}
|
||||
|
||||
async getConversationItem(
|
||||
@@ -800,34 +808,68 @@ class ApiClient {
|
||||
yield* this.streamOpenAIResponse(openAIRequest, request.conversation_id, signal);
|
||||
}
|
||||
|
||||
// REMOVED: Legacy streaming methods - use streamAgentExecutionOpenAI and streamWorkflowExecutionOpenAI instead
|
||||
// ========================================
|
||||
// Non-Streaming Execution Methods
|
||||
// ========================================
|
||||
|
||||
// Non-streaming execution (for testing)
|
||||
async runAgent(
|
||||
// Non-streaming agent execution using /v1/responses with stream=false
|
||||
async runAgentSync(
|
||||
agentId: string,
|
||||
request: RunAgentRequest
|
||||
): Promise<{
|
||||
conversation_id: string;
|
||||
result: unknown[];
|
||||
message_count: number;
|
||||
}> {
|
||||
return this.request(`/agents/${agentId}/run`, {
|
||||
): Promise<import("@/types/openai").OpenAIResponse> {
|
||||
// Check if OAI proxy mode is enabled
|
||||
const { oaiMode } = await import("@/stores").then((m) => ({
|
||||
oaiMode: m.useDevUIStore.getState().oaiMode,
|
||||
}));
|
||||
|
||||
const openAIRequest: AgentFrameworkRequest = {
|
||||
metadata: { entity_id: agentId },
|
||||
input: request.input,
|
||||
stream: false,
|
||||
conversation: request.conversation_id,
|
||||
};
|
||||
|
||||
// Apply OAI mode settings if enabled
|
||||
if (oaiMode.enabled) {
|
||||
openAIRequest.model = oaiMode.model;
|
||||
if (oaiMode.temperature !== undefined) {
|
||||
openAIRequest.temperature = oaiMode.temperature;
|
||||
}
|
||||
if (oaiMode.max_output_tokens !== undefined) {
|
||||
openAIRequest.max_output_tokens = oaiMode.max_output_tokens;
|
||||
}
|
||||
}
|
||||
|
||||
const headers: Record<string, string> = {};
|
||||
if (oaiMode.enabled) {
|
||||
headers["X-Proxy-Backend"] = "openai";
|
||||
}
|
||||
|
||||
return this.request<import("@/types/openai").OpenAIResponse>("/v1/responses", {
|
||||
method: "POST",
|
||||
body: JSON.stringify(request),
|
||||
headers,
|
||||
body: JSON.stringify(openAIRequest),
|
||||
});
|
||||
}
|
||||
|
||||
async runWorkflow(
|
||||
// Non-streaming workflow execution using /v1/responses with stream=false
|
||||
async runWorkflowSync(
|
||||
workflowId: string,
|
||||
request: RunWorkflowRequest
|
||||
): Promise<{
|
||||
result: string;
|
||||
events: number;
|
||||
message_count: number;
|
||||
}> {
|
||||
return this.request(`/workflows/${workflowId}/run`, {
|
||||
): Promise<import("@/types/openai").OpenAIResponse> {
|
||||
const openAIRequest: AgentFrameworkRequest = {
|
||||
metadata: { entity_id: workflowId },
|
||||
input: JSON.stringify(request.input_data || {}),
|
||||
stream: false,
|
||||
conversation: request.conversation_id,
|
||||
extra_body: request.checkpoint_id
|
||||
? { entity_id: workflowId, checkpoint_id: request.checkpoint_id }
|
||||
: undefined,
|
||||
};
|
||||
|
||||
return this.request<import("@/types/openai").OpenAIResponse>("/v1/responses", {
|
||||
method: "POST",
|
||||
body: JSON.stringify(request),
|
||||
body: JSON.stringify(openAIRequest),
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
@@ -60,6 +60,13 @@ interface DevUIState {
|
||||
debugEvents: ExtendedResponseStreamEvent[];
|
||||
isResizing: boolean;
|
||||
showToolCalls: boolean; // UI setting to show/hide tool calls in chat
|
||||
streamingEnabled: boolean; // Whether to use streaming mode for responses
|
||||
|
||||
// Debug Panel Preferences (persisted)
|
||||
debugPanelTab: "events" | "traces" | "tools"; // Main debug panel tab
|
||||
debugTraceSubTab: "spans" | "context"; // OTel Spans vs Context Inspector
|
||||
contextInspectorViewMode: "tokens" | "composition";
|
||||
contextInspectorCumulative: boolean;
|
||||
|
||||
// Modal Slice
|
||||
showAboutModal: boolean;
|
||||
@@ -82,7 +89,7 @@ interface DevUIState {
|
||||
uiMode: "developer" | "user";
|
||||
runtime: "python" | "dotnet";
|
||||
serverCapabilities: {
|
||||
tracing: boolean;
|
||||
instrumentation: boolean;
|
||||
openai_proxy: boolean;
|
||||
deployment: boolean;
|
||||
};
|
||||
@@ -146,6 +153,13 @@ interface DevUIActions {
|
||||
clearDebugEvents: () => void;
|
||||
setIsResizing: (resizing: boolean) => void;
|
||||
setShowToolCalls: (show: boolean) => void;
|
||||
setStreamingEnabled: (enabled: boolean) => void;
|
||||
|
||||
// Debug Panel Preference Actions
|
||||
setDebugPanelTab: (tab: "events" | "traces" | "tools") => void;
|
||||
setDebugTraceSubTab: (tab: "spans" | "context") => void;
|
||||
setContextInspectorViewMode: (mode: "tokens" | "composition") => void;
|
||||
setContextInspectorCumulative: (cumulative: boolean) => void;
|
||||
|
||||
// Modal Actions
|
||||
setShowAboutModal: (show: boolean) => void;
|
||||
@@ -166,7 +180,7 @@ interface DevUIActions {
|
||||
toggleOAIMode: () => void;
|
||||
|
||||
// Server Meta Actions
|
||||
setServerMeta: (meta: { uiMode: "developer" | "user"; runtime: "python" | "dotnet"; capabilities: { tracing: boolean; openai_proxy: boolean; deployment: boolean }; authRequired: boolean; version?: string }) => void;
|
||||
setServerMeta: (meta: { uiMode: "developer" | "user"; runtime: "python" | "dotnet"; capabilities: { instrumentation: boolean; openai_proxy: boolean; deployment: boolean }; authRequired: boolean; version?: string }) => void;
|
||||
|
||||
// Deployment Actions
|
||||
startDeployment: () => void;
|
||||
@@ -228,6 +242,13 @@ export const useDevUIStore = create<DevUIStore>()(
|
||||
debugEvents: [],
|
||||
isResizing: false,
|
||||
showToolCalls: true, // Default to showing tool calls
|
||||
streamingEnabled: true, // Default to streaming mode (recommended)
|
||||
|
||||
// Debug Panel Preferences (persisted)
|
||||
debugPanelTab: "events", // Default to events tab
|
||||
debugTraceSubTab: "spans", // Default to spans sub-tab
|
||||
contextInspectorViewMode: "tokens", // Default to tokens view
|
||||
contextInspectorCumulative: false, // Default to per-message view
|
||||
|
||||
// Modal State
|
||||
showAboutModal: false,
|
||||
@@ -248,7 +269,7 @@ export const useDevUIStore = create<DevUIStore>()(
|
||||
uiMode: "developer", // Default to developer mode
|
||||
runtime: "python", // Default to Python runtime
|
||||
serverCapabilities: {
|
||||
tracing: false,
|
||||
instrumentation: false,
|
||||
openai_proxy: false,
|
||||
deployment: false,
|
||||
},
|
||||
@@ -371,14 +392,16 @@ export const useDevUIStore = create<DevUIStore>()(
|
||||
setDebugPanelMinimized: (minimized) => set({ debugPanelMinimized: minimized }),
|
||||
setDebugPanelWidth: (width) => set({ debugPanelWidth: width }),
|
||||
setShowToolCalls: (show) => set({ showToolCalls: show }),
|
||||
setStreamingEnabled: (enabled) => set({ streamingEnabled: enabled }),
|
||||
addDebugEvent: (event) =>
|
||||
set((state) => {
|
||||
// Generate unique timestamp for each event
|
||||
// Use current time + small increment to ensure uniqueness even for rapid events
|
||||
const baseTimestamp = Math.floor(Date.now() / 1000);
|
||||
const lastTimestamp = state.debugEvents.length > 0
|
||||
? (state.debugEvents[state.debugEvents.length - 1] as any)._uiTimestamp || 0
|
||||
: 0;
|
||||
const lastEvent = state.debugEvents.length > 0
|
||||
? state.debugEvents[state.debugEvents.length - 1] as { _uiTimestamp?: number }
|
||||
: null;
|
||||
const lastTimestamp = lastEvent?._uiTimestamp ?? 0;
|
||||
// Ensure new timestamp is always greater than the last one
|
||||
const uniqueTimestamp = Math.max(baseTimestamp, lastTimestamp + 1);
|
||||
|
||||
@@ -399,6 +422,12 @@ export const useDevUIStore = create<DevUIStore>()(
|
||||
clearDebugEvents: () => set({ debugEvents: [] }),
|
||||
setIsResizing: (resizing) => set({ isResizing: resizing }),
|
||||
|
||||
// Debug Panel Preference Actions
|
||||
setDebugPanelTab: (tab) => set({ debugPanelTab: tab }),
|
||||
setDebugTraceSubTab: (tab) => set({ debugTraceSubTab: tab }),
|
||||
setContextInspectorViewMode: (mode) => set({ contextInspectorViewMode: mode }),
|
||||
setContextInspectorCumulative: (cumulative) => set({ contextInspectorCumulative: cumulative }),
|
||||
|
||||
// ========================================
|
||||
// Modal Actions
|
||||
// ========================================
|
||||
@@ -605,8 +634,14 @@ export const useDevUIStore = create<DevUIStore>()(
|
||||
debugPanelMinimized: state.debugPanelMinimized,
|
||||
debugPanelWidth: state.debugPanelWidth,
|
||||
showToolCalls: state.showToolCalls, // Persist tool calls visibility preference
|
||||
streamingEnabled: state.streamingEnabled, // Persist streaming mode preference
|
||||
oaiMode: state.oaiMode, // Persist OpenAI proxy mode settings
|
||||
azureDeploymentEnabled: state.azureDeploymentEnabled, // Persist Azure deployment preference
|
||||
// Debug panel tab preferences
|
||||
debugPanelTab: state.debugPanelTab,
|
||||
debugTraceSubTab: state.debugTraceSubTab,
|
||||
contextInspectorViewMode: state.contextInspectorViewMode,
|
||||
contextInspectorCumulative: state.contextInspectorCumulative,
|
||||
}),
|
||||
}
|
||||
),
|
||||
|
||||
@@ -130,6 +130,7 @@ export type {
|
||||
ResponseCompletedEvent,
|
||||
ResponseFailedEvent,
|
||||
ResponseFunctionResultComplete,
|
||||
ResponseFunctionToolCall,
|
||||
StructuredEvent,
|
||||
WorkflowItem,
|
||||
ExecutorActionItem,
|
||||
@@ -159,7 +160,7 @@ export interface MetaResponse {
|
||||
framework: string;
|
||||
runtime: "python" | "dotnet";
|
||||
capabilities: {
|
||||
tracing: boolean;
|
||||
instrumentation: boolean;
|
||||
openai_proxy: boolean;
|
||||
deployment: boolean;
|
||||
};
|
||||
@@ -266,6 +267,29 @@ export interface CheckpointInfo {
|
||||
metadata?: Record<string, unknown>;
|
||||
}
|
||||
|
||||
// Full checkpoint data structure
|
||||
export interface FullCheckpoint {
|
||||
checkpoint_id: string;
|
||||
workflow_id: string;
|
||||
timestamp: string;
|
||||
messages: Record<string, unknown[]>;
|
||||
shared_state: Record<string, unknown>;
|
||||
pending_request_info_events: Record<string, PendingRequestInfoEvent>;
|
||||
iteration_count: number;
|
||||
metadata: Record<string, unknown>;
|
||||
version: string;
|
||||
}
|
||||
|
||||
// Pending request info event data
|
||||
export interface PendingRequestInfoEvent {
|
||||
source_executor_id: string;
|
||||
request_type?: string;
|
||||
response_type?: string;
|
||||
request_data?: Record<string, unknown>;
|
||||
request_schema?: Record<string, unknown>;
|
||||
timestamp?: string;
|
||||
}
|
||||
|
||||
// Checkpoint item from conversation items API
|
||||
export interface CheckpointItem {
|
||||
id: string;
|
||||
@@ -281,16 +305,6 @@ export interface CheckpointItem {
|
||||
message_count: number;
|
||||
size_bytes?: number;
|
||||
version: string;
|
||||
full_checkpoint?: {
|
||||
checkpoint_id: string;
|
||||
workflow_id: string;
|
||||
timestamp: string;
|
||||
messages: Record<string, unknown[]>;
|
||||
shared_state: Record<string, unknown>;
|
||||
pending_request_info_events: Record<string, unknown>;
|
||||
iteration_count: number;
|
||||
metadata: Record<string, unknown>;
|
||||
version: string;
|
||||
};
|
||||
full_checkpoint?: FullCheckpoint;
|
||||
};
|
||||
}
|
||||
|
||||
@@ -3,6 +3,45 @@
|
||||
* Based on OpenAI's official response types
|
||||
*/
|
||||
|
||||
// OpenAI Response Error (from response_error.py)
|
||||
export type ResponseErrorCode =
|
||||
| "server_error"
|
||||
| "rate_limit_exceeded"
|
||||
| "invalid_prompt"
|
||||
| "vector_store_timeout"
|
||||
| "invalid_image"
|
||||
| "invalid_image_format"
|
||||
| "invalid_base64_image"
|
||||
| "invalid_image_url"
|
||||
| "image_too_large"
|
||||
| "image_too_small"
|
||||
| "image_parse_error"
|
||||
| "image_content_policy_violation"
|
||||
| "invalid_image_mode"
|
||||
| "image_file_too_large"
|
||||
| "unsupported_image_media_type"
|
||||
| "empty_image_file"
|
||||
| "failed_to_download_image"
|
||||
| "image_file_not_found";
|
||||
|
||||
export interface ResponseError {
|
||||
code: ResponseErrorCode;
|
||||
message: string;
|
||||
}
|
||||
|
||||
// OpenAI Response Usage (from response_usage.py)
|
||||
export interface ResponseUsage {
|
||||
input_tokens: number;
|
||||
output_tokens: number;
|
||||
total_tokens: number;
|
||||
input_tokens_details?: {
|
||||
cached_tokens: number;
|
||||
};
|
||||
output_tokens_details?: {
|
||||
reasoning_tokens: number;
|
||||
};
|
||||
}
|
||||
|
||||
// Core OpenAI Response Stream Event
|
||||
export interface ResponseStreamEvent {
|
||||
type: string;
|
||||
@@ -28,7 +67,7 @@ export interface ResponseCreatedEvent {
|
||||
id: string;
|
||||
status: "in_progress";
|
||||
created_at: number;
|
||||
output?: any[];
|
||||
output?: ResponseOutputItem[];
|
||||
};
|
||||
sequence_number?: number;
|
||||
}
|
||||
@@ -47,9 +86,11 @@ export interface ResponseCompletedEvent {
|
||||
response: {
|
||||
id: string;
|
||||
status?: "completed";
|
||||
usage?: any; // Optional usage information
|
||||
usage?: ResponseUsage; // Optional usage information
|
||||
model?: string; // Optional model information
|
||||
[key: string]: any; // Allow any additional fields
|
||||
output?: ResponseOutputItem[]; // Output items
|
||||
error?: ResponseError; // Error if failed
|
||||
metadata?: Record<string, unknown>; // Additional metadata
|
||||
};
|
||||
sequence_number?: number;
|
||||
}
|
||||
@@ -59,7 +100,7 @@ export interface ResponseFailedEvent {
|
||||
response: {
|
||||
id: string;
|
||||
status: "failed";
|
||||
error?: any;
|
||||
error?: ResponseError;
|
||||
};
|
||||
sequence_number?: number;
|
||||
}
|
||||
@@ -157,16 +198,16 @@ export interface WorkflowItem {
|
||||
type: string; // "executor_action", "workflow_action", "message", or any future type
|
||||
id: string;
|
||||
status?: "in_progress" | "completed" | "failed" | "cancelled";
|
||||
[key: string]: any; // Allow any additional fields
|
||||
[key: string]: unknown; // Allow any additional fields with unknown type
|
||||
}
|
||||
|
||||
// Executor Action Item (DevUI specific)
|
||||
export interface ExecutorActionItem extends WorkflowItem {
|
||||
type: "executor_action";
|
||||
executor_id: string;
|
||||
metadata?: Record<string, any>;
|
||||
result?: any;
|
||||
error?: any;
|
||||
metadata?: Record<string, unknown>;
|
||||
result?: unknown;
|
||||
error?: unknown;
|
||||
}
|
||||
|
||||
// Type guard for executor actions
|
||||
@@ -364,18 +405,7 @@ export interface ResponseOutputText {
|
||||
annotations: Record<string, unknown>[];
|
||||
}
|
||||
|
||||
export interface ResponseUsage {
|
||||
input_tokens: number;
|
||||
output_tokens: number;
|
||||
total_tokens: number;
|
||||
input_tokens_details: {
|
||||
cached_tokens: number;
|
||||
};
|
||||
output_tokens_details: {
|
||||
reasoning_tokens: number;
|
||||
};
|
||||
}
|
||||
|
||||
// Note: ResponseUsage is defined at the top of this file
|
||||
|
||||
// Request format for Agent Framework
|
||||
// AgentFrameworkRequest moved to agent-framework.ts to avoid conflicts
|
||||
@@ -404,11 +434,62 @@ export interface MessageInputTextContent {
|
||||
text: string;
|
||||
}
|
||||
|
||||
// Annotation types for output text (from response_output_text.py)
|
||||
export interface AnnotationFileCitation {
|
||||
type: "file_citation";
|
||||
file_id: string;
|
||||
filename: string;
|
||||
index: number;
|
||||
}
|
||||
|
||||
export interface AnnotationURLCitation {
|
||||
type: "url_citation";
|
||||
url: string;
|
||||
title: string;
|
||||
start_index: number;
|
||||
end_index: number;
|
||||
}
|
||||
|
||||
export interface AnnotationContainerFileCitation {
|
||||
type: "container_file_citation";
|
||||
container_id: string;
|
||||
file_id: string;
|
||||
filename: string;
|
||||
start_index: number;
|
||||
end_index: number;
|
||||
}
|
||||
|
||||
export interface AnnotationFilePath {
|
||||
type: "file_path";
|
||||
file_id: string;
|
||||
index: number;
|
||||
}
|
||||
|
||||
export type OutputTextAnnotation =
|
||||
| AnnotationFileCitation
|
||||
| AnnotationURLCitation
|
||||
| AnnotationContainerFileCitation
|
||||
| AnnotationFilePath;
|
||||
|
||||
// Logprob types for output text
|
||||
export interface LogprobTopLogprob {
|
||||
token: string;
|
||||
bytes: number[];
|
||||
logprob: number;
|
||||
}
|
||||
|
||||
export interface Logprob {
|
||||
token: string;
|
||||
bytes: number[];
|
||||
logprob: number;
|
||||
top_logprobs: LogprobTopLogprob[];
|
||||
}
|
||||
|
||||
export interface MessageOutputTextContent {
|
||||
type: "output_text";
|
||||
text: string;
|
||||
annotations?: any[];
|
||||
logprobs?: any[];
|
||||
annotations?: OutputTextAnnotation[];
|
||||
logprobs?: Logprob[];
|
||||
}
|
||||
|
||||
export interface MessageInputImage {
|
||||
@@ -541,9 +622,218 @@ export interface Conversation {
|
||||
metadata?: Record<string, unknown>;
|
||||
}
|
||||
|
||||
// List response
|
||||
// ============================================================================
|
||||
// OpenTelemetry Trace Attribute Keys
|
||||
// Mirrored from Python: agent_framework/observability.py ObservabilityAttributes
|
||||
// ============================================================================
|
||||
|
||||
/**
|
||||
* Standard attribute keys for OpenTelemetry traces.
|
||||
* These match the Python ObservabilityAttributes enum exactly.
|
||||
*/
|
||||
export const TraceAttributes = {
|
||||
// Request attributes
|
||||
MODEL: "gen_ai.request.model",
|
||||
MAX_TOKENS: "gen_ai.request.max_tokens",
|
||||
TEMPERATURE: "gen_ai.request.temperature",
|
||||
TOP_P: "gen_ai.request.top_p",
|
||||
SEED: "gen_ai.request.seed",
|
||||
FREQUENCY_PENALTY: "gen_ai.request.frequency_penalty",
|
||||
PRESENCE_PENALTY: "gen_ai.request.presence_penalty",
|
||||
STOP_SEQUENCES: "gen_ai.request.stop_sequences",
|
||||
|
||||
// Response attributes
|
||||
FINISH_REASONS: "gen_ai.response.finish_reasons",
|
||||
RESPONSE_ID: "gen_ai.response.id",
|
||||
|
||||
// Usage attributes
|
||||
INPUT_TOKENS: "gen_ai.usage.input_tokens",
|
||||
OUTPUT_TOKENS: "gen_ai.usage.output_tokens",
|
||||
|
||||
// Content attributes (messages sent/received)
|
||||
INPUT_MESSAGES: "gen_ai.input.messages",
|
||||
OUTPUT_MESSAGES: "gen_ai.output.messages",
|
||||
SYSTEM_INSTRUCTIONS: "gen_ai.system_instructions",
|
||||
OUTPUT_TYPE: "gen_ai.output.type",
|
||||
|
||||
// Tool attributes
|
||||
TOOL_CALL_ID: "gen_ai.tool.call.id",
|
||||
TOOL_NAME: "gen_ai.tool.name",
|
||||
TOOL_TYPE: "gen_ai.tool.type",
|
||||
TOOL_DEFINITIONS: "gen_ai.tool.definitions",
|
||||
TOOL_ARGUMENTS: "gen_ai.tool.call.arguments",
|
||||
TOOL_RESULT: "gen_ai.tool.call.result",
|
||||
|
||||
// Agent attributes
|
||||
AGENT_ID: "gen_ai.agent.id",
|
||||
AGENT_NAME: "gen_ai.agent.name",
|
||||
AGENT_DESCRIPTION: "gen_ai.agent.description",
|
||||
CONVERSATION_ID: "gen_ai.conversation.id",
|
||||
|
||||
// Workflow attributes
|
||||
WORKFLOW_ID: "workflow.id",
|
||||
WORKFLOW_NAME: "workflow.name",
|
||||
EXECUTOR_ID: "executor.id",
|
||||
EXECUTOR_TYPE: "executor.type",
|
||||
} as const;
|
||||
|
||||
/**
|
||||
* Type for trace attribute keys - ensures type safety when accessing attributes
|
||||
*/
|
||||
export type TraceAttributeKey = (typeof TraceAttributes)[keyof typeof TraceAttributes];
|
||||
|
||||
/**
|
||||
* Typed interface for known trace attributes.
|
||||
* Using this instead of Record<string, unknown> provides compile-time safety.
|
||||
*/
|
||||
export interface TypedTraceAttributes {
|
||||
// Request attributes
|
||||
[TraceAttributes.MODEL]?: string;
|
||||
[TraceAttributes.MAX_TOKENS]?: number;
|
||||
[TraceAttributes.TEMPERATURE]?: number;
|
||||
[TraceAttributes.TOP_P]?: number;
|
||||
[TraceAttributes.SEED]?: number;
|
||||
|
||||
// Usage attributes
|
||||
[TraceAttributes.INPUT_TOKENS]?: number;
|
||||
[TraceAttributes.OUTPUT_TOKENS]?: number;
|
||||
|
||||
// Content attributes (JSON strings that need parsing)
|
||||
[TraceAttributes.INPUT_MESSAGES]?: string;
|
||||
[TraceAttributes.OUTPUT_MESSAGES]?: string;
|
||||
[TraceAttributes.SYSTEM_INSTRUCTIONS]?: string;
|
||||
|
||||
// Tool attributes
|
||||
[TraceAttributes.TOOL_NAME]?: string;
|
||||
[TraceAttributes.TOOL_DEFINITIONS]?: string;
|
||||
[TraceAttributes.TOOL_ARGUMENTS]?: string;
|
||||
[TraceAttributes.TOOL_RESULT]?: string;
|
||||
|
||||
// Agent/workflow attributes
|
||||
[TraceAttributes.AGENT_NAME]?: string;
|
||||
[TraceAttributes.WORKFLOW_NAME]?: string;
|
||||
[TraceAttributes.EXECUTOR_ID]?: string;
|
||||
|
||||
// Allow additional unknown attributes
|
||||
[key: string]: unknown;
|
||||
}
|
||||
|
||||
/**
|
||||
* Message part types used in gen_ai.input.messages / gen_ai.output.messages
|
||||
*
|
||||
* Source: Python agent_framework/observability.py _to_otel_part()
|
||||
*
|
||||
* Python produces:
|
||||
* - text: {"type": "text", "content": "..."}
|
||||
* - function_call: {"type": "tool_call", "id": "...", "name": "...", "arguments": "..."}
|
||||
* - function_result: {"type": "tool_call_response", "id": "...", "response": "..."}
|
||||
*/
|
||||
|
||||
// Text content part
|
||||
// Python: {"type": "text", "content": content.text}
|
||||
export interface TraceTextPart {
|
||||
type: "text";
|
||||
content?: string; // Agent Framework format (from Python)
|
||||
text?: string; // Alternative field name (OpenAI format)
|
||||
}
|
||||
|
||||
// Tool/function call part (from assistant)
|
||||
// Python: {"type": "tool_call", "id": content.call_id, "name": content.name, "arguments": content.arguments}
|
||||
export interface TraceToolCallPart {
|
||||
type: "tool_call" | "function_call";
|
||||
id?: string; // Tool call ID for correlation
|
||||
name?: string; // Function name
|
||||
arguments?: string; // JSON string of arguments
|
||||
}
|
||||
|
||||
// Tool/function result part (response to tool call)
|
||||
// Python: {"type": "tool_call_response", "id": content.call_id, "response": response}
|
||||
export interface TraceToolResultPart {
|
||||
type: "tool_call_response" | "tool_result" | "function_result";
|
||||
id?: string; // Tool call ID for correlation
|
||||
response?: string; // Agent Framework format (from Python)
|
||||
result?: string; // Alternative field name (other formats)
|
||||
}
|
||||
|
||||
// Union type for all message parts
|
||||
export type TraceMessagePart = TraceTextPart | TraceToolCallPart | TraceToolResultPart;
|
||||
|
||||
// Helper type guard functions
|
||||
export function isTextPart(part: TraceMessagePart): part is TraceTextPart {
|
||||
return part.type === "text";
|
||||
}
|
||||
|
||||
export function isToolCallPart(part: TraceMessagePart): part is TraceToolCallPart {
|
||||
return part.type === "tool_call" || part.type === "function_call";
|
||||
}
|
||||
|
||||
export function isToolResultPart(part: TraceMessagePart): part is TraceToolResultPart {
|
||||
return (
|
||||
part.type === "tool_result" ||
|
||||
part.type === "function_result" ||
|
||||
part.type === "tool_call_response"
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Message structure in gen_ai.input.messages / gen_ai.output.messages
|
||||
* Format: [{role: "system"|"user"|"assistant"|"tool", parts: [...]}]
|
||||
*/
|
||||
export interface TraceMessage {
|
||||
role: "system" | "user" | "assistant" | "tool";
|
||||
parts: TraceMessagePart[];
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper to safely get a typed attribute value
|
||||
*/
|
||||
export function getTraceAttribute<K extends keyof TypedTraceAttributes>(
|
||||
attributes: TypedTraceAttributes,
|
||||
key: K
|
||||
): TypedTraceAttributes[K] {
|
||||
return attributes[key];
|
||||
}
|
||||
|
||||
/**
|
||||
* Helper to parse JSON message array from trace attributes
|
||||
*/
|
||||
export function parseTraceMessages(jsonString: string | undefined): TraceMessage[] {
|
||||
if (!jsonString) return [];
|
||||
try {
|
||||
return JSON.parse(jsonString) as TraceMessage[];
|
||||
} catch {
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
// Stored trace span (from conversation metadata)
|
||||
export interface TraceSpan {
|
||||
type?: string;
|
||||
span_id: string;
|
||||
trace_id: string;
|
||||
parent_span_id?: string | null;
|
||||
operation_name: string;
|
||||
start_time: number;
|
||||
end_time?: number;
|
||||
duration_ms?: number;
|
||||
attributes: TypedTraceAttributes;
|
||||
status: string;
|
||||
response_id?: string | null;
|
||||
entity_id?: string;
|
||||
events?: Array<{
|
||||
name: string;
|
||||
timestamp: number;
|
||||
attributes?: Record<string, unknown>;
|
||||
}>;
|
||||
error?: string;
|
||||
}
|
||||
|
||||
// List response with trace metadata (DevUI extension)
|
||||
export interface ConversationItemsListResponse {
|
||||
object: "list";
|
||||
data: ConversationItem[];
|
||||
has_more: boolean;
|
||||
metadata?: {
|
||||
traces?: TraceSpan[];
|
||||
};
|
||||
}
|
||||
|
||||
@@ -99,54 +99,54 @@ export interface Workflow {
|
||||
* Type guards for runtime type checking
|
||||
*/
|
||||
export function isWorkflow(obj: unknown): obj is Workflow {
|
||||
if (typeof obj !== "object" || obj === null) return false;
|
||||
const record = obj as Record<string, unknown>;
|
||||
return (
|
||||
typeof obj === "object" &&
|
||||
obj !== null &&
|
||||
"id" in obj &&
|
||||
"edge_groups" in obj &&
|
||||
"executors" in obj &&
|
||||
"start_executor_id" in obj &&
|
||||
"max_iterations" in obj &&
|
||||
typeof (obj as any).id === "string" &&
|
||||
Array.isArray((obj as any).edge_groups) &&
|
||||
typeof (obj as any).executors === "object" &&
|
||||
typeof (obj as any).start_executor_id === "string" &&
|
||||
typeof (obj as any).max_iterations === "number"
|
||||
"id" in record &&
|
||||
"edge_groups" in record &&
|
||||
"executors" in record &&
|
||||
"start_executor_id" in record &&
|
||||
"max_iterations" in record &&
|
||||
typeof record.id === "string" &&
|
||||
Array.isArray(record.edge_groups) &&
|
||||
typeof record.executors === "object" &&
|
||||
typeof record.start_executor_id === "string" &&
|
||||
typeof record.max_iterations === "number"
|
||||
);
|
||||
}
|
||||
|
||||
export function isExecutor(obj: unknown): obj is Executor {
|
||||
if (typeof obj !== "object" || obj === null) return false;
|
||||
const record = obj as Record<string, unknown>;
|
||||
return (
|
||||
typeof obj === "object" &&
|
||||
obj !== null &&
|
||||
"id" in obj &&
|
||||
"type" in obj &&
|
||||
typeof (obj as any).id === "string" &&
|
||||
typeof (obj as any).type === "string"
|
||||
"id" in record &&
|
||||
"type" in record &&
|
||||
typeof record.id === "string" &&
|
||||
typeof record.type === "string"
|
||||
);
|
||||
}
|
||||
|
||||
export function isEdge(obj: unknown): obj is Edge {
|
||||
if (typeof obj !== "object" || obj === null) return false;
|
||||
const record = obj as Record<string, unknown>;
|
||||
return (
|
||||
typeof obj === "object" &&
|
||||
obj !== null &&
|
||||
"source_id" in obj &&
|
||||
"target_id" in obj &&
|
||||
typeof (obj as any).source_id === "string" &&
|
||||
typeof (obj as any).target_id === "string"
|
||||
"source_id" in record &&
|
||||
"target_id" in record &&
|
||||
typeof record.source_id === "string" &&
|
||||
typeof record.target_id === "string"
|
||||
);
|
||||
}
|
||||
|
||||
export function isEdgeGroup(obj: unknown): obj is EdgeGroup {
|
||||
if (typeof obj !== "object" || obj === null) return false;
|
||||
const record = obj as Record<string, unknown>;
|
||||
return (
|
||||
typeof obj === "object" &&
|
||||
obj !== null &&
|
||||
"id" in obj &&
|
||||
"type" in obj &&
|
||||
"edges" in obj &&
|
||||
typeof (obj as any).id === "string" &&
|
||||
typeof (obj as any).type === "string" &&
|
||||
Array.isArray((obj as any).edges)
|
||||
"id" in record &&
|
||||
"type" in record &&
|
||||
"edges" in record &&
|
||||
typeof record.id === "string" &&
|
||||
typeof record.type === "string" &&
|
||||
Array.isArray(record.edges)
|
||||
);
|
||||
}
|
||||
|
||||
|
||||
@@ -7,6 +7,8 @@ import type {
|
||||
import type {
|
||||
ExtendedResponseStreamEvent,
|
||||
ResponseWorkflowEventComplete,
|
||||
ResponseOutputItemAddedEvent,
|
||||
ResponseOutputItemDoneEvent,
|
||||
JSONSchemaProperty,
|
||||
} from "@/types";
|
||||
import type { Workflow } from "@/types/workflow";
|
||||
@@ -389,9 +391,10 @@ export function processWorkflowEvents(
|
||||
events.forEach((event) => {
|
||||
// Handle new standard OpenAI events
|
||||
if (event.type === "response.output_item.added" || event.type === "response.output_item.done") {
|
||||
const item = (event as any).item;
|
||||
if (item && item.type === "executor_action" && item.executor_id) {
|
||||
const executorId = item.executor_id;
|
||||
const outputEvent = event as ResponseOutputItemAddedEvent | ResponseOutputItemDoneEvent;
|
||||
const item = outputEvent.item;
|
||||
if (item && item.type === "executor_action" && "executor_id" in item) {
|
||||
const executorId = item.executor_id as string;
|
||||
const itemId = item.id;
|
||||
|
||||
// Track the latest item ID for this executor
|
||||
@@ -492,16 +495,17 @@ export function processWorkflowEvents(
|
||||
// This prevents setting to "running" after the executor has already completed
|
||||
const hasCompletionEvent = events.some((event) => {
|
||||
if (event.type === "response.output_item.done") {
|
||||
const item = (event as any).item;
|
||||
return item && item.type === "executor_action" && item.executor_id === startExecutorId;
|
||||
const outputEvent = event as ResponseOutputItemDoneEvent;
|
||||
const item = outputEvent.item;
|
||||
return item && item.type === "executor_action" && "executor_id" in item && item.executor_id === startExecutorId;
|
||||
}
|
||||
if (event.type === "response.workflow_event.completed" && "data" in event && event.data) {
|
||||
const data = event.data as any;
|
||||
const data = event.data as Record<string, unknown>;
|
||||
return data.executor_id === startExecutorId &&
|
||||
(data.event_type === "ExecutorCompletedEvent" ||
|
||||
data.event_type === "ExecutorFailedEvent" ||
|
||||
data.event_type?.includes("Error") ||
|
||||
data.event_type?.includes("Failed"));
|
||||
(typeof data.event_type === "string" && data.event_type.includes("Error")) ||
|
||||
(typeof data.event_type === "string" && data.event_type.includes("Failed")));
|
||||
}
|
||||
return false;
|
||||
});
|
||||
@@ -565,9 +569,10 @@ export function getCurrentlyExecutingExecutors(
|
||||
events.forEach((event) => {
|
||||
// Handle new standard OpenAI events
|
||||
if (event.type === "response.output_item.added" || event.type === "response.output_item.done") {
|
||||
const item = (event as any).item;
|
||||
if (item && item.type === "executor_action" && item.executor_id) {
|
||||
const executorId = item.executor_id;
|
||||
const outputEvent = event as ResponseOutputItemAddedEvent | ResponseOutputItemDoneEvent;
|
||||
const item = outputEvent.item;
|
||||
if (item && item.type === "executor_action" && "executor_id" in item) {
|
||||
const executorId = item.executor_id as string;
|
||||
|
||||
executorTimeline[executorId] = {
|
||||
lastEvent: event.type === "response.output_item.added" ? "ExecutorInvokedEvent" : "ExecutorCompletedEvent",
|
||||
|
||||
@@ -169,17 +169,17 @@
|
||||
"@babel/helper-validator-identifier" "^7.27.1"
|
||||
|
||||
"@emnapi/core@^1.4.3", "@emnapi/core@^1.4.5":
|
||||
version "1.7.1"
|
||||
resolved "https://registry.yarnpkg.com/@emnapi/core/-/core-1.7.1.tgz#3a79a02dbc84f45884a1806ebb98e5746bdfaac4"
|
||||
integrity sha512-o1uhUASyo921r2XtHYOHy7gdkGLge8ghBEQHMWmyJFoXlpU58kIrhhN3w26lpQb6dspetweapMn2CSNwQ8I4wg==
|
||||
version "1.8.1"
|
||||
resolved "https://registry.yarnpkg.com/@emnapi/core/-/core-1.8.1.tgz#fd9efe721a616288345ffee17a1f26ac5dd01349"
|
||||
integrity sha512-AvT9QFpxK0Zd8J0jopedNm+w/2fIzvtPKPjqyw9jwvBaReTTqPBk9Hixaz7KbjimP+QNz605/XnjFcDAL2pqBg==
|
||||
dependencies:
|
||||
"@emnapi/wasi-threads" "1.1.0"
|
||||
tslib "^2.4.0"
|
||||
|
||||
"@emnapi/runtime@^1.4.3", "@emnapi/runtime@^1.4.5":
|
||||
version "1.7.1"
|
||||
resolved "https://registry.yarnpkg.com/@emnapi/runtime/-/runtime-1.7.1.tgz#a73784e23f5d57287369c808197288b52276b791"
|
||||
integrity sha512-PVtJr5CmLwYAU9PZDMITZoR5iAOShYREoR45EyyLrbntV50mdePTgUn4AmOw90Ifcj+x2kRjdzr1HP3RrNiHGA==
|
||||
version "1.8.1"
|
||||
resolved "https://registry.yarnpkg.com/@emnapi/runtime/-/runtime-1.8.1.tgz#550fa7e3c0d49c5fb175a116e8cd70614f9a22a5"
|
||||
integrity sha512-mehfKSMWjjNol8659Z8KxEMrdSJDDot5SXMq00dM8BN4o+CLNXQ0xH2V7EchNHV4RmbZLmmPdEaXZc5H2FXmDg==
|
||||
dependencies:
|
||||
tslib "^2.4.0"
|
||||
|
||||
@@ -212,7 +212,7 @@
|
||||
|
||||
"@esbuild/darwin-arm64@0.25.9":
|
||||
version "0.25.9"
|
||||
resolved "https://registry.yarnpkg.com/@esbuild/darwin-arm64/-/darwin-arm64-0.25.9.tgz#f1513eaf9ec8fa15dcaf4c341b0f005d3e8b47ae"
|
||||
resolved "https://registry.npmjs.org/@esbuild/darwin-arm64/-/darwin-arm64-0.25.9.tgz"
|
||||
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|
||||
|
||||
"@esbuild/darwin-x64@0.25.9":
|
||||
@@ -317,7 +317,7 @@
|
||||
|
||||
"@esbuild/win32-x64@0.25.9":
|
||||
version "0.25.9"
|
||||
resolved "https://registry.npmjs.org/@esbuild/win32-x64/-/win32-x64-0.25.9.tgz"
|
||||
resolved "https://registry.yarnpkg.com/@esbuild/win32-x64/-/win32-x64-0.25.9.tgz#585624dc829cfb6e7c0aa6c3ca7d7e6daa87e34f"
|
||||
integrity sha512-PPOl1mi6lpLNQxnGoyAfschAodRFYXJ+9fs6WHXz7CSWKbOqiMZsubC+BQsVKuul+3vKLuwTHsS2c2y9EoKwxQ==
|
||||
|
||||
"@eslint-community/eslint-utils@^4.2.0", "@eslint-community/eslint-utils@^4.7.0":
|
||||
@@ -742,7 +742,7 @@
|
||||
|
||||
"@radix-ui/react-separator@^1.1.7":
|
||||
version "1.1.7"
|
||||
resolved "https://registry.yarnpkg.com/@radix-ui/react-separator/-/react-separator-1.1.7.tgz#a18bd7fd07c10fda1bba14f2a3032e7b1a2b3470"
|
||||
resolved "https://registry.npmjs.org/@radix-ui/react-separator/-/react-separator-1.1.7.tgz"
|
||||
integrity sha512-0HEb8R9E8A+jZjvmFCy/J4xhbXy3TV+9XSnGJ3KvTtjlIUy/YQ/p6UYZvi7YbeoeXdyU9+Y3scizK6hkY37baA==
|
||||
dependencies:
|
||||
"@radix-ui/react-primitive" "2.1.3"
|
||||
@@ -756,7 +756,7 @@
|
||||
|
||||
"@radix-ui/react-switch@^1.2.6":
|
||||
version "1.2.6"
|
||||
resolved "https://registry.yarnpkg.com/@radix-ui/react-switch/-/react-switch-1.2.6.tgz#ff79acb831f0d5ea9216cfcc5b939912571358e3"
|
||||
resolved "https://registry.npmjs.org/@radix-ui/react-switch/-/react-switch-1.2.6.tgz"
|
||||
integrity sha512-bByzr1+ep1zk4VubeEVViV592vu2lHE2BZY5OnzehZqOOgogN80+mNtCqPkhn2gklJqOpxWgPoYTSnhBCqpOXQ==
|
||||
dependencies:
|
||||
"@radix-ui/primitive" "1.1.3"
|
||||
@@ -781,6 +781,24 @@
|
||||
"@radix-ui/react-roving-focus" "1.1.11"
|
||||
"@radix-ui/react-use-controllable-state" "1.2.2"
|
||||
|
||||
"@radix-ui/react-tooltip@^1.2.8":
|
||||
version "1.2.8"
|
||||
resolved "https://registry.npmjs.org/@radix-ui/react-tooltip/-/react-tooltip-1.2.8.tgz"
|
||||
integrity sha512-tY7sVt1yL9ozIxvmbtN5qtmH2krXcBCfjEiCgKGLqunJHvgvZG2Pcl2oQ3kbcZARb1BGEHdkLzcYGO8ynVlieg==
|
||||
dependencies:
|
||||
"@radix-ui/primitive" "1.1.3"
|
||||
"@radix-ui/react-compose-refs" "1.1.2"
|
||||
"@radix-ui/react-context" "1.1.2"
|
||||
"@radix-ui/react-dismissable-layer" "1.1.11"
|
||||
"@radix-ui/react-id" "1.1.1"
|
||||
"@radix-ui/react-popper" "1.2.8"
|
||||
"@radix-ui/react-portal" "1.1.9"
|
||||
"@radix-ui/react-presence" "1.1.5"
|
||||
"@radix-ui/react-primitive" "2.1.3"
|
||||
"@radix-ui/react-slot" "1.2.3"
|
||||
"@radix-ui/react-use-controllable-state" "1.2.2"
|
||||
"@radix-ui/react-visually-hidden" "1.2.3"
|
||||
|
||||
"@radix-ui/react-use-callback-ref@1.1.1":
|
||||
version "1.1.1"
|
||||
resolved "https://registry.npmjs.org/@radix-ui/react-use-callback-ref/-/react-use-callback-ref-1.1.1.tgz"
|
||||
@@ -861,7 +879,7 @@
|
||||
|
||||
"@rollup/rollup-darwin-arm64@4.47.1":
|
||||
version "4.47.1"
|
||||
resolved "https://registry.yarnpkg.com/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.47.1.tgz#fa41e413c8e73d61039d6375b234595f24b1e5e3"
|
||||
resolved "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.47.1.tgz"
|
||||
integrity sha512-tV6reObmxBDS4DDyLzTDIpymthNlxrLBGAoQx6m2a7eifSNEZdkXQl1PE4ZjCkEDPVgNXSzND/k9AQ3mC4IOEQ==
|
||||
|
||||
"@rollup/rollup-darwin-x64@4.47.1":
|
||||
@@ -946,7 +964,7 @@
|
||||
|
||||
"@rollup/rollup-win32-x64-msvc@4.47.1":
|
||||
version "4.47.1"
|
||||
resolved "https://registry.npmjs.org/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.47.1.tgz"
|
||||
resolved "https://registry.yarnpkg.com/@rollup/rollup-win32-x64-msvc/-/rollup-win32-x64-msvc-4.47.1.tgz#e5e0a0bae2c9d4858cc9b8dc508b2e10d7f0df8b"
|
||||
integrity sha512-CpKnYa8eHthJa3c+C38v/E+/KZyF1Jdh2Cz3DyKZqEWYgrM1IHFArXNWvBLPQCKUEsAqqKX27tTqVEFbDNUcOA==
|
||||
|
||||
"@tailwindcss/node@4.1.12":
|
||||
@@ -969,7 +987,7 @@
|
||||
|
||||
"@tailwindcss/oxide-darwin-arm64@4.1.12":
|
||||
version "4.1.12"
|
||||
resolved "https://registry.yarnpkg.com/@tailwindcss/oxide-darwin-arm64/-/oxide-darwin-arm64-4.1.12.tgz#e8bd4798f26ec1d012bf0683aeb77449f71505cd"
|
||||
resolved "https://registry.npmjs.org/@tailwindcss/oxide-darwin-arm64/-/oxide-darwin-arm64-4.1.12.tgz"
|
||||
integrity sha512-cq1qmq2HEtDV9HvZlTtrj671mCdGB93bVY6J29mwCyaMYCP/JaUBXxrQQQm7Qn33AXXASPUb2HFZlWiiHWFytw==
|
||||
|
||||
"@tailwindcss/oxide-darwin-x64@4.1.12":
|
||||
@@ -1026,7 +1044,7 @@
|
||||
|
||||
"@tailwindcss/oxide-win32-x64-msvc@4.1.12":
|
||||
version "4.1.12"
|
||||
resolved "https://registry.npmjs.org/@tailwindcss/oxide-win32-x64-msvc/-/oxide-win32-x64-msvc-4.1.12.tgz"
|
||||
resolved "https://registry.yarnpkg.com/@tailwindcss/oxide-win32-x64-msvc/-/oxide-win32-x64-msvc-4.1.12.tgz#b1ee2ed0ef2c4095ddec3684a1987e2b3613af36"
|
||||
integrity sha512-NKIh5rzw6CpEodv/++r0hGLlfgT/gFN+5WNdZtvh6wpU2BpGNgdjvj6H2oFc8nCM839QM1YOhjpgbAONUb4IxA==
|
||||
|
||||
"@tailwindcss/oxide@4.1.12":
|
||||
@@ -1925,7 +1943,7 @@ levn@^0.4.1:
|
||||
|
||||
lightningcss-darwin-arm64@1.30.1:
|
||||
version "1.30.1"
|
||||
resolved "https://registry.yarnpkg.com/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.30.1.tgz#3d47ce5e221b9567c703950edf2529ca4a3700ae"
|
||||
resolved "https://registry.npmjs.org/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.30.1.tgz"
|
||||
integrity sha512-c8JK7hyE65X1MHMN+Viq9n11RRC7hgin3HhYKhrMyaXflk5GVplZ60IxyoVtzILeKr+xAJwg6zK6sjTBJ0FKYQ==
|
||||
|
||||
lightningcss-darwin-x64@1.30.1:
|
||||
@@ -1970,7 +1988,7 @@ lightningcss-win32-arm64-msvc@1.30.1:
|
||||
|
||||
lightningcss-win32-x64-msvc@1.30.1:
|
||||
version "1.30.1"
|
||||
resolved "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.30.1.tgz"
|
||||
resolved "https://registry.yarnpkg.com/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.30.1.tgz#fd7dd008ea98494b85d24b4bea016793f2e0e352"
|
||||
integrity sha512-PVqXh48wh4T53F/1CCu8PIPCxLzWyCnn/9T5W1Jpmdy5h9Cwd+0YQS6/LwhHXSafuc61/xg9Lv5OrCby6a++jg==
|
||||
|
||||
lightningcss@1.30.1:
|
||||
|
||||
@@ -4,7 +4,7 @@ description = "Debug UI for Microsoft Agent Framework with OpenAI-compatible API
|
||||
authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
version = "1.0.0b251223"
|
||||
version = "1.0.0b260107"
|
||||
license-files = ["LICENSE"]
|
||||
urls.homepage = "https://github.com/microsoft/agent-framework"
|
||||
urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
|
||||
|
||||
@@ -8,8 +8,10 @@ import pytest
|
||||
from agent_framework import (
|
||||
Executor,
|
||||
InMemoryCheckpointStorage,
|
||||
RequestInfoEvent,
|
||||
WorkflowBuilder,
|
||||
WorkflowContext,
|
||||
WorkflowStatusEvent,
|
||||
handler,
|
||||
response_handler,
|
||||
)
|
||||
@@ -426,14 +428,11 @@ class TestIntegration:
|
||||
# Run workflow until it reaches IDLE_WITH_PENDING_REQUESTS (after checkpoint is created)
|
||||
saw_request_event = False
|
||||
async for event in test_workflow.run_stream(WorkflowTestData(value="test")):
|
||||
if hasattr(event, "__class__"):
|
||||
if event.__class__.__name__ == "RequestInfoEvent":
|
||||
saw_request_event = True
|
||||
# Wait for IDLE_WITH_PENDING_REQUESTS status (comes after checkpoint creation)
|
||||
is_status_event = event.__class__.__name__ == "WorkflowStatusEvent"
|
||||
has_pending_status = hasattr(event, "status") and "IDLE_WITH_PENDING_REQUESTS" in str(event.status)
|
||||
if is_status_event and has_pending_status:
|
||||
break
|
||||
if isinstance(event, RequestInfoEvent):
|
||||
saw_request_event = True
|
||||
# Wait for IDLE_WITH_PENDING_REQUESTS status (comes after checkpoint creation)
|
||||
if isinstance(event, WorkflowStatusEvent) and "IDLE_WITH_PENDING_REQUESTS" in str(event.state):
|
||||
break
|
||||
|
||||
assert saw_request_event, "Test workflow should have emitted RequestInfoEvent"
|
||||
|
||||
|
||||
@@ -234,7 +234,7 @@ async def test_list_items_converts_function_calls():
|
||||
{
|
||||
"type": "function_result",
|
||||
"call_id": "call_test123",
|
||||
"output": '{"temperature": 65, "condition": "sunny"}',
|
||||
"result": '{"temperature": 65, "condition": "sunny"}',
|
||||
}
|
||||
],
|
||||
),
|
||||
|
||||
@@ -441,73 +441,119 @@ async def test_workflow_status_event(mapper: MessageMapper, test_request: AgentF
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MagenticAgentDeltaEvent Tests
|
||||
# Magentic Event Tests - Testing REAL AgentRunUpdateEvent with additional_properties
|
||||
# =============================================================================
|
||||
|
||||
|
||||
async def test_magentic_agent_delta_creates_message_container(
|
||||
async def test_magentic_agent_run_update_event_with_agent_delta_metadata(
|
||||
mapper: MessageMapper, test_request: AgentFrameworkRequest
|
||||
) -> None:
|
||||
"""Test that MagenticAgentDeltaEvent creates message containers when no executor context."""
|
||||
from dataclasses import dataclass
|
||||
"""Test that AgentRunUpdateEvent with magentic_event_type='agent_delta' is handled correctly.
|
||||
|
||||
from agent_framework import WorkflowEvent
|
||||
This tests the ACTUAL event format Magentic emits - not a fake MagenticAgentDeltaEvent class.
|
||||
Magentic uses AgentRunUpdateEvent with additional_properties containing magentic_event_type.
|
||||
"""
|
||||
from agent_framework._types import AgentRunResponseUpdate, Role, TextContent
|
||||
from agent_framework._workflows._events import AgentRunUpdateEvent
|
||||
|
||||
@dataclass
|
||||
class MagenticAgentDeltaEvent(WorkflowEvent):
|
||||
agent_id: str
|
||||
text: str | None = None
|
||||
# Create the REAL event format that Magentic emits
|
||||
update = AgentRunResponseUpdate(
|
||||
contents=[TextContent(text="Hello from agent")],
|
||||
role=Role.ASSISTANT,
|
||||
author_name="Writer",
|
||||
additional_properties={
|
||||
"magentic_event_type": "agent_delta",
|
||||
"agent_id": "writer_agent",
|
||||
},
|
||||
)
|
||||
event = AgentRunUpdateEvent(executor_id="magentic_executor", data=update)
|
||||
|
||||
# First delta should create message container
|
||||
first_delta = MagenticAgentDeltaEvent(agent_id="test_agent", text="Hello ")
|
||||
events = await mapper.convert_event(first_delta, test_request)
|
||||
events = await mapper.convert_event(event, test_request)
|
||||
|
||||
# Should emit 3 events: message container, content part, and text delta
|
||||
assert len(events) == 3
|
||||
assert events[0].type == "response.output_item.added"
|
||||
assert events[0].item.type == "message"
|
||||
assert events[0].item.metadata["agent_id"] == "test_agent"
|
||||
message_id = events[0].item.id
|
||||
|
||||
# Second delta should NOT create new container
|
||||
second_delta = MagenticAgentDeltaEvent(agent_id="test_agent", text="world!")
|
||||
events = await mapper.convert_event(second_delta, test_request)
|
||||
|
||||
assert len(events) == 1
|
||||
assert events[0].type == "response.output_text.delta"
|
||||
assert events[0].item_id == message_id
|
||||
# Should be treated as a regular AgentRunUpdateEvent with text content
|
||||
# The mapper should emit text delta events
|
||||
assert len(events) >= 1
|
||||
text_events = [e for e in events if getattr(e, "type", "") == "response.output_text.delta"]
|
||||
assert len(text_events) >= 1
|
||||
assert text_events[0].delta == "Hello from agent"
|
||||
|
||||
|
||||
async def test_magentic_agent_delta_routes_to_executor_item(
|
||||
async def test_magentic_orchestrator_message_event(mapper: MessageMapper, test_request: AgentFrameworkRequest) -> None:
|
||||
"""Test that AgentRunUpdateEvent with magentic_event_type='orchestrator_message' is handled.
|
||||
|
||||
Magentic emits orchestrator planning/instruction messages using AgentRunUpdateEvent
|
||||
with additional_properties containing magentic_event_type='orchestrator_message'.
|
||||
"""
|
||||
from agent_framework._types import AgentRunResponseUpdate, Role, TextContent
|
||||
from agent_framework._workflows._events import AgentRunUpdateEvent
|
||||
|
||||
# Create orchestrator message event (REAL format from Magentic)
|
||||
update = AgentRunResponseUpdate(
|
||||
contents=[TextContent(text="Planning: First, the writer will create content...")],
|
||||
role=Role.ASSISTANT,
|
||||
author_name="Orchestrator",
|
||||
additional_properties={
|
||||
"magentic_event_type": "orchestrator_message",
|
||||
"orchestrator_message_kind": "task_ledger",
|
||||
"orchestrator_id": "magentic_orchestrator",
|
||||
},
|
||||
)
|
||||
event = AgentRunUpdateEvent(executor_id="magentic_orchestrator", data=update)
|
||||
|
||||
events = await mapper.convert_event(event, test_request)
|
||||
|
||||
# Currently, mapper treats this as regular AgentRunUpdateEvent (no special handling)
|
||||
# This test documents the current behavior
|
||||
assert len(events) >= 1
|
||||
text_events = [e for e in events if getattr(e, "type", "") == "response.output_text.delta"]
|
||||
assert len(text_events) >= 1
|
||||
assert "Planning:" in text_events[0].delta
|
||||
|
||||
|
||||
async def test_magentic_events_use_same_event_class_as_other_workflows(
|
||||
mapper: MessageMapper, test_request: AgentFrameworkRequest
|
||||
) -> None:
|
||||
"""Test that MagenticAgentDeltaEvent routes to executor item when executor context is present."""
|
||||
from dataclasses import dataclass
|
||||
"""Verify Magentic uses the same AgentRunUpdateEvent class as other workflows.
|
||||
|
||||
from agent_framework import WorkflowEvent
|
||||
This test documents that Magentic does NOT define separate event classes like
|
||||
MagenticAgentDeltaEvent - it reuses AgentRunUpdateEvent with metadata in
|
||||
additional_properties. Any mapper code checking for 'MagenticAgentDeltaEvent'
|
||||
class names is dead code.
|
||||
"""
|
||||
from agent_framework._types import AgentRunResponseUpdate, Role, TextContent
|
||||
from agent_framework._workflows._events import AgentRunUpdateEvent
|
||||
|
||||
@dataclass
|
||||
class MagenticAgentDeltaEvent(WorkflowEvent):
|
||||
agent_id: str
|
||||
text: str | None = None
|
||||
# Create events the way different workflows do it
|
||||
# 1. Regular workflow (no additional_properties)
|
||||
regular_update = AgentRunResponseUpdate(
|
||||
contents=[TextContent(text="Regular workflow response")],
|
||||
role=Role.ASSISTANT,
|
||||
)
|
||||
regular_event = AgentRunUpdateEvent(executor_id="regular_executor", data=regular_update)
|
||||
|
||||
# First, invoke an executor (sets current_executor_id in context)
|
||||
executor_event = create_executor_invoked_event(executor_id="agent_writer")
|
||||
executor_events = await mapper.convert_event(executor_event, test_request)
|
||||
# 2. Magentic workflow (with additional_properties)
|
||||
magentic_update = AgentRunResponseUpdate(
|
||||
contents=[TextContent(text="Magentic workflow response")],
|
||||
role=Role.ASSISTANT,
|
||||
additional_properties={"magentic_event_type": "agent_delta"},
|
||||
)
|
||||
magentic_event = AgentRunUpdateEvent(executor_id="magentic_executor", data=magentic_update)
|
||||
|
||||
assert len(executor_events) == 1
|
||||
assert executor_events[0].type == "response.output_item.added"
|
||||
executor_item_id = executor_events[0].item.id
|
||||
# Both should be the SAME class
|
||||
assert type(regular_event) is type(magentic_event)
|
||||
assert isinstance(regular_event, AgentRunUpdateEvent)
|
||||
assert isinstance(magentic_event, AgentRunUpdateEvent)
|
||||
|
||||
# Now send Magentic delta - should route to executor's item
|
||||
delta = MagenticAgentDeltaEvent(agent_id="writer", text="Hello world")
|
||||
delta_events = await mapper.convert_event(delta, test_request)
|
||||
# Both should be handled by the same isinstance check in mapper
|
||||
regular_events = await mapper.convert_event(regular_event, test_request)
|
||||
magentic_events = await mapper.convert_event(magentic_event, test_request)
|
||||
|
||||
# Should only emit 1 event: text delta routed to executor's item
|
||||
assert len(delta_events) == 1
|
||||
assert delta_events[0].type == "response.output_text.delta"
|
||||
assert delta_events[0].item_id == executor_item_id
|
||||
assert delta_events[0].delta == "Hello world"
|
||||
# Both produce text delta events
|
||||
regular_text = [e for e in regular_events if getattr(e, "type", "") == "response.output_text.delta"]
|
||||
magentic_text = [e for e in magentic_events if getattr(e, "type", "") == "response.output_text.delta"]
|
||||
|
||||
assert len(regular_text) >= 1
|
||||
assert len(magentic_text) >= 1
|
||||
|
||||
|
||||
# =============================================================================
|
||||
|
||||
Reference in New Issue
Block a user