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Evan Mattson 5e8fe0be1f Python: Stop emitting duplicate reasoning content from OpenAI response.reasoning_text.done and response.reasoning_summary_text.done events (#5162 )

* Fix reasoning text done events duplicating streamed delta content (#5157)

The OpenAI Responses API sends both reasoning_text.delta (incremental
chunks) and reasoning_text.done (full accumulated text) events. The
chat client was emitting Content for both, causing ag-ui to append the
full done text onto already-accumulated delta text, producing
duplicated reasoning output.

Stop emitting Content for reasoning_text.done and
reasoning_summary_text.done events, matching how output_text.done is
already handled (not emitted). The deltas contain all the content;
the done event is redundant.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(openai): emit reasoning done content as fallback when no deltas observed (#5157)

Address PR review feedback:
- Track item_ids that received reasoning deltas via seen_reasoning_delta_item_ids set
- Emit content from done events only when no deltas were received for the
  item_id, preventing silent content loss on stream resumption
- Add comment documenting code_interpreter done event asymmetry
- Replace redundant ag-ui test with deduplication-focused test
- Add integration test for delta+done sequence in OpenAI chat client tests
- Add fallback path tests for done events without preceding deltas

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address review feedback for #5157: Python: [Bug]: "type": "response.reasoning_text.delta" and "response.reasoning_text.done" both get exposed as "text_reasoning"

* Fix AG-UI reasoning streaming to use proper Start/End pattern (#5157)

_emit_text_reasoning now follows the same streaming pattern as _emit_text:
- Emits ReasoningStartEvent/ReasoningMessageStartEvent only on the first
  delta for a given message_id
- Emits only ReasoningMessageContentEvent for subsequent deltas
- Defers ReasoningMessageEndEvent/ReasoningEndEvent until
  _close_reasoning_block is called (on content type switch or end-of-run)

This produces the correct protocol pattern:
  ReasoningStartEvent
    ReasoningMessageStartEvent
    ReasoningMessageContentEvent(delta1)
    ReasoningMessageContentEvent(delta2)
    ReasoningMessageEndEvent
  ReasoningEndEvent

Instead of wrapping every delta in a full Start→End sequence.

Backward compatibility is preserved: calling _emit_text_reasoning without
a flow argument still produces the full sequence per call.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix import ordering lint error in AG-UI test file (#5157)

Move inline import of TextMessageContentEvent to the top-level import
block and ensure alphabetical ordering to satisfy ruff I001 rule.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix mypy error: rename loop variable to avoid type conflict with WorkflowEvent

The 'event' variable was already typed as WorkflowEvent[Any] from the
async for loop at line 590. Reusing it in the _close_reasoning_block
loop (which returns list[BaseEvent]) caused an incompatible assignment
error. Renamed to 'reasoning_evt' to avoid the conflict.

Fixes #5162

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address review feedback for #5157: review comment fixes

* narrow test result reporting to explicit pytest JUnit XML

* Fix test args

* Fix pytest-results-action in merge workflow and remove committed test artifacts

Apply the same JUnit XML fix from python-tests.yml to python-merge-tests.yml:
add --junitxml=pytest.xml to all test commands and narrow the results action
path from ./python/**.xml to ./python/pytest.xml. Also remove accidentally
committed pytest.xml and python-coverage.xml and add them to .gitignore.

---------

Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

5e8fe0be1f · 2026-04-09 22:44:59 +00:00

History

01-get-started

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

02-agents

Python: Stop emitting duplicate reasoning content from OpenAI response.reasoning_text.done and response.reasoning_summary_text.done events (#5162 )

2026-04-09 22:44:59 +00:00

03-workflows

Python: Stop emitting duplicate reasoning content from OpenAI response.reasoning_text.done and response.reasoning_summary_text.done events (#5162 )

2026-04-09 22:44:59 +00:00

04-hosting

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

05-end-to-end

Fix handoff workflow context management and improve AG-UI demo (#5136 )

2026-04-08 04:08:24 +00:00

autogen-migration

Fix handoff workflow context management and improve AG-UI demo (#5136 )

2026-04-08 04:08:24 +00:00

semantic-kernel-migration

Fix handoff workflow context management and improve AG-UI demo (#5136 )

2026-04-08 04:08:24 +00:00

shared

Python: Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference (#4207 )

2026-02-25 17:45:08 +00:00

__init__.py

Python telemetry (#223 )

2025-07-28 07:33:42 +00:00

AGENTS.md

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

README.md

Python: [BREAKING] update to v1.0.0 (#5062 )

2026-04-02 15:26:30 +00:00

SAMPLE_GUIDELINES.md

Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818 )

2026-03-25 09:56:29 +00:00

README.md

Python Samples

This directory contains samples demonstrating the capabilities of Microsoft Agent Framework for Python.

Structure

Folder	Description
`01-get-started/`	Progressive tutorial: hello agent → hosting
`02-agents/`	Deep-dive by concept: tools, middleware, providers, orchestrations
`03-workflows/`	Workflow patterns: sequential, concurrent, state, declarative
`04-hosting/`	Deployment: Azure Functions, Durable Tasks, A2A
`05-end-to-end/`	Full applications, evaluation, demos

Getting Started

Start with 01-get-started/ and work through the numbered files:

01_hello_agent.py — Create and run your first agent
02_add_tools.py — Add function tools with @tool
03_multi_turn.py — Multi-turn conversations with AgentSession
04_memory.py — Agent memory with ContextProvider
05_first_workflow.py — Build a workflow with executors and edges
06_host_your_agent.py — Host your agent via Azure Functions

Prerequisites

pip install agent-framework

Environment Variables

Samples call load_dotenv() to automatically load environment variables from a .env file in the python/ directory. This is a convenience for local development and testing.

For local development, set up your environment using any of these methods:

Option 1: Using a .env file (recommended for local development):

Copy .env.example to .env in the python/ directory:
```
cp .env.example .env
```
Edit .env and set your values (API keys, endpoints, etc.)

Option 2: Export environment variables directly:

export FOUNDRY_PROJECT_ENDPOINT="your-foundry-project-endpoint"
export FOUNDRY_MODEL="gpt-4o"

Option 3: Using env_file_path parameter (for per-client configuration):

All client classes (e.g., OpenAIChatClient, OpenAIChatCompletionClient) support an env_file_path parameter to load environment variables from a specific file:

from agent_framework.openai import OpenAIChatClient

# Load from a custom .env file
client = OpenAIChatClient(env_file_path="path/to/custom.env")

This allows different clients to use different configuration files if needed.

For the generic OpenAI clients (OpenAIChatClient and OpenAIChatCompletionClient), routing precedence is:

Explicit Azure inputs such as credential, azure_endpoint, or api_version
OPENAI_API_KEY / explicit OpenAI API-key parameters
Azure environment fallback such as AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY

If you keep both OpenAI and Azure variables in your shell, the generic clients stay on OpenAI until you pass an explicit Azure input.

For the getting-started samples, you'll need at minimum:

FOUNDRY_PROJECT_ENDPOINT="your-foundry-project-endpoint"
FOUNDRY_MODEL="gpt-4o"

Consolidated sample env inventory

This is the single source of truth for package-level environment variables read by packages included by agent-framework-core[all]. It intentionally excludes variables that are only read by standalone samples, package sample folders, or tests. When package code adds, removes, or renames an environment variable, update this table in the same change.

Example values below are illustrative. For entries not backed by a single public class, the class column names the closest public surface, helper, or package-level initialization point that reads the variable.

package	class	env var	example value
`agent-framework-anthropic`	`AnthropicClient`	`ANTHROPIC_API_KEY`	`sk-ant-api03-...`
`agent-framework-anthropic`	`AnthropicClient`	`ANTHROPIC_CHAT_MODEL`	`claude-sonnet-4-5-20250929`
`agent-framework-foundry`	`FoundryEmbeddingClient`	`FOUNDRY_MODELS_ENDPOINT`	`https://my-endpoint.inference.ai.azure.com`
`agent-framework-foundry`	`FoundryEmbeddingClient`	`FOUNDRY_MODELS_API_KEY`	`env-key`
`agent-framework-foundry`	`FoundryEmbeddingClient`	`FOUNDRY_EMBEDDING_MODEL`	`text-embedding-3-small`
`agent-framework-foundry`	`FoundryEmbeddingClient`	`FOUNDRY_IMAGE_EMBEDDING_MODEL`	`Cohere-embed-v3-english`
`agent-framework-azure-ai-search`	`AzureAISearchContextProvider`	`AZURE_SEARCH_ENDPOINT`	`https://my-search.search.windows.net`
`agent-framework-azure-ai-search`	`AzureAISearchContextProvider`	`AZURE_SEARCH_API_KEY`	`search-key`
`agent-framework-azure-ai-search`	`AzureAISearchContextProvider`	`AZURE_SEARCH_INDEX_NAME`	`hotels-index`
`agent-framework-azure-ai-search`	`AzureAISearchContextProvider`	`AZURE_SEARCH_KNOWLEDGE_BASE_NAME`	`hotels-kb`
`agent-framework-azure-cosmos`	`CosmosHistoryProvider`	`AZURE_COSMOS_ENDPOINT`	`https://my-cosmos.documents.azure.com:443/`
`agent-framework-azure-cosmos`	`CosmosHistoryProvider`	`AZURE_COSMOS_DATABASE_NAME`	`agent-history`
`agent-framework-azure-cosmos`	`CosmosHistoryProvider`	`AZURE_COSMOS_CONTAINER_NAME`	`messages`
`agent-framework-azure-cosmos`	`CosmosHistoryProvider`	`AZURE_COSMOS_KEY`	`C2F...==`
`agent-framework-bedrock`	`BedrockChatClient`	`BEDROCK_REGION`	`us-east-1`
`agent-framework-bedrock`	`BedrockChatClient`	`BEDROCK_CHAT_MODEL`	`anthropic.claude-3-5-sonnet-20241022-v2:0`
`agent-framework-bedrock`	`BedrockEmbeddingClient`	`BEDROCK_REGION`	`us-east-1`
`agent-framework-bedrock`	`BedrockEmbeddingClient`	`BEDROCK_EMBEDDING_MODEL`	`amazon.titan-embed-text-v2:0`
`agent-framework-bedrock`	`BedrockChatClient / BedrockEmbeddingClient`	`AWS_ACCESS_KEY_ID`	`AKIAIOSFODNN7EXAMPLE`
`agent-framework-bedrock`	`BedrockChatClient / BedrockEmbeddingClient`	`AWS_SECRET_ACCESS_KEY`	`wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY`
`agent-framework-bedrock`	`BedrockChatClient / BedrockEmbeddingClient`	`AWS_SESSION_TOKEN`	`IQoJb3JpZ2luX2VjEO7//////////wEaCXVzLXdlc3QtMiJHMEUCIQD...`
`agent-framework-copilotstudio`	`CopilotStudioAgent`	`COPILOTSTUDIOAGENT__ENVIRONMENTID`	`00000000-0000-0000-0000-000000000000`
`agent-framework-copilotstudio`	`CopilotStudioAgent`	`COPILOTSTUDIOAGENT__SCHEMANAME`	`cr123_agentname`
`agent-framework-copilotstudio`	`CopilotStudioAgent`	`COPILOTSTUDIOAGENT__TENANTID`	`11111111-1111-1111-1111-111111111111`
`agent-framework-copilotstudio`	`CopilotStudioAgent`	`COPILOTSTUDIOAGENT__AGENTAPPID`	`22222222-2222-2222-2222-222222222222`
`agent-framework-core`	`enable_instrumentation()`	`ENABLE_INSTRUMENTATION`	`true`
`agent-framework-core`	`enable_instrumentation()`	`ENABLE_SENSITIVE_DATA`	`false`
`agent-framework-core`	`enable_instrumentation()`	`ENABLE_CONSOLE_EXPORTERS`	`true`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_ENDPOINT`	`http://localhost:4317`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_TRACES_ENDPOINT`	`http://localhost:4318/v1/traces`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_METRICS_ENDPOINT`	`http://localhost:4318/v1/metrics`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_LOGS_ENDPOINT`	`http://localhost:4318/v1/logs`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_PROTOCOL`	`grpc`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_HEADERS`	`api-key=demo`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_TRACES_HEADERS`	`api-key=trace-demo`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_METRICS_HEADERS`	`api-key=metric-demo`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_EXPORTER_OTLP_LOGS_HEADERS`	`api-key=log-demo`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_SERVICE_NAME`	`sample-agent`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_SERVICE_VERSION`	`1.0.0`
`agent-framework-core`	`enable_instrumentation()`	`OTEL_RESOURCE_ATTRIBUTES`	`deployment.environment=dev,service.namespace=agent-framework`
`agent-framework-devui`	`DevUI server`	`DEVUI_AUTH_TOKEN`	`my-devui-token`
`agent-framework-foundry`	`FoundryChatClient`	`FOUNDRY_PROJECT_ENDPOINT`	`https://my-project.services.ai.azure.com/api/projects/my-project`
`agent-framework-foundry`	`FoundryChatClient`	`FOUNDRY_MODEL`	`gpt-4o`
`agent-framework-foundry`	`FoundryAgent`	`FOUNDRY_AGENT_NAME`	`travel-planner`
`agent-framework-foundry`	`FoundryAgent`	`FOUNDRY_AGENT_VERSION`	`v1`
`agent-framework-github-copilot`	`GitHubCopilotAgent`	`GITHUB_COPILOT_CLI_PATH`	`copilot`
`agent-framework-github-copilot`	`GitHubCopilotAgent`	`GITHUB_COPILOT_MODEL`	`gpt-5`
`agent-framework-github-copilot`	`GitHubCopilotAgent`	`GITHUB_COPILOT_TIMEOUT`	`60`
`agent-framework-github-copilot`	`GitHubCopilotAgent`	`GITHUB_COPILOT_LOG_LEVEL`	`info`
`agent-framework-mem0`	`agent_framework_mem0 package import`	`MEM0_TELEMETRY`	`false`
`agent-framework-ollama`	`OllamaChatClient`	`OLLAMA_HOST`	`http://localhost:11434`
`agent-framework-ollama`	`OllamaChatClient`	`OLLAMA_MODEL`	`llama3.1:8b`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`OPENAI_API_KEY`	`sk-proj-...`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`OPENAI_MODEL`	`gpt-4o-mini`
`agent-framework-openai`	`OpenAIChatClient`	`OPENAI_CHAT_MODEL`	`gpt-4.1-mini`
`agent-framework-openai`	`OpenAIChatCompletionClient`	`OPENAI_CHAT_COMPLETION_MODEL`	`gpt-4o`
`agent-framework-openai`	`OpenAIEmbeddingClient`	`OPENAI_EMBEDDING_MODEL`	`text-embedding-3-small`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`OPENAI_BASE_URL`	`https://api.openai.com/v1/`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`OPENAI_ORG_ID`	`org_123456789`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_ENDPOINT`	`https://my-resource.openai.azure.com/`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_API_KEY`	`sk-azure-...`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_API_VERSION`	`2024-10-21`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_BASE_URL`	`https://my-resource.openai.azure.com/openai/v1/`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_MODEL`	`gpt-4o`
`agent-framework-openai`	`OpenAIChatClient`	`AZURE_OPENAI_CHAT_MODEL`	`gpt-4.1`
`agent-framework-openai`	`OpenAIChatCompletionClient`	`AZURE_OPENAI_CHAT_COMPLETION_MODEL`	`gpt-4o-mini`
`agent-framework-openai`	`OpenAIEmbeddingClient`	`AZURE_OPENAI_EMBEDDING_MODEL`	`text-embedding-3-large`
`agent-framework-openai`	`OpenAIChatClient / OpenAIChatCompletionClient / OpenAIEmbeddingClient`	`AZURE_OPENAI_RESOURCE_URL`	`https://cognitiveservices.azure.com/`

agent-framework-openai supports the Azure OpenAI client-specific deployment aliases listed above; keep packages/openai/README.md as the authoritative reference for the exact fallback order and package-specific behavior.

Note for production: In production environments, set environment variables through your deployment platform (e.g., Azure App Settings, Kubernetes ConfigMaps/Secrets) rather than using .env files. The load_dotenv() call in samples will have no effect when a .env file is not present, allowing environment variables to be loaded from the system.

For Azure authentication, run az login before running samples.

Note on XML tags

Some sample files include XML-style snippet tags (for example <snippet_name> and </snippet_name>). These are used by our documentation tooling and can be ignored or removed when you use the samples outside this repository.

Additional Resources

Agent Framework Documentation
AGENTS.md — Structure documentation for maintainers
SAMPLE_GUIDELINES.md — Coding conventions for samples