Eduard van Valkenburg
6e95517659
* Python: Split type checkers by target (pyright source, 5 checkers on tests/samples) Rework the typing setup along the lines of the 'too many type checkers' approach: - Pyright (strict) is now the sole source-code type checker; mypy is removed from source and its [tool.mypy] block becomes a relaxed profile used only for tests/samples. - Tests are checked by all five checkers (pyright relaxed, mypy, pyrefly, ty, zuban); samples by pyright, pyrefly, and ty. All run in a relaxed/ basic profile so authors aren't forced into over-annotation. - Add pyrightconfig.tests.json and bump sample pyright configs to basic. - Unify test/sample typing onto the same parallel fan-out used by source pyright via run_command_items in task_runner.py. - Make version-conditional imports symmetric: keep or drop the '# type: ignore' on both branches so results match across interpreter versions (local vs CI). - Update SKILL.md, DEV_SETUP.md, and CODING_STANDARD.md for the five gating checkers and pyright on source+tests+samples. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix merge regressions from main (typing + runtime) Merging main into the type-checker split branch surfaced regressions that the new five-checker test suite and unit tests caught: Runtime fixes: - anthropic: restore the dropped `cache_read_input_token_count` mapping in _parse_usage_from_anthropic (lost during merge conflict resolution). - gemini: _get_function_calling_mode test helper returned str(enum) ('FunctionCallingConfigMode.AUTO') instead of the enum value ('AUTO'). - openai: _response_id_from_token test helper was an infinite self-recursion; return token['response_id']. - orchestrations: reset output_events per approval iteration so the terminal output assertion counts only the final run. - core: drop a stale duplicate harness test whose message ('non-negative') contradicted the source ('positive'). - purview: import PolicyLocation/PolicyScope/ProtectionScopeActivities/ ExecutionMode used by the processor tests. Type-checker fixes (tests, relaxed profile): - core: pyright/mypy/pyrefly/ty/zuban green-ups across the harness, MCP, observability and types tests. - anthropic/openai: route provider-namespaced UsageDetails keys through a dict cast (extra_items TypedDict unsupported by mypy/ty). - purview: typed model constructors and cache-mock casts. - ag-ui: annotate WorkflowContext[Any, Any] so yield_output accepts test payloads, guard Optional forwarded_props, and ty-ignore intentional bad args. Source pyright (sole source checker) flagged unnecessary ignores newly introduced by merged code in core _tools.py and declarative _declarative_base.py. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Isolate per-package mypy cache in test-typing fan-out The parallel test-typing fan-out runs many mypy processes concurrently, all defaulting to a single shared ./.mypy_cache. Concurrent writes corrupt the cache and mypy aborts with INTERNAL ERROR (intermittently, depending on worker timing) -- which is why CI's Test Typing job failed on a shifting set of packages while a single-package run was fine. Give each mypy invocation an isolated cache dir keyed by its target paths so incremental caching still works per package without races. Other checkers (zuban/pyrefly/ty/pyright) maintain their own caches and are unaffected. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Make lab pyright-only on source (drop source mypy) Lab was the last package still running mypy on its source code, requiring mypy-only `# type: ignore` comments that pyright (the sole source checker everywhere else) flags as unnecessary. Align lab with the rest of the monorepo: - Remove the lab source mypy poe tasks (mypy-gaia/lightning/tau2) and the now-dead strict [tool.mypy] config block. - Drop the 'Run lab mypy' CI step; lab source is type-checked by pyright only. Lab tests remain covered by the workspace test-typing fan-out (mypy, pyrefly, ty, zuban, pyright over tests using the relaxed root config). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix test-typing regressions from latest main merge A fresh merge from main brought in new test code never run under the five-checker test-typing suite. Green up across the affected packages: - core: narrow Optional span.attributes with 'and' guards in span filters and assert+cast the json.loads(...attributes[...]) reads (test_observability); match the existing as_agent ignore on the protocol-typed fixture (test_clients). - openai: align new streaming tests with the established chat_options dict pattern (ChatOptions TypedDict isn't assignable to dict), route Optional .annotations[0] access through a small _first_annotation helper (mirrors the file's assert-not-None convention), and annotate a mapped ResponseStream. - foundry_hosting: annotate error: dict[str, Any] = body.get(...) or {} (zuban needs the annotation). - foundry: narrow ignores for the live AIProjectClient credential arg (pyrefly) and connections.get_default (zuban) SDK type gaps. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated pyright version * pyright fix * Python: Fix source typing for pyright 1.1.410 Pyright 1.1.410 tightened several checks. Apply the same source fixes as upstream PR #6275: - anthropic: import AsyncAnthropicBedrock from anthropic.lib.bedrock and AsyncAnthropicVertex from anthropic.lib.vertex (no longer re-exported from the anthropic top-level package -> reportPrivateImportUsage). - core _types.py: cast the transform-hook result to UpdateT (reportAssignmentType). - core _workflows/_events.py: annotate the @contextmanager helper as Generator[None] instead of Iterator[None] (reportDeprecated). - redis: build the combined filter expression with an explicit loop instead of reduce(and_, ...), which pyright could no longer fully type (drops the now unused functools.reduce / operator.and_ imports). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Accept plain-text body in Azure Functions workflow/run endpoint The workflow_orchestrator already accepts plain strings as well as JSON objects via context.get_input(), but the start_workflow_orchestration HTTP handler only accepted JSON and returned 400 for any non-JSON body. This made the functions integration tests that POST text/plain to /api/workflow/run (e.g. test_09_workflow_shared_state) fail consistently with 400 != 202. Fall back to the raw request body (decoded as UTF-8) when the body is not JSON, rejecting only a truly empty body. The JSON path is unchanged. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
agent-framework-openai
OpenAI integration for Microsoft Agent Framework.
This package provides:
OpenAIChatClientfor the OpenAI Responses APIOpenAIChatCompletionClientfor the Chat Completions APIOpenAIEmbeddingClientfor embeddings
Installation
pip install agent-framework-openai
Which chat client should I use?
Use OpenAIChatClient for new work unless you specifically need the Chat Completions API.
OpenAIChatClientuses the Responses API and is the preferred general-purpose chat client.OpenAIChatCompletionClientuses the Chat Completions API and is mainly for compatibility with existing Chat Completions-based integrations.
The previous deprecated Responses alias has been removed. Use OpenAIChatClient directly.
Environment variables
OpenAI
These variables are used when the client is configured for OpenAI:
| Variable | Purpose |
|---|---|
OPENAI_API_KEY |
OpenAI API key |
OPENAI_ORG_ID |
OpenAI organization ID |
OPENAI_BASE_URL |
Custom OpenAI-compatible base URL |
OPENAI_MODEL |
Generic fallback model |
OPENAI_CHAT_MODEL |
Preferred model for OpenAIChatClient |
OPENAI_CHAT_COMPLETION_MODEL |
Preferred model for OpenAIChatCompletionClient |
OPENAI_EMBEDDING_MODEL |
Preferred model for OpenAIEmbeddingClient |
Model lookup order:
OpenAIChatClient:OPENAI_CHAT_MODEL->OPENAI_MODELOpenAIChatCompletionClient:OPENAI_CHAT_COMPLETION_MODEL->OPENAI_MODELOpenAIEmbeddingClient:OPENAI_EMBEDDING_MODEL->OPENAI_MODEL
These model variables are only consulted when you do not pass model= directly. In other words,
OpenAIChatClient(model="...") ignores OPENAI_CHAT_MODEL, and
OpenAIChatCompletionClient(model="...") ignores OPENAI_CHAT_COMPLETION_MODEL.
Azure OpenAI
These variables are used when the client is configured for Azure OpenAI:
| Variable | Purpose |
|---|---|
AZURE_OPENAI_ENDPOINT |
Azure OpenAI resource endpoint |
AZURE_OPENAI_BASE_URL |
Full Azure OpenAI base URL (.../openai/v1) |
AZURE_OPENAI_API_KEY |
Azure OpenAI API key |
AZURE_OPENAI_API_VERSION |
Azure OpenAI API version |
AZURE_OPENAI_MODEL |
Generic fallback deployment |
AZURE_OPENAI_CHAT_MODEL |
Preferred deployment for OpenAIChatClient |
AZURE_OPENAI_CHAT_COMPLETION_MODEL |
Preferred deployment for OpenAIChatCompletionClient |
AZURE_OPENAI_EMBEDDING_MODEL |
Preferred deployment for OpenAIEmbeddingClient |
Deployment lookup order:
OpenAIChatClient:AZURE_OPENAI_CHAT_MODEL->AZURE_OPENAI_MODELOpenAIChatCompletionClient:AZURE_OPENAI_CHAT_COMPLETION_MODEL->AZURE_OPENAI_MODELOpenAIEmbeddingClient:AZURE_OPENAI_EMBEDDING_MODEL->AZURE_OPENAI_MODEL
For Azure routing, the same rule applies: the client-specific deployment variable is checked first,
then the generic AZURE_OPENAI_MODEL fallback. Passing model= overrides both environment variables.
When both OpenAI and Azure environment variables are present, the generic clients prefer OpenAI
when OPENAI_API_KEY is configured. To use Azure explicitly, pass azure_endpoint or
credential.
OpenAI example
from agent_framework.openai import OpenAIChatClient
client = OpenAIChatClient(model="gpt-4.1")
Azure OpenAI example
from azure.identity.aio import AzureCliCredential
from agent_framework.openai import OpenAIChatClient
client = OpenAIChatClient(
model="my-responses-deployment",
azure_endpoint="https://my-resource.openai.azure.com",
credential=AzureCliCredential(),
)
ChatClient vs ChatCompletionClient
Use OpenAIChatClient when you want the Responses API as your default chat surface.
Use OpenAIChatCompletionClient when you specifically need the Chat Completions API:
from agent_framework.openai import OpenAIChatCompletionClient
client = OpenAIChatCompletionClient(model="gpt-4o-mini")