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>
Hosting Samples
This directory contains Python samples that demonstrate different ways to host Agent Framework agents. Use this page to choose the hosting model that best fits your scenario, then continue to the README in the relevant subdirectory.
Hosting Options
| Option | Use this when you need... | Start here |
|---|---|---|
| A2A | Agent-to-Agent protocol interoperability or remote agent invocation. | a2a/README.md |
| Azure Functions | HTTP or serverless hosting on Azure Functions. | azure_functions/README.md |
| Durable Task | Durable execution, long-running flows, or orchestration patterns. | durabletask/README.md |
| Foundry Hosted Agents | Azure AI Foundry hosted agent deployment. | foundry-hosted-agents/README.md |
How to Choose
- Start with A2A if you want one agent to call or expose another agent over the A2A protocol.
- Start with Azure Functions if you want an HTTP-hosted or serverless entry point using Azure Functions.
- Start with Durable Task if you need persistent state, durable workflows, or orchestration across multiple steps.
- Start with Foundry Hosted Agents if you want to package and deploy an agent as a hosted agent in Azure AI Foundry.
Common Prerequisites
Most hosting samples share a small set of prerequisites:
- A supported Python environment for running the samples locally.
- An Azure AI Foundry project endpoint and model deployment name for
FOUNDRY_PROJECT_ENDPOINTandFOUNDRY_MODEL. - Azure CLI authentication via
az loginwhen the sample usesAzureCliCredential. - Any hosting-specific tools or extra services called out in the subdirectory README.
Next Steps
- Pick the hosting approach that matches your scenario.
- Open the corresponding README for setup and run instructions.
- Follow that sample's environment, dependency, and execution steps.