Files
Eduard van Valkenburg 6e95517659 Python: Split type checkers by target (pyright source, 5 checkers on tests/samples) (#6443)
* 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>
2026-06-18 15:06:20 +00:00

236 lines
7.8 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from collections.abc import AsyncIterable, Awaitable
from typing import Any, Literal, overload
from agent_framework import (
AgentResponse,
AgentResponseUpdate,
AgentSession,
BaseAgent,
Content,
InMemoryHistoryProvider,
Message,
normalize_messages,
)
"""
Custom Agent Implementation Example
This sample demonstrates implementing a custom agent by extending BaseAgent class,
showing the minimal requirements for both streaming and non-streaming responses.
"""
class EchoAgent(BaseAgent):
"""A simple custom agent that echoes user messages with a prefix.
This demonstrates how to create a fully custom agent by extending BaseAgent
and implementing the required run() method with stream support.
"""
echo_prefix: str = "Echo: "
def __init__(
self,
*,
name: str | None = None,
description: str | None = None,
echo_prefix: str = "Echo: ",
**kwargs: Any,
) -> None:
"""Initialize the EchoAgent.
Args:
name: The name of the agent.
description: The description of the agent.
echo_prefix: The prefix to add to echoed messages.
**kwargs: Additional keyword arguments passed to BaseAgent.
"""
super().__init__(
name=name,
description=description,
**kwargs,
)
self.echo_prefix = echo_prefix
@overload
def run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
stream: Literal[False] = False,
session: AgentSession | None = None,
**kwargs: Any,
) -> asyncio.Future[AgentResponse]: ...
@overload
def run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
stream: Literal[True],
session: AgentSession | None = None,
**kwargs: Any,
) -> AsyncIterable[AgentResponseUpdate]: ...
def run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
stream: bool = False,
session: AgentSession | None = None,
**kwargs: Any,
) -> "AsyncIterable[AgentResponseUpdate] | Awaitable[AgentResponse]":
"""Execute the agent and return a response.
Args:
messages: The message(s) to process.
stream: If True, return an async iterable of updates. If False, return an awaitable response.
session: The conversation session (optional).
**kwargs: Additional keyword arguments.
Returns:
When stream=False: An awaitable AgentResponse containing the agent's reply.
When stream=True: An async iterable of AgentResponseUpdate objects.
"""
if stream:
return self._run_stream(messages=messages, session=session, **kwargs)
return self._run(messages=messages, session=session, **kwargs)
async def _run(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
session: AgentSession | None = None,
**kwargs: Any,
) -> AgentResponse:
"""Non-streaming implementation."""
# Normalize input messages to a list
normalized_messages = normalize_messages(messages)
if not normalized_messages:
response_message = Message(
role="assistant",
contents=[
Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")
],
)
else:
# For simplicity, echo the last user message
last_message = normalized_messages[-1]
if last_message.text:
echo_text = f"{self.echo_prefix}{last_message.text}"
else:
echo_text = f"{self.echo_prefix}[Non-text message received]"
response_message = Message(role="assistant", contents=[Content.from_text(text=echo_text)])
# Store messages in session state if provided
if session is not None:
stored = session.state.setdefault(InMemoryHistoryProvider.DEFAULT_SOURCE_ID, {}).setdefault("messages", [])
stored.extend(normalized_messages)
stored.append(response_message)
return AgentResponse(messages=[response_message])
async def _run_stream(
self,
messages: str | Message | list[str] | list[Message] | None = None,
*,
session: AgentSession | None = None,
**kwargs: Any,
) -> AsyncIterable[AgentResponseUpdate]:
"""Streaming implementation."""
# Normalize input messages to a list
normalized_messages = normalize_messages(messages)
if not normalized_messages:
response_text = "Hello! I'm a custom echo agent. Send me a message and I'll echo it back."
else:
# For simplicity, echo the last user message
last_message = normalized_messages[-1]
if last_message.text:
response_text = f"{self.echo_prefix}{last_message.text}"
else:
response_text = f"{self.echo_prefix}[Non-text message received]"
# Simulate streaming by yielding the response word by word
words = response_text.split()
for i, word in enumerate(words):
# Add space before word except for the first one
chunk_text = f" {word}" if i > 0 else word
yield AgentResponseUpdate(
contents=[Content.from_text(text=chunk_text)],
role="assistant",
)
# Small delay to simulate streaming
await asyncio.sleep(0.1)
# Store messages in session state if provided
if session is not None:
complete_response = Message(role="assistant", contents=[Content.from_text(text=response_text)])
stored = session.state.setdefault(InMemoryHistoryProvider.DEFAULT_SOURCE_ID, {}).setdefault("messages", [])
stored.extend(normalized_messages)
stored.append(complete_response)
async def main() -> None:
"""Demonstrates how to use the custom EchoAgent."""
print("=== Custom Agent Example ===\n")
# Create EchoAgent
print("--- EchoAgent Example ---")
echo_agent = EchoAgent(
name="EchoBot", description="A simple agent that echoes messages with a prefix", echo_prefix="🔊 Echo: "
)
# Test non-streaming
print(f"Agent Name: {echo_agent.name}")
print(f"Agent ID: {echo_agent.id}")
query = "Hello, custom agent!"
print(f"\nUser: {query}")
result = await echo_agent.run(query)
print(f"Agent: {result.messages[0].text}")
# Test streaming
query2 = "This is a streaming test"
print(f"\nUser: {query2}")
print("Agent: ", end="", flush=True)
stream = echo_agent.run(query2, stream=True)
assert isinstance(stream, AsyncIterable)
async for chunk in stream:
if chunk.text:
print(chunk.text, end="", flush=True)
print()
# Example with sessions
print("\n--- Using Custom Agent with Session ---")
session = echo_agent.create_session()
# First message
result1 = await echo_agent.run("First message", session=session)
print("User: First message")
print(f"Agent: {result1.messages[0].text}")
# Second message in same thread
result2 = await echo_agent.run("Second message", session=session)
print("User: Second message")
print(f"Agent: {result2.messages[0].text}")
# Check conversation history
memory_state = session.state.get(InMemoryHistoryProvider.DEFAULT_SOURCE_ID, {})
messages = memory_state.get("messages", [])
if messages:
print(f"\nSession contains {len(messages)} messages in history")
else:
print("\nSession has no messages stored")
if __name__ == "__main__":
asyncio.run(main())