Foundry Evals integration for Python

Merged and refactored eval module per Eduard's PR review:

- Merge _eval.py + _local_eval.py into single _evaluation.py
- Convert EvalItem from dataclass to regular class
- Rename to_dict() to to_eval_data()
- Convert _AgentEvalData to TypedDict
- Simplify check system: unified async pattern with isawaitable
- Parallelize checks and evaluators with asyncio.gather
- Add all/any mode to tool_called_check
- Fix bool(passed) truthy bug in _coerce_result
- Remove deprecated function_evaluator/async_function_evaluator aliases
- Remove _MinimalAgent, tighten evaluate_agent signature
- Set self.name in __init__ (LocalEvaluator, FoundryEvals)
- Limit FoundryEvals to AsyncOpenAI only
- Type project_client as AIProjectClient
- Remove NotImplementedError continuous eval code
- Add evaluation samples in 02-agents/ and 03-workflows/
- Update all imports and tests (167 passing)

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

python/samples/02-agents/evaluation/evaluate_agent.py

@@ -0,0 +1,68 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Evaluate an agent with local checks — no API keys needed.
+
+Demonstrates the simplest evaluation workflow:
+1. Define checks using the @evaluator decorator
+2. Run evaluate_agent() which calls agent.run() under the covers
+3. Assert results in CI or inspect interactively
+
+Usage:
+    uv run python samples/02-agents/evaluation/evaluate_agent.py
+"""
+
+import asyncio
+
+from agent_framework import (
+    Agent,
+    LocalEvaluator,
+    evaluate_agent,
+    evaluator,
+    keyword_check,
+)
+
+
+# A custom check — parameter names determine what data you receive
+@evaluator
+def is_helpful(response: str) -> bool:
+    """Check the response isn't empty or a refusal."""
+    refusals = ["i can't", "i'm not able", "i don't know"]
+    return len(response) > 10 and not any(r in response.lower() for r in refusals)
+
+
+async def main():
+    agent = Agent(
+        model="gpt-4o-mini",
+        instructions="You are a helpful weather assistant.",
+    )
+
+    # Combine built-in and custom checks
+    local = LocalEvaluator(
+        keyword_check("weather"),  # response must mention "weather"
+        is_helpful,  # custom check
+    )
+
+    # evaluate_agent() calls agent.run() for each query, then evaluates
+    results = await evaluate_agent(
+        agent=agent,
+        queries=[
+            "What's the weather like in Seattle?",
+            "Will it rain in London tomorrow?",
+            "What should I wear for 30°C weather?",
+        ],
+        evaluators=local,
+    )
+
+    for r in results:
+        print(f"{r.provider}: {r.passed}/{r.total} passed")
+        for item in r.items:
+            print(f"  [{item.status}] Q: {item.input_text[:50]}  A: {item.output_text[:50]}...")
+            for score in item.scores:
+                print(f"    {score.name}: {'✓' if score.passed else '✗'}")
+
+    # Use in CI: will raise AssertionError if any check fails
+    # results[0].assert_passed()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/02-agents/evaluation/evaluate_with_expected.py

@@ -0,0 +1,64 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Evaluate an agent with expected outputs and tool call checks.
+
+Demonstrates ground-truth comparison and tool usage evaluation:
+1. Provide expected outputs alongside queries
+2. Use built-in tool_calls_present for tool verification
+3. Combine multiple evaluation criteria
+
+Usage:
+    uv run python samples/02-agents/evaluation/evaluate_with_expected.py
+"""
+
+import asyncio
+
+from agent_framework import (
+    Agent,
+    LocalEvaluator,
+    evaluate_agent,
+    evaluator,
+    tool_calls_present,
+)
+
+
+@evaluator
+def response_matches_expected(response: str, expected_output: str) -> float:
+    """Score based on word overlap with expected output."""
+    if not expected_output:
+        return 1.0
+    response_words = set(response.lower().split())
+    expected_words = set(expected_output.lower().split())
+    return len(response_words & expected_words) / max(len(expected_words), 1)
+
+
+async def main():
+    agent = Agent(
+        model="gpt-4o-mini",
+        instructions="You are a math tutor. Answer concisely.",
+    )
+
+    local = LocalEvaluator(
+        response_matches_expected,
+        tool_calls_present,  # verifies expected tools were called
+    )
+
+    results = await evaluate_agent(
+        agent=agent,
+        queries=["What is 2 + 2?", "What is the square root of 144?"],
+        expected_output=["4", "12"],
+        expected_tool_calls=[
+            [],  # no tools expected for simple math
+            [],
+        ],
+        evaluators=local,
+    )
+
+    for r in results:
+        print(f"{r.provider}: {r.passed}/{r.total} passed")
+        for item in r.items:
+            print(f"  [{item.status}] {item.input_text} → {item.output_text[:80]}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())