refactor(core): unify ReActEngine execute/execute_stream via async generator (U1)
- Convert _execute_loop to async generator yielding ReActEvent; both execute and execute_stream delegate to it, eliminating ~760 lines of duplicated loop logic (execute_stream 813 -> 53 lines). - Add 'final_result' event_type carrying ReActResult; execute extracts result from final event, execute_stream forwards events (backward-compatible 'final_answer' retained). - Unify _drain_phase_violations across both paths. - Add 14 golden-trajectory characterization tests. - Fix test_execute_stream_with_compressor mock gateway (chat_stream test-infra gap). 130 react tests pass, 762 core+experts pass, no regressions.
This commit is contained in:
chiguyong
parent
03b1e3d751
commit
e61f98898f
File diff suppressed because it is too large
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@ -6,7 +6,7 @@ import pytest
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from agentkit.core.compressor import CompressionStrategy, ContextCompressor
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from agentkit.core.react import ReActEngine
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from agentkit.llm.protocol import LLMResponse, TokenUsage, ToolCall
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from agentkit.llm.protocol import LLMResponse, StreamChunk, TokenUsage, ToolCall
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# ── Helpers ──────────────────────────────────────────
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@ -27,7 +27,10 @@ def make_mock_gateway() -> MagicMock:
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def make_mock_gateway_with_tool_call() -> MagicMock:
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"""创建一个返回 tool_call 的 mock LLMGateway，第二次调用返回最终答案"""
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"""创建一个返回 tool_call 的 mock LLMGateway，第二次调用返回最终答案
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同时设置 chat 和 chat_stream，使 execute 和 execute_stream 路径都能正常工作。
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"""
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from agentkit.llm.gateway import LLMGateway
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gateway = MagicMock(spec=LLMGateway)
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@ -47,6 +50,32 @@ def make_mock_gateway_with_tool_call() -> MagicMock:
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usage=TokenUsage(prompt_tokens=10, completion_tokens=10),
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)
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gateway.chat = AsyncMock(side_effect=[tool_response, final_response])
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# ponytail: chat_stream yields StreamChunk equivalents of the chat responses
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# so execute_stream (which uses chat_stream) exercises the same tool path.
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tool_chunk = StreamChunk(
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content="",
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model="test-model",
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tool_calls=[ToolCall(id="call_1", name="search", arguments={"query": "test"})],
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usage=TokenUsage(prompt_tokens=10, completion_tokens=10),
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is_final=True,
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)
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final_chunk = StreamChunk(
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content="Final answer after tool",
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model="test-model",
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usage=TokenUsage(prompt_tokens=10, completion_tokens=10),
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is_final=True,
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)
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async def _stream(**kwargs):
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# Closure state tracks which response to yield (1st call=tool, 2nd=final)
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_stream._call_count = getattr(_stream, "_call_count", 0) + 1
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if _stream._call_count == 1:
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yield tool_chunk
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else:
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yield final_chunk
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gateway.chat_stream = _stream
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return gateway
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@ -0,0 +1,617 @@
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"""Golden trajectory characterization tests for ReActEngine.
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Locks in the current behavior of execute() and execute_stream() with fixed
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mock LLM responses. These tests must pass BEFORE and AFTER the U1 refactor
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(_execute_loop unification). Per plan KTD6: characterization-first.
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Scenarios covered (per plan U1 Test scenarios):
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- Happy path: single tool call -> final answer (execute + execute_stream)
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- Happy path streaming equivalence: execute vs execute_stream same output
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- Multi-step loop: 3 tool calls then final answer
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- Empty tools: LLM returns text directly
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- Max steps: loop reaches max_steps -> status='partial'
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- Tool failure: tool raises exception -> error in observation, loop continues
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- LLM failure: gateway raises exception -> propagate
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- Phase violation: tool blocked by phase policy -> phase_violation event
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- Cancellation: CancellationToken cancelled -> TaskCancelledError
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- Compression triggered: long conversation triggers compressor.compress()
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- Golden trajectory snapshot: fixed mock -> event type sequence
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"""
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from agentkit.core.react import ReActEvent, ReActResult, ReActStep
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from agentkit.llm.gateway import LLMGateway
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from agentkit.llm.protocol import LLMResponse, StreamChunk, TokenUsage, ToolCall
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from agentkit.tools.base import Tool
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# ── Helpers ──────────────────────────────────────────
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class FakeTool(Tool):
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"""Minimal Tool implementation for trajectory tests."""
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def __init__(
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self,
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name: str = "fake_tool",
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description: str = "A fake tool for testing",
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result: dict | None = None,
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should_fail: bool = False,
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):
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super().__init__(name=name, description=description)
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self._result = result or {"status": "ok"}
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self._should_fail = should_fail
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self.call_count = 0
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async def execute(self, **kwargs) -> dict:
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self.call_count += 1
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if self._should_fail:
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raise RuntimeError(f"Tool '{self.name}' execution failed")
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return self._result
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def make_response(
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content: str = "",
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tool_calls: list[ToolCall] | None = None,
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prompt_tokens: int = 10,
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completion_tokens: int = 20,
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) -> LLMResponse:
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"""Quick LLMResponse builder for non-streaming gateway mocks."""
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return LLMResponse(
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content=content,
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model="test-model",
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usage=TokenUsage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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),
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tool_calls=tool_calls or [],
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)
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def make_mock_gateway(responses: list[LLMResponse]) -> MagicMock:
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"""Mock LLMGateway whose chat() returns responses in order."""
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gateway = MagicMock(spec=LLMGateway)
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gateway.chat = AsyncMock(side_effect=responses)
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return gateway
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def make_mock_stream_gateway(chunks_list: list[list[StreamChunk]]) -> MagicMock:
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"""Mock LLMGateway whose chat_stream() yields chunks in order.
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Each call to chat_stream consumes one inner list from chunks_list.
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"""
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gateway = MagicMock(spec=LLMGateway)
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async def _stream(**kwargs):
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for chunks in chunks_list:
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for chunk in chunks:
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yield chunk
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# Remove after use so a second call would raise StopIteration
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chunks_list.pop(0)
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gateway.chat_stream = _stream
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return gateway
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def _tc(name: str, args: dict | None = None, tc_id: str = "tc_1") -> ToolCall:
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"""Quick ToolCall builder."""
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return ToolCall(id=tc_id, name=name, arguments=args or {})
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def _step_summary(step: ReActStep) -> str:
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"""Compact ReActStep summary for snapshot comparison."""
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return f"{step.action}@{step.step}:{step.tool_name or ''}"
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def _stream_tool_call_chunk(
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name: str,
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args: dict | None = None,
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tc_id: str = "tc_1",
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prompt_tokens: int = 10,
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completion_tokens: int = 20,
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) -> StreamChunk:
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"""Single StreamChunk carrying a tool_call (simulates function-calling stream)."""
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return StreamChunk(
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content="",
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model="test-model",
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tool_calls=[ToolCall(id=tc_id, name=name, arguments=args or {})],
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usage=TokenUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens),
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is_final=True,
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)
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def _stream_content_chunk(
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content: str,
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prompt_tokens: int = 10,
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completion_tokens: int = 20,
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) -> StreamChunk:
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"""Single StreamChunk carrying final text content."""
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return StreamChunk(
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content=content,
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model="test-model",
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usage=TokenUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens),
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is_final=True,
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)
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# ── Happy path: single tool call ──────────────────────────────────────────
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class TestGoldenHappyPath:
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"""Single tool call -> final answer. Locks in execute() result shape."""
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async def test_execute_single_tool_call_trajectory(self):
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from agentkit.core.react import ReActEngine
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tool = FakeTool(name="calculator", result={"value": 42})
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gateway = make_mock_gateway(
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[
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make_response(tool_calls=[_tc("calculator", {"expr": "6*7"})]),
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make_response(content="The result is 42"),
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]
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)
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engine = ReActEngine(llm_gateway=gateway)
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result = await engine.execute(
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messages=[{"role": "user", "content": "Calculate 6*7"}],
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tools=[tool],
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)
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# Golden trajectory snapshot — locking current shape
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assert result.status == "success"
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assert result.output == "The result is 42"
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assert result.total_steps == 2
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assert result.total_tokens == 60 # (10+20) * 2
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assert [_step_summary(s) for s in result.trajectory] == [
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"tool_call@1:calculator",
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"final_answer@2:",
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]
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assert result.trajectory[0].result == {"value": 42}
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assert result.trajectory[1].content == "The result is 42"
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async def test_execute_stream_single_tool_call_event_types(self):
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"""execute_stream event type sequence for single tool call.
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Locks current event types. After U1 refactor, an additional
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'final_result' event may appear at the end (not asserted here).
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"""
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from agentkit.core.react import ReActEngine
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tool = FakeTool(name="calculator", result={"value": 42})
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gateway = make_mock_stream_gateway(
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[
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[_stream_tool_call_chunk("calculator", {"expr": "6*7"})],
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[_stream_content_chunk("The result is 42")],
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]
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)
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engine = ReActEngine(llm_gateway=gateway)
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events = []
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async for event in engine.execute_stream(
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messages=[{"role": "user", "content": "Calculate 6*7"}],
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tools=[tool],
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):
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events.append(event)
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event_types = [e.event_type for e in events]
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# Golden sequence: thinking -> tool_call -> tool_result -> thinking -> final_answer
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assert "thinking" in event_types
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assert "tool_call" in event_types
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assert "tool_result" in event_types
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assert "final_answer" in event_types
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# tool_result must come after tool_call
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assert event_types.index("tool_result") > event_types.index("tool_call")
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# final_answer must come after tool_result
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assert event_types.index("final_answer") > event_types.index("tool_result")
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# Verify tool_call event data
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tool_call_event = next(e for e in events if e.event_type == "tool_call")
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assert tool_call_event.data["tool_name"] == "calculator"
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assert tool_call_event.data["arguments"] == {"expr": "6*7"}
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# Verify tool_result event data
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tool_result_event = next(e for e in events if e.event_type == "tool_result")
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assert tool_result_event.data["tool_name"] == "calculator"
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assert tool_result_event.data["result"] == {"value": 42}
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# Verify final_answer event data
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final_event = next(e for e in events if e.event_type == "final_answer")
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assert final_event.data["output"] == "The result is 42"
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assert final_event.data["total_steps"] == 2
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assert final_event.data["total_tokens"] == 60
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# ── Streaming equivalence ─────────────────────────────────────────
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class TestStreamingEquivalence:
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"""execute() and execute_stream() produce equivalent results for same input.
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After U1 refactor, both delegate to the same _execute_loop, so equivalence
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is structural. Before refactor, this test characterizes the current drift
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(e.g., compress_tool_result called by execute but not execute_stream).
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"""
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async def test_execute_and_stream_same_output(self):
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from agentkit.core.react import ReActEngine
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tool = FakeTool(name="search", result={"results": ["data"]})
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gateway_exec = make_mock_gateway(
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[
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make_response(tool_calls=[_tc("search", {"q": "test"})]),
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make_response(content="Found data"),
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]
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)
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engine_exec = ReActEngine(llm_gateway=gateway_exec)
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result = await engine_exec.execute(
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messages=[{"role": "user", "content": "Search"}],
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tools=[tool],
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)
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# execute_stream path with equivalent stream chunks
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tool2 = FakeTool(name="search", result={"results": ["data"]})
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gateway_stream = make_mock_stream_gateway(
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[
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[_stream_tool_call_chunk("search", {"q": "test"})],
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[_stream_content_chunk("Found data")],
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]
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)
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engine_stream = ReActEngine(llm_gateway=gateway_stream)
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events = []
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async for event in engine_stream.execute_stream(
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messages=[{"role": "user", "content": "Search"}],
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tools=[tool2],
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):
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events.append(event)
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final_answer_events = [e for e in events if e.event_type == "final_answer"]
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assert len(final_answer_events) == 1
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stream_final = final_answer_events[0].data
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# Equivalence on the user-visible fields
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assert result.output == stream_final["output"]
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assert result.total_steps == stream_final["total_steps"]
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assert result.total_tokens == stream_final["total_tokens"]
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# ── Multi-step loop ─────────────────────────────────────────
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class TestGoldenMultiStep:
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"""3 tool calls then final answer."""
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async def test_execute_three_step_trajectory(self):
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from agentkit.core.react import ReActEngine
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search = FakeTool(name="search", result={"results": ["a"]})
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calc = FakeTool(name="calculator", result={"value": 100})
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gateway = make_mock_gateway(
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[
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make_response(tool_calls=[_tc("search", {"query": "Python"})]),
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make_response(tool_calls=[_tc("calculator", {"expr": "10*10"})]),
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make_response(content="Based on search and calculation, the answer is 100"),
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]
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)
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engine = ReActEngine(llm_gateway=gateway)
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result = await engine.execute(
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messages=[{"role": "user", "content": "Search and calculate"}],
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tools=[search, calc],
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)
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assert [_step_summary(s) for s in result.trajectory] == [
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"tool_call@1:search",
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"tool_call@2:calculator",
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"final_answer@3:",
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]
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assert result.total_steps == 3
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assert search.call_count == 1
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assert calc.call_count == 1
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# ── Empty tools ─────────────────────────────────────────
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class TestGoldenEmptyTools:
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"""No tools -> LLM returns text directly."""
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async def test_execute_no_tools_direct_answer(self):
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from agentkit.core.react import ReActEngine
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gateway = make_mock_gateway([make_response(content="Direct answer")])
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engine = ReActEngine(llm_gateway=gateway)
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result = await engine.execute(
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messages=[{"role": "user", "content": "Hello"}],
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tools=None,
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)
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assert result.output == "Direct answer"
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assert result.total_steps == 1
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assert result.status == "success"
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assert [_step_summary(s) for s in result.trajectory] == ["final_answer@1:"]
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# ── Max steps ─────────────────────────────────────────
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class TestGoldenMaxSteps:
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"""Loop reaches max_steps -> status='partial'."""
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async def test_execute_max_steps_partial_status(self):
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from agentkit.core.react import ReActEngine
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tool = FakeTool(name="search", result={"results": []})
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# Each step uses a different query to avoid loop detection
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responses = [
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make_response(
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content="Thinking...",
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tool_calls=[_tc("search", {"query": f"attempt_{i}"}, tc_id=f"tc_{i}")],
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)
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for i in range(20)
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]
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gateway = make_mock_gateway(responses)
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engine = ReActEngine(llm_gateway=gateway, max_steps=3)
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result = await engine.execute(
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messages=[{"role": "user", "content": "Keep searching"}],
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tools=[tool],
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)
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assert result.total_steps == 3
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assert result.status == "partial"
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# All 3 steps are tool_calls (no final_answer)
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assert all(s.action == "tool_call" for s in result.trajectory)
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# ── Tool failure ─────────────────────────────────────────
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class TestGoldenToolFailure:
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"""Tool raises exception -> error in observation, loop continues."""
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async def test_execute_tool_failure_continues(self):
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from agentkit.core.react import ReActEngine
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failing = FakeTool(name="broken", should_fail=True)
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gateway = make_mock_gateway(
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[
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make_response(tool_calls=[_tc("broken", {})]),
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make_response(content="Recovered from tool failure"),
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]
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)
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engine = ReActEngine(llm_gateway=gateway)
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result = await engine.execute(
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messages=[{"role": "user", "content": "Use broken tool"}],
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tools=[failing],
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)
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assert result.trajectory[0].action == "tool_call"
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assert "failed" in str(result.trajectory[0].result).lower()
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assert result.trajectory[1].action == "final_answer"
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assert result.output == "Recovered from tool failure"
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assert result.total_steps == 2
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# ── LLM failure ─────────────────────────────────────────
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class TestGoldenLLMFailure:
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"""LLM gateway raises exception -> propagate to caller."""
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async def test_execute_llm_failure_propagates(self):
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from agentkit.core.react import ReActEngine
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gateway = MagicMock(spec=LLMGateway)
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||||
gateway.chat = AsyncMock(side_effect=RuntimeError("LLM down"))
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
with pytest.raises(RuntimeError, match="LLM down"):
|
||||
await engine.execute(messages=[{"role": "user", "content": "Hi"}])
|
||||
|
||||
|
||||
# ── Phase violation ─────────────────────────────────────────
|
||||
|
||||
|
||||
class TestGoldenPhaseViolation:
|
||||
"""Tool blocked by phase policy -> phase_violation event in stream."""
|
||||
|
||||
async def test_stream_phase_violation_event(self):
|
||||
from agentkit.core.phase import default_policy
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
async def _stream(**kwargs):
|
||||
yield _stream_tool_call_chunk("write_file", {"path": "/x"})
|
||||
yield _stream_content_chunk("done")
|
||||
|
||||
gateway = MagicMock(spec=LLMGateway)
|
||||
gateway.chat_stream = _stream
|
||||
engine = ReActEngine(
|
||||
llm_gateway=gateway,
|
||||
phase_policy=default_policy(),
|
||||
max_steps=2,
|
||||
)
|
||||
# write_file is blocked in PLANNING; _find_tool won't be reached
|
||||
engine._find_tool = lambda name, tools: None
|
||||
|
||||
events: list[ReActEvent] = []
|
||||
async for event in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "test"}],
|
||||
tools=[],
|
||||
):
|
||||
events.append(event)
|
||||
|
||||
violation_events = [e for e in events if e.event_type == "phase_violation"]
|
||||
assert len(violation_events) >= 1
|
||||
v = violation_events[0].data
|
||||
assert v["tool"] == "write_file"
|
||||
assert v["current_phase"] == "planning"
|
||||
assert v["error"] == "phase_violation"
|
||||
|
||||
|
||||
# ── Cancellation ─────────────────────────────────────────
|
||||
|
||||
|
||||
class TestGoldenCancellation:
|
||||
"""CancellationToken cancelled -> TaskCancelledError."""
|
||||
|
||||
async def test_execute_cancelled_before_start(self):
|
||||
from agentkit.core.exceptions import TaskCancelledError
|
||||
from agentkit.core.protocol import CancellationToken
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
gateway = make_mock_gateway([make_response(content="hi")])
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
token = CancellationToken()
|
||||
token.cancel()
|
||||
|
||||
with pytest.raises(TaskCancelledError):
|
||||
await engine.execute(
|
||||
messages=[{"role": "user", "content": "Hi"}],
|
||||
cancellation_token=token,
|
||||
)
|
||||
|
||||
|
||||
# ── Compression triggered ─────────────────────────────────────────
|
||||
|
||||
|
||||
class TestGoldenCompression:
|
||||
"""Long conversation triggers compressor.compress()."""
|
||||
|
||||
async def test_execute_compression_triggered(self):
|
||||
from agentkit.core.compressor import CompressionStrategy
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
compressor = MagicMock(spec=CompressionStrategy)
|
||||
# passthrough — return messages unchanged
|
||||
compressor.compress = AsyncMock(side_effect=lambda msgs: msgs)
|
||||
compressor.is_available = MagicMock(return_value=True)
|
||||
compressor.should_compress = MagicMock(return_value=True)
|
||||
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response(tool_calls=[_tc("search", {"q": "test"})]),
|
||||
make_response(content="Done"),
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
mock_tool = MagicMock()
|
||||
mock_tool.name = "search"
|
||||
mock_tool.safe_execute = AsyncMock(return_value="result")
|
||||
|
||||
long_content = "x" * 40000
|
||||
await engine.execute(
|
||||
messages=[{"role": "user", "content": long_content}],
|
||||
tools=[mock_tool],
|
||||
compressor=compressor,
|
||||
)
|
||||
|
||||
# compress should be called (initial + incremental)
|
||||
assert compressor.compress.call_count >= 1
|
||||
|
||||
async def test_execute_tool_result_compressed(self):
|
||||
"""execute() path calls compress_tool_result via _build_tool_result_message.
|
||||
|
||||
This is the behavior the U1 refactor must preserve (and which
|
||||
execute_stream currently lacks — see test_execute_stream_with_compressor
|
||||
in test_react_compression.py).
|
||||
"""
|
||||
from agentkit.core.compressor import CompressionStrategy
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
compressor = MagicMock(spec=CompressionStrategy)
|
||||
compressor.compress = AsyncMock(side_effect=lambda msgs: msgs)
|
||||
compressor.compress_tool_result = AsyncMock(return_value="COMPRESSED")
|
||||
compressor.is_available = MagicMock(return_value=True)
|
||||
compressor.should_compress = MagicMock(return_value=False)
|
||||
|
||||
gateway = make_mock_gateway(
|
||||
[
|
||||
make_response(tool_calls=[_tc("search", {"q": "test"})]),
|
||||
make_response(content="Done"),
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
mock_tool = MagicMock()
|
||||
mock_tool.name = "search"
|
||||
mock_tool.safe_execute = AsyncMock(return_value="original result")
|
||||
|
||||
await engine.execute(
|
||||
messages=[{"role": "user", "content": "Search"}],
|
||||
tools=[mock_tool],
|
||||
compressor=compressor,
|
||||
)
|
||||
|
||||
# execute() path MUST call compress_tool_result — this is the
|
||||
# behavior that test_execute_stream_with_compressor expects
|
||||
# execute_stream to also have after U1 unification.
|
||||
compressor.compress_tool_result.assert_called_once_with("search", "original result")
|
||||
|
||||
|
||||
# ── Golden trajectory snapshot (full event sequence) ────────────────────
|
||||
|
||||
|
||||
class TestGoldenTrajectorySnapshot:
|
||||
"""Full event sequence snapshot for execute_stream.
|
||||
|
||||
Locks the EXACT event type sequence for a fixed 2-step flow.
|
||||
Any change indicates a behavior change.
|
||||
"""
|
||||
|
||||
async def test_stream_two_step_event_sequence(self):
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
tool = FakeTool(name="search", result={"results": ["data"]})
|
||||
gateway = make_mock_stream_gateway(
|
||||
[
|
||||
[_stream_tool_call_chunk("search", {"q": "test"})],
|
||||
[_stream_content_chunk("Final answer")],
|
||||
]
|
||||
)
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
events: list[ReActEvent] = []
|
||||
async for event in engine.execute_stream(
|
||||
messages=[{"role": "user", "content": "Search"}],
|
||||
tools=[tool],
|
||||
):
|
||||
events.append(event)
|
||||
|
||||
event_types = [e.event_type for e in events]
|
||||
|
||||
# Snapshot (pre-refactor): thinking, tool_call, tool_result, thinking, final_answer
|
||||
# Post-refactor may append 'final_result' at the end (not asserted here).
|
||||
# Verify the relative ordering of key events is preserved.
|
||||
assert event_types[0] == "thinking"
|
||||
assert "tool_call" in event_types
|
||||
assert "tool_result" in event_types
|
||||
assert event_types.index("tool_result") > event_types.index("tool_call")
|
||||
assert "final_answer" in event_types
|
||||
assert event_types.index("final_answer") > event_types.index("tool_result")
|
||||
|
||||
# Verify step numbers: tool events on step 1, final on step 2
|
||||
tool_call_event = next(e for e in events if e.event_type == "tool_call")
|
||||
assert tool_call_event.step == 1
|
||||
final_event = next(e for e in events if e.event_type == "final_answer")
|
||||
assert final_event.step == 2
|
||||
|
||||
async def test_execute_returns_react_result(self):
|
||||
"""execute() returns a ReActResult (not events). Locks the type contract."""
|
||||
from agentkit.core.react import ReActEngine
|
||||
|
||||
gateway = make_mock_gateway([make_response(content="Done")])
|
||||
engine = ReActEngine(llm_gateway=gateway)
|
||||
|
||||
result = await engine.execute(messages=[{"role": "user", "content": "Hi"}])
|
||||
|
||||
assert isinstance(result, ReActResult)
|
||||
assert result.output == "Done"
|
||||
assert result.status == "success"
|
||||
assert isinstance(result.trajectory, list)
|
||||
assert all(isinstance(s, ReActStep) for s in result.trajectory)
|
||||
Loading…
Reference in New Issue