Timothy
1236 lines
39 KiB
Python
1236 lines
39 KiB
Python
"""
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Integration tests for EventLoopNode lifecycle
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Default: real LLM (cerebras/zai-glm-4.7).
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Set HIVE_TEST_LLM_MODE=mock for fast, deterministic, no-API tests.
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Set HIVE_TEST_LLM_MODEL=<model> to override the real model.
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"""
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from __future__ import annotations
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import os
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from collections.abc import AsyncIterator, Callable
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from dataclasses import dataclass
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from framework.graph.edge import EdgeCondition, EdgeSpec, GraphSpec
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from framework.graph.event_loop_node import (
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EventLoopNode,
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JudgeVerdict,
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LoopConfig,
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)
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from framework.graph.executor import GraphExecutor
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from framework.graph.goal import Goal
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from framework.graph.node import (
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NodeContext,
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NodeProtocol,
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NodeResult,
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NodeSpec,
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SharedMemory,
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)
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from framework.llm.provider import LLMProvider, LLMResponse, Tool, ToolResult, ToolUse
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from framework.llm.stream_events import (
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FinishEvent,
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StreamEvent,
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TextDeltaEvent,
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ToolCallEvent,
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)
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from framework.runtime.core import Runtime
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from framework.runtime.event_bus import AgentEvent, EventBus, EventType
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# ---------------------------------------------------------------------------
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# Config: mock / real toggle
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# ---------------------------------------------------------------------------
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USE_MOCK_LLM = os.environ.get("HIVE_TEST_LLM_MODE", "mock").lower() == "mock"
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LLM_MODEL = os.environ.get("HIVE_TEST_LLM_MODEL", "cerebras/zai-glm-4.7")
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# ---------------------------------------------------------------------------
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# ScriptableMockLLMProvider
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# ---------------------------------------------------------------------------
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@dataclass
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class StreamScript:
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"""One scripted stream() invocation.
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- text only -> yields TextDeltaEvent + FinishEvent (turn ends)
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- tool_calls -> yields ToolCallEvent(s) + FinishEvent (node executes tools, calls stream again)
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"""
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text: str = ""
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tool_calls: list[dict] | None = None # [{name, id, input}, ...]
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class ScriptableMockLLMProvider(LLMProvider):
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"""Mock LLM that plays back a flat list of StreamScript entries.
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Each call to stream() pops the next entry and yields the corresponding events.
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complete() returns a fixed summary (used by _generate_compaction_summary).
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"""
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def __init__(self, scripts: list[StreamScript] | None = None):
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self._scripts: list[StreamScript] = list(scripts or [])
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self._call_index = 0
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self.model = "mock-scriptable"
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def complete(
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self,
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messages: list[dict[str, Any]],
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system: str = "",
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tools: list[Tool] | None = None,
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max_tokens: int = 1024,
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response_format: dict[str, Any] | None = None,
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json_mode: bool = False,
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) -> LLMResponse:
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return LLMResponse(
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content="Conversation summary for compaction.",
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model=self.model,
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input_tokens=10,
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output_tokens=10,
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)
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def complete_with_tools(
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self,
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messages: list[dict[str, Any]],
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system: str = "",
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tools: list[Tool] | None = None,
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tool_executor: Callable[[ToolUse], ToolResult] | None = None,
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max_iterations: int = 10,
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max_tokens: int = 1024,
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) -> LLMResponse:
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return self.complete(messages, system, tools, max_tokens)
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async def stream(
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self,
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messages: list[dict[str, Any]],
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system: str = "",
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tools: list[Tool] | None = None,
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max_tokens: int = 4096,
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) -> AsyncIterator[StreamEvent]:
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if self._call_index >= len(self._scripts):
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# Fallback: yield empty text finish so node can terminate
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yield TextDeltaEvent(content="(no more scripts)", snapshot="(no more scripts)")
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yield FinishEvent(stop_reason="end_turn", input_tokens=5, output_tokens=5)
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return
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script = self._scripts[self._call_index]
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self._call_index += 1
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if script.tool_calls:
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# Yield tool call events
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for tc in script.tool_calls:
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yield ToolCallEvent(
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tool_use_id=tc.get("id", f"tc_{self._call_index}"),
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tool_name=tc["name"],
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tool_input=tc.get("input", {}),
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)
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if script.text:
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yield TextDeltaEvent(content=script.text, snapshot=script.text)
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yield FinishEvent(stop_reason="tool_use", input_tokens=10, output_tokens=10)
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else:
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# Text-only response
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if script.text:
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yield TextDeltaEvent(content=script.text, snapshot=script.text)
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yield FinishEvent(stop_reason="end_turn", input_tokens=10, output_tokens=10)
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# ---------------------------------------------------------------------------
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# MockConversationStore
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# ---------------------------------------------------------------------------
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class MockConversationStore:
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"""In-memory ConversationStore for testing persistence and restore."""
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def __init__(self) -> None:
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self._parts: dict[int, dict[str, Any]] = {}
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self._meta: dict[str, Any] | None = None
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self._cursor: dict[str, Any] | None = None
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async def write_part(self, seq: int, data: dict[str, Any]) -> None:
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self._parts[seq] = data
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async def read_parts(self) -> list[dict[str, Any]]:
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return [self._parts[k] for k in sorted(self._parts)]
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async def write_meta(self, data: dict[str, Any]) -> None:
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self._meta = data
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async def read_meta(self) -> dict[str, Any] | None:
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return self._meta
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async def write_cursor(self, data: dict[str, Any]) -> None:
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self._cursor = data
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async def read_cursor(self) -> dict[str, Any] | None:
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return self._cursor
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async def delete_parts_before(self, seq: int) -> None:
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keys_to_delete = [k for k in self._parts if k < seq]
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for k in keys_to_delete:
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del self._parts[k]
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async def close(self) -> None:
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pass
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async def destroy(self) -> None:
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self._parts.clear()
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self._meta = None
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self._cursor = None
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# ---------------------------------------------------------------------------
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# Judge helpers
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# ---------------------------------------------------------------------------
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class AlwaysAcceptJudge:
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"""Judge that always accepts."""
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async def evaluate(self, context: dict[str, Any]) -> JudgeVerdict:
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return JudgeVerdict(action="ACCEPT")
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class AlwaysRetryJudge:
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"""Judge that always retries with feedback."""
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async def evaluate(self, context: dict[str, Any]) -> JudgeVerdict:
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return JudgeVerdict(action="RETRY", feedback="Try harder.")
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class CountingJudge:
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"""Judge that retries N times then accepts."""
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def __init__(self, retry_count: int = 1):
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self._retry_count = retry_count
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self._calls = 0
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async def evaluate(self, context: dict[str, Any]) -> JudgeVerdict:
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self._calls += 1
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if self._calls <= self._retry_count:
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return JudgeVerdict(action="RETRY", feedback=f"Retry {self._calls}")
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return JudgeVerdict(action="ACCEPT")
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def make_llm(scripts: list[StreamScript] | None = None) -> LLMProvider:
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"""Create an LLM provider based on the test mode."""
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if USE_MOCK_LLM:
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return ScriptableMockLLMProvider(scripts)
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# Real mode: use LiteLLM
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from framework.llm.litellm import LiteLLMProvider
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return LiteLLMProvider(model=LLM_MODEL)
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def make_tool_executor(results_map: dict[str, str]) -> Callable:
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"""Create a tool executor that returns predetermined results."""
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def executor(tool_use: ToolUse) -> ToolResult:
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content = results_map.get(tool_use.name, f"Unknown tool: {tool_use.name}")
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return ToolResult(
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tool_use_id=tool_use.id,
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content=content,
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is_error=tool_use.name not in results_map,
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)
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return executor
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def make_ctx(
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node_id: str = "test_node",
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llm: LLMProvider | None = None,
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output_keys: list[str] | None = None,
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input_keys: list[str] | None = None,
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input_data: dict[str, Any] | None = None,
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system_prompt: str = "You are a test assistant.",
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client_facing: bool = False,
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available_tools: list[Tool] | None = None,
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) -> NodeContext:
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"""Build a NodeContext for direct EventLoopNode testing."""
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runtime = MagicMock(spec=Runtime)
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runtime.start_run = MagicMock(return_value="run_id")
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runtime.decide = MagicMock(return_value="dec_id")
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runtime.record_outcome = MagicMock()
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runtime.end_run = MagicMock()
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runtime.report_problem = MagicMock()
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runtime.set_node = MagicMock()
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spec = NodeSpec(
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id=node_id,
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name=f"Test {node_id}",
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description="test node",
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node_type="event_loop",
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output_keys=output_keys or [],
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input_keys=input_keys or [],
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system_prompt=system_prompt,
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client_facing=client_facing,
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)
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memory = SharedMemory()
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return NodeContext(
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runtime=runtime,
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node_id=node_id,
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node_spec=spec,
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memory=memory,
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input_data=input_data or {},
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llm=llm,
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available_tools=available_tools or [],
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)
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# ---------------------------------------------------------------------------
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# Fixtures
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# ---------------------------------------------------------------------------
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@pytest.fixture
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def runtime():
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"""Create a mock Runtime."""
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rt = MagicMock(spec=Runtime)
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rt.start_run = MagicMock(return_value="test_run_id")
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rt.decide = MagicMock(return_value="test_decision_id")
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rt.record_outcome = MagicMock()
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rt.end_run = MagicMock()
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rt.report_problem = MagicMock()
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rt.set_node = MagicMock()
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return rt
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@pytest.fixture
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def event_bus():
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"""Create a real EventBus."""
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return EventBus()
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@pytest.fixture(autouse=True)
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def fast_sleep(monkeypatch):
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"""Mock asyncio.sleep to avoid real delays from exponential backoff."""
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monkeypatch.setattr("asyncio.sleep", AsyncMock())
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# ===========================================================================
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# Group 1: Core Lifecycle
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# ===========================================================================
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@pytest.mark.asyncio
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async def test_event_loop_node_in_graph(runtime):
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"""EventLoopNode runs inside GraphExecutor, produces output."""
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scripts = [
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# stream 1: call set_output("result", "ok")
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StreamScript(
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tool_calls=[
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{"name": "set_output", "id": "tc_1", "input": {"key": "result", "value": "ok"}}
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],
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),
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# stream 2: text finish (turn ends, implicit judge accepts because all keys present)
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StreamScript(text="Done."),
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]
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llm = make_llm(scripts)
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node_spec = NodeSpec(
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id="el_node",
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name="Event Loop Node",
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description="test event loop",
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node_type="event_loop",
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output_keys=["result"],
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)
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graph = GraphSpec(
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id="test_graph",
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goal_id="test_goal",
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name="Test Graph",
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entry_node="el_node",
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nodes=[node_spec],
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edges=[],
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terminal_nodes=["el_node"],
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)
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goal = Goal(id="test_goal", name="Test Goal", description="test")
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executor = GraphExecutor(runtime=runtime, llm=llm)
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el_node = EventLoopNode(config=LoopConfig(max_iterations=5))
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executor.register_node("el_node", el_node)
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result = await executor.execute(graph, goal, {})
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assert result.success
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if USE_MOCK_LLM:
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assert result.output.get("result") == "ok"
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else:
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assert "result" in result.output
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@pytest.mark.asyncio
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async def test_event_loop_with_event_bus():
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"""Lifecycle events are published correctly to EventBus."""
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recorded: list[AgentEvent] = []
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async def handler(event: AgentEvent) -> None:
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recorded.append(event)
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bus = EventBus()
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bus.subscribe(
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event_types=[
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EventType.NODE_LOOP_STARTED,
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EventType.NODE_LOOP_ITERATION,
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EventType.NODE_LOOP_COMPLETED,
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],
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handler=handler,
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)
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scripts = [StreamScript(text="All done.")]
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llm = make_llm(scripts)
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ctx = make_ctx(llm=llm, output_keys=[])
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node = EventLoopNode(
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event_bus=bus,
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config=LoopConfig(max_iterations=5),
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)
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result = await node.execute(ctx)
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assert result.success
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event_types = [e.type for e in recorded]
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assert EventType.NODE_LOOP_STARTED in event_types
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assert EventType.NODE_LOOP_ITERATION in event_types
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assert EventType.NODE_LOOP_COMPLETED in event_types
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# Verify ordering: STARTED before ITERATION before COMPLETED
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started_idx = event_types.index(EventType.NODE_LOOP_STARTED)
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iteration_idx = event_types.index(EventType.NODE_LOOP_ITERATION)
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completed_idx = event_types.index(EventType.NODE_LOOP_COMPLETED)
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assert started_idx < iteration_idx < completed_idx
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@pytest.mark.asyncio
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async def test_event_loop_tool_execution():
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"""Custom tools execute, results feed back to LLM."""
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recorded_events: list[AgentEvent] = []
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async def handler(event: AgentEvent) -> None:
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recorded_events.append(event)
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bus = EventBus()
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bus.subscribe(
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event_types=[EventType.TOOL_CALL_STARTED, EventType.TOOL_CALL_COMPLETED],
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handler=handler,
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)
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scripts = [
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# stream 1: call search_crm tool
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StreamScript(
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tool_calls=[{"name": "search_crm", "id": "tc_crm", "input": {"query": "TechCorp"}}],
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),
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# stream 2: call set_output with result
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StreamScript(
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tool_calls=[
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{
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"name": "set_output",
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"id": "tc_so",
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"input": {"key": "result", "value": "Found: TechCorp"},
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}
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],
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),
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# stream 3: text finish
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StreamScript(text="Search complete."),
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]
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llm = make_llm(scripts)
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ctx = make_ctx(llm=llm, output_keys=["result"])
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search_tool = Tool(
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name="search_crm",
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description="Search CRM",
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parameters={"type": "object", "properties": {"query": {"type": "string"}}},
|
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)
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ctx.available_tools = [search_tool]
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tool_executor = make_tool_executor({"search_crm": "Found: TechCorp"})
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node = EventLoopNode(
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event_bus=bus,
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tool_executor=tool_executor,
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config=LoopConfig(max_iterations=5),
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)
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result = await node.execute(ctx)
|
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assert result.success
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# Check tool events were published
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tool_event_types = [e.type for e in recorded_events]
|
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assert EventType.TOOL_CALL_STARTED in tool_event_types
|
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assert EventType.TOOL_CALL_COMPLETED in tool_event_types
|
|
|
|
|
|
# ===========================================================================
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# Group 2: Output Collection
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# ===========================================================================
|
|
|
|
|
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@pytest.mark.asyncio
|
|
async def test_event_loop_set_output():
|
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"""set_output tool sets values in NodeResult.output."""
|
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scripts = [
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# stream 1: set lead_score
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StreamScript(
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tool_calls=[
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{"name": "set_output", "id": "tc_1", "input": {"key": "lead_score", "value": "87"}}
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],
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),
|
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# stream 2: set company
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StreamScript(
|
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tool_calls=[
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{
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"name": "set_output",
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"id": "tc_2",
|
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"input": {"key": "company", "value": "TechCorp"},
|
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}
|
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],
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),
|
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# stream 3: text finish
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StreamScript(text="Outputs set."),
|
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]
|
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llm = make_llm(scripts)
|
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ctx = make_ctx(llm=llm, output_keys=["lead_score", "company"])
|
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|
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node = EventLoopNode(config=LoopConfig(max_iterations=5))
|
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result = await node.execute(ctx)
|
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assert result.success
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if USE_MOCK_LLM:
|
|
assert result.output == {"lead_score": 87, "company": "TechCorp"}
|
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else:
|
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assert "lead_score" in result.output
|
|
assert "company" in result.output
|
|
assert len(result.output["lead_score"]) > 0
|
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assert len(result.output["company"]) > 0
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_missing_output_keys_retried():
|
|
"""Missing output keys trigger implicit judge retry."""
|
|
scripts = [
|
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# Iteration 1: only set "score" (missing "reason")
|
|
StreamScript(
|
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tool_calls=[
|
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{"name": "set_output", "id": "tc_1", "input": {"key": "score", "value": "87"}}
|
|
],
|
|
),
|
|
StreamScript(text="Scored the lead."),
|
|
# Iteration 2 (after implicit retry feedback): set "reason"
|
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StreamScript(
|
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tool_calls=[
|
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{
|
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"name": "set_output",
|
|
"id": "tc_2",
|
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"input": {"key": "reason", "value": "good fit"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(text="Complete."),
|
|
]
|
|
llm = make_llm(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=["score", "reason"])
|
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|
|
node = EventLoopNode(config=LoopConfig(max_iterations=10))
|
|
result = await node.execute(ctx)
|
|
|
|
assert result.success
|
|
assert "score" in result.output
|
|
assert "reason" in result.output
|
|
if USE_MOCK_LLM:
|
|
assert result.output["score"] == 87
|
|
assert result.output["reason"] == "good fit"
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 3: Compaction
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_conversation_compaction():
|
|
"""Long conversations compact, output keys survive."""
|
|
# Build enough scripts for 4 iterations (CountingJudge retries 3 times then accepts)
|
|
scripts = []
|
|
for i in range(4):
|
|
scripts.append(
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": f"tc_{i}",
|
|
"input": {"key": "result", "value": f"val_{i}"},
|
|
}
|
|
],
|
|
)
|
|
)
|
|
scripts.append(StreamScript(text=f"Iteration {i} done. " + "x" * 200))
|
|
|
|
llm = make_llm(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=["result"])
|
|
|
|
judge = CountingJudge(retry_count=3)
|
|
node = EventLoopNode(
|
|
judge=judge,
|
|
config=LoopConfig(max_iterations=10, max_history_tokens=200),
|
|
)
|
|
result = await node.execute(ctx)
|
|
|
|
assert result.success
|
|
assert "result" in result.output
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 4: Crash Recovery
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_checkpoint_and_restore():
|
|
"""Crash mid-loop, resume from checkpoint via ConversationStore."""
|
|
store = MockConversationStore()
|
|
|
|
# Phase 1: Run with max_iterations=2, judge always retries -> fails at max
|
|
scripts_phase1 = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_p1", "input": {"key": "score", "value": "50"}}
|
|
],
|
|
),
|
|
StreamScript(text="Phase 1 iter 0."),
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_p1b", "input": {"key": "score", "value": "60"}}
|
|
],
|
|
),
|
|
StreamScript(text="Phase 1 iter 1."),
|
|
]
|
|
llm1 = ScriptableMockLLMProvider(scripts_phase1)
|
|
ctx1 = make_ctx(node_id="el_restore", llm=llm1, output_keys=["score", "reason"])
|
|
|
|
node1 = EventLoopNode(
|
|
judge=AlwaysRetryJudge(),
|
|
config=LoopConfig(max_iterations=2),
|
|
conversation_store=store,
|
|
)
|
|
result1 = await node1.execute(ctx1)
|
|
|
|
# Phase 1 should fail (max iterations)
|
|
assert not result1.success
|
|
assert "max iterations" in result1.error.lower()
|
|
|
|
# Store should have persisted data (meta + parts from conversation write-through)
|
|
meta = await store.read_meta()
|
|
assert meta is not None # Conversation was persisted
|
|
parts = await store.read_parts()
|
|
assert len(parts) > 0 # Messages were written
|
|
|
|
# The cursor may be overwritten by conversation's _persist (which writes {next_seq})
|
|
# after _write_cursor (which writes {iteration, ...}). This is expected behavior:
|
|
# the last write wins. What matters for restore is that meta and parts exist.
|
|
|
|
# Phase 2: Resume with higher limit, implicit judge (accepts when all keys present).
|
|
# The cursor's "outputs" may have been overwritten by conversation _persist,
|
|
# so the accumulator may not have "score". Re-set both keys to be safe.
|
|
scripts_phase2 = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_p2a", "input": {"key": "score", "value": "75"}}
|
|
],
|
|
),
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": "tc_p2b",
|
|
"input": {"key": "reason", "value": "recovered"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(text="Phase 2 done."),
|
|
]
|
|
llm2 = ScriptableMockLLMProvider(scripts_phase2)
|
|
ctx2 = make_ctx(node_id="el_restore", llm=llm2, output_keys=["score", "reason"])
|
|
|
|
node2 = EventLoopNode(
|
|
config=LoopConfig(max_iterations=10),
|
|
conversation_store=store,
|
|
)
|
|
result2 = await node2.execute(ctx2)
|
|
|
|
assert result2.success
|
|
assert "score" in result2.output
|
|
assert "reason" in result2.output
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 5: External Injection
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_external_injection():
|
|
"""inject_event() appears as user message in conversation."""
|
|
store = MockConversationStore()
|
|
|
|
scripts = [
|
|
StreamScript(text="First response."),
|
|
StreamScript(text="Second response after injection."),
|
|
]
|
|
llm = ScriptableMockLLMProvider(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=[])
|
|
|
|
judge = CountingJudge(retry_count=1) # RETRY once then ACCEPT
|
|
node = EventLoopNode(
|
|
judge=judge,
|
|
config=LoopConfig(max_iterations=5),
|
|
conversation_store=store,
|
|
)
|
|
|
|
# Run in a task so we can inject mid-execution
|
|
async def run_with_injection():
|
|
# Inject before running - will be drained at iteration start
|
|
await node.inject_event("Priority: CEO email")
|
|
return await node.execute(ctx)
|
|
|
|
result = await run_with_injection()
|
|
assert result.success
|
|
|
|
# Check that the injection appeared in the stored messages
|
|
parts = await store.read_parts()
|
|
all_content = " ".join(p.get("content", "") for p in parts)
|
|
assert "[External event]: Priority: CEO email" in all_content
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 6: Pause/Resume
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_pause_and_resume():
|
|
"""Pause triggers early return, resume continues."""
|
|
store = MockConversationStore()
|
|
|
|
# Phase 1: pause_requested=True -> immediate return
|
|
scripts_phase1 = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": "tc_p",
|
|
"input": {"key": "partial", "value": "started"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(text="Should not reach here in phase 1."),
|
|
]
|
|
llm1 = ScriptableMockLLMProvider(scripts_phase1)
|
|
ctx1 = make_ctx(
|
|
llm=llm1, output_keys=["partial", "final"], input_data={"pause_requested": True}
|
|
)
|
|
|
|
node1 = EventLoopNode(
|
|
config=LoopConfig(max_iterations=5),
|
|
conversation_store=store,
|
|
)
|
|
result1 = await node1.execute(ctx1)
|
|
|
|
# Pause returns success immediately (before any LLM call)
|
|
assert result1.success
|
|
|
|
# Phase 2: Resume without pause
|
|
scripts_phase2 = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": "tc_r1",
|
|
"input": {"key": "partial", "value": "resumed"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_r2", "input": {"key": "final", "value": "done"}}
|
|
],
|
|
),
|
|
StreamScript(text="Resume complete."),
|
|
]
|
|
llm2 = ScriptableMockLLMProvider(scripts_phase2)
|
|
ctx2 = make_ctx(llm=llm2, output_keys=["partial", "final"], input_data={})
|
|
|
|
node2 = EventLoopNode(
|
|
config=LoopConfig(max_iterations=10),
|
|
conversation_store=store,
|
|
)
|
|
result2 = await node2.execute(ctx2)
|
|
|
|
assert result2.success
|
|
assert "final" in result2.output
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 7: Executor Retry Enforcement
|
|
# ===========================================================================
|
|
|
|
|
|
class AlwaysFailsNode(NodeProtocol):
|
|
"""A test node that always fails (for retry enforcement testing)."""
|
|
|
|
def __init__(self):
|
|
self.attempt_count = 0
|
|
|
|
async def execute(self, ctx: NodeContext) -> NodeResult:
|
|
self.attempt_count += 1
|
|
return NodeResult(success=False, error=f"Permanent error (attempt {self.attempt_count})")
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_event_loop_no_executor_retry(runtime):
|
|
"""Executor runs event_loop exactly once (no retry)."""
|
|
node_spec = NodeSpec(
|
|
id="el_fail",
|
|
name="Failing Event Loop",
|
|
description="event loop that fails",
|
|
node_type="event_loop",
|
|
max_retries=3,
|
|
output_keys=["result"],
|
|
)
|
|
graph = GraphSpec(
|
|
id="test_graph",
|
|
goal_id="test_goal",
|
|
name="Test Graph",
|
|
entry_node="el_fail",
|
|
nodes=[node_spec],
|
|
edges=[],
|
|
terminal_nodes=["el_fail"],
|
|
)
|
|
goal = Goal(id="test_goal", name="Test", description="test")
|
|
|
|
executor = GraphExecutor(runtime=runtime)
|
|
failing_node = AlwaysFailsNode()
|
|
executor.register_node("el_fail", failing_node)
|
|
|
|
result = await executor.execute(graph, goal, {})
|
|
|
|
assert not result.success
|
|
assert failing_node.attempt_count == 1 # Executor forced max_retries to 0
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 8: Context Handoff
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_context_handoff_between_nodes(runtime):
|
|
"""Output from one event_loop feeds into next via shared memory."""
|
|
# Enrichment node scripts: set lead_score
|
|
enrichment_scripts = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_e", "input": {"key": "lead_score", "value": "92"}}
|
|
],
|
|
),
|
|
StreamScript(text="Enrichment complete."),
|
|
]
|
|
enrichment_llm = ScriptableMockLLMProvider(enrichment_scripts)
|
|
|
|
# Strategy node scripts: set strategy
|
|
strategy_scripts = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": "tc_s",
|
|
"input": {"key": "strategy", "value": "premium"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(text="Strategy determined."),
|
|
]
|
|
enrichment_spec = NodeSpec(
|
|
id="enrichment",
|
|
name="Enrichment",
|
|
description="Enrich lead data",
|
|
node_type="event_loop",
|
|
output_keys=["lead_score"],
|
|
)
|
|
strategy_spec = NodeSpec(
|
|
id="strategy",
|
|
name="Strategy",
|
|
description="Determine strategy",
|
|
node_type="event_loop",
|
|
# Note: input_keys left empty so scoped memory allows reading all keys.
|
|
# EventLoopNode._check_pause() reads "pause_requested" from memory,
|
|
# and a restrictive scope would block it. The node still receives
|
|
# lead_score via input_data mapping from the edge.
|
|
output_keys=["strategy"],
|
|
)
|
|
|
|
graph = GraphSpec(
|
|
id="handoff_graph",
|
|
goal_id="test_goal",
|
|
name="Handoff Graph",
|
|
entry_node="enrichment",
|
|
nodes=[enrichment_spec, strategy_spec],
|
|
edges=[
|
|
EdgeSpec(
|
|
id="e_to_s",
|
|
source="enrichment",
|
|
target="strategy",
|
|
condition=EdgeCondition.ON_SUCCESS,
|
|
),
|
|
],
|
|
terminal_nodes=["strategy"],
|
|
)
|
|
goal = Goal(id="test_goal", name="Handoff Test", description="test context handoff")
|
|
|
|
executor = GraphExecutor(runtime=runtime, llm=enrichment_llm)
|
|
|
|
el_enrichment = EventLoopNode(config=LoopConfig(max_iterations=5))
|
|
el_strategy = EventLoopNode(config=LoopConfig(max_iterations=5))
|
|
|
|
executor.register_node("enrichment", el_enrichment)
|
|
executor.register_node("strategy", el_strategy)
|
|
|
|
# Override: the executor uses self.llm for all nodes, but EventLoopNode uses ctx.llm.
|
|
# For this test, we need different LLMs per node. Since the executor passes self.llm
|
|
# via context, and EventLoopNode uses ctx.llm, we need a workaround.
|
|
# The simplest approach: use one LLM that serves both scripts sequentially.
|
|
combined_scripts = enrichment_scripts + strategy_scripts
|
|
combined_llm = ScriptableMockLLMProvider(combined_scripts)
|
|
executor.llm = combined_llm
|
|
|
|
result = await executor.execute(graph, goal, {})
|
|
|
|
assert result.success
|
|
assert "lead_score" in result.output
|
|
assert "strategy" in result.output
|
|
if USE_MOCK_LLM:
|
|
assert result.output["lead_score"] == 92
|
|
assert result.output["strategy"] == "premium"
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 9: Client I/O
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_client_facing_node_streams_output():
|
|
"""Client-facing node emits CLIENT_OUTPUT_DELTA events."""
|
|
recorded: list[AgentEvent] = []
|
|
|
|
async def handler(event: AgentEvent) -> None:
|
|
recorded.append(event)
|
|
|
|
bus = EventBus()
|
|
bus.subscribe(
|
|
event_types=[EventType.CLIENT_OUTPUT_DELTA, EventType.LLM_TEXT_DELTA],
|
|
handler=handler,
|
|
)
|
|
|
|
scripts = [StreamScript(text="Hello, user!")]
|
|
llm = make_llm(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=[], client_facing=True)
|
|
|
|
node = EventLoopNode(
|
|
event_bus=bus,
|
|
config=LoopConfig(max_iterations=5),
|
|
)
|
|
|
|
# Text-only on client_facing does not block (no ask_user called),
|
|
# so the node completes without needing a shutdown workaround.
|
|
result = await node.execute(ctx)
|
|
|
|
assert result.success
|
|
|
|
event_types = [e.type for e in recorded]
|
|
assert EventType.CLIENT_OUTPUT_DELTA in event_types
|
|
# Should NOT have LLM_TEXT_DELTA (that's for internal nodes)
|
|
assert EventType.LLM_TEXT_DELTA not in event_types
|
|
|
|
# Verify node_id is correct
|
|
client_events = [e for e in recorded if e.type == EventType.CLIENT_OUTPUT_DELTA]
|
|
assert all(e.node_id == "test_node" for e in client_events)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_internal_node_no_client_output():
|
|
"""Internal node emits LLM_TEXT_DELTA, not CLIENT_OUTPUT_DELTA."""
|
|
recorded: list[AgentEvent] = []
|
|
|
|
async def handler(event: AgentEvent) -> None:
|
|
recorded.append(event)
|
|
|
|
bus = EventBus()
|
|
bus.subscribe(
|
|
event_types=[EventType.CLIENT_OUTPUT_DELTA, EventType.LLM_TEXT_DELTA],
|
|
handler=handler,
|
|
)
|
|
|
|
scripts = [StreamScript(text="Internal processing.")]
|
|
llm = make_llm(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=[], client_facing=False)
|
|
|
|
node = EventLoopNode(
|
|
event_bus=bus,
|
|
config=LoopConfig(max_iterations=5),
|
|
)
|
|
result = await node.execute(ctx)
|
|
|
|
assert result.success
|
|
|
|
event_types = [e.type for e in recorded]
|
|
assert EventType.LLM_TEXT_DELTA in event_types
|
|
assert EventType.CLIENT_OUTPUT_DELTA not in event_types
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 10: Full Pipeline
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_mixed_node_graph(runtime):
|
|
"""function -> event_loop -> function end-to-end."""
|
|
|
|
# Function 1: write leads to memory
|
|
def load_leads(**kwargs):
|
|
return ["lead_A", "lead_B", "lead_C"]
|
|
|
|
# Event loop: process leads, produce summary
|
|
el_scripts = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{
|
|
"name": "set_output",
|
|
"id": "tc_sum",
|
|
"input": {"key": "summary", "value": "3 leads processed"},
|
|
}
|
|
],
|
|
),
|
|
StreamScript(text="Processing complete."),
|
|
]
|
|
el_llm = ScriptableMockLLMProvider(el_scripts)
|
|
|
|
# Function 2: format final output
|
|
def format_output(**kwargs):
|
|
summary = kwargs.get("summary", "no summary")
|
|
return f"Report: {summary}"
|
|
|
|
# Node specs
|
|
load_spec = NodeSpec(
|
|
id="load",
|
|
name="Load Leads",
|
|
description="Load lead data",
|
|
node_type="function",
|
|
function="load_leads",
|
|
output_keys=["leads"],
|
|
)
|
|
process_spec = NodeSpec(
|
|
id="process",
|
|
name="Process Leads",
|
|
description="Process leads with LLM",
|
|
node_type="event_loop",
|
|
# input_keys left empty: EventLoopNode._check_pause() reads "pause_requested"
|
|
# from memory, and a restrictive scope would block it. Data flows via input_data.
|
|
output_keys=["summary"],
|
|
)
|
|
format_spec = NodeSpec(
|
|
id="format",
|
|
name="Format Output",
|
|
description="Format final report",
|
|
node_type="function",
|
|
function="format_output",
|
|
# input_keys left empty for same scoping reason with FunctionNode
|
|
output_keys=["report"],
|
|
)
|
|
|
|
graph = GraphSpec(
|
|
id="pipeline_graph",
|
|
goal_id="test_goal",
|
|
name="Pipeline Graph",
|
|
entry_node="load",
|
|
nodes=[load_spec, process_spec, format_spec],
|
|
edges=[
|
|
EdgeSpec(id="e1", source="load", target="process", condition=EdgeCondition.ON_SUCCESS),
|
|
EdgeSpec(
|
|
id="e2", source="process", target="format", condition=EdgeCondition.ON_SUCCESS
|
|
),
|
|
],
|
|
terminal_nodes=["format"],
|
|
)
|
|
goal = Goal(id="test_goal", name="Pipeline Test", description="test full pipeline")
|
|
|
|
executor = GraphExecutor(runtime=runtime, llm=el_llm)
|
|
executor.register_function("load", load_leads)
|
|
executor.register_node("process", EventLoopNode(config=LoopConfig(max_iterations=5)))
|
|
executor.register_function("format", format_output)
|
|
|
|
result = await executor.execute(graph, goal, {})
|
|
|
|
assert result.success
|
|
assert "summary" in result.output
|
|
assert "report" in result.output
|
|
if USE_MOCK_LLM:
|
|
assert "3 leads processed" in result.output["summary"]
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 11: Validation
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_fan_out_rejects_overlapping_output_keys(runtime):
|
|
"""Parallel event_loop nodes with same output_keys fail at execution.
|
|
|
|
The GraphExecutor's parallel execution with overlapping keys uses
|
|
last-wins memory strategy, which can cause data corruption.
|
|
We verify the behavior is at least deterministic (both branches execute).
|
|
"""
|
|
scripts_a = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_a", "input": {"key": "result", "value": "from_A"}}
|
|
],
|
|
),
|
|
StreamScript(text="A done."),
|
|
]
|
|
scripts_b = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_b", "input": {"key": "result", "value": "from_B"}}
|
|
],
|
|
),
|
|
StreamScript(text="B done."),
|
|
]
|
|
# Combined scripts: A's scripts then B's scripts
|
|
combined = scripts_a + scripts_b
|
|
|
|
source_spec = NodeSpec(
|
|
id="source",
|
|
name="Source",
|
|
description="Source node",
|
|
node_type="event_loop",
|
|
output_keys=["trigger"],
|
|
)
|
|
branch_a_spec = NodeSpec(
|
|
id="branch_a",
|
|
name="Branch A",
|
|
description="Parallel branch A",
|
|
node_type="event_loop",
|
|
output_keys=["result"],
|
|
)
|
|
branch_b_spec = NodeSpec(
|
|
id="branch_b",
|
|
name="Branch B",
|
|
description="Parallel branch B",
|
|
node_type="event_loop",
|
|
output_keys=["result"], # Same key as branch A
|
|
)
|
|
|
|
graph = GraphSpec(
|
|
id="fanout_graph",
|
|
goal_id="test_goal",
|
|
name="Fan Out Graph",
|
|
entry_node="source",
|
|
nodes=[source_spec, branch_a_spec, branch_b_spec],
|
|
edges=[
|
|
EdgeSpec(
|
|
id="e_a", source="source", target="branch_a", condition=EdgeCondition.ON_SUCCESS
|
|
),
|
|
EdgeSpec(
|
|
id="e_b", source="source", target="branch_b", condition=EdgeCondition.ON_SUCCESS
|
|
),
|
|
],
|
|
terminal_nodes=["branch_a", "branch_b"],
|
|
)
|
|
goal = Goal(id="test_goal", name="Fanout Test", description="test fanout")
|
|
|
|
# Source node: simple success
|
|
source_scripts = [
|
|
StreamScript(
|
|
tool_calls=[
|
|
{"name": "set_output", "id": "tc_src", "input": {"key": "trigger", "value": "go"}}
|
|
],
|
|
),
|
|
StreamScript(text="Source done."),
|
|
]
|
|
all_scripts = source_scripts + combined
|
|
all_llm = ScriptableMockLLMProvider(all_scripts)
|
|
|
|
executor = GraphExecutor(runtime=runtime, llm=all_llm)
|
|
executor.register_node("source", EventLoopNode(config=LoopConfig(max_iterations=5)))
|
|
executor.register_node("branch_a", EventLoopNode(config=LoopConfig(max_iterations=5)))
|
|
executor.register_node("branch_b", EventLoopNode(config=LoopConfig(max_iterations=5)))
|
|
|
|
result = await executor.execute(graph, goal, {})
|
|
|
|
# GraphSpec.validate() catches overlapping output_keys on parallel
|
|
# event_loop branches and rejects the graph before execution starts.
|
|
assert not result.success
|
|
assert "Invalid graph" in result.error
|
|
|
|
|
|
# ===========================================================================
|
|
# Group 12: Edge Cases
|
|
# ===========================================================================
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_max_iterations_exceeded():
|
|
"""Loop hits max_iterations, returns failure."""
|
|
scripts = [
|
|
StreamScript(text="Response 1."),
|
|
StreamScript(text="Response 2."),
|
|
StreamScript(text="Response 3."), # Extra safety
|
|
]
|
|
llm = ScriptableMockLLMProvider(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=[])
|
|
|
|
node = EventLoopNode(
|
|
judge=AlwaysRetryJudge(),
|
|
config=LoopConfig(max_iterations=2),
|
|
)
|
|
result = await node.execute(ctx)
|
|
|
|
assert not result.success
|
|
assert "max iterations" in result.error.lower()
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_stall_detection():
|
|
"""N identical responses trigger stall failure."""
|
|
# 3 identical text responses will trigger stall (threshold=3)
|
|
scripts = [
|
|
StreamScript(text="I am stuck"),
|
|
StreamScript(text="I am stuck"),
|
|
StreamScript(text="I am stuck"),
|
|
StreamScript(text="I am stuck"), # Extra safety
|
|
]
|
|
llm = ScriptableMockLLMProvider(scripts)
|
|
ctx = make_ctx(llm=llm, output_keys=[])
|
|
|
|
node = EventLoopNode(
|
|
judge=AlwaysRetryJudge(),
|
|
config=LoopConfig(stall_detection_threshold=3, max_iterations=10),
|
|
)
|
|
result = await node.execute(ctx)
|
|
|
|
assert not result.success
|
|
assert "stall" in result.error.lower()
|