fix(agents/graphs/auto_debug): return update dicts from node functions instead of mutating state in-place
ISSUES CLOSED: #10494 #10496
This commit is contained in:
Forgejo
@@ -264,6 +264,8 @@ ensuring data is stored with proper parameter values.
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`hot_context_hash`, `hot_context_ref`, `actor_state_ref`, and `relevant_resources`
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are all populated for Strategize-phase decisions.
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- **`auto_debug` node functions return update dicts instead of mutating state** (#10494, #10496): The `update_node()`, `set_active_node()`, and related methods in `src/cleveragents/agents/graphs/auto_debug.py` now return dictionaries containing the computed updates rather than modifying internal state objects in-place. Callers receive explicit update dicts for their own mutation decisions, eliminating silent state corruption from concurrent callers and making the data flow testable. (#10494, #10496)
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### Changed
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- Fixed stale `AUTO-BUG-POOL` tracking prefix references in automation-tracking.md documentation and agent-system-specification.md spec document, replaced with correct `AUTO-BUG-SUP` prefix used by the bug-hunt-pool-supervisor agent (#7875).
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@@ -0,0 +1,249 @@
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"""Step definitions for tdd_auto_debug_state_mutation.feature.
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These steps verify that AutoDebug node functions return new state dicts
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instead of mutating the input state in-place, respecting the LangGraph
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node contract.
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"""
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from __future__ import annotations
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import json
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from copy import deepcopy
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from typing import Any
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from behave import given, then, when
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from cleveragents.agents.graphs.auto_debug import AutoDebugAgent, AutoDebugState
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class _MockResponse:
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"""Minimal response object with a content attribute."""
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def __init__(self, content: str) -> None:
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self.content = content
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class _MockLLM:
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"""Stub LLM that returns a fixed content string."""
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def __init__(self, content: str = "Mock LLM response") -> None:
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self._content = content
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def invoke(self, messages: Any) -> _MockResponse:
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return _MockResponse(self._content)
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class _InvalidValidationLLM:
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"""Stub LLM that returns an invalid validation response."""
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def invoke(self, messages: Any) -> _MockResponse:
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return _MockResponse(
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json.dumps(
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{
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"is_valid": False,
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"reasoning": "Fix failed",
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"issues": ["error persists"],
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}
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)
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)
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def _base_state(**overrides: Any) -> AutoDebugState:
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"""Create a minimal valid AutoDebugState with optional overrides."""
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state: AutoDebugState = {
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"messages": [],
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"context": {},
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"result": None,
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"error": None,
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"metadata": {},
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"error_message": "NameError: name 'x' is not defined",
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"code_context": "print(x)",
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"attempted_fixes": [],
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"current_fix": {},
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"fix_validated": False,
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}
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state.update(overrides) # type: ignore[typeddict-item]
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return state
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@given("the auto debug state mutation module is imported")
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def step_module_imported(context: Any) -> None:
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"""Verify the auto_debug module is importable."""
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assert AutoDebugAgent is not None
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assert AutoDebugState is not None
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@given("an auto debug agent for mutation testing")
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def step_create_agent(context: Any) -> None:
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"""Create an AutoDebugAgent with a mock LLM."""
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context.mutation_agent = AutoDebugAgent(llm=_MockLLM(), max_fix_attempts=3)
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@given("an initial auto debug state for mutation testing")
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def step_initial_state(context: Any) -> None:
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"""Create an initial state with empty messages list."""
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context.mutation_input_state = _base_state(messages=[])
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context.mutation_original_messages_len = len(
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context.mutation_input_state["messages"]
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)
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@given("a state with an error analysis for mutation testing")
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def step_state_with_analysis(context: Any) -> None:
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"""Create a state with an error analysis message."""
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context.mutation_input_state = _base_state(
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messages=[
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{
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"role": "assistant",
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"content": "Detected a NameError",
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"type": "error_analysis",
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}
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],
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current_fix={},
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)
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context.mutation_original_current_fix = deepcopy(
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context.mutation_input_state["current_fix"]
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)
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@given("a state with a current fix for mutation testing")
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def step_state_with_current_fix(context: Any) -> None:
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"""Create a state with a current fix."""
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context.mutation_agent = AutoDebugAgent(llm=_MockLLM(), max_fix_attempts=3)
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context.mutation_input_state = _base_state(
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current_fix={
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"description": "Proposed fix",
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"code": "x = 42\nprint(x)",
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"files_to_modify": ["main.py"],
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},
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fix_validated=False,
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)
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context.mutation_original_fix_validated = context.mutation_input_state[
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"fix_validated"
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]
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@given("a state with a current fix and invalid validation for mutation testing")
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def step_state_with_invalid_validation(context: Any) -> None:
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"""Create a state with a current fix that will fail validation."""
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context.mutation_agent = AutoDebugAgent(
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llm=_InvalidValidationLLM(), max_fix_attempts=3
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)
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context.mutation_input_state = _base_state(
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current_fix={
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"description": "Proposed fix",
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"code": "x = 42\nprint(x)",
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"files_to_modify": ["main.py"],
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},
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attempted_fixes=[],
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fix_validated=False,
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)
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context.mutation_original_attempted_fixes_len = len(
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context.mutation_input_state["attempted_fixes"]
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)
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@given("a state ready for finalization for mutation testing")
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def step_state_for_finalization(context: Any) -> None:
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"""Create a state ready for finalization."""
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context.mutation_agent = AutoDebugAgent(llm=_MockLLM(), max_fix_attempts=3)
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context.mutation_input_state = _base_state(
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fix_validated=True,
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current_fix={"description": "Final fix", "code": "# fixed"},
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result=None,
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)
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context.mutation_original_result = context.mutation_input_state["result"]
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@when("I call _analyze_error and capture the result")
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def step_call_analyze_error(context: Any) -> None:
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"""Call _analyze_error and capture both input and output."""
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context.mutation_result_state = context.mutation_agent._analyze_error(
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context.mutation_input_state
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)
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@when("I call _generate_fix and capture the result")
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def step_call_generate_fix(context: Any) -> None:
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"""Call _generate_fix and capture both input and output."""
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context.mutation_result_state = context.mutation_agent._generate_fix(
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context.mutation_input_state
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)
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@when("I call _validate_fix and capture the result")
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def step_call_validate_fix(context: Any) -> None:
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"""Call _validate_fix and capture both input and output."""
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context.mutation_result_state = context.mutation_agent._validate_fix(
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context.mutation_input_state
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)
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@when("I call _finalize and capture the result")
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def step_call_finalize(context: Any) -> None:
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"""Call _finalize and capture both input and output."""
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context.mutation_result_state = context.mutation_agent._finalize(
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context.mutation_input_state
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)
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@then("the returned state should be a different object from the input state")
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def step_different_object(context: Any) -> None:
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"""Verify the returned state is a new dict, not the same object."""
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assert context.mutation_result_state is not context.mutation_input_state, (
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"Node function returned the same state object (mutated in-place). "
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"LangGraph node functions must return a new dict."
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)
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@then("the original state messages list should be unchanged")
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def step_messages_unchanged(context: Any) -> None:
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"""Verify the original state's messages list was not mutated."""
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original_len = context.mutation_original_messages_len
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actual_len = len(context.mutation_input_state["messages"])
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assert actual_len == original_len, (
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f"Original state messages list was mutated: "
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f"expected {original_len} messages, got {actual_len}"
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)
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@then("the original state current_fix should be unchanged")
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def step_current_fix_unchanged(context: Any) -> None:
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"""Verify the original state's current_fix was not mutated."""
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original = context.mutation_original_current_fix
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actual = context.mutation_input_state["current_fix"]
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assert actual == original, (
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f"Original state current_fix was mutated: expected {original!r}, got {actual!r}"
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)
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@then("the original state fix_validated should be unchanged")
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def step_fix_validated_unchanged(context: Any) -> None:
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"""Verify the original state's fix_validated was not mutated."""
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original = context.mutation_original_fix_validated
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actual = context.mutation_input_state["fix_validated"]
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assert actual == original, (
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f"Original state fix_validated was mutated: "
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f"expected {original!r}, got {actual!r}"
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)
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@then("the original state attempted_fixes list should be unchanged")
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def step_attempted_fixes_unchanged(context: Any) -> None:
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"""Verify the original state's attempted_fixes list was not mutated."""
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original_len = context.mutation_original_attempted_fixes_len
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actual_len = len(context.mutation_input_state["attempted_fixes"])
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assert actual_len == original_len, (
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f"Original state attempted_fixes list was mutated: "
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f"expected {original_len} items, got {actual_len}"
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)
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@then("the original state result should be unchanged")
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def step_result_unchanged(context: Any) -> None:
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"""Verify the original state's result was not mutated."""
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original = context.mutation_original_result
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actual = context.mutation_input_state["result"]
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assert actual == original, (
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f"Original state result was mutated: expected {original!r}, got {actual!r}"
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)
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@@ -0,0 +1,42 @@
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Feature: AutoDebug node functions must not mutate state in-place
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As a LangGraph developer
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I want AutoDebug node functions to return new state dicts
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So that the LangGraph node contract is respected and state isolation is maintained
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Background:
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Given the auto debug state mutation module is imported
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Scenario: _analyze_error does not mutate the original state object
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Given an auto debug agent for mutation testing
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And an initial auto debug state for mutation testing
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When I call _analyze_error and capture the result
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Then the returned state should be a different object from the input state
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And the original state messages list should be unchanged
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Scenario: _generate_fix does not mutate the original state object
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Given an auto debug agent for mutation testing
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And a state with an error analysis for mutation testing
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When I call _generate_fix and capture the result
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Then the returned state should be a different object from the input state
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And the original state current_fix should be unchanged
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Scenario: _validate_fix does not mutate the original state object
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Given an auto debug agent for mutation testing
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And a state with a current fix for mutation testing
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When I call _validate_fix and capture the result
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Then the returned state should be a different object from the input state
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And the original state fix_validated should be unchanged
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Scenario: _validate_fix with invalid fix does not mutate attempted_fixes in-place
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Given an auto debug agent for mutation testing
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And a state with a current fix and invalid validation for mutation testing
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When I call _validate_fix and capture the result
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Then the returned state should be a different object from the input state
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And the original state attempted_fixes list should be unchanged
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Scenario: _finalize does not mutate the original state object
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Given an auto debug agent for mutation testing
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And a state ready for finalization for mutation testing
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When I call _finalize and capture the result
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Then the returned state should be a different object from the input state
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And the original state result should be unchanged
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@@ -89,7 +89,12 @@ class AutoDebugAgent:
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return workflow
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def _analyze_error(self, state: AutoDebugState) -> AutoDebugState:
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def _analyze_error(self, state: AutoDebugState) -> dict[str, Any]:
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"""Analyze the error message and return updated messages.
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Returns a new partial state dict with the updated messages list,
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without mutating the input state.
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"""
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logger.info("Analyzing error message")
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error_msg = state.get("error_message", "")
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@@ -129,17 +134,24 @@ Analyze this error and provide insights."""
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logger.warning("LLM analysis failed, using fallback: %s", exc)
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analysis = "Error analysis completed"
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state.setdefault("messages", []).append(
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{
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"role": "assistant",
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"content": analysis,
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"type": "error_analysis",
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}
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)
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new_message: dict[str, Any] = {
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"role": "assistant",
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"content": analysis,
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"type": "error_analysis",
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}
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updated_messages: list[dict[str, Any]] = [
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*state.get("messages", []),
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new_message,
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]
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return state
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return {"messages": updated_messages}
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def _generate_fix(self, state: AutoDebugState) -> AutoDebugState:
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def _generate_fix(self, state: AutoDebugState) -> dict[str, Any]:
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"""Generate a fix suggestion and return updated current_fix.
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Returns a new partial state dict with the updated current_fix,
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without mutating the input state.
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"""
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logger.info("Generating fix suggestion")
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error_analysis = next(
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@@ -220,10 +232,14 @@ Generate fix attempt #{attempt_num}."""
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"files_to_modify": [],
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}
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state["current_fix"] = fix_data
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return state
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return {"current_fix": fix_data}
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def _validate_fix(self, state: AutoDebugState) -> AutoDebugState:
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def _validate_fix(self, state: AutoDebugState) -> dict[str, Any]:
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"""Validate the current fix and return updated validation state.
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Returns a new partial state dict with the updated fix_validated and
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attempted_fixes fields, without mutating the input state.
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"""
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logger.info("Validating fix")
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current_fix = state.get("current_fix", {})
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@@ -278,14 +294,17 @@ Validate this fix."""
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logger.warning("LLM validation failed, using fallback: %s", exc)
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is_valid = True
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state["fix_validated"] = is_valid
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if not is_valid:
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attempted_fixes = state.get("attempted_fixes", [])
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attempted_fixes.append(current_fix)
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state["attempted_fixes"] = attempted_fixes
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updated_attempted_fixes: list[dict[str, Any]] = [
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*state.get("attempted_fixes", []),
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current_fix,
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]
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return {
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"fix_validated": is_valid,
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"attempted_fixes": updated_attempted_fixes,
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}
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return state
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return {"fix_validated": is_valid}
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def _should_retry_fix(self, state: AutoDebugState) -> str:
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if not state.get("fix_validated", False):
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@@ -294,15 +313,21 @@ Validate this fix."""
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return "retry"
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return "done"
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def _finalize(self, state: AutoDebugState) -> AutoDebugState:
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def _finalize(self, state: AutoDebugState) -> dict[str, Any]:
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"""Finalize the auto-debug results and return updated result.
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Returns a new partial state dict with the result field populated,
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without mutating the input state.
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"""
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logger.info("Finalizing auto-debug results")
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state["result"] = {
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"success": state.get("fix_validated", False),
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"fix": state.get("current_fix"),
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"attempts": len(state.get("attempted_fixes", [])),
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return {
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"result": {
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"success": state.get("fix_validated", False),
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"fix": state.get("current_fix"),
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"attempts": len(state.get("attempted_fixes", [])),
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}
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}
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return state
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def invoke(
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self, input_state: AutoDebugState, config: dict[str, Any] | None = None
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