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cleveractors-core/features/steps/runtime_coverage_steps.py
hurui200320 9ec578f229
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feat(agents): add tool_agent_class parameter to AgentFactory and create_executor
Extends AgentFactory.__init__ and create_executor() with a keyword-only
tool_agent_class: type[ToolAgent] = ToolAgent parameter so callers can
inject a custom ToolAgent subclass at runtime without monkey-patching
module globals (resolves the five-module setattr loop in cleveragents-webapp).

**AgentFactory** (agents/factory.py):
- Accepts tool_agent_class keyword argument; validates it is a ToolAgent
  subclass at construction time (fail-fast, CONTRIBUTING.md §Argument
  Validation).
- Stores as self._tool_agent_class and uses it to populate
  self.agent_types['tool'], so every create_agent('tool') call
  instantiates the supplied subclass.
- Forwards tool_agent_class to LLMAgent when creating 'llm'-typed agents
  so the LLM tool-calling loop honours the injected subclass.
- Default ToolAgent is preserved, so all existing callers are unaffected.

**LLMAgent** (agents/llm.py):
- Accepts a keyword-only tool_agent_class: type[ToolAgent] = ToolAgent
  argument, validated as a ToolAgent subclass at construction time.
- The multi-turn tool-call loop (_execute_tool_loop) constructs its
  ephemeral tool executors via self._tool_agent_class(...) across all
  three dispatch sites (regular loop, budget-exhaustion synthesis round,
  and stuck-model synthesis round), so the LLM tool-calling path no
  longer falls back to the module-level ToolAgent. This closes the gap
  that previously prevented removing the webapp monkey-patch for the LLM
  tool-calling path.

**Executor / create_executor** (runtime.py):
- Executor.__init__ accepts and validates tool_agent_class; stores as
  self.tool_agent_class.
- create_executor() accepts tool_agent_class and forwards it to Executor.

**Dispatch paths** (runtime_dispatch.py):
- _execute_tool: uses executor.tool_agent_class(...) instead of the
  module-level ToolAgent(...) so the direct-construction path (bypassing
  AgentFactory) also honours the injected subclass.
- _execute_graph and _execute_graph_stream: pass
  tool_agent_class=executor.tool_agent_class to AgentFactory so graph
  nodes using tool-typed agents pick up the subclass.
- _execute_llm and _execute_llm_stream: pass
  tool_agent_class=executor.tool_agent_class to AgentFactory so the
  single-LLM actor's internally-created LLMAgent (and its tool-calling
  loop) honours the injected subclass. Without this, a single LLM actor
  that makes tool calls silently fell back to the base ToolAgent.
- _execute_multi_actor: passes tool_agent_class=executor.tool_agent_class
  to the sub-Executor so every actor inside a multi-actor bundle (tool,
  graph, or LLM) honours the injected subclass. Without this, all
  dispatch paths reachable through a multi-actor bundle fell back to the
  base ToolAgent.
- Removed the now-unused module-level ToolAgent import; the _execute_tool
  docstring cross-reference is fully qualified so it still resolves.
  All six dispatch paths that construct or dispatch tool agents now
  forward the injected subclass.

**ReactiveCleverAgentsApp** (core/application.py):
- Removed the redundant register_agent_type('tool', ToolAgent) call from
  load_configuration and the dead type_map re-registration block in
  _create_agents; AgentFactory.__init__ already owns the 'tool'
  registration. This drops the last hardcoded module-level ToolAgent
  references flagged in the issue #73 audit.

**Tests** (features/tool_agent_class_injection.feature + steps):
- 17 BDD scenarios covering: default class when parameter omitted;
  custom subclass stored on factory and executor; invalid class rejected
  with ConfigurationError (factory, executor, and LLMAgent); factory
  create_agent returns custom instance; _execute_tool dispatch
  instantiates custom subclass; the LLM tool loop instantiates the
  injected subclass; isinstance LSP compatibility through the factory;
  the _execute_llm dispatch path forwarding tool_agent_class to
  AgentFactory (single-LLM actor making a tool call); and the
  _execute_multi_actor dispatch path forwarding tool_agent_class to the
  sub-Executor. The two new dispatch-path scenarios were verified to
  fail without the source fixes (instantiation_count=0), confirming
  they guard the injection contract for the single-LLM and multi-actor
  paths.

**Updated tests** (features/steps/runtime_coverage_steps.py,
features/steps/runtime_extended_coverage_steps.py):
- Removed the now-dead patch('cleveractors.runtime_dispatch.ToolAgent')
  mocks (and their mock instances) that no longer intercept _execute_tool
  after it switched to executor.tool_agent_class. The tool-actor scenario
  continues to pass via the real ToolAgent; the normal-coverage branch
  uses patch.object(ToolAgent, 'process_message', ...) to stub tool
  execution.

**Updated test** (features/steps/credential_executor_paths_steps.py):
- The _execute_multi_actor credentials-forwarding scenario intercepts
  Executor.__init__ with a capturing_init to assert the credentials
  dict reaches the sub-Executor. Now that _execute_multi_actor forwards
  tool_agent_class to the sub-Executor, capturing_init accepts and
  forwards that keyword so the assertion continues to pass.

**Removed obsolete test** (features/application_coverage_gaps.feature):
- Dropped the '_create_agents registers built-in agent types' scenario
  and its step definitions; the behaviour it asserted (redundant
  re-registration) was removed from _create_agents.

ISSUES CLOSED: #73
2026-07-08 09:23:34 +00:00

232 lines
8.1 KiB
Python

"""Step definitions for Runtime Executor API BDD tests.
Given steps (environment, config, credentials, limits, pricing) are defined
in ``runtime_config_steps.py`` (extracted to stay under the 500-line file
limit, CONTRIBUTING.md General Principles).
"""
from __future__ import annotations
from typing import Any
from unittest.mock import AsyncMock, MagicMock, patch
from behave import given, then, when
from behave.api.async_step import async_run_until_complete
from cleveractors.core.exceptions import ConfigurationError, ExecutionError
from cleveractors.result import ActorResult, NodeUsage
from cleveractors.runtime import (
Executor,
create_executor,
)
@given("the LLM agent is configured to fail")
def step_llm_fail(context: Any) -> None:
context._llm_should_fail = True
@when("I call create_executor")
def step_call_create_executor(context: Any) -> None:
context.test_executor = create_executor(
config_dict=context.config_dict,
credentials=context.credentials,
limits=context.limits if hasattr(context, "limits") else None,
pricing=context.pricing if hasattr(context, "pricing") else None,
)
@when("I call create_executor with None limits and pricing")
def step_call_create_executor_none(context: Any) -> None:
context.test_executor = create_executor(
config_dict=context.config_dict,
credentials=context.credentials,
limits=None,
pricing=None,
)
@when("I execute the actor with message {msg}")
@async_run_until_complete
async def step_execute_actor(context: Any, msg: str) -> None:
msg = msg.strip('"')
executor = create_executor(
config_dict=context.config_dict,
credentials=context.credentials,
limits=context.limits if hasattr(context, "limits") and context.limits else {},
pricing=context.pricing
if hasattr(context, "pricing") and context.pricing
else {},
)
context.test_executor = executor
with (
patch("cleveractors.runtime_dispatch.TemplateRenderer") as mock_renderer,
patch("cleveractors.agents.llm.LLMAgent") as mock_llm,
patch("cleveractors.runtime_dispatch.AgentFactory") as mock_factory,
patch("cleveractors.runtime_dispatch.PureLangGraph") as mock_pure_graph,
):
mock_renderer_instance = MagicMock()
mock_renderer.return_value = mock_renderer_instance
if getattr(context, "_llm_should_fail", False):
mock_llm_instance = MagicMock()
mock_llm_instance.process_message = AsyncMock(
side_effect=Exception("LLM API error")
)
mock_llm_instance.cleanup = AsyncMock()
mock_llm_instance._last_token_usage = (0, 0)
else:
mock_llm_instance = MagicMock()
mock_llm_instance.process_message = AsyncMock(
return_value="Mock LLM response"
)
mock_llm_instance.cleanup = AsyncMock()
# Simulate real token usage from LLMAgent._last_token_usage (AC2)
mock_llm_instance._last_token_usage = (100, 50)
mock_llm.return_value = mock_llm_instance
mock_graph_instance = MagicMock()
# PureLangGraph.execute() returns a 3-tuple (AC4): (response, state, node_usages)
mock_graph_instance.execute = AsyncMock(
return_value=(
"Mock graph response",
{},
[("entry", "openai", "gpt-3.5-turbo", 100, 50)],
)
)
mock_pure_graph.return_value = mock_graph_instance
mock_factory_instance = MagicMock()
mock_factory_instance.create_agent = MagicMock(return_value=mock_llm_instance)
mock_factory.return_value = mock_factory_instance
try:
context.test_result = await executor.execute(msg)
context.test_error = None
except Exception as e:
context.test_error = e
context.test_result = None
@then("an Executor instance should be returned")
def step_assert_executor(context: Any) -> None:
assert context.test_executor is not None
assert isinstance(context.test_executor, Executor)
@then("the executor config should match the config dict")
def step_assert_config(context: Any) -> None:
assert context.test_executor.config == context.config_dict
@then("the executor credentials should match the credentials dict")
def step_assert_credentials(context: Any) -> None:
assert context.test_executor.credentials == context.credentials
@then("the executor limits should match the limits dict")
def step_assert_limits(context: Any) -> None:
assert context.test_executor.limits == context.limits
@then("the executor pricing should match the pricing dict")
def step_assert_pricing(context: Any) -> None:
assert context.test_executor.pricing == context.pricing
@then("the executor limits should be an empty dictionary")
def step_assert_limits_empty(context: Any) -> None:
assert context.test_executor.limits == {}
@then("the executor pricing should be an empty dictionary")
def step_assert_pricing_empty(context: Any) -> None:
assert context.test_executor.pricing == {}
@then("the execution should return an ActorResult")
def step_assert_actor_result(context: Any) -> None:
assert context.test_result is not None
assert isinstance(context.test_result, ActorResult)
@then("the ActorResult response should be non-empty")
def step_assert_response(context: Any) -> None:
assert context.test_result.response is not None
assert len(context.test_result.response) > 0
@then("the ActorResult should have token usage tracked")
def step_assert_tokens(context: Any) -> None:
assert context.test_result.prompt_tokens == 100, (
f"Expected prompt_tokens == 100, got {context.test_result.prompt_tokens}"
)
assert context.test_result.completion_tokens == 50, (
f"Expected completion_tokens == 50, got {context.test_result.completion_tokens}"
)
@then("the ActorResult should have at least one node usage entry")
def step_assert_nodes(context: Any) -> None:
assert len(context.test_result.nodes) >= 1
@then("the ActorResult should have zero prompt tokens for tool agents")
def step_assert_zero_tokens(context: Any) -> None:
assert context.test_result.prompt_tokens == 0
assert context.test_result.completion_tokens == 0
@then("the node usage IDs should be prefixed with the default actor name")
def step_assert_node_prefix(context: Any) -> None:
for node in context.test_result.nodes:
assert node.node_id.startswith("default.")
@then("a ConfigurationError should be raised for runtime")
def step_assert_config_error(context: Any) -> None:
assert context.test_error is not None
assert isinstance(context.test_error, ConfigurationError)
@then("the error message should mention the unknown actor type")
def step_assert_error_unknown_type(context: Any) -> None:
assert "unknown_type" in str(context.test_error).lower()
@then(
"an ExecutionError should be raised for runtime with the original cause suppressed"
)
def step_assert_execution_error_cause_suppressed(context: Any) -> None:
assert context.test_error is not None
assert isinstance(context.test_error, ExecutionError)
assert context.test_error.__cause__ is None, (
f"Expected ExecutionError with suppressed cause, "
f"but got __cause__={context.test_error.__cause__!r}"
)
@then("a ConfigurationError should be raised for runtime about no actors")
def step_assert_no_actors_error(context: Any) -> None:
assert context.test_error is not None
assert isinstance(context.test_error, ConfigurationError)
assert "no actors" in str(context.test_error).lower(), (
f"Expected error to mention 'no actors', got: {context.test_error}"
)
@when("I import from cleaveractors.runtime_types")
def step_import_runtime_types(context):
from cleveractors.runtime_types import ActorResult, NodeUsage
context.rt_actor_result = ActorResult
context.rt_node_usage = NodeUsage
@then("ActorResult and NodeUsage should be importable")
def step_assert_rt_imports(context):
assert context.rt_actor_result is not None
assert context.rt_node_usage is not None