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"""Unit tests for BudgetControlMiddleware."""
from typing import Any
from unittest.async_case import IsolatedAsyncioTestCase
from utils import MockModel
from agentscope.agent import Agent
from agentscope.message import UserMsg, TextBlock, ToolCallBlock, HintBlock
from agentscope.middleware import ReplyBudgetControlMiddleware
from agentscope.model import ChatResponse, ChatUsage
from agentscope.permission import (
PermissionBehavior,
PermissionContext,
PermissionDecision,
)
from agentscope.event import UserConfirmResultEvent, ConfirmResult
from agentscope.tool import ToolBase, Toolkit, ToolChunk
def _response(
text: str,
input_tokens: int,
output_tokens: int,
) -> ChatResponse:
"""Build a non-streaming ChatResponse with usage."""
return ChatResponse(
content=[TextBlock(text=text)],
is_last=True,
usage=ChatUsage(
input_tokens=input_tokens,
output_tokens=output_tokens,
time=0.0,
),
)
class DummyTool(ToolBase):
"""Minimal tool that always allows and returns a fixed result."""
name: str = "dummy"
description: str = "A dummy tool for testing"
input_schema: dict[str, Any] = {"type": "object", "properties": {}}
is_concurrency_safe: bool = True
is_read_only: bool = True
is_external_tool: bool = False
is_mcp: bool = False
async def check_permissions(
self,
tool_input: dict[str, Any],
context: PermissionContext,
) -> PermissionDecision:
"""Always allow."""
return PermissionDecision(
behavior=PermissionBehavior.ALLOW,
decision_reason="Dummy tool always allows",
message="Dummy tool always allows",
)
async def __call__(self, **kwargs: Any) -> ToolChunk:
"""Return a fixed result."""
return ToolChunk(content=[TextBlock(text="ok")])
class ConfirmRequiredTool(ToolBase):
"""Minimal tool that always requires user confirmation before running."""
name: str = "confirm_required"
description: str = "A tool that requires user confirmation"
input_schema: dict[str, Any] = {"type": "object", "properties": {}}
is_concurrency_safe: bool = False
is_read_only: bool = False
is_external_tool: bool = False
is_mcp: bool = False
async def check_permissions(
self,
tool_input: dict[str, Any],
context: PermissionContext,
) -> PermissionDecision:
"""Always require user confirmation."""
return PermissionDecision(
behavior=PermissionBehavior.ASK,
decision_reason="Confirmation required",
message="Confirmation required",
)
async def __call__(self, **kwargs: Any) -> ToolChunk:
"""Return a fixed result."""
return ToolChunk(content=[TextBlock(text="confirmed result")])
def _has_hint_block(msg: Any, hint_message: str) -> bool:
"""Return True if *msg* contains a HintBlock with *hint_message*."""
content = getattr(msg, "content", None)
if not isinstance(content, list):
return False
return any(
isinstance(b, HintBlock) and hint_message in b.hint for b in content
)
class TestBudgetControlMiddleware(IsolatedAsyncioTestCase):
"""Test cases for BudgetControlMiddleware."""
async def asyncSetUp(self) -> None:
"""Set up shared fixtures."""
self.toolkit = Toolkit()
async def test_under_budget_no_hint_injected(self) -> None:
"""When token usage stays below the budget, no hint is injected."""
model = MockModel()
model.set_responses(
[_response("done", input_tokens=10, output_tokens=5)],
)
middleware = ReplyBudgetControlMiddleware(token_budget=1000)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=self.toolkit,
middlewares=[middleware],
)
context_before = len(agent.state.context)
await agent.reply(UserMsg("user", "hello"))
# No HintBlock should have been added to context
hint_msgs = [
m
for m in agent.state.context[context_before:]
if _has_hint_block(m, middleware.hint_message)
]
self.assertEqual(len(hint_msgs), 0)
async def test_budget_exceeded_injects_hint(self) -> None:
"""When the budget is exceeded, the hint block is injected.
Uses token_budget=0 so the budget condition fires on the very first
reasoning call (0 used >= 0 max).
"""
model = MockModel()
model.set_responses(
[_response("wrap up", input_tokens=10, output_tokens=5)],
)
middleware = ReplyBudgetControlMiddleware(token_budget=0)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=self.toolkit,
middlewares=[middleware],
)
context_before = len(agent.state.context)
await agent.reply(UserMsg("user", "hello"))
hint_msgs = [
m
for m in agent.state.context[context_before:]
if _has_hint_block(m, middleware.hint_message)
]
self.assertGreater(len(hint_msgs), 0)
async def test_budget_exceeded_forces_tool_choice_none(self) -> None:
"""When budget is exceeded, tool_choice forwarded to model is none.
Uses token_budget=0 so the override fires on the first reasoning call.
"""
received_tool_choices: list = []
class TrackingModel(MockModel):
"""Model that records tool_choice on every call."""
async def _call_api(
self,
*args: Any,
**kwargs: Any,
) -> ChatResponse:
"""Record tool_choice and delegate to mock."""
received_tool_choices.append(kwargs.get("tool_choice"))
return await super()._call_api(*args, **kwargs)
model = TrackingModel()
model.set_responses(
[_response("wrap up", input_tokens=10, output_tokens=5)],
)
middleware = ReplyBudgetControlMiddleware(token_budget=0)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=self.toolkit,
middlewares=[middleware],
)
await agent.reply(UserMsg("user", "hello"))
# At least one call must have received tool_choice with mode="none"
self.assertTrue(
any(
getattr(tc, "mode", None) == "none"
for tc in received_tool_choices
if tc is not None
),
)
async def test_token_accumulation_across_steps(self) -> None:
"""Tokens accumulate across steps and trigger enforcement correctly.
Step 1: tool call costs 200+100=300 tokens (token_budget=300 so
step 2 sees used >= max and injects the hint).
"""
toolkit = Toolkit(tools=[DummyTool()])
model = MockModel()
model.set_responses(
[
[
ChatResponse(
content=[
ToolCallBlock(
id="tc_1",
name="dummy",
input="{}",
),
],
is_last=True,
usage=ChatUsage(
input_tokens=200,
output_tokens=100,
time=0.0,
),
),
],
[
ChatResponse(
content=[TextBlock(text="done")],
is_last=True,
usage=ChatUsage(
input_tokens=150,
output_tokens=50,
time=0.0,
),
),
],
],
)
middleware = ReplyBudgetControlMiddleware(token_budget=300)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=toolkit,
middlewares=[middleware],
)
context_before = len(agent.state.context)
await agent.reply(UserMsg("user", "hello"))
hint_msgs = [
m
for m in agent.state.context[context_before:]
if _has_hint_block(m, middleware.hint_message)
]
self.assertGreater(len(hint_msgs), 0)
async def test_weighted_token_calculation(self) -> None:
"""output_token_weight scales output tokens in the budget calculation.
With input_token_weight=1, output_token_weight=3, token_budget=200:
- Step 1: 50 input * 1 + 50 output * 3 = 200 → budget hit exactly
- Step 2: hint should be injected before the model call
"""
toolkit = Toolkit(tools=[DummyTool()])
model = MockModel()
model.set_responses(
[
[
ChatResponse(
content=[
ToolCallBlock(
id="tc_1",
name="dummy",
input="{}",
),
],
is_last=True,
usage=ChatUsage(
input_tokens=50,
output_tokens=50,
time=0.0,
),
),
],
[
ChatResponse(
content=[TextBlock(text="done")],
is_last=True,
usage=ChatUsage(
input_tokens=30,
output_tokens=10,
time=0.0,
),
),
],
],
)
# 50*1 + 50*3 = 200 == token_budget → step 2 triggers enforcement
middleware = ReplyBudgetControlMiddleware(
token_budget=200,
input_token_weight=1,
output_token_weight=3,
)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=toolkit,
middlewares=[middleware],
)
context_before = len(agent.state.context)
await agent.reply(UserMsg("user", "hello"))
hint_msgs = [
m
for m in agent.state.context[context_before:]
if _has_hint_block(m, middleware.hint_message)
]
self.assertGreater(len(hint_msgs), 0)
async def test_state_cleanup_after_reply(self) -> None:
"""middle_context entry for the reply is removed after reply ends."""
model = MockModel()
model.set_responses(
[_response("done", input_tokens=10, output_tokens=5)],
)
middleware = ReplyBudgetControlMiddleware(token_budget=1000)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=self.toolkit,
middlewares=[middleware],
)
await agent.reply(UserMsg("user", "hello"))
middleware_key = await middleware.get_middleware_key()
bucket = agent.state.middle_context.get(middleware_key, {})
# All per-reply entries must have been cleaned up
self.assertEqual(len(bucket), 0)
async def test_token_accumulation_persists_across_hitl(self) -> None:
"""Token accumulation in middle_context persists across HITL boundary.
Scenario:
- token_budget=300, both weights default to 1.
- First reply_stream call: model call costs 200 input + 100 output
= 300 tokens, then pauses at REQUIRE_USER_CONFIRM (no ReplyEndEvent
is emitted). The 300-token count is stored in middle_context.
- Second reply_stream call with UserConfirmResultEvent: the same
reply_id resumes. on_reasoning reads 300 >= 300 from middle_context
and injects the hint + forces tool_choice=none before the final
model call, proving budget state survived the HITL round-trip.
"""
tool_call_id = "tc_hitl"
tool_input = "{}"
toolkit = Toolkit(tools=[ConfirmRequiredTool()])
model = MockModel()
model.set_responses(
[
# Step 1: model produces a tool call that requires confirmation
[
ChatResponse(
content=[
ToolCallBlock(
id=tool_call_id,
name="confirm_required",
input=tool_input,
),
],
is_last=True,
usage=ChatUsage(
input_tokens=200,
output_tokens=100,
time=0.0,
),
),
],
# Step 2 (after confirmation): final wrap-up text
[
ChatResponse(
content=[TextBlock(text="wrap up")],
is_last=True,
usage=ChatUsage(
input_tokens=50,
output_tokens=20,
time=0.0,
),
),
],
],
)
# 200*1 + 100*1 = 300 == token_budget → reasoning after resume
# injects hint
middleware = ReplyBudgetControlMiddleware(token_budget=300)
agent = Agent(
name="test_agent",
system_prompt="you are helpful",
model=model,
toolkit=toolkit,
middlewares=[middleware],
)
# --- First call: pauses at REQUIRE_USER_CONFIRM ---
events = []
async for event in agent.reply_stream(UserMsg("user", "hello")):
events.append(event)
event_types = [e.type for e in events]
self.assertIn("REQUIRE_USER_CONFIRM", event_types)
self.assertNotIn("REPLY_END", event_types)
reply_id = agent.state.reply_id
# Token count must be stored in middle_context (survived the pause)
middleware_key = await middleware.get_middleware_key()
stored = agent.state.middle_context.get(middleware_key, {})
self.assertAlmostEqual(stored.get(reply_id, 0), 300.0)
# --- Second call: resume with user confirmation ---
user_confirm_event = UserConfirmResultEvent(
reply_id=reply_id,
confirm_results=[
ConfirmResult(
confirmed=True,
tool_call=ToolCallBlock(
id=tool_call_id,
name="confirm_required",
input=tool_input,
),
),
],
)
resume_events = []
async for event in agent.reply_stream(inputs=user_confirm_event):
resume_events.append(event)
resume_event_types = [e.type for e in resume_events]
self.assertIn("REPLY_END", resume_event_types)
# Hint is appended to the existing assistant message (which was created
# in the first call), so we search the full context rather than a
# slice.
hint_msgs = [
m
for m in agent.state.context
if _has_hint_block(m, middleware.hint_message)
]
self.assertGreater(len(hint_msgs), 0)
# middle_context must be cleaned up after reply ends
bucket = agent.state.middle_context.get(middleware_key, {})
self.assertNotIn(reply_id, bucket)
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