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Fix structured output leaks in tool-calling loops (#5897)

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* Fix structured output leaks in tool-calling loops

* addressing comments

* drop scripts

* Update Gemini agent tests to include structured output with thoughts and bump model version to 2.5-flash

* merge

* Update Anthropic test cases to use new model and tool structure

- Changed the model from "claude-3-5-haiku-20241022" to "claude-sonnet-4-6" in the test setup.
- Updated the request and response formats in the YAML test cassette to reflect the new tool structure and improved content formatting.
- Adjusted the expected response body to match the new output format from the assistant, including changes in tool usage and response details.
- Increased rate limit values in the response headers for better testing scenarios.

* adjusted bedrock cassettes

* adjusting cassettes for bedrock

* fix test

* Update VCR configuration to use 'host' instead of 'bedrock_host' for request matching
This commit is contained in:
Lorenze Jay
2026-05-27 13:20:53 -07:00
committed by GitHub
parent 90a37c94c1
commit a1033e4bfe
34 changed files with 1071 additions and 1053 deletions
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37
conftest.py
View File

@@ -5,6 +5,7 @@ from collections.abc import Generator
import gzip
import os
from pathlib import Path
import re
import tempfile
from typing import Any
@@ -20,8 +21,25 @@ except ModuleNotFoundError:
env_test_path = Path(__file__).parent / ".env.test"
load_dotenv(env_test_path, override=True)
load_dotenv(override=True)
load_dotenv(env_test_path, override=False)
load_dotenv(override=False)
BEDROCK_HOST_PLACEHOLDER = "bedrock-runtime.vcr.amazonaws.com"
_BEDROCK_HOST_RE = re.compile(r"^bedrock-runtime\.[a-z0-9-]+\.amazonaws\.com$")
def _normalize_bedrock_host(host: str) -> str:
if _BEDROCK_HOST_RE.match(host):
return BEDROCK_HOST_PLACEHOLDER
return host
def bedrock_host_matcher(r1: Request, r2: Request) -> bool: # type: ignore[no-any-unimported]
"""Match Bedrock requests across AWS regions (CI uses us-east-1, local may use us-west-2)."""
return _normalize_bedrock_host(r1.host or "") == _normalize_bedrock_host(
r2.host or ""
)
def _patched_make_vcr_request(httpx_request: Any, **kwargs: Any) -> Any:
@@ -188,6 +206,7 @@ HEADERS_TO_FILTER = {
"anthropic-ratelimit-tokens-remaining": "ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX",
"anthropic-ratelimit-tokens-reset": "ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX",
"x-amz-date": "X-AMZ-DATE-XXX",
"x-amz-security-token": "X-AMZ-SECURITY-TOKEN-XXX",
"amz-sdk-invocation-id": "AMZ-SDK-INVOCATION-ID-XXX",
"accept-encoding": "ACCEPT-ENCODING-XXX",
"x-amzn-requestid": "X-AMZN-REQUESTID-XXX",
@@ -212,6 +231,10 @@ def _filter_request_headers(request: Request) -> Request: # type: ignore[no-any
placeholder_host = "fake-azure-endpoint.openai.azure.com"
request.uri = request.uri.replace(original_host, placeholder_host)
# Normalize Bedrock regional endpoints so cassettes work in any AWS region.
if request.host and _BEDROCK_HOST_RE.match(request.host):
request.uri = request.uri.replace(request.host, BEDROCK_HOST_PLACEHOLDER)
return request
@@ -229,6 +252,11 @@ def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any] | None:
if body == "" or body == b"" or content_length == ["0"]:
return None
status_code = response.get("status", {}).get("code")
if isinstance(status_code, int) and status_code >= 400:
# Avoid persisting auth/model errors when re-recording without valid AWS creds.
return None
for encoding_header in ["Content-Encoding", "content-encoding"]:
if encoding_header in headers:
encoding = headers.pop(encoding_header)
@@ -279,6 +307,11 @@ def vcr_cassette_dir(request: Any) -> str:
return str(cassette_dir)
def pytest_recording_configure(vcr: Any, config: Any) -> None:
"""Register custom VCR matchers for each test cassette session."""
vcr.register_matcher("bedrock_host", bedrock_host_matcher)
@pytest.fixture(scope="module")
def vcr_config(vcr_cassette_dir: str) -> dict[str, Any]:
"""Configure VCR with organized cassette storage."""

41
lib/crewai/src/crewai/agent/core.py
View File

@@ -28,6 +28,7 @@ from pydantic import (
ConfigDict,
Field,
PrivateAttr,
ValidationError,
model_validator,
)
from pydantic.functional_serializers import PlainSerializer
@@ -1710,24 +1711,32 @@ class Agent(BaseAgent):
elif response_format:
raw_output = str(output) if not isinstance(output, str) else output
try:
model_schema = generate_model_description(response_format)
schema = json.dumps(model_schema, indent=2)
instructions = I18N_DEFAULT.slice("formatted_task_instructions").format(
output_format=schema
)
formatted_result = response_format.model_validate_json(raw_output)
except ValidationError:
# Direct JSON validation failed; fall back to converter-based parsing below.
formatted_result = None
converter = Converter(
llm=cast(BaseLLM, self.llm),
text=raw_output,
model=response_format,
instructions=instructions,
)
if formatted_result is None:
try:
model_schema = generate_model_description(response_format)
schema = json.dumps(model_schema, indent=2)
instructions = I18N_DEFAULT.slice(
"formatted_task_instructions"
).format(output_format=schema)
conversion_result = converter.to_pydantic()
if isinstance(conversion_result, BaseModel):
formatted_result = conversion_result
except ConverterError:
pass
converter = Converter(
llm=cast(BaseLLM, self.llm),
text=raw_output,
model=response_format,
instructions=instructions,
)
conversion_result = converter.to_pydantic()
if isinstance(conversion_result, BaseModel):
formatted_result = conversion_result
except ConverterError:
# Conversion failure is non-fatal; raw output is preserved below.
pass
else:
raw_output = str(output) if not isinstance(output, str) else output

20
lib/crewai/src/crewai/agents/crew_agent_executor.py
View File

@@ -350,6 +350,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
enforce_rpm_limit(self.request_within_rpm_limit)
effective_response_model = (
None if self.original_tools else self.response_model
)
answer = get_llm_response(
llm=cast("BaseLLM", self.llm),
messages=self.messages,
@@ -357,11 +361,11 @@ class CrewAgentExecutor(BaseAgentExecutor):
printer=PRINTER,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=effective_response_model,
executor_context=self,
verbose=self.agent.verbose,
)
if self.response_model is not None:
if effective_response_model is not None:
try:
if isinstance(answer, BaseModel):
output_json = answer.model_dump_json()
@@ -502,7 +506,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
available_functions=None,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=None,
executor_context=self,
verbose=self.agent.verbose,
)
@@ -1159,6 +1163,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
enforce_rpm_limit(self.request_within_rpm_limit)
effective_response_model = (
None if self.original_tools else self.response_model
)
answer = await aget_llm_response(
llm=cast("BaseLLM", self.llm),
messages=self.messages,
@@ -1166,12 +1174,12 @@ class CrewAgentExecutor(BaseAgentExecutor):
printer=PRINTER,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=effective_response_model,
executor_context=self,
verbose=self.agent.verbose,
)
if self.response_model is not None:
if effective_response_model is not None:
try:
if isinstance(answer, BaseModel):
output_json = answer.model_dump_json()
@@ -1311,7 +1319,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
available_functions=None,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=None,
executor_context=self,
verbose=self.agent.verbose,
)

8
lib/crewai/src/crewai/experimental/agent_executor.py
View File

@@ -1273,6 +1273,10 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
try:
enforce_rpm_limit(self.request_within_rpm_limit)
effective_response_model = (
None if self.original_tools else self.response_model
)
answer = get_llm_response(
llm=self.llm,
messages=list(self.state.messages),
@@ -1280,7 +1284,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
printer=PRINTER,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=effective_response_model,
executor_context=self,
verbose=self.agent.verbose,
)
@@ -1368,7 +1372,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
available_functions=None,
from_task=self.task,
from_agent=self.agent,
response_model=self.response_model,
response_model=None,
executor_context=self,
verbose=self.agent.verbose,
)

46
lib/crewai/src/crewai/lite_agent.py
View File

@@ -23,6 +23,7 @@ from pydantic import (
BaseModel,
Field,
PrivateAttr,
ValidationError,
field_serializer,
field_validator,
model_validator,
@@ -639,29 +640,38 @@ class LiteAgent(FlowTrackable, BaseModel):
formatted_result = agent_finish.output
elif active_response_format:
try:
model_schema = generate_model_description(active_response_format)
schema = json.dumps(model_schema, indent=2)
instructions = I18N_DEFAULT.slice("formatted_task_instructions").format(
output_format=schema
formatted_result = active_response_format.model_validate_json(
str(agent_finish.output)
)
except ValidationError:
# Direct JSON validation failed; fall back to converter-based parsing below.
formatted_result = None
converter = Converter(
llm=self.llm,
text=agent_finish.output,
model=active_response_format,
instructions=instructions,
)
if formatted_result is None:
try:
model_schema = generate_model_description(active_response_format)
schema = json.dumps(model_schema, indent=2)
instructions = I18N_DEFAULT.slice(
"formatted_task_instructions"
).format(output_format=schema)
result = converter.to_pydantic()
if isinstance(result, BaseModel):
formatted_result = result
except ConverterError as e:
if self.verbose:
PRINTER.print(
content=f"Failed to parse output into response format after retries: {e.message}",
color="yellow",
converter = Converter(
llm=self.llm,
text=agent_finish.output,
model=active_response_format,
instructions=instructions,
)
result = converter.to_pydantic()
if isinstance(result, BaseModel):
formatted_result = result
except ConverterError as e:
if self.verbose:
PRINTER.print(
content=f"Failed to parse output into response format after retries: {e.message}",
color="yellow",
)
if isinstance(self.llm, BaseLLM):
usage_metrics = self.llm.get_token_usage_summary()
else:

47
lib/crewai/tests/agents/test_agent_executor.py
View File

@@ -12,6 +12,7 @@ from typing import Any
from unittest.mock import AsyncMock, Mock, patch
import pytest
from pydantic import BaseModel
from crewai.agents.tools_handler import ToolsHandler as _ToolsHandler
from crewai.agents.step_executor import StepExecutor
@@ -108,6 +109,9 @@ class TestAgentExecutorState:
class TestAgentExecutor:
"""Test AgentExecutor class."""
class StructuredResult(BaseModel):
value: str
@pytest.fixture
def mock_dependencies(self):
"""Create mock dependencies for executor."""
@@ -215,6 +219,49 @@ class TestAgentExecutor:
assert result == "check_iteration"
def test_call_llm_and_parse_does_not_pass_response_model_with_tools(
self, mock_dependencies
):
"""Structured output should not be requested during ReAct tool loops."""
executor = _build_executor(
**mock_dependencies,
original_tools=[Mock()],
response_model=self.StructuredResult,
callbacks=[],
)
executor.state.messages = [{"role": "user", "content": "Use a tool"}]
with patch(
"crewai.experimental.agent_executor.get_llm_response",
return_value="Thought: done\nFinal Answer: complete",
) as get_llm_response_mock:
result = executor.call_llm_and_parse()
assert result == "parsed"
assert get_llm_response_mock.call_args.kwargs["response_model"] is None
def test_call_llm_native_tools_does_not_pass_response_model_with_tools(
self, mock_dependencies
):
"""Structured output should not be requested during native tool calls."""
executor = _build_executor(
**mock_dependencies,
original_tools=[Mock()],
response_model=self.StructuredResult,
callbacks=[],
)
executor._openai_tools = [{"type": "function", "function": {"name": "lookup"}}]
executor.state.messages = [{"role": "user", "content": "Use a tool"}]
with patch(
"crewai.experimental.agent_executor.get_llm_response",
return_value="complete",
) as get_llm_response_mock:
result = executor.call_llm_native_tools()
assert result == "native_finished"
assert get_llm_response_mock.call_args.kwargs["response_model"] is None
def test_finalize_success(self, mock_dependencies):
"""Test finalize with valid AgentFinish."""
with patch.object(AgentExecutor, "_show_logs") as mock_show_logs:

6
lib/crewai/tests/agents/test_native_tool_calling.py
View File

@@ -824,7 +824,7 @@ class TestBedrockNativeToolCalling:
self, calculator_tool: CalculatorTool
) -> None:
"""Test Bedrock agent kickoff with mocked LLM call."""
llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
agent = Agent(
role="Math Assistant",
@@ -858,7 +858,7 @@ class TestBedrockNativeToolCalling:
goal="Use both tools exactly as instructed",
backstory="You follow tool instructions precisely.",
tools=parallel_tools,
llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"),
llm=LLM(model="bedrock/us.anthropic.claude-sonnet-4-6"),
verbose=False,
max_iter=3,
)
@@ -881,7 +881,7 @@ class TestBedrockNativeToolCalling:
goal="Use both tools exactly as instructed",
backstory="You follow tool instructions precisely.",
tools=parallel_tools,
llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"),
llm=LLM(model="bedrock/us.anthropic.claude-sonnet-4-6"),
verbose=False,
max_iter=3,
)

58
lib/crewai/tests/cassettes/TestCrewMultimodalBedrock.test_image_file[bedrock-anthropic.claude-3-haiku-20240307-v1-0].yaml
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File diff suppressed because one or more lines are too long

51
lib/crewai/tests/cassettes/TestCrewMultimodalBedrock.test_image_file[bedrock-us.anthropic.claude-sonnet-4-6].yaml Normal file
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@@ -0,0 +1,51 @@
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complete response:"}]}], "inferenceConfig": {"stopSequences": ["\nObservation:"]},
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57
lib/crewai/tests/cassettes/TestCrewMultimodalBedrock.test_pdf_file[bedrock-anthropic.claude-3-haiku-20240307-v1-0].yaml
View File

@@ -1,57 +0,0 @@
interactions:
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body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Describe
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53
lib/crewai/tests/cassettes/TestCrewMultimodalBedrock.test_pdf_file[bedrock-us.anthropic.claude-sonnet-4-6].yaml Normal file
View File

@@ -0,0 +1,53 @@
interactions:
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complete response:"}]}], "inferenceConfig": {"stopSequences": ["\nObservation:"]},
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428
lib/crewai/tests/cassettes/agents/TestBedrockNativeToolCalling.test_bedrock_agent_kickoff_with_tools_mocked.yaml
View File

@@ -2,134 +2,17 @@ interactions:
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lib/crewai/tests/cassettes/llms/bedrock/test_bedrock_agent_kickoff_structured_output_without_tools.yaml
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lib/crewai/tests/cassettes/llms/google/test_gemini_crew_structured_output_with_tools.yaml
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2
lib/crewai/tests/llms/anthropic/test_anthropic.py
View File

@@ -923,7 +923,7 @@ def test_anthropic_agent_kickoff_structured_output_with_tools():
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="anthropic/claude-3-5-haiku-20241022"),
llm=LLM(model="anthropic/claude-sonnet-4-6"),
tools=[add_numbers],
verbose=True,
)

4
lib/crewai/tests/llms/bedrock/test_bedrock.py
View File

@@ -844,7 +844,7 @@ def test_bedrock_agent_kickoff_structured_output_without_tools():
role="Analyst",
goal="Provide structured analysis on topics",
backstory="You are an expert analyst who provides clear, structured insights.",
llm=LLM(model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"),
llm=LLM(model="bedrock/us.anthropic.claude-sonnet-4-6"),
tools=[],
verbose=True,
)
@@ -886,7 +886,7 @@ def test_bedrock_agent_kickoff_structured_output_with_tools():
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0"),
llm=LLM(model="bedrock/us.anthropic.claude-sonnet-4-6"),
tools=[add_numbers],
verbose=True,
)

15
lib/crewai/tests/llms/bedrock/test_bedrock_async.py
View File

@@ -17,7 +17,7 @@ SKIP_REASON = "VCR does not support aiobotocore async HTTP client"
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_basic_call():
"""Test basic async call with Bedrock."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
result = await llm.acall("Say hello")
@@ -31,7 +31,7 @@ async def test_bedrock_async_basic_call():
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_temperature():
"""Test async call with temperature parameter."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0", temperature=0.1)
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6", temperature=0.1)
result = await llm.acall("Say the word 'test' once")
@@ -44,7 +44,7 @@ async def test_bedrock_async_with_temperature():
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_max_tokens():
"""Test async call with max_tokens parameter."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0", max_tokens=10)
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6", max_tokens=10)
result = await llm.acall("Write a very long story about a dragon.")
@@ -58,7 +58,7 @@ async def test_bedrock_async_with_max_tokens():
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_with_system_message():
"""Test async call with system message."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
messages = [
{"role": "system", "content": "You are a helpful assistant."},
@@ -76,7 +76,7 @@ async def test_bedrock_async_with_system_message():
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_conversation():
"""Test async call with conversation history."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
messages = [
{"role": "user", "content": "My name is Alice."},
@@ -95,7 +95,7 @@ async def test_bedrock_async_conversation():
@pytest.mark.skip(reason=SKIP_REASON)
async def test_bedrock_async_multiple_calls():
"""Test making multiple async calls in sequence."""
llm = LLM(model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
result1 = await llm.acall("What is 1+1?")
result2 = await llm.acall("What is 2+2?")
@@ -112,10 +112,9 @@ async def test_bedrock_async_multiple_calls():
async def test_bedrock_async_with_parameters():
"""Test async call with multiple parameters."""
llm = LLM(
model="bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0",
model="bedrock/us.anthropic.claude-sonnet-4-6",
temperature=0.7,
max_tokens=100,
top_p=0.9
)
result = await llm.acall("Tell me a short fact")

2
lib/crewai/tests/llms/google/test_google.py
View File

@@ -969,7 +969,7 @@ def test_gemini_crew_structured_output_with_tools():
role="Calculator",
goal="Perform calculations using available tools",
backstory="You are a calculator assistant that uses tools to compute results.",
llm=LLM(model="google/gemini-2.0-flash-001"),
llm=LLM(model="google/gemini-2.5-flash"),
tools=[add_numbers],
)

4
lib/crewai/tests/llms/test_multimodal_integration.py
View File

@@ -290,7 +290,7 @@ class TestBedrockMultimodalIntegration:
@pytest.mark.vcr()
def test_describe_image(self, test_image_bytes: bytes) -> None:
"""Test Bedrock Claude can describe an image."""
llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
files = {"image": ImageFile(source=test_image_bytes)}
messages = _build_multimodal_message(
@@ -308,7 +308,7 @@ class TestBedrockMultimodalIntegration:
@pytest.mark.vcr()
def test_analyze_pdf(self) -> None:
"""Test Bedrock Claude can analyze a PDF."""
llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
llm = LLM(model="bedrock/us.anthropic.claude-sonnet-4-6")
files = {"document": PDFFile(source=MINIMAL_PDF)}
messages = _build_multimodal_message(

2
lib/crewai/tests/test_crew_multimodal.py
View File

@@ -42,7 +42,7 @@ GEMINI_MODELS = [
]
BEDROCK_MODELS = [
"bedrock/anthropic.claude-3-haiku-20240307-v1:0",
"bedrock/us.anthropic.claude-sonnet-4-6",
]