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fix: ensure lite agents course-correct on validation errors
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* fix: ensure lite agents course-correct on validation errors

* chore: update cassettes and test expectations

* fix: ensure multiple guardrails propogate
This commit is contained in:
Greyson LaLonde
2025-11-05 19:02:11 -05:00
committed by GitHub
parent 61ad1fb112
commit 7e6171d5bc
28 changed files with 8125 additions and 10295 deletions
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89
lib/crewai/src/crewai/lite_agent.py
View File

@@ -1,6 +1,7 @@
import asyncio
from collections.abc import Callable
import inspect
import json
from typing import (
Any,
Literal,
@@ -58,7 +59,11 @@ from crewai.utilities.agent_utils import (
process_llm_response,
render_text_description_and_args,
)
from crewai.utilities.converter import generate_model_description
from crewai.utilities.converter import (
Converter,
ConverterError,
generate_model_description,
)
from crewai.utilities.guardrail import process_guardrail
from crewai.utilities.guardrail_types import GuardrailCallable, GuardrailType
from crewai.utilities.i18n import I18N, get_i18n
@@ -241,7 +246,11 @@ class LiteAgent(FlowTrackable, BaseModel):
"""Return the original role for compatibility with tool interfaces."""
return self.role
def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
def kickoff(
self,
messages: str | list[LLMMessage],
response_format: type[BaseModel] | None = None,
) -> LiteAgentOutput:
"""
Execute the agent with the given messages.
@@ -249,6 +258,8 @@ class LiteAgent(FlowTrackable, BaseModel):
messages: Either a string query or a list of message dictionaries.
If a string is provided, it will be converted to a user message.
If a list is provided, each dict should have 'role' and 'content' keys.
response_format: Optional Pydantic model for structured output. If provided,
overrides self.response_format for this execution.
Returns:
LiteAgentOutput: The result of the agent execution.
@@ -269,9 +280,13 @@ class LiteAgent(FlowTrackable, BaseModel):
self.tools_results = []
# Format messages for the LLM
self._messages = self._format_messages(messages)
self._messages = self._format_messages(
messages, response_format=response_format
)
return self._execute_core(agent_info=agent_info)
return self._execute_core(
agent_info=agent_info, response_format=response_format
)
except Exception as e:
self._printer.print(
@@ -289,7 +304,9 @@ class LiteAgent(FlowTrackable, BaseModel):
)
raise e
def _execute_core(self, agent_info: dict[str, Any]) -> LiteAgentOutput:
def _execute_core(
self, agent_info: dict[str, Any], response_format: type[BaseModel] | None = None
) -> LiteAgentOutput:
# Emit event for agent execution start
crewai_event_bus.emit(
self,
@@ -303,15 +320,29 @@ class LiteAgent(FlowTrackable, BaseModel):
# Execute the agent using invoke loop
agent_finish = self._invoke_loop()
formatted_result: BaseModel | None = None
if self.response_format:
active_response_format = response_format or self.response_format
if active_response_format:
try:
# Cast to BaseModel to ensure type safety
result = self.response_format.model_validate_json(agent_finish.output)
model_schema = generate_model_description(active_response_format)
schema = json.dumps(model_schema, indent=2)
instructions = self.i18n.slice("formatted_task_instructions").format(
output_format=schema
)
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 Exception as e:
except ConverterError as e:
self._printer.print(
content=f"Failed to parse output into response format: {e!s}",
content=f"Failed to parse output into response format after retries: {e.message}",
color="yellow",
)
@@ -400,8 +431,14 @@ class LiteAgent(FlowTrackable, BaseModel):
"""
return await asyncio.to_thread(self.kickoff, messages)
def _get_default_system_prompt(self) -> str:
"""Get the default system prompt for the agent."""
def _get_default_system_prompt(
self, response_format: type[BaseModel] | None = None
) -> str:
"""Get the default system prompt for the agent.
Args:
response_format: Optional response format to use instead of self.response_format
"""
base_prompt = ""
if self._parsed_tools:
# Use the prompt template for agents with tools
@@ -422,21 +459,31 @@ class LiteAgent(FlowTrackable, BaseModel):
goal=self.goal,
)
# Add response format instructions if specified
if self.response_format:
schema = generate_model_description(self.response_format)
active_response_format = response_format or self.response_format
if active_response_format:
model_description = generate_model_description(active_response_format)
schema_json = json.dumps(model_description, indent=2)
base_prompt += self.i18n.slice("lite_agent_response_format").format(
response_format=schema
response_format=schema_json
)
return base_prompt
def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
"""Format messages for the LLM."""
def _format_messages(
self,
messages: str | list[LLMMessage],
response_format: type[BaseModel] | None = None,
) -> list[LLMMessage]:
"""Format messages for the LLM.
Args:
messages: Input messages to format
response_format: Optional response format to use instead of self.response_format
"""
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
system_prompt = self._get_default_system_prompt()
system_prompt = self._get_default_system_prompt(response_format=response_format)
# Add system message at the beginning
formatted_messages: list[LLMMessage] = [
@@ -506,6 +553,10 @@ class LiteAgent(FlowTrackable, BaseModel):
self._append_message(formatted_answer.text, role="assistant")
except OutputParserError as e: # noqa: PERF203
self._printer.print(
content="Failed to parse LLM output. Retrying...",
color="yellow",
)
formatted_answer = handle_output_parser_exception(
e=e,
messages=self._messages,

6
lib/crewai/src/crewai/task.py
View File

@@ -525,7 +525,11 @@ class Task(BaseModel):
tools=tools,
)
pydantic_output, json_output = self._export_output(result)
if not self._guardrails and not self._guardrail:
pydantic_output, json_output = self._export_output(result)
else:
pydantic_output, json_output = None, None
task_output = TaskOutput(
name=self.name or self.description,
description=self.description,

4
lib/crewai/src/crewai/translations/en.json
View File

@@ -22,12 +22,12 @@
"summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
"summary": "This is a summary of our conversation so far:\n{merged_summary}",
"manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
"formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
"formatted_task_instructions": "Ensure your final answer strictly adheres to the following OpenAPI schema: {output_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
"conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
"feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
"lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
"lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
"lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
"lite_agent_response_format": "Ensure your final answer strictly adheres to the following OpenAPI schema: {response_format}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python.",
"knowledge_search_query": "The original query is: {task_prompt}.",
"knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
},

37
lib/crewai/src/crewai/utilities/converter.py
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@@ -10,9 +10,9 @@ from pydantic import BaseModel, ValidationError
from typing_extensions import Unpack
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
from crewai.utilities.i18n import get_i18n
from crewai.utilities.internal_instructor import InternalInstructor
from crewai.utilities.printer import Printer
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
if TYPE_CHECKING:
@@ -22,6 +22,7 @@ if TYPE_CHECKING:
from crewai.llms.base_llm import BaseLLM
_JSON_PATTERN: Final[re.Pattern[str]] = re.compile(r"({.*})", re.DOTALL)
_I18N = get_i18n()
class ConverterError(Exception):
@@ -87,8 +88,7 @@ class Converter(OutputConverter):
result = self.model.model_validate(result)
elif isinstance(result, str):
try:
parsed = json.loads(result)
result = self.model.model_validate(parsed)
result = self.model.model_validate_json(result)
except Exception as parse_err:
raise ConverterError(
f"Failed to convert partial JSON result into Pydantic: {parse_err}"
@@ -172,6 +172,16 @@ def convert_to_model(
model = output_pydantic or output_json
if model is None:
return result
if converter_cls:
return convert_with_instructions(
result=result,
model=model,
is_json_output=bool(output_json),
agent=agent,
converter_cls=converter_cls,
)
try:
escaped_result = json.dumps(json.loads(result, strict=False))
return validate_model(
@@ -251,7 +261,7 @@ def handle_partial_json(
except json.JSONDecodeError:
pass
except ValidationError:
pass
raise
except Exception as e:
Printer().print(
content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
@@ -335,25 +345,26 @@ def get_conversion_instructions(
Returns:
"""
instructions = "Please convert the following text into valid JSON."
instructions = ""
if (
llm
and not isinstance(llm, str)
and hasattr(llm, "supports_function_calling")
and llm.supports_function_calling()
):
model_schema = PydanticSchemaParser(model=model).get_schema()
instructions += (
f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
f"Use this format exactly:\n```json\n{model_schema}\n```"
schema_dict = generate_model_description(model)
schema = json.dumps(schema_dict, indent=2)
formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
output_format=schema
)
instructions += formatted_task_instructions
else:
model_description = generate_model_description(model)
schema_json = json.dumps(model_description["json_schema"]["schema"], indent=2)
instructions += (
f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
f"Use this format exactly:\n```json\n{schema_json}\n```"
schema_json = json.dumps(model_description, indent=2)
formatted_task_instructions = _I18N.slice("formatted_task_instructions").format(
output_format=schema_json
)
instructions += formatted_task_instructions
return instructions

4
lib/crewai/tests/agents/test_lite_agent.py
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@@ -382,8 +382,8 @@ def test_guardrail_is_called_using_string():
assert not guardrail_events["completed"][0].success
assert guardrail_events["completed"][1].success
assert (
"Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
in result.raw
"top 10 best Brazilian soccer players" in result.raw or
"Brazilian players" in result.raw
)

2309
lib/crewai/tests/cassettes/test_after_kickoff_modification.yaml
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lib/crewai/tests/cassettes/test_before_kickoff_modification.yaml
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lib/crewai/tests/cassettes/test_before_kickoff_with_none_input.yaml
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lib/crewai/tests/cassettes/test_custom_converter_cls.yaml
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lib/crewai/tests/test_task.py
View File

@@ -340,7 +340,7 @@ def test_output_pydantic_hierarchical():
)
result = crew.kickoff()
assert isinstance(result.pydantic, ScoreOutput)
assert result.to_dict() == {"score": 0}
assert result.to_dict() == {"score": 4}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -599,7 +599,7 @@ def test_output_pydantic_to_another_task():
assert isinstance(pydantic_result, ScoreOutput), (
"Expected pydantic result to be of type ScoreOutput"
)
assert pydantic_result.score == 4
assert pydantic_result.score == 5
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -630,7 +630,7 @@ def test_output_json_to_another_task():
crew = Crew(agents=[scorer], tasks=[task1, task2])
result = crew.kickoff()
assert '{"score": 4}' == result.json
assert '{"score": 3}' == result.json
@pytest.mark.vcr(filter_headers=["authorization"])

7
lib/crewai/tests/test_task_guardrails.py
View File

@@ -181,7 +181,7 @@ def test_task_guardrail_process_output(task_output):
result = guardrail(task_output)
assert result[0] is False
assert "exceeding the guardrail limit of fewer than" in result[1].lower()
assert result[1] == "The task result contains more than 10 words, violating the guardrail. The text provided contains about 21 words."
guardrail = LLMGuardrail(
description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
@@ -252,10 +252,7 @@ def test_guardrail_emits_events(sample_agent):
{
"success": False,
"result": None,
"error": "The task result does not comply with the guardrail because none of "
"the listed authors are from Italy. All authors mentioned are from "
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"which violates the requirement that authors must be Italian.",
"error": "The output indicates that none of the authors mentioned are from Italy, while the guardrail requires authors to be from Italy. Therefore, the output does not comply with the guardrail.",
"retry_count": 0,
},
{"success": True, "result": result.raw, "error": None, "retry_count": 1},

26
lib/crewai/tests/utilities/test_converter.py
View File

@@ -227,28 +227,22 @@ def test_get_conversion_instructions_gpt() -> None:
with patch.object(LLM, "supports_function_calling") as supports_function_calling:
supports_function_calling.return_value = True
instructions = get_conversion_instructions(SimpleModel, llm)
model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
expected_instructions = (
"Please convert the following text into valid JSON.\n\n"
"Output ONLY the valid JSON and nothing else.\n\n"
"Use this format exactly:\n```json\n"
f"{model_schema}\n```"
)
assert instructions == expected_instructions
# Now using OpenAPI schema format for all models
assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
assert '"type": "json_schema"' in instructions
assert '"name": "SimpleModel"' in instructions
assert "Do not include the OpenAPI schema in the final output" in instructions
def test_get_conversion_instructions_non_gpt() -> None:
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
with patch.object(LLM, "supports_function_calling", return_value=False):
instructions = get_conversion_instructions(SimpleModel, llm)
# Check that the JSON schema is properly formatted
assert "Please convert the following text into valid JSON" in instructions
assert "Output ONLY the valid JSON and nothing else" in instructions
assert "Use this format exactly" in instructions
assert "```json" in instructions
assert '"type": "object"' in instructions
assert '"properties"' in instructions
assert "'type': 'json_schema'" not in instructions
# Now using OpenAPI schema format for all models
assert "Ensure your final answer strictly adheres to the following OpenAPI schema:" in instructions
assert '"type": "json_schema"' in instructions
assert '"name": "SimpleModel"' in instructions
assert "Do not include the OpenAPI schema in the final output" in instructions
# Tests for is_gpt