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chore: merge main into release/v1.0.0

Browse Source 
Resolved merge conflicts:
- agent.py: kept main's docker validation, restored get_platform_tools method, fixed KnowledgeRetrievalStartedEvent position
- agent_utils.py: merged both LLM and LiteAgent imports
- test_cache_hitting.yaml: accepted main's test recordings
- Removed 4 deprecated test cassettes from old tests/ directory

Key changes from main:
- Enhanced knowledge event handling with from_agent/from_task parameters
- Updated LLMMessage typing in kickoff methods
- Added guardrail parameter to async kickoff
- Restored runtime validation in guardrail.py
This commit is contained in:
Greyson LaLonde
2025-10-08 16:52:55 -04:00
parent 8b9186311f 6f2e39c0dd
commit 79d4e42e62
103 changed files with 4218 additions and 1821 deletions
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34
lib/crewai/src/crewai/agent.py
View File

@@ -5,7 +5,6 @@ from collections.abc import Callable, Sequence
from typing import (
Any,
Literal,
cast,
)
from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -54,6 +53,7 @@ from crewai.utilities.converter import generate_model_description
from crewai.utilities.llm_utils import create_llm
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.utilities.types import LLMMessage
class Agent(BaseAgent):
@@ -348,12 +348,13 @@ class Agent(BaseAgent):
crewai_event_bus.emit(
self,
event=KnowledgeRetrievalStartedEvent(
agent=self,
from_task=task,
from_agent=self,
),
)
try:
self.knowledge_search_query = self._get_knowledge_search_query(
task_prompt
task_prompt, task
)
if self.knowledge_search_query:
# Quering agent specific knowledge
@@ -383,7 +384,8 @@ class Agent(BaseAgent):
self,
event=KnowledgeRetrievalCompletedEvent(
query=self.knowledge_search_query,
agent=self,
from_task=task,
from_agent=self,
retrieved_knowledge=(
(self.agent_knowledge_context or "")
+ (
@@ -401,8 +403,9 @@ class Agent(BaseAgent):
self,
event=KnowledgeSearchQueryFailedEvent(
query=self.knowledge_search_query or "",
agent=self,
error=str(e),
from_task=task,
from_agent=self,
),
)
@@ -577,7 +580,7 @@ class Agent(BaseAgent):
agent=self,
crew=self.crew,
tools=parsed_tools,
prompt=cast(dict[str, str], prompt),
prompt=prompt, # type: ignore[arg-type]
original_tools=raw_tools,
stop_words=stop_words,
max_iter=self.max_iter,
@@ -744,13 +747,14 @@ class Agent(BaseAgent):
def set_fingerprint(self, fingerprint: Fingerprint):
self.security_config.fingerprint = fingerprint
def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
"""Generate a search query for the knowledge base based on the task description."""
crewai_event_bus.emit(
self,
event=KnowledgeQueryStartedEvent(
task_prompt=task_prompt,
agent=self,
from_task=task,
from_agent=self,
),
)
query = self.i18n.slice("knowledge_search_query").format(
@@ -765,8 +769,9 @@ class Agent(BaseAgent):
crewai_event_bus.emit(
self,
event=KnowledgeQueryFailedEvent(
agent=self,
error="LLM is not compatible with knowledge search queries",
from_task=task,
from_agent=self,
),
)
return None
@@ -785,7 +790,8 @@ class Agent(BaseAgent):
self,
event=KnowledgeQueryCompletedEvent(
query=query,
agent=self,
from_task=task,
from_agent=self,
),
)
return rewritten_query
@@ -793,15 +799,16 @@ class Agent(BaseAgent):
crewai_event_bus.emit(
self,
event=KnowledgeQueryFailedEvent(
agent=self,
error=str(e),
from_task=task,
from_agent=self,
),
)
return None
def kickoff(
self,
messages: str | list[dict[str, str]],
messages: str | list[LLMMessage],
response_format: type[Any] | None = None,
) -> LiteAgentOutput:
"""
@@ -841,7 +848,7 @@ class Agent(BaseAgent):
async def kickoff_async(
self,
messages: str | list[dict[str, str]],
messages: str | list[LLMMessage],
response_format: type[Any] | None = None,
) -> LiteAgentOutput:
"""
@@ -871,6 +878,7 @@ class Agent(BaseAgent):
response_format=response_format,
i18n=self.i18n,
original_agent=self,
guardrail=self.guardrail,
)
return await lite_agent.kickoff_async(messages)

27
lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py
View File

@@ -32,6 +32,13 @@ from crewai.events.types.flow_events import (
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
from crewai.events.types.knowledge_events import (
KnowledgeQueryCompletedEvent,
KnowledgeQueryFailedEvent,
KnowledgeQueryStartedEvent,
KnowledgeRetrievalCompletedEvent,
KnowledgeRetrievalStartedEvent,
)
from crewai.events.types.llm_events import (
LLMCallCompletedEvent,
LLMCallFailedEvent,
@@ -310,6 +317,26 @@ class TraceCollectionListener(BaseEventListener):
def on_agent_reasoning_failed(source, event):
self._handle_action_event("agent_reasoning_failed", source, event)
@event_bus.on(KnowledgeRetrievalStartedEvent)
def on_knowledge_retrieval_started(source, event):
self._handle_action_event("knowledge_retrieval_started", source, event)
@event_bus.on(KnowledgeRetrievalCompletedEvent)
def on_knowledge_retrieval_completed(source, event):
self._handle_action_event("knowledge_retrieval_completed", source, event)
@event_bus.on(KnowledgeQueryStartedEvent)
def on_knowledge_query_started(source, event):
self._handle_action_event("knowledge_query_started", source, event)
@event_bus.on(KnowledgeQueryCompletedEvent)
def on_knowledge_query_completed(source, event):
self._handle_action_event("knowledge_query_completed", source, event)
@event_bus.on(KnowledgeQueryFailedEvent)
def on_knowledge_query_failed(source, event):
self._handle_action_event("knowledge_query_failed", source, event)
def _initialize_crew_batch(self, source: Any, event: Any):
"""Initialize trace batch"""
user_context = self._get_user_context()

35
lib/crewai/src/crewai/events/types/knowledge_events.py
View File

@@ -1,51 +1,60 @@
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import Any
from crewai.events.base_events import BaseEvent
class KnowledgeRetrievalStartedEvent(BaseEvent):
class KnowledgeEventBase(BaseEvent):
task_id: str | None = None
task_name: str | None = None
from_task: Any | None = None
from_agent: Any | None = None
agent_role: str | None = None
agent_id: str | None = None
def __init__(self, **data):
super().__init__(**data)
self._set_agent_params(data)
self._set_task_params(data)
class KnowledgeRetrievalStartedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge retrieval is started."""
type: str = "knowledge_search_query_started"
agent: BaseAgent
class KnowledgeRetrievalCompletedEvent(BaseEvent):
class KnowledgeRetrievalCompletedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge retrieval is completed."""
query: str
type: str = "knowledge_search_query_completed"
agent: BaseAgent
retrieved_knowledge: str
class KnowledgeQueryStartedEvent(BaseEvent):
class KnowledgeQueryStartedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge query is started."""
task_prompt: str
type: str = "knowledge_query_started"
agent: BaseAgent
class KnowledgeQueryFailedEvent(BaseEvent):
class KnowledgeQueryFailedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge query fails."""
type: str = "knowledge_query_failed"
agent: BaseAgent
error: str
class KnowledgeQueryCompletedEvent(BaseEvent):
class KnowledgeQueryCompletedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge query is completed."""
query: str
type: str = "knowledge_query_completed"
agent: BaseAgent
class KnowledgeSearchQueryFailedEvent(BaseEvent):
class KnowledgeSearchQueryFailedEvent(KnowledgeEventBase):
"""Event emitted when a knowledge search query fails."""
query: str
type: str = "knowledge_search_query_failed"
agent: BaseAgent
error: str

31
lib/crewai/src/crewai/events/types/llm_guardrail_events.py
View File

@@ -5,7 +5,21 @@ from typing import Any
from crewai.events.base_events import BaseEvent
class LLMGuardrailStartedEvent(BaseEvent):
class LLMGuardrailBaseEvent(BaseEvent):
task_id: str | None = None
task_name: str | None = None
from_task: Any | None = None
from_agent: Any | None = None
agent_role: str | None = None
agent_id: str | None = None
def __init__(self, **data):
super().__init__(**data)
self._set_agent_params(data)
self._set_task_params(data)
class LLMGuardrailStartedEvent(LLMGuardrailBaseEvent):
"""Event emitted when a guardrail task starts
Attributes:
@@ -29,7 +43,7 @@ class LLMGuardrailStartedEvent(BaseEvent):
self.guardrail = getsource(self.guardrail).strip()
class LLMGuardrailCompletedEvent(BaseEvent):
class LLMGuardrailCompletedEvent(LLMGuardrailBaseEvent):
"""Event emitted when a guardrail task completes
Attributes:
@@ -44,3 +58,16 @@ class LLMGuardrailCompletedEvent(BaseEvent):
result: Any
error: str | None = None
retry_count: int
class LLMGuardrailFailedEvent(LLMGuardrailBaseEvent):
"""Event emitted when a guardrail task fails
Attributes:
error: The error message
retry_count: The number of times the guardrail has been retried
"""
type: str = "llm_guardrail_failed"
error: str
retry_count: int

2
lib/crewai/src/crewai/events/utils/console_formatter.py
View File

@@ -1377,7 +1377,7 @@ class ConsoleFormatter:
if isinstance(formatted_answer, AgentAction):
thought = re.sub(r"\n+", "\n", formatted_answer.thought)
formatted_json = json.dumps(
formatted_answer.tool_input,
json.loads(formatted_answer.tool_input),
indent=2,
ensure_ascii=False,
)

26
lib/crewai/src/crewai/lite_agent.py
View File

@@ -3,6 +3,7 @@ from collections.abc import Callable
import inspect
from typing import (
Any,
Literal,
cast,
get_args,
get_origin,
@@ -62,6 +63,7 @@ from crewai.utilities.llm_utils import create_llm
from crewai.utilities.printer import Printer
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.tool_utils import execute_tool_and_check_finality
from crewai.utilities.types import LLMMessage
class LiteAgentOutput(BaseModel):
@@ -180,7 +182,7 @@ class LiteAgent(FlowTrackable, BaseModel):
_token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
_cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
_key: str = PrivateAttr(default_factory=lambda: str(uuid.uuid4()))
_messages: list[dict[str, str]] = PrivateAttr(default_factory=list)
_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
_iterations: int = PrivateAttr(default=0)
_printer: Printer = PrivateAttr(default_factory=Printer)
_guardrail: Callable | None = PrivateAttr(default=None)
@@ -219,7 +221,6 @@ class LiteAgent(FlowTrackable, BaseModel):
raise TypeError(
f"Guardrail requires LLM instance of type BaseLLM, got {type(self.llm).__name__}"
)
self._guardrail = LLMGuardrail(description=self.guardrail, llm=self.llm)
return self
@@ -276,7 +277,7 @@ class LiteAgent(FlowTrackable, BaseModel):
"""Return the original role for compatibility with tool interfaces."""
return self.role
def kickoff(self, messages: str | list[dict[str, str]]) -> LiteAgentOutput:
def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
"""
Execute the agent with the given messages.
@@ -371,6 +372,7 @@ class LiteAgent(FlowTrackable, BaseModel):
guardrail=self._guardrail,
retry_count=self._guardrail_retry_count,
event_source=self,
from_agent=self,
)
if not guardrail_result.success:
@@ -420,9 +422,7 @@ class LiteAgent(FlowTrackable, BaseModel):
return output
async def kickoff_async(
self, messages: str | list[dict[str, str]]
) -> LiteAgentOutput:
async def kickoff_async(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
"""
Execute the agent asynchronously with the given messages.
@@ -467,9 +467,7 @@ class LiteAgent(FlowTrackable, BaseModel):
return base_prompt
def _format_messages(
self, messages: str | list[dict[str, str]]
) -> list[dict[str, str]]:
def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
"""Format messages for the LLM."""
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
@@ -477,7 +475,9 @@ class LiteAgent(FlowTrackable, BaseModel):
system_prompt = self._get_default_system_prompt()
# Add system message at the beginning
formatted_messages = [{"role": "system", "content": system_prompt}]
formatted_messages: list[LLMMessage] = [
{"role": "system", "content": system_prompt}
]
# Add the rest of the messages
formatted_messages.extend(messages)
@@ -589,6 +589,8 @@ class LiteAgent(FlowTrackable, BaseModel):
),
)
def _append_message(self, text: str, role: str = "assistant") -> None:
def _append_message(
self, text: str, role: Literal["user", "assistant", "system"] = "assistant"
) -> None:
"""Append a message to the message list with the given role."""
self._messages.append(format_message_for_llm(text, role=role))
self._messages.append(cast(LLMMessage, format_message_for_llm(text, role=role)))

2
lib/crewai/src/crewai/task.py
View File

@@ -462,6 +462,8 @@ class Task(BaseModel):
guardrail=self._guardrail,
retry_count=self.retry_count,
event_source=self,
from_task=self,
from_agent=agent,
)
if not guardrail_result.success:
if self.retry_count >= self.guardrail_max_retries:

3
lib/crewai/src/crewai/utilities/agent_utils.py
View File

@@ -31,6 +31,7 @@ from crewai.utilities.types import LLMMessage
if TYPE_CHECKING:
from crewai.agent import Agent
from crewai.lite_agent import LiteAgent
from crewai.llm import LLM
from crewai.task import Task
@@ -224,7 +225,7 @@ def get_llm_response(
callbacks: list[Callable[..., Any]],
printer: Printer,
from_task: Task | None = None,
from_agent: Agent | None = None,
from_agent: Agent | LiteAgent | None = None,
) -> str:
"""Call the LLM and return the response, handling any invalid responses.

20
lib/crewai/src/crewai/utilities/converter.py
View File

@@ -14,6 +14,7 @@ from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
if TYPE_CHECKING:
from crewai.agent import Agent
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.llm import LLM
from crewai.llms.base_llm import BaseLLM
@@ -143,7 +144,7 @@ def convert_to_model(
result: str,
output_pydantic: type[BaseModel] | None,
output_json: type[BaseModel] | None,
agent: Agent | None = None,
agent: Agent | BaseAgent | None = None,
converter_cls: type[Converter] | None = None,
) -> dict[str, Any] | BaseModel | str:
"""Convert a result string to a Pydantic model or JSON.
@@ -215,7 +216,7 @@ def handle_partial_json(
result: str,
model: type[BaseModel],
is_json_output: bool,
agent: Agent | None,
agent: Agent | BaseAgent | None,
converter_cls: type[Converter] | None = None,
) -> dict[str, Any] | BaseModel | str:
"""Handle partial JSON in a result string and convert to Pydantic model or dict.
@@ -260,7 +261,7 @@ def convert_with_instructions(
result: str,
model: type[BaseModel],
is_json_output: bool,
agent: Agent | None,
agent: Agent | BaseAgent | None,
converter_cls: type[Converter] | None = None,
) -> dict | BaseModel | str:
"""Convert a result string to a Pydantic model or JSON using instructions.
@@ -283,7 +284,12 @@ def convert_with_instructions(
"""
if agent is None:
raise TypeError("Agent must be provided if converter_cls is not specified.")
llm = agent.function_calling_llm or agent.llm
llm = getattr(agent, "function_calling_llm", None) or agent.llm
if llm is None:
raise ValueError("Agent must have a valid LLM instance for conversion")
instructions = get_conversion_instructions(model=model, llm=llm)
converter = create_converter(
agent=agent,
@@ -299,7 +305,7 @@ def convert_with_instructions(
if isinstance(exported_result, ConverterError):
Printer().print(
content=f"{exported_result.message} Using raw output instead.",
content=f"Failed to convert result to model: {exported_result}",
color="red",
)
return result
@@ -308,7 +314,7 @@ def convert_with_instructions(
def get_conversion_instructions(
model: type[BaseModel], llm: BaseLLM | LLM | str
model: type[BaseModel], llm: BaseLLM | LLM | str | Any
) -> str:
"""Generate conversion instructions based on the model and LLM capabilities.
@@ -357,7 +363,7 @@ class CreateConverterKwargs(TypedDict, total=False):
def create_converter(
agent: Agent | None = None,
agent: Agent | BaseAgent | None = None,
converter_cls: type[Converter] | None = None,
*args: Any,
**kwargs: Unpack[CreateConverterKwargs],

15
lib/crewai/src/crewai/utilities/guardrail.py
View File

@@ -7,7 +7,9 @@ from pydantic import BaseModel, Field, field_validator
from typing_extensions import Self
if TYPE_CHECKING:
from crewai.lite_agent import LiteAgentOutput
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.lite_agent import LiteAgent, LiteAgentOutput
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
@@ -79,6 +81,8 @@ def process_guardrail(
guardrail: Callable[[Any], tuple[bool, Any | str]],
retry_count: int,
event_source: Any | None = None,
from_agent: BaseAgent | LiteAgent | None = None,
from_task: Task | None = None,
) -> GuardrailResult:
"""Process the guardrail for the agent output.
@@ -111,7 +115,12 @@ def process_guardrail(
crewai_event_bus.emit(
event_source,
LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=retry_count),
LLMGuardrailStartedEvent(
guardrail=guardrail,
retry_count=retry_count,
from_agent=from_agent,
from_task=from_task,
),
)
result = guardrail(output)
@@ -124,6 +133,8 @@ def process_guardrail(
result=guardrail_result.result,
error=guardrail_result.error,
retry_count=retry_count,
from_agent=from_agent,
from_task=from_task,
),
)

3
lib/crewai/src/crewai/utilities/tool_utils.py
View File

@@ -12,6 +12,7 @@ from crewai.utilities.i18n import I18N
if TYPE_CHECKING:
from crewai.agent import Agent
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.llm import LLM
from crewai.llms.base_llm import BaseLLM
from crewai.task import Task
@@ -25,7 +26,7 @@ def execute_tool_and_check_finality(
agent_role: str | None = None,
tools_handler: ToolsHandler | None = None,
task: Task | None = None,
agent: Agent | None = None,
agent: Agent | BaseAgent | None = None,
function_calling_llm: BaseLLM | LLM | None = None,
fingerprint_context: dict[str, str] | None = None,
) -> ToolResult: