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Code Issues Actions 4 Packages Projects Releases Wiki Activity

General Clean UP (#2042)

Browse Source 
* clean up. fix type safety. address memory config docs

* improve manager

* Include fix for o1 models not supporting system messages

* more broad with o1

* address fix: Typo in expected_output string #2045

* drop prints

* drop prints

* wip

* wip

* fix failing memory tests

* Fix memory provider issue

* clean up short term memory

* revert ltm

* drop
This commit is contained in:
Brandon Hancock (bhancock_ai)
2025-02-07 14:45:36 -05:00
committed by Devin AI
parent df55278476
commit ef1ef8fd80
12 changed files with 113 additions and 77 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
docs/concepts/memory.mdx
View File

@@ -185,7 +185,12 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model="text-embedding-3-small"),
embedder={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
}
)
```
@@ -242,13 +247,15 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder=OpenAIEmbeddingFunction(
api_key="YOUR_API_KEY",
api_base="YOUR_API_BASE_PATH",
api_type="azure",
api_version="YOUR_API_VERSION",
model="text-embedding-3-small"
)
embedder={
"provider": "openai",
"config": {
"api_key": "YOUR_API_KEY",
"api_base": "YOUR_API_BASE_PATH",
"api_version": "YOUR_API_VERSION",
"model_name": 'text-embedding-3-small'
}
}
)
```
@@ -264,12 +271,15 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder=GoogleVertexEmbeddingFunction(
project_id="YOUR_PROJECT_ID",
region="YOUR_REGION",
api_key="YOUR_API_KEY",
model="textembedding-gecko"
)
embedder={
"provider": "vertexai",
"config": {
"project_id"="YOUR_PROJECT_ID",
"region"="YOUR_REGION",
"api_key"="YOUR_API_KEY",
"model_name"="textembedding-gecko"
}
}
)
```

24
src/crewai/agent.py
View File

@@ -1,7 +1,7 @@
import re
import shutil
import subprocess
from typing import Any, Dict, List, Literal, Optional, Union
from typing import Any, Dict, List, Literal, Optional, Sequence, Union
from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -55,7 +55,6 @@ class Agent(BaseAgent):
llm: The language model that will run the agent.
function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
max_iter: Maximum number of iterations for an agent to execute a task.
memory: Whether the agent should have memory or not.
max_rpm: Maximum number of requests per minute for the agent execution to be respected.
verbose: Whether the agent execution should be in verbose mode.
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
@@ -72,9 +71,6 @@ class Agent(BaseAgent):
)
agent_ops_agent_name: str = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
agent_ops_agent_id: str = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
cache_handler: InstanceOf[CacheHandler] = Field(
default=None, description="An instance of the CacheHandler class."
)
step_callback: Optional[Any] = Field(
default=None,
description="Callback to be executed after each step of the agent execution.",
@@ -108,10 +104,6 @@ class Agent(BaseAgent):
default=True,
description="Keep messages under the context window size by summarizing content.",
)
max_iter: int = Field(
default=20,
description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
)
max_retry_limit: int = Field(
default=2,
description="Maximum number of retries for an agent to execute a task when an error occurs.",
@@ -197,13 +189,15 @@ class Agent(BaseAgent):
if task.output_json:
# schema = json.dumps(task.output_json, indent=2)
schema = generate_model_description(task.output_json)
task_prompt += "\n" + self.i18n.slice(
"formatted_task_instructions"
).format(output_format=schema)
elif task.output_pydantic:
schema = generate_model_description(task.output_pydantic)
task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
output_format=schema
)
task_prompt += "\n" + self.i18n.slice(
"formatted_task_instructions"
).format(output_format=schema)
if context:
task_prompt = self.i18n.slice("task_with_context").format(
@@ -331,14 +325,14 @@ class Agent(BaseAgent):
tools = agent_tools.tools()
return tools
def get_multimodal_tools(self) -> List[Tool]:
def get_multimodal_tools(self) -> Sequence[BaseTool]:
from crewai.tools.agent_tools.add_image_tool import AddImageTool
return [AddImageTool()]
def get_code_execution_tools(self):
try:
from crewai_tools import CodeInterpreterTool
from crewai_tools import CodeInterpreterTool # type: ignore
# Set the unsafe_mode based on the code_execution_mode attribute
unsafe_mode = self.code_execution_mode == "unsafe"

12
src/crewai/agents/agent_builder/base_agent.py
View File

@@ -24,6 +24,7 @@ from crewai.tools import BaseTool
from crewai.tools.base_tool import Tool
from crewai.utilities import I18N, Logger, RPMController
from crewai.utilities.config import process_config
from crewai.utilities.converter import Converter
T = TypeVar("T", bound="BaseAgent")
@@ -42,7 +43,7 @@ class BaseAgent(ABC, BaseModel):
max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
allow_delegation (bool): Allow delegation of tasks to agents.
tools (Optional[List[Any]]): Tools at the agent's disposal.
max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
max_iter (int): Maximum iterations for an agent to execute a task.
agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
llm (Any): Language model that will run the agent.
crew (Any): Crew to which the agent belongs.
@@ -114,7 +115,7 @@ class BaseAgent(ABC, BaseModel):
tools: Optional[List[Any]] = Field(
default_factory=list, description="Tools at agents' disposal"
)
max_iter: Optional[int] = Field(
max_iter: int = Field(
default=25, description="Maximum iterations for an agent to execute a task"
)
agent_executor: InstanceOf = Field(
@@ -125,11 +126,12 @@ class BaseAgent(ABC, BaseModel):
)
crew: Any = Field(default=None, description="Crew to which the agent belongs.")
i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
cache_handler: InstanceOf[CacheHandler] = Field(
cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
default=None, description="An instance of the CacheHandler class."
)
tools_handler: InstanceOf[ToolsHandler] = Field(
default=None, description="An instance of the ToolsHandler class."
default_factory=ToolsHandler,
description="An instance of the ToolsHandler class.",
)
max_tokens: Optional[int] = Field(
default=None, description="Maximum number of tokens for the agent's execution."
@@ -254,7 +256,7 @@ class BaseAgent(ABC, BaseModel):
@abstractmethod
def get_output_converter(
self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
):
) -> Converter:
"""Get the converter class for the agent to create json/pydantic outputs."""
pass

33
src/crewai/flow/flow.py
View File

@@ -600,7 +600,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
```
"""
try:
if not hasattr(self, '_state'):
if not hasattr(self, "_state"):
return ""
if isinstance(self._state, dict):
@@ -706,26 +706,31 @@ class Flow(Generic[T], metaclass=FlowMeta):
inputs: Optional dictionary containing input values and potentially a state ID to restore
"""
# Handle state restoration if ID is provided in inputs
if inputs and 'id' in inputs and self._persistence is not None:
restore_uuid = inputs['id']
if inputs and "id" in inputs and self._persistence is not None:
restore_uuid = inputs["id"]
stored_state = self._persistence.load_state(restore_uuid)
# Override the id in the state if it exists in inputs
if 'id' in inputs:
if "id" in inputs:
if isinstance(self._state, dict):
self._state['id'] = inputs['id']
self._state["id"] = inputs["id"]
elif isinstance(self._state, BaseModel):
setattr(self._state, 'id', inputs['id'])
setattr(self._state, "id", inputs["id"])
if stored_state:
self._log_flow_event(f"Loading flow state from memory for UUID: {restore_uuid}", color="yellow")
self._log_flow_event(
f"Loading flow state from memory for UUID: {restore_uuid}",
color="yellow",
)
# Restore the state
self._restore_state(stored_state)
else:
self._log_flow_event(f"No flow state found for UUID: {restore_uuid}", color="red")
self._log_flow_event(
f"No flow state found for UUID: {restore_uuid}", color="red"
)
# Apply any additional inputs after restoration
filtered_inputs = {k: v for k, v in inputs.items() if k != 'id'}
filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
if filtered_inputs:
self._initialize_state(filtered_inputs)
@@ -737,9 +742,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
flow_name=self.__class__.__name__,
),
)
self._log_flow_event(f"Flow started with ID: {self.flow_id}", color="bold_magenta")
self._log_flow_event(
f"Flow started with ID: {self.flow_id}", color="bold_magenta"
)
if inputs is not None and 'id' not in inputs:
if inputs is not None and "id" not in inputs:
self._initialize_state(inputs)
return asyncio.run(self.kickoff_async())
@@ -984,7 +991,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
traceback.print_exc()
def _log_flow_event(self, message: str, color: str = "yellow", level: str = "info") -> None:
def _log_flow_event(
self, message: str, color: str = "yellow", level: str = "info"
) -> None:
"""Centralized logging method for flow events.
This method provides a consistent interface for logging flow-related events,

7
src/crewai/llm.py
View File

@@ -221,6 +221,13 @@ class LLM:
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
# For O1 models, system messages are not supported.
# Convert any system messages into assistant messages.
if "o1" in self.model.lower():
for message in messages:
if message.get("role") == "system":
message["role"] = "assistant"
with suppress_warnings():
if callbacks and len(callbacks) > 0:
self.set_callbacks(callbacks)

24
src/crewai/memory/entity/entity_memory.py
View File

@@ -1,3 +1,7 @@
from typing import Any, Optional
from pydantic import PrivateAttr
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.memory import Memory
from crewai.memory.storage.rag_storage import RAGStorage
@@ -10,13 +14,15 @@ class EntityMemory(Memory):
Inherits from the Memory class.
"""
def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
if hasattr(crew, "memory_config") and crew.memory_config is not None:
self.memory_provider = crew.memory_config.get("provider")
else:
self.memory_provider = None
_memory_provider: Optional[str] = PrivateAttr()
if self.memory_provider == "mem0":
def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
memory_provider = crew.memory_config.get("provider")
else:
memory_provider = None
if memory_provider == "mem0":
try:
from crewai.memory.storage.mem0_storage import Mem0Storage
except ImportError:
@@ -36,11 +42,13 @@ class EntityMemory(Memory):
path=path,
)
)
super().__init__(storage)
super().__init__(storage=storage)
self._memory_provider = memory_provider
def save(self, item: EntityMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
"""Saves an entity item into the SQLite storage."""
if self.memory_provider == "mem0":
if self._memory_provider == "mem0":
data = f"""
Remember details about the following entity:
Name: {item.name}

2
src/crewai/memory/long_term/long_term_memory.py
View File

@@ -17,7 +17,7 @@ class LongTermMemory(Memory):
def __init__(self, storage=None, path=None):
if not storage:
storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
super().__init__(storage)
super().__init__(storage=storage)
def save(self, item: LongTermMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
metadata = item.metadata

12
src/crewai/memory/memory.py
View File

@@ -1,15 +1,19 @@
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, Union
from pydantic import BaseModel
from crewai.memory.storage.rag_storage import RAGStorage
class Memory:
class Memory(BaseModel):
"""
Base class for memory, now supporting agent tags and generic metadata.
"""
def __init__(self, storage: RAGStorage):
self.storage = storage
storage: Any
def __init__(self, storage: Any, **data: Any):
super().__init__(storage=storage, **data)
def save(
self,

21
src/crewai/memory/short_term/short_term_memory.py
View File

@@ -1,5 +1,7 @@
from typing import Any, Dict, Optional
from pydantic import PrivateAttr
from crewai.memory.memory import Memory
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.memory.storage.rag_storage import RAGStorage
@@ -14,13 +16,15 @@ class ShortTermMemory(Memory):
MemoryItem instances.
"""
def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
if hasattr(crew, "memory_config") and crew.memory_config is not None:
self.memory_provider = crew.memory_config.get("provider")
else:
self.memory_provider = None
_memory_provider: Optional[str] = PrivateAttr()
if self.memory_provider == "mem0":
def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
memory_provider = crew.memory_config.get("provider")
else:
memory_provider = None
if memory_provider == "mem0":
try:
from crewai.memory.storage.mem0_storage import Mem0Storage
except ImportError:
@@ -39,7 +43,8 @@ class ShortTermMemory(Memory):
path=path,
)
)
super().__init__(storage)
super().__init__(storage=storage)
self._memory_provider = memory_provider
def save(
self,
@@ -48,7 +53,7 @@ class ShortTermMemory(Memory):
agent: Optional[str] = None,
) -> None:
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
if self.memory_provider == "mem0":
if self._memory_provider == "mem0":
item.data = f"Remember the following insights from Agent run: {item.data}"
super().save(value=item.data, metadata=item.metadata, agent=item.agent)

11
src/crewai/tools/agent_tools/add_image_tool.py
View File

@@ -7,11 +7,11 @@ from crewai.utilities import I18N
i18n = I18N()
class AddImageToolSchema(BaseModel):
image_url: str = Field(..., description="The URL or path of the image to add")
action: Optional[str] = Field(
default=None,
description="Optional context or question about the image"
default=None, description="Optional context or question about the image"
)
@@ -36,10 +36,7 @@ class AddImageTool(BaseTool):
"image_url": {
"url": image_url,
},
}
},
]
return {
"role": "user",
"content": content
}
return {"role": "user", "content": content}

2
src/crewai/translations/en.json
View File

@@ -15,7 +15,7 @@
"final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\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\n```",
"task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
"expected_output": "\nThis is the expected criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
"human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
"getting_input": "This is the agent's final answer: {final_answer}\n\n",
"summarizer_system_message": "You are a helpful assistant that summarizes text.",

4
tests/agent_test.py
View File

@@ -1183,7 +1183,7 @@ def test_agent_max_retry_limit():
[
mock.call(
{
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"ask_for_human_input": True,
@@ -1191,7 +1191,7 @@ def test_agent_max_retry_limit():
),
mock.call(
{
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"ask_for_human_input": True,