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Add support for retrieving user preferences and memories using Mem0 (#1209)
* Integrate Mem0 * Update src/crewai/memory/contextual/contextual_memory.py Co-authored-by: Deshraj Yadav <deshraj@gatech.edu> * pending commit for _fetch_user_memories * update poetry.lock * fixes mypy issues * fix mypy checks * New fixes for user_id * remove memory_provider * handle memory_provider * checks for memory_config * add mem0 to dependency * Update pyproject.toml Co-authored-by: Deshraj Yadav <deshraj@gatech.edu> * update docs * update doc * bump mem0 version * fix api error msg and mypy issue * mypy fix * resolve comments * fix memory usage without mem0 * mem0 version bump * lazy import mem0 --------- Co-authored-by: Deshraj Yadav <deshraj@gatech.edu> Co-authored-by: João Moura <joaomdmoura@gmail.com> Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
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@@ -27,6 +27,7 @@ from crewai.llm import LLM
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from crewai.memory.entity.entity_memory import EntityMemory
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from crewai.memory.long_term.long_term_memory import LongTermMemory
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from crewai.memory.short_term.short_term_memory import ShortTermMemory
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from crewai.memory.user.user_memory import UserMemory
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from crewai.process import Process
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from crewai.task import Task
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from crewai.tasks.conditional_task import ConditionalTask
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@@ -71,6 +72,7 @@ class Crew(BaseModel):
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manager_llm: The language model that will run manager agent.
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manager_agent: Custom agent that will be used as manager.
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memory: Whether the crew should use memory to store memories of it's execution.
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memory_config: Configuration for the memory to be used for the crew.
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cache: Whether the crew should use a cache to store the results of the tools execution.
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function_calling_llm: The language model that will run the tool calling for all the agents.
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process: The process flow that the crew will follow (e.g., sequential, hierarchical).
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@@ -94,6 +96,7 @@ class Crew(BaseModel):
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_short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
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_long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
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_entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
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_user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
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_train: Optional[bool] = PrivateAttr(default=False)
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_train_iteration: Optional[int] = PrivateAttr()
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_inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
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@@ -114,6 +117,10 @@ class Crew(BaseModel):
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default=False,
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description="Whether the crew should use memory to store memories of it's execution",
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)
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memory_config: Optional[Dict[str, Any]] = Field(
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default=None,
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description="Configuration for the memory to be used for the crew.",
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)
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short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
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default=None,
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description="An Instance of the ShortTermMemory to be used by the Crew",
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@@ -126,7 +133,11 @@ class Crew(BaseModel):
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default=None,
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description="An Instance of the EntityMemory to be used by the Crew",
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)
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embedder: Optional[Any] = Field(
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user_memory: Optional[InstanceOf[UserMemory]] = Field(
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default=None,
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description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
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)
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embedder: Optional[dict] = Field(
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default=None,
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description="Configuration for the embedder to be used for the crew.",
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)
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@@ -238,13 +249,22 @@ class Crew(BaseModel):
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self._short_term_memory = (
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self.short_term_memory
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if self.short_term_memory
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else ShortTermMemory(crew=self, embedder_config=self.embedder)
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else ShortTermMemory(
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crew=self,
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embedder_config=self.embedder,
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)
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)
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self._entity_memory = (
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self.entity_memory
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if self.entity_memory
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else EntityMemory(crew=self, embedder_config=self.embedder)
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)
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if hasattr(self, "memory_config") and self.memory_config is not None:
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self._user_memory = (
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self.user_memory if self.user_memory else UserMemory(crew=self)
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)
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else:
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self._user_memory = None
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return self
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@model_validator(mode="after")
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