Add support for retrieving user preferences and memories using Mem0 #1209
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Co-authored-by: Deshraj Yadav <[email protected]>
Dev-Khant
changed the title
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Hey @joaomdmoura @theCyberTech could you please add the Mem0 API key to the CI variables to enable the Mem0 tests to run? |
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Sure! I'm testing this again tomorrow so we cna merge :) |
Co-authored-by: Deshraj Yadav <[email protected]>
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Hey @joaomdmoura @bhancockio Can you please review the PR, I have made the necessary changes. |
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If I create a crew like this: crew = Crew(
agents=[qa_agent],
tasks=[qa_task],
verbose=True,
memory=True,
memory_config={
"provider": "mem0",
"config": {"user_id": "Personal Travel Planner Agent"},
},
process=Process.sequential,
)And I do not include a mem0 API key, I get the following error: It's not clear that the Mem0 API key is missing which will make it hard for our users to know how to fix this bug |
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Hey @bhancockio I have fixed |
src/crewai/crew.py
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| self.entity_memory | ||
| if self.entity_memory | ||
| else EntityMemory(crew=self, embedder_config=self.embedder) | ||
| self._entity_memory = EntityMemory( |
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Could you please add back in the check for if entity memory exists?
| em: EntityMemory, | ||
| um: UserMemory, | ||
| ): | ||
| self.memory_provider = stm.memory_provider |
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Do we want to tie together memory provider with the short term memory?
Shouldn't we update the parameters of ContextualMemory to include memory_config?
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Yes makes sense. I have updated the params for ContextualMemory.
| storage | ||
| if storage | ||
| else RAGStorage( | ||
| if hasattr(crew, "memory_config") and crew.memory_config is not None: |
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Why are we tying together entity memory with user memory? Shouldn't they be separate?
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The root issue is that we are an agnostic platform and this PR directly couples CrewAI memory with mem0.
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Hey, thanks for reviewing the PR!
In this case, we're using Mem0 within entity_memory when the user opts for Mem0. So, it's not only the user memory being utilized but also the entity memory.
Let me know if we only want to use user_memory when user opts for Mem0 and keep other memories as is.
| if storage | ||
| else RAGStorage( | ||
| type="short_term", embedder_config=embedder_config, crew=crew | ||
| if hasattr(crew, "memory_config") and crew.memory_config is not None: |
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Once again, why are we connecting short term with mem0? Shouldn't we only be checking user memory?
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Same here when user opts for Mem0 we use mem0 inside short_term memory as well.
So let me know if we only want to use user_memory when user opts for Mem0 and keep other memories as is.
| @@ -7,8 +7,10 @@ class Storage: | |||
| def save(self, value: Any, metadata: Dict[str, Any]) -> None: | |||
| pass | |||
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| def search(self, key: str) -> List[Dict[str, Any]]: # type: ignore | |||
| pass | |||
| def search( | |||
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Search is supposed to return a list of dictionaries not just a single dictionary.
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Hey here I included to have either Dict[str, Any] or List[Any]
| @@ -160,7 +162,7 @@ def search( | |||
| self, | |||
| query: str, | |||
| limit: int = 3, | |||
| filter: Optional[dict] = None, | |||
| filters: Optional[dict] = None, | |||
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It doesn't look like filters is being used.
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Yes I have removed it.
pyproject.toml
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| @@ -21,6 +21,7 @@ dependencies = [ | |||
| "python-dotenv>=1.0.0", | |||
| "appdirs>=1.4.4", | |||
| "jsonref>=1.1.0", | |||
| "mem0ai>=0.1.24", | |||
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This should be an optional dependency that users who want to use mem0 can add.
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Yes, moved to optional dependency.
| storage | ||
| if storage | ||
| else RAGStorage( | ||
| if hasattr(crew, "memory_config") and crew.memory_config is not None: |
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The root issue is that we are an agnostic platform and this PR directly couples CrewAI memory with mem0.
Hey @bhancockio, latest commit resolves the issue mentioned here. |
pyproject.toml
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| @@ -39,6 +39,7 @@ Repository = "https://github.com/crewAIInc/crewAI" | |||
| [project.optional-dependencies] | |||
| tools = ["crewai-tools>=0.13.4"] | |||
| agentops = ["agentops>=0.3.0"] | |||
| mem0 = ["mem0ai>=0.1.24"] | |||
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@Dev-Khant Should this be 0.1.29 now?
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Yes bumped up the version
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@Dev-Khant here are my final remarks. We are super close to getting this merged in! https://www.loom.com/share/4610bf10afc147379aec202f0aca005b?sid=f1a0773a-c66b-447c-a199-905485e8b8f3 |
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Hey @bhancockio I have resolved the issue mentioned here: https://www.loom.com/share/4610bf10afc147379aec202f0aca005b?sid=f1a0773a-c66b-447c-a199-905485e8b8f3. Thanks! |
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@Dev-Khant can we sync main and get this in ? |
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Hey @lorenzejay Did the merge. |
Changes
Comparison
User preferences provided through Mem0:
Full notebook: https://colab.research.google.com/drive/1nJi71bZOsFf6MPS385X-eqJVrAUXGRGJ?usp=sharing
TL;DR
Agent with UserMemory shows significant improvements in personalization:
This TL;DR highlights the key differences between the agent outputs, specifically showing how the agent with memory provides more personalized and relevant recommendations based on the user preferences you provided in the example.
Travel Planner Agent Output without Memory
Travel Planner Agent Output with Memory