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Code Issues Actions 4 Packages Projects Releases Wiki Activity

Integrate Mem0

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Dev-Khant
2024-08-17 12:21:16 +05:30
parent ddda8f6bda
commit 2607ac3a1f
17 changed files with 499 additions and 26 deletions
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1
docs/core-concepts/Collaboration.md
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@@ -25,6 +25,7 @@ The `Crew` class has been enriched with several attributes to support advanced f
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
- **Memory Provider (`memory_provider`)**: Specifies the memory provider to be used by the crew for storing memories.
- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
- **Cache Management (`cache`)**: Determines whether the crew should use a cache to store the results of tool executions, optimizing performance.
- **Output Logging (`output_log_file`)**: Specifies the file path for logging the output of the crew execution.

1
docs/core-concepts/Crews.md
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@@ -22,6 +22,7 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. |
| **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Memory Provider** _(optional)_ | `memory_provider` | Specifies the memory provider to be used by the crew for storing memories. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. |

19
docs/core-concepts/Memory.md
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@@ -197,6 +197,25 @@ crewai reset_memories [OPTIONS]
- **Type:** Flag (boolean)
- **Default:** False
## Use Mem0 to store memories
[Mem0](https://www.mem0.ai/) enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.
In order to use Mem0 as the memory provider for your crew, you will have to set `MEM0_API_KEY` as an environment variable. Refer to the [Mem0 documentation](https://docs.mem0.ai/platform/quickstart) for more information on how to obtain an API key.
### Example: Using Mem0 as the Memory Provider
```python
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
memory_provider="mem0"
)
```
## Benefits of Using crewAI's Memory System
- **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
- **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.

19
poetry.lock generated
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@@ -1005,13 +1005,13 @@ files = [
[[package]]
name = "embedchain"
version = "0.1.120"
version = "0.1.121"
description = "Simplest open source retrieval (RAG) framework"
optional = false
python-versions = "<=3.13,>=3.9"
files = [
{file = "embedchain-0.1.120-py3-none-any.whl", hash = "sha256:9eaa946f8a7b394080c56067849d7852a78361dd5e7b099ebf42989c07a1814d"},
{file = "embedchain-0.1.120.tar.gz", hash = "sha256:6061c261a054d677e5b9c4062146d45e04e8572c67152120913d61aee4c22ae3"},
{file = "embedchain-0.1.121-py3-none-any.whl", hash = "sha256:c756e8750fb9e3431b6d2a0b0dfbb0dfebeae2d7669d3dd6894311a632abfe77"},
{file = "embedchain-0.1.121.tar.gz", hash = "sha256:1427a43fd92b0e5303d0d733ebcd5310df14da8bd8dba0b08818d0d3658e7c3e"},
]
[package.dependencies]
@@ -1025,7 +1025,7 @@ langchain = ">0.2,<=0.3"
langchain-cohere = ">=0.1.4,<0.2.0"
langchain-community = ">=0.2.6,<0.3.0"
langchain-openai = ">=0.1.7,<0.2.0"
mem0ai = ">=0.0.9,<0.0.10"
mem0ai = ">=0.0.20,<0.0.21"
openai = ">=1.1.1"
posthog = ">=3.0.2,<4.0.0"
pypdf = ">=4.0.1,<5.0.0"
@@ -2707,20 +2707,22 @@ files = [
[[package]]
name = "mem0ai"
version = "0.0.9"
version = "0.0.20"
description = "Long-term memory for AI Agents"
optional = false
python-versions = "<4.0,>=3.8"
files = [
{file = "mem0ai-0.0.9-py3-none-any.whl", hash = "sha256:d4de435729af4fd3d597d022ffb2af89a0630d6c3b4769792bbe27d2ce816858"},
{file = "mem0ai-0.0.9.tar.gz", hash = "sha256:e4374d5d04aa3f543cd3325f700e4b62f5358ae1c6fa5c44b2ff790c10c4e5f1"},
{file = "mem0ai-0.0.20-py3-none-any.whl", hash = "sha256:c19b2082173c818f3516279f0924bfd763e2d18175560332c94e415e5131fd3b"},
{file = "mem0ai-0.0.20.tar.gz", hash = "sha256:459b96850156c8e51e321e3ab4e5f86fb00d75532c16ad41a3eb09578e0ce00a"},
]
[package.dependencies]
openai = ">=1.33.0,<2.0.0"
posthog = ">=3.5.0,<4.0.0"
pydantic = ">=2.7.3,<3.0.0"
pytz = ">=2024.1,<2025.0"
qdrant-client = ">=1.9.1,<2.0.0"
sqlalchemy = ">=2.0.31,<3.0.0"
[[package]]
name = "mergedeep"
@@ -5572,6 +5574,7 @@ description = "Automatically mock your HTTP interactions to simplify and speed u
optional = false
python-versions = ">=3.8"
files = [
{file = "vcrpy-6.0.1-py2.py3-none-any.whl", hash = "sha256:621c3fb2d6bd8aa9f87532c688e4575bcbbde0c0afeb5ebdb7e14cac409edfdd"},
{file = "vcrpy-6.0.1.tar.gz", hash = "sha256:9e023fee7f892baa0bbda2f7da7c8ac51165c1c6e38ff8688683a12a4bde9278"},
]
@@ -6073,4 +6076,4 @@ tools = ["crewai-tools"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<=3.13"
content-hash = "91ba982ea96ca7be017d536784223d4ef83e86de05d11eb1c3ce0fc1b726f283"
content-hash = "a69ccfe611edf0c3857cdf1dbcbdbd443b0130bff418713dce3993025cc14311"

2
pyproject.toml
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@@ -27,7 +27,7 @@ python-dotenv = "^1.0.0"
appdirs = "^1.4.4"
jsonref = "^1.1.0"
agentops = { version = "^0.3.0", optional = true }
embedchain = "^0.1.114"
embedchain = "^0.1.121"
json-repair = "^0.25.2"
[tool.poetry.extras]

2
src/crewai/agent.py
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@@ -176,9 +176,11 @@ class Agent(BaseAgent):
if self.crew and self.crew.memory:
contextual_memory = ContextualMemory(
self.crew.memory_provider,
self.crew._short_term_memory,
self.crew._long_term_memory,
self.crew._entity_memory,
self.crew._user_memory,
)
memory = contextual_memory.build_context_for_task(task, context)
if memory.strip() != "":

17
src/crewai/crew.py
View File

@@ -26,6 +26,7 @@ from crewai.crews.crew_output import CrewOutput
from crewai.memory.entity.entity_memory import EntityMemory
from crewai.memory.long_term.long_term_memory import LongTermMemory
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.memory.user.user_memory import UserMemory
from crewai.process import Process
from crewai.task import Task
from crewai.tasks.conditional_task import ConditionalTask
@@ -92,6 +93,7 @@ class Crew(BaseModel):
_short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
_long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
_entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
_user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
_train: Optional[bool] = PrivateAttr(default=False)
_train_iteration: Optional[int] = PrivateAttr()
_inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
@@ -112,6 +114,10 @@ class Crew(BaseModel):
default=False,
description="Whether the crew should use memory to store memories of it's execution",
)
memory_provider: Optional[str] = Field(
default=None,
description="The memory provider to be used for the crew.",
)
embedder: Optional[dict] = Field(
default={"provider": "openai"},
description="Configuration for the embedder to be used for the crew.",
@@ -215,9 +221,16 @@ class Crew(BaseModel):
if self.memory:
self._long_term_memory = LongTermMemory()
self._short_term_memory = ShortTermMemory(
crew=self, embedder_config=self.embedder
memory_provider=self.memory_provider,
crew=self,
embedder_config=self.embedder,
)
self._entity_memory = EntityMemory(crew=self, embedder_config=self.embedder)
self._entity_memory = EntityMemory(
memory_provider=self.memory_provider,
crew=self,
embedder_config=self.embedder,
)
self._user_memory = UserMemory(crew=self)
return self
@model_validator(mode="after")

1
src/crewai/memory/__init__.py
View File

@@ -1,3 +1,4 @@
from .entity.entity_memory import EntityMemory
from .long_term.long_term_memory import LongTermMemory
from .short_term.short_term_memory import ShortTermMemory
from .user.user_memory import UserMemory

30
src/crewai/memory/contextual/contextual_memory.py
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@@ -1,13 +1,22 @@
from typing import Optional
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
class ContextualMemory:
def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
def __init__(
self,
memory_provider: str,
stm: ShortTermMemory,
ltm: LongTermMemory,
em: EntityMemory,
um: UserMemory,
):
self.memory_provider = memory_provider
self.stm = stm
self.ltm = ltm
self.em = em
self.um = um
def build_context_for_task(self, task, context) -> str:
"""
@@ -23,6 +32,8 @@ class ContextualMemory:
context.append(self._fetch_ltm_context(task.description))
context.append(self._fetch_stm_context(query))
context.append(self._fetch_entity_context(query))
if self.memory_provider == "mem0":
context.append(self._fetch_user_memory(query))
return "\n".join(filter(None, context))
def _fetch_stm_context(self, query) -> str:
@@ -60,6 +71,19 @@ class ContextualMemory:
"""
em_results = self.em.search(query)
formatted_results = "\n".join(
[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
[
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
for result in em_results
] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
)
return f"Entities:\n{formatted_results}" if em_results else ""
def _fetch_user_memory(self, query) -> str:
"""
Fetches relevant user memory information from User Memory related to the task's description and expected_output,
"""
um_results = self.um.search(query)
formatted_results = "\n".join(
[f"- {result['memory']}" for result in um_results]
)
return f"User memories/preferences:\n{formatted_results}" if um_results else ""

32
src/crewai/memory/entity/entity_memory.py
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@@ -1,6 +1,7 @@
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.memory import Memory
from crewai.memory.storage.rag_storage import RAGStorage
from crewai.memory.storage.mem0_storage import Mem0Storage
class EntityMemory(Memory):
@@ -10,18 +11,33 @@ class EntityMemory(Memory):
Inherits from the Memory class.
"""
def __init__(self, crew=None, embedder_config=None):
storage = RAGStorage(
type="entities",
allow_reset=False,
embedder_config=embedder_config,
crew=crew,
)
def __init__(self, memory_provider, crew=None, embedder_config=None):
self.memory_provider = memory_provider
if self.memory_provider == "mem0":
storage = Mem0Storage(
type="entities",
crew=crew,
)
else:
storage = RAGStorage(
type="entities",
allow_reset=False,
embedder_config=embedder_config,
crew=crew,
)
super().__init__(storage)
def save(self, item: EntityMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
"""Saves an entity item into the SQLite storage."""
data = f"{item.name}({item.type}): {item.description}"
if self.memory_provider == "mem0":
data = f"""
Remember details about the following entity:
Name: {item.name}
Type: {item.type}
Entity Description: {item.description}
"""
else:
data = f"{item.name}({item.type}): {item.description}"
super().save(data, item.metadata)
def reset(self) -> None:

15
src/crewai/memory/short_term/short_term_memory.py
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@@ -2,6 +2,7 @@ from typing import Any, Dict, Optional
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
from crewai.memory.storage.mem0_storage import Mem0Storage
class ShortTermMemory(Memory):
@@ -13,10 +14,14 @@ class ShortTermMemory(Memory):
MemoryItem instances.
"""
def __init__(self, crew=None, embedder_config=None):
storage = RAGStorage(
type="short_term", embedder_config=embedder_config, crew=crew
)
def __init__(self, memory_provider=None, crew=None, embedder_config=None):
self.memory_provider = memory_provider
if self.memory_provider == "mem0":
storage = Mem0Storage(type="short_term", crew=crew)
else:
storage = RAGStorage(
type="short_term", embedder_config=embedder_config, crew=crew
)
super().__init__(storage)
def save(
@@ -26,6 +31,8 @@ class ShortTermMemory(Memory):
agent: Optional[str] = None,
) -> None:
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
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)

42
src/crewai/memory/storage/mem0_storage.py Normal file
View File

@@ -0,0 +1,42 @@
import os
from typing import Any, Dict, List, Optional
from mem0 import MemoryClient
from crewai.memory.storage.interface import Storage
class Mem0Storage(Storage):
"""
Extends Storage to handle embedding and searching across entities using Mem0.
"""
def __init__(self, type, crew=None):
super().__init__()
if (
not os.getenv("OPENAI_API_KEY")
and not os.getenv("OPENAI_BASE_URL") == "https://api.openai.com/v1"
):
os.environ["OPENAI_API_KEY"] = "fake"
agents = crew.agents if crew else []
agents = [agent.role for agent in agents]
agents = "_".join(agents)
self.app_id = agents
self.memory = MemoryClient(api_key=os.getenv("MEM0_API_KEY"))
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
self.memory.add(value, metadata=metadata, app_id=self.app_id)
def search(
self,
query: str,
limit: int = 3,
filters: Optional[dict] = None,
score_threshold: float = 0.35,
) -> List[Any]:
params = {"query": query, "limit": limit, "app_id": self.app_id}
if filters:
params["filters"] = filters
results = self.memory.search(**params)
return [r for r in results if r["score"] >= score_threshold]

0
src/crewai/memory/user/__init__.py Normal file
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23
src/crewai/memory/user/user_memory.py Normal file
View File

@@ -0,0 +1,23 @@
from crewai.memory.memory import Memory
from crewai.memory.user.user_memory_item import UserMemoryItem
from crewai.memory.storage.mem0_storage import Mem0Storage
class UserMemory(Memory):
"""
UserMemory class for handling user memory storage and retrieval.
Inherits from the Memory class and utilizes an instance of a class that
adheres to the Storage for data storage, specifically working with
MemoryItem instances.
"""
def __init__(self, crew=None):
storage = Mem0Storage(type="user", crew=crew)
super().__init__(storage)
def save(self, item: UserMemoryItem) -> None:
data = f"Remember the details about the user: {item.data}"
super().save(data, item.metadata, user=item.user)
def search(self, query: str, score_threshold: float = 0.35):
return self.storage.search(query=query, score_threshold=score_threshold)

8
src/crewai/memory/user/user_memory_item.py Normal file
View File

@@ -0,0 +1,8 @@
from typing import Any, Dict, Optional
class UserMemoryItem:
def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
self.data = data
self.user = user
self.metadata = metadata if metadata is not None else {}

270
tests/memory/cassettes/test_save_and_search_with_provider.yaml Normal file
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@@ -0,0 +1,270 @@
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43
tests/memory/short_term_memory_test.py
View File

@@ -44,3 +44,46 @@ def test_save_and_search(short_term_memory):
find = short_term_memory.search("test value", score_threshold=0.01)[0]
assert find["context"] == memory.data, "Data value mismatch."
assert find["metadata"]["agent"] == "test_agent", "Agent value mismatch."
@pytest.fixture
def short_term_memory_with_provider():
"""Fixture to create a ShortTermMemory instance with a specific memory provider"""
agent = Agent(
role="Researcher",
goal="Search relevant data and provide results",
backstory="You are a researcher at a leading tech think tank.",
tools=[],
verbose=True,
)
task = Task(
description="Perform a search on specific topics.",
expected_output="A list of relevant URLs based on the search query.",
agent=agent,
)
return ShortTermMemory(
crew=Crew(agents=[agent], tasks=[task]), memory_provider="mem0"
)
def test_save_and_search_with_provider(short_term_memory_with_provider):
memory = ShortTermMemoryItem(
data="Loves to do research on the latest technologies.",
agent="test_agent_provider",
metadata={"task": "test_task_provider"},
)
short_term_memory_with_provider.save(
value=memory.data,
metadata=memory.metadata,
agent=memory.agent,
)
find = short_term_memory_with_provider.search(
"Loves to do research on the latest technologies.", score_threshold=0.01
)[0]
assert find["memory"] in memory.data, "Data value mismatch."
assert find["metadata"]["agent"] == "test_agent_provider", "Agent value mismatch."
assert (
short_term_memory_with_provider.memory_provider == "mem0"
), "Memory provider mismatch."