fix: Add empty thought since its required

.github/workflows/security-checker.yml

@@ -19,5 +19,5 @@ jobs:
         run: pip install bandit
 
       - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -ll
+        run: bandit -c pyproject.toml -r src/ -lll
 

docs/concepts/crews.mdx

@@ -22,8 +22,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
 | **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 Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`.                                                                                                                                                       |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
 | **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |

docs/concepts/memory.mdx

@@ -18,7 +18,6 @@ reason, and learn from past interactions.
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
-| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
 
 ## How Memory Systems Empower Agents
 
@@ -93,47 +92,6 @@ my_crew = Crew(
 )
 ```
 
-## Integrating Mem0 for Enhanced User Memory
-
-[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
-
-To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
-
-
-```python Code
-import os
-from crewai import Crew, Process
-from mem0 import MemoryClient
-
-# Set environment variables for Mem0
-os.environ["MEM0_API_KEY"] = "m0-xx"
-
-# Step 1: Record preferences based on past conversation or user input
-client = MemoryClient()
-messages = [
-    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
-    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
-    {"role": "user", "content": "I am more of a beach person than a mountain person."},
-    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
-    {"role": "user", "content": "I like Airbnb more."},
-]
-client.add(messages, user_id="john")
-
-# Step 2: Create a Crew with User Memory
-
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    verbose=True,
-    process=Process.sequential,
-    memory=True,
-    memory_config={
-        "provider": "mem0",
-        "config": {"user_id": "john"},
-    },
-)
-```
-
 
 ## Additional Embedding Providers
 

docs/tools/githubsearchtool.mdx

@@ -34,7 +34,6 @@ from crewai_tools import GithubSearchTool
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
 	github_repo='https://github.com/example/repo',
-	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
@@ -42,7 +41,6 @@ tool = GithubSearchTool(
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
-	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
@@ -50,7 +48,6 @@ tool = GithubSearchTool(
 ## Arguments
 
 - `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
-- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens.
 - `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, 
 `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. 
 This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
@@ -80,4 +77,5 @@ tool = GithubSearchTool(
             ),
         ),
     )
-)
+)
+```

poetry.lock

@@ -1597,12 +1597,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = [
-    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
@@ -4286,8 +4286,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"

pyproject.toml

@@ -27,8 +27,8 @@ dependencies = [
     "pyvis>=0.3.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
+    "chromadb>=0.4.24",
     "tomli>=2.0.2",
-    "chromadb>=0.5.18",
 ]
 
 [project.urls]
@@ -39,7 +39,6 @@ Repository = "https://github.com/crewAIInc/crewAI"
 [project.optional-dependencies]
 tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
-mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/agent.py

@@ -262,11 +262,9 @@ class Agent(BaseAgent):
 
         if self.crew and self.crew.memory:
             contextual_memory = ContextualMemory(
-                self.crew.memory_config,
                 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() != "":

src/crewai/agents/agent_builder/utilities/base_token_process.py

@@ -4,7 +4,6 @@ from crewai.types.usage_metrics import UsageMetrics
 class TokenProcess:
     total_tokens: int = 0
     prompt_tokens: int = 0
-    cached_prompt_tokens: int = 0
     completion_tokens: int = 0
     successful_requests: int = 0
 
@@ -16,9 +15,6 @@ class TokenProcess:
         self.completion_tokens = self.completion_tokens + tokens
         self.total_tokens = self.total_tokens + tokens
 
-    def sum_cached_prompt_tokens(self, tokens: int):
-        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
-
     def sum_successful_requests(self, requests: int):
         self.successful_requests = self.successful_requests + requests
 
@@ -26,7 +22,6 @@ class TokenProcess:
         return UsageMetrics(
             total_tokens=self.total_tokens,
             prompt_tokens=self.prompt_tokens,
-            cached_prompt_tokens=self.cached_prompt_tokens,
             completion_tokens=self.completion_tokens,
             successful_requests=self.successful_requests,
         )

src/crewai/cli/authentication/main.py

@@ -34,9 +34,7 @@ class AuthenticationCommand:
             "scope": "openid",
             "audience": AUTH0_AUDIENCE,
         }
-        response = requests.post(
-            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
-        )
+        response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
         response.raise_for_status()
         return response.json()
 
@@ -56,7 +54,7 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
+            response = requests.post(self.TOKEN_URL, data=token_payload)
             token_data = response.json()
 
             if response.status_code == 200:

src/crewai/crew.py

@@ -27,7 +27,6 @@ from crewai.llm import LLM
 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
@@ -72,7 +71,6 @@ class Crew(BaseModel):
         manager_llm: The language model that will run manager agent.
         manager_agent: Custom agent that will be used as manager.
         memory: Whether the crew should use memory to store memories of it's execution.
-        memory_config: Configuration for the memory to be used for the crew.
         cache: Whether the crew should use a cache to store the results of the tools execution.
         function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -96,7 +94,6 @@ 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)
@@ -117,10 +114,6 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
-    memory_config: Optional[Dict[str, Any]] = Field(
-        default=None,
-        description="Configuration for the memory to be used for the crew.",
-    )
     short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
         default=None,
         description="An Instance of the ShortTermMemory to be used by the Crew",
@@ -133,11 +126,7 @@ class Crew(BaseModel):
         default=None,
         description="An Instance of the EntityMemory to be used by the Crew",
     )
-    user_memory: Optional[InstanceOf[UserMemory]] = Field(
-        default=None,
-        description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
-    )
-    embedder: Optional[dict] = Field(
+    embedder: Optional[Any] = Field(
         default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -249,22 +238,13 @@ class Crew(BaseModel):
             self._short_term_memory = (
                 self.short_term_memory
                 if self.short_term_memory
-                else ShortTermMemory(
-                    crew=self,
-                    embedder_config=self.embedder,
-                )
+                else ShortTermMemory(crew=self, embedder_config=self.embedder)
             )
             self._entity_memory = (
                 self.entity_memory
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            if hasattr(self, "memory_config") and self.memory_config is not None:
-                self._user_memory = (
-                    self.user_memory if self.user_memory else UserMemory(crew=self)
-                )
-            else:
-                self._user_memory = None
         return self
 
     @model_validator(mode="after")

src/crewai/memory/__init__.py

@@ -1,6 +1,5 @@
 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
 
-__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]
+__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,25 +1,13 @@
-from typing import Optional, Dict, Any
+from typing import Optional
 
-from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
+from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
 
 
 class ContextualMemory:
-    def __init__(
-        self,
-        memory_config: Optional[Dict[str, Any]],
-        stm: ShortTermMemory,
-        ltm: LongTermMemory,
-        em: EntityMemory,
-        um: UserMemory,
-    ):
-        if memory_config is not None:
-            self.memory_provider = memory_config.get("provider")
-        else:
-            self.memory_provider = None
+    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
         self.stm = stm
         self.ltm = ltm
         self.em = em
-        self.um = um
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -35,8 +23,6 @@ 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_context(query))
         return "\n".join(filter(None, context))
 
     def _fetch_stm_context(self, query) -> str:
@@ -46,10 +32,7 @@ class ContextualMemory:
         """
         stm_results = self.stm.search(query)
         formatted_results = "\n".join(
-            [
-                f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
-                for result in stm_results
-            ]
+            [f"- {result['context']}" for result in stm_results]
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
@@ -79,26 +62,6 @@ class ContextualMemory:
         """
         em_results = self.em.search(query)
         formatted_results = "\n".join(
-            [
-                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"
+            [f"- {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_context(self, query: str) -> str:
-        """
-        Fetches and formats relevant user information from User Memory.
-        Args:
-            query (str): The search query to find relevant user memories.
-        Returns:
-            str: Formatted user memories as bullet points, or an empty string if none found.
-        """
-        user_memories = self.um.search(query)
-        if not user_memories:
-            return ""
-
-        formatted_memories = "\n".join(
-            f"- {result['memory']}" for result in user_memories
-        )
-        return f"User memories/preferences:\n{formatted_memories}"

src/crewai/memory/entity/entity_memory.py

@@ -11,43 +11,21 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=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
-
-        if self.memory_provider == "mem0":
-            try:
-                from crewai.memory.storage.mem0_storage import Mem0Storage
-            except ImportError:
-                raise ImportError(
-                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
-                )
-            storage = Mem0Storage(type="entities", crew=crew)
-        else:
-            storage = (
-                storage
-                if storage
-                else RAGStorage(
-                    type="entities",
-                    allow_reset=True,
-                    embedder_config=embedder_config,
-                    crew=crew,
-                )
+        storage = (
+            storage
+            if storage
+            else RAGStorage(
+                type="entities",
+                allow_reset=True,
+                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."""
-        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}"
+        data = f"{item.name}({item.type}): {item.description}"
         super().save(data, item.metadata)
 
     def reset(self) -> None:

src/crewai/memory/memory.py

@@ -23,12 +23,5 @@ class Memory:
 
         self.storage.save(value, metadata)
 
-    def search(
-        self,
-        query: str,
-        limit: int = 3,
-        score_threshold: float = 0.35,
-    ) -> List[Any]:
-        return self.storage.search(
-            query=query, limit=limit, score_threshold=score_threshold
-        )
+    def search(self, query: str) -> List[Dict[str, Any]]:
+        return self.storage.search(query)

src/crewai/memory/short_term/short_term_memory.py

@@ -14,27 +14,13 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=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
-
-        if self.memory_provider == "mem0":
-            try:
-                from crewai.memory.storage.mem0_storage import Mem0Storage
-            except ImportError:
-                raise ImportError(
-                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
-                )
-            storage = Mem0Storage(type="short_term", crew=crew)
-        else:
-            storage = (
-                storage
-                if storage
-                else RAGStorage(
-                    type="short_term", embedder_config=embedder_config, crew=crew
-                )
+        storage = (
+            storage
+            if storage
+            else RAGStorage(
+                type="short_term", embedder_config=embedder_config, crew=crew
             )
+        )
         super().__init__(storage)
 
     def save(
@@ -44,20 +30,11 @@ 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)
 
-    def search(
-        self,
-        query: str,
-        limit: int = 3,
-        score_threshold: float = 0.35,
-    ):
-        return self.storage.search(
-            query=query, limit=limit, score_threshold=score_threshold
-        )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+    def search(self, query: str, score_threshold: float = 0.35):
+        return self.storage.search(query=query, score_threshold=score_threshold)  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
 
     def reset(self) -> None:
         try:

src/crewai/memory/storage/interface.py

@@ -7,10 +7,8 @@ class Storage:
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         pass
 
-    def search(
-        self, query: str, limit: int, score_threshold: float
-    ) -> Dict[str, Any] | List[Any]:
-        return {}
+    def search(self, key: str) -> List[Dict[str, Any]]:  # type: ignore
+        pass
 
     def reset(self) -> None:
         pass

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         else value
                     )
 
-                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"  # nosec
+                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
                 values.append(task_index)
 
                 cursor.execute(query, tuple(values))

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -83,7 +83,7 @@ class LTMSQLiteStorage:
                     WHERE task_description = ?
                     ORDER BY datetime DESC, score ASC
                     LIMIT {latest_n}
-                """,  # nosec
+                """,
                     (task_description,),
                 )
                 rows = cursor.fetchall()

src/crewai/memory/storage/mem0_storage.py

@@ -1,104 +0,0 @@
-import os
-from typing import Any, Dict, List
-
-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 type not in ["user", "short_term", "long_term", "entities"]:
-            raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
-
-        self.memory_type = type
-        self.crew = crew
-        self.memory_config = crew.memory_config
-
-        # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
-        user_id = self._get_user_id()
-        if type == "user" and not user_id:
-            raise ValueError("User ID is required for user memory type")
-
-        # API key in memory config overrides the environment variable
-        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
-            "MEM0_API_KEY"
-        )
-        self.memory = MemoryClient(api_key=mem0_api_key)
-
-    def _sanitize_role(self, role: str) -> str:
-        """
-        Sanitizes agent roles to ensure valid directory names.
-        """
-        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
-
-    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        user_id = self._get_user_id()
-        agent_name = self._get_agent_name()
-        if self.memory_type == "user":
-            self.memory.add(value, user_id=user_id, metadata={**metadata})
-        elif self.memory_type == "short_term":
-            agent_name = self._get_agent_name()
-            self.memory.add(
-                value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
-            )
-        elif self.memory_type == "long_term":
-            agent_name = self._get_agent_name()
-            self.memory.add(
-                value,
-                agent_id=agent_name,
-                infer=False,
-                metadata={"type": "long_term", **metadata},
-            )
-        elif self.memory_type == "entities":
-            entity_name = None
-            self.memory.add(
-                value, user_id=entity_name, metadata={"type": "entity", **metadata}
-            )
-
-    def search(
-        self,
-        query: str,
-        limit: int = 3,
-        score_threshold: float = 0.35,
-    ) -> List[Any]:
-        params = {"query": query, "limit": limit}
-        if self.memory_type == "user":
-            user_id = self._get_user_id()
-            params["user_id"] = user_id
-        elif self.memory_type == "short_term":
-            agent_name = self._get_agent_name()
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "short_term"}
-        elif self.memory_type == "long_term":
-            agent_name = self._get_agent_name()
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "long_term"}
-        elif self.memory_type == "entities":
-            agent_name = self._get_agent_name()
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "entity"}
-
-        # Discard the filters for now since we create the filters
-        # automatically when the crew is created.
-        results = self.memory.search(**params)
-        return [r for r in results if r["score"] >= score_threshold]
-
-    def _get_user_id(self):
-        if self.memory_type == "user":
-            if hasattr(self, "memory_config") and self.memory_config is not None:
-                return self.memory_config.get("config", {}).get("user_id")
-            else:
-                return None
-        return None
-
-    def _get_agent_name(self):
-        agents = self.crew.agents if self.crew else []
-        agents = [self._sanitize_role(agent.role) for agent in agents]
-        agents = "_".join(agents)
-        return agents

src/crewai/memory/storage/rag_storage.py

@@ -51,6 +51,8 @@ class RAGStorage(BaseRAGStorage):
         self._initialize_app()
 
     def _set_embedder_config(self):
+        import chromadb.utils.embedding_functions as embedding_functions
+
         if self.embedder_config is None:
             self.embedder_config = self._create_default_embedding_function()
 
@@ -59,20 +61,12 @@ class RAGStorage(BaseRAGStorage):
             config = self.embedder_config.get("config", {})
             model_name = config.get("model")
             if provider == "openai":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
+                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
                     api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
                     model_name=model_name,
                 )
             elif provider == "azure":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
+                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
                     api_key=config.get("api_key"),
                     api_base=config.get("api_base"),
                     api_type=config.get("api_type", "azure"),
@@ -80,55 +74,45 @@ class RAGStorage(BaseRAGStorage):
                     model_name=model_name,
                 )
             elif provider == "ollama":
-                from chromadb.utils.embedding_functions.ollama_embedding_function import (
-                    OllamaEmbeddingFunction,
-                )
+                from openai import OpenAI
 
-                self.embedder_config = OllamaEmbeddingFunction(
-                    url=config.get("url", "http://localhost:11434/api/embeddings"),
-                    model_name=model_name,
-                )
+                class OllamaEmbeddingFunction(EmbeddingFunction):
+                    def __call__(self, input: Documents) -> Embeddings:
+                        client = OpenAI(
+                            base_url="http://localhost:11434/v1",
+                            api_key=config.get("api_key", "ollama"),
+                        )
+                        try:
+                            response = client.embeddings.create(
+                                input=input, model=model_name
+                            )
+                            embeddings = [item.embedding for item in response.data]
+                            return cast(Embeddings, embeddings)
+                        except Exception as e:
+                            raise e
+
+                self.embedder_config = OllamaEmbeddingFunction()
             elif provider == "vertexai":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleVertexEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleVertexEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
+                self.embedder_config = (
+                    embedding_functions.GoogleVertexEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
                 )
             elif provider == "google":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleGenerativeAiEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleGenerativeAiEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
+                self.embedder_config = (
+                    embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
                 )
             elif provider == "cohere":
-                from chromadb.utils.embedding_functions.cohere_embedding_function import (
-                    CohereEmbeddingFunction,
-                )
-
-                self.embedder_config = CohereEmbeddingFunction(
+                self.embedder_config = embedding_functions.CohereEmbeddingFunction(
                     model_name=model_name,
                     api_key=config.get("api_key"),
                 )
-            elif provider == "bedrock":
-                from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-                    AmazonBedrockEmbeddingFunction,
-                )
-
-                self.embedder_config = AmazonBedrockEmbeddingFunction(
-                    session=config.get("session"),
-                )
             elif provider == "huggingface":
-                from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-                    HuggingFaceEmbeddingServer,
-                )
-
-                self.embedder_config = HuggingFaceEmbeddingServer(
+                self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
                     url=config.get("api_url"),
                 )
             elif provider == "watson":
@@ -269,10 +253,8 @@ class RAGStorage(BaseRAGStorage):
                 )
 
     def _create_default_embedding_function(self):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
+        import chromadb.utils.embedding_functions as embedding_functions
 
-        return OpenAIEmbeddingFunction(
+        return embedding_functions.OpenAIEmbeddingFunction(
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )

src/crewai/memory/user/user_memory.py

@@ -1,45 +0,0 @@
-from typing import Any, Dict, Optional
-
-from crewai.memory.memory import Memory
-
-
-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):
-        try:
-            from crewai.memory.storage.mem0_storage import Mem0Storage
-        except ImportError:
-            raise ImportError(
-                "Mem0 is not installed. Please install it with `pip install mem0ai`."
-            )
-        storage = Mem0Storage(type="user", crew=crew)
-        super().__init__(storage)
-
-    def save(
-        self,
-        value,
-        metadata: Optional[Dict[str, Any]] = None,
-        agent: Optional[str] = None,
-    ) -> None:
-        # TODO: Change this function since we want to take care of the case where we save memories for the usr
-        data = f"Remember the details about the user: {value}"
-        super().save(data, metadata)
-
-    def search(
-        self,
-        query: str,
-        limit: int = 3,
-        score_threshold: float = 0.35,
-    ):
-        results = super().search(
-            query=query,
-            limit=limit,
-            score_threshold=score_threshold,
-        )
-        return results

src/crewai/memory/user/user_memory_item.py

@@ -1,8 +0,0 @@
-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 {}

src/crewai/types/usage_metrics.py

@@ -8,7 +8,6 @@ class UsageMetrics(BaseModel):
     Attributes:
         total_tokens: Total number of tokens used.
         prompt_tokens: Number of tokens used in prompts.
-        cached_prompt_tokens: Number of cached prompt tokens used.
         completion_tokens: Number of tokens used in completions.
         successful_requests: Number of successful requests made.
     """
@@ -17,9 +16,6 @@ class UsageMetrics(BaseModel):
     prompt_tokens: int = Field(
         default=0, description="Number of tokens used in prompts."
     )
-    cached_prompt_tokens: int = Field(
-        default=0, description="Number of cached prompt tokens used."
-    )
     completion_tokens: int = Field(
         default=0, description="Number of tokens used in completions."
     )
@@ -36,6 +32,5 @@ class UsageMetrics(BaseModel):
         """
         self.total_tokens += usage_metrics.total_tokens
         self.prompt_tokens += usage_metrics.prompt_tokens
-        self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
         self.completion_tokens += usage_metrics.completion_tokens
         self.successful_requests += usage_metrics.successful_requests

src/crewai/utilities/file_handler.py

@@ -16,11 +16,7 @@ class FileHandler:
 
     def log(self, **kwargs):
         now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        message = (
-            f"{now}: "
-            + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
-            + "\n"
-        )
+        message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
         with open(self._path, "a", encoding="utf-8") as file:
             file.write(message + "\n")
 
@@ -67,7 +63,7 @@ class PickleHandler:
 
         with open(self.file_path, "rb") as file:
             try:
-                return pickle.load(file)  # nosec
+                return pickle.load(file)
             except EOFError:
                 return {}  # Return an empty dictionary if the file is empty or corrupted
             except Exception:

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,5 @@
 from litellm.integrations.custom_logger import CustomLogger
-from litellm.types.utils import Usage
+
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 
 
@@ -11,11 +11,8 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
-        usage : Usage = response_obj["usage"]
         self.token_cost_process.sum_successful_requests(1)
-        self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
-        if usage.prompt_tokens_details:
-            self.token_cost_process.sum_cached_prompt_tokens(
-                usage.prompt_tokens_details.cached_tokens
-            )
+        self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
+        self.token_cost_process.sum_completion_tokens(
+            response_obj["usage"].completion_tokens
+        )

tests/cassettes/test_crew_kickoff_usage_metrics.yaml

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         assert result.token_usage.prompt_tokens > 0
         assert result.token_usage.completion_tokens > 0
         assert result.token_usage.successful_requests > 0
-        assert result.token_usage.cached_prompt_tokens == 0
 
 
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-    crew_training_handler().load.assert_called()
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-    crew_training_handler.assert_any_call("trained_agents_data.pkl")
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-        mock.call(
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-        )
-    ])
+    crew_training_handler.assert_has_calls(
+        [
+            mock.call("training_data.pkl"),
+            mock.call().load(),
+            mock.call("trained_agents_data.pkl"),
+            mock.call().save_trained_data(
+                agent_id="Researcher",
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
+            ),
+            mock.call("trained_agents_data.pkl"),
+            mock.call().save_trained_data(
+                agent_id="Senior Writer",
+                trained_data=task_evaluator().evaluate_training_data().model_dump(),
+            ),
+            mock.call(),
+            mock.call().load(),
+            mock.call(),
+            mock.call().load(),
+        ]
+    )
 
 
 def test_crew_train_error():

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