Update pyproject.toml and uv.lock to drop crewai-tools as a default requirement in crewai repo

docs/concepts/crews.mdx

@@ -41,155 +41,6 @@ A crew in crewAI represents a collaborative group of agents working together to
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
 </Tip>
 
-## Creating Crews
-
-There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**.
-
-### YAML Configuration (Recommended)
-
-Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
-
-After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
-
-#### Example Crew Class with Decorators
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-
-
-@CrewBase
-class YourCrewName:
-    """Description of your crew"""
-
-    # Paths to your YAML configuration files
-    # To see an example agent and task defined in YAML, checkout the following:
-    # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml' 
-    tasks_config = 'config/tasks.yaml' 
-
-    @before_kickoff
-    def prepare_inputs(self, inputs):
-        # Modify inputs before the crew starts
-        inputs['additional_data'] = "Some extra information"
-        return inputs
-
-    @after_kickoff
-    def process_output(self, output):
-        # Modify output after the crew finishes
-        output.raw += "\nProcessed after kickoff."
-        return output
-
-    @agent
-    def agent_one(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_one'],
-            verbose=True
-        )
-
-    @agent
-    def agent_two(self) -> Agent:
-        return Agent(
-            config=self.agents_config['agent_two'],
-            verbose=True
-        )
-
-    @task
-    def task_one(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_one']
-        )
-
-    @task
-    def task_two(self) -> Task:
-        return Task(
-            config=self.tasks_config['task_two']
-        )
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator. 
-            process=Process.sequential,
-            verbose=True,
-        )
-```
-
-<Note>
-Tasks will be executed in the order they are defined.
-</Note>
-
-The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management.
-
-#### Decorators overview from `annotations.py`
-
-CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling:
-
-- `@CrewBase`: Marks the class as a crew base class.
-- `@agent`: Denotes a method that returns an `Agent` object.
-- `@task`: Denotes a method that returns a `Task` object.
-- `@crew`: Denotes the method that returns the `Crew` object.
-- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts.
-- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes.
-
-These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them.
-
-### Direct Code Definition (Alternative)
-
-Alternatively, you can define the crew directly in code without using YAML configuration files.
-
-```python code
-from crewai import Agent, Crew, Task, Process
-from crewai_tools import YourCustomTool
-
-class YourCrewName:
-    def agent_one(self) -> Agent:
-        return Agent(
-            role="Data Analyst",
-            goal="Analyze data trends in the market",
-            backstory="An experienced data analyst with a background in economics",
-            verbose=True,
-            tools=[YourCustomTool()]
-        )
-
-    def agent_two(self) -> Agent:
-        return Agent(
-            role="Market Researcher",
-            goal="Gather information on market dynamics",
-            backstory="A diligent researcher with a keen eye for detail",
-            verbose=True
-        )
-
-    def task_one(self) -> Task:
-        return Task(
-            description="Collect recent market data and identify trends.",
-            expected_output="A report summarizing key trends in the market.",
-            agent=self.agent_one()
-        )
-
-    def task_two(self) -> Task:
-        return Task(
-            description="Research factors affecting market dynamics.",
-            expected_output="An analysis of factors influencing the market.",
-            agent=self.agent_two()
-        )
-
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.agent_one(), self.agent_two()],
-            tasks=[self.task_one(), self.task_two()],
-            process=Process.sequential,
-            verbose=True
-        )
-```
-
-In this example:
-
-- Agents and tasks are defined directly within the class without decorators.
-- We manually create and manage the list of agents and tasks.
-- This approach provides more control but can be less maintainable for larger projects.
 
 ## Crew Output
 
@@ -337,4 +188,4 @@ Then, to replay from a specific task, use:
 crewai replay -t <task_id>
 ```
 
-These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/concepts/knowledge.mdx

@@ -48,6 +48,7 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc
 content = "Users name is John. He is 30 years old and lives in San Francisco."
 string_source = StringKnowledgeSource(
     content=content, 
+    metadata={"preference": "personal"}
 )
 
 # Create an LLM with a temperature of 0 to ensure deterministic outputs
@@ -73,7 +74,10 @@ crew = Crew(
     tasks=[task],
     verbose=True,
     process=Process.sequential,
-    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+    knowledge={
+      "sources": [string_source], 
+      "metadata": {"preference": "personal"}
+    }, # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
 )
 
 result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
@@ -81,6 +85,17 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 
 ## Knowledge Configuration
 
+### Metadata and Filtering
+
+Knowledge sources support metadata for better organization and filtering. Metadata is used to filter the knowledge sources when querying the knowledge store.
+
+```python Code
+knowledge_source = StringKnowledgeSource(
+    content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"} # Metadata is used to filter the knowledge sources
+)
+```
+
 ### Chunking Configuration
 
 Control how content is split for processing by setting the chunk size and overlap.
@@ -101,28 +116,21 @@ You can also configure the embedder for the knowledge store. This is useful if y
 ...
 string_source = StringKnowledgeSource(
     content="Users name is John. He is 30 years old and lives in San Francisco.",
+    metadata={"preference": "personal"}
 )
 crew = Crew(
     ...
-    knowledge_sources=[string_source],
-    embedder={
-        "provider": "openai",
-        "config": {"model": "text-embedding-3-small"},
+    knowledge={
+        "sources": [string_source],
+        "metadata": {"preference": "personal"},
+        "embedder_config": {
+          "provider": "openai", # Default embedder provider; can be "ollama", "gemini", e.t.c.
+          "config": {"model": "text-embedding-3-small"} # Default embedder model; can be "mxbai-embed-large", "nomic-embed-tex", e.t.c.
+        },
     },
 )
 ```
 
-## Clearing Knowledge
-
-If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
-
-```bash Command
-crewai reset-memories --knowledge
-```
-
-This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
-
-
 ## Custom Knowledge Sources
 
 CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
@@ -166,12 +174,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
         formatted = "Space News Articles:\n\n"
         for article in articles:
             formatted += f"""
-                Title: {article['title']}
-                Published: {article['published_at']}
-                Summary: {article['summary']}
-                News Site: {article['news_site']}
-                URL: {article['url']}
-                -------------------"""
+Title: {article['title']}
+Published: {article['published_at']}
+Summary: {article['summary']}
+News Site: {article['news_site']}
+URL: {article['url']}
+-------------------"""
         return formatted
 
     def add(self) -> None:
@@ -181,12 +189,17 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
             chunks = self._chunk_text(text)
             self.chunks.extend(chunks)
         
-        self._save_documents()
+        self.save_documents(metadata={
+            "source": "space_news_api",
+            "timestamp": datetime.now().isoformat(),
+            "article_count": self.limit
+        })
 
 # Create knowledge source
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=10,
+    metadata={"category": "recent_news", "source": "spaceflight_news"}
 )
 
 # Create specialized agent
@@ -252,7 +265,7 @@ The latest developments in space exploration, based on recent space news article
    - Implements three key methods:
      - `load_content()`: Fetches articles from the API
      - `_format_articles()`: Structures the articles into readable text
-     - `add()`: Processes and stores the content
+     - `add()`: Processes and stores the content with metadata
 
 2. **Agent Configuration**:
    - Specialized role as a Space News Analyst
@@ -286,12 +299,14 @@ You can customize the API query by modifying the endpoint URL:
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
     limit=20,  # Increase the number of articles
+    metadata={"category": "recent_news"}
 )
 
 # Add search parameters
 recent_news = SpaceNewsKnowledgeSource(
     api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
     limit=10,
+    metadata={"category": "nasa_news"}
 )
 ```
 
@@ -299,14 +314,16 @@ recent_news = SpaceNewsKnowledgeSource(
 
 <AccordionGroup>
   <Accordion title="Content Organization">
+    - Use descriptive metadata for better filtering
     - Keep chunk sizes appropriate for your content type
     - Consider content overlap for context preservation
     - Organize related information into separate knowledge sources
   </Accordion>
   
   <Accordion title="Performance Tips">
+    - Use metadata filtering to narrow search scope
     - Adjust chunk sizes based on content complexity
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing
   </Accordion>
-</AccordionGroup>
+</AccordionGroup>

docs/how-to/agentops-observability.mdx

@@ -57,7 +57,7 @@ This feature is useful for debugging and understanding how agents interact with
    <Step title="Install AgentOps">
       Install AgentOps with:
       ```bash
-      pip install 'crewai[agentops]'
+      pip install crewai[agentops]
       ```
       or
       ```bash

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.86.0"
+version = "0.85.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
@@ -15,7 +15,6 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.17.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.86.0"
+__version__ = "0.85.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -8,7 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
+from crewai.cli.constants import ENV_VARS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -181,11 +181,20 @@ class Agent(BaseAgent):
                         if key_name and key_name not in unaccepted_attributes:
                             env_value = os.environ.get(key_name)
                             if env_value:
-                                key_name = key_name.lower()
-                                for pattern in LITELLM_PARAMS:
-                                    if pattern in key_name:
-                                        key_name = pattern
-                                        break
+                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = (
+                                    "api_key" if "API_KEY" in key_name else key_name
+                                )
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base" if "API_BASE" in key_name else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in key_name
+                                    else key_name
+                                )
                                 llm_params[key_name] = env_value
                         # Check for default values if the environment variable is not set
                         elif env_var.get("default", False):

src/crewai/cli/cli.py

@@ -25,7 +25,6 @@ from .update_crew import update_crew
 
 
 @click.group()
-@click.version_option(pkg_resources.get_distribution("crewai").version)
 def crewai():
     """Top-level command group for crewai."""
 
@@ -51,10 +50,7 @@ def create(type, name, provider, skip_provider=False):
 )
 def version(tools):
     """Show the installed version of crewai."""
-    try:
-        crewai_version = pkg_resources.get_distribution("crewai").version
-    except Exception:
-        crewai_version = "unknown version"
+    crewai_version = pkg_resources.get_distribution("crewai").version
     click.echo(f"crewai version: {crewai_version}")
 
     if tools:

src/crewai/cli/constants.py

@@ -159,6 +159,3 @@ MODELS = {
 }
 
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
-
-
-LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

src/crewai/cli/templates/crew/config/tasks.yaml

@@ -12,6 +12,6 @@ reporting_task:
     Review the context you got and expand each topic into a full section for a report.
     Make sure the report is detailed and contains any and all relevant information.
   expected_output: >
-    A fully fledged report with the main topics, each with a full section of information.
+    A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst

src/crewai/cli/templates/crew/crew.py

@@ -1,26 +1,37 @@
 from crewai import Agent, Crew, Process, Task
-from crewai.project import CrewBase, agent, crew, task
+from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
+# Uncomment the following line to use an example of a custom tool
+# from {{folder_name}}.tools.custom_tool import MyCustomTool
+# Uncomment the following line to use an example of a knowledge source
+# from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
-# If you want to run a snippet of code before or after the crew starts, 
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
+# Check our tools documentations for more information on how to use them
+# from crewai_tools import SerperDevTool
 
 @CrewBase
 class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
-	# Learn more about YAML configuration files here:
-	# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-	# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
 	agents_config = 'config/agents.yaml'
 	tasks_config = 'config/tasks.yaml'
 
-	# If you would like to add tools to your agents, you can learn more about it here:
-	# https://docs.crewai.com/concepts/agents#agent-tools
+	@before_kickoff # Optional hook to be executed before the crew starts
+	def pull_data_example(self, inputs):
+		# Example of pulling data from an external API, dynamically changing the inputs
+		inputs['extra_data'] = "This is extra data"
+		return inputs
+
+	@after_kickoff # Optional hook to be executed after the crew has finished
+	def log_results(self, output):
+		# Example of logging results, dynamically changing the output
+		print(f"Results: {output}")
+		return output
+
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
 			config=self.agents_config['researcher'],
+			# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
 			verbose=True
 		)
 
@@ -31,9 +42,6 @@ class {{crew_name}}():
 			verbose=True
 		)
 
-	# To learn more about structured task outputs, 
-	# task dependencies, and task callbacks, check out the documentation:
-	# https://docs.crewai.com/concepts/tasks#overview-of-a-task
 	@task
 	def research_task(self) -> Task:
 		return Task(
@@ -50,8 +58,14 @@ class {{crew_name}}():
 	@crew
 	def crew(self) -> Crew:
 		"""Creates the {{crew_name}} crew"""
-		# To learn how to add knowledge sources to your crew, check out the documentation:
-		# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
+		# You can add knowledge sources here
+		# knowledge_path = "user_preference.txt"
+		# sources = [
+		# 	TextFileKnowledgeSource(
+		# 		file_path="knowledge/user_preference.txt",
+		# 		metadata={"preference": "personal"}
+		# 	),
+		# ]
 
 		return Crew(
 			agents=self.agents, # Automatically created by the @agent decorator
@@ -59,4 +73,5 @@ class {{crew_name}}():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
+			# knowledge_sources=sources, # In the case you want to add knowledge sources
 		)

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0"
+    "crewai[tools]>=0.85.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py

@@ -1,47 +1,31 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 
-# If you want to run a snippet of code before or after the crew starts,
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
-
-
 @CrewBase
-class PoemCrew:
-    """Poem Crew"""
+class PoemCrew():
+	"""Poem Crew"""
 
-    # Learn more about YAML configuration files here:
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
 
-    # If you would lik to add tools to your crew, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
-    @agent
-    def poem_writer(self) -> Agent:
-        return Agent(
-            config=self.agents_config["poem_writer"],
-        )
+	@agent
+	def poem_writer(self) -> Agent:
+		return Agent(
+			config=self.agents_config['poem_writer'],
+		)
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
-    @task
-    def write_poem(self) -> Task:
-        return Task(
-            config=self.tasks_config["write_poem"],
-        )
+	@task
+	def write_poem(self) -> Task:
+		return Task(
+			config=self.tasks_config['write_poem'],
+		)
 
-    @crew
-    def crew(self) -> Crew:
-        """Creates the Research Crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-
-        return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
-            process=Process.sequential,
-            verbose=True,
-        )
+	@crew
+	def crew(self) -> Crew:
+		"""Creates the Research Crew"""
+		return Crew(
+			agents=self.agents, # Automatically created by the @agent decorator
+			tasks=self.tasks, # Automatically created by the @task decorator
+			process=Process.sequential,
+			verbose=True,
+		)

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0,<1.0.0",
+    "crewai[tools]>=0.85.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/pipeline/pyproject.toml

@@ -1,17 +0,0 @@
-[tool.poetry]
-name = "{{folder_name}}"
-version = "0.1.0"
-description = "{{name}} using crewAI"
-authors = ["Your Name <you@example.com>"]
-
-[tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.86.0,<1.0.0" }
-asyncio = "*"
-
-[tool.poetry.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.86.0"
+    "crewai[tools]>=0.85.0"
 ]
 

src/crewai/knowledge/knowledge.py

@@ -1,10 +1,11 @@
 import os
-from typing import Any, Dict, List, Optional
 
+from typing import List, Optional, Dict, Any
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # removes logging from fastembed
 
@@ -45,7 +46,9 @@ class Knowledge(BaseModel):
             source.storage = self.storage
             source.add()
 
-    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
+    def query(
+        self, query: List[str], limit: int = 3, preference: Optional[str] = None
+    ) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -54,6 +57,8 @@ class Knowledge(BaseModel):
         results = self.storage.search(
             query,
             limit,
+            filter={"preference": preference} if preference else None,
+            score_threshold=DEFAULT_SCORE_THRESHOLD,
         )
         return results
 

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -1,13 +1,13 @@
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, List, Union
+from typing import Union, List, Dict, Any
 
 from pydantic import Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.utilities.logger import Logger
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
-from crewai.utilities.logger import Logger
 
 
 class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
@@ -49,9 +49,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
                     color="red",
                 )
 
-    def _save_documents(self):
+    def save_documents(self, metadata: Dict[str, Any]):
         """Save the documents to the storage."""
-        self.storage.save(self.chunks)
+        chunk_metadatas = [metadata.copy() for _ in self.chunks]
+        self.storage.save(self.chunks, chunk_metadatas)
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import List, Dict, Any, Optional
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict, Field
@@ -17,7 +17,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
-    metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
+    metadata: Dict[str, Any] = Field(default_factory=dict)
     collection_name: Optional[str] = Field(default=None)
 
     @abstractmethod
@@ -41,9 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC):
             for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
         ]
 
-    def _save_documents(self):
+    def save_documents(self, metadata: Dict[str, Any]):
         """
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks)
+        self.storage.save(self.chunks, metadata)

src/crewai/knowledge/source/csv_knowledge_source.py

@@ -1,6 +1,6 @@
 import csv
-from pathlib import Path
 from typing import Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -30,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,6 +1,5 @@
-from pathlib import Path
 from typing import Dict, List
-
+from pathlib import Path
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
 
@@ -45,7 +44,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
 
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/json_knowledge_source.py

@@ -1,6 +1,6 @@
 import json
-from pathlib import Path
 from typing import Any, Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -42,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
         )
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/pdf_knowledge_source.py

@@ -1,5 +1,5 @@
+from typing import List, Dict
 from pathlib import Path
-from typing import Dict, List
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -43,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/string_knowledge_source.py

@@ -24,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
         """Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
         new_chunks = self._chunk_text(self.content)
         self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/source/text_file_knowledge_source.py

@@ -1,5 +1,5 @@
-from pathlib import Path
 from typing import Dict, List
+from pathlib import Path
 
 from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
 
@@ -24,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
         for _, text in self.content.items():
             new_chunks = self._chunk_text(text)
             self.chunks.extend(new_chunks)
-        self._save_documents()
+        self.save_documents(metadata=self.metadata)
 
     def _chunk_text(self, text: str) -> List[str]:
         """Utility method to split text into chunks."""

src/crewai/knowledge/storage/knowledge_storage.py

@@ -1,20 +1,18 @@
 import contextlib
-import hashlib
 import io
 import logging
-import os
-from typing import Any, Dict, List, Optional, Union, cast
-
 import chromadb
-import chromadb.errors
-from chromadb.api import ClientAPI
-from chromadb.api.types import OneOrMany
-from chromadb.config import Settings
+import os
 
-from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.logger import Logger
+import chromadb.errors
 from crewai.utilities.paths import db_storage_path
+from typing import Optional, List, Dict, Any, Union
+from crewai.utilities import EmbeddingConfigurator
+from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+import hashlib
+from chromadb.config import Settings
+from chromadb.api import ClientAPI
+from crewai.utilities.logger import Logger
 
 
 @contextlib.contextmanager
@@ -118,16 +116,11 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     def save(
         self,
         documents: List[str],
-        metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
+        metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
     ):
         if self.collection:
             try:
-                if metadata is None:
-                    metadatas: Optional[OneOrMany[chromadb.Metadata]] = None
-                elif isinstance(metadata, list):
-                    metadatas = [cast(chromadb.Metadata, m) for m in metadata]
-                else:
-                    metadatas = cast(chromadb.Metadata, metadata)
+                metadatas = [metadata] if isinstance(metadata, dict) else metadata
 
                 ids = [
                     hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents

src/crewai/llm.py

@@ -1,5 +1,4 @@
 import logging
-import os
 import sys
 import threading
 import warnings
@@ -129,7 +128,6 @@ class LLM:
         litellm.drop_params = True
         litellm.set_verbose = False
         self.set_callbacks(callbacks)
-        self.set_env_callbacks()
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
@@ -204,39 +202,3 @@ class LLM:
                 litellm._async_success_callback.remove(callback)
 
         litellm.callbacks = callbacks
-
-    def set_env_callbacks(self):
-        """
-        Sets the success and failure callbacks for the LiteLLM library from environment variables.
-
-        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
-        environment variables, which should contain comma-separated lists of callback names.
-        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
-        respectively.
-
-        If the environment variables are not set or are empty, the corresponding callback lists
-        will be set to empty lists.
-
-        Example:
-            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
-            LITELLM_FAILURE_CALLBACKS="langfuse"
-
-        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
-        `litellm.failure_callback` to ["langfuse"].
-        """
-        success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-        success_callbacks = []
-        if success_callbacks_str:
-            success_callbacks = [
-                callback.strip() for callback in success_callbacks_str.split(",")
-            ]
-
-        failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-        failure_callbacks = []
-        if failure_callbacks_str:
-            failure_callbacks = [
-                callback.strip() for callback in failure_callbacks_str.split(",")
-            ]
-
-        litellm.success_callback = success_callbacks
-        litellm.failure_callback = failure_callbacks

src/crewai/memory/storage/rag_storage.py

@@ -4,14 +4,12 @@ import logging
 import os
 import shutil
 import uuid
+
 from typing import Any, Dict, List, Optional
-
 from chromadb.api import ClientAPI
-
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities import EmbeddingConfigurator
-from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities import EmbeddingConfigurator
 
 
 @contextlib.contextmanager
@@ -39,15 +37,12 @@ class RAGStorage(BaseRAGStorage):
 
     app: ClientAPI | None = None
 
-    def __init__(
-        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
-    ):
+    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None, path=None):
         super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
         self.agents = agents
-        self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
 
@@ -65,7 +60,7 @@ class RAGStorage(BaseRAGStorage):
 
         self._set_embedder_config()
         chroma_client = chromadb.PersistentClient(
-            path=self.path if self.path else self.storage_file_name,
+            path=self.path if self.path else f"{db_storage_path()}/{self.type}/{self.agents}",
             settings=Settings(allow_reset=self.allow_reset),
         )
 
@@ -86,20 +81,6 @@ class RAGStorage(BaseRAGStorage):
         """
         return role.replace("\n", "").replace(" ", "_").replace("/", "_")
 
-    def _build_storage_file_name(self, type: str, file_name: str) -> str:
-        """
-        Ensures file name does not exceed max allowed by OS
-        """
-        base_path = f"{db_storage_path()}/{type}"
-
-        if len(file_name) > MAX_FILE_NAME_LENGTH:
-            logging.warning(
-                f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
-            )
-            file_name = file_name[:MAX_FILE_NAME_LENGTH]
-
-        return f"{base_path}/{file_name}"
-
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()

src/crewai/utilities/constants.py

@@ -3,4 +3,3 @@ TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
 DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
-MAX_FILE_NAME_LENGTH = 255

tests/cli/cli_test.py

@@ -131,13 +131,6 @@ def test_reset_no_memory_flags(runner):
     )
 
 
-def test_version_flag(runner):
-    result = runner.invoke(version)
-
-    assert result.exit_code == 0
-    assert "crewai version:" in result.output
-
-
 def test_version_command(runner):
     result = runner.invoke(version)
 

uv.lock

@@ -608,14 +608,13 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.86.0"
+version = "0.85.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
     { name = "auth0-python" },
     { name = "chromadb" },
     { name = "click" },
-    { name = "crewai-tools" },
     { name = "instructor" },
     { name = "json-repair" },
     { name = "jsonref" },
@@ -685,7 +684,6 @@ requires-dist = [
     { name = "auth0-python", specifier = ">=4.7.1" },
     { name = "chromadb", specifier = ">=0.5.18" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -733,7 +731,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.17.0"
+version = "0.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -742,6 +740,7 @@ dependencies = [
     { name = "docx2txt" },
     { name = "embedchain" },
     { name = "lancedb" },
+    { name = "langchain" },
     { name = "openai" },
     { name = "pydantic" },
     { name = "pyright" },
@@ -750,9 +749,9 @@ dependencies = [
     { name = "requests" },
     { name = "selenium" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/cc/15/365f74e0e8313e7a3399bf01d908aa73575c823275f9196ec14c23159878/crewai_tools-0.17.0.tar.gz", hash = "sha256:2a2986000775c76bad45b9f3a2be857d293cf5daffe5f316abc052e630b1e5ce", size = 818983 }
+sdist = { url = "https://files.pythonhosted.org/packages/9b/6d/4fa91b481b120f83bb58f365203d8aa8564e8ced1035d79f8aedb7d71e2f/crewai_tools-0.14.0.tar.gz", hash = "sha256:510f3a194bcda4fdae4314bd775521964b5f229ddbe451e5d9e0216cae57f4e3", size = 815892 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/1d/976adc2a4e5237cb03625de412cd051dea7d524084ed442adedfda871526/crewai_tools-0.17.0-py3-none-any.whl", hash = "sha256:85cf15286684ecad579b5a497888c6bf8a079ca443f7dd63a52bf1709655e4a3", size = 467975 },
+    { url = "https://files.pythonhosted.org/packages/c8/ed/9f4e64e1507062957b0118085332d38b621c1000874baef2d1c4069bfd97/crewai_tools-0.14.0-py3-none-any.whl", hash = "sha256:0a804a828c29869c3af3253f4fc4c3967a3f80f06dab22e9bbe9526608a31564", size = 462980 },
 ]
 
 [[package]]