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Merge branch 'main' into bug_fix

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Lucas Gomide
2025-04-02 10:00:53 -03:00
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13
README.md
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@@ -401,11 +401,16 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
### Using Crews and Flows Together
CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
- `or_`: Triggers when any of the specified conditions are met.
- `and_`Triggers when all of the specified conditions are met.
Here's how you can orchestrate multiple Crews within a Flow:
```python
from crewai.flow.flow import Flow, listen, start, router
from crewai import Crew, Agent, Task
from crewai.flow.flow import Flow, listen, start, router, or_
from crewai import Crew, Agent, Task, Process
from pydantic import BaseModel
# Define structured state for precise control
@@ -479,7 +484,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
)
return strategy_crew.kickoff()
@listen("medium_confidence", "low_confidence")
@listen(or_("medium_confidence", "low_confidence"))
def request_additional_analysis(self):
self.state.recommendations.append("Gather more data")
return "Additional analysis required"

4
docs/concepts/event-listener.mdx
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@@ -13,7 +13,7 @@ CrewAI provides a powerful event system that allows you to listen for and react
CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
2. **CrewEvent**: Base class for all events in the system
2. **BaseEvent**: Base class for all events in the system
3. **BaseEventListener**: Abstract base class for creating custom event listeners
When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
@@ -234,7 +234,7 @@ Each event handler receives two parameters:
1. **source**: The object that emitted the event
2. **event**: The event instance, containing event-specific data
The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
- **timestamp**: The time when the event was emitted
- **type**: A string identifier for the event type

85
docs/concepts/memory.mdx
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@@ -164,7 +164,10 @@ crew = Crew(
[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.
### Using Mem0 API platform
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. In this case `user_memory` is set to `MemoryClient` from mem0.
```python Code
@@ -175,18 +178,7 @@ 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
# Step 1: Create a Crew with User Memory
crew = Crew(
agents=[...],
@@ -197,11 +189,12 @@ crew = Crew(
memory_config={
"provider": "mem0",
"config": {"user_id": "john"},
"user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
},
)
```
## Memory Configuration Options
#### Additional Memory Configuration Options
If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
```python Code
@@ -215,10 +208,74 @@ crew = Crew(
memory_config={
"provider": "mem0",
"config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
"user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
},
)
```
### Using Local Mem0 memory
If you want to use local mem0 memory, with a custom configuration, you can set a parameter `local_mem0_config` in the config itself.
If both os environment key is set and local_mem0_config is given, the API platform takes higher priority over the local configuration.
Check [this](https://docs.mem0.ai/open-source/python-quickstart#run-mem0-locally) mem0 local configuration docs for more understanding.
In this case `user_memory` is set to `Memory` from mem0.
```python Code
from crewai import Crew
#local mem0 config
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333
}
},
"llm": {
"provider": "openai",
"config": {
"api_key": "your-api-key",
"model": "gpt-4"
}
},
"embedder": {
"provider": "openai",
"config": {
"api_key": "your-api-key",
"model": "text-embedding-3-small"
}
},
"graph_store": {
"provider": "neo4j",
"config": {
"url": "neo4j+s://your-instance",
"username": "neo4j",
"password": "password"
}
},
"history_db_path": "/path/to/history.db",
"version": "v1.1",
"custom_fact_extraction_prompt": "Optional custom prompt for fact extraction for memory",
"custom_update_memory_prompt": "Optional custom prompt for update memory"
}
crew = Crew(
agents=[...],
tasks=[...],
verbose=True,
memory=True,
memory_config={
"provider": "mem0",
"config": {"user_id": "john", 'local_mem0_config': config},
"user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
},
)
```
## Additional Embedding Providers
### Using OpenAI embeddings (already default)

10
docs/docs.json
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@@ -1,6 +1,6 @@
{
"$schema": "https://mintlify.com/docs.json",
"theme": "palm",
"theme": "mint",
"name": "CrewAI",
"colors": {
"primary": "#EB6658",
@@ -103,13 +103,15 @@
{
"group": "Agent Monitoring & Observability",
"pages": [
"how-to/weave-integration",
"how-to/agentops-observability",
"how-to/langfuse-observability",
"how-to/langtrace-observability",
"how-to/mlflow-observability",
"how-to/openlit-observability",
"how-to/portkey-observability"
"how-to/opik-observability",
"how-to/phoenix-observability",
"how-to/portkey-observability",
"how-to/weave-integration"
]
},
{
@@ -228,4 +230,4 @@
"reddit": "https://www.reddit.com/r/crewAIInc/"
}
}
}
}

6
docs/how-to/kickoff-async.mdx
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@@ -92,12 +92,14 @@ coding_agent = Agent(
# Create tasks that require code execution
task_1 = Task(
description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
agent=coding_agent
agent=coding_agent,
expected_output="The average age of the participants."
)
task_2 = Task(
description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
agent=coding_agent
agent=coding_agent,
expected_output="The average age of the participants."
)
# Create two crews and add tasks

129
docs/how-to/opik-observability.mdx Normal file
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@@ -0,0 +1,129 @@
---
title: Opik Integration
description: Learn how to use Comet Opik to debug, evaluate, and monitor your CrewAI applications with comprehensive tracing, automated evaluations, and production-ready dashboards.
icon: meteor
---
# Opik Overview
With [Comet Opik](https://www.comet.com/docs/opik/), debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
<Frame caption="Opik Agent Dashboard">
<img src="/images/opik-crewai-dashboard.png" alt="Opik agent monitoring example with CrewAI" />
</Frame>
Opik provides comprehensive support for every stage of your CrewAI application development:
- **Log Traces and Spans**: Automatically track LLM calls and application logic to debug and analyze development and production systems. Manually or programmatically annotate, view, and compare responses across projects.
- **Evaluate Your LLM Application's Performance**: Evaluate against a custom test set and run built-in evaluation metrics or define your own metrics in the SDK or UI.
- **Test Within Your CI/CD Pipeline**: Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Run online evaluations for continuous monitoring in production.
- **Monitor & Analyze Production Data**: Understand your models' performance on unseen data in production and generate datasets for new dev iterations.
## Setup
Comet provides a hosted version of the Opik platform, or you can run the platform locally.
To use the hosted version, simply [create a free Comet account](https://www.comet.com/signup?utm_medium=github&utm_source=crewai_docs) and grab you API Key.
To run the Opik platform locally, see our [installation guide](https://www.comet.com/docs/opik/self-host/overview/) for more information.
For this guide we will use CrewAI’s quickstart example.
<Steps>
<Step title="Install required packages">
```shell
pip install crewai crewai-tools opik --upgrade
```
</Step>
<Step title="Configure Opik">
```python
import opik
opik.configure(use_local=False)
```
</Step>
<Step title="Prepare environment">
First, we set up our API keys for our LLM-provider as environment variables:
```python
import os
import getpass
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
```
</Step>
<Step title="Using CrewAI">
The first step is to create our project. We will use an example from CrewAI’s documentation:
```python
from crewai import Agent, Crew, Task, Process
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,
)
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(
name="Collect Data 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(
name="Market Research 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,
)
```
Now we can import Opik’s tracker and run our crew:
```python
from opik.integrations.crewai import track_crewai
track_crewai(project_name="crewai-integration-demo")
my_crew = YourCrewName().crew()
result = my_crew.kickoff()
print(result)
```
After running your CrewAI application, visit the Opik app to view:
- LLM traces, spans, and their metadata
- Agent interactions and task execution flow
- Performance metrics like latency and token usage
- Evaluation metrics (built-in or custom)
</Step>
</Steps>
## Resources
- [🦉 Opik Documentation](https://www.comet.com/docs/opik/)
- [👉 Opik + CrewAI Colab](https://colab.research.google.com/github/comet-ml/opik/blob/main/apps/opik-documentation/documentation/docs/cookbook/crewai.ipynb)
- [🐦 X](https://x.com/cometml)
- [💬 Slack](https://slack.comet.com/)

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docs/how-to/phoenix-observability.mdx Normal file
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@@ -0,0 +1,145 @@
---
title: Agent Monitoring with Arize Phoenix
description: Learn how to integrate Arize Phoenix with CrewAI via OpenTelemetry using OpenInference
icon: magnifying-glass-chart
---
# Integrate Arize Phoenix with CrewAI
This guide demonstrates how to integrate **Arize Phoenix** with **CrewAI** using OpenTelemetry via the [OpenInference](https://github.com/openinference/openinference) SDK. By the end of this guide, you will be able to trace your CrewAI agents and easily debug your agents.
> **What is Arize Phoenix?** [Arize Phoenix](https://phoenix.arize.com) is an LLM observability platform that provides tracing and evaluation for AI applications.
[![Watch a Video Demo of Our Integration with Phoenix](https://storage.googleapis.com/arize-assets/fixtures/setup_crewai.png)](https://www.youtube.com/watch?v=Yc5q3l6F7Ww)
## Get Started
We'll walk through a simple example of using CrewAI and integrating it with Arize Phoenix via OpenTelemetry using OpenInference.
You can also access this guide on [Google Colab](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/crewai_tracing_tutorial.ipynb).
### Step 1: Install Dependencies
```bash
pip install openinference-instrumentation-crewai crewai crewai-tools arize-phoenix-otel
```
### Step 2: Set Up Environment Variables
Setup Phoenix Cloud API keys and configure OpenTelemetry to send traces to Phoenix. Phoenix Cloud is a hosted version of Arize Phoenix, but it is not required to use this integration.
You can get your free Serper API key [here](https://serper.dev/).
```python
import os
from getpass import getpass
# Get your Phoenix Cloud credentials
PHOENIX_API_KEY = getpass("🔑 Enter your Phoenix Cloud API Key: ")
# Get API keys for services
OPENAI_API_KEY = getpass("🔑 Enter your OpenAI API key: ")
SERPER_API_KEY = getpass("🔑 Enter your Serper API key: ")
# Set environment variables
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com" # Phoenix Cloud, change this to your own endpoint if you are using a self-hosted instance
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["SERPER_API_KEY"] = SERPER_API_KEY
```
### Step 3: Initialize OpenTelemetry with Phoenix
Initialize the OpenInference OpenTelemetry instrumentation SDK to start capturing traces and send them to Phoenix.
```python
from phoenix.otel import register
tracer_provider = register(
project_name="crewai-tracing-demo",
auto_instrument=True,
)
```
### Step 4: Create a CrewAI Application
We'll create a CrewAI application where two agents collaborate to research and write a blog post about AI advancements.
```python
from crewai import Agent, Crew, Process, Task
from crewai_tools import SerperDevTool
search_tool = SerperDevTool()
# Define your agents with roles and goals
researcher = Agent(
role="Senior Research Analyst",
goal="Uncover cutting-edge developments in AI and data science",
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting actionable insights.""",
verbose=True,
allow_delegation=False,
# You can pass an optional llm attribute specifying what model you wanna use.
# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
tools=[search_tool],
)
writer = Agent(
role="Tech Content Strategist",
goal="Craft compelling content on tech advancements",
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True,
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.""",
expected_output="Full analysis report in bullet points",
agent=researcher,
)
task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
expected_output="Full blog post of at least 4 paragraphs",
agent=writer,
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer], tasks=[task1, task2], verbose=1, process=Process.sequential
)
# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)
```
### Step 5: View Traces in Phoenix
After running the agent, you can view the traces generated by your CrewAI application in Phoenix. You should see detailed steps of the agent interactions and LLM calls, which can help you debug and optimize your AI agents.
Log into your Phoenix Cloud account and navigate to the project you specified in the `project_name` parameter. You'll see a timeline view of your trace with all the agent interactions, tool usages, and LLM calls.
![Example trace in Phoenix showing agent interactions](https://storage.googleapis.com/arize-assets/fixtures/crewai_traces.png)
### Version Compatibility Information
- Python 3.8+
- CrewAI >= 0.86.0
- Arize Phoenix >= 7.0.1
- OpenTelemetry SDK >= 1.31.0
### References
- [Phoenix Documentation](https://docs.arize.com/phoenix/) - Overview of the Phoenix platform.
- [CrewAI Documentation](https://docs.crewai.com/) - Overview of the CrewAI framework.
- [OpenTelemetry Docs](https://opentelemetry.io/docs/) - OpenTelemetry guide
- [OpenInference GitHub](https://github.com/openinference/openinference) - Source code for OpenInference SDK.

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2
pyproject.toml
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@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = ["crewai-tools>=0.37.0"]
tools = ["crewai-tools~=0.38.0"]
embeddings = [
"tiktoken~=0.7.0"
]

4
src/crewai/agent.py
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@@ -142,15 +142,13 @@ class Agent(BaseAgent):
self.embedder = crew_embedder
if self.knowledge_sources:
full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
if isinstance(self.knowledge_sources, list) and all(
isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
):
self.knowledge = Knowledge(
sources=self.knowledge_sources,
embedder=self.embedder,
collection_name=knowledge_agent_name,
collection_name=self.role,
storage=self.knowledge_storage or None,
)
except (TypeError, ValueError) as e:

72
src/crewai/agents/crew_agent_executor.py
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@@ -20,7 +20,6 @@ from crewai.utilities import I18N, Printer
from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
from crewai.utilities.events import (
ToolUsageErrorEvent,
ToolUsageStartedEvent,
crewai_event_bus,
)
from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
@@ -153,8 +152,21 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
formatted_answer = self._process_llm_response(answer)
if isinstance(formatted_answer, AgentAction):
# Extract agent fingerprint if available
fingerprint_context = {}
if (
self.agent
and hasattr(self.agent, "security_config")
and hasattr(self.agent.security_config, "fingerprint")
):
fingerprint_context = {
"agent_fingerprint": str(
self.agent.security_config.fingerprint
)
}
tool_result = self._execute_tool_and_check_finality(
formatted_answer
formatted_answer, fingerprint_context=fingerprint_context
)
formatted_answer = self._handle_agent_action(
formatted_answer, tool_result
@@ -360,19 +372,35 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
)
def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
def _execute_tool_and_check_finality(
self,
agent_action: AgentAction,
fingerprint_context: Optional[Dict[str, str]] = None,
) -> ToolResult:
try:
fingerprint_context = fingerprint_context or {}
if self.agent:
# Create tool usage event with fingerprint information
event_data = {
"agent_key": self.agent.key,
"agent_role": self.agent.role,
"tool_name": agent_action.tool,
"tool_args": agent_action.tool_input,
"tool_class": agent_action.tool,
"agent": self.agent, # Pass the agent object for fingerprint extraction
}
# Include fingerprint context
if fingerprint_context:
event_data.update(fingerprint_context)
# Emit the tool usage started event with agent information
crewai_event_bus.emit(
self,
event=ToolUsageStartedEvent(
agent_key=self.agent.key,
agent_role=self.agent.role,
tool_name=agent_action.tool,
tool_args=agent_action.tool_input,
tool_class=agent_action.tool,
),
event=ToolUsageStartedEvent(**event_data),
)
tool_usage = ToolUsage(
tools_handler=self.tools_handler,
tools=self.tools,
@@ -383,6 +411,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
task=self.task, # type: ignore[arg-type]
agent=self.agent,
action=agent_action,
fingerprint_context=fingerprint_context, # Pass fingerprint context
)
tool_calling = tool_usage.parse_tool_calling(agent_action.text)
@@ -411,16 +440,23 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
except Exception as e:
# TODO: drop
if self.agent:
error_event_data = {
"agent_key": self.agent.key,
"agent_role": self.agent.role,
"tool_name": agent_action.tool,
"tool_args": agent_action.tool_input,
"tool_class": agent_action.tool,
"error": str(e),
"agent": self.agent, # Pass the agent object for fingerprint extraction
}
# Include fingerprint context
if fingerprint_context:
error_event_data.update(fingerprint_context)
crewai_event_bus.emit(
self,
event=ToolUsageErrorEvent( # validation error
agent_key=self.agent.key,
agent_role=self.agent.role,
tool_name=agent_action.tool,
tool_args=agent_action.tool_input,
tool_class=agent_action.tool,
error=str(e),
),
event=ToolUsageErrorEvent(**error_event_data),
)
raise e

35
src/crewai/crew.py
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@@ -6,9 +6,8 @@ import warnings
from concurrent.futures import Future
from copy import copy as shallow_copy
from hashlib import md5
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TypeVar, Union, cast
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union, cast
from langchain_core.tools import BaseTool as LangchainBaseTool
from pydantic import (
UUID4,
BaseModel,
@@ -291,23 +290,17 @@ class Crew(BaseModel):
else EntityMemory(crew=self, embedder_config=self.embedder)
)
if (
self.memory_config and "user_memory" in self.memory_config
self.memory_config
and "user_memory" in self.memory_config
and self.memory_config.get("provider") == "mem0"
): # Check for user_memory in config
user_memory_config = self.memory_config["user_memory"]
if isinstance(
user_memory_config, UserMemory
): # Check if it is already an instance
self._user_memory = user_memory_config
elif isinstance(
user_memory_config, dict
): # Check if it's a configuration dict
self._user_memory = UserMemory(
crew=self, **user_memory_config
) # Initialize with config
self._user_memory = UserMemory(crew=self)
else:
raise TypeError(
"user_memory must be a UserMemory instance or a configuration dictionary"
)
raise TypeError("user_memory must be a configuration dictionary")
else:
self._user_memory = None # No user memory if not in config
return self
@@ -490,7 +483,7 @@ class Crew(BaseModel):
task.key for task in self.tasks
]
return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
@property
def fingerprint(self) -> Fingerprint:
"""
@@ -1157,7 +1150,12 @@ class Crew(BaseModel):
return required_inputs
def copy(self):
"""Create a deep copy of the Crew."""
"""
Creates a deep copy of the Crew instance.
Returns:
Crew: A new instance with copied components
"""
exclude = {
"id",
@@ -1169,13 +1167,18 @@ class Crew(BaseModel):
"_short_term_memory",
"_long_term_memory",
"_entity_memory",
"_telemetry",
"agents",
"tasks",
"knowledge_sources",
"knowledge",
"manager_agent",
"manager_llm",
}
cloned_agents = [agent.copy() for agent in self.agents]
manager_agent = self.manager_agent.copy() if self.manager_agent else None
manager_llm = shallow_copy(self.manager_llm) if self.manager_llm else None
task_mapping = {}
@@ -1208,6 +1211,8 @@ class Crew(BaseModel):
tasks=cloned_tasks,
knowledge_sources=existing_knowledge_sources,
knowledge=existing_knowledge,
manager_agent=manager_agent,
manager_llm=manager_llm,
)
return copied_crew

4
src/crewai/knowledge/storage/knowledge_storage.py
View File

@@ -14,6 +14,7 @@ from chromadb.config import Settings
from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
from crewai.utilities import EmbeddingConfigurator
from crewai.utilities.chromadb import sanitize_collection_name
from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
from crewai.utilities.logger import Logger
from crewai.utilities.paths import db_storage_path
@@ -99,7 +100,8 @@ class KnowledgeStorage(BaseKnowledgeStorage):
)
if self.app:
self.collection = self.app.get_or_create_collection(
name=collection_name, embedding_function=self.embedder
name=sanitize_collection_name(collection_name),
embedding_function=self.embedder,
)
else:
raise Exception("Vector Database Client not initialized")

4
src/crewai/memory/contextual/contextual_memory.py
View File

@@ -94,6 +94,10 @@ class ContextualMemory:
Returns:
str: Formatted user memories as bullet points, or an empty string if none found.
"""
if self.um is None:
return ""
user_memories = self.um.search(query)
if not user_memories:
return ""

10
src/crewai/memory/storage/mem0_storage.py
View File

@@ -31,6 +31,7 @@ class Mem0Storage(Storage):
mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
mem0_org_id = config.get("org_id")
mem0_project_id = config.get("project_id")
mem0_local_config = config.get("local_mem0_config")
# Initialize MemoryClient or Memory based on the presence of the mem0_api_key
if mem0_api_key:
@@ -41,7 +42,10 @@ class Mem0Storage(Storage):
else:
self.memory = MemoryClient(api_key=mem0_api_key)
else:
self.memory = Memory() # Fallback to Memory if no Mem0 API key is provided
if mem0_local_config and len(mem0_local_config):
self.memory = Memory.from_config(config)
else:
self.memory = Memory()
def _sanitize_role(self, role: str) -> str:
"""
@@ -114,3 +118,7 @@ class Mem0Storage(Storage):
agents = [self._sanitize_role(agent.role) for agent in agents]
agents = "_".join(agents)
return agents
def reset(self):
if self.memory:
self.memory.reset()

8
src/crewai/memory/user/user_memory.py
View File

@@ -43,3 +43,11 @@ class UserMemory(Memory):
score_threshold=score_threshold,
)
return results
def reset(self) -> None:
try:
self.storage.reset()
except Exception as e:
raise Exception(
f"An error occurred while resetting the user memory: {e}"
)

18
src/crewai/task.py
View File

@@ -388,7 +388,7 @@ class Task(BaseModel):
tools = tools or self.tools or []
self.processed_by_agents.add(agent.role)
crewai_event_bus.emit(self, TaskStartedEvent(context=context))
crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))
result = agent.execute_task(
task=self,
context=context,
@@ -464,11 +464,11 @@ class Task(BaseModel):
)
)
self._save_file(content)
crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output))
crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output, task=self))
return task_output
except Exception as e:
self.end_time = datetime.datetime.now()
crewai_event_bus.emit(self, TaskFailedEvent(error=str(e)))
crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))
raise e # Re-raise the exception after emitting the event
def prompt(self) -> str:
@@ -572,7 +572,15 @@ class Task(BaseModel):
def copy(
self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
) -> "Task":
"""Create a deep copy of the Task."""
"""Creates a deep copy of the Task while preserving its original class type.
Args:
agents: List of agents available for the task.
task_mapping: Dictionary mapping task IDs to Task instances.
Returns:
A copy of the task with the same class type as the original.
"""
exclude = {
"id",
"agent",
@@ -595,7 +603,7 @@ class Task(BaseModel):
cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
cloned_tools = copy(self.tools) if self.tools else []
copied_task = Task(
copied_task = self.__class__(
**copied_data,
context=cloned_context,
agent=cloned_agent,

172
src/crewai/telemetry/telemetry.py
View File

@@ -112,6 +112,23 @@ class Telemetry:
self._add_attribute(span, "crew_memory", crew.memory)
self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
# Add fingerprint data
if hasattr(crew, "fingerprint") and crew.fingerprint:
self._add_attribute(span, "crew_fingerprint", crew.fingerprint.uuid_str)
self._add_attribute(
span,
"crew_fingerprint_created_at",
crew.fingerprint.created_at.isoformat(),
)
# Add fingerprint metadata if it exists
if hasattr(crew.fingerprint, "metadata") and crew.fingerprint.metadata:
self._add_attribute(
span,
"crew_fingerprint_metadata",
json.dumps(crew.fingerprint.metadata),
)
if crew.share_crew:
self._add_attribute(
span,
@@ -129,17 +146,43 @@ class Telemetry:
"max_rpm": agent.max_rpm,
"i18n": agent.i18n.prompt_file,
"function_calling_llm": (
agent.function_calling_llm.model
if agent.function_calling_llm
getattr(
getattr(agent, "function_calling_llm", None),
"model",
"",
)
if getattr(agent, "function_calling_llm", None)
else ""
),
"llm": agent.llm.model,
"delegation_enabled?": agent.allow_delegation,
"allow_code_execution?": agent.allow_code_execution,
"max_retry_limit": agent.max_retry_limit,
"allow_code_execution?": getattr(
agent, "allow_code_execution", False
),
"max_retry_limit": getattr(agent, "max_retry_limit", 3),
"tools_names": [
tool.name.casefold() for tool in agent.tools or []
],
# Add agent fingerprint data if sharing crew details
"fingerprint": (
getattr(
getattr(agent, "fingerprint", None),
"uuid_str",
None,
)
),
"fingerprint_created_at": (
created_at.isoformat()
if (
created_at := getattr(
getattr(agent, "fingerprint", None),
"created_at",
None,
)
)
is not None
else None
),
}
for agent in crew.agents
]
@@ -169,6 +212,17 @@ class Telemetry:
"tools_names": [
tool.name.casefold() for tool in task.tools or []
],
# Add task fingerprint data if sharing crew details
"fingerprint": (
task.fingerprint.uuid_str
if hasattr(task, "fingerprint") and task.fingerprint
else None
),
"fingerprint_created_at": (
task.fingerprint.created_at.isoformat()
if hasattr(task, "fingerprint") and task.fingerprint
else None
),
}
for task in crew.tasks
]
@@ -196,14 +250,20 @@ class Telemetry:
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"function_calling_llm": (
agent.function_calling_llm.model
if agent.function_calling_llm
getattr(
getattr(agent, "function_calling_llm", None),
"model",
"",
)
if getattr(agent, "function_calling_llm", None)
else ""
),
"llm": agent.llm.model,
"delegation_enabled?": agent.allow_delegation,
"allow_code_execution?": agent.allow_code_execution,
"max_retry_limit": agent.max_retry_limit,
"allow_code_execution?": getattr(
agent, "allow_code_execution", False
),
"max_retry_limit": getattr(agent, "max_retry_limit", 3),
"tools_names": [
tool.name.casefold() for tool in agent.tools or []
],
@@ -252,6 +312,39 @@ class Telemetry:
self._add_attribute(created_span, "task_key", task.key)
self._add_attribute(created_span, "task_id", str(task.id))
# Add fingerprint data
if hasattr(crew, "fingerprint") and crew.fingerprint:
self._add_attribute(
created_span, "crew_fingerprint", crew.fingerprint.uuid_str
)
if hasattr(task, "fingerprint") and task.fingerprint:
self._add_attribute(
created_span, "task_fingerprint", task.fingerprint.uuid_str
)
self._add_attribute(
created_span,
"task_fingerprint_created_at",
task.fingerprint.created_at.isoformat(),
)
# Add fingerprint metadata if it exists
if hasattr(task.fingerprint, "metadata") and task.fingerprint.metadata:
self._add_attribute(
created_span,
"task_fingerprint_metadata",
json.dumps(task.fingerprint.metadata),
)
# Add agent fingerprint if task has an assigned agent
if hasattr(task, "agent") and task.agent:
agent_fingerprint = getattr(
getattr(task.agent, "fingerprint", None), "uuid_str", None
)
if agent_fingerprint:
self._add_attribute(
created_span, "agent_fingerprint", agent_fingerprint
)
if crew.share_crew:
self._add_attribute(
created_span, "formatted_description", task.description
@@ -270,6 +363,21 @@ class Telemetry:
self._add_attribute(span, "task_key", task.key)
self._add_attribute(span, "task_id", str(task.id))
# Add fingerprint data to execution span
if hasattr(crew, "fingerprint") and crew.fingerprint:
self._add_attribute(span, "crew_fingerprint", crew.fingerprint.uuid_str)
if hasattr(task, "fingerprint") and task.fingerprint:
self._add_attribute(span, "task_fingerprint", task.fingerprint.uuid_str)
# Add agent fingerprint if task has an assigned agent
if hasattr(task, "agent") and task.agent:
agent_fingerprint = getattr(
getattr(task.agent, "fingerprint", None), "uuid_str", None
)
if agent_fingerprint:
self._add_attribute(span, "agent_fingerprint", agent_fingerprint)
if crew.share_crew:
self._add_attribute(span, "formatted_description", task.description)
self._add_attribute(
@@ -291,7 +399,12 @@ class Telemetry:
Note:
If share_crew is enabled, this will also record the task output
"""
def operation():
# Ensure fingerprint data is present on completion span
if hasattr(task, "fingerprint") and task.fingerprint:
self._add_attribute(span, "task_fingerprint", task.fingerprint.uuid_str)
if crew.share_crew:
self._add_attribute(
span,
@@ -312,6 +425,7 @@ class Telemetry:
tool_name (str): Name of the tool being repeatedly used
attempts (int): Number of attempts made with this tool
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Tool Repeated Usage")
@@ -329,14 +443,16 @@ class Telemetry:
self._safe_telemetry_operation(operation)
def tool_usage(self, llm: Any, tool_name: str, attempts: int):
def tool_usage(self, llm: Any, tool_name: str, attempts: int, agent: Any = None):
"""Records the usage of a tool by an agent.
Args:
llm (Any): The language model being used
tool_name (str): Name of the tool being used
attempts (int): Number of attempts made with this tool
agent (Any, optional): The agent using the tool
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Tool Usage")
@@ -349,17 +465,31 @@ class Telemetry:
self._add_attribute(span, "attempts", attempts)
if llm:
self._add_attribute(span, "llm", llm.model)
# Add agent fingerprint data if available
if agent and hasattr(agent, "fingerprint") and agent.fingerprint:
self._add_attribute(
span, "agent_fingerprint", agent.fingerprint.uuid_str
)
if hasattr(agent, "role"):
self._add_attribute(span, "agent_role", agent.role)
span.set_status(Status(StatusCode.OK))
span.end()
self._safe_telemetry_operation(operation)
def tool_usage_error(self, llm: Any):
def tool_usage_error(
self, llm: Any, agent: Any = None, tool_name: Optional[str] = None
):
"""Records when a tool usage results in an error.
Args:
llm (Any): The language model being used when the error occurred
agent (Any, optional): The agent using the tool
tool_name (str, optional): Name of the tool that caused the error
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Tool Usage Error")
@@ -370,6 +500,18 @@ class Telemetry:
)
if llm:
self._add_attribute(span, "llm", llm.model)
if tool_name:
self._add_attribute(span, "tool_name", tool_name)
# Add agent fingerprint data if available
if agent and hasattr(agent, "fingerprint") and agent.fingerprint:
self._add_attribute(
span, "agent_fingerprint", agent.fingerprint.uuid_str
)
if hasattr(agent, "role"):
self._add_attribute(span, "agent_role", agent.role)
span.set_status(Status(StatusCode.OK))
span.end()
@@ -386,6 +528,7 @@ class Telemetry:
exec_time (int): Execution time in seconds
model_name (str): Name of the model used
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Crew Individual Test Result")
@@ -420,6 +563,7 @@ class Telemetry:
inputs (dict[str, Any] | None): Input parameters for the test
model_name (str): Name of the model used in testing
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Crew Test Execution")
@@ -446,6 +590,7 @@ class Telemetry:
def deploy_signup_error_span(self):
"""Records when an error occurs during the deployment signup process."""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Deploy Signup Error")
@@ -460,6 +605,7 @@ class Telemetry:
Args:
uuid (Optional[str]): Unique identifier for the deployment
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Start Deployment")
@@ -472,6 +618,7 @@ class Telemetry:
def create_crew_deployment_span(self):
"""Records the creation of a new crew deployment."""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Create Crew Deployment")
@@ -487,6 +634,7 @@ class Telemetry:
uuid (Optional[str]): Unique identifier for the crew
log_type (str, optional): Type of logs being retrieved. Defaults to "deployment".
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Get Crew Logs")
@@ -504,6 +652,7 @@ class Telemetry:
Args:
uuid (Optional[str]): Unique identifier for the crew being removed
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Remove Crew")
@@ -634,6 +783,7 @@ class Telemetry:
Args:
flow_name (str): Name of the flow being created
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Flow Creation")
@@ -650,6 +800,7 @@ class Telemetry:
flow_name (str): Name of the flow being plotted
node_names (list[str]): List of node names in the flow
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Flow Plotting")
@@ -667,6 +818,7 @@ class Telemetry:
flow_name (str): Name of the flow being executed
node_names (list[str]): List of nodes being executed in the flow
"""
def operation():
tracer = trace.get_tracer("crewai.telemetry")
span = tracer.start_span("Flow Execution")

15
src/crewai/tools/base_tool.py
View File

@@ -7,29 +7,27 @@ from pydantic import (
BaseModel,
ConfigDict,
Field,
PydanticDeprecatedSince20,
create_model,
validator,
field_validator,
)
from pydantic import BaseModel as PydanticBaseModel
from crewai.tools.structured_tool import CrewStructuredTool
# Ignore all "PydanticDeprecatedSince20" warnings globally
warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
class BaseTool(BaseModel, ABC):
class _ArgsSchemaPlaceholder(PydanticBaseModel):
pass
model_config = ConfigDict()
model_config = ConfigDict(arbitrary_types_allowed=True)
name: str
"""The unique name of the tool that clearly communicates its purpose."""
description: str
"""Used to tell the model how/when/why to use the tool."""
args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
args_schema: Type[PydanticBaseModel] = Field(
default_factory=_ArgsSchemaPlaceholder, validate_default=True
)
"""The schema for the arguments that the tool accepts."""
description_updated: bool = False
"""Flag to check if the description has been updated."""
@@ -38,7 +36,8 @@ class BaseTool(BaseModel, ABC):
result_as_answer: bool = False
"""Flag to check if the tool should be the final agent answer."""
@validator("args_schema", always=True, pre=True)
@field_validator("args_schema", mode="before")
@classmethod
def _default_args_schema(
cls, v: Type[PydanticBaseModel]
) -> Type[PydanticBaseModel]:

83
src/crewai/tools/tool_usage.py
View File

@@ -22,6 +22,7 @@ from crewai.utilities.events.tool_usage_events import (
ToolSelectionErrorEvent,
ToolUsageErrorEvent,
ToolUsageFinishedEvent,
ToolUsageStartedEvent,
ToolValidateInputErrorEvent,
)
@@ -69,6 +70,7 @@ class ToolUsage:
function_calling_llm: Any,
agent: Any,
action: Any,
fingerprint_context: Optional[Dict[str, str]] = None,
) -> None:
self._i18n: I18N = agent.i18n
self._printer: Printer = Printer()
@@ -85,6 +87,7 @@ class ToolUsage:
self.task = task
self.action = action
self.function_calling_llm = function_calling_llm
self.fingerprint_context = fingerprint_context or {}
# Set the maximum parsing attempts for bigger models
if (
@@ -117,7 +120,10 @@ class ToolUsage:
self._printer.print(content=f"\n\n{error}\n", color="red")
return error
if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]: # type: ignore
if (
isinstance(tool, CrewStructuredTool)
and tool.name == self._i18n.tools("add_image")["name"] # type: ignore
):
try:
result = self._use(tool_string=tool_string, tool=tool, calling=calling)
return result
@@ -181,18 +187,26 @@ class ToolUsage:
if calling.arguments:
try:
acceptable_args = tool.args_schema.model_json_schema()["properties"].keys() # type: ignore
acceptable_args = tool.args_schema.model_json_schema()[
"properties"
].keys() # type: ignore
arguments = {
k: v
for k, v in calling.arguments.items()
if k in acceptable_args
}
# Add fingerprint metadata if available
arguments = self._add_fingerprint_metadata(arguments)
result = tool.invoke(input=arguments)
except Exception:
arguments = calling.arguments
# Add fingerprint metadata if available
arguments = self._add_fingerprint_metadata(arguments)
result = tool.invoke(input=arguments)
else:
result = tool.invoke(input={})
# Add fingerprint metadata even to empty arguments
arguments = self._add_fingerprint_metadata({})
result = tool.invoke(input=arguments)
except Exception as e:
self.on_tool_error(tool=tool, tool_calling=calling, e=e)
self._run_attempts += 1
@@ -202,7 +216,7 @@ class ToolUsage:
error=e, tool=tool.name, tool_inputs=tool.description
)
error = ToolUsageErrorException(
f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
).message
self.task.increment_tools_errors()
if self.agent.verbose:
@@ -244,6 +258,7 @@ class ToolUsage:
tool_calling=calling,
from_cache=from_cache,
started_at=started_at,
result=result,
)
if (
@@ -380,7 +395,7 @@ class ToolUsage:
raise
else:
return ToolUsageErrorException(
f'{self._i18n.errors("tool_arguments_error")}'
f"{self._i18n.errors('tool_arguments_error')}"
)
if not isinstance(arguments, dict):
@@ -388,7 +403,7 @@ class ToolUsage:
raise
else:
return ToolUsageErrorException(
f'{self._i18n.errors("tool_arguments_error")}'
f"{self._i18n.errors('tool_arguments_error')}"
)
return ToolCalling(
@@ -416,7 +431,7 @@ class ToolUsage:
if self.agent.verbose:
self._printer.print(content=f"\n\n{e}\n", color="red")
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
)
return self._tool_calling(tool_string)
@@ -480,8 +495,13 @@ class ToolUsage:
"tool_name": self.action.tool,
"tool_args": str(self.action.tool_input),
"tool_class": self.__class__.__name__,
"agent": self.agent, # Adding agent for fingerprint extraction
}
# Include fingerprint context if available
if self.fingerprint_context:
tool_selection_data.update(self.fingerprint_context)
crewai_event_bus.emit(
self,
ToolValidateInputErrorEvent(**tool_selection_data, error=final_error),
@@ -492,7 +512,12 @@ class ToolUsage:
crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
def on_tool_use_finished(
self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
self,
tool: Any,
tool_calling: ToolCalling,
from_cache: bool,
started_at: float,
result: Any,
) -> None:
finished_at = time.time()
event_data = self._prepare_event_data(tool, tool_calling)
@@ -501,12 +526,13 @@ class ToolUsage:
"started_at": datetime.datetime.fromtimestamp(started_at),
"finished_at": datetime.datetime.fromtimestamp(finished_at),
"from_cache": from_cache,
"output": result,
}
)
crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
return {
event_data = {
"agent_key": self.agent.key,
"agent_role": (self.agent._original_role or self.agent.role),
"run_attempts": self._run_attempts,
@@ -514,4 +540,43 @@ class ToolUsage:
"tool_name": tool.name,
"tool_args": tool_calling.arguments,
"tool_class": tool.__class__.__name__,
"agent": self.agent, # Adding agent for fingerprint extraction
}
# Include fingerprint context if available
if self.fingerprint_context:
event_data.update(self.fingerprint_context)
return event_data
def _add_fingerprint_metadata(self, arguments: dict) -> dict:
"""Add fingerprint metadata to tool arguments if available.
Args:
arguments: The original tool arguments
Returns:
Updated arguments dictionary with fingerprint metadata
"""
# Create a shallow copy to avoid modifying the original
arguments = arguments.copy()
# Add security metadata under a designated key
if not "security_context" in arguments:
arguments["security_context"] = {}
security_context = arguments["security_context"]
# Add agent fingerprint if available
if hasattr(self, "agent") and hasattr(self.agent, "security_config"):
security_context["agent_fingerprint"] = self.agent.security_config.fingerprint.to_dict()
# Add task fingerprint if available
if hasattr(self, "task") and hasattr(self.task, "security_config"):
security_context["task_fingerprint"] = self.task.security_config.fingerprint.to_dict()
# Add crew fingerprint if available
if hasattr(self, "crew") and hasattr(self.crew, "security_config"):
security_context["crew_fingerprint"] = self.crew.security_config.fingerprint.to_dict()
return arguments

62
src/crewai/utilities/chromadb.py Normal file
View File

@@ -0,0 +1,62 @@
import re
from typing import Optional
MIN_COLLECTION_LENGTH = 3
MAX_COLLECTION_LENGTH = 63
DEFAULT_COLLECTION = "default_collection"
# Compiled regex patterns for better performance
INVALID_CHARS_PATTERN = re.compile(r"[^a-zA-Z0-9_-]")
IPV4_PATTERN = re.compile(r"^(\d{1,3}\.){3}\d{1,3}$")
def is_ipv4_pattern(name: str) -> bool:
"""
Check if a string matches an IPv4 address pattern.
Args:
name: The string to check
Returns:
True if the string matches an IPv4 pattern, False otherwise
"""
return bool(IPV4_PATTERN.match(name))
def sanitize_collection_name(name: Optional[str]) -> str:
"""
Sanitize a collection name to meet ChromaDB requirements:
1. 3-63 characters long
2. Starts and ends with alphanumeric character
3. Contains only alphanumeric characters, underscores, or hyphens
4. No consecutive periods
5. Not a valid IPv4 address
Args:
name: The original collection name to sanitize
Returns:
A sanitized collection name that meets ChromaDB requirements
"""
if not name:
return DEFAULT_COLLECTION
if is_ipv4_pattern(name):
name = f"ip_{name}"
sanitized = INVALID_CHARS_PATTERN.sub("_", name)
if not sanitized[0].isalnum():
sanitized = "a" + sanitized
if not sanitized[-1].isalnum():
sanitized = sanitized[:-1] + "z"
if len(sanitized) < MIN_COLLECTION_LENGTH:
sanitized = sanitized + "x" * (MIN_COLLECTION_LENGTH - len(sanitized))
if len(sanitized) > MAX_COLLECTION_LENGTH:
sanitized = sanitized[:MAX_COLLECTION_LENGTH]
if not sanitized[-1].isalnum():
sanitized = sanitized[:-1] + "z"
return sanitized

5
src/crewai/utilities/converter.py
View File

@@ -287,8 +287,9 @@ def generate_model_description(model: Type[BaseModel]) -> str:
else:
return str(field_type)
fields = model.__annotations__
fields = model.model_fields
field_descriptions = [
f'"{name}": {describe_field(type_)}' for name, type_ in fields.items()
f'"{name}": {describe_field(field.annotation)}'
for name, field in fields.items()
]
return "{\n " + ",\n ".join(field_descriptions) + "\n}"

2
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -45,7 +45,7 @@ class TaskEvaluator:
def evaluate(self, task, output) -> TaskEvaluation:
crewai_event_bus.emit(
self, TaskEvaluationEvent(evaluation_type="task_evaluation")
self, TaskEvaluationEvent(evaluation_type="task_evaluation", task=task)
)
evaluation_query = (
f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"

44
src/crewai/utilities/events/agent_events.py
View File

@@ -4,13 +4,13 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.tools.base_tool import BaseTool
from crewai.tools.structured_tool import CrewStructuredTool
from .base_events import CrewEvent
from .base_events import BaseEvent
if TYPE_CHECKING:
from crewai.agents.agent_builder.base_agent import BaseAgent
class AgentExecutionStartedEvent(CrewEvent):
class AgentExecutionStartedEvent(BaseEvent):
"""Event emitted when an agent starts executing a task"""
agent: BaseAgent
@@ -21,8 +21,20 @@ class AgentExecutionStartedEvent(CrewEvent):
model_config = {"arbitrary_types_allowed": True}
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the agent
if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str
self.source_type = "agent"
if (
hasattr(self.agent.fingerprint, "metadata")
and self.agent.fingerprint.metadata
):
self.fingerprint_metadata = self.agent.fingerprint.metadata
class AgentExecutionCompletedEvent(CrewEvent):
class AgentExecutionCompletedEvent(BaseEvent):
"""Event emitted when an agent completes executing a task"""
agent: BaseAgent
@@ -30,11 +42,35 @@ class AgentExecutionCompletedEvent(CrewEvent):
output: str
type: str = "agent_execution_completed"
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the agent
if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str
self.source_type = "agent"
if (
hasattr(self.agent.fingerprint, "metadata")
and self.agent.fingerprint.metadata
):
self.fingerprint_metadata = self.agent.fingerprint.metadata
class AgentExecutionErrorEvent(CrewEvent):
class AgentExecutionErrorEvent(BaseEvent):
"""Event emitted when an agent encounters an error during execution"""
agent: BaseAgent
task: Any
error: str
type: str = "agent_execution_error"
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the agent
if hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str
self.source_type = "agent"
if (
hasattr(self.agent.fingerprint, "metadata")
and self.agent.fingerprint.metadata
):
self.fingerprint_metadata = self.agent.fingerprint.metadata

22
src/crewai/utilities/events/base_events.py
View File

@@ -1,10 +1,28 @@
from datetime import datetime
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from crewai.utilities.serialization import to_serializable
class CrewEvent(BaseModel):
"""Base class for all crew events"""
class BaseEvent(BaseModel):
"""Base class for all events"""
timestamp: datetime = Field(default_factory=datetime.now)
type: str
source_fingerprint: Optional[str] = None # UUID string of the source entity
source_type: Optional[str] = None # "agent", "task", "crew"
fingerprint_metadata: Optional[Dict[str, Any]] = None # Any relevant metadata
def to_json(self, exclude: set[str] | None = None):
"""
Converts the event to a JSON-serializable dictionary.
Args:
exclude (set[str], optional): Set of keys to exclude from the result. Defaults to None.
Returns:
dict: A JSON-serializable dictionary.
"""
return to_serializable(self, exclude=exclude)

63
src/crewai/utilities/events/crew_events.py
View File

@@ -1,81 +1,102 @@
from typing import Any, Dict, Optional, Union
from typing import TYPE_CHECKING, Any, Dict, Optional, Union
from pydantic import InstanceOf
from crewai.utilities.events.base_events import BaseEvent
from crewai.utilities.events.base_events import CrewEvent
if TYPE_CHECKING:
from crewai.crew import Crew
else:
Crew = Any
class CrewKickoffStartedEvent(CrewEvent):
"""Event emitted when a crew starts execution"""
class CrewBaseEvent(BaseEvent):
"""Base class for crew events with fingerprint handling"""
crew_name: Optional[str]
crew: Optional[Crew] = None
def __init__(self, **data):
super().__init__(**data)
self.set_crew_fingerprint()
def set_crew_fingerprint(self) -> None:
if self.crew and hasattr(self.crew, "fingerprint") and self.crew.fingerprint:
self.source_fingerprint = self.crew.fingerprint.uuid_str
self.source_type = "crew"
if (
hasattr(self.crew.fingerprint, "metadata")
and self.crew.fingerprint.metadata
):
self.fingerprint_metadata = self.crew.fingerprint.metadata
def to_json(self, exclude: set[str] | None = None):
if exclude is None:
exclude = set()
exclude.add("crew")
return super().to_json(exclude=exclude)
class CrewKickoffStartedEvent(CrewBaseEvent):
"""Event emitted when a crew starts execution"""
inputs: Optional[Dict[str, Any]]
type: str = "crew_kickoff_started"
class CrewKickoffCompletedEvent(CrewEvent):
class CrewKickoffCompletedEvent(CrewBaseEvent):
"""Event emitted when a crew completes execution"""
crew_name: Optional[str]
output: Any
type: str = "crew_kickoff_completed"
class CrewKickoffFailedEvent(CrewEvent):
class CrewKickoffFailedEvent(CrewBaseEvent):
"""Event emitted when a crew fails to complete execution"""
error: str
crew_name: Optional[str]
type: str = "crew_kickoff_failed"
class CrewTrainStartedEvent(CrewEvent):
class CrewTrainStartedEvent(CrewBaseEvent):
"""Event emitted when a crew starts training"""
crew_name: Optional[str]
n_iterations: int
filename: str
inputs: Optional[Dict[str, Any]]
type: str = "crew_train_started"
class CrewTrainCompletedEvent(CrewEvent):
class CrewTrainCompletedEvent(CrewBaseEvent):
"""Event emitted when a crew completes training"""
crew_name: Optional[str]
n_iterations: int
filename: str
type: str = "crew_train_completed"
class CrewTrainFailedEvent(CrewEvent):
class CrewTrainFailedEvent(CrewBaseEvent):
"""Event emitted when a crew fails to complete training"""
error: str
crew_name: Optional[str]
type: str = "crew_train_failed"
class CrewTestStartedEvent(CrewEvent):
class CrewTestStartedEvent(CrewBaseEvent):
"""Event emitted when a crew starts testing"""
crew_name: Optional[str]
n_iterations: int
eval_llm: Optional[Union[str, Any]]
inputs: Optional[Dict[str, Any]]
type: str = "crew_test_started"
class CrewTestCompletedEvent(CrewEvent):
class CrewTestCompletedEvent(CrewBaseEvent):
"""Event emitted when a crew completes testing"""
crew_name: Optional[str]
type: str = "crew_test_completed"
class CrewTestFailedEvent(CrewEvent):
class CrewTestFailedEvent(CrewBaseEvent):
"""Event emitted when a crew fails to complete testing"""
error: str
crew_name: Optional[str]
type: str = "crew_test_failed"

8
src/crewai/utilities/events/crewai_event_bus.py
View File

@@ -4,10 +4,10 @@ from typing import Any, Callable, Dict, List, Type, TypeVar, cast
from blinker import Signal
from crewai.utilities.events.base_events import CrewEvent
from crewai.utilities.events.base_events import BaseEvent
from crewai.utilities.events.event_types import EventTypes
EventT = TypeVar("EventT", bound=CrewEvent)
EventT = TypeVar("EventT", bound=BaseEvent)
class CrewAIEventsBus:
@@ -30,7 +30,7 @@ class CrewAIEventsBus:
def _initialize(self) -> None:
"""Initialize the event bus internal state"""
self._signal = Signal("crewai_event_bus")
self._handlers: Dict[Type[CrewEvent], List[Callable]] = {}
self._handlers: Dict[Type[BaseEvent], List[Callable]] = {}
def on(
self, event_type: Type[EventT]
@@ -59,7 +59,7 @@ class CrewAIEventsBus:
return decorator
def emit(self, source: Any, event: CrewEvent) -> None:
def emit(self, source: Any, event: BaseEvent) -> None:
"""
Emit an event to all registered handlers

4
src/crewai/utilities/events/flow_events.py
View File

@@ -2,10 +2,10 @@ from typing import Any, Dict, Optional, Union
from pydantic import BaseModel, ConfigDict
from .base_events import CrewEvent
from .base_events import BaseEvent
class FlowEvent(CrewEvent):
class FlowEvent(BaseEvent):
"""Base class for all flow events"""
type: str

19
src/crewai/utilities/events/llm_events.py
View File

@@ -1,7 +1,7 @@
from enum import Enum
from typing import Any, Dict, List, Optional, Union
from crewai.utilities.events.base_events import CrewEvent
from crewai.utilities.events.base_events import BaseEvent
class LLMCallType(Enum):
@@ -11,17 +11,22 @@ class LLMCallType(Enum):
LLM_CALL = "llm_call"
class LLMCallStartedEvent(CrewEvent):
"""Event emitted when a LLM call starts"""
class LLMCallStartedEvent(BaseEvent):
"""Event emitted when a LLM call starts
Attributes:
messages: Content can be either a string or a list of dictionaries that support
multimodal content (text, images, etc.)
"""
type: str = "llm_call_started"
messages: Union[str, List[Dict[str, str]]]
messages: Union[str, List[Dict[str, Any]]]
tools: Optional[List[dict]] = None
callbacks: Optional[List[Any]] = None
available_functions: Optional[Dict[str, Any]] = None
class LLMCallCompletedEvent(CrewEvent):
class LLMCallCompletedEvent(BaseEvent):
"""Event emitted when a LLM call completes"""
type: str = "llm_call_completed"
@@ -29,14 +34,14 @@ class LLMCallCompletedEvent(CrewEvent):
call_type: LLMCallType
class LLMCallFailedEvent(CrewEvent):
class LLMCallFailedEvent(BaseEvent):
"""Event emitted when a LLM call fails"""
error: str
type: str = "llm_call_failed"
class LLMStreamChunkEvent(CrewEvent):
class LLMStreamChunkEvent(BaseEvent):
"""Event emitted when a streaming chunk is received"""
type: str = "llm_stream_chunk"

64
src/crewai/utilities/events/task_events.py
View File

@@ -1,32 +1,84 @@
from typing import Optional
from typing import Any, Optional
from crewai.tasks.task_output import TaskOutput
from crewai.utilities.events.base_events import CrewEvent
from crewai.utilities.events.base_events import BaseEvent
class TaskStartedEvent(CrewEvent):
class TaskStartedEvent(BaseEvent):
"""Event emitted when a task starts"""
type: str = "task_started"
context: Optional[str]
task: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the task
if hasattr(self.task, "fingerprint") and self.task.fingerprint:
self.source_fingerprint = self.task.fingerprint.uuid_str
self.source_type = "task"
if (
hasattr(self.task.fingerprint, "metadata")
and self.task.fingerprint.metadata
):
self.fingerprint_metadata = self.task.fingerprint.metadata
class TaskCompletedEvent(CrewEvent):
class TaskCompletedEvent(BaseEvent):
"""Event emitted when a task completes"""
output: TaskOutput
type: str = "task_completed"
task: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the task
if hasattr(self.task, "fingerprint") and self.task.fingerprint:
self.source_fingerprint = self.task.fingerprint.uuid_str
self.source_type = "task"
if (
hasattr(self.task.fingerprint, "metadata")
and self.task.fingerprint.metadata
):
self.fingerprint_metadata = self.task.fingerprint.metadata
class TaskFailedEvent(CrewEvent):
class TaskFailedEvent(BaseEvent):
"""Event emitted when a task fails"""
error: str
type: str = "task_failed"
task: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the task
if hasattr(self.task, "fingerprint") and self.task.fingerprint:
self.source_fingerprint = self.task.fingerprint.uuid_str
self.source_type = "task"
if (
hasattr(self.task.fingerprint, "metadata")
and self.task.fingerprint.metadata
):
self.fingerprint_metadata = self.task.fingerprint.metadata
class TaskEvaluationEvent(CrewEvent):
class TaskEvaluationEvent(BaseEvent):
"""Event emitted when a task evaluation is completed"""
type: str = "task_evaluation"
evaluation_type: str
task: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the task
if hasattr(self.task, "fingerprint") and self.task.fingerprint:
self.source_fingerprint = self.task.fingerprint.uuid_str
self.source_type = "task"
if (
hasattr(self.task.fingerprint, "metadata")
and self.task.fingerprint.metadata
):
self.fingerprint_metadata = self.task.fingerprint.metadata

35
src/crewai/utilities/events/tool_usage_events.py
View File

@@ -1,10 +1,10 @@
from datetime import datetime
from typing import Any, Callable, Dict
from typing import Any, Callable, Dict, Optional
from .base_events import CrewEvent
from .base_events import BaseEvent
class ToolUsageEvent(CrewEvent):
class ToolUsageEvent(BaseEvent):
"""Base event for tool usage tracking"""
agent_key: str
@@ -14,9 +14,22 @@ class ToolUsageEvent(CrewEvent):
tool_class: str
run_attempts: int | None = None
delegations: int | None = None
agent: Optional[Any] = None
model_config = {"arbitrary_types_allowed": True}
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the agent
if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str
self.source_type = "agent"
if (
hasattr(self.agent.fingerprint, "metadata")
and self.agent.fingerprint.metadata
):
self.fingerprint_metadata = self.agent.fingerprint.metadata
class ToolUsageStartedEvent(ToolUsageEvent):
"""Event emitted when a tool execution is started"""
@@ -30,6 +43,7 @@ class ToolUsageFinishedEvent(ToolUsageEvent):
started_at: datetime
finished_at: datetime
from_cache: bool = False
output: Any
type: str = "tool_usage_finished"
@@ -54,7 +68,7 @@ class ToolSelectionErrorEvent(ToolUsageEvent):
type: str = "tool_selection_error"
class ToolExecutionErrorEvent(CrewEvent):
class ToolExecutionErrorEvent(BaseEvent):
"""Event emitted when a tool execution encounters an error"""
error: Any
@@ -62,3 +76,16 @@ class ToolExecutionErrorEvent(CrewEvent):
tool_name: str
tool_args: Dict[str, Any]
tool_class: Callable
agent: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
# Set fingerprint data from the agent
if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str
self.source_type = "agent"
if (
hasattr(self.agent.fingerprint, "metadata")
and self.agent.fingerprint.metadata
):
self.fingerprint_metadata = self.agent.fingerprint.metadata

8
src/crewai/utilities/events/utils/console_formatter.py
View File

@@ -507,9 +507,10 @@ class ConsoleFormatter:
# Remove the thinking status node when complete
if "Thinking" in str(tool_branch.label):
agent_branch.children.remove(tool_branch)
self.print(crew_tree)
self.print()
if tool_branch in agent_branch.children:
agent_branch.children.remove(tool_branch)
self.print(crew_tree)
self.print()
def handle_llm_call_failed(
self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -587,6 +588,7 @@ class ConsoleFormatter:
for child in flow_tree.children:
if "Running tests" in str(child.label):
child.label = Text("✅ Tests completed successfully", style="green")
break
self.print(flow_tree)
self.print()

52
src/crewai/flow/state_utils.py → src/crewai/utilities/serialization.py
View File

@@ -1,38 +1,21 @@
import json
import uuid
from datetime import date, datetime
from typing import Any, Dict, List, Union
from pydantic import BaseModel
from crewai.flow import Flow
SerializablePrimitive = Union[str, int, float, bool, None]
Serializable = Union[
SerializablePrimitive, List["Serializable"], Dict[str, "Serializable"]
]
def export_state(flow: Flow) -> dict[str, Serializable]:
"""Exports the Flow's internal state as JSON-compatible data structures.
Performs a one-way transformation of a Flow's state into basic Python types
that can be safely serialized to JSON. To prevent infinite recursion with
circular references, the conversion is limited to a depth of 5 levels.
Args:
flow: The Flow object whose state needs to be exported
Returns:
dict[str, Any]: The transformed state using JSON-compatible Python
types.
"""
result = to_serializable(flow._state)
assert isinstance(result, dict)
return result
def to_serializable(
obj: Any, max_depth: int = 5, _current_depth: int = 0
obj: Any,
exclude: set[str] | None = None,
max_depth: int = 5,
_current_depth: int = 0,
) -> Serializable:
"""Converts a Python object into a JSON-compatible representation.
@@ -42,6 +25,7 @@ def to_serializable(
Args:
obj (Any): Object to transform.
exclude (set[str], optional): Set of keys to exclude from the result.
max_depth (int, optional): Maximum recursion depth. Defaults to 5.
Returns:
@@ -50,21 +34,39 @@ def to_serializable(
if _current_depth >= max_depth:
return repr(obj)
if exclude is None:
exclude = set()
if isinstance(obj, (str, int, float, bool, type(None))):
return obj
elif isinstance(obj, uuid.UUID):
return str(obj)
elif isinstance(obj, (date, datetime)):
return obj.isoformat()
elif isinstance(obj, (list, tuple, set)):
return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
return [
to_serializable(
item, max_depth=max_depth, _current_depth=_current_depth + 1
)
for item in obj
]
elif isinstance(obj, dict):
return {
_to_serializable_key(key): to_serializable(
value, max_depth, _current_depth + 1
obj=value,
exclude=exclude,
max_depth=max_depth,
_current_depth=_current_depth + 1,
)
for key, value in obj.items()
if key not in exclude
}
elif isinstance(obj, BaseModel):
return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
return to_serializable(
obj=obj.model_dump(exclude=exclude),
max_depth=max_depth,
_current_depth=_current_depth + 1,
)
else:
return repr(obj)

32
tests/agent_test.py
View File

@@ -1621,6 +1621,38 @@ def test_agent_with_knowledge_sources():
assert "red" in result.raw.lower()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_with_knowledge_sources_extensive_role():
content = "Brandon's favorite color is red and he likes Mexican food."
string_source = StringKnowledgeSource(content=content)
with patch(
"crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
) as MockKnowledge:
mock_knowledge_instance = MockKnowledge.return_value
mock_knowledge_instance.sources = [string_source]
mock_knowledge_instance.query.return_value = [{"content": content}]
agent = Agent(
role="Information Agent with extensive role description that is longer than 80 characters",
goal="Provide information based on knowledge sources",
backstory="You have access to specific knowledge sources.",
llm=LLM(model="gpt-4o-mini"),
knowledge_sources=[string_source],
)
task = Task(
description="What is Brandon's favorite color?",
expected_output="Brandon's favorite color.",
agent=agent,
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
assert "red" in result.raw.lower()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_with_knowledge_sources_works_with_copy():
content = "Brandon's favorite color is red and he likes Mexican food."

382
tests/cassettes/test_agent_with_knowledge_sources_extensive_role.yaml Normal file
View File

File diff suppressed because one or more lines are too long

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tests/cassettes/test_before_kickoff_callback.yaml
View File

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89
tests/crew_test.py
View File

@@ -11,7 +11,9 @@ import pydantic_core
import pytest
from crewai.agent import Agent
from crewai.agents import CacheHandler
from crewai.agents.cache import CacheHandler
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.crew import Crew
from crewai.crews.crew_output import CrewOutput
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -3731,6 +3733,44 @@ def test_multimodal_agent_image_tool_handling():
assert result["content"][1]["type"] == "image_url"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_multimodal_agent_describing_image_successfully():
"""
Test that a multimodal agent can process images without validation errors.
This test reproduces the scenario from issue #2475.
"""
llm = LLM(model="openai/gpt-4o", temperature=0.7) # model with vision capabilities
expert_analyst = Agent(
role="Visual Quality Inspector",
goal="Perform detailed quality analysis of product images",
backstory="Senior quality control expert with expertise in visual inspection",
llm=llm,
verbose=True,
allow_delegation=False,
multimodal=True,
)
inspection_task = Task(
description="""
Analyze the product image at https://www.us.maguireshoes.com/cdn/shop/files/FW24-Edito-Lucena-Distressed-01_1920x.jpg?v=1736371244 with focus on:
1. Quality of materials
2. Manufacturing defects
3. Compliance with standards
Provide a detailed report highlighting any issues found.
""",
expected_output="A detailed report highlighting any issues found",
agent=expert_analyst,
)
crew = Crew(agents=[expert_analyst], tasks=[inspection_task])
result = crew.kickoff()
task_output = result.tasks_output[0]
assert isinstance(task_output, TaskOutput)
assert task_output.raw == result.raw
@pytest.mark.vcr(filter_headers=["authorization"])
def test_multimodal_agent_live_image_analysis():
"""
@@ -4025,3 +4065,52 @@ def test_crew_with_knowledge_sources_works_with_copy():
assert len(crew_copy.tasks) == len(crew.tasks)
assert len(crew_copy.tasks) == len(crew.tasks)
def test_crew_kickoff_for_each_works_with_manager_agent_copy():
researcher = Agent(
role="Researcher",
goal="Conduct thorough research and analysis on AI and AI agents",
backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
allow_delegation=False
)
writer = Agent(
role="Senior Writer",
goal="Create compelling content about AI and AI agents",
backstory="You're a senior writer, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently writing content for a new client.",
allow_delegation=False
)
# Define task
task = Task(
description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
expected_output="5 bullet points, each with a paragraph and accompanying notes.",
)
# Define manager agent
manager = Agent(
role="Project Manager",
goal="Efficiently manage the crew and ensure high-quality task completion",
backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
allow_delegation=True
)
# Instantiate crew with a custom manager
crew = Crew(
agents=[researcher, writer],
tasks=[task],
manager_agent=manager,
process=Process.hierarchical,
verbose=True
)
crew_copy = crew.copy()
assert crew_copy.manager_agent is not None
assert crew_copy.manager_agent.id != crew.manager_agent.id
assert crew_copy.manager_agent.role == crew.manager_agent.role
assert crew_copy.manager_agent.goal == crew.manager_agent.goal
assert crew_copy.manager_agent.backstory == crew.manager_agent.backstory
assert isinstance(crew_copy.manager_agent.agent_executor, CrewAgentExecutor)
assert isinstance(crew_copy.manager_agent.cache_handler, CacheHandler)

68
tests/memory/user_memory_test.py Normal file
View File

@@ -0,0 +1,68 @@
from unittest.mock import MagicMock, patch
import pytest
from mem0.memory.main import Memory
from crewai.memory.user.user_memory import UserMemory
from crewai.memory.user.user_memory_item import UserMemoryItem
class MockCrew:
def __init__(self, memory_config):
self.memory_config = memory_config
@pytest.fixture
def user_memory():
"""Fixture to create a UserMemory instance"""
crew = MockCrew(
memory_config={
"provider": "mem0",
"config": {"user_id": "john"},
"user_memory" : {}
}
)
user_memory = MagicMock(spec=UserMemory)
with patch.object(Memory,'__new__',return_value=user_memory):
user_memory_instance = UserMemory(crew=crew)
return user_memory_instance
def test_save_and_search(user_memory):
memory = UserMemoryItem(
data="""test value test value test value test value test value test value
test value test value test value test value test value test value
test value test value test value test value test value test value""",
user="test_user",
metadata={"task": "test_task"},
)
with patch.object(UserMemory, "save") as mock_save:
user_memory.save(
value=memory.data,
metadata=memory.metadata,
user=memory.user
)
mock_save.assert_called_once_with(
value=memory.data,
metadata=memory.metadata,
user=memory.user
)
expected_result = [
{
"context": memory.data,
"metadata": {"agent": "test_agent"},
"score": 0.95,
}
]
expected_result = ["mocked_result"]
# Use patch.object to mock UserMemory's search method
with patch.object(UserMemory, 'search', return_value=expected_result) as mock_search:
find = UserMemory.search("test value", score_threshold=0.01)[0]
mock_search.assert_called_once_with("test value", score_threshold=0.01)
assert find == expected_result[0]

114
tests/storage/test_mem0_storage.py Normal file
View File

@@ -0,0 +1,114 @@
import os
from unittest.mock import MagicMock, patch
import pytest
from mem0.client.main import MemoryClient
from mem0.memory.main import Memory
from crewai.agent import Agent
from crewai.crew import Crew
from crewai.memory.storage.mem0_storage import Mem0Storage
from crewai.task import Task
# Define the class (if not already defined)
class MockCrew:
def __init__(self, memory_config):
self.memory_config = memory_config
@pytest.fixture
def mock_mem0_memory():
"""Fixture to create a mock Memory instance"""
mock_memory = MagicMock(spec=Memory)
return mock_memory
@pytest.fixture
def mem0_storage_with_mocked_config(mock_mem0_memory):
"""Fixture to create a Mem0Storage instance with mocked dependencies"""
# Patch the Memory class to return our mock
with patch('mem0.memory.main.Memory.from_config', return_value=mock_mem0_memory):
config = {
"vector_store": {
"provider": "mock_vector_store",
"config": {
"host": "localhost",
"port": 6333
}
},
"llm": {
"provider": "mock_llm",
"config": {
"api_key": "mock-api-key",
"model": "mock-model"
}
},
"embedder": {
"provider": "mock_embedder",
"config": {
"api_key": "mock-api-key",
"model": "mock-model"
}
},
"graph_store": {
"provider": "mock_graph_store",
"config": {
"url": "mock-url",
"username": "mock-user",
"password": "mock-password"
}
},
"history_db_path": "/mock/path",
"version": "test-version",
"custom_fact_extraction_prompt": "mock prompt 1",
"custom_update_memory_prompt": "mock prompt 2"
}
# Instantiate the class with memory_config
crew = MockCrew(
memory_config={
"provider": "mem0",
"config": {"user_id": "test_user", "local_mem0_config": config},
}
)
mem0_storage = Mem0Storage(type="short_term", crew=crew)
return mem0_storage
def test_mem0_storage_initialization(mem0_storage_with_mocked_config, mock_mem0_memory):
"""Test that Mem0Storage initializes correctly with the mocked config"""
assert mem0_storage_with_mocked_config.memory_type == "short_term"
assert mem0_storage_with_mocked_config.memory is mock_mem0_memory
@pytest.fixture
def mock_mem0_memory_client():
"""Fixture to create a mock MemoryClient instance"""
mock_memory = MagicMock(spec=MemoryClient)
return mock_memory
@pytest.fixture
def mem0_storage_with_memory_client(mock_mem0_memory_client):
"""Fixture to create a Mem0Storage instance with mocked dependencies"""
# We need to patch the MemoryClient before it's instantiated
with patch.object(MemoryClient, '__new__', return_value=mock_mem0_memory_client):
crew = MockCrew(
memory_config={
"provider": "mem0",
"config": {"user_id": "test_user", "api_key": "ABCDEFGH", "org_id": "my_org_id", "project_id": "my_project_id"},
}
)
mem0_storage = Mem0Storage(type="short_term", crew=crew)
return mem0_storage
def test_mem0_storage_with_memory_client_initialization(mem0_storage_with_memory_client, mock_mem0_memory_client):
"""Test Mem0Storage initialization with MemoryClient"""
assert mem0_storage_with_memory_client.memory_type == "short_term"
assert mem0_storage_with_memory_client.memory is mock_mem0_memory_client

19
tests/task_test.py
View File

@@ -787,6 +787,25 @@ def test_conditional_task_definition_based_on_dict():
assert task.agent is None
def test_conditional_task_copy_preserves_type():
task_config = {
"description": "Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work', check examples to based your evaluation.",
"expected_output": "The score of the title.",
}
original_task = Task(**task_config)
copied_task = original_task.copy(agents=[], task_mapping={})
assert isinstance(copied_task, Task)
original_conditional_config = {
"description": "Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'. Check examples to base your evaluation on.",
"expected_output": "The score of the title.",
"condition": lambda x: True,
}
original_conditional_task = ConditionalTask(**original_conditional_config)
copied_conditional_task = original_conditional_task.copy(agents=[], task_mapping={})
assert isinstance(copied_conditional_task, ConditionalTask)
def test_interpolate_inputs():
task = Task(
description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",

46
tests/test_multimodal_validation.py Normal file
View File

@@ -0,0 +1,46 @@
import os
import pytest
from crewai import LLM, Agent, Crew, Task
@pytest.mark.skip(reason="Only run manually with valid API keys")
def test_multimodal_agent_with_image_url():
"""
Test that a multimodal agent can process images without validation errors.
This test reproduces the scenario from issue #2475.
"""
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
pytest.skip("OPENAI_API_KEY environment variable not set")
llm = LLM(
model="openai/gpt-4o", # model with vision capabilities
api_key=OPENAI_API_KEY,
temperature=0.7
)
expert_analyst = Agent(
role="Visual Quality Inspector",
goal="Perform detailed quality analysis of product images",
backstory="Senior quality control expert with expertise in visual inspection",
llm=llm,
verbose=True,
allow_delegation=False,
multimodal=True
)
inspection_task = Task(
description="""
Analyze the product image at https://www.us.maguireshoes.com/collections/spring-25/products/lucena-black-boot with focus on:
1. Quality of materials
2. Manufacturing defects
3. Compliance with standards
Provide a detailed report highlighting any issues found.
""",
expected_output="A detailed report highlighting any issues found",
agent=expert_analyst
)
crew = Crew(agents=[expert_analyst], tasks=[inspection_task])

161
tests/tools/test_tool_usage.py
View File

@@ -1,5 +1,7 @@
import datetime
import json
import random
import time
from unittest.mock import MagicMock, patch
import pytest
@@ -11,6 +13,7 @@ from crewai.tools.tool_usage import ToolUsage
from crewai.utilities.events import crewai_event_bus
from crewai.utilities.events.tool_usage_events import (
ToolSelectionErrorEvent,
ToolUsageFinishedEvent,
ToolValidateInputErrorEvent,
)
@@ -624,3 +627,161 @@ def test_tool_validate_input_error_event():
assert event.agent_role == "test_role"
assert event.tool_name == "test_tool"
assert "must be a valid dictionary" in event.error
def test_tool_usage_finished_event_with_result():
"""Test that ToolUsageFinishedEvent is emitted with correct result attributes."""
# Create mock agent with proper string values
mock_agent = MagicMock()
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
# Create mock tool
class TestTool(BaseTool):
name: str = "Test Tool"
description: str = "A test tool"
def _run(self, input: dict) -> str:
return "test result"
test_tool = TestTool()
# Create mock tool calling
mock_tool_calling = MagicMock()
mock_tool_calling.arguments = {"arg1": "value1"}
# Create ToolUsage instance
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
action=MagicMock(),
)
# Track received events
received_events = []
@crewai_event_bus.on(ToolUsageFinishedEvent)
def event_handler(source, event):
received_events.append(event)
# Call on_tool_use_finished with test data
started_at = time.time()
result = "test output result"
tool_usage.on_tool_use_finished(
tool=test_tool,
tool_calling=mock_tool_calling,
from_cache=False,
started_at=started_at,
result=result,
)
# Verify event was emitted
assert len(received_events) == 1, "Expected one event to be emitted"
event = received_events[0]
assert isinstance(event, ToolUsageFinishedEvent)
# Verify event attributes
assert event.agent_key == "test_agent_key"
assert event.agent_role == "test_agent_role"
assert event.tool_name == "Test Tool"
assert event.tool_args == {"arg1": "value1"}
assert event.tool_class == "TestTool"
assert event.run_attempts == 1 # Default value from ToolUsage
assert event.delegations == 0
assert event.from_cache is False
assert event.output == "test output result"
assert isinstance(event.started_at, datetime.datetime)
assert isinstance(event.finished_at, datetime.datetime)
assert event.type == "tool_usage_finished"
def test_tool_usage_finished_event_with_cached_result():
"""Test that ToolUsageFinishedEvent is emitted with correct result attributes when using cached result."""
# Create mock agent with proper string values
mock_agent = MagicMock()
mock_agent.key = "test_agent_key"
mock_agent.role = "test_agent_role"
mock_agent._original_role = "test_agent_role"
mock_agent.i18n = MagicMock()
mock_agent.verbose = False
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
# Create mock tool
class TestTool(BaseTool):
name: str = "Test Tool"
description: str = "A test tool"
def _run(self, input: dict) -> str:
return "test result"
test_tool = TestTool()
# Create mock tool calling
mock_tool_calling = MagicMock()
mock_tool_calling.arguments = {"arg1": "value1"}
# Create ToolUsage instance
tool_usage = ToolUsage(
tools_handler=MagicMock(),
tools=[test_tool],
original_tools=[test_tool],
tools_description="Test Tool Description",
tools_names="Test Tool",
task=mock_task,
function_calling_llm=None,
agent=mock_agent,
action=MagicMock(),
)
# Track received events
received_events = []
@crewai_event_bus.on(ToolUsageFinishedEvent)
def event_handler(source, event):
received_events.append(event)
# Call on_tool_use_finished with test data and from_cache=True
started_at = time.time()
result = "cached test output result"
tool_usage.on_tool_use_finished(
tool=test_tool,
tool_calling=mock_tool_calling,
from_cache=True,
started_at=started_at,
result=result,
)
# Verify event was emitted
assert len(received_events) == 1, "Expected one event to be emitted"
event = received_events[0]
assert isinstance(event, ToolUsageFinishedEvent)
# Verify event attributes
assert event.agent_key == "test_agent_key"
assert event.agent_role == "test_agent_role"
assert event.tool_name == "Test Tool"
assert event.tool_args == {"arg1": "value1"}
assert event.tool_class == "TestTool"
assert event.run_attempts == 1 # Default value from ToolUsage
assert event.delegations == 0
assert event.from_cache is True
assert event.output == "cached test output result"
assert isinstance(event.started_at, datetime.datetime)
assert isinstance(event.finished_at, datetime.datetime)
assert event.type == "tool_usage_finished"

6
tests/utilities/events/test_crewai_event_bus.py
View File

@@ -1,10 +1,10 @@
from unittest.mock import Mock
from crewai.utilities.events.base_events import CrewEvent
from crewai.utilities.events.base_events import BaseEvent
from crewai.utilities.events.crewai_event_bus import crewai_event_bus
class TestEvent(CrewEvent):
class TestEvent(BaseEvent):
pass
@@ -24,7 +24,7 @@ def test_specific_event_handler():
def test_wildcard_event_handler():
mock_handler = Mock()
@crewai_event_bus.on(CrewEvent)
@crewai_event_bus.on(BaseEvent)
def handler(source, event):
mock_handler(source, event)

81
tests/utilities/test_chromadb_utils.py Normal file
View File

@@ -0,0 +1,81 @@
import unittest
from typing import Any, Dict, List, Union
import pytest
from crewai.utilities.chromadb import (
MAX_COLLECTION_LENGTH,
MIN_COLLECTION_LENGTH,
is_ipv4_pattern,
sanitize_collection_name,
)
class TestChromadbUtils(unittest.TestCase):
def test_sanitize_collection_name_long_name(self):
"""Test sanitizing a very long collection name."""
long_name = "This is an extremely long role name that will definitely exceed the ChromaDB collection name limit of 63 characters and cause an error when used as a collection name"
sanitized = sanitize_collection_name(long_name)
self.assertLessEqual(len(sanitized), MAX_COLLECTION_LENGTH)
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())
self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
def test_sanitize_collection_name_special_chars(self):
"""Test sanitizing a name with special characters."""
special_chars = "Agent@123!#$%^&*()"
sanitized = sanitize_collection_name(special_chars)
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())
self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
def test_sanitize_collection_name_short_name(self):
"""Test sanitizing a very short name."""
short_name = "A"
sanitized = sanitize_collection_name(short_name)
self.assertGreaterEqual(len(sanitized), MIN_COLLECTION_LENGTH)
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())
def test_sanitize_collection_name_bad_ends(self):
"""Test sanitizing a name with non-alphanumeric start/end."""
bad_ends = "_Agent_"
sanitized = sanitize_collection_name(bad_ends)
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())
def test_sanitize_collection_name_none(self):
"""Test sanitizing a None value."""
sanitized = sanitize_collection_name(None)
self.assertEqual(sanitized, "default_collection")
def test_sanitize_collection_name_ipv4_pattern(self):
"""Test sanitizing an IPv4 address."""
ipv4 = "192.168.1.1"
sanitized = sanitize_collection_name(ipv4)
self.assertTrue(sanitized.startswith("ip_"))
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())
self.assertTrue(all(c.isalnum() or c in ["_", "-"] for c in sanitized))
def test_is_ipv4_pattern(self):
"""Test IPv4 pattern detection."""
self.assertTrue(is_ipv4_pattern("192.168.1.1"))
self.assertFalse(is_ipv4_pattern("not.an.ip.address"))
def test_sanitize_collection_name_properties(self):
"""Test that sanitized collection names always meet ChromaDB requirements."""
test_cases = [
"A" * 100, # Very long name
"_start_with_underscore",
"end_with_underscore_",
"contains@special#characters",
"192.168.1.1", # IPv4 address
"a" * 2, # Too short
]
for test_case in test_cases:
sanitized = sanitize_collection_name(test_case)
self.assertGreaterEqual(len(sanitized), MIN_COLLECTION_LENGTH)
self.assertLessEqual(len(sanitized), MAX_COLLECTION_LENGTH)
self.assertTrue(sanitized[0].isalnum())
self.assertTrue(sanitized[-1].isalnum())

76
tests/flow/test_state_utils.py → tests/utilities/test_serialization.py
View File

@@ -5,8 +5,7 @@ from unittest.mock import Mock
import pytest
from pydantic import BaseModel
from crewai.flow import Flow
from crewai.flow.state_utils import export_state, to_string
from crewai.utilities.serialization import to_serializable, to_string
class Address(BaseModel):
@@ -23,16 +22,6 @@ class Person(BaseModel):
skills: List[str]
@pytest.fixture
def mock_flow():
def create_flow(state):
flow = Mock(spec=Flow)
flow._state = state
return flow
return create_flow
@pytest.mark.parametrize(
"test_input,expected",
[
@@ -47,9 +36,8 @@ def mock_flow():
({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
],
)
def test_basic_serialization(mock_flow, test_input, expected):
flow = mock_flow(test_input)
result = export_state(flow)
def test_basic_serialization(test_input, expected):
result = to_serializable(test_input)
assert result == expected
@@ -60,9 +48,8 @@ def test_basic_serialization(mock_flow, test_input, expected):
(datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
],
)
def test_temporal_serialization(mock_flow, input_date, expected):
flow = mock_flow({"date": input_date})
result = export_state(flow)
def test_temporal_serialization(input_date, expected):
result = to_serializable({"date": input_date})
assert result["date"] == expected
@@ -75,9 +62,8 @@ def test_temporal_serialization(mock_flow, input_date, expected):
("normal", "value", str),
],
)
def test_dictionary_key_serialization(mock_flow, key, value, expected_key_type):
flow = mock_flow({key: value})
result = export_state(flow)
def test_dictionary_key_serialization(key, value, expected_key_type):
result = to_serializable({key: value})
assert len(result) == 1
result_key = next(iter(result.keys()))
assert isinstance(result_key, expected_key_type)
@@ -91,14 +77,13 @@ def test_dictionary_key_serialization(mock_flow, key, value, expected_key_type):
(str.upper, "upper"),
],
)
def test_callable_serialization(mock_flow, callable_obj, expected_in_result):
flow = mock_flow({"func": callable_obj})
result = export_state(flow)
def test_callable_serialization(callable_obj, expected_in_result):
result = to_serializable({"func": callable_obj})
assert isinstance(result["func"], str)
assert expected_in_result in result["func"].lower()
def test_pydantic_model_serialization(mock_flow):
def test_pydantic_model_serialization():
address = Address(street="123 Main St", city="Tech City", country="Pythonia")
person = Person(
@@ -109,23 +94,21 @@ def test_pydantic_model_serialization(mock_flow):
skills=["Python", "Testing"],
)
flow = mock_flow(
{
"single_model": address,
"nested_model": person,
"model_list": [address, address],
"model_dict": {"home": address},
}
)
data = {
"single_model": address,
"nested_model": person,
"model_list": [address, address],
"model_dict": {"home": address},
}
result = export_state(flow)
result = to_serializable(data)
assert (
to_string(result)
== '{"single_model": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "nested_model": {"name": "John Doe", "age": 30, "address": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "birthday": "1994-01-01", "skills": ["Python", "Testing"]}, "model_list": [{"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}], "model_dict": {"home": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}}}'
)
def test_depth_limit(mock_flow):
def test_depth_limit():
"""Test max depth handling with a deeply nested structure"""
def create_nested(depth):
@@ -134,8 +117,7 @@ def test_depth_limit(mock_flow):
return {"next": create_nested(depth - 1)}
deep_structure = create_nested(10)
flow = mock_flow(deep_structure)
result = export_state(flow)
result = to_serializable(deep_structure)
assert result == {
"next": {
@@ -148,3 +130,23 @@ def test_depth_limit(mock_flow):
}
}
}
def test_exclude_keys():
result = to_serializable({"key1": "value1", "key2": "value2"}, exclude={"key1"})
assert result == {"key2": "value2"}
model = Person(
name="John Doe",
age=30,
address=Address(street="123 Main St", city="Tech City", country="Pythonia"),
birthday=date(1994, 1, 1),
skills=["Python", "Testing"],
)
result = to_serializable(model, exclude={"address"})
assert result == {
"name": "John Doe",
"age": 30,
"birthday": "1994-01-01",
"skills": ["Python", "Testing"],
}

421
uv.lock generated
View File

@@ -1,42 +1,19 @@
version = 1
revision = 1
requires-python = ">=3.10, <3.13"
resolution-markers = [
"python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
"(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
"(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
"python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
"(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
"python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'",
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
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version = "1.2.2.post1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "os_name == 'nt'" },
{ name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
{ name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
{ name = "packaging" },
{ name = "pyproject-hooks" },
@@ -580,7 +556,7 @@ name = "click"
version = "8.1.8"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "platform_system == 'Windows'" },
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