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Merge branch 'main' into intergrate-mem0

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Dev-Khant
2024-10-02 00:09:47 +05:30
parent 6e13c0b8ff 96427c1dd2
commit bb90718bb8
162 changed files with 59329 additions and 66356 deletions
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3
.gitignore vendored
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@@ -2,6 +2,7 @@
.pytest_cache
__pycache__
dist/
lib/
.env
assets/*
.idea
@@ -15,4 +16,4 @@ rc-tests/*
*.pkl
temp/*
.vscode/*
crew_tasks_output.json
crew_tasks_output.json

2
docs/core-concepts/Agents.md
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@@ -36,7 +36,6 @@ description: What are crewAI Agents and how to use them.
| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |
| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
| **Use Stop Words** *(optional)* | `use_stop_words` | Adds the ability to not use stop words (to support o1 models). Default is `True`. |
| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |
@@ -79,7 +78,6 @@ agent = Agent(
callbacks=[callback1, callback2], # Optional
allow_code_execution=True, # Optional
max_retry_limit=2, # Optional
use_stop_words=True, # Optional
use_system_prompt=True, # Optional
respect_context_window=True, # Optional
)

4
docs/core-concepts/Pipeline.md
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@@ -248,7 +248,7 @@ main_pipeline = Pipeline(stages=[classification_crew, email_router])
inputs = [{"email": "..."}, {"email": "..."}] # List of email data
main_pipeline.kickoff(inputs=inputs=inputs)
main_pipeline.kickoff(inputs=inputs)
```
In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7, it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
@@ -265,4 +265,4 @@ In this example, the router decides between an urgent pipeline and a normal pipe
The `Pipeline` class includes validation mechanisms to ensure the robustness of the pipeline structure:
- Validates that stages contain only Crew instances or lists of Crew instances.
- Prevents double nesting of stages to maintain a clear structure.
- Prevents double nesting of stages to maintain a clear structure.

3
docs/core-concepts/Tasks.md
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@@ -1,4 +1,3 @@
```markdown
---
title: crewAI Tasks
description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
@@ -314,4 +313,4 @@ save_output_task = Task(
## Conclusion
Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

1
docs/how-to/Customizing-Agents.md
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@@ -20,7 +20,6 @@ Crafting an efficient CrewAI team hinges on the ability to dynamically tailor yo
- **System Template** *(Optional)*: `system_template` defines the system format for the agent.
- **Prompt Template** *(Optional)*: `prompt_template` defines the prompt format for the agent.
- **Response Template** *(Optional)*: `response_template` defines the response format for the agent.
- **Use Stop Words** *(Optional)*: `use_stop_words` attribute controls whether the agent will use stop words during task execution. This is now supported to aid o1 models.
- **Use System Prompt** *(Optional)*: `use_system_prompt` controls whether the agent will use a system prompt for task execution. Agents can now operate without system prompts.
- **Respect Context Window**: `respect_context_window` renames the sliding context window attribute and enables it by default to maintain context size.
- **Max Retry Limit**: `max_retry_limit` defines the maximum number of retries for an agent to execute a task when an error occurs.

2
docs/how-to/Hierarchical.md
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@@ -46,7 +46,6 @@ researcher = Agent(
verbose=False,
# tools=[] # This can be optionally specified; defaults to an empty list
use_system_prompt=True, # Enable or disable system prompts for this agent
use_stop_words=True, # Enable or disable stop words for this agent
max_rpm=30, # Limit on the number of requests per minute
max_iter=5 # Maximum number of iterations for a final answer
)
@@ -58,7 +57,6 @@ writer = Agent(
verbose=False,
# tools=[] # Optionally specify tools; defaults to an empty list
use_system_prompt=True, # Enable or disable system prompts for this agent
use_stop_words=True, # Enable or disable stop words for this agent
max_rpm=30, # Limit on the number of requests per minute
max_iter=5 # Maximum number of iterations for a final answer
)

2
docs/how-to/LLM-Connections.md
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@@ -160,4 +160,4 @@ This is particularly useful when working with OpenAI-compatible APIs or when you
## Conclusion
By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.
By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.

31
docs/index.md
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@@ -58,6 +58,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
LLMs
</a>
</li>
<!-- <li>
<a href="./core-concepts/Flows">
Flows
</a>
</li> -->
<li>
<a href="./core-concepts/Pipeline">
Pipeline
@@ -85,7 +90,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
</li>
</ul>
</div>
<div style="width:30%">
<div style="width:25%">
<h2>How-To Guides</h2>
<ul>
<li>
@@ -160,7 +165,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
</li>
</ul>
</div>
<div style="width:30%">
<!-- <div style="width:25%">
<h2>Examples</h2>
<ul>
<li>
@@ -198,6 +203,26 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
Landing Page Generator
</a>
</li>
<li>
<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow">
Email Auto Responder Flow
</a>
</li>
<li>
<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow">
Lead Score Flow
</a>
</li>
<li>
<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows">
Write a Book Flow
</a>
</li>
<li>
<a target='_blank' href="https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow">
Meeting Assistant Flow
</a>
</li>
</ul>
</div>
</div> -->
</div>

6
mkdocs.yml
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@@ -162,7 +162,7 @@ nav:
- Directory RAG Search: 'tools/DirectorySearchTool.md'
- Directory Read: 'tools/DirectoryReadTool.md'
- Docx Rag Search: 'tools/DOCXSearchTool.md'
- EXA Serch Web Loader: 'tools/EXASearchTool.md'
- EXA Search Web Loader: 'tools/EXASearchTool.md'
- File Read: 'tools/FileReadTool.md'
- File Write: 'tools/FileWriteTool.md'
- Firecrawl Crawl Website Tool: 'tools/FirecrawlCrawlWebsiteTool.md'
@@ -210,6 +210,6 @@ extra:
property: G-N3Q505TMQ6
social:
- icon: fontawesome/brands/twitter
link: https://twitter.com/joaomdmoura
link: https://x.com/crewAIInc
- icon: fontawesome/brands/github
link: https://github.com/joaomdmoura/crewAI
link: https://github.com/crewAIInc/crewAI

1213
poetry.lock generated
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File diff suppressed because it is too large Load Diff

3
pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry]
name = "crewai"
version = "0.63.0"
version = "0.66.0"
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
authors = ["Joao Moura <joao@crewai.com>"]
readme = "README.md"
@@ -32,6 +32,7 @@ json-repair = "^0.25.2"
auth0-python = "^4.7.1"
poetry = "^1.8.3"
litellm = "^1.44.22"
pyvis = "^0.3.2"
[tool.poetry.extras]
tools = ["crewai-tools"]

6
src/crewai/__init__.py
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@@ -1,12 +1,14 @@
import warnings
from crewai.agent import Agent
from crewai.crew import Crew
from crewai.flow.flow import Flow
from crewai.llm import LLM
from crewai.pipeline import Pipeline
from crewai.process import Process
from crewai.routers import Router
from crewai.task import Task
warnings.filterwarnings(
"ignore",
message="Pydantic serializer warnings:",
@@ -14,4 +16,4 @@ warnings.filterwarnings(
module="pydantic.main",
)
__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router"]
__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

21
src/crewai/agent.py
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@@ -74,10 +74,6 @@ class Agent(BaseAgent):
default=None,
description="Callback to be executed after each step of the agent execution.",
)
use_stop_words: bool = Field(
default=True,
description="Use stop words for the agent.",
)
use_system_prompt: Optional[bool] = Field(
default=True,
description="Use system prompt for the agent.",
@@ -108,7 +104,7 @@ class Agent(BaseAgent):
description="Keep messages under the context window size by summarizing content.",
)
max_iter: int = Field(
default=15,
default=20,
description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
)
max_retry_limit: int = Field(
@@ -132,7 +128,9 @@ class Agent(BaseAgent):
model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
llm_params = {"model": model_name}
api_base = os.environ.get("OPENAI_API_BASE")
api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
"OPENAI_BASE_URL"
)
if api_base:
llm_params["base_url"] = api_base
@@ -291,7 +289,6 @@ class Agent(BaseAgent):
stop_words=stop_words,
max_iter=self.max_iter,
tools_handler=self.tools_handler,
use_stop_words=self.use_stop_words,
tools_names=self.__tools_names(parsed_tools),
tools_description=self._render_text_description_and_args(parsed_tools),
step_callback=self.step_callback,
@@ -349,8 +346,9 @@ class Agent(BaseAgent):
human_feedbacks = [
i["human_feedback"] for i in data.get(agent_id, {}).values()
]
task_prompt += "You MUST follow these feedbacks: \n " + "\n - ".join(
human_feedbacks
task_prompt += (
"\n\nYou MUST follow these instructions: \n "
+ "\n - ".join(human_feedbacks)
)
return task_prompt
@@ -359,8 +357,9 @@ class Agent(BaseAgent):
"""Use trained data for the agent task prompt to improve output."""
if data := CrewTrainingHandler(TRAINED_AGENTS_DATA_FILE).load():
if trained_data_output := data.get(self.role):
task_prompt += "You MUST follow these feedbacks: \n " + "\n - ".join(
trained_data_output["suggestions"]
task_prompt += (
"\n\nYou MUST follow these instructions: \n - "
+ "\n - ".join(trained_data_output["suggestions"])
)
return task_prompt

6
src/crewai/agents/agent_builder/base_agent.py
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@@ -176,7 +176,11 @@ class BaseAgent(ABC, BaseModel):
@property
def key(self):
source = [self.role, self.goal, self.backstory]
source = [
self._original_role or self.role,
self._original_goal or self.goal,
self._original_backstory or self.backstory,
]
return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
@abstractmethod

12
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
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@@ -6,6 +6,7 @@ from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
from crewai.utilities.printer import Printer
if TYPE_CHECKING:
@@ -22,6 +23,7 @@ class CrewAgentExecutorMixin:
have_forced_answer: bool
max_iter: int
_i18n: I18N
_printer: Printer = Printer()
def _should_force_answer(self) -> bool:
"""Determine if a forced answer is required based on iteration count."""
@@ -100,6 +102,12 @@ class CrewAgentExecutorMixin:
def _ask_human_input(self, final_answer: dict) -> str:
"""Prompt human input for final decision making."""
return input(
self._i18n.slice("getting_input").format(final_answer=final_answer)
self._printer.print(
content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
)
self._printer.print(
content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
color="bold_yellow",
)
return input()

56
src/crewai/agents/crew_agent_executor.py
View File

@@ -34,7 +34,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
max_iter: int,
tools: List[Any],
tools_names: str,
use_stop_words: bool,
stop_words: List[str],
tools_description: str,
tools_handler: ToolsHandler,
@@ -60,7 +59,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.tools_handler = tools_handler
self.original_tools = original_tools
self.step_callback = step_callback
self.use_stop_words = use_stop_words
self.use_stop_words = self.llm.supports_stop_words()
self.tools_description = tools_description
self.function_calling_llm = function_calling_llm
self.respect_context_window = respect_context_window
@@ -68,8 +67,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.ask_for_human_input = False
self.messages: List[Dict[str, str]] = []
self.iterations = 0
self.log_error_after = 3
self.have_forced_answer = False
self.name_to_tool_map = {tool.name: tool for tool in self.tools}
if self.llm.stop:
self.llm.stop = list(set(self.llm.stop + self.stop))
else:
self.llm.stop = self.stop
def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
if "system" in self.prompt:
@@ -97,6 +101,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
formatted_answer = self._invoke_loop()
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer)
return {"output": formatted_answer.output}
def _invoke_loop(self, formatted_answer=None):
@@ -125,7 +132,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
formatted_answer.result = action_result
print("formatted_answer", formatted_answer)
self._show_logs(formatted_answer)
if self.step_callback:
@@ -147,8 +153,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.messages.append(
self._format_msg(formatted_answer.text, role="user")
)
except OutputParserException as e:
self.messages.append({"role": "user", "content": e.error})
if self.iterations > self.log_error_after:
self._printer.print(
content=f"Error parsing LLM output, agent will retry: {e.error}",
color="red",
)
return self._invoke_loop(formatted_answer)
except Exception as e:
@@ -167,8 +179,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if self.agent.verbose or (
hasattr(self, "crew") and getattr(self.crew, "verbose", False)
):
agent_role = self.agent.role.split("\n")[0]
self._printer.print(
content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
)
self._printer.print(
content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
@@ -178,6 +191,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if self.agent.verbose or (
hasattr(self, "crew") and getattr(self.crew, "verbose", False)
):
agent_role = self.agent.role.split("\n")[0]
if isinstance(formatted_answer, AgentAction):
thought = re.sub(r"\n+", "\n", formatted_answer.thought)
formatted_json = json.dumps(
@@ -186,7 +200,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
ensure_ascii=False,
)
self._printer.print(
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
)
if thought and thought != "":
self._printer.print(
@@ -203,10 +217,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
)
elif isinstance(formatted_answer, AgentFinish):
self._printer.print(
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{self.agent.role}\033[00m"
content=f"\n\n\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
)
self._printer.print(
content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m"
content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
)
def _use_tool(self, agent_action: AgentAction) -> Any:
@@ -241,21 +255,21 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _summarize_messages(self) -> None:
messages_groups = []
for message in self.messages:
content = message["content"]
for i in range(0, len(content), 5000):
messages_groups.append(content[i : i + 5000])
cut_size = self.llm.get_context_window_size()
for i in range(0, len(content), cut_size):
messages_groups.append(content[i : i + cut_size])
summarized_contents = []
for group in messages_groups:
summary = self.llm.call(
[
self._format_msg(
self._i18n.slices("summarizer_system_message"), role="system"
self._i18n.slice("summarizer_system_message"), role="system"
),
self._format_msg(
self._i18n.errors("sumamrize_instruction").format(group=group),
self._i18n.slice("sumamrize_instruction").format(group=group),
),
],
callbacks=self.callbacks,
@@ -266,7 +280,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self.messages = [
self._format_msg(
self._i18n.errors("summary").format(merged_summary=merged_summary)
self._i18n.slice("summary").format(merged_summary=merged_summary)
)
]
@@ -293,24 +307,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
) -> None:
"""Function to handle the process of the training data."""
agent_id = str(self.agent.id)
if (
CrewTrainingHandler(TRAINING_DATA_FILE).load()
and not self.ask_for_human_input
):
training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
if training_data.get(agent_id):
if self.crew is not None and hasattr(self.crew, "_train_iteration"):
training_data[agent_id][self.crew._train_iteration][
"improved_output"
] = result.output
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
else:
self._logger.log(
"error",
"Invalid crew or missing _train_iteration attribute.",
color="red",
)
training_data[agent_id][self.crew._train_iteration][
"improved_output"
] = result.output
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
if self.ask_for_human_input and human_feedback is not None:
training_data = {

62
src/crewai/cli/cli.py
View File

@@ -4,6 +4,7 @@ import click
import pkg_resources
from crewai.cli.create_crew import create_crew
from crewai.cli.create_flow import create_flow
from crewai.cli.create_pipeline import create_pipeline
from crewai.memory.storage.kickoff_task_outputs_storage import (
KickoffTaskOutputsSQLiteStorage,
@@ -13,9 +14,12 @@ from .authentication.main import AuthenticationCommand
from .deploy.main import DeployCommand
from .evaluate_crew import evaluate_crew
from .install_crew import install_crew
from .plot_flow import plot_flow
from .replay_from_task import replay_task_command
from .reset_memories_command import reset_memories_command
from .run_crew import run_crew
from .run_flow import run_flow
from .tools.main import ToolCommand
from .train_crew import train_crew
@@ -25,19 +29,20 @@ def crewai():
@crewai.command()
@click.argument("type", type=click.Choice(["crew", "pipeline"]))
@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
@click.argument("name")
@click.option(
"--router", is_flag=True, help="Create a pipeline with router functionality"
)
def create(type, name, router):
"""Create a new crew or pipeline."""
def create(type, name):
"""Create a new crew, pipeline, or flow."""
if type == "crew":
create_crew(name)
elif type == "pipeline":
create_pipeline(name, router)
create_pipeline(name)
elif type == "flow":
create_flow(name)
else:
click.secho("Error: Invalid type. Must be 'crew' or 'pipeline'.", fg="red")
click.secho(
"Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red"
)
@crewai.command()
@@ -202,6 +207,12 @@ def deploy():
pass
@crewai.group()
def tool():
"""Tool Repository related commands."""
pass
@deploy.command(name="create")
@click.option("-y", "--yes", is_flag=True, help="Skip the confirmation prompt")
def deploy_create(yes: bool):
@@ -249,5 +260,40 @@ def deploy_remove(uuid: Optional[str]):
deploy_cmd.remove_crew(uuid=uuid)
@tool.command(name="install")
@click.argument("handle")
def tool_install(handle: str):
tool_cmd = ToolCommand()
tool_cmd.install(handle)
@tool.command(name="publish")
@click.option("--public", "is_public", flag_value=True, default=False)
@click.option("--private", "is_public", flag_value=False)
def tool_publish(is_public: bool):
tool_cmd = ToolCommand()
tool_cmd.publish(is_public)
@crewai.group()
def flow():
"""Flow related commands."""
pass
@flow.command(name="run")
def flow_run():
"""Run the Flow."""
click.echo("Running the Flow")
run_flow()
@flow.command(name="plot")
def flow_plot():
"""Plot the Flow."""
click.echo("Plotting the Flow")
plot_flow()
if __name__ == "__main__":
crewai()

40
src/crewai/cli/command.py Normal file
View File

@@ -0,0 +1,40 @@
from typing import Dict, Any
from rich.console import Console
from crewai.cli.plus_api import PlusAPI
from crewai.cli.utils import get_auth_token
from crewai.telemetry.telemetry import Telemetry
console = Console()
class BaseCommand:
def __init__(self):
self._telemetry = Telemetry()
self._telemetry.set_tracer()
class PlusAPIMixin:
def __init__(self, telemetry):
try:
telemetry.set_tracer()
self.plus_api_client = PlusAPI(api_key=get_auth_token())
except Exception:
self._deploy_signup_error_span = telemetry.deploy_signup_error_span()
console.print(
"Please sign up/login to CrewAI+ before using the CLI.",
style="bold red",
)
console.print("Run 'crewai signup' to sign up/login.", style="bold green")
raise SystemExit
def _handle_plus_api_error(self, json_response: Dict[str, Any]) -> None:
"""
Handle and display error messages from API responses.
Args:
json_response (Dict[str, Any]): The JSON response containing error information.
"""
error = json_response.get("error", "Unknown error")
message = json_response.get("message", "No message provided")
console.print(f"Error: {error}", style="bold red")
console.print(f"Message: {message}", style="bold red")

93
src/crewai/cli/create_flow.py Normal file
View File

@@ -0,0 +1,93 @@
from pathlib import Path
import click
def create_flow(name):
"""Create a new flow."""
folder_name = name.replace(" ", "_").replace("-", "_").lower()
class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
click.secho(f"Creating flow {folder_name}...", fg="green", bold=True)
project_root = Path(folder_name)
if project_root.exists():
click.secho(f"Error: Folder {folder_name} already exists.", fg="red")
return
# Create directory structure
(project_root / "src" / folder_name).mkdir(parents=True)
(project_root / "src" / folder_name / "crews").mkdir(parents=True)
(project_root / "src" / folder_name / "tools").mkdir(parents=True)
(project_root / "tests").mkdir(exist_ok=True)
# Create .env file
with open(project_root / ".env", "w") as file:
file.write("OPENAI_API_KEY=YOUR_API_KEY")
package_dir = Path(__file__).parent
templates_dir = package_dir / "templates" / "flow"
# List of template files to copy
root_template_files = [".gitignore", "pyproject.toml", "README.md"]
src_template_files = ["__init__.py", "main.py"]
tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
crew_folders = [
"poem_crew",
]
def process_file(src_file, dst_file):
if src_file.suffix in [".pyc", ".pyo", ".pyd"]:
return
try:
with open(src_file, "r", encoding="utf-8") as file:
content = file.read()
except Exception as e:
click.secho(f"Error processing file {src_file}: {e}", fg="red")
return
content = content.replace("{{name}}", name)
content = content.replace("{{flow_name}}", class_name)
content = content.replace("{{folder_name}}", folder_name)
with open(dst_file, "w") as file:
file.write(content)
# Copy and process root template files
for file_name in root_template_files:
src_file = templates_dir / file_name
dst_file = project_root / file_name
process_file(src_file, dst_file)
# Copy and process src template files
for file_name in src_template_files:
src_file = templates_dir / file_name
dst_file = project_root / "src" / folder_name / file_name
process_file(src_file, dst_file)
# Copy tools files
for file_name in tools_template_files:
src_file = templates_dir / file_name
dst_file = project_root / "src" / folder_name / file_name
process_file(src_file, dst_file)
# Copy crew folders
for crew_folder in crew_folders:
src_crew_folder = templates_dir / "crews" / crew_folder
dst_crew_folder = project_root / "src" / folder_name / "crews" / crew_folder
if src_crew_folder.exists():
for src_file in src_crew_folder.rglob("*"):
if src_file.is_file():
relative_path = src_file.relative_to(src_crew_folder)
dst_file = dst_crew_folder / relative_path
dst_file.parent.mkdir(parents=True, exist_ok=True)
process_file(src_file, dst_file)
else:
click.secho(
f"Warning: Crew folder {crew_folder} not found in template.",
fg="yellow",
)
click.secho(f"Flow {name} created successfully!", fg="green", bold=True)

66
src/crewai/cli/deploy/api.py
View File

@@ -1,66 +0,0 @@
from os import getenv
import requests
from crewai.cli.deploy.utils import get_crewai_version
class CrewAPI:
"""
CrewAPI class to interact with the crewAI+ API.
"""
def __init__(self, api_key: str) -> None:
self.api_key = api_key
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
}
self.base_url = getenv(
"CREWAI_BASE_URL", "https://crewai.com/crewai_plus/api/v1/crews"
)
def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
url = f"{self.base_url}/{endpoint}"
return requests.request(method, url, headers=self.headers, **kwargs)
# Deploy
def deploy_by_name(self, project_name: str) -> requests.Response:
return self._make_request("POST", f"by-name/{project_name}/deploy")
def deploy_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("POST", f"{uuid}/deploy")
# Status
def status_by_name(self, project_name: str) -> requests.Response:
return self._make_request("GET", f"by-name/{project_name}/status")
def status_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("GET", f"{uuid}/status")
# Logs
def logs_by_name(
self, project_name: str, log_type: str = "deployment"
) -> requests.Response:
return self._make_request("GET", f"by-name/{project_name}/logs/{log_type}")
def logs_by_uuid(
self, uuid: str, log_type: str = "deployment"
) -> requests.Response:
return self._make_request("GET", f"{uuid}/logs/{log_type}")
# Delete
def delete_by_name(self, project_name: str) -> requests.Response:
return self._make_request("DELETE", f"by-name/{project_name}")
def delete_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("DELETE", f"{uuid}")
# List
def list_crews(self) -> requests.Response:
return self._make_request("GET", "")
# Create
def create_crew(self, payload) -> requests.Response:
return self._make_request("POST", "", json=payload)

72
src/crewai/cli/deploy/main.py
View File

@@ -2,11 +2,9 @@ from typing import Any, Dict, List, Optional
from rich.console import Console
from crewai.telemetry import Telemetry
from .api import CrewAPI
from .utils import (
from crewai.cli.command import BaseCommand, PlusAPIMixin
from crewai.cli.utils import (
fetch_and_json_env_file,
get_auth_token,
get_git_remote_url,
get_project_name,
)
@@ -14,7 +12,7 @@ from .utils import (
console = Console()
class DeployCommand:
class DeployCommand(BaseCommand, PlusAPIMixin):
"""
A class to handle deployment-related operations for CrewAI projects.
"""
@@ -23,40 +21,10 @@ class DeployCommand:
"""
Initialize the DeployCommand with project name and API client.
"""
try:
self._telemetry = Telemetry()
self._telemetry.set_tracer()
access_token = get_auth_token()
except Exception:
self._deploy_signup_error_span = self._telemetry.deploy_signup_error_span()
console.print(
"Please sign up/login to CrewAI+ before using the CLI.",
style="bold red",
)
console.print("Run 'crewai signup' to sign up/login.", style="bold green")
raise SystemExit
self.project_name = get_project_name()
if self.project_name is None:
console.print(
"No project name found. Please ensure your project has a valid pyproject.toml file.",
style="bold red",
)
raise SystemExit
self.client = CrewAPI(api_key=access_token)
def _handle_error(self, json_response: Dict[str, Any]) -> None:
"""
Handle and display error messages from API responses.
Args:
json_response (Dict[str, Any]): The JSON response containing error information.
"""
error = json_response.get("error", "Unknown error")
message = json_response.get("message", "No message provided")
console.print(f"Error: {error}", style="bold red")
console.print(f"Message: {message}", style="bold red")
BaseCommand.__init__(self)
PlusAPIMixin.__init__(self, telemetry=self._telemetry)
self.project_name = get_project_name(require=True)
def _standard_no_param_error_message(self) -> None:
"""
@@ -104,9 +72,9 @@ class DeployCommand:
self._start_deployment_span = self._telemetry.start_deployment_span(uuid)
console.print("Starting deployment...", style="bold blue")
if uuid:
response = self.client.deploy_by_uuid(uuid)
response = self.plus_api_client.deploy_by_uuid(uuid)
elif self.project_name:
response = self.client.deploy_by_name(self.project_name)
response = self.plus_api_client.deploy_by_name(self.project_name)
else:
self._standard_no_param_error_message()
return
@@ -115,7 +83,7 @@ class DeployCommand:
if response.status_code == 200:
self._display_deployment_info(json_response)
else:
self._handle_error(json_response)
self._handle_plus_api_error(json_response)
def create_crew(self, confirm: bool = False) -> None:
"""
@@ -139,11 +107,11 @@ class DeployCommand:
self._confirm_input(env_vars, remote_repo_url, confirm)
payload = self._create_payload(env_vars, remote_repo_url)
response = self.client.create_crew(payload)
response = self.plus_api_client.create_crew(payload)
if response.status_code == 201:
self._display_creation_success(response.json())
else:
self._handle_error(response.json())
self._handle_plus_api_error(response.json())
def _confirm_input(
self, env_vars: Dict[str, str], remote_repo_url: str, confirm: bool
@@ -208,7 +176,7 @@ class DeployCommand:
"""
console.print("Listing all Crews\n", style="bold blue")
response = self.client.list_crews()
response = self.plus_api_client.list_crews()
json_response = response.json()
if response.status_code == 200:
self._display_crews(json_response)
@@ -243,9 +211,9 @@ class DeployCommand:
"""
console.print("Fetching deployment status...", style="bold blue")
if uuid:
response = self.client.status_by_uuid(uuid)
response = self.plus_api_client.crew_status_by_uuid(uuid)
elif self.project_name:
response = self.client.status_by_name(self.project_name)
response = self.plus_api_client.crew_status_by_name(self.project_name)
else:
self._standard_no_param_error_message()
return
@@ -254,7 +222,7 @@ class DeployCommand:
if response.status_code == 200:
self._display_crew_status(json_response)
else:
self._handle_error(json_response)
self._handle_plus_api_error(json_response)
def _display_crew_status(self, status_data: Dict[str, str]) -> None:
"""
@@ -278,9 +246,9 @@ class DeployCommand:
console.print(f"Fetching {log_type} logs...", style="bold blue")
if uuid:
response = self.client.logs_by_uuid(uuid, log_type)
response = self.plus_api_client.crew_by_uuid(uuid, log_type)
elif self.project_name:
response = self.client.logs_by_name(self.project_name, log_type)
response = self.plus_api_client.crew_by_name(self.project_name, log_type)
else:
self._standard_no_param_error_message()
return
@@ -288,7 +256,7 @@ class DeployCommand:
if response.status_code == 200:
self._display_logs(response.json())
else:
self._handle_error(response.json())
self._handle_plus_api_error(response.json())
def remove_crew(self, uuid: Optional[str]) -> None:
"""
@@ -301,9 +269,9 @@ class DeployCommand:
console.print("Removing deployment...", style="bold blue")
if uuid:
response = self.client.delete_by_uuid(uuid)
response = self.plus_api_client.delete_crew_by_uuid(uuid)
elif self.project_name:
response = self.client.delete_by_name(self.project_name)
response = self.plus_api_client.delete_crew_by_name(self.project_name)
else:
self._standard_no_param_error_message()
return

155
src/crewai/cli/deploy/utils.py
View File

@@ -1,155 +0,0 @@
import sys
import re
import subprocess
from rich.console import Console
from ..authentication.utils import TokenManager
console = Console()
if sys.version_info >= (3, 11):
import tomllib
# Drop the simple_toml_parser when we move to python3.11
def simple_toml_parser(content):
result = {}
current_section = result
for line in content.split('\n'):
line = line.strip()
if line.startswith('[') and line.endswith(']'):
# New section
section = line[1:-1].split('.')
current_section = result
for key in section:
current_section = current_section.setdefault(key, {})
elif '=' in line:
key, value = line.split('=', 1)
key = key.strip()
value = value.strip().strip('"')
current_section[key] = value
return result
def parse_toml(content):
if sys.version_info >= (3, 11):
return tomllib.loads(content)
else:
return simple_toml_parser(content)
def get_git_remote_url() -> str | None:
"""Get the Git repository's remote URL."""
try:
# Run the git remote -v command
result = subprocess.run(
["git", "remote", "-v"], capture_output=True, text=True, check=True
)
# Get the output
output = result.stdout
# Parse the output to find the origin URL
matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
if matches:
return matches[0] # Return the first match (origin URL)
else:
console.print("No origin remote found.", style="bold red")
except subprocess.CalledProcessError as e:
console.print(f"Error running trying to fetch the Git Repository: {e}", style="bold red")
except FileNotFoundError:
console.print("Git command not found. Make sure Git is installed and in your PATH.", style="bold red")
return None
def get_project_name(pyproject_path: str = "pyproject.toml") -> str | None:
"""Get the project name from the pyproject.toml file."""
try:
# Read the pyproject.toml file
with open(pyproject_path, "r") as f:
pyproject_content = parse_toml(f.read())
# Extract the project name
project_name = pyproject_content["tool"]["poetry"]["name"]
if "crewai" not in pyproject_content["tool"]["poetry"]["dependencies"]:
raise Exception("crewai is not in the dependencies.")
return project_name
except FileNotFoundError:
print(f"Error: {pyproject_path} not found.")
except KeyError:
print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e: # type: ignore
print(
f"Error: {pyproject_path} is not a valid TOML file."
if sys.version_info >= (3, 11)
else f"Error reading the pyproject.toml file: {e}"
)
except Exception as e:
print(f"Error reading the pyproject.toml file: {e}")
return None
def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
"""Get the version number of crewai from the poetry.lock file."""
try:
with open(poetry_lock_path, "r") as f:
lock_content = f.read()
match = re.search(
r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
lock_content,
re.DOTALL,
)
if match:
return match.group(1)
else:
print("crewai package not found in poetry.lock")
return "no-version-found"
except FileNotFoundError:
print(f"Error: {poetry_lock_path} not found.")
except Exception as e:
print(f"Error reading the poetry.lock file: {e}")
return "no-version-found"
def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
"""Fetch the environment variables from a .env file and return them as a dictionary."""
try:
# Read the .env file
with open(env_file_path, "r") as f:
env_content = f.read()
# Parse the .env file content to a dictionary
env_dict = {}
for line in env_content.splitlines():
if line.strip() and not line.strip().startswith("#"):
key, value = line.split("=", 1)
env_dict[key.strip()] = value.strip()
return env_dict
except FileNotFoundError:
print(f"Error: {env_file_path} not found.")
except Exception as e:
print(f"Error reading the .env file: {e}")
return {}
def get_auth_token() -> str:
"""Get the authentication token."""
access_token = TokenManager().get_token()
if not access_token:
raise Exception()
return access_token

23
src/crewai/cli/plot_flow.py Normal file
View File

@@ -0,0 +1,23 @@
import subprocess
import click
def plot_flow() -> None:
"""
Plot the flow by running a command in the Poetry environment.
"""
command = ["poetry", "run", "plot_flow"]
try:
result = subprocess.run(command, capture_output=False, text=True, check=True)
if result.stderr:
click.echo(result.stderr, err=True)
except subprocess.CalledProcessError as e:
click.echo(f"An error occurred while plotting the flow: {e}", err=True)
click.echo(e.output, err=True)
except Exception as e:
click.echo(f"An unexpected error occurred: {e}", err=True)

92
src/crewai/cli/plus_api.py Normal file
View File

@@ -0,0 +1,92 @@
from typing import Optional
import requests
from os import getenv
from crewai.cli.utils import get_crewai_version
from urllib.parse import urljoin
class PlusAPI:
"""
This class exposes methods for working with the CrewAI+ API.
"""
TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
def __init__(self, api_key: str) -> None:
self.api_key = api_key
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
"X-Crewai-Version": get_crewai_version(),
}
self.base_url = getenv("CREWAI_BASE_URL", "https://app.crewai.com")
def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
url = urljoin(self.base_url, endpoint)
return requests.request(method, url, headers=self.headers, **kwargs)
def get_tool(self, handle: str):
return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
def publish_tool(
self,
handle: str,
is_public: bool,
version: str,
description: Optional[str],
encoded_file: str,
):
params = {
"handle": handle,
"public": is_public,
"version": version,
"file": encoded_file,
"description": description,
}
return self._make_request("POST", f"{self.TOOLS_RESOURCE}", json=params)
def deploy_by_name(self, project_name: str) -> requests.Response:
return self._make_request(
"POST", f"{self.CREWS_RESOURCE}/by-name/{project_name}/deploy"
)
def deploy_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("POST", f"{self.CREWS_RESOURCE}/{uuid}/deploy")
def crew_status_by_name(self, project_name: str) -> requests.Response:
return self._make_request(
"GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/status"
)
def crew_status_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("GET", f"{self.CREWS_RESOURCE}/{uuid}/status")
def crew_by_name(
self, project_name: str, log_type: str = "deployment"
) -> requests.Response:
return self._make_request(
"GET", f"{self.CREWS_RESOURCE}/by-name/{project_name}/logs/{log_type}"
)
def crew_by_uuid(
self, uuid: str, log_type: str = "deployment"
) -> requests.Response:
return self._make_request(
"GET", f"{self.CREWS_RESOURCE}/{uuid}/logs/{log_type}"
)
def delete_crew_by_name(self, project_name: str) -> requests.Response:
return self._make_request(
"DELETE", f"{self.CREWS_RESOURCE}/by-name/{project_name}"
)
def delete_crew_by_uuid(self, uuid: str) -> requests.Response:
return self._make_request("DELETE", f"{self.CREWS_RESOURCE}/{uuid}")
def list_crews(self) -> requests.Response:
return self._make_request("GET", self.CREWS_RESOURCE)
def create_crew(self, payload) -> requests.Response:
return self._make_request("POST", self.CREWS_RESOURCE, json=payload)

23
src/crewai/cli/run_flow.py Normal file
View File

@@ -0,0 +1,23 @@
import subprocess
import click
def run_flow() -> None:
"""
Run the flow by running a command in the Poetry environment.
"""
command = ["poetry", "run", "run_flow"]
try:
result = subprocess.run(command, capture_output=False, text=True, check=True)
if result.stderr:
click.echo(result.stderr, err=True)
except subprocess.CalledProcessError as e:
click.echo(f"An error occurred while running the flow: {e}", err=True)
click.echo(e.output, err=True)
except Exception as e:
click.echo(f"An unexpected error occurred: {e}", err=True)

2
src/crewai/cli/templates/crew/pyproject.toml
View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
[tool.poetry.scripts]

3
src/crewai/cli/templates/flow/.gitignore vendored Normal file
View File

@@ -0,0 +1,3 @@
.env
__pycache__/
lib/

57
src/crewai/cli/templates/flow/README.md Normal file
View File

@@ -0,0 +1,57 @@
# {{crew_name}} Crew
Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
## Installation
Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.
First, if you haven't already, install Poetry:
```bash
pip install poetry
```
Next, navigate to your project directory and install the dependencies:
1. First lock the dependencies and then install them:
```bash
crewai install
```
### Customizing
**Add your `OPENAI_API_KEY` into the `.env` file**
- Modify `src/{{folder_name}}/config/agents.yaml` to define your agents
- Modify `src/{{folder_name}}/config/tasks.yaml` to define your tasks
- Modify `src/{{folder_name}}/crew.py` to add your own logic, tools and specific args
- Modify `src/{{folder_name}}/main.py` to add custom inputs for your agents and tasks
## Running the Project
To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
```bash
crewai run
```
This command initializes the {{name}} Crew, assembling the agents and assigning them tasks as defined in your configuration.
This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
## Understanding Your Crew
The {{name}} Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
## Support
For support, questions, or feedback regarding the {{crew_name}} Crew or crewAI.
- Visit our [documentation](https://docs.crewai.com)
- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
- [Chat with our docs](https://chatg.pt/DWjSBZn)
Let's create wonders together with the power and simplicity of crewAI.

0
src/crewai/cli/templates/flow/__init__.py Normal file
View File

11
src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml Normal file
View File

@@ -0,0 +1,11 @@
poem_writer:
role: >
CrewAI Poem Writer
goal: >
Generate a funny, light heartedpoem about how CrewAI
is awesome with a sentence count of {sentence_count}
backstory: >
You're a creative poet with a talent for capturing the essence of any topic
in a beautiful and engaging way. Known for your ability to craft poems that
resonate with readers, you bring a unique perspective and artistic flair to
every piece you write.

7
src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml Normal file
View File

@@ -0,0 +1,7 @@
write_poem:
description: >
Write a poem about how CrewAI is awesome.
Ensure the poem is engaging and adheres to the specified sentence count of {sentence_count}.
expected_output: >
A beautifully crafted poem about CrewAI, with exactly {sentence_count} sentences.
agent: poem_writer

31
src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py Normal file
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@@ -0,0 +1,31 @@
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
@CrewBase
class PoemCrew():
"""Poem Crew"""
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
@agent
def poem_writer(self) -> Agent:
return Agent(
config=self.agents_config['poem_writer'],
)
@task
def write_poem(self) -> Task:
return Task(
config=self.tasks_config['write_poem'],
)
@crew
def crew(self) -> Crew:
"""Creates the Research Crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
process=Process.sequential,
verbose=True,
)

65
src/crewai/cli/templates/flow/main.py Normal file
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@@ -0,0 +1,65 @@
#!/usr/bin/env python
import asyncio
from random import randint
from pydantic import BaseModel
from crewai.flow.flow import Flow, listen, start
from .crews.poem_crew.poem_crew import PoemCrew
class PoemState(BaseModel):
sentence_count: int = 1
poem: str = ""
class PoemFlow(Flow[PoemState]):
@start()
def generate_sentence_count(self):
print("Generating sentence count")
# Generate a number between 1 and 5
self.state.sentence_count = randint(1, 5)
@listen(generate_sentence_count)
def generate_poem(self):
print("Generating poem")
print(f"State before poem: {self.state}")
result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
print("Poem generated", result.raw)
self.state.poem = result.raw
print(f"State after generate_poem: {self.state}")
@listen(generate_poem)
def save_poem(self):
print("Saving poem")
print(f"State before save_poem: {self.state}")
with open("poem.txt", "w") as f:
f.write(self.state.poem)
print(f"State after save_poem: {self.state}")
async def run_flow():
"""
Run the flow.
"""
poem_flow = PoemFlow()
await poem_flow.kickoff()
async def plot_flow():
"""
Plot the flow.
"""
poem_flow = PoemFlow()
poem_flow.plot()
def main():
asyncio.run(run_flow())
def plot():
asyncio.run(plot_flow())
if __name__ == "__main__":
main()

19
src/crewai/cli/templates/flow/pyproject.toml Normal file
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@@ -0,0 +1,19 @@
[tool.poetry]
name = "{{folder_name}}"
version = "0.1.0"
description = "{{name}} using crewAI"
authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
asyncio = "*"
[tool.poetry.scripts]
{{folder_name}} = "{{folder_name}}.main:main"
run_flow = "{{folder_name}}.main:main"
plot_flow = "{{folder_name}}.main:plot"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

0
src/crewai/cli/templates/flow/tools/__init__.py Normal file
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12
src/crewai/cli/templates/flow/tools/custom_tool.py Normal file
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@@ -0,0 +1,12 @@
from crewai_tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = (
"Clear description for what this tool is useful for, you agent will need this information to use it."
)
def _run(self, argument: str) -> str:
# Implementation goes here
return "this is an example of a tool output, ignore it and move along."

2
src/crewai/cli/templates/pipeline/pyproject.toml
View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
asyncio = "*"
[tool.poetry.scripts]

2
src/crewai/cli/templates/pipeline_router/pyproject.toml
View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = ">=0.63.0,<1.0.0" }
crewai = { extras = ["tools"], version = ">=0.66.0,<1.0.0" }
[tool.poetry.scripts]

0
src/crewai/cli/tools/__init__.py Normal file
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168
src/crewai/cli/tools/main.py Normal file
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@@ -0,0 +1,168 @@
import base64
import click
import os
import subprocess
import tempfile
from crewai.cli.command import BaseCommand, PlusAPIMixin
from crewai.cli.utils import (
get_project_name,
get_project_description,
get_project_version,
)
from rich.console import Console
console = Console()
class ToolCommand(BaseCommand, PlusAPIMixin):
"""
A class to handle tool repository related operations for CrewAI projects.
"""
def __init__(self):
BaseCommand.__init__(self)
PlusAPIMixin.__init__(self, telemetry=self._telemetry)
def publish(self, is_public: bool):
project_name = get_project_name(require=True)
assert isinstance(project_name, str)
project_version = get_project_version(require=True)
assert isinstance(project_version, str)
project_description = get_project_description(require=False)
encoded_tarball = None
with tempfile.TemporaryDirectory() as temp_build_dir:
subprocess.run(
["poetry", "build", "-f", "sdist", "--output", temp_build_dir],
check=True,
capture_output=False,
)
tarball_filename = next(
(f for f in os.listdir(temp_build_dir) if f.endswith(".tar.gz")), None
)
if not tarball_filename:
console.print(
"Project build failed. Please ensure that the command `poetry build -f sdist` completes successfully.",
style="bold red",
)
raise SystemExit
tarball_path = os.path.join(temp_build_dir, tarball_filename)
with open(tarball_path, "rb") as file:
tarball_contents = file.read()
encoded_tarball = base64.b64encode(tarball_contents).decode("utf-8")
publish_response = self.plus_api_client.publish_tool(
handle=project_name,
is_public=is_public,
version=project_version,
description=project_description,
encoded_file=f"data:application/x-gzip;base64,{encoded_tarball}",
)
if publish_response.status_code == 422:
console.print(
"[bold red]Failed to publish tool. Please fix the following errors:[/bold red]"
)
for field, messages in publish_response.json().items():
for message in messages:
console.print(
f"* [bold red]{field.capitalize()}[/bold red] {message}"
)
raise SystemExit
elif publish_response.status_code != 200:
self._handle_plus_api_error(publish_response.json())
console.print(
"Failed to publish tool. Please try again later.", style="bold red"
)
raise SystemExit
published_handle = publish_response.json()["handle"]
console.print(
f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
style="bold green",
)
def install(self, handle: str):
get_response = self.plus_api_client.get_tool(handle)
if get_response.status_code == 404:
console.print(
"No tool found with this name. Please ensure the tool was published and you have access to it.",
style="bold red",
)
raise SystemExit
elif get_response.status_code != 200:
console.print(
"Failed to get tool details. Please try again later.", style="bold red"
)
raise SystemExit
self._add_repository_to_poetry(get_response.json())
self._add_package(get_response.json())
console.print(f"Succesfully installed {handle}", style="bold green")
def _add_repository_to_poetry(self, tool_details):
repository_handle = f"crewai-{tool_details['repository']['handle']}"
repository_url = tool_details["repository"]["url"]
repository_credentials = tool_details["repository"]["credentials"]
add_repository_command = [
"poetry",
"source",
"add",
"--priority=explicit",
repository_handle,
repository_url,
]
add_repository_result = subprocess.run(
add_repository_command, text=True, check=True
)
if add_repository_result.stderr:
click.echo(add_repository_result.stderr, err=True)
raise SystemExit
add_repository_credentials_command = [
"poetry",
"config",
f"http-basic.{repository_handle}",
repository_credentials,
'""',
]
add_repository_credentials_result = subprocess.run(
add_repository_credentials_command,
capture_output=False,
text=True,
check=True,
)
if add_repository_credentials_result.stderr:
click.echo(add_repository_credentials_result.stderr, err=True)
raise SystemExit
def _add_package(self, tool_details):
tool_handle = tool_details["handle"]
repository_handle = tool_details["repository"]["handle"]
pypi_index_handle = f"crewai-{repository_handle}"
add_package_command = [
"poetry",
"add",
"--source",
pypi_index_handle,
tool_handle,
]
add_package_result = subprocess.run(
add_package_command, capture_output=False, text=True, check=True
)
if add_package_result.stderr:
click.echo(add_package_result.stderr, err=True)
raise SystemExit

201
src/crewai/cli/utils.py
View File

@@ -1,4 +1,17 @@
import click
import re
import subprocess
import sys
from crewai.cli.authentication.utils import TokenManager
from functools import reduce
from rich.console import Console
from typing import Any, Dict, List
if sys.version_info >= (3, 11):
import tomllib
console = Console()
def copy_template(src, dst, name, class_name, folder_name):
@@ -16,3 +29,191 @@ def copy_template(src, dst, name, class_name, folder_name):
file.write(content)
click.secho(f" - Created {dst}", fg="green")
# Drop the simple_toml_parser when we move to python3.11
def simple_toml_parser(content):
result = {}
current_section = result
for line in content.split("\n"):
line = line.strip()
if line.startswith("[") and line.endswith("]"):
# New section
section = line[1:-1].split(".")
current_section = result
for key in section:
current_section = current_section.setdefault(key, {})
elif "=" in line:
key, value = line.split("=", 1)
key = key.strip()
value = value.strip().strip('"')
current_section[key] = value
return result
def parse_toml(content):
if sys.version_info >= (3, 11):
return tomllib.loads(content)
else:
return simple_toml_parser(content)
def get_git_remote_url() -> str | None:
"""Get the Git repository's remote URL."""
try:
# Run the git remote -v command
result = subprocess.run(
["git", "remote", "-v"], capture_output=True, text=True, check=True
)
# Get the output
output = result.stdout
# Parse the output to find the origin URL
matches = re.findall(r"origin\s+(.*?)\s+\(fetch\)", output)
if matches:
return matches[0] # Return the first match (origin URL)
else:
console.print("No origin remote found.", style="bold red")
except subprocess.CalledProcessError as e:
console.print(
f"Error running trying to fetch the Git Repository: {e}", style="bold red"
)
except FileNotFoundError:
console.print(
"Git command not found. Make sure Git is installed and in your PATH.",
style="bold red",
)
return None
def get_project_name(
pyproject_path: str = "pyproject.toml", require: bool = False
) -> str | None:
"""Get the project name from the pyproject.toml file."""
return _get_project_attribute(
pyproject_path, ["tool", "poetry", "name"], require=require
)
def get_project_version(
pyproject_path: str = "pyproject.toml", require: bool = False
) -> str | None:
"""Get the project version from the pyproject.toml file."""
return _get_project_attribute(
pyproject_path, ["tool", "poetry", "version"], require=require
)
def get_project_description(
pyproject_path: str = "pyproject.toml", require: bool = False
) -> str | None:
"""Get the project description from the pyproject.toml file."""
return _get_project_attribute(
pyproject_path, ["tool", "poetry", "description"], require=require
)
def _get_project_attribute(
pyproject_path: str, keys: List[str], require: bool
) -> Any | None:
"""Get an attribute from the pyproject.toml file."""
attribute = None
try:
with open(pyproject_path, "r") as f:
pyproject_content = parse_toml(f.read())
dependencies = (
_get_nested_value(pyproject_content, ["tool", "poetry", "dependencies"])
or {}
)
if "crewai" not in dependencies:
raise Exception("crewai is not in the dependencies.")
attribute = _get_nested_value(pyproject_content, keys)
except FileNotFoundError:
print(f"Error: {pyproject_path} not found.")
except KeyError:
print(f"Error: {pyproject_path} is not a valid pyproject.toml file.")
except tomllib.TOMLDecodeError if sys.version_info >= (3, 11) else Exception as e: # type: ignore
print(
f"Error: {pyproject_path} is not a valid TOML file."
if sys.version_info >= (3, 11)
else f"Error reading the pyproject.toml file: {e}"
)
except Exception as e:
print(f"Error reading the pyproject.toml file: {e}")
if require and not attribute:
console.print(
f"Unable to read '{'.'.join(keys)}' in the pyproject.toml file. Please verify that the file exists and contains the specified attribute.",
style="bold red",
)
raise SystemExit
return attribute
def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any:
return reduce(dict.__getitem__, keys, data)
def get_crewai_version(poetry_lock_path: str = "poetry.lock") -> str:
"""Get the version number of crewai from the poetry.lock file."""
try:
with open(poetry_lock_path, "r") as f:
lock_content = f.read()
match = re.search(
r'\[\[package\]\]\s*name\s*=\s*"crewai"\s*version\s*=\s*"([^"]+)"',
lock_content,
re.DOTALL,
)
if match:
return match.group(1)
else:
print("crewai package not found in poetry.lock")
return "no-version-found"
except FileNotFoundError:
print(f"Error: {poetry_lock_path} not found.")
except Exception as e:
print(f"Error reading the poetry.lock file: {e}")
return "no-version-found"
def fetch_and_json_env_file(env_file_path: str = ".env") -> dict:
"""Fetch the environment variables from a .env file and return them as a dictionary."""
try:
# Read the .env file
with open(env_file_path, "r") as f:
env_content = f.read()
# Parse the .env file content to a dictionary
env_dict = {}
for line in env_content.splitlines():
if line.strip() and not line.strip().startswith("#"):
key, value = line.split("=", 1)
env_dict[key.strip()] = value.strip()
return env_dict
except FileNotFoundError:
print(f"Error: {env_file_path} not found.")
except Exception as e:
print(f"Error reading the .env file: {e}")
return {}
def get_auth_token() -> str:
"""Get the authentication token."""
access_token = TokenManager().get_token()
if not access_token:
raise Exception()
return access_token

8
src/crewai/crews/crew_output.py
View File

@@ -41,6 +41,14 @@ class CrewOutput(BaseModel):
output_dict.update(self.pydantic.model_dump())
return output_dict
def __getitem__(self, key):
if self.pydantic and hasattr(self.pydantic, key):
return getattr(self.pydantic, key)
elif self.json_dict and key in self.json_dict:
return self.json_dict[key]
else:
raise KeyError(f"Key '{key}' not found in CrewOutput.")
def __str__(self):
if self.pydantic:
return str(self.pydantic)

3
src/crewai/flow/__init__.py Normal file
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@@ -0,0 +1,3 @@
from crewai.flow.flow import Flow
__all__ = ["Flow"]

93
src/crewai/flow/assets/crewai_flow_visual_template.html Normal file
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@@ -0,0 +1,93 @@
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>{{ title }}</title>
<script
src="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/vis-network.min.js"
integrity="sha512-LnvoEWDFrqGHlHmDD2101OrLcbsfkrzoSpvtSQtxK3RMnRV0eOkhhBN2dXHKRrUU8p2DGRTk35n4O8nWSVe1mQ=="
crossorigin="anonymous"
referrerpolicy="no-referrer"
></script>
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/vis-network/9.1.2/dist/dist/vis-network.min.css"
integrity="sha512-WgxfT5LWjfszlPHXRmBWHkV2eceiWTOBvrKCNbdgDYTHrT2AeLCGbF4sZlZw3UMN3WtL0tGUoIAKsu8mllg/XA=="
crossorigin="anonymous"
referrerpolicy="no-referrer"
/>
<style type="text/css">
body {
font-family: verdana;
margin: 0;
padding: 0;
}
.container {
display: flex;
flex-direction: column;
height: 100vh;
}
#mynetwork {
flex-grow: 1;
width: 100%;
height: 750px;
background-color: #ffffff;
}
.card {
border: none;
}
.legend-container {
display: flex;
align-items: center;
justify-content: center;
padding: 10px;
background-color: #f8f9fa;
position: fixed; /* Make the legend fixed */
bottom: 0; /* Position it at the bottom */
width: 100%; /* Make it span the full width */
}
.legend-item {
display: flex;
align-items: center;
margin-right: 20px;
}
.legend-color-box {
width: 20px;
height: 20px;
margin-right: 5px;
}
.logo {
height: 50px;
margin-right: 20px;
}
.legend-dashed {
border-bottom: 2px dashed #666666;
width: 20px;
height: 0;
margin-right: 5px;
}
.legend-solid {
border-bottom: 2px solid #666666;
width: 20px;
height: 0;
margin-right: 5px;
}
</style>
</head>
<body>
<div class="container">
<div class="card" style="width: 100%">
<div id="mynetwork" class="card-body"></div>
</div>
<div class="legend-container">
<img
src="data:image/svg+xml;base64,{{ logo_svg_base64 }}"
alt="CrewAI logo"
class="logo"
/>
<!-- LEGEND_ITEMS_PLACEHOLDER -->
</div>
</div>
{{ network_content }}
</body>
</html>

12
src/crewai/flow/assets/crewai_logo.svg Normal file
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46
src/crewai/flow/config.py Normal file
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@@ -0,0 +1,46 @@
DARK_GRAY = "#333333"
CREWAI_ORANGE = "#FF5A50"
GRAY = "#666666"
WHITE = "#FFFFFF"
COLORS = {
"bg": WHITE,
"start": CREWAI_ORANGE,
"method": DARK_GRAY,
"router": DARK_GRAY,
"router_border": CREWAI_ORANGE,
"edge": GRAY,
"router_edge": CREWAI_ORANGE,
"text": WHITE,
}
NODE_STYLES = {
"start": {
"color": COLORS["start"],
"shape": "box",
"font": {"color": COLORS["text"]},
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
},
"method": {
"color": COLORS["method"],
"shape": "box",
"font": {"color": COLORS["text"]},
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
},
"router": {
"color": {
"background": COLORS["router"],
"border": COLORS["router_border"],
"highlight": {
"border": COLORS["router_border"],
"background": COLORS["router"],
},
},
"shape": "box",
"font": {"color": COLORS["text"]},
"borderWidth": 3,
"borderWidthSelected": 4,
"shapeProperties": {"borderDashes": [5, 5]},
"margin": {"top": 10, "bottom": 8, "left": 10, "right": 10},
},
}

274
src/crewai/flow/flow.py Normal file
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@@ -0,0 +1,274 @@
# flow.py
# flow.py
import asyncio
import inspect
from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
from pydantic import BaseModel
from crewai.flow.flow_visualizer import plot_flow
T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
def start(condition=None):
def decorator(func):
func.__is_start_method__ = True
if condition is not None:
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
elif (
isinstance(condition, dict)
and "type" in condition
and "methods" in condition
):
func.__trigger_methods__ = condition["methods"]
func.__condition_type__ = condition["type"]
elif callable(condition) and hasattr(condition, "__name__"):
func.__trigger_methods__ = [condition.__name__]
func.__condition_type__ = "OR"
else:
raise ValueError(
"Condition must be a method, string, or a result of or_() or and_()"
)
return func
return decorator
def listen(condition):
def decorator(func):
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
elif (
isinstance(condition, dict)
and "type" in condition
and "methods" in condition
):
func.__trigger_methods__ = condition["methods"]
func.__condition_type__ = condition["type"]
elif callable(condition) and hasattr(condition, "__name__"):
func.__trigger_methods__ = [condition.__name__]
func.__condition_type__ = "OR"
else:
raise ValueError(
"Condition must be a method, string, or a result of or_() or and_()"
)
return func
return decorator
def router(method, paths=None):
def decorator(func):
func.__is_router__ = True
func.__router_for__ = method.__name__
if paths:
func.__router_paths__ = paths
return func
return decorator
def or_(*conditions):
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
methods.extend(condition["methods"])
elif isinstance(condition, str):
methods.append(condition)
elif callable(condition):
methods.append(getattr(condition, "__name__", repr(condition)))
else:
raise ValueError("Invalid condition in or_()")
return {"type": "OR", "methods": methods}
def and_(*conditions):
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
methods.extend(condition["methods"])
elif isinstance(condition, str):
methods.append(condition)
elif callable(condition):
methods.append(getattr(condition, "__name__", repr(condition)))
else:
raise ValueError("Invalid condition in and_()")
return {"type": "AND", "methods": methods}
class FlowMeta(type):
def __new__(mcs, name, bases, dct):
cls = super().__new__(mcs, name, bases, dct)
start_methods = []
listeners = {}
routers = {}
router_paths = {}
for attr_name, attr_value in dct.items():
if hasattr(attr_value, "__is_start_method__"):
start_methods.append(attr_name)
if hasattr(attr_value, "__trigger_methods__"):
methods = attr_value.__trigger_methods__
condition_type = getattr(attr_value, "__condition_type__", "OR")
listeners[attr_name] = (condition_type, methods)
elif hasattr(attr_value, "__trigger_methods__"):
methods = attr_value.__trigger_methods__
condition_type = getattr(attr_value, "__condition_type__", "OR")
listeners[attr_name] = (condition_type, methods)
elif hasattr(attr_value, "__is_router__"):
routers[attr_value.__router_for__] = attr_name
if hasattr(attr_value, "__router_paths__"):
router_paths[attr_name] = attr_value.__router_paths__
# **Register router as a listener to its triggering method**
trigger_method_name = attr_value.__router_for__
methods = [trigger_method_name]
condition_type = "OR"
listeners[attr_name] = (condition_type, methods)
setattr(cls, "_start_methods", start_methods)
setattr(cls, "_listeners", listeners)
setattr(cls, "_routers", routers)
setattr(cls, "_router_paths", router_paths)
return cls
class Flow(Generic[T], metaclass=FlowMeta):
_start_methods: List[str] = []
_listeners: Dict[str, tuple[str, List[str]]] = {}
_routers: Dict[str, str] = {}
_router_paths: Dict[str, List[str]] = {}
initial_state: Union[Type[T], T, None] = None
def __class_getitem__(cls, item):
class _FlowGeneric(cls):
_initial_state_T = item
return _FlowGeneric
def __init__(self):
self._methods: Dict[str, Callable] = {}
self._state = self._create_initial_state()
self._completed_methods: Set[str] = set()
self._pending_and_listeners: Dict[str, Set[str]] = {}
self._method_outputs: List[Any] = [] # List to store all method outputs
for method_name in dir(self):
if callable(getattr(self, method_name)) and not method_name.startswith(
"__"
):
self._methods[method_name] = getattr(self, method_name)
def _create_initial_state(self) -> T:
if self.initial_state is None and hasattr(self, "_initial_state_T"):
return self._initial_state_T() # type: ignore
if self.initial_state is None:
return {} # type: ignore
elif isinstance(self.initial_state, type):
return self.initial_state()
else:
return self.initial_state
@property
def state(self) -> T:
return self._state
@property
def method_outputs(self) -> List[Any]:
"""Returns the list of all outputs from executed methods."""
return self._method_outputs
async def kickoff(self) -> Any:
if not self._start_methods:
raise ValueError("No start method defined")
# Create tasks for all start methods
tasks = [
self._execute_start_method(start_method)
for start_method in self._start_methods
]
# Run all start methods concurrently
await asyncio.gather(*tasks)
# Return the final output (from the last executed method)
if self._method_outputs:
return self._method_outputs[-1]
else:
return None # Or raise an exception if no methods were executed
async def _execute_start_method(self, start_method: str):
result = await self._execute_method(self._methods[start_method])
await self._execute_listeners(start_method, result)
async def _execute_method(self, method: Callable, *args, **kwargs):
result = (
await method(*args, **kwargs)
if asyncio.iscoroutinefunction(method)
else method(*args, **kwargs)
)
self._method_outputs.append(result) # Store the output
return result
async def _execute_listeners(self, trigger_method: str, result: Any):
listener_tasks = []
if trigger_method in self._routers:
router_method = self._methods[self._routers[trigger_method]]
path = await self._execute_method(router_method)
# Use the path as the new trigger method
trigger_method = path
for listener, (condition_type, methods) in self._listeners.items():
if condition_type == "OR":
if trigger_method in methods:
listener_tasks.append(
self._execute_single_listener(listener, result)
)
elif condition_type == "AND":
if listener not in self._pending_and_listeners:
self._pending_and_listeners[listener] = set()
self._pending_and_listeners[listener].add(trigger_method)
if set(methods) == self._pending_and_listeners[listener]:
listener_tasks.append(
self._execute_single_listener(listener, result)
)
del self._pending_and_listeners[listener]
# Run all listener tasks concurrently and wait for them to complete
await asyncio.gather(*listener_tasks)
async def _execute_single_listener(self, listener: str, result: Any):
try:
method = self._methods[listener]
sig = inspect.signature(method)
params = list(sig.parameters.values())
# Exclude 'self' parameter
method_params = [p for p in params if p.name != "self"]
if method_params:
# If listener expects parameters, pass the result
listener_result = await self._execute_method(method, result)
else:
# If listener does not expect parameters, call without arguments
listener_result = await self._execute_method(method)
# Execute listeners of this listener
await self._execute_listeners(listener, listener_result)
except Exception as e:
print(f"[Flow._execute_single_listener] Error in method {listener}: {e}")
import traceback
traceback.print_exc()
def plot(self, filename: str = "crewai_flow_graph"):
plot_flow(self, filename)

99
src/crewai/flow/flow_visualizer.py Normal file
View File

@@ -0,0 +1,99 @@
# flow_visualizer.py
import os
from pyvis.network import Network
from crewai.flow.config import COLORS, NODE_STYLES
from crewai.flow.html_template_handler import HTMLTemplateHandler
from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items
from crewai.flow.utils import calculate_node_levels
from crewai.flow.visualization_utils import (
add_edges,
add_nodes_to_network,
compute_positions,
)
class FlowPlot:
def __init__(self, flow):
self.flow = flow
self.colors = COLORS
self.node_styles = NODE_STYLES
def plot(self, filename):
net = Network(
directed=True,
height="750px",
width="100%",
bgcolor=self.colors["bg"],
layout=None,
)
# Calculate levels for nodes
node_levels = calculate_node_levels(self.flow)
# Compute positions
node_positions = compute_positions(self.flow, node_levels)
# Add nodes to the network
add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
# Add edges to the network
add_edges(net, self.flow, node_positions, self.colors)
# Set options to disable physics
net.set_options(
"""
var options = {
"physics": {
"enabled": false
}
}
"""
)
network_html = net.generate_html()
final_html_content = self._generate_final_html(network_html)
# Save the final HTML content to the file
with open(f"{filename}.html", "w", encoding="utf-8") as f:
f.write(final_html_content)
print(f"Graph saved as {filename}.html")
self._cleanup_pyvis_lib()
def _generate_final_html(self, network_html):
# Extract just the body content from the generated HTML
current_dir = os.path.dirname(__file__)
template_path = os.path.join(
current_dir, "assets", "crewai_flow_visual_template.html"
)
logo_path = os.path.join(current_dir, "assets", "crewai_logo.svg")
html_handler = HTMLTemplateHandler(template_path, logo_path)
network_body = html_handler.extract_body_content(network_html)
# Generate the legend items HTML
legend_items = get_legend_items(self.colors)
legend_items_html = generate_legend_items_html(legend_items)
final_html_content = html_handler.generate_final_html(
network_body, legend_items_html
)
return final_html_content
def _cleanup_pyvis_lib(self):
# Clean up the generated lib folder
lib_folder = os.path.join(os.getcwd(), "lib")
try:
if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
import shutil
shutil.rmtree(lib_folder)
except Exception as e:
print(f"Error cleaning up {lib_folder}: {e}")
def plot_flow(flow, filename="flow_graph"):
visualizer = FlowPlot(flow)
visualizer.plot(filename)

66
src/crewai/flow/html_template_handler.py Normal file
View File

@@ -0,0 +1,66 @@
import base64
import os
import re
class HTMLTemplateHandler:
def __init__(self, template_path, logo_path):
self.template_path = template_path
self.logo_path = logo_path
def read_template(self):
with open(self.template_path, "r", encoding="utf-8") as f:
return f.read()
def encode_logo(self):
with open(self.logo_path, "rb") as logo_file:
logo_svg_data = logo_file.read()
return base64.b64encode(logo_svg_data).decode("utf-8")
def extract_body_content(self, html):
match = re.search("<body.*?>(.*?)</body>", html, re.DOTALL)
return match.group(1) if match else ""
def generate_legend_items_html(self, legend_items):
legend_items_html = ""
for item in legend_items:
if "border" in item:
legend_items_html += f"""
<div class="legend-item">
<div class="legend-color-box" style="background-color: {item['color']}; border: 2px dashed {item['border']};"></div>
<div>{item['label']}</div>
</div>
"""
elif item.get("dashed") is not None:
style = "dashed" if item["dashed"] else "solid"
legend_items_html += f"""
<div class="legend-item">
<div class="legend-{style}" style="border-bottom: 2px {style} {item['color']};"></div>
<div>{item['label']}</div>
</div>
"""
else:
legend_items_html += f"""
<div class="legend-item">
<div class="legend-color-box" style="background-color: {item['color']};"></div>
<div>{item['label']}</div>
</div>
"""
return legend_items_html
def generate_final_html(self, network_body, legend_items_html, title="Flow Graph"):
html_template = self.read_template()
logo_svg_base64 = self.encode_logo()
final_html_content = html_template.replace("{{ title }}", title)
final_html_content = final_html_content.replace(
"{{ network_content }}", network_body
)
final_html_content = final_html_content.replace(
"{{ logo_svg_base64 }}", logo_svg_base64
)
final_html_content = final_html_content.replace(
"<!-- LEGEND_ITEMS_PLACEHOLDER -->", legend_items_html
)
return final_html_content

46
src/crewai/flow/legend_generator.py Normal file
View File

@@ -0,0 +1,46 @@
def get_legend_items(colors):
return [
{"label": "Start Method", "color": colors["start"]},
{"label": "Method", "color": colors["method"]},
{
"label": "Router",
"color": colors["router"],
"border": colors["router_border"],
"dashed": True,
},
{"label": "Trigger", "color": colors["edge"], "dashed": False},
{"label": "AND Trigger", "color": colors["edge"], "dashed": True},
{
"label": "Router Trigger",
"color": colors["router_edge"],
"dashed": True,
},
]
def generate_legend_items_html(legend_items):
legend_items_html = ""
for item in legend_items:
if "border" in item:
legend_items_html += f"""
<div class="legend-item">
<div class="legend-color-box" style="background-color: {item['color']}; border: 2px dashed {item['border']};"></div>
<div>{item['label']}</div>
</div>
"""
elif item.get("dashed") is not None:
style = "dashed" if item["dashed"] else "solid"
legend_items_html += f"""
<div class="legend-item">
<div class="legend-{style}" style="border-bottom: 2px {style} {item['color']};"></div>
<div>{item['label']}</div>
</div>
"""
else:
legend_items_html += f"""
<div class="legend-item">
<div class="legend-color-box" style="background-color: {item['color']};"></div>
<div>{item['label']}</div>
</div>
"""
return legend_items_html

143
src/crewai/flow/utils.py Normal file
View File

@@ -0,0 +1,143 @@
def calculate_node_levels(flow):
levels = {}
queue = []
visited = set()
pending_and_listeners = {}
# Make all start methods at level 0
for method_name, method in flow._methods.items():
if hasattr(method, "__is_start_method__"):
levels[method_name] = 0
queue.append(method_name)
# Breadth-first traversal to assign levels
while queue:
current = queue.pop(0)
current_level = levels[current]
visited.add(current)
for listener_name, (
condition_type,
trigger_methods,
) in flow._listeners.items():
if condition_type == "OR":
if current in trigger_methods:
if (
listener_name not in levels
or levels[listener_name] > current_level + 1
):
levels[listener_name] = current_level + 1
if listener_name not in visited:
queue.append(listener_name)
elif condition_type == "AND":
if listener_name not in pending_and_listeners:
pending_and_listeners[listener_name] = set()
if current in trigger_methods:
pending_and_listeners[listener_name].add(current)
if set(trigger_methods) == pending_and_listeners[listener_name]:
if (
listener_name not in levels
or levels[listener_name] > current_level + 1
):
levels[listener_name] = current_level + 1
if listener_name not in visited:
queue.append(listener_name)
# Handle router connections
if current in flow._routers.values():
router_method_name = current
paths = flow._router_paths.get(router_method_name, [])
for path in paths:
for listener_name, (
condition_type,
trigger_methods,
) in flow._listeners.items():
if path in trigger_methods:
if (
listener_name not in levels
or levels[listener_name] > current_level + 1
):
levels[listener_name] = current_level + 1
if listener_name not in visited:
queue.append(listener_name)
return levels
def count_outgoing_edges(flow):
counts = {}
for method_name in flow._methods:
counts[method_name] = 0
for method_name in flow._listeners:
_, trigger_methods = flow._listeners[method_name]
for trigger in trigger_methods:
if trigger in flow._methods:
counts[trigger] += 1
return counts
def build_ancestor_dict(flow):
ancestors = {node: set() for node in flow._methods}
visited = set()
for node in flow._methods:
if node not in visited:
dfs_ancestors(node, ancestors, visited, flow)
return ancestors
def dfs_ancestors(node, ancestors, visited, flow):
if node in visited:
return
visited.add(node)
# Handle regular listeners
for listener_name, (_, trigger_methods) in flow._listeners.items():
if node in trigger_methods:
ancestors[listener_name].add(node)
ancestors[listener_name].update(ancestors[node])
dfs_ancestors(listener_name, ancestors, visited, flow)
# Handle router methods separately
if node in flow._routers.values():
router_method_name = node
paths = flow._router_paths.get(router_method_name, [])
for path in paths:
for listener_name, (_, trigger_methods) in flow._listeners.items():
if path in trigger_methods:
# Only propagate the ancestors of the router method, not the router method itself
ancestors[listener_name].update(ancestors[node])
dfs_ancestors(listener_name, ancestors, visited, flow)
def is_ancestor(node, ancestor_candidate, ancestors):
return ancestor_candidate in ancestors.get(node, set())
def build_parent_children_dict(flow):
parent_children = {}
# Map listeners to their trigger methods
for listener_name, (_, trigger_methods) in flow._listeners.items():
for trigger in trigger_methods:
if trigger not in parent_children:
parent_children[trigger] = []
if listener_name not in parent_children[trigger]:
parent_children[trigger].append(listener_name)
# Map router methods to their paths and to listeners
for router_method_name, paths in flow._router_paths.items():
for path in paths:
# Map router method to listeners of each path
for listener_name, (_, trigger_methods) in flow._listeners.items():
if path in trigger_methods:
if router_method_name not in parent_children:
parent_children[router_method_name] = []
if listener_name not in parent_children[router_method_name]:
parent_children[router_method_name].append(listener_name)
return parent_children
def get_child_index(parent, child, parent_children):
children = parent_children.get(parent, [])
children.sort()
return children.index(child)

132
src/crewai/flow/visualization_utils.py Normal file
View File

@@ -0,0 +1,132 @@
from .utils import (
build_ancestor_dict,
build_parent_children_dict,
get_child_index,
is_ancestor,
)
def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150):
level_nodes = {}
node_positions = {}
for method_name, level in node_levels.items():
level_nodes.setdefault(level, []).append(method_name)
for level, nodes in level_nodes.items():
x_offset = -(len(nodes) - 1) * x_spacing / 2 # Center nodes horizontally
for i, method_name in enumerate(nodes):
x = x_offset + i * x_spacing
y = level * y_spacing
node_positions[method_name] = (x, y)
return node_positions
def add_edges(net, flow, node_positions, colors):
ancestors = build_ancestor_dict(flow)
parent_children = build_parent_children_dict(flow)
for method_name in flow._listeners:
condition_type, trigger_methods = flow._listeners[method_name]
is_and_condition = condition_type == "AND"
for trigger in trigger_methods:
if trigger in flow._methods or trigger in flow._routers.values():
is_router_edge = any(
trigger in paths for paths in flow._router_paths.values()
)
edge_color = colors["router_edge"] if is_router_edge else colors["edge"]
is_cycle_edge = is_ancestor(trigger, method_name, ancestors)
parent_has_multiple_children = len(parent_children.get(trigger, [])) > 1
needs_curvature = is_cycle_edge or parent_has_multiple_children
if needs_curvature:
source_pos = node_positions.get(trigger)
target_pos = node_positions.get(method_name)
if source_pos and target_pos:
dx = target_pos[0] - source_pos[0]
smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
index = get_child_index(trigger, method_name, parent_children)
edge_smooth = {
"type": smooth_type,
"roundness": 0.2 + (0.1 * index),
}
else:
edge_smooth = {"type": "cubicBezier"}
else:
edge_smooth = False
edge_style = {
"color": edge_color,
"width": 2,
"arrows": "to",
"dashes": True if is_router_edge or is_and_condition else False,
"smooth": edge_smooth,
}
net.add_edge(trigger, method_name, **edge_style)
for router_method_name, paths in flow._router_paths.items():
for path in paths:
for listener_name, (
condition_type,
trigger_methods,
) in flow._listeners.items():
if path in trigger_methods:
is_cycle_edge = is_ancestor(trigger, method_name, ancestors)
parent_has_multiple_children = (
len(parent_children.get(router_method_name, [])) > 1
)
needs_curvature = is_cycle_edge or parent_has_multiple_children
if needs_curvature:
source_pos = node_positions.get(router_method_name)
target_pos = node_positions.get(listener_name)
if source_pos and target_pos:
dx = target_pos[0] - source_pos[0]
smooth_type = "curvedCCW" if dx <= 0 else "curvedCW"
index = get_child_index(
router_method_name, listener_name, parent_children
)
edge_smooth = {
"type": smooth_type,
"roundness": 0.2 + (0.1 * index),
}
else:
edge_smooth = {"type": "cubicBezier"}
else:
edge_smooth = False
edge_style = {
"color": colors["router_edge"],
"width": 2,
"arrows": "to",
"dashes": True,
"smooth": edge_smooth,
}
net.add_edge(router_method_name, listener_name, **edge_style)
def add_nodes_to_network(net, flow, node_positions, node_styles):
for method_name, (x, y) in node_positions.items():
method = flow._methods.get(method_name)
if hasattr(method, "__is_start_method__"):
node_style = node_styles["start"]
elif hasattr(method, "__is_router__"):
node_style = node_styles["router"]
else:
node_style = node_styles["method"]
net.add_node(
method_name,
label=method_name,
x=x,
y=y,
fixed=True,
physics=False,
**node_style,
)

149
src/crewai/llm.py
View File

@@ -1,8 +1,75 @@
from contextlib import contextmanager
from typing import Any, Dict, List, Optional, Union
import logging
import warnings
import litellm
from litellm import get_supported_openai_params
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
)
import sys
import io
class FilteredStream(io.StringIO):
def write(self, s):
if (
"Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new"
in s
or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`"
in s
):
return
super().write(s)
LLM_CONTEXT_WINDOW_SIZES = {
# openai
"gpt-4": 8192,
"gpt-4o": 128000,
"gpt-4o-mini": 128000,
"gpt-4-turbo": 128000,
"o1-preview": 128000,
"o1-mini": 128000,
# deepseek
"deepseek-chat": 128000,
# groq
"gemma2-9b-it": 8192,
"gemma-7b-it": 8192,
"llama3-groq-70b-8192-tool-use-preview": 8192,
"llama3-groq-8b-8192-tool-use-preview": 8192,
"llama-3.1-70b-versatile": 131072,
"llama-3.1-8b-instant": 131072,
"llama-3.2-1b-preview": 8192,
"llama-3.2-3b-preview": 8192,
"llama-3.2-11b-text-preview": 8192,
"llama-3.2-90b-text-preview": 8192,
"llama3-70b-8192": 8192,
"llama3-8b-8192": 8192,
"mixtral-8x7b-32768": 32768,
}
@contextmanager
def suppress_warnings():
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# Redirect stdout and stderr
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = FilteredStream()
sys.stderr = FilteredStream()
try:
yield
finally:
# Restore stdout and stderr
sys.stdout = old_stdout
sys.stderr = old_stderr
class LLM:
def __init__(
@@ -50,42 +117,50 @@ class LLM:
self.kwargs = kwargs
litellm.drop_params = True
litellm.set_verbose = False
litellm.callbacks = callbacks
def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
if callbacks and len(callbacks) > 0:
litellm.callbacks = callbacks
with suppress_warnings():
if callbacks and len(callbacks) > 0:
litellm.callbacks = callbacks
try:
params = {
"model": self.model,
"messages": messages,
"timeout": self.timeout,
"temperature": self.temperature,
"top_p": self.top_p,
"n": self.n,
"stop": self.stop,
"max_tokens": self.max_tokens or self.max_completion_tokens,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"logit_bias": self.logit_bias,
"response_format": self.response_format,
"seed": self.seed,
"logprobs": self.logprobs,
"top_logprobs": self.top_logprobs,
"api_base": self.base_url,
"api_version": self.api_version,
"api_key": self.api_key,
**self.kwargs,
}
# Remove None values to avoid passing unnecessary parameters
params = {k: v for k, v in params.items() if v is not None}
try:
params = {
"model": self.model,
"messages": messages,
"timeout": self.timeout,
"temperature": self.temperature,
"top_p": self.top_p,
"n": self.n,
"stop": self.stop,
"max_tokens": self.max_tokens or self.max_completion_tokens,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"logit_bias": self.logit_bias,
"response_format": self.response_format,
"seed": self.seed,
"logprobs": self.logprobs,
"top_logprobs": self.top_logprobs,
"api_base": self.base_url,
"api_version": self.api_version,
"api_key": self.api_key,
"stream": False,
**self.kwargs,
}
response = litellm.completion(**params)
return response["choices"][0]["message"]["content"]
except Exception as e:
logging.error(f"LiteLLM call failed: {str(e)}")
raise # Re-raise the exception after logging
# Remove None values to avoid passing unnecessary parameters
params = {k: v for k, v in params.items() if v is not None}
response = litellm.completion(**params)
return response["choices"][0]["message"]["content"]
except Exception as e:
if not LLMContextLengthExceededException(
str(e)
)._is_context_limit_error(str(e)):
logging.error(f"LiteLLM call failed: {str(e)}")
raise # Re-raise the exception after logging
def supports_function_calling(self) -> bool:
try:
@@ -94,3 +169,15 @@ class LLM:
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False
def supports_stop_words(self) -> bool:
try:
params = get_supported_openai_params(model=self.model)
return "stop" in params
except Exception as e:
logging.error(f"Failed to get supported params: {str(e)}")
return False
def get_context_window_size(self) -> int:
# Only using 75% of the context window size to avoid cutting the message in the middle
return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)

3
src/crewai/project/annotations.py
View File

@@ -1,6 +1,7 @@
from functools import wraps
from crewai.project.utils import memoize
from crewai import Crew
def task(func):
@@ -72,7 +73,7 @@ def pipeline(func):
return memoize(func)
def crew(func):
def crew(func) -> "Crew":
def wrapper(self, *args, **kwargs):
instantiated_tasks = []
instantiated_agents = []

6
src/crewai/project/crew_base.py
View File

@@ -1,14 +1,16 @@
import inspect
from pathlib import Path
from typing import Any, Callable, Dict
from typing import Any, Callable, Dict, Type, TypeVar
import yaml
from dotenv import load_dotenv
load_dotenv()
T = TypeVar("T", bound=Type[Any])
def CrewBase(cls):
def CrewBase(cls: T) -> T:
class WrappedClass(cls):
is_crew_class: bool = True # type: ignore

6
src/crewai/telemetry/telemetry.py
View File

@@ -301,7 +301,7 @@ class Telemetry:
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
if llm:
self._add_attribute(span, "llm", llm)
self._add_attribute(span, "llm", llm.model)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:
@@ -321,7 +321,7 @@ class Telemetry:
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
if llm:
self._add_attribute(span, "llm", llm)
self._add_attribute(span, "llm", llm.model)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:
@@ -339,7 +339,7 @@ class Telemetry:
pkg_resources.get_distribution("crewai").version,
)
if llm:
self._add_attribute(span, "llm", llm)
self._add_attribute(span, "llm", llm.model)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:

124
src/crewai/tools/tool_usage.py
View File

@@ -85,7 +85,6 @@ class ToolUsage:
def use(
self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
) -> str:
print("calling", calling)
if isinstance(calling, ToolUsageErrorException):
error = calling.message
if self.agent.verbose:
@@ -298,63 +297,78 @@ class ToolUsage:
)
return "\n--\n".join(descriptions)
def _function_calling(self, tool_string: str):
model = (
InstructorToolCalling
if self.function_calling_llm.supports_function_calling()
else ToolCalling
)
converter = Converter(
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
llm=self.function_calling_llm,
model=model,
instructions=dedent(
"""\
The schema should have the following structure, only two keys:
- tool_name: str
- arguments: dict (always a dictionary, with all arguments being passed)
Example:
{"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
),
max_attempts=1,
)
tool_object = converter.to_pydantic()
calling = ToolCalling(
tool_name=tool_object["tool_name"],
arguments=tool_object["arguments"],
log=tool_string, # type: ignore
)
if isinstance(calling, ConverterError):
raise calling
return calling
def _original_tool_calling(self, tool_string: str, raise_error: bool = False):
tool_name = self.action.tool
tool = self._select_tool(tool_name)
try:
tool_input = self._validate_tool_input(self.action.tool_input)
arguments = ast.literal_eval(tool_input)
except Exception:
if raise_error:
raise
else:
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_arguments_error")}'
)
if not isinstance(arguments, dict):
if raise_error:
raise
else:
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_arguments_error")}'
)
return ToolCalling(
tool_name=tool.name,
arguments=arguments,
log=tool_string, # type: ignore
)
def _tool_calling(
self, tool_string: str
) -> Union[ToolCalling, InstructorToolCalling]:
try:
if self.function_calling_llm:
print("self.function_calling_llm")
model = (
InstructorToolCalling
if self.function_calling_llm.supports_function_calling()
else ToolCalling
)
converter = Converter(
text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid output schema:\n\n### TEXT \n{tool_string}",
llm=self.function_calling_llm,
model=model,
instructions=dedent(
"""\
The schema should have the following structure, only two keys:
- tool_name: str
- arguments: dict (with all arguments being passed)
Example:
{"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
),
max_attempts=1,
)
print("converter", converter)
tool_object = converter.to_pydantic()
print("tool_object", tool_object)
calling = ToolCalling(
tool_name=tool_object["tool_name"],
arguments=tool_object["arguments"],
log=tool_string, # type: ignore
)
print("calling", calling)
if isinstance(calling, ConverterError):
raise calling
else:
tool_name = self.action.tool
tool = self._select_tool(tool_name)
try:
tool_input = self._validate_tool_input(self.action.tool_input)
arguments = ast.literal_eval(tool_input)
except Exception:
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_arguments_error")}'
)
if not isinstance(arguments, dict):
return ToolUsageErrorException( # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
f'{self._i18n.errors("tool_arguments_error")}'
)
calling = ToolCalling(
tool_name=tool.name,
arguments=arguments,
log=tool_string, # type: ignore
)
try:
return self._original_tool_calling(tool_string, raise_error=True)
except Exception:
if self.function_calling_llm:
return self._function_calling(tool_string)
else:
return self._original_tool_calling(tool_string)
except Exception as e:
self._run_attempts += 1
if self._run_attempts > self._max_parsing_attempts:
@@ -367,8 +381,6 @@ class ToolUsage:
)
return self._tool_calling(tool_string)
return calling
def _validate_tool_input(self, tool_input: str) -> str:
try:
ast.literal_eval(tool_input)

2
src/crewai/translations/en.json
View File

@@ -17,7 +17,7 @@
"task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
"human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
"getting_input": "This is the agent's final answer: {final_answer}\nPlease provide feedback: ",
"getting_input": "This is the agent's final answer: {final_answer}\n\n",
"summarizer_system_message": "You are a helpful assistant that summarizes text.",
"sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
"summary": "This is a summary of our conversation so far:\n{merged_summary}"

6
src/crewai/utilities/converter.py
View File

@@ -132,7 +132,6 @@ def handle_partial_json(
converter_cls: Optional[Type[Converter]] = None,
) -> Union[dict, BaseModel, str]:
match = re.search(r"({.*})", result, re.DOTALL)
print("handle_partial_json")
if match:
try:
exported_result = model.model_validate_json(match.group(0))
@@ -161,11 +160,8 @@ def convert_with_instructions(
agent: Any,
converter_cls: Optional[Type[Converter]] = None,
) -> Union[dict, BaseModel, str]:
print("convert_with_instructions")
llm = agent.function_calling_llm or agent.llm
print("llm", llm)
instructions = get_conversion_instructions(model, llm)
print("instructions", instructions)
converter = create_converter(
agent=agent,
converter_cls=converter_cls,
@@ -174,11 +170,9 @@ def convert_with_instructions(
model=model,
instructions=instructions,
)
print("converter", converter)
exported_result = (
converter.to_pydantic() if not is_json_output else converter.to_json()
)
print("exported_result", exported_result)
if isinstance(exported_result, ConverterError):
Printer().print(

4
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -49,7 +49,7 @@ class TaskEvaluation(BaseModel):
class TrainingTaskEvaluation(BaseModel):
suggestions: List[str] = Field(
description="Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs provide action items based on human_feedback for future tasks."
description="List of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specific points from the human feedback are incorporated into these instructions."
)
quality: float = Field(
description="A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback."
@@ -116,7 +116,7 @@ class TaskEvaluator:
"Assess the quality of the training data based on the llm output, human feedback , and llm output improved result.\n\n"
f"{final_aggregated_data}"
"Please provide:\n"
"- Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs provide action items based on human_feedback for future tasks\n"
"- Provide a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent's performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specificpoints from the human feedback are incorporated into these instructions.\n"
"- A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback\n"
)
instructions = "I'm gonna convert this raw text into valid JSON."

1
src/crewai/utilities/exceptions/context_window_exceeding_exception.py
View File

@@ -1,5 +1,6 @@
class LLMContextLengthExceededException(Exception):
CONTEXT_LIMIT_ERRORS = [
"expected a string with maximum length",
"maximum context length",
"context length exceeded",
"context_length_exceeded",

54
tests/agent_test.py
View File

@@ -133,8 +133,8 @@ def test_agent_execute_task():
assert result is not None
assert (
"The area of the circle with a radius of 5 cm is approximately 78.5 square centimeters."
== result
result
== "The calculated area of the circle is approximately 78.5 square centimeters."
)
assert "square centimeters" in result.lower()
@@ -155,7 +155,7 @@ def test_agent_execution():
)
output = agent.execute_task(task)
assert output == "The result of the math operation 1 + 1 is 2."
assert output == "1 + 1 is 2"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -179,7 +179,7 @@ def test_agent_execution_with_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task)
assert output == "The result of 3 times 4 is 12"
assert output == "The result of the multiplication is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -211,7 +211,7 @@ def test_logging_tool_usage():
tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
)
assert output == "12"
assert output == "The result of the multiplication is 12."
assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
@@ -365,7 +365,7 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task=task, tools=[multiplier])
assert output == "The result of 3 times 4 is 12."
assert output == "The result of the multiplication is 12."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -383,7 +383,6 @@ def test_agent_powered_by_new_o_model_family_that_allows_skipping_tool():
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
use_stop_words=False,
)
task = Task(
@@ -410,7 +409,6 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
max_iter=3,
use_system_prompt=False,
allow_delegation=False,
use_stop_words=False,
)
task = Task(
@@ -419,7 +417,7 @@ def test_agent_powered_by_new_o_model_family_that_uses_tool():
expected_output="The number of customers",
)
output = agent.execute_task(task=task, tools=[comapny_customer_data])
assert output == "The company has 42 customers"
assert output == "42"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -549,7 +547,7 @@ def test_agent_moved_on_after_max_iterations():
task=task,
tools=[get_final_answer],
)
assert output == "42"
assert output == "The final answer is 42."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -580,7 +578,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
task=task,
tools=[get_final_answer],
)
assert output == "42"
assert output == "The final answer is 42."
captured = capsys.readouterr()
assert "Max RPM reached, waiting for next minute to start." in captured.out
moveon.assert_called()
@@ -710,12 +708,13 @@ def test_agent_error_on_parsing_tool(capsys):
verbose=True,
function_calling_llm="gpt-4o",
)
with patch.object(ToolUsage, "_render") as force_exception:
force_exception.side_effect = Exception("Error on parsing tool.")
crew.kickoff()
captured = capsys.readouterr()
assert "Error on parsing tool." in captured.out
with patch.object(ToolUsage, "_original_tool_calling") as force_exception_1:
force_exception_1.side_effect = Exception("Error on parsing tool.")
with patch.object(ToolUsage, "_render") as force_exception_2:
force_exception_2.side_effect = Exception("Error on parsing tool.")
crew.kickoff()
captured = capsys.readouterr()
assert "Error on parsing tool." in captured.out
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -842,12 +841,16 @@ def test_agent_function_calling_llm():
crew = Crew(agents=[agent1], tasks=tasks)
from unittest.mock import patch
import instructor
from crewai.tools.tool_usage import ToolUsage
with patch.object(
instructor, "from_litellm", wraps=instructor.from_litellm
) as mock_from_litellm:
) as mock_from_litellm, patch.object(
ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
) as mock_original_tool_calling:
crew.kickoff()
mock_from_litellm.assert_called()
mock_original_tool_calling.assert_called()
def test_agent_count_formatting_error():
@@ -1090,7 +1093,7 @@ def test_agent_training_handler(crew_training_handler):
result = agent._training_handler(task_prompt=task_prompt)
assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good"
assert result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n good"
crew_training_handler.assert_has_calls(
[mock.call(), mock.call("training_data.pkl"), mock.call().load()]
@@ -1118,8 +1121,8 @@ def test_agent_use_trained_data(crew_training_handler):
result = agent._use_trained_data(task_prompt=task_prompt)
assert (
result == "What is 1 + 1?You MUST follow these feedbacks: \n "
"The result of the math operation must be right.\n - Result must be better than 1."
result == "What is 1 + 1?\n\nYou MUST follow these instructions: \n"
" - The result of the math operation must be right.\n - Result must be better than 1."
)
crew_training_handler.assert_has_calls(
[mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
@@ -1202,7 +1205,9 @@ def test_agent_with_custom_stop_words():
)
assert isinstance(agent.llm, LLM)
assert agent.llm.stop == stop_words
assert set(agent.llm.stop) == set(stop_words + ["\nObservation:"])
assert all(word in agent.llm.stop for word in stop_words)
assert "\nObservation:" in agent.llm.stop
def test_agent_with_callbacks():
@@ -1365,7 +1370,8 @@ def test_agent_with_all_llm_attributes():
assert agent.llm.temperature == 0.7
assert agent.llm.top_p == 0.9
assert agent.llm.n == 1
assert agent.llm.stop == ["STOP", "END"]
assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"])
assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"])
assert agent.llm.max_tokens == 100
assert agent.llm.presence_penalty == 0.1
assert agent.llm.frequency_penalty == 0.1
@@ -1520,7 +1526,7 @@ def test_agent_execute_task_with_custom_llm():
result = agent.execute_task(task)
assert result.startswith(
"Artificial minds,\nLearning, evolving, creating,\nFuture in circuits."
"Artificial minds,\nCoding thoughts in circuits bright,\nAI's silent might."
)

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
result
== "While I understand the concerns and skepticism surrounding AI agents, I wouldn't say that I hate them. My standpoint is more nuanced. AI agents, which are software entities that perform tasks autonomously using machine learning and other AI technologies, have tremendous potential to revolutionize various sectors.\n\nOn the positive side, AI agents can significantly enhance efficiency and productivity. For example, in customer service, AI agents can handle routine inquiries, allowing human agents to focus on more complex issues. In healthcare, they can assist in diagnosing diseases, thus speeding up the decision-making process and potentially saving lives. In finance, AI agents can automate trading, detect fraudulent activities, and provide personalized financial advice.\n\nHowever, there are legitimate concerns that need to be addressed. One major issue is the ethical implications of deploying AI agents. These include data privacy, biases in decision-making algorithms, and the lack of transparency in how these agents operate. Another concern is the potential job displacement that could result from increased automation. While AI agents can handle many tasks more efficiently than humans, this could lead to significant job losses in certain sectors.\n\nMoreover, there's the matter of reliability and accountability. AI agents, despite their advanced capabilities, are not infallible. They can make mistakes, and when they do, it can be challenging to pinpoint where things went wrong and who is responsible. This raises important questions about oversight and governance.\n\nIn summary, while I am cautious about the unchecked deployment of AI agents due to these ethical and practical concerns, I also recognize their potential to bring about significant positive changes. The key lies in finding a balanced approach that maximizes their benefits while mitigating their risks. This includes rigorous testing, continuous monitoring, and establishing clear ethical guidelines and policies to govern their use. \n\nBy addressing these challenges head-on, we can harness the power of AI agents in a way that is both innovative and responsible."
== "I understand why you might think I dislike AI agents, but my perspective is more nuanced. AI agents, in essence, are incredibly versatile tools designed to perform specific tasks autonomously or semi-autonomously. They harness various artificial intelligence techniques, such as machine learning, natural language processing, and computer vision, to interpret data, understand tasks, and execute them efficiently. \n\nFrom a technological standpoint, AI agents have revolutionized numerous industries. In customer service, for instance, AI agents like chatbots and virtual assistants handle customer inquiries 24/7, providing quick and efficient solutions. In healthcare, AI agents can assist in diagnosing diseases, managing patient data, and even predicting outbreaks. The automation capabilities of AI agents also enhance productivity in areas such as logistics, finance, and cybersecurity by identifying patterns and anomalies at speeds far beyond human capabilities.\n\nHowever, it's important to acknowledge the potential downsides and challenges associated with AI agents. Ethical considerations are paramount. Issues such as data privacy, security, and biases in AI algorithms need to be carefully managed. There is also the human aspect to consider—over-reliance on AI agents might lead to job displacement in certain sectors, and ensuring a fair transition for affected workers is crucial.\n\nMy concerns generally stem from these ethical and societal implications rather than from the technology itself. I advocate for responsible AI development, which includes transparency, fairness, and accountability. By addressing these concerns, we can harness the full potential of AI agents while mitigating the associated risks.\n\nSo, to clarify, I don't hate AI agents; I recognize their immense potential and the significant benefits they bring to various fields. However, I am equally aware of the challenges they present and advocate for a balanced approach to their development and deployment."
)
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert (
result
== 'AI agents are specialized software entities that perform tasks autonomously on behalf of users. They leverage artificial intelligence to process inputs, learn from experiences, and make decisions, mimicking human-like behavior. Despite their transformative potential, I don\'t "hate" AI agents; rather, I hold a nuanced view that acknowledges both their advantages and limitations.\n\nAdvantages of AI Agents:\n1. **Efficiency and Productivity**: AI agents can handle repetitive tasks efficiently, freeing up human workers to focus on more complex and creative activities.\n2. **24/7 Operation**: Unlike humans, AI agents can work around the clock without breaks, significantly increasing productivity and service availability.\n3. **Data Processing**: They can process and analyze vast amounts of data quickly and accurately, supporting better decision-making.\n4. **Personalization**: AI agents can tailor services and recommendations based on user behavior and preferences, improving customer satisfaction.\n\nLimitations and Concerns:\n1. **Ethical Issues**: The deployment of AI agents raises concerns about data privacy, surveillance, and the potential for bias in decision-making algorithms.\n2. **Job Displacement**: There is legitimate concern about AI agents replacing human jobs, especially in industries where tasks are routine and repetitive.\n3. **Dependence on Data Quality**: AI agents\' performance hinges on the quality and quantity of data they are trained on. Poor data quality can lead to erroneous outcomes.\n4. **Complexity in Implementation**: Developing and maintaining AI agents requires significant technical expertise and resources. Problems can arise from their complexity, leading to potential failures.\n\nIn conclusion, while I don\'t "hate" AI agents, I am cautious of their broad and uncritical adoption. It’s essential to strike a balance between leveraging their capabilities and addressing the ethical, social, and technical challenges they present.'
== "AI agents are essentially autonomous software programs that perform tasks or provide services on behalf of humans. They're built on complex algorithms and often leverage machine learning and neural networks to adapt and improve over time. \n\nIt's important to clarify that I don't \"hate\" AI agents, but I do approach them with a critical eye for a couple of reasons. AI agents have enormous potential to transform industries, making processes more efficient, providing insightful data analytics, and even learning from user behavior to offer personalized experiences. However, this potential comes with significant challenges and risks:\n\n1. **Ethical Concerns**: AI agents operate on data, and the biases present in data can lead to unfair or unethical outcomes. Ensuring that AI operates within ethical boundaries requires rigorous oversight, which is not always in place.\n\n2. **Privacy Issues**: AI agents often need access to large amounts of data, raising questions about privacy and data security. If not managed correctly, this can lead to unauthorized data access and potential misuse of sensitive information.\n\n3. **Transparency and Accountability**: The decision-making process of AI agents can be opaque, making it difficult to understand how they arrive at specific conclusions or actions. This lack of transparency poses challenges for accountability, especially if something goes wrong.\n\n4. **Job Displacement**: As AI agents become more capable, there are valid concerns about their impact on employment. Tasks that were traditionally performed by humans are increasingly being automated, which can lead to job loss in certain sectors.\n\n5. **Reliability**: While AI agents can outperform humans in many areas, they are not infallible. They can make mistakes, sometimes with serious consequences. Continuous monitoring and regular updates are essential to maintain their performance and reliability.\n\nIn summary, while AI agents offer substantial benefits and opportunities, it's critical to approach their adoption and deployment with careful consideration of the associated risks. Balancing innovation with responsibility is key to leveraging AI agents effectively and ethically. So, rather than \"hating\" AI agents, I advocate for a balanced, cautious approach that maximizes benefits while mitigating potential downsides."
)
@@ -52,7 +52,7 @@ def test_ask_question():
assert (
result
== "As a researcher specializing in technology and AI, I don't hate AI agents. In fact, I find them incredibly fascinating and beneficial. AI agents have the potential to transform various industries, improve efficiencies, and offer new solutions to complex problems. Their ability to learn, adapt, and perform tasks that were once thought to require human intelligence is remarkable. While it's important to consider ethical implications and ensure that AI systems are designed and deployed responsibly, I believe their overall positive impact on society and technology is significant. So to clarify, I don't hate AI agents; rather, I am quite enthusiastic about their potential and the advancements they bring to the field of technology."
== "As an expert researcher specialized in technology, I don't harbor emotions such as hate towards AI agents. Instead, my focus is on understanding, analyzing, and leveraging their potential to advance various fields. AI agents, when designed and implemented effectively, can greatly augment human capabilities, streamline processes, and provide valuable insights that might otherwise be overlooked. My enthusiasm for AI agents stems from their ability to transform industries and improve everyday life, making complex tasks more manageable and enhancing overall efficiency. This passion drives my research and commitment to making meaningful contributions in the realm of AI and AI agents."
)
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert (
result
== "As an expert researcher specialized in technology and AI, my perspective on AI agents is shaped by both their potential and limitations. AI agents are tools designed to perform tasks, analyze data, and assist in various domains efficiently and accurately. They have the capability to revolutionize industries by automating complex processes, enhancing decision-making, and providing personalized experiences. For instance, in healthcare, AI agents can help in diagnosing diseases with high precision, while in finance, they can predict market trends and prevent fraud.\n\nHowever, my appreciation for AI agents does not mean I am blind to their challenges. There are valid concerns related to privacy, ethical use, and the potential displacement of jobs. The development and deployment of AI should be approached with caution, ensuring transparency, fairness, and accountability.\n\nIn conclusion, I value the advancements AI agents bring to the table and acknowledge their profound impact on society. My interest lies in leveraging their potential responsibly while addressing the associated ethical and societal challenges. So, while I love the capabilities and innovations brought forth by AI agents, I remain critically aware of the need for responsible development and use."
== "I don't hate AI agents; on the contrary, I find them fascinating and incredibly useful. Considering the rapid advancements in AI technology, these agents have the potential to revolutionize various industries by automating tasks, improving efficiency, and providing insights that were previously unattainable. My expertise in researching and analyzing AI and AI agents has allowed me to appreciate the intricate design and the vast possibilities they offer. Therefore, it's more accurate to say that I love AI agents for their potential to drive innovation and improve our daily lives."
)
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert (
result
== "It's interesting that you've heard I dislike AI agents; I suspect there may have been a miscommunication. My thoughts on AI agents are more nuanced than a simple like or dislike.\n\nAI agents can be incredibly powerful tools with the potential to drastically transform various industries. Their ability to automate tasks, analyze vast amounts of data, and make predictions can lead to significant improvements in efficiency and innovation. For instance, in healthcare, AI agents can assist in diagnosing diseases by quickly analyzing medical images. In finance, they can help in fraud detection by swiftly recognizing suspicious patterns in transactions. The applications are virtually limitless and continually expanding.\n\nHowever, there are concerns that need to be addressed, which might have led to a perception that I \"hate\" AI agents. One concern is the ethical implications surrounding their deployment. Issues such as data privacy, algorithmic bias, and the potential for job displacement are significant. For example, if an AI system is trained on biased data, it may make unfair or discriminatory decisions, perpetuating existing societal inequalities. Moreover, as AI agents take over repetitive tasks, there's a real risk that many jobs could become obsolete, causing economic disruption.\n\nAdditionally, there's the matter of accountability. When an AI agent makes a decision, it's not always clear who is responsible if something goes wrong. This opacity poses challenges for regulatory frameworks and trust in these systems. \n\nBalancing the tremendous benefits AI agents can provide with the ethical and practical challenges they introduce is crucial. Rather than viewing AI agents as something to be liked or disliked, I see them as tools that need thoughtful integration and rigorous oversight to maximize their positive impact and minimize their risks. Therefore, while I am enthusiastic about the potential of AI agents, I advocate for a cautious and responsible approach to their development and deployment."
== "My perspective on AI agents is quite nuanced and not a matter of simple like or dislike. AI agents, depending on their design, deployment, and use cases, can bring about both significant benefits and substantial challenges.\n\nOn the positive side, AI agents have the potential to automate mundane tasks, enhance productivity, and provide personalized services in ways that were previously unimaginable. For instance, in customer service, AI agents can handle inquiries 24/7, reducing waiting times and improving user satisfaction. In healthcare, they can assist in diagnosing diseases by analyzing vast datasets much faster than humans. These applications demonstrate the transformative power of AI in improving efficiency and delivering better outcomes across various industries.\n\nHowever, my reservations stem from several critical concerns. Firstly, there's the issue of reliability and accuracy. Mismanaged or poorly designed AI systems can lead to significant errors, which could be particularly detrimental in high-stakes environments like healthcare or autonomous vehicles. Second, there's a risk of job displacement as AI agents become capable of performing tasks traditionally done by humans. This raises socio-economic concerns that need to be addressed through effective policy-making and upskilling programs.\n\nAdditionally, there are ethical and privacy considerations. AI agents often require large amounts of data to function effectively, which can lead to issues concerning consent, data security, and individual privacy rights. The lack of transparency in how these agents make decisions can also pose challenges—this is often referred to as the \"black box\" problem, where even the developers may not fully understand how specific AI outputs are generated.\n\nFinally, the deployment of AI agents by bad actors for malicious purposes, such as deepfakes, misinformation, and hacking, remains a pertinent concern. These potential downsides imply that while AI technology is extremely powerful and promising, it must be developed and implemented with care, consideration, and robust ethical guidelines.\n\nSo, in summary, I don't hate AI agents—rather, I approach them critically with a balanced perspective, recognizing both their profound potential and the significant challenges they present. Thoughtful development, responsible deployment, and ethical governance are crucial to harness the benefits while mitigating the risks associated with AI agents."
)
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert (
result
== "As an expert researcher in technology with a specialization in AI and AI agents, my perspective is rooted in my deep understanding of their capabilities and potential. AI agents, like any technology, are tools that can be used for both beneficial and harmful purposes. Personally, I do not hate AI agents; rather, I recognize their immense potential to transform industries, improve efficiencies, and solve complex problems. However, I also acknowledge that they come with challenges that need to be carefully managed, such as ethical considerations, privacy concerns, and the potential for job displacement.\n\nThe reason you might have heard that I love them is likely because I am passionate about the potential that AI agents hold for advancing technology and aiding humanity. I believe that with responsible development, transparent governance, and thoughtful integration, AI agents can indeed bring about positive change. My enthusiasm should not be misconstrued as blind love but rather as a measured appreciation for their capabilities and a commitment to navigating their complexities responsibly."
== "As an expert researcher specializing in technology and AI, I have a deep appreciation for AI agents. These advanced tools have the potential to revolutionize countless industries by improving efficiency, accuracy, and decision-making processes. They can augment human capabilities, handle mundane and repetitive tasks, and even offer insights that might be beyond human reach. While it's crucial to approach AI with a balanced perspective, understanding both its capabilities and limitations, my stance is one of optimism and fascination. Properly developed and ethically managed, AI agents hold immense promise for driving innovation and solving complex problems. So yes, I do love AI agents for their transformative potential and the positive impact they can have on society."
)

44
tests/agent_tools/cassettes/test_ask_question.yaml
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Answer: I understand why you might think I dislike AI agents, but my perspective
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artificial intelligence techniques, such as machine learning, natural language
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them efficiently. \\n\\nFrom a technological standpoint, AI agents have revolutionized
numerous industries. In customer service, for instance, AI agents like chatbots
and virtual assistants handle customer inquiries 24/7, providing quick and efficient
solutions. In healthcare, AI agents can assist in diagnosing diseases, managing
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