From 4fb3331c6af231568e49a671a5e333bf6fd85cfe Mon Sep 17 00:00:00 2001 From: Brandon Hancock Date: Wed, 4 Dec 2024 10:46:39 -0500 Subject: [PATCH 01/11] Talk about getting structured consistent outputs with tasks. --- docs/concepts/tasks.mdx | 163 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 162 insertions(+), 1 deletion(-) diff --git a/docs/concepts/tasks.mdx b/docs/concepts/tasks.mdx index fd48c242d..9ca90fcb5 100644 --- a/docs/concepts/tasks.mdx +++ b/docs/concepts/tasks.mdx @@ -263,6 +263,167 @@ analysis_task = Task( ) ``` +## Getting Structured Consistent Outputs from Tasks +When you need to ensure that a task outputs a structured and consistent format, you can use the `output_pydantic` or `output_json` properties on a task. These properties allow you to define the expected output structure, making it easier to parse and utilize the results in your application. + + +It's also important to note that the output of the final task of a crew becomes the final output of the actual crew itself. + + +### Using `output_pydantic` +The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model. + +Here’s an example demonstrating how to use output_pydantic: + +```python Code +import json + +from crewai import Agent, Crew, Process, Task +from pydantic import BaseModel + + +class Blog(BaseModel): + title: str + content: str + + +blog_agent = Agent( + role="Blog Content Generator Agent", + goal="Generate a blog title and content", + backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""", + verbose=False, + allow_delegation=False, + llm="gpt-4o", +) + +task1 = Task( + description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""", + expected_output="A compelling blog title and well-written content.", + agent=blog_agent, + output_pydantic=Blog, +) + +# Instantiate your crew with a sequential process +crew = Crew( + agents=[blog_agent], + tasks=[task1], + verbose=True, + process=Process.sequential, +) + +result = crew.kickoff() + +# Option 1: Accessing Properties Using Dictionary-Style Indexing +print("Accessing Properties - Option 1") +title = result["title"] +content = result["content"] +print("Title:", title) +print("Content:", content) + +# Option 2: Accessing Properties Directly from the Pydantic Model +print("Accessing Properties - Option 2") +title = result.pydantic.title +content = result.pydantic.content +print("Title:", title) +print("Content:", content) + +# Option 3: Accessing Properties Using the to_dict() Method +print("Accessing Properties - Option 3") +output_dict = result.to_dict() +title = output_dict["title"] +content = output_dict["content"] +print("Title:", title) +print("Content:", content) + +# Option 4: Printing the Entire Blog Object +print("Accessing Properties - Option 5") +print("Blog:", result) + +``` +In this example: +* A Pydantic model Blog is defined with title and content fields. +* The task task1 uses the output_pydantic property to specify that its output should conform to the Blog model. +* After executing the crew, you can access the structured output in multiple ways as shown. + +#### Explanation of Accessing the Output +1. Dictionary-Style Indexing: You can directly access the fields using result["field_name"]. This works because the CrewOutput class implements the __getitem__ method. +2. Directly from Pydantic Model: Access the attributes directly from the result.pydantic object. +3. Using to_dict() Method: Convert the output to a dictionary and access the fields. +4. Printing the Entire Object: Simply print the result object to see the structured output. + +### Using `output_json` +The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application. + +Here’s an example demonstrating how to use `output_json`: + +```python Code +import json + +from crewai import Agent, Crew, Process, Task +from pydantic import BaseModel + + +# Define the Pydantic model for the blog +class Blog(BaseModel): + title: str + content: str + + +# Define the agent +blog_agent = Agent( + role="Blog Content Generator Agent", + goal="Generate a blog title and content", + backstory="""You are an expert content creator, skilled in crafting engaging and informative blog posts.""", + verbose=False, + allow_delegation=False, + llm="gpt-4o", +) + +# Define the task with output_json set to the Blog model +task1 = Task( + description="""Create a blog title and content on a given topic. Make sure the content is under 200 words.""", + expected_output="A JSON object with 'title' and 'content' fields.", + agent=blog_agent, + output_json=Blog, +) + +# Instantiate the crew with a sequential process +crew = Crew( + agents=[blog_agent], + tasks=[task1], + verbose=True, + process=Process.sequential, +) + +# Kickoff the crew to execute the task +result = crew.kickoff() + +# Option 1: Accessing Properties Using Dictionary-Style Indexing +print("Accessing Properties - Option 1") +title = result["title"] +content = result["content"] +print("Title:", title) +print("Content:", content) + +# Option 2: Printing the Entire Blog Object +print("Accessing Properties - Option 2") +print("Blog:", result) +``` + +In this example: +* A Pydantic model Blog is defined with title and content fields, which is used to specify the structure of the JSON output. +* The task task1 uses the output_json property to indicate that it expects a JSON output conforming to the Blog model. +* After executing the crew, you can access the structured JSON output in two ways as shown. + +#### Explanation of Accessing the Output + +1. Accessing Properties Using Dictionary-Style Indexing: You can access the fields directly using result["field_name"]. This is possible because the CrewOutput class implements the __getitem__ method, allowing you to treat the output like a dictionary. In this option, we're retrieving the title and content from the result. +2. Printing the Entire Blog Object: By printing result, you get the string representation of the CrewOutput object. Since the __str__ method is implemented to return the JSON output, this will display the entire output as a formatted string representing the Blog object. + +--- + +By using output_pydantic or output_json, you ensure that your tasks produce outputs in a consistent and structured format, making it easier to process and utilize the data within your application or across multiple tasks. + ## Integrating Tools with Tasks Leverage tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction. @@ -471,4 +632,4 @@ save_output_task = Task( 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. \ No newline at end of file +ensuring agents are effectively prepared for their assignments and that tasks are executed as intended. From bbea797b0cdff5c30821ab877aaa30162d69523e Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Wed, 4 Dec 2024 12:39:34 -0500 Subject: [PATCH 02/11] remove all references to pipeline and pipeline router (#1661) * remove all references to pipeline and router * fix linting * drop poetry.lock --- docs/concepts/cli.mdx | 9 +- mkdocs.yml | 1 - poetry.lock | 7507 ----------------- src/crewai/__init__.py | 4 - src/crewai/cli/cli.py | 11 +- src/crewai/cli/create_pipeline.py | 107 - src/crewai/cli/templates/pipeline/.gitignore | 2 - src/crewai/cli/templates/pipeline/README.md | 57 - src/crewai/cli/templates/pipeline/__init__.py | 0 .../crews/research_crew/config/agents.yaml | 19 - .../crews/research_crew/config/tasks.yaml | 16 - .../crews/research_crew/research_crew.py | 58 - .../write_linkedin_crew/config/agents.yaml | 0 .../write_linkedin_crew/config/tasks.yaml | 0 .../write_linkedin_crew.py | 51 - .../crews/write_x_crew/config/agents.yaml | 14 - .../crews/write_x_crew/config/tasks.yaml | 22 - .../crews/write_x_crew/write_x_crew.py | 36 - src/crewai/cli/templates/pipeline/main.py | 26 - .../templates/pipeline/pipelines/__init__.py | 0 .../templates/pipeline/pipelines/pipeline.py | 87 - .../cli/templates/pipeline/pyproject.toml | 17 - .../cli/templates/pipeline/tools/__init__.py | 0 .../templates/pipeline/tools/custom_tool.py | 19 - .../cli/templates/pipeline_router/.gitignore | 2 - .../cli/templates/pipeline_router/README.md | 54 - .../cli/templates/pipeline_router/__init__.py | 0 .../pipeline_router/config/agents.yaml | 19 - .../pipeline_router/config/tasks.yaml | 17 - .../crews/classifier_crew/classifier_crew.py | 40 - .../crews/classifier_crew/config/agents.yaml | 7 - .../crews/classifier_crew/config/tasks.yaml | 7 - .../crews/normal_crew/config/agents.yaml | 7 - .../crews/normal_crew/config/tasks.yaml | 6 - .../crews/normal_crew/normal_crew.py | 36 - .../crews/urgent_crew/config/agents.yaml | 7 - .../crews/urgent_crew/config/tasks.yaml | 6 - .../crews/urgent_crew/urgent_crew.py | 36 - .../cli/templates/pipeline_router/main.py | 75 - .../pipeline_router/pipelines/__init__.py | 0 .../pipelines/pipeline_classifier.py | 24 - .../pipelines/pipeline_normal.py | 24 - .../pipelines/pipeline_urgent.py | 23 - .../templates/pipeline_router/pyproject.toml | 21 - .../pipeline_router/tools/__init__.py | 0 .../pipeline_router/tools/custom_tool.py | 19 - src/crewai/crew.py | 20 +- src/crewai/pipeline/__init__.py | 5 - src/crewai/pipeline/pipeline.py | 405 - .../pipeline/pipeline_kickoff_result.py | 61 - src/crewai/pipeline/pipeline_output.py | 20 - src/crewai/project/__init__.py | 4 - src/crewai/project/annotations.py | 15 - src/crewai/project/pipeline_base.py | 58 - src/crewai/routers/__init__.py | 3 - src/crewai/routers/router.py | 84 - tests/pipeline/test_pipeline.py | 921 -- 57 files changed, 10 insertions(+), 10079 deletions(-) delete mode 100644 poetry.lock delete mode 100644 src/crewai/cli/create_pipeline.py delete mode 100644 src/crewai/cli/templates/pipeline/.gitignore delete mode 100644 src/crewai/cli/templates/pipeline/README.md delete mode 100644 src/crewai/cli/templates/pipeline/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/research_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/research_crew/research_crew.py delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/write_linkedin_crew.py delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_x_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_x_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline/crews/write_x_crew/write_x_crew.py delete mode 100644 src/crewai/cli/templates/pipeline/main.py delete mode 100644 src/crewai/cli/templates/pipeline/pipelines/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline/pipelines/pipeline.py delete mode 100644 src/crewai/cli/templates/pipeline/pyproject.toml delete mode 100644 src/crewai/cli/templates/pipeline/tools/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline/tools/custom_tool.py delete mode 100644 src/crewai/cli/templates/pipeline_router/.gitignore delete mode 100644 src/crewai/cli/templates/pipeline_router/README.md delete mode 100644 src/crewai/cli/templates/pipeline_router/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline_router/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/classifier_crew/classifier_crew.py delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/normal_crew/normal_crew.py delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/agents.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/tasks.yaml delete mode 100644 src/crewai/cli/templates/pipeline_router/crews/urgent_crew/urgent_crew.py delete mode 100644 src/crewai/cli/templates/pipeline_router/main.py delete mode 100644 src/crewai/cli/templates/pipeline_router/pipelines/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline_router/pipelines/pipeline_classifier.py delete mode 100644 src/crewai/cli/templates/pipeline_router/pipelines/pipeline_normal.py delete mode 100644 src/crewai/cli/templates/pipeline_router/pipelines/pipeline_urgent.py delete mode 100644 src/crewai/cli/templates/pipeline_router/pyproject.toml delete mode 100644 src/crewai/cli/templates/pipeline_router/tools/__init__.py delete mode 100644 src/crewai/cli/templates/pipeline_router/tools/custom_tool.py delete mode 100644 src/crewai/pipeline/__init__.py delete mode 100644 src/crewai/pipeline/pipeline.py delete mode 100644 src/crewai/pipeline/pipeline_kickoff_result.py delete mode 100644 src/crewai/pipeline/pipeline_output.py delete mode 100644 src/crewai/project/pipeline_base.py delete mode 100644 src/crewai/routers/__init__.py delete mode 100644 src/crewai/routers/router.py delete mode 100644 tests/pipeline/test_pipeline.py diff --git a/docs/concepts/cli.mdx b/docs/concepts/cli.mdx index 2afc6b56c..ab21232ad 100644 --- a/docs/concepts/cli.mdx +++ b/docs/concepts/cli.mdx @@ -28,20 +28,19 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS] ### 1. Create -Create a new crew or pipeline. +Create a new crew or flow. ```shell crewai create [OPTIONS] TYPE NAME ``` -- `TYPE`: Choose between "crew" or "pipeline" -- `NAME`: Name of the crew or pipeline -- `--router`: (Optional) Create a pipeline with router functionality +- `TYPE`: Choose between "crew" or "flow" +- `NAME`: Name of the crew or flow Example: ```shell crewai create crew my_new_crew -crewai create pipeline my_new_pipeline --router +crewai create flow my_new_flow ``` ### 2. 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"Pipeline", - "Router", "LLM", "Flow", "Knowledge", diff --git a/src/crewai/cli/cli.py b/src/crewai/cli/cli.py index 600eb6142..6e8560133 100644 --- a/src/crewai/cli/cli.py +++ b/src/crewai/cli/cli.py @@ -6,7 +6,6 @@ import pkg_resources from crewai.cli.add_crew_to_flow import add_crew_to_flow 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, ) @@ -31,22 +30,18 @@ def crewai(): @crewai.command() -@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"])) +@click.argument("type", type=click.Choice(["crew", "flow"])) @click.argument("name") @click.option("--provider", type=str, help="The provider to use for the crew") @click.option("--skip_provider", is_flag=True, help="Skip provider validation") def create(type, name, provider, skip_provider=False): - """Create a new crew, pipeline, or flow.""" + """Create a new crew, or flow.""" if type == "crew": create_crew(name, provider, skip_provider) - elif type == "pipeline": - create_pipeline(name) elif type == "flow": create_flow(name) else: - click.secho( - "Error: Invalid type. Must be 'crew', 'pipeline', or 'flow'.", fg="red" - ) + click.secho("Error: Invalid type. Must be 'crew' or 'flow'.", fg="red") @crewai.command() diff --git a/src/crewai/cli/create_pipeline.py b/src/crewai/cli/create_pipeline.py deleted file mode 100644 index b26acf818..000000000 --- a/src/crewai/cli/create_pipeline.py +++ /dev/null @@ -1,107 +0,0 @@ -import shutil -from pathlib import Path - -import click - - -def create_pipeline(name, router=False): - """Create a new pipeline project.""" - folder_name = name.replace(" ", "_").replace("-", "_").lower() - class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "") - - click.secho(f"Creating pipeline {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 / "pipelines").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 - template_folder = "pipeline_router" if router else "pipeline" - templates_dir = package_dir / "templates" / template_folder - - # 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"] - - if router: - crew_folders = [ - "classifier_crew", - "normal_crew", - "urgent_crew", - ] - pipelines_folders = [ - "pipelines/__init__.py", - "pipelines/pipeline_classifier.py", - "pipelines/pipeline_normal.py", - "pipelines/pipeline_urgent.py", - ] - else: - crew_folders = [ - "research_crew", - "write_linkedin_crew", - "write_x_crew", - ] - pipelines_folders = ["pipelines/__init__.py", "pipelines/pipeline.py"] - - def process_file(src_file, dst_file): - with open(src_file, "r") as file: - content = file.read() - - content = content.replace("{{name}}", name) - content = content.replace("{{crew_name}}", class_name) - content = content.replace("{{folder_name}}", folder_name) - content = content.replace("{{pipeline_name}}", class_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 - shutil.copy(src_file, dst_file) - - # Copy pipelines folders - for file_name in pipelines_folders: - 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(): - shutil.copytree(src_crew_folder, dst_crew_folder) - else: - click.secho( - f"Warning: Crew folder {crew_folder} not found in template.", - fg="yellow", - ) - - click.secho(f"Pipeline {name} created successfully!", fg="green", bold=True) diff --git a/src/crewai/cli/templates/pipeline/.gitignore b/src/crewai/cli/templates/pipeline/.gitignore deleted file mode 100644 index d50a09fc9..000000000 --- a/src/crewai/cli/templates/pipeline/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -.env -__pycache__/ diff --git a/src/crewai/cli/templates/pipeline/README.md b/src/crewai/cli/templates/pipeline/README.md deleted file mode 100644 index 433e43abe..000000000 --- a/src/crewai/cli/templates/pipeline/README.md +++ /dev/null @@ -1,57 +0,0 @@ -# {{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. diff --git a/src/crewai/cli/templates/pipeline/__init__.py b/src/crewai/cli/templates/pipeline/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml deleted file mode 100644 index f8cf1f5c1..000000000 --- a/src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml +++ /dev/null @@ -1,19 +0,0 @@ -researcher: - role: > - {topic} Senior Data Researcher - goal: > - Uncover cutting-edge developments in {topic} - backstory: > - You're a seasoned researcher with a knack for uncovering the latest - developments in {topic}. Known for your ability to find the most relevant - information and present it in a clear and concise manner. - -reporting_analyst: - role: > - {topic} Reporting Analyst - goal: > - Create detailed reports based on {topic} data analysis and research findings - backstory: > - You're a meticulous analyst with a keen eye for detail. You're known for - your ability to turn complex data into clear and concise reports, making - it easy for others to understand and act on the information you provide. diff --git a/src/crewai/cli/templates/pipeline/crews/research_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline/crews/research_crew/config/tasks.yaml deleted file mode 100644 index e78091842..000000000 --- a/src/crewai/cli/templates/pipeline/crews/research_crew/config/tasks.yaml +++ /dev/null @@ -1,16 +0,0 @@ -research_task: - description: > - Conduct a thorough research about {topic} - Make sure you find any interesting and relevant information given - the current year is 2024. - expected_output: > - A list with 10 bullet points of the most relevant information about {topic} - agent: researcher - -reporting_task: - description: > - Review the context you got and expand each topic into a full section for a report. - Make sure the report is detailed and contains any and all relevant information. - expected_output: > - A fully fledge reports with a title, mains topics, each with a full section of information. - agent: reporting_analyst diff --git a/src/crewai/cli/templates/pipeline/crews/research_crew/research_crew.py b/src/crewai/cli/templates/pipeline/crews/research_crew/research_crew.py deleted file mode 100644 index f26ad712a..000000000 --- a/src/crewai/cli/templates/pipeline/crews/research_crew/research_crew.py +++ /dev/null @@ -1,58 +0,0 @@ -from pydantic import BaseModel -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# Uncomment the following line to use an example of a custom tool -# from demo_pipeline.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - - -class ResearchReport(BaseModel): - """Research Report""" - title: str - body: str - -@CrewBase -class ResearchCrew(): - """Research Crew""" - agents_config = 'config/agents.yaml' - tasks_config = 'config/tasks.yaml' - - @agent - def researcher(self) -> Agent: - return Agent( - config=self.agents_config['researcher'], - verbose=True - ) - - @agent - def reporting_analyst(self) -> Agent: - return Agent( - config=self.agents_config['reporting_analyst'], - verbose=True - ) - - @task - def research_task(self) -> Task: - return Task( - config=self.tasks_config['research_task'], - ) - - @task - def reporting_task(self) -> Task: - return Task( - config=self.tasks_config['reporting_task'], - output_pydantic=ResearchReport - ) - - @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, - ) \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/agents.yaml deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/tasks.yaml deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/write_linkedin_crew.py b/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/write_linkedin_crew.py deleted file mode 100644 index 4a40c3fb4..000000000 --- a/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/write_linkedin_crew.py +++ /dev/null @@ -1,51 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# Uncomment the following line to use an example of a custom tool -# from {{folder_name}}.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - -@CrewBase -class WriteLinkedInCrew(): - """Research Crew""" - agents_config = 'config/agents.yaml' - tasks_config = 'config/tasks.yaml' - - @agent - def researcher(self) -> Agent: - return Agent( - config=self.agents_config['researcher'], - verbose=True - ) - - @agent - def reporting_analyst(self) -> Agent: - return Agent( - config=self.agents_config['reporting_analyst'], - verbose=True - ) - - @task - def research_task(self) -> Task: - return Task( - config=self.tasks_config['research_task'], - ) - - @task - def reporting_task(self) -> Task: - return Task( - config=self.tasks_config['reporting_task'], - output_file='report.md' - ) - - @crew - def crew(self) -> Crew: - """Creates the {{crew_name}} 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, - ) \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/agents.yaml deleted file mode 100644 index 1401dcbe0..000000000 --- a/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/agents.yaml +++ /dev/null @@ -1,14 +0,0 @@ -x_writer_agent: - role: > - Expert Social Media Content Creator specializing in short form written content - goal: > - Create viral-worthy, engaging short form posts that distill complex {topic} information - into compelling 280-character messages - backstory: > - You're a social media virtuoso with a particular talent for short form content. Your posts - consistently go viral due to your ability to craft hooks that stop users mid-scroll. - You've studied the techniques of social media masters like Justin Welsh, Dickie Bush, - Nicolas Cole, and Shaan Puri, incorporating their best practices into your own unique style. - Your superpower is taking intricate {topic} concepts and transforming them into - bite-sized, shareable content that resonates with a wide audience. You know exactly - how to structure a post for maximum impact and engagement. diff --git a/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/tasks.yaml deleted file mode 100644 index 1ffbc207a..000000000 --- a/src/crewai/cli/templates/pipeline/crews/write_x_crew/config/tasks.yaml +++ /dev/null @@ -1,22 +0,0 @@ -write_x_task: - description: > - Using the research report provided, create an engaging short form post about {topic}. - Your post should have a great hook, summarize key points, and be structured for easy - consumption on a digital platform. The post must be under 280 characters. - Follow these guidelines: - 1. Start with an attention-grabbing hook - 2. Condense the main insights from the research - 3. Use clear, concise language - 4. Include a call-to-action or thought-provoking question if space allows - 5. Ensure the post flows well and is easy to read quickly - - Here is the title of the research report you will be using - - Title: {title} - Research: - {body} - - expected_output: > - A compelling X post under 280 characters that effectively summarizes the key findings - about {topic}, starts with a strong hook, and is optimized for engagement on the platform. - agent: x_writer_agent diff --git a/src/crewai/cli/templates/pipeline/crews/write_x_crew/write_x_crew.py b/src/crewai/cli/templates/pipeline/crews/write_x_crew/write_x_crew.py deleted file mode 100644 index 454aafdc0..000000000 --- a/src/crewai/cli/templates/pipeline/crews/write_x_crew/write_x_crew.py +++ /dev/null @@ -1,36 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# Uncomment the following line to use an example of a custom tool -# from demo_pipeline.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - - -@CrewBase -class WriteXCrew: - """Research Crew""" - - agents_config = "config/agents.yaml" - tasks_config = "config/tasks.yaml" - - @agent - def x_writer_agent(self) -> Agent: - return Agent(config=self.agents_config["x_writer_agent"], verbose=True) - - @task - def write_x_task(self) -> Task: - return Task( - config=self.tasks_config["write_x_task"], - ) - - @crew - def crew(self) -> Crew: - """Creates the Write X 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, - ) diff --git a/src/crewai/cli/templates/pipeline/main.py b/src/crewai/cli/templates/pipeline/main.py deleted file mode 100644 index 376693330..000000000 --- a/src/crewai/cli/templates/pipeline/main.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python -import asyncio -from {{folder_name}}.pipelines.pipeline import {{pipeline_name}}Pipeline - -async def run(): - """ - Run the pipeline. - """ - inputs = [ - {"topic": "AI wearables"}, - ] - pipeline = {{pipeline_name}}Pipeline() - results = await pipeline.kickoff(inputs) - - # Process and print results - for result in results: - print(f"Raw output: {result.raw}") - if result.json_dict: - print(f"JSON output: {result.json_dict}") - print("\n") - -def main(): - asyncio.run(run()) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline/pipelines/__init__.py b/src/crewai/cli/templates/pipeline/pipelines/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline/pipelines/pipeline.py b/src/crewai/cli/templates/pipeline/pipelines/pipeline.py deleted file mode 100644 index 8ca8dfa44..000000000 --- a/src/crewai/cli/templates/pipeline/pipelines/pipeline.py +++ /dev/null @@ -1,87 +0,0 @@ -""" -This pipeline file includes two different examples to demonstrate the flexibility of crewAI pipelines. - -Example 1: Two-Stage Pipeline ------------------------------ -This pipeline consists of two crews: -1. ResearchCrew: Performs research on a given topic. -2. WriteXCrew: Generates an X (Twitter) post based on the research findings. - -Key features: -- The ResearchCrew's final task uses output_json to store all research findings in a JSON object. -- This JSON object is then passed to the WriteXCrew, where tasks can access the research findings. - -Example 2: Two-Stage Pipeline with Parallel Execution -------------------------------------------------------- -This pipeline consists of three crews: -1. ResearchCrew: Performs research on a given topic. -2. WriteXCrew and WriteLinkedInCrew: Run in parallel, using the research findings to generate posts for X and LinkedIn, respectively. - -Key features: -- Demonstrates the ability to run multiple crews in parallel. -- Shows how to structure a pipeline with both sequential and parallel stages. - -Usage: -- To switch between examples, comment/uncomment the respective code blocks below. -- Ensure that you have implemented all necessary crew classes (ResearchCrew, WriteXCrew, WriteLinkedInCrew) before running. -""" - -# Common imports for both examples -from crewai import Pipeline - - - -# Uncomment the crews you need for your chosen example -from ..crews.research_crew.research_crew import ResearchCrew -from ..crews.write_x_crew.write_x_crew import WriteXCrew -# from .crews.write_linkedin_crew.write_linkedin_crew import WriteLinkedInCrew # Uncomment for Example 2 - -# EXAMPLE 1: Two-Stage Pipeline -# ----------------------------- -# Uncomment the following code block to use Example 1 - -class {{pipeline_name}}Pipeline: - def __init__(self): - # Initialize crews - self.research_crew = ResearchCrew().crew() - self.write_x_crew = WriteXCrew().crew() - - def create_pipeline(self): - return Pipeline( - stages=[ - self.research_crew, - self.write_x_crew - ] - ) - - async def kickoff(self, inputs): - pipeline = self.create_pipeline() - results = await pipeline.kickoff(inputs) - return results - - -# EXAMPLE 2: Two-Stage Pipeline with Parallel Execution -# ------------------------------------------------------- -# Uncomment the following code block to use Example 2 - -# @PipelineBase -# class {{pipeline_name}}Pipeline: -# def __init__(self): -# # Initialize crews -# self.research_crew = ResearchCrew().crew() -# self.write_x_crew = WriteXCrew().crew() -# self.write_linkedin_crew = WriteLinkedInCrew().crew() - -# @pipeline -# def create_pipeline(self): -# return Pipeline( -# stages=[ -# self.research_crew, -# [self.write_x_crew, self.write_linkedin_crew] # Parallel execution -# ] -# ) - -# async def run(self, inputs): -# pipeline = self.create_pipeline() -# results = await pipeline.kickoff(inputs) -# return results \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline/pyproject.toml b/src/crewai/cli/templates/pipeline/pyproject.toml deleted file mode 100644 index 1e7f4efd5..000000000 --- a/src/crewai/cli/templates/pipeline/pyproject.toml +++ /dev/null @@ -1,17 +0,0 @@ -[tool.poetry] -name = "{{folder_name}}" -version = "0.1.0" -description = "{{name}} using crewAI" -authors = ["Your Name "] - -[tool.poetry.dependencies] -python = ">=3.10,<=3.13" -crewai = { extras = ["tools"], version = ">=0.85.0,<1.0.0" } -asyncio = "*" - -[tool.poetry.scripts] -{{folder_name}} = "{{folder_name}}.main:main" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline/tools/__init__.py b/src/crewai/cli/templates/pipeline/tools/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline/tools/custom_tool.py b/src/crewai/cli/templates/pipeline/tools/custom_tool.py deleted file mode 100644 index fbd53cf02..000000000 --- a/src/crewai/cli/templates/pipeline/tools/custom_tool.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Type -from crewai.tools import BaseTool -from pydantic import BaseModel, Field - - -class MyCustomToolInput(BaseModel): - """Input schema for MyCustomTool.""" - argument: str = Field(..., description="Description of the argument.") - -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." - ) - args_schema: Type[BaseModel] = MyCustomToolInput - - def _run(self, argument: str) -> str: - # Implementation goes here - return "this is an example of a tool output, ignore it and move along." diff --git a/src/crewai/cli/templates/pipeline_router/.gitignore b/src/crewai/cli/templates/pipeline_router/.gitignore deleted file mode 100644 index d50a09fc9..000000000 --- a/src/crewai/cli/templates/pipeline_router/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -.env -__pycache__/ diff --git a/src/crewai/cli/templates/pipeline_router/README.md b/src/crewai/cli/templates/pipeline_router/README.md deleted file mode 100644 index d710c341a..000000000 --- a/src/crewai/cli/templates/pipeline_router/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# {{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. diff --git a/src/crewai/cli/templates/pipeline_router/__init__.py b/src/crewai/cli/templates/pipeline_router/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline_router/config/agents.yaml b/src/crewai/cli/templates/pipeline_router/config/agents.yaml deleted file mode 100644 index 72ed6939e..000000000 --- a/src/crewai/cli/templates/pipeline_router/config/agents.yaml +++ /dev/null @@ -1,19 +0,0 @@ -researcher: - role: > - {topic} Senior Data Researcher - goal: > - Uncover cutting-edge developments in {topic} - backstory: > - You're a seasoned researcher with a knack for uncovering the latest - developments in {topic}. Known for your ability to find the most relevant - information and present it in a clear and concise manner. - -reporting_analyst: - role: > - {topic} Reporting Analyst - goal: > - Create detailed reports based on {topic} data analysis and research findings - backstory: > - You're a meticulous analyst with a keen eye for detail. You're known for - your ability to turn complex data into clear and concise reports, making - it easy for others to understand and act on the information you provide. \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline_router/config/tasks.yaml b/src/crewai/cli/templates/pipeline_router/config/tasks.yaml deleted file mode 100644 index f30820855..000000000 --- a/src/crewai/cli/templates/pipeline_router/config/tasks.yaml +++ /dev/null @@ -1,17 +0,0 @@ -research_task: - description: > - Conduct a thorough research about {topic} - Make sure you find any interesting and relevant information given - the current year is 2024. - expected_output: > - A list with 10 bullet points of the most relevant information about {topic} - agent: researcher - -reporting_task: - description: > - Review the context you got and expand each topic into a full section for a report. - Make sure the report is detailed and contains any and all relevant information. - expected_output: > - A fully fledge reports with the mains topics, each with a full section of information. - Formatted as markdown without '```' - agent: reporting_analyst diff --git a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/classifier_crew.py b/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/classifier_crew.py deleted file mode 100644 index af6be20ab..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/classifier_crew.py +++ /dev/null @@ -1,40 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task -from pydantic import BaseModel - -# Uncomment the following line to use an example of a custom tool -# from demo_pipeline.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - -class UrgencyScore(BaseModel): - urgency_score: int - -@CrewBase -class ClassifierCrew: - """Email Classifier Crew""" - - agents_config = "config/agents.yaml" - tasks_config = "config/tasks.yaml" - - @agent - def classifier(self) -> Agent: - return Agent(config=self.agents_config["classifier"], verbose=True) - - @task - def urgent_task(self) -> Task: - return Task( - config=self.tasks_config["classify_email"], - output_pydantic=UrgencyScore, - ) - - @crew - def crew(self) -> Crew: - """Creates the Email Classifier 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, - ) diff --git a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/agents.yaml deleted file mode 100644 index 45506d038..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/agents.yaml +++ /dev/null @@ -1,7 +0,0 @@ -classifier: - role: > - Email Classifier - goal: > - Classify the email: {email} as urgent or normal from a score of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only.` - backstory: > - You are a highly efficient and experienced email classifier, trained to quickly assess and classify emails. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations. diff --git a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/tasks.yaml deleted file mode 100644 index cd843fd1a..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/classifier_crew/config/tasks.yaml +++ /dev/null @@ -1,7 +0,0 @@ -classify_email: - description: > - Classify the email: {email} - as urgent or normal. - expected_output: > - Classify the email from a scale of 1 to 10, where 1 is not urgent and 10 is urgent. Return the urgency score only. - agent: classifier diff --git a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/agents.yaml deleted file mode 100644 index c847ce8f5..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/agents.yaml +++ /dev/null @@ -1,7 +0,0 @@ -normal_handler: - role: > - Normal Email Processor - goal: > - Process normal emails and create an email to respond to the sender. - backstory: > - You are a highly efficient and experienced normal email handler, trained to quickly assess and respond to normal communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing normal situations and maintaining smooth operations. diff --git a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/tasks.yaml deleted file mode 100644 index 341303e90..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/config/tasks.yaml +++ /dev/null @@ -1,6 +0,0 @@ -normal_task: - description: > - Process and respond to normal email quickly. - expected_output: > - An email response to the normal email. - agent: normal_handler diff --git a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/normal_crew.py b/src/crewai/cli/templates/pipeline_router/crews/normal_crew/normal_crew.py deleted file mode 100644 index c240acfd1..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/normal_crew/normal_crew.py +++ /dev/null @@ -1,36 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# Uncomment the following line to use an example of a custom tool -# from demo_pipeline.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - - -@CrewBase -class NormalCrew: - """Normal Email Crew""" - - agents_config = "config/agents.yaml" - tasks_config = "config/tasks.yaml" - - @agent - def normal_handler(self) -> Agent: - return Agent(config=self.agents_config["normal_handler"], verbose=True) - - @task - def urgent_task(self) -> Task: - return Task( - config=self.tasks_config["normal_task"], - ) - - @crew - def crew(self) -> Crew: - """Creates the Normal Email 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, - ) diff --git a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/agents.yaml b/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/agents.yaml deleted file mode 100644 index 52804a9c1..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/agents.yaml +++ /dev/null @@ -1,7 +0,0 @@ -urgent_handler: - role: > - Urgent Email Processor - goal: > - Process urgent emails and create an email to respond to the sender. - backstory: > - You are a highly efficient and experienced urgent email handler, trained to quickly assess and respond to time-sensitive communications. Your ability to remain calm under pressure and provide concise, actionable responses has made you an invaluable asset in managing critical situations and maintaining smooth operations. diff --git a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/tasks.yaml b/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/tasks.yaml deleted file mode 100644 index dc2ee1c2a..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/config/tasks.yaml +++ /dev/null @@ -1,6 +0,0 @@ -urgent_task: - description: > - Process and respond to urgent email quickly. - expected_output: > - An email response to the urgent email. - agent: urgent_handler diff --git a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/urgent_crew.py b/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/urgent_crew.py deleted file mode 100644 index 54c804c79..000000000 --- a/src/crewai/cli/templates/pipeline_router/crews/urgent_crew/urgent_crew.py +++ /dev/null @@ -1,36 +0,0 @@ -from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task - -# Uncomment the following line to use an example of a custom tool -# from demo_pipeline.tools.custom_tool import MyCustomTool - -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool - - -@CrewBase -class UrgentCrew: - """Urgent Email Crew""" - - agents_config = "config/agents.yaml" - tasks_config = "config/tasks.yaml" - - @agent - def urgent_handler(self) -> Agent: - return Agent(config=self.agents_config["urgent_handler"], verbose=True) - - @task - def urgent_task(self) -> Task: - return Task( - config=self.tasks_config["urgent_task"], - ) - - @crew - def crew(self) -> Crew: - """Creates the Urgent Email 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, - ) diff --git a/src/crewai/cli/templates/pipeline_router/main.py b/src/crewai/cli/templates/pipeline_router/main.py deleted file mode 100644 index 5d1cdc370..000000000 --- a/src/crewai/cli/templates/pipeline_router/main.py +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python -import asyncio -from crewai.routers.router import Route -from crewai.routers.router import Router - -from {{folder_name}}.pipelines.pipeline_classifier import EmailClassifierPipeline -from {{folder_name}}.pipelines.pipeline_normal import NormalPipeline -from {{folder_name}}.pipelines.pipeline_urgent import UrgentPipeline - -async def run(): - """ - Run the pipeline. - """ - inputs = [ - { - "email": """ - Subject: URGENT: Marketing Campaign Launch - Immediate Action Required - Dear Team, - I'm reaching out regarding our upcoming marketing campaign that requires your immediate attention and swift action. We're facing a critical deadline, and our success hinges on our ability to mobilize quickly. - Key points: - - Campaign launch: 48 hours from now - Target audience: 250,000 potential customers - Expected ROI: 35% increase in Q3 sales - - What we need from you NOW: - - Final approval on creative assets (due in 3 hours) - Confirmation of media placements (due by end of day) - Last-minute budget allocation for paid social media push - - Our competitors are poised to launch similar campaigns, and we must act fast to maintain our market advantage. Delays could result in significant lost opportunities and potential revenue. - Please prioritize this campaign above all other tasks. I'll be available for the next 24 hours to address any concerns or roadblocks. - Let's make this happen! - [Your Name] - Marketing Director - P.S. I'll be scheduling an emergency team meeting in 1 hour to discuss our action plan. Attendance is mandatory. - """ - } - ] - - pipeline_classifier = EmailClassifierPipeline().create_pipeline() - pipeline_urgent = UrgentPipeline().create_pipeline() - pipeline_normal = NormalPipeline().create_pipeline() - - router = Router( - routes={ - "high_urgency": Route( - condition=lambda x: x.get("urgency_score", 0) > 7, - pipeline=pipeline_urgent - ), - "low_urgency": Route( - condition=lambda x: x.get("urgency_score", 0) <= 7, - pipeline=pipeline_normal - ) - }, - default=pipeline_normal - ) - - pipeline = pipeline_classifier >> router - - results = await pipeline.kickoff(inputs) - - # Process and print results - for result in results: - print(f"Raw output: {result.raw}") - if result.json_dict: - print(f"JSON output: {result.json_dict}") - print("\n") - -def main(): - asyncio.run(run()) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline_router/pipelines/__init__.py b/src/crewai/cli/templates/pipeline_router/pipelines/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_classifier.py b/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_classifier.py deleted file mode 100644 index 5047d1602..000000000 --- a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_classifier.py +++ /dev/null @@ -1,24 +0,0 @@ -from crewai import Pipeline -from crewai.project import PipelineBase -from ..crews.classifier_crew.classifier_crew import ClassifierCrew - - -@PipelineBase -class EmailClassifierPipeline: - def __init__(self): - # Initialize crews - self.classifier_crew = ClassifierCrew().crew() - - def create_pipeline(self): - return Pipeline( - stages=[ - self.classifier_crew - ] - ) - - async def kickoff(self, inputs): - pipeline = self.create_pipeline() - results = await pipeline.kickoff(inputs) - return results - - diff --git a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_normal.py b/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_normal.py deleted file mode 100644 index 936af4176..000000000 --- a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_normal.py +++ /dev/null @@ -1,24 +0,0 @@ -from crewai import Pipeline -from crewai.project import PipelineBase -from ..crews.normal_crew.normal_crew import NormalCrew - - -@PipelineBase -class NormalPipeline: - def __init__(self): - # Initialize crews - self.normal_crew = NormalCrew().crew() - - def create_pipeline(self): - return Pipeline( - stages=[ - self.normal_crew - ] - ) - - async def kickoff(self, inputs): - pipeline = self.create_pipeline() - results = await pipeline.kickoff(inputs) - return results - - diff --git a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_urgent.py b/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_urgent.py deleted file mode 100644 index 07297bf75..000000000 --- a/src/crewai/cli/templates/pipeline_router/pipelines/pipeline_urgent.py +++ /dev/null @@ -1,23 +0,0 @@ -from crewai import Pipeline -from crewai.project import PipelineBase -from ..crews.urgent_crew.urgent_crew import UrgentCrew - -@PipelineBase -class UrgentPipeline: - def __init__(self): - # Initialize crews - self.urgent_crew = UrgentCrew().crew() - - def create_pipeline(self): - return Pipeline( - stages=[ - self.urgent_crew - ] - ) - - async def kickoff(self, inputs): - pipeline = self.create_pipeline() - results = await pipeline.kickoff(inputs) - return results - - diff --git a/src/crewai/cli/templates/pipeline_router/pyproject.toml b/src/crewai/cli/templates/pipeline_router/pyproject.toml deleted file mode 100644 index 49208d120..000000000 --- a/src/crewai/cli/templates/pipeline_router/pyproject.toml +++ /dev/null @@ -1,21 +0,0 @@ -[project] -name = "{{folder_name}}" -version = "0.1.0" -description = "{{name}} using crewAI" -authors = ["Your Name "] -requires-python = ">=3.10,<=3.13" -dependencies = [ - "crewai[tools]>=0.85.0,<1.0.0" -] - -[project.scripts] -{{folder_name}} = "{{folder_name}}.main:main" -run_crew = "{{folder_name}}.main:main" -train = "{{folder_name}}.main:train" -replay = "{{folder_name}}.main:replay" -test = "{{folder_name}}.main:test" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - diff --git a/src/crewai/cli/templates/pipeline_router/tools/__init__.py b/src/crewai/cli/templates/pipeline_router/tools/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/src/crewai/cli/templates/pipeline_router/tools/custom_tool.py b/src/crewai/cli/templates/pipeline_router/tools/custom_tool.py deleted file mode 100644 index fbd53cf02..000000000 --- a/src/crewai/cli/templates/pipeline_router/tools/custom_tool.py +++ /dev/null @@ -1,19 +0,0 @@ -from typing import Type -from crewai.tools import BaseTool -from pydantic import BaseModel, Field - - -class MyCustomToolInput(BaseModel): - """Input schema for MyCustomTool.""" - argument: str = Field(..., description="Description of the argument.") - -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." - ) - args_schema: Type[BaseModel] = MyCustomToolInput - - def _run(self, argument: str) -> str: - # Implementation goes here - return "this is an example of a tool output, ignore it and move along." diff --git a/src/crewai/crew.py b/src/crewai/crew.py index 3b6ee44b4..943320ceb 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -5,7 +5,7 @@ import uuid import warnings from concurrent.futures import Future from hashlib import md5 -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union from pydantic import ( UUID4, @@ -23,12 +23,12 @@ from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.cache import CacheHandler from crewai.crews.crew_output import CrewOutput +from crewai.knowledge.knowledge import Knowledge +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.llm import LLM from crewai.memory.entity.entity_memory import EntityMemory from crewai.memory.long_term.long_term_memory import LongTermMemory from crewai.memory.short_term.short_term_memory import ShortTermMemory -from crewai.knowledge.knowledge import Knowledge -from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.memory.user.user_memory import UserMemory from crewai.process import Process from crewai.task import Task @@ -56,8 +56,6 @@ if os.environ.get("AGENTOPS_API_KEY"): except ImportError: pass -if TYPE_CHECKING: - from crewai.pipeline.pipeline import Pipeline warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd") @@ -1073,17 +1071,5 @@ class Crew(BaseModel): evaluator.print_crew_evaluation_result() - def __rshift__(self, other: "Crew") -> "Pipeline": - """ - Implements the >> operator to add another Crew to an existing Pipeline. - """ - from crewai.pipeline.pipeline import Pipeline - - if not isinstance(other, Crew): - raise TypeError( - f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'" - ) - return Pipeline(stages=[self, other]) - def __repr__(self): return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})" diff --git a/src/crewai/pipeline/__init__.py b/src/crewai/pipeline/__init__.py deleted file mode 100644 index d9821e34c..000000000 --- a/src/crewai/pipeline/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from crewai.pipeline.pipeline import Pipeline -from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult -from crewai.pipeline.pipeline_output import PipelineOutput - -__all__ = ["Pipeline", "PipelineKickoffResult", "PipelineOutput"] diff --git a/src/crewai/pipeline/pipeline.py b/src/crewai/pipeline/pipeline.py deleted file mode 100644 index e65529092..000000000 --- a/src/crewai/pipeline/pipeline.py +++ /dev/null @@ -1,405 +0,0 @@ -import asyncio -import copy -from typing import Any, Dict, List, Tuple, Union - -from pydantic import BaseModel, Field, model_validator - -from crewai.crew import Crew -from crewai.crews.crew_output import CrewOutput -from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult -from crewai.routers.router import Router -from crewai.types.usage_metrics import UsageMetrics - -Trace = Union[Union[str, Dict[str, Any]], List[Union[str, Dict[str, Any]]]] -PipelineStage = Union[Crew, List[Crew], Router] - -""" -Developer Notes: - -This module defines a Pipeline class that represents a sequence of operations (stages) -to process inputs. Each stage can be either sequential or parallel, and the pipeline -can process multiple kickoffs concurrently. - -Core Loop Explanation: -1. The `process_kickoffs` method processes multiple kickoffs in parallel, each going through - all pipeline stages. -2. The `process_single_kickoff` method handles the processing of a single kickouff through - all stages, updating metrics and input data along the way. -3. The `_process_stage` method determines whether a stage is sequential or parallel - and processes it accordingly. -4. The `_process_single_crew` and `_process_parallel_crews` methods handle the - execution of single and parallel crew stages. -5. The `_update_metrics_and_input` method updates usage metrics and the current input - with the outputs from a stage. -6. The `_build_pipeline_kickoff_results` method constructs the final results of the - pipeline kickoff, including traces and outputs. - -Handling Traces and Crew Outputs: -- During the processing of stages, we handle the results (traces and crew outputs) - for all stages except the last one differently from the final stage. -- For intermediate stages, the primary focus is on passing the input data between stages. - This involves merging the output dictionaries from all crews in a stage into a single - dictionary and passing it to the next stage. This merged dictionary allows for smooth - data flow between stages. -- For the final stage, in addition to passing the input data, we also need to prepare - the final outputs and traces to be returned as the overall result of the pipeline kickoff. - In this case, we do not merge the results, as each result needs to be included - separately in its own pipeline kickoff result. - -Pipeline Terminology: -- Pipeline: The overall structure that defines a sequence of operations. -- Stage: A distinct part of the pipeline, which can be either sequential or parallel. -- Kickoff: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline. -- Branch: Parallel executions within a stage (e.g., concurrent crew operations). -- Trace: The journey of an individual input through the entire pipeline. - -Example pipeline structure: -crew1 >> crew2 >> crew3 - -This represents a pipeline with three sequential stages: -1. crew1 is the first stage, which processes the input and passes its output to crew2. -2. crew2 is the second stage, which takes the output from crew1 as its input, processes it, and passes its output to crew3. -3. crew3 is the final stage, which takes the output from crew2 as its input and produces the final output of the pipeline. - -Each input creates its own kickoff, flowing through all stages of the pipeline. -Multiple kickoffss can be processed concurrently, each following the defined pipeline structure. - -Another example pipeline structure: -crew1 >> [crew2, crew3] >> crew4 - -This represents a pipeline with three stages: -1. A sequential stage (crew1) -2. A parallel stage with two branches (crew2 and crew3 executing concurrently) -3. Another sequential stage (crew4) - -Each input creates its own kickoff, flowing through all stages of the pipeline. -Multiple kickoffs can be processed concurrently, each following the defined pipeline structure. -""" - - -class Pipeline(BaseModel): - stages: List[PipelineStage] = Field( - ..., description="List of crews representing stages to be executed in sequence" - ) - - @model_validator(mode="before") - @classmethod - def validate_stages(cls, values): - stages = values.get("stages", []) - - def check_nesting_and_type(item, depth=0): - if depth > 1: - raise ValueError("Double nesting is not allowed in pipeline stages") - if isinstance(item, list): - for sub_item in item: - check_nesting_and_type(sub_item, depth + 1) - elif not isinstance(item, (Crew, Router)): - raise ValueError( - f"Expected Crew instance, Router instance, or list of Crews, got {type(item)}" - ) - - for stage in stages: - check_nesting_and_type(stage) - return values - - async def kickoff( - self, inputs: List[Dict[str, Any]] - ) -> List[PipelineKickoffResult]: - """ - Processes multiple runs in parallel, each going through all pipeline stages. - - Args: - inputs (List[Dict[str, Any]]): List of inputs for each run. - - Returns: - List[PipelineKickoffResult]: List of results from each run. - """ - pipeline_results: List[PipelineKickoffResult] = [] - - # Process all runs in parallel - all_run_results = await asyncio.gather( - *(self.process_single_kickoff(input_data) for input_data in inputs) - ) - - # Flatten the list of lists into a single list of results - pipeline_results.extend( - result for run_result in all_run_results for result in run_result - ) - - return pipeline_results - - async def process_single_kickoff( - self, kickoff_input: Dict[str, Any] - ) -> List[PipelineKickoffResult]: - """ - Processes a single run through all pipeline stages. - - Args: - input (Dict[str, Any]): The input for the run. - - Returns: - List[PipelineKickoffResult]: The results of processing the run. - """ - initial_input = copy.deepcopy(kickoff_input) - current_input = copy.deepcopy(kickoff_input) - stages = self._copy_stages() - pipeline_usage_metrics: Dict[str, UsageMetrics] = {} - all_stage_outputs: List[List[CrewOutput]] = [] - traces: List[List[Union[str, Dict[str, Any]]]] = [[initial_input]] - - stage_index = 0 - while stage_index < len(stages): - stage = stages[stage_index] - stage_input = copy.deepcopy(current_input) - - if isinstance(stage, Router): - next_pipeline, route_taken = stage.route(stage_input) - stages = ( - stages[: stage_index + 1] - + list(next_pipeline.stages) - + stages[stage_index + 1 :] - ) - traces.append([{"route_taken": route_taken}]) - stage_index += 1 - continue - - stage_outputs, stage_trace = await self._process_stage(stage, stage_input) - - self._update_metrics_and_input( - pipeline_usage_metrics, current_input, stage, stage_outputs - ) - traces.append(stage_trace) - all_stage_outputs.append(stage_outputs) - stage_index += 1 - - return self._build_pipeline_kickoff_results( - all_stage_outputs, traces, pipeline_usage_metrics - ) - - async def _process_stage( - self, stage: PipelineStage, current_input: Dict[str, Any] - ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: - """ - Processes a single stage of the pipeline, which can be either sequential or parallel. - - Args: - stage (Union[Crew, List[Crew]]): The stage to process. - current_input (Dict[str, Any]): The input for the stage. - - Returns: - Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and trace of the stage. - """ - if isinstance(stage, Crew): - return await self._process_single_crew(stage, current_input) - elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage): - return await self._process_parallel_crews(stage, current_input) - else: - raise ValueError(f"Unsupported stage type: {type(stage)}") - - async def _process_single_crew( - self, crew: Crew, current_input: Dict[str, Any] - ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: - """ - Processes a single crew. - - Args: - crew (Crew): The crew to process. - current_input (Dict[str, Any]): The input for the crew. - - Returns: - Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The output and trace of the crew. - """ - output = await crew.kickoff_async(inputs=current_input) - return [output], [crew.name or str(crew.id)] - - async def _process_parallel_crews( - self, crews: List[Crew], current_input: Dict[str, Any] - ) -> Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: - """ - Processes multiple crews in parallel. - - Args: - crews (List[Crew]): The list of crews to process in parallel. - current_input (Dict[str, Any]): The input for the crews. - - Returns: - Tuple[List[CrewOutput], List[Union[str, Dict[str, Any]]]]: The outputs and traces of the crews. - """ - parallel_outputs = await asyncio.gather( - *[crew.kickoff_async(inputs=current_input) for crew in crews] - ) - return parallel_outputs, [crew.name or str(crew.id) for crew in crews] - - def _update_metrics_and_input( - self, - usage_metrics: Dict[str, UsageMetrics], - current_input: Dict[str, Any], - stage: PipelineStage, - outputs: List[CrewOutput], - ) -> None: - """ - Updates metrics and current input with the outputs of a stage. - - Args: - usage_metrics (Dict[str, Any]): The usage metrics to update. - current_input (Dict[str, Any]): The current input to update. - stage (Union[Crew, List[Crew]]): The stage that was processed. - outputs (List[CrewOutput]): The outputs of the stage. - """ - if isinstance(stage, Crew): - usage_metrics[stage.name or str(stage.id)] = outputs[0].token_usage - current_input.update(outputs[0].to_dict()) - elif isinstance(stage, list) and all(isinstance(crew, Crew) for crew in stage): - for crew, output in zip(stage, outputs): - usage_metrics[crew.name or str(crew.id)] = output.token_usage - current_input.update(output.to_dict()) - else: - raise ValueError(f"Unsupported stage type: {type(stage)}") - - def _build_pipeline_kickoff_results( - self, - all_stage_outputs: List[List[CrewOutput]], - traces: List[List[Union[str, Dict[str, Any]]]], - token_usage: Dict[str, UsageMetrics], - ) -> List[PipelineKickoffResult]: - """ - Builds the results of a pipeline run. - - Args: - all_stage_outputs (List[List[CrewOutput]]): All stage outputs. - traces (List[List[Union[str, Dict[str, Any]]]]): All traces. - token_usage (Dict[str, Any]): Token usage metrics. - - Returns: - List[PipelineKickoffResult]: The results of the pipeline run. - """ - formatted_traces = self._format_traces(traces) - formatted_crew_outputs = self._format_crew_outputs(all_stage_outputs) - - return [ - PipelineKickoffResult( - token_usage=token_usage, - trace=formatted_trace, - raw=crews_outputs[-1].raw, - pydantic=crews_outputs[-1].pydantic, - json_dict=crews_outputs[-1].json_dict, - crews_outputs=crews_outputs, - ) - for crews_outputs, formatted_trace in zip( - formatted_crew_outputs, formatted_traces - ) - ] - - def _format_traces( - self, traces: List[List[Union[str, Dict[str, Any]]]] - ) -> List[List[Trace]]: - """ - Formats the traces of a pipeline run. - - Args: - traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format. - - Returns: - List[List[Trace]]: The formatted traces. - """ - formatted_traces: List[Trace] = self._format_single_trace(traces[:-1]) - return self._format_multiple_traces(formatted_traces, traces[-1]) - - def _format_single_trace( - self, traces: List[List[Union[str, Dict[str, Any]]]] - ) -> List[Trace]: - """ - Formats single traces. - - Args: - traces (List[List[Union[str, Dict[str, Any]]]]): The traces to format. - - Returns: - List[Trace]: The formatted single traces. - """ - formatted_traces: List[Trace] = [] - for trace in traces: - formatted_traces.append(trace[0] if len(trace) == 1 else trace) - return formatted_traces - - def _format_multiple_traces( - self, - formatted_traces: List[Trace], - final_trace: List[Union[str, Dict[str, Any]]], - ) -> List[List[Trace]]: - """ - Formats multiple traces. - - Args: - formatted_traces (List[Trace]): The formatted single traces. - final_trace (List[Union[str, Dict[str, Any]]]): The final trace to format. - - Returns: - List[List[Trace]]: The formatted multiple traces. - """ - traces_to_return: List[List[Trace]] = [] - if len(final_trace) == 1: - formatted_traces.append(final_trace[0]) - traces_to_return.append(formatted_traces) - else: - for trace in final_trace: - copied_traces = formatted_traces.copy() - copied_traces.append(trace) - traces_to_return.append(copied_traces) - return traces_to_return - - def _format_crew_outputs( - self, all_stage_outputs: List[List[CrewOutput]] - ) -> List[List[CrewOutput]]: - """ - Formats the outputs of all stages into a list of crew outputs. - - Args: - all_stage_outputs (List[List[CrewOutput]]): All stage outputs. - - Returns: - List[List[CrewOutput]]: Formatted crew outputs. - """ - crew_outputs: List[CrewOutput] = [ - output - for stage_outputs in all_stage_outputs[:-1] - for output in stage_outputs - ] - return [crew_outputs + [output] for output in all_stage_outputs[-1]] - - def _copy_stages(self): - """Create a deep copy of the Pipeline's stages.""" - new_stages = [] - for stage in self.stages: - if isinstance(stage, list): - new_stages.append( - [ - crew.copy() if hasattr(crew, "copy") else copy.deepcopy(crew) - for crew in stage - ] - ) - elif hasattr(stage, "copy"): - new_stages.append(stage.copy()) - else: - new_stages.append(copy.deepcopy(stage)) - - return new_stages - - def __rshift__(self, other: PipelineStage) -> "Pipeline": - """ - Implements the >> operator to add another Stage (Crew or List[Crew]) to an existing Pipeline. - - Args: - other (Any): The stage to add. - - Returns: - Pipeline: A new pipeline with the added stage. - """ - if isinstance(other, (Crew, Router)) or ( - isinstance(other, list) and all(isinstance(item, Crew) for item in other) - ): - return type(self)(stages=self.stages + [other]) - else: - raise TypeError( - f"Unsupported operand type for >>: '{type(self).__name__}' and '{type(other).__name__}'" - ) diff --git a/src/crewai/pipeline/pipeline_kickoff_result.py b/src/crewai/pipeline/pipeline_kickoff_result.py deleted file mode 100644 index 7bde238cd..000000000 --- a/src/crewai/pipeline/pipeline_kickoff_result.py +++ /dev/null @@ -1,61 +0,0 @@ -import json -import uuid -from typing import Any, Dict, List, Optional, Union - -from pydantic import UUID4, BaseModel, Field - -from crewai.crews.crew_output import CrewOutput -from crewai.types.usage_metrics import UsageMetrics - - -class PipelineKickoffResult(BaseModel): - """Class that represents the result of a pipeline run.""" - - id: UUID4 = Field( - default_factory=uuid.uuid4, - frozen=True, - description="Unique identifier for the object, not set by user.", - ) - raw: str = Field(description="Raw output of the pipeline run", default="") - pydantic: Any = Field( - description="Pydantic output of the pipeline run", default=None - ) - json_dict: Union[Dict[str, Any], None] = Field( - description="JSON dict output of the pipeline run", default={} - ) - - token_usage: Dict[str, UsageMetrics] = Field( - description="Token usage for each crew in the run" - ) - trace: List[Any] = Field( - description="Trace of the journey of inputs through the run" - ) - crews_outputs: List[CrewOutput] = Field( - description="Output from each crew in the run", - default=[], - ) - - @property - def json(self) -> Optional[str]: - if self.crews_outputs[-1].tasks_output[-1].output_format != "json": - raise ValueError( - "No JSON output found in the final task of the final crew. Please make sure to set the output_json property in the final task in your crew." - ) - - return json.dumps(self.json_dict) - - def to_dict(self) -> Dict[str, Any]: - """Convert json_output and pydantic_output to a dictionary.""" - output_dict = {} - if self.json_dict: - output_dict.update(self.json_dict) - elif self.pydantic: - output_dict.update(self.pydantic.model_dump()) - return output_dict - - def __str__(self): - if self.pydantic: - return str(self.pydantic) - if self.json_dict: - return str(self.json_dict) - return self.raw diff --git a/src/crewai/pipeline/pipeline_output.py b/src/crewai/pipeline/pipeline_output.py deleted file mode 100644 index d9875b64a..000000000 --- a/src/crewai/pipeline/pipeline_output.py +++ /dev/null @@ -1,20 +0,0 @@ -import uuid -from typing import List - -from pydantic import UUID4, BaseModel, Field - -from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult - - -class PipelineOutput(BaseModel): - id: UUID4 = Field( - default_factory=uuid.uuid4, - frozen=True, - description="Unique identifier for the object, not set by user.", - ) - run_results: List[PipelineKickoffResult] = Field( - description="List of results for each run through the pipeline", default=[] - ) - - def add_run_result(self, result: PipelineKickoffResult): - self.run_results.append(result) diff --git a/src/crewai/project/__init__.py b/src/crewai/project/__init__.py index 25f6f5e04..d60212153 100644 --- a/src/crewai/project/__init__.py +++ b/src/crewai/project/__init__.py @@ -8,12 +8,10 @@ from .annotations import ( llm, output_json, output_pydantic, - pipeline, task, tool, ) from .crew_base import CrewBase -from .pipeline_base import PipelineBase __all__ = [ "agent", @@ -24,10 +22,8 @@ __all__ = [ "tool", "callback", "CrewBase", - "PipelineBase", "llm", "cache_handler", - "pipeline", "before_kickoff", "after_kickoff", ] diff --git a/src/crewai/project/annotations.py b/src/crewai/project/annotations.py index 83e396a4c..be4710b2b 100644 --- a/src/crewai/project/annotations.py +++ b/src/crewai/project/annotations.py @@ -65,21 +65,6 @@ def cache_handler(func): return memoize(func) -def stage(func): - func.is_stage = True - return memoize(func) - - -def router(func): - func.is_router = True - return memoize(func) - - -def pipeline(func): - func.is_pipeline = True - return memoize(func) - - def crew(func) -> Callable[..., Crew]: def wrapper(self, *args, **kwargs) -> Crew: instantiated_tasks = [] diff --git a/src/crewai/project/pipeline_base.py b/src/crewai/project/pipeline_base.py deleted file mode 100644 index 268cb3901..000000000 --- a/src/crewai/project/pipeline_base.py +++ /dev/null @@ -1,58 +0,0 @@ -from typing import Any, Callable, Dict, List, Type, Union - -from crewai.crew import Crew -from crewai.pipeline.pipeline import Pipeline -from crewai.routers.router import Router - -PipelineStage = Union[Crew, List[Crew], Router] - - -# TODO: Could potentially remove. Need to check with @joao and @gui if this is needed for CrewAI+ -def PipelineBase(cls: Type[Any]) -> Type[Any]: - class WrappedClass(cls): - is_pipeline_class: bool = True # type: ignore - stages: List[PipelineStage] - - def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.stages = [] - self._map_pipeline_components() - - def _get_all_functions(self) -> Dict[str, Callable[..., Any]]: - return { - name: getattr(self, name) - for name in dir(self) - if callable(getattr(self, name)) - } - - def _filter_functions( - self, functions: Dict[str, Callable[..., Any]], attribute: str - ) -> Dict[str, Callable[..., Any]]: - return { - name: func - for name, func in functions.items() - if hasattr(func, attribute) - } - - def _map_pipeline_components(self) -> None: - all_functions = self._get_all_functions() - crew_functions = self._filter_functions(all_functions, "is_crew") - router_functions = self._filter_functions(all_functions, "is_router") - - for stage_attr in dir(self): - stage = getattr(self, stage_attr) - if isinstance(stage, (Crew, Router)): - self.stages.append(stage) - elif callable(stage) and hasattr(stage, "is_crew"): - self.stages.append(crew_functions[stage_attr]()) - elif callable(stage) and hasattr(stage, "is_router"): - self.stages.append(router_functions[stage_attr]()) - elif isinstance(stage, list) and all( - isinstance(item, Crew) for item in stage - ): - self.stages.append(stage) - - def build_pipeline(self) -> Pipeline: - return Pipeline(stages=self.stages) - - return WrappedClass diff --git a/src/crewai/routers/__init__.py b/src/crewai/routers/__init__.py deleted file mode 100644 index c53802bef..000000000 --- a/src/crewai/routers/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from crewai.routers.router import Router - -__all__ = ["Router"] diff --git a/src/crewai/routers/router.py b/src/crewai/routers/router.py deleted file mode 100644 index e85ce2256..000000000 --- a/src/crewai/routers/router.py +++ /dev/null @@ -1,84 +0,0 @@ -from copy import deepcopy -from typing import Any, Callable, Dict, Tuple - -from pydantic import BaseModel, Field, PrivateAttr - - -class Route(BaseModel): - condition: Callable[[Dict[str, Any]], bool] - pipeline: Any - - -class Router(BaseModel): - routes: Dict[str, Route] = Field( - default_factory=dict, - description="Dictionary of route names to (condition, pipeline) tuples", - ) - default: Any = Field(..., description="Default pipeline if no conditions are met") - _route_types: Dict[str, type] = PrivateAttr(default_factory=dict) - - class Config: - arbitrary_types_allowed = True - - def __init__(self, routes: Dict[str, Route], default: Any, **data): - super().__init__(routes=routes, default=default, **data) - self._check_copyable(default) - for name, route in routes.items(): - self._check_copyable(route.pipeline) - self._route_types[name] = type(route.pipeline) - - @staticmethod - def _check_copyable(obj: Any) -> None: - if not hasattr(obj, "copy") or not callable(getattr(obj, "copy")): - raise ValueError(f"Object of type {type(obj)} must have a 'copy' method") - - def add_route( - self, - name: str, - condition: Callable[[Dict[str, Any]], bool], - pipeline: Any, - ) -> "Router": - """ - Add a named route with its condition and corresponding pipeline to the router. - - Args: - name: A unique name for this route - condition: A function that takes a dictionary input and returns a boolean - pipeline: The Pipeline to execute if the condition is met - - Returns: - The Router instance for method chaining - """ - self._check_copyable(pipeline) - self.routes[name] = Route(condition=condition, pipeline=pipeline) - self._route_types[name] = type(pipeline) - return self - - def route(self, input_data: Dict[str, Any]) -> Tuple[Any, str]: - """ - Evaluate the input against the conditions and return the appropriate pipeline. - - Args: - input_data: The input dictionary to be evaluated - - Returns: - A tuple containing the next Pipeline to be executed and the name of the route taken - """ - for name, route in self.routes.items(): - if route.condition(input_data): - return route.pipeline, name - - return self.default, "default" - - def copy(self) -> "Router": - """Create a deep copy of the Router.""" - new_routes = { - name: Route( - condition=deepcopy(route.condition), - pipeline=route.pipeline.copy(), - ) - for name, route in self.routes.items() - } - new_default = self.default.copy() - - return Router(routes=new_routes, default=new_default) diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py deleted file mode 100644 index bd0a38183..000000000 --- a/tests/pipeline/test_pipeline.py +++ /dev/null @@ -1,921 +0,0 @@ -import json -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest -from crewai.agent import Agent -from crewai.crew import Crew -from crewai.crews.crew_output import CrewOutput -from crewai.pipeline.pipeline import Pipeline -from crewai.pipeline.pipeline_kickoff_result import PipelineKickoffResult -from crewai.process import Process -from crewai.routers.router import Route, Router -from crewai.task import Task -from crewai.tasks.task_output import TaskOutput -from crewai.types.usage_metrics import UsageMetrics -from pydantic import BaseModel, ValidationError - -DEFAULT_TOKEN_USAGE = UsageMetrics( - total_tokens=100, prompt_tokens=50, completion_tokens=50, successful_requests=3 -) - - -@pytest.fixture -def mock_crew_factory(): - def _create_mock_crew(name: str, output_json_dict=None, pydantic_output=None): - MockCrewClass = type("MockCrew", (MagicMock, Crew), {}) - - class MockCrew(MockCrewClass): - def __deepcopy__(self): - result = MockCrewClass() - result.kickoff_async = self.kickoff_async - result.name = self.name - return result - - def copy( - self, - ): - return self - - crew = MockCrew() - crew.name = name - crew.knowledge = None - - task_output = TaskOutput( - description="Test task", raw="Task output", agent="Test Agent" - ) - crew_output = CrewOutput( - raw="Test output", - tasks_output=[task_output], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict=output_json_dict if output_json_dict else None, - pydantic=pydantic_output, - ) - - async def kickoff_async(inputs=None): - return crew_output - - # Create an AsyncMock for kickoff_async - crew.kickoff_async = AsyncMock(side_effect=kickoff_async) - - # Mock the synchronous kickoff method - crew.kickoff = MagicMock(return_value=crew_output) - - # Add more attributes that Procedure might be expecting - crew.verbose = False - crew.output_log_file = None - crew.max_rpm = None - crew.memory = False - crew.process = Process.sequential - crew.config = None - crew.cache = True - crew.embedder = None - - # Add non-empty agents and tasks - mock_agent = MagicMock(spec=Agent) - mock_task = MagicMock(spec=Task) - mock_task.agent = mock_agent - mock_task.async_execution = False - mock_task.context = None - - crew.agents = [mock_agent] - crew.tasks = [mock_task] - - return crew - - return _create_mock_crew - - -@pytest.fixture -def mock_router_factory(mock_crew_factory): - def _create_mock_router(): - crew1 = mock_crew_factory(name="Crew 1", output_json_dict={"output": "crew1"}) - crew2 = mock_crew_factory(name="Crew 2", output_json_dict={"output": "crew2"}) - crew3 = mock_crew_factory(name="Crew 3", output_json_dict={"output": "crew3"}) - - MockRouterClass = type("MockRouter", (MagicMock, Router), {}) - - class MockRouter(MockRouterClass): - def __deepcopy__(self, memo): - result = MockRouterClass() - result.route = self.route - return result - - mock_router = MockRouter() - mock_router.route = MagicMock( - side_effect=lambda x: ( - ( - Pipeline(stages=[crew1]) - if x.get("score", 0) > 80 - else ( - Pipeline(stages=[crew2]) - if x.get("score", 0) > 50 - else Pipeline(stages=[crew3]) - ) - ), - ( - "route1" - if x.get("score", 0) > 80 - else "route2" - if x.get("score", 0) > 50 - else "default" - ), - ) - ) - - return mock_router - - return _create_mock_router - - -def test_pipeline_initialization(mock_crew_factory): - """ - Test that a Pipeline is correctly initialized with the given stages. - """ - crew1 = mock_crew_factory(name="Crew 1") - crew2 = mock_crew_factory(name="Crew 2") - - pipeline = Pipeline(stages=[crew1, crew2]) - assert len(pipeline.stages) == 2 - assert pipeline.stages[0] == crew1 - assert pipeline.stages[1] == crew2 - - -@pytest.mark.asyncio -async def test_pipeline_with_empty_input(mock_crew_factory): - """ - Ensure the pipeline handles an empty input list correctly. - """ - crew = mock_crew_factory(name="Test Crew") - pipeline = Pipeline(stages=[crew]) - - input_data = [] - pipeline_results = await pipeline.kickoff(input_data) - - assert ( - len(pipeline_results) == 0 - ), "Pipeline should return empty results for empty input" - - -agent = Agent( - role="Test Role", - goal="Test Goal", - backstory="Test Backstory", - allow_delegation=False, - verbose=False, -) -task = Task( - description="Return: Test output", - expected_output="Test output", - agent=agent, - async_execution=False, - context=None, -) - - -@pytest.mark.asyncio -async def test_pipeline_process_streams_single_input(): - """ - Test that Pipeline.process_streams() correctly processes a single input - and returns the expected CrewOutput. - """ - crew_name = "Test Crew" - mock_crew = Crew( - agents=[agent], - tasks=[task], - process=Process.sequential, - ) - mock_crew.name = crew_name - pipeline = Pipeline(stages=[mock_crew]) - input_data = [{"key": "value"}] - with patch.object(Crew, "kickoff_async") as mock_kickoff: - task_output = TaskOutput( - description="Test task", raw="Task output", agent="Test Agent" - ) - mock_kickoff.return_value = CrewOutput( - raw="Test output", - tasks_output=[task_output], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict=None, - pydantic=None, - ) - pipeline_results = await pipeline.kickoff(input_data) - mock_crew.kickoff_async.assert_called_once_with(inputs={"key": "value"}) - - for pipeline_result in pipeline_results: - assert isinstance(pipeline_result, PipelineKickoffResult) - assert pipeline_result.raw == "Test output" - assert len(pipeline_result.crews_outputs) == 1 - assert pipeline_result.token_usage == {crew_name: DEFAULT_TOKEN_USAGE} - assert pipeline_result.trace == [input_data[0], "Test Crew"] - - -@pytest.mark.asyncio -async def test_pipeline_result_ordering(): - """ - Ensure that results are returned in the same order as the inputs, especially with parallel processing. - """ - crew1 = Crew( - name="Crew 1", - agents=[agent], - tasks=[task], - ) - crew2 = Crew( - name="Crew 2", - agents=[agent], - tasks=[task], - ) - crew3 = Crew( - name="Crew 3", - agents=[agent], - tasks=[task], - ) - - pipeline = Pipeline( - stages=[crew1, [crew2, crew3]] - ) # Parallel stage to test ordering - input_data = [{"id": 1}, {"id": 2}, {"id": 3}] - - def create_crew_output(crew_name): - return CrewOutput( - raw=f"Test output from {crew_name}", - tasks_output=[ - TaskOutput( - description="Test task", - raw=f"Task output from {crew_name}", - agent="Test Agent", - ) - ], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": crew_name.lower().replace(" ", "")}, - pydantic=None, - ) - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.side_effect = [ - create_crew_output("Crew 1"), - create_crew_output("Crew 2"), - create_crew_output("Crew 3"), - ] * 3 - pipeline_results = await pipeline.kickoff(input_data) - mock_kickoff.call_count = 3 - - assert ( - len(pipeline_results) == 6 - ), "Should have 2 results for each input due to the parallel final stage" - - # Group results by their original input id - grouped_results = {} - for result in pipeline_results: - input_id = result.trace[0]["id"] - if input_id not in grouped_results: - grouped_results[input_id] = [] - grouped_results[input_id].append(result) - - # Check that we have the correct number of groups and results per group - assert len(grouped_results) == 3, "Should have results for each of the 3 inputs" - for input_id, results in grouped_results.items(): - assert ( - len(results) == 2 - ), f"Each input should have 2 results, but input {input_id} has {len(results)}" - - # Check the ordering and content of the results - for input_id in range(1, 4): - group = grouped_results[input_id] - assert group[0].trace == [ - {"id": input_id}, - "Crew 1", - "Crew 2", - ], f"Unexpected trace for first result of input {input_id}" - assert group[1].trace == [ - {"id": input_id}, - "Crew 1", - "Crew 3", - ], f"Unexpected trace for second result of input {input_id}" - - -class TestPydanticOutput(BaseModel): - key: str - value: int - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_pipeline_process_streams_single_input_pydantic_output(): - crew_name = "Test Crew" - task = Task( - description="Return: Key:value", - expected_output="Key:Value", - agent=agent, - async_execution=False, - context=None, - output_pydantic=TestPydanticOutput, - ) - mock_crew = Crew( - name=crew_name, - agents=[agent], - tasks=[task], - ) - - pipeline = Pipeline(stages=[mock_crew]) - input_data = [{"key": "value"}] - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_crew_output = CrewOutput( - raw="Test output", - tasks_output=[ - TaskOutput( - description="Return: Key:value", raw="Key:Value", agent="Test Agent" - ) - ], - token_usage=UsageMetrics( - total_tokens=171, - prompt_tokens=154, - completion_tokens=17, - successful_requests=1, - ), - pydantic=TestPydanticOutput(key="test", value=42), - ) - mock_kickoff.return_value = mock_crew_output - pipeline_results = await pipeline.kickoff(input_data) - - assert len(pipeline_results) == 1 - pipeline_result = pipeline_results[0] - - assert isinstance(pipeline_result, PipelineKickoffResult) - assert pipeline_result.raw == "Test output" - assert len(pipeline_result.crews_outputs) == 1 - assert pipeline_result.token_usage == { - crew_name: UsageMetrics( - total_tokens=171, - prompt_tokens=154, - completion_tokens=17, - successful_requests=1, - ) - } - - assert pipeline_result.trace == [input_data[0], "Test Crew"] - assert isinstance(pipeline_result.pydantic, TestPydanticOutput) - assert pipeline_result.pydantic.key == "test" - assert pipeline_result.pydantic.value == 42 - assert pipeline_result.json_dict is None - - -@pytest.mark.asyncio -async def test_pipeline_preserves_original_input(mock_crew_factory): - crew_name = "Test Crew" - mock_crew = mock_crew_factory( - name=crew_name, - output_json_dict={"new_key": "new_value"}, - ) - pipeline = Pipeline(stages=[mock_crew]) - - # Create a deep copy of the input data to ensure we're not comparing references - original_input_data = [{"key": "value", "nested": {"a": 1}}] - input_data = json.loads(json.dumps(original_input_data)) - - await pipeline.kickoff(input_data) - - # Assert that the original input hasn't been modified - assert ( - input_data == original_input_data - ), "The original input data should not be modified" - - # Ensure that even nested structures haven't been modified - assert ( - input_data[0]["nested"] == original_input_data[0]["nested"] - ), "Nested structures should not be modified" - - # Verify that adding new keys to the crew output doesn't affect the original input - assert ( - "new_key" not in input_data[0] - ), "New keys from crew output should not be added to the original input" - - -@pytest.mark.asyncio -async def test_pipeline_process_streams_multiple_inputs(): - """ - Test that Pipeline.process_streams() correctly processes multiple inputs - and returns the expected CrewOutputs. - """ - mock_crew = Crew(name="Test Crew", tasks=[task], agents=[agent]) - pipeline = Pipeline(stages=[mock_crew]) - input_data = [{"key1": "value1"}, {"key2": "value2"}] - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.return_value = CrewOutput( - raw="Test output", - tasks_output=[ - TaskOutput( - description="Test task", raw="Task output", agent="Test Agent" - ) - ], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict=None, - pydantic=None, - ) - pipeline_results = await pipeline.kickoff(input_data) - assert mock_kickoff.call_count == 2 - assert len(pipeline_results) == 2 - - for pipeline_result in pipeline_results: - assert all( - isinstance(crew_output, CrewOutput) - for crew_output in pipeline_result.crews_outputs - ) - - -@pytest.mark.asyncio -async def test_pipeline_with_parallel_stages(): - """ - Test that Pipeline correctly handles parallel stages. - """ - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent]) - - pipeline = Pipeline(stages=[crew1, [crew2, crew3]]) - input_data = [{"initial": "data"}] - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.return_value = CrewOutput( - raw="Test output", - tasks_output=[ - TaskOutput( - description="Test task", raw="Task output", agent="Test Agent" - ) - ], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict=None, - pydantic=None, - ) - pipeline_result = await pipeline.kickoff(input_data) - mock_kickoff.assert_called_with(inputs={"initial": "data"}) - - assert len(pipeline_result) == 2 - pipeline_result_1, pipeline_result_2 = pipeline_result - - pipeline_result_1.trace = [ - "Crew 1", - "Crew 2", - ] - pipeline_result_2.trace = [ - "Crew 1", - "Crew 3", - ] - - expected_token_usage = { - "Crew 1": DEFAULT_TOKEN_USAGE, - "Crew 2": DEFAULT_TOKEN_USAGE, - "Crew 3": DEFAULT_TOKEN_USAGE, - } - - assert pipeline_result_1.token_usage == expected_token_usage - assert pipeline_result_2.token_usage == expected_token_usage - - -@pytest.mark.asyncio -async def test_pipeline_with_parallel_stages_end_in_single_stage(mock_crew_factory): - """ - Test that Pipeline correctly handles parallel stages. - """ - crew1 = mock_crew_factory(name="Crew 1") - crew2 = mock_crew_factory(name="Crew 2") - crew3 = mock_crew_factory(name="Crew 3") - crew4 = mock_crew_factory(name="Crew 4") - - pipeline = Pipeline(stages=[crew1, [crew2, crew3], crew4]) - input_data = [{"initial": "data"}] - - pipeline_result = await pipeline.kickoff(input_data) - - crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"}) - - assert len(pipeline_result) == 1 - pipeline_result_1 = pipeline_result[0] - - pipeline_result_1.trace = [ - input_data[0], - "Crew 1", - ["Crew 2", "Crew 3"], - "Crew 4", - ] - - expected_token_usage = { - "Crew 1": DEFAULT_TOKEN_USAGE, - "Crew 2": DEFAULT_TOKEN_USAGE, - "Crew 3": DEFAULT_TOKEN_USAGE, - "Crew 4": DEFAULT_TOKEN_USAGE, - } - - assert pipeline_result_1.token_usage == expected_token_usage - - -def test_pipeline_rshift_operator(mock_crew_factory): - """ - Test that the >> operator correctly creates a Pipeline from Crews and lists of Crews. - """ - crew1 = mock_crew_factory(name="Crew 1") - crew2 = mock_crew_factory(name="Crew 2") - crew3 = mock_crew_factory(name="Crew 3") - - # Test single crew addition - pipeline = Pipeline(stages=[]) >> crew1 - assert len(pipeline.stages) == 1 - assert pipeline.stages[0] == crew1 - - # Test adding a list of crews - pipeline = Pipeline(stages=[crew1]) - pipeline = pipeline >> [crew2, crew3] - assert len(pipeline.stages) == 2 - assert pipeline.stages[1] == [crew2, crew3] - - # Test error case: trying to shift with non-Crew object - with pytest.raises(TypeError): - pipeline >> "not a crew" - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_pipeline_parallel_crews_to_parallel_crews(): - """ - Test that feeding parallel crews to parallel crews works correctly. - """ - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent]) - crew4 = Crew(name="Crew 4", tasks=[task], agents=[agent]) - # output_json_dict={"output1": "crew1"} - pipeline = Pipeline(stages=[[crew1, crew2], [crew3, crew4]]) - - input_data = [{"input": "test"}] - - def create_crew_output(crew_name): - return CrewOutput( - raw=f"Test output from {crew_name}", - tasks_output=[ - TaskOutput( - description="Test task", - raw=f"Task output from {crew_name}", - agent="Test Agent", - ) - ], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": crew_name.lower().replace(" ", "")}, - pydantic=None, - ) - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.side_effect = [ - create_crew_output(crew_name) - for crew_name in ["Crew 1", "Crew 2", "Crew 3", "Crew 4"] - ] - pipeline_results = await pipeline.kickoff(input_data) - - assert len(pipeline_results) == 2, "Should have 2 results for final parallel stage" - - pipeline_result_1, pipeline_result_2 = pipeline_results - - # Check the outputs - assert pipeline_result_1.json_dict == {"output": "crew3"} - assert pipeline_result_2.json_dict == {"output": "crew4"} - - # Check the traces - expected_traces = [ - [{"input": "test"}, ["Crew 1", "Crew 2"], "Crew 3"], - [{"input": "test"}, ["Crew 1", "Crew 2"], "Crew 4"], - ] - - for result, expected_trace in zip(pipeline_results, expected_traces): - assert result.trace == expected_trace, f"Unexpected trace: {result.trace}" - - -def test_pipeline_double_nesting_not_allowed(mock_crew_factory): - """ - Test that double nesting in pipeline stages is not allowed. - """ - crew1 = mock_crew_factory(name="Crew 1") - crew2 = mock_crew_factory(name="Crew 2") - crew3 = mock_crew_factory(name="Crew 3") - crew4 = mock_crew_factory(name="Crew 4") - - with pytest.raises(ValidationError) as exc_info: - Pipeline(stages=[crew1, [[crew2, crew3], crew4]]) - - error_msg = str(exc_info.value) - - assert ( - "Double nesting is not allowed in pipeline stages" in error_msg - ), f"Unexpected error message: {error_msg}" - - -def test_pipeline_invalid_crew(mock_crew_factory): - """ - Test that non-Crew objects are not allowed in pipeline stages. - """ - crew1 = mock_crew_factory(name="Crew 1") - not_a_crew = "This is not a crew" - - with pytest.raises(ValidationError) as exc_info: - Pipeline(stages=[crew1, not_a_crew]) - - error_msg = str(exc_info.value) - assert ( - "Expected Crew instance, Router instance, or list of Crews, got " - in error_msg - ), f"Unexpected error message: {error_msg}" - - -""" -TODO: Figure out what is the proper output for a pipeline with multiple stages - -Options: -- Should the final output only include the last stage's output? -- Should the final output include the accumulation of previous stages' outputs? -""" - - -@pytest.mark.asyncio -async def test_pipeline_data_accumulation(mock_crew_factory): - crew1 = mock_crew_factory(name="Crew 1", output_json_dict={"key1": "value1"}) - crew2 = mock_crew_factory(name="Crew 2", output_json_dict={"key2": "value2"}) - - pipeline = Pipeline(stages=[crew1, crew2]) - input_data = [{"initial": "data"}] - results = await pipeline.kickoff(input_data) - - # Check that crew1 was called with only the initial input - crew1.kickoff_async.assert_called_once_with(inputs={"initial": "data"}) - - # Check that crew2 was called with the combined input from the initial data and crew1's output - crew2.kickoff_async.assert_called_once_with( - inputs={"initial": "data", "key1": "value1"} - ) - - # Check the final output - assert len(results) == 1 - final_result = results[0] - assert final_result.json_dict == {"key2": "value2"} - - # Check that the trace includes all stages - assert final_result.trace == [{"initial": "data"}, "Crew 1", "Crew 2"] - - # Check that crews_outputs contain the correct information - assert len(final_result.crews_outputs) == 2 - assert final_result.crews_outputs[0].json_dict == {"key1": "value1"} - assert final_result.crews_outputs[1].json_dict == {"key2": "value2"} - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_pipeline_with_router(): - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent]) - routes = { - "route1": Route( - condition=lambda x: x.get("score", 0) > 80, - pipeline=Pipeline(stages=[crew1]), - ), - "route2": Route( - condition=lambda x: 50 < x.get("score", 0) <= 80, - pipeline=Pipeline(stages=[crew2]), - ), - } - router = Router( - routes=routes, - default=Pipeline(stages=[crew3]), - ) - # Test high score route - pipeline = Pipeline(stages=[router]) - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.return_value = CrewOutput( - raw="Test output from Crew 1", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew1"}, - pydantic=None, - ) - result_high = await pipeline.kickoff([{"score": 90}]) - - assert len(result_high) == 1 - assert result_high[0].json_dict is not None - assert result_high[0].json_dict["output"] == "crew1" - assert result_high[0].trace == [ - {"score": 90}, - {"route_taken": "route1"}, - "Crew 1", - ] - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.return_value = CrewOutput( - raw="Test output from Crew 2", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew2"}, - pydantic=None, - ) - # Test medium score route - pipeline = Pipeline(stages=[router]) - result_medium = await pipeline.kickoff([{"score": 60}]) - assert len(result_medium) == 1 - assert result_medium[0].json_dict is not None - assert result_medium[0].json_dict["output"] == "crew2" - assert result_medium[0].trace == [ - {"score": 60}, - {"route_taken": "route2"}, - "Crew 2", - ] - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.return_value = CrewOutput( - raw="Test output from Crew 3", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew3"}, - pydantic=None, - ) - # Test low score route - pipeline = Pipeline(stages=[router]) - result_low = await pipeline.kickoff([{"score": 30}]) - assert len(result_low) == 1 - assert result_low[0].json_dict is not None - assert result_low[0].json_dict["output"] == "crew3" - assert result_low[0].trace == [ - {"score": 30}, - {"route_taken": "default"}, - "Crew 3", - ] - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_router_with_multiple_inputs(): - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent]) - router = Router( - routes={ - "route1": Route( - condition=lambda x: x.get("score", 0) > 80, - pipeline=Pipeline(stages=[crew1]), - ), - "route2": Route( - condition=lambda x: 50 < x.get("score", 0) <= 80, - pipeline=Pipeline(stages=[crew2]), - ), - }, - default=Pipeline(stages=[crew3]), - ) - pipeline = Pipeline(stages=[router]) - - inputs = [{"score": 90}, {"score": 60}, {"score": 30}] - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.side_effect = [ - CrewOutput( - raw="Test output from Crew 1", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew1"}, - pydantic=None, - ), - CrewOutput( - raw="Test output from Crew 2", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew2"}, - pydantic=None, - ), - CrewOutput( - raw="Test output from Crew 3", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew3"}, - pydantic=None, - ), - ] - results = await pipeline.kickoff(inputs) - - assert len(results) == 3 - assert results[0].json_dict is not None - assert results[0].json_dict["output"] == "crew1" - assert results[1].json_dict is not None - assert results[1].json_dict["output"] == "crew2" - assert results[2].json_dict is not None - assert results[2].json_dict["output"] == "crew3" - - assert results[0].trace[1]["route_taken"] == "route1" - assert results[1].trace[1]["route_taken"] == "route2" - assert results[2].trace[1]["route_taken"] == "default" - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_pipeline_with_multiple_routers(): - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - router1 = Router( - routes={ - "route1": Route( - condition=lambda x: x.get("score", 0) > 80, - pipeline=Pipeline(stages=[crew1]), - ), - }, - default=Pipeline(stages=[crew2]), - ) - router2 = Router( - routes={ - "route2": Route( - condition=lambda x: 50 < x.get("score", 0) <= 80, - pipeline=Pipeline(stages=[crew2]), - ), - }, - default=Pipeline(stages=[crew2]), - ) - final_crew = Crew(name="Final Crew", tasks=[task], agents=[agent]) - - pipeline = Pipeline(stages=[router1, router2, final_crew]) - - with patch.object(Crew, "kickoff_async") as mock_kickoff: - mock_kickoff.side_effect = [ - CrewOutput( - raw="Test output from Crew 1", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew1"}, - pydantic=None, - ), - CrewOutput( - raw="Test output from Crew 2", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "crew2"}, - pydantic=None, - ), - CrewOutput( - raw="Test output from Final Crew", - tasks_output=[], - token_usage=DEFAULT_TOKEN_USAGE, - json_dict={"output": "final"}, - pydantic=None, - ), - ] - result = await pipeline.kickoff([{"score": 75}]) - - assert len(result) == 1 - assert result[0].json_dict is not None - assert result[0].json_dict["output"] == "final" - assert ( - len(result[0].trace) == 6 - ) # Input, Router1, Crew2, Router2, Crew2, Final Crew - assert result[0].trace[1]["route_taken"] == "default" - assert result[0].trace[3]["route_taken"] == "route2" - - -@pytest.mark.asyncio -async def test_router_default_route(mock_crew_factory): - default_crew = mock_crew_factory( - name="Default Crew", output_json_dict={"output": "default"} - ) - router = Router( - routes={ - "route1": Route( - condition=lambda x: False, - pipeline=Pipeline(stages=[mock_crew_factory(name="Never Used")]), - ), - }, - default=Pipeline(stages=[default_crew]), - ) - - pipeline = Pipeline(stages=[router]) - result = await pipeline.kickoff([{"score": 100}]) - - assert len(result) == 1 - assert result[0].json_dict is not None - assert result[0].json_dict["output"] == "default" - assert result[0].trace[1]["route_taken"] == "default" - - -@pytest.mark.asyncio -@pytest.mark.vcr(filter_headers=["authorization"]) -async def test_router_with_empty_input(): - crew1 = Crew(name="Crew 1", tasks=[task], agents=[agent]) - crew2 = Crew(name="Crew 2", tasks=[task], agents=[agent]) - crew3 = Crew(name="Crew 3", tasks=[task], agents=[agent]) - router = Router( - routes={ - "route1": Route( - condition=lambda x: x.get("score", 0) > 80, - pipeline=Pipeline(stages=[crew1]), - ), - "route2": Route( - condition=lambda x: 50 < x.get("score", 0) <= 80, - pipeline=Pipeline(stages=[crew2]), - ), - }, - default=Pipeline(stages=[crew3]), - ) - pipeline = Pipeline(stages=[router]) - - result = await pipeline.kickoff([{}]) - - assert len(result) == 1 - assert result[0].trace[1]["route_taken"] == "default" From 48bb8ef775392b67aba84ad04f6c7f8beda0802d Mon Sep 17 00:00:00 2001 From: Rashmi Pawar <168514198+raspawar@users.noreply.github.com> Date: Thu, 5 Dec 2024 02:08:46 +0530 Subject: [PATCH 03/11] docs: add nvidia as provider (#1632) Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com> --- docs/how-to/llm-connections.mdx | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/how-to/llm-connections.mdx b/docs/how-to/llm-connections.mdx index a2fc540cc..a69aa4c86 100644 --- a/docs/how-to/llm-connections.mdx +++ b/docs/how-to/llm-connections.mdx @@ -32,6 +32,7 @@ LiteLLM supports a wide range of providers, including but not limited to: - Cloudflare Workers AI - DeepInfra - Groq +- [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1) - And many more! For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers). From fa373f9660410b766c2f81ea33fbfd864bae4f47 Mon Sep 17 00:00:00 2001 From: Tony Kipkemboi Date: Thu, 5 Dec 2024 09:49:44 -0500 Subject: [PATCH 04/11] add knowledge demo + improve knowledge docs (#1706) --- docs/concepts/knowledge.mdx | 370 +++++++++++++++++++++++------------- 1 file changed, 234 insertions(+), 136 deletions(-) diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx index a00b2c2f0..06c2eb947 100644 --- a/docs/concepts/knowledge.mdx +++ b/docs/concepts/knowledge.mdx @@ -1,6 +1,6 @@ --- title: Knowledge -description: Understand what knowledge is in CrewAI and how to effectively use it. +description: What is knowledge in CrewAI and how to use it. icon: book --- @@ -8,7 +8,8 @@ icon: book ## What is Knowledge? -Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. Think of it as giving your agents a reference library they can consult while working. +Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks. +Think of it as giving your agents a reference library they can consult while working. Key benefits of using Knowledge: @@ -37,151 +38,275 @@ CrewAI supports various types of knowledge sources out of the box: ## Quick Start -Here's a simple example using string-based knowledge: +Here's an example using string-based knowledge: -```python -from crewai import Agent, Task, Crew -from crewai.knowledge import StringKnowledgeSource +```python Code +from crewai import Agent, Task, Crew, Process, LLM +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource -# 1. Create a knowledge source -product_info = StringKnowledgeSource( - content="""Our product X1000 has the following features: - - 10-hour battery life - - Water-resistant - - Available in black and silver - Price: $299.99""", - metadata={"category": "product"} +# Create a knowledge source +content = "Users name is John. He is 30 years old and lives in San Francisco." +string_source = StringKnowledgeSource( + content=content, + metadata={"preference": "personal"} ) -# 2. Create an agent with knowledge -sales_agent = Agent( - role="Sales Representative", - goal="Accurately answer customer questions about products", - backstory="Expert in product features and customer service", - knowledge_sources=[product_info] # Attach knowledge to agent +# Create an LLM with a temperature of 0 to ensure deterministic outputs +llm = LLM(model="gpt-4o-mini", temperature=0) + +# Create an agent with the knowledge store +agent = Agent( + role="About User", + goal="You know everything about the user.", + backstory="""You are a master at understanding people and their preferences.""", + verbose=True, + allow_delegation=False, + llm=llm, +) +task = Task( + description="Answer the following questions about the user: {question}", + expected_output="An answer to the question.", + agent=agent, ) -# 3. Create a task -answer_task = Task( - description="Answer: What colors is the X1000 available in and how much does it cost?", - agent=sales_agent -) - -# 4. Create and run the crew crew = Crew( - agents=[sales_agent], - tasks=[answer_task] + agents=[agent], + tasks=[task], + verbose=True, + process=Process.sequential, + knowledge={ + "sources": [string_source], + "metadata": {"preference": "personal"} + }, # Enable knowledge by adding the sources here. You can also add more sources to the sources list. ) -result = crew.kickoff() +result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) ``` ## Knowledge Configuration -### Collection Names - -Knowledge sources are organized into collections for better management: - -```python -# Create knowledge sources with specific collections -tech_specs = StringKnowledgeSource( - content="Technical specifications...", - collection_name="product_tech_specs" -) - -pricing_info = StringKnowledgeSource( - content="Pricing information...", - collection_name="product_pricing" -) -``` - ### Metadata and Filtering -Add metadata to organize and filter knowledge: +Knowledge sources support metadata for better organization and filtering. Metadata is used to filter the knowledge sources when querying the knowledge store. -```python +```python Code knowledge_source = StringKnowledgeSource( - content="Product details...", - metadata={ - "category": "electronics", - "product_line": "premium", - "last_updated": "2024-03" - } + content="Users name is John. He is 30 years old and lives in San Francisco.", + metadata={"preference": "personal"} # Metadata is used to filter the knowledge sources ) ``` ### Chunking Configuration -Control how your content is split for processing: +Control how content is split for processing by setting the chunk size and overlap. -```python -knowledge_source = PDFKnowledgeSource( - file_path="product_manual.pdf", - chunk_size=2000, # Characters per chunk - chunk_overlap=200 # Overlap between chunks +```python Code +knowledge_source = StringKnowledgeSource( + content="Long content...", + chunk_size=4000, # Characters per chunk (default) + chunk_overlap=200 # Overlap between chunks (default) ) ``` -## Advanced Usage +## Embedder Configuration -### Custom Knowledge Sources +You can also configure the embedder for the knowledge store. This is useful if you want to use a different embedder for the knowledge store than the one used for the agents. -Create your own knowledge source by extending the base class: +```python Code +... +string_source = StringKnowledgeSource( + content="Users name is John. He is 30 years old and lives in San Francisco.", + metadata={"preference": "personal"} +) +crew = Crew( + ... + knowledge={ + "sources": [string_source], + "metadata": {"preference": "personal"}, + "embedder_config": { + "provider": "openai", # Default embedder provider; can be "ollama", "gemini", e.t.c. + "config": {"model": "text-embedding-3-small"} # Default embedder model; can be "mxbai-embed-large", "nomic-embed-tex", e.t.c. + }, + }, +) +``` -```python -from crewai.knowledge.source import BaseKnowledgeSource +## Custom Knowledge Sources -class APIKnowledgeSource(BaseKnowledgeSource): - def __init__(self, api_endpoint: str, **kwargs): - super().__init__(**kwargs) - self.api_endpoint = api_endpoint +CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles. + +#### Space News Knowledge Source Example + + + +```python Code +from crewai import Agent, Task, Crew, Process, LLM +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +import requests +from datetime import datetime +from typing import Dict, Any +from pydantic import BaseModel, Field + +class SpaceNewsKnowledgeSource(BaseKnowledgeSource): + """Knowledge source that fetches data from Space News API.""" - def load_content(self): - # Implement API data fetching - response = requests.get(self.api_endpoint) - return response.json() + api_endpoint: str = Field(description="API endpoint URL") + limit: int = Field(default=10, description="Number of articles to fetch") - def add(self): + def load_content(self) -> Dict[Any, str]: + """Fetch and format space news articles.""" + try: + response = requests.get( + f"{self.api_endpoint}?limit={self.limit}" + ) + response.raise_for_status() + + data = response.json() + articles = data.get('results', []) + + formatted_data = self._format_articles(articles) + return {self.api_endpoint: formatted_data} + except Exception as e: + raise ValueError(f"Failed to fetch space news: {str(e)}") + + def _format_articles(self, articles: list) -> str: + """Format articles into readable text.""" + formatted = "Space News Articles:\n\n" + for article in articles: + formatted += f""" +Title: {article['title']} +Published: {article['published_at']} +Summary: {article['summary']} +News Site: {article['news_site']} +URL: {article['url']} +-------------------""" + return formatted + + def add(self) -> None: + """Process and store the articles.""" content = self.load_content() - # Process and store content - self.save_documents({"source": "api"}) -``` + for _, text in content.items(): + chunks = self._chunk_text(text) + self.chunks.extend(chunks) + + self.save_documents(metadata={ + "source": "space_news_api", + "timestamp": datetime.now().isoformat(), + "article_count": self.limit + }) -### Embedder Configuration +# Create knowledge source +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", + limit=10, + metadata={"category": "recent_news", "source": "spaceflight_news"} +) -Customize the embedding process: +# Create specialized agent +space_analyst = Agent( + role="Space News Analyst", + goal="Answer questions about space news accurately and comprehensively", + backstory="""You are a space industry analyst with expertise in space exploration, + satellite technology, and space industry trends. You excel at answering questions + about space news and providing detailed, accurate information.""", + knowledge_sources=[recent_news], + llm=LLM(model="gpt-4", temperature=0.0) +) -```python +# Create task that handles user questions +analysis_task = Task( + description="Answer this question about space news: {user_question}", + expected_output="A detailed answer based on the recent space news articles", + agent=space_analyst +) + +# Create and run the crew crew = Crew( - agents=[agent], - tasks=[task], - knowledge_sources=[source], - embedder={ - "provider": "ollama", - "config": {"model": "nomic-embed-text:latest"}, - } + agents=[space_analyst], + tasks=[analysis_task], + verbose=True, + process=Process.sequential +) + +# Example usage +result = crew.kickoff( + inputs={"user_question": "What are the latest developments in space exploration?"} ) ``` +```output Output +# Agent: Space News Analyst +## Task: Answer this question about space news: What are the latest developments in space exploration? -### Referencing Sources -You can reference knowledge sources by their collection name or metadata. +# Agent: Space News Analyst +## Final Answer: +The latest developments in space exploration, based on recent space news articles, include the following: -* Add a directory to your crew project called `knowledge`: -* File paths in knowledge can be referenced relative to the `knowledge` directory. +1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/) -Example: -A file inside the `knowledge` directory called `example.txt` can be referenced as `example.txt`. +2. SpaceX has also informed the US Federal Communications Commission (FCC) that it plans to begin launching its first next-generation Starlink Gen2 satellites. This represents a major upgrade to the Starlink satellite internet service, which aims to provide high-speed internet access worldwide. [Source: Teslarati](https://www.teslarati.com/spacex-first-starlink-gen2-satellite-launch-2022/) +3. AI startup Synthetaic has raised $15 million in Series B funding. The company uses artificial intelligence to analyze data from space and air sensors, which could have significant applications in space exploration and satellite technology. [Source: SpaceNews](https://spacenews.com/ai-startup-synthetaic-raises-15-million-in-series-b-funding/) + +4. The Space Force has formally established a unit within the U.S. Indo-Pacific Command, marking a permanent presence in the Indo-Pacific region. This could have significant implications for space security and geopolitics. [Source: SpaceNews](https://spacenews.com/space-force-establishes-permanent-presence-in-indo-pacific-region/) + +5. Slingshot Aerospace, a space tracking and data analytics company, is expanding its network of ground-based optical telescopes to increase coverage of low Earth orbit. This could improve our ability to track and analyze objects in low Earth orbit, including satellites and space debris. [Source: SpaceNews](https://spacenews.com/slingshots-space-tracking-network-to-extend-coverage-of-low-earth-orbit/) + +6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/) + +7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/) +``` + +#### Key Components Explained + +1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**: + - Extends `BaseKnowledgeSource` for integration with CrewAI + - Configurable API endpoint and article limit + - Implements three key methods: + - `load_content()`: Fetches articles from the API + - `_format_articles()`: Structures the articles into readable text + - `add()`: Processes and stores the content with metadata + +2. **Agent Configuration**: + - Specialized role as a Space News Analyst + - Uses the knowledge source to access space news + +3. **Task Setup**: + - Takes a user question as input through `{user_question}` + - Designed to provide detailed answers based on the knowledge source + +4. **Crew Orchestration**: + - Manages the workflow between agent and task + - Handles input/output through the kickoff method + +This example demonstrates how to: +- Create a custom knowledge source that fetches real-time data +- Process and format external data for AI consumption +- Use the knowledge source to answer specific user questions +- Integrate everything seamlessly with CrewAI's agent system + +#### About the Spaceflight News API + +The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/documentation), which: +- Provides free access to space-related news articles +- Requires no authentication +- Returns structured data about space news +- Supports pagination and filtering + +You can customize the API query by modifying the endpoint URL: ```python -source = TextFileKnowledgeSource( - file_path="example.txt", # or /example.txt - collection_name="example" +# Fetch more articles +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", + limit=20, # Increase the number of articles + metadata={"category": "recent_news"} ) -crew = Crew( - agents=[agent], - tasks=[task], - knowledge_sources=[source], + +# Add search parameters +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news + limit=10, + metadata={"category": "nasa_news"} ) ``` @@ -189,43 +314,16 @@ crew = Crew( - - Use meaningful collection names - - Add detailed metadata for filtering - - Keep chunk sizes appropriate for your content + - Use descriptive metadata for better filtering + - Keep chunk sizes appropriate for your content type - Consider content overlap for context preservation + - Organize related information into separate knowledge sources - - Use smaller chunk sizes for precise retrieval - - Implement metadata filtering for faster searches - - Choose appropriate embedding models for your use case - - Cache frequently accessed knowledge + - Use metadata filtering to narrow search scope + - Adjust chunk sizes based on content complexity + - Configure appropriate embedding models + - Consider using local embedding providers for faster processing - - - - Validate knowledge source content - - Handle missing or corrupted files - - Monitor embedding generation - - Implement fallback options - - - -## Common Issues and Solutions - - - - If agents can't find relevant information: - - Check chunk sizes - - Verify knowledge source loading - - Review metadata filters - - Test with simpler queries first - - - - If knowledge retrieval is slow: - - Reduce chunk sizes - - Optimize metadata filtering - - Consider using a lighter embedding model - - Cache frequently accessed content - - + \ No newline at end of file From 611526596a026a68f9657ca47479d546a80d6ca6 Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Thu, 5 Dec 2024 10:14:04 -0500 Subject: [PATCH 05/11] Brandon/cre 509 hitl multiple rounds of followup (#1702) * v1 of HITL working * Drop print statements * HITL code more robust. Still needs to be refactored. * refactor and more clear messages * Fix type issue * fix tests * Fix test again * Drop extra print --- .../base_agent_executor_mixin.py | 14 +- src/crewai/agents/crew_agent_executor.py | 123 +++++-- src/crewai/translations/en.json | 3 +- src/crewai/utilities/constants.py | 1 + tests/agent_test.py | 30 +- tests/cassettes/test_agent_human_input.yaml | 321 +++++++++++++++--- 6 files changed, 403 insertions(+), 89 deletions(-) diff --git a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py b/src/crewai/agents/agent_builder/base_agent_executor_mixin.py index c452848e8..19d5401a3 100644 --- a/src/crewai/agents/agent_builder/base_agent_executor_mixin.py +++ b/src/crewai/agents/agent_builder/base_agent_executor_mixin.py @@ -3,16 +3,15 @@ from typing import TYPE_CHECKING, Optional from crewai.memory.entity.entity_memory_item import EntityMemoryItem from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem +from crewai.utilities import I18N 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: + from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.crew import Crew from crewai.task import Task - from crewai.agents.agent_builder.base_agent import BaseAgent class CrewAgentExecutorMixin: @@ -100,14 +99,19 @@ class CrewAgentExecutorMixin: print(f"Failed to add to long term memory: {e}") pass - def _ask_human_input(self, final_answer: dict) -> str: + def _ask_human_input(self, final_answer: str) -> str: """Prompt human input for final decision making.""" 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:", + content=( + "\n\n=====\n" + "## Please provide feedback on the Final Result and the Agent's actions. " + "Respond with 'looks good' or a similar phrase when you're satisfied.\n" + "=====\n" + ), color="bold_yellow", ) return input() diff --git a/src/crewai/agents/crew_agent_executor.py b/src/crewai/agents/crew_agent_executor.py index 2c880a634..00545e760 100644 --- a/src/crewai/agents/crew_agent_executor.py +++ b/src/crewai/agents/crew_agent_executor.py @@ -16,7 +16,7 @@ from crewai.agents.tools_handler import ToolsHandler from crewai.tools.base_tool import BaseTool from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException from crewai.utilities import I18N, Printer -from crewai.utilities.constants import TRAINING_DATA_FILE +from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, ) @@ -90,7 +90,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if "system" in self.prompt: system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs) user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs) - self.messages.append(self._format_msg(system_prompt, role="system")) self.messages.append(self._format_msg(user_prompt)) else: @@ -103,17 +102,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): formatted_answer = self._invoke_loop() if self.ask_for_human_input: - human_feedback = self._ask_human_input(formatted_answer.output) - if self.crew and self.crew._train: - self._handle_crew_training_output(formatted_answer, human_feedback) + formatted_answer = self._handle_human_feedback(formatted_answer) - # Making sure we only ask for it once, so disabling for the next thought loop - self.ask_for_human_input = False - 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) self._create_short_term_memory(formatted_answer) self._create_long_term_memory(formatted_answer) return {"output": formatted_answer.output} @@ -326,16 +316,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): def _handle_context_length(self) -> None: if self.respect_context_window: - self._logger.log( - "debug", - "Context length exceeded. Summarizing content to fit the model context window.", + self._printer.print( + content="Context length exceeded. Summarizing content to fit the model context window.", color="yellow", ) self._summarize_messages() else: - self._logger.log( - "debug", - "Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.", + self._printer.print( + content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.", color="red", ) raise SystemExit( @@ -362,15 +350,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): ] = result.output training_handler.save(training_data) else: - self._logger.log( - "error", - "Invalid train iteration type or agent_id not in training data.", + self._printer.print( + content="Invalid train iteration type or agent_id not in training data.", color="red", ) else: - self._logger.log( - "error", - "Crew is None or does not have _train_iteration attribute.", + self._printer.print( + content="Crew is None or does not have _train_iteration attribute.", color="red", ) @@ -388,15 +374,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): train_iteration, agent_id, training_data ) else: - self._logger.log( - "error", - "Invalid train iteration type. Expected int.", + self._printer.print( + content="Invalid train iteration type. Expected int.", color="red", ) else: - self._logger.log( - "error", - "Crew is None or does not have _train_iteration attribute.", + self._printer.print( + content="Crew is None or does not have _train_iteration attribute.", color="red", ) @@ -412,3 +396,82 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]: prompt = prompt.rstrip() return {"role": role, "content": prompt} + + def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish: + """ + Handles the human feedback loop, allowing the user to provide feedback + on the agent's output and determining if additional iterations are needed. + + Parameters: + formatted_answer (AgentFinish): The initial output from the agent. + + Returns: + AgentFinish: The final output after incorporating human feedback. + """ + while self.ask_for_human_input: + human_feedback = self._ask_human_input(formatted_answer.output) + print("Human feedback: ", human_feedback) + + if self.crew and self.crew._train: + self._handle_crew_training_output(formatted_answer, human_feedback) + + # Make an LLM call to verify if additional changes are requested based on human feedback + additional_changes_prompt = self._i18n.slice( + "human_feedback_classification" + ).format(feedback=human_feedback) + + retry_count = 0 + llm_call_successful = False + additional_changes_response = None + + while retry_count < MAX_LLM_RETRY and not llm_call_successful: + try: + additional_changes_response = ( + self.llm.call( + [ + self._format_msg( + additional_changes_prompt, role="system" + ) + ], + callbacks=self.callbacks, + ) + .strip() + .lower() + ) + llm_call_successful = True + except Exception as e: + retry_count += 1 + + self._printer.print( + content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})", + color="red", + ) + + if not llm_call_successful: + self._printer.print( + content="Error processing feedback after multiple attempts.", + color="red", + ) + self.ask_for_human_input = False + break + + if additional_changes_response == "false": + self.ask_for_human_input = False + elif additional_changes_response == "true": + self.ask_for_human_input = True + # Add human feedback to messages + self.messages.append(self._format_msg(f"Feedback: {human_feedback}")) + # Invoke the loop again with updated messages + formatted_answer = self._invoke_loop() + + if self.crew and self.crew._train: + self._handle_crew_training_output(formatted_answer) + else: + # Unexpected response + self._printer.print( + content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.", + color="red", + ) + self.ask_for_human_input = False + + return formatted_answer diff --git a/src/crewai/translations/en.json b/src/crewai/translations/en.json index d79076bb7..d30606dc0 100644 --- a/src/crewai/translations/en.json +++ b/src/crewai/translations/en.json @@ -22,7 +22,8 @@ "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}", "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.", - "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python." + "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.", + "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"" }, "errors": { "force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}", diff --git a/src/crewai/utilities/constants.py b/src/crewai/utilities/constants.py index 97fadda48..3578b7ece 100644 --- a/src/crewai/utilities/constants.py +++ b/src/crewai/utilities/constants.py @@ -2,3 +2,4 @@ TRAINING_DATA_FILE = "training_data.pkl" TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl" DEFAULT_SCORE_THRESHOLD = 0.35 KNOWLEDGE_DIRECTORY = "knowledge" +MAX_LLM_RETRY = 3 diff --git a/tests/agent_test.py b/tests/agent_test.py index 1f4b62fd0..46f6edc29 100644 --- a/tests/agent_test.py +++ b/tests/agent_test.py @@ -3,19 +3,20 @@ import os from unittest import mock from unittest.mock import patch + import pytest from crewai import Agent, Crew, Task from crewai.agents.cache import CacheHandler from crewai.agents.crew_agent_executor import CrewAgentExecutor from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.llm import LLM from crewai.tools import tool from crewai.tools.tool_calling import InstructorToolCalling from crewai.tools.tool_usage import ToolUsage from crewai.tools.tool_usage_events import ToolUsageFinished from crewai.utilities import RPMController -from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.utilities.events import Emitter @@ -985,8 +986,7 @@ def test_agent_definition_based_on_dict(): # test for human input @pytest.mark.vcr(filter_headers=["authorization"]) def test_agent_human_input(): - from unittest.mock import patch - + # Agent configuration config = { "role": "test role", "goal": "test goal", @@ -995,6 +995,7 @@ def test_agent_human_input(): agent = Agent(**config) + # Task configuration with human input enabled task = Task( agent=agent, description="Say the word: Hi", @@ -1002,11 +1003,26 @@ def test_agent_human_input(): human_input=True, ) - with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input: - mock_human_input.return_value = "Don't say hi, say Hello instead!" + # Side effect function for _ask_human_input to simulate multiple feedback iterations + feedback_responses = iter( + [ + "Don't say hi, say Hello instead!", # First feedback + "looks good", # Second feedback to exit loop + ] + ) + + def ask_human_input_side_effect(*args, **kwargs): + return next(feedback_responses) + + with patch.object( + CrewAgentExecutor, "_ask_human_input", side_effect=ask_human_input_side_effect + ) as mock_human_input: + # Execute the task output = agent.execute_task(task) - mock_human_input.assert_called_once() - assert output == "Hello" + + # Assertions to ensure the agent behaves correctly + assert mock_human_input.call_count == 2 # Should have asked for feedback twice + assert output.strip().lower() == "hello" # Final output should be 'Hello' def test_interpolate_inputs(): diff --git a/tests/cassettes/test_agent_human_input.yaml b/tests/cassettes/test_agent_human_input.yaml index 4a336247e..16b9ac9a5 100644 --- a/tests/cassettes/test_agent_human_input.yaml +++ b/tests/cassettes/test_agent_human_input.yaml @@ -9,7 +9,8 @@ interactions: is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, - your job depends on it!\n\nThought:"}], "model": "gpt-4o"}' + your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"], + "stream": false}' headers: accept: - application/json @@ -18,16 +19,13 @@ interactions: connection: - keep-alive content-length: - - '774' + - '824' content-type: - application/json - cookie: - - __cf_bm=rb61BZH2ejzD5YPmLaEJqI7km71QqyNJGTVdNxBq6qk-1727213194-1.0.1.1-pJ49onmgX9IugEMuYQMralzD7oj_6W.CHbSu4Su1z3NyjTGYg.rhgJZWng8feFYah._oSnoYlkTjpK1Wd2C9FA; - _cfuvid=lbRdAddVWV6W3f5Dm9SaOPWDUOxqtZBSPr_fTW26nEA-1727213194587-0.0.1.1-604800000 host: - api.openai.com user-agent: - - OpenAI/Python 1.47.0 + - OpenAI/Python 1.52.1 x-stainless-arch: - arm64 x-stainless-async: @@ -37,29 +35,34 @@ interactions: x-stainless-os: - MacOS x-stainless-package-version: - - 1.47.0 + - 1.52.1 x-stainless-raw-response: - 'true' + x-stainless-retry-count: + - '0' x-stainless-runtime: - CPython x-stainless-runtime-version: - - 3.11.7 + - 3.12.7 method: POST uri: https://api.openai.com/v1/chat/completions response: - content: "{\n \"id\": \"chatcmpl-AB7WMYMmqACvaemh26N6a62wxlxvx\",\n \"object\": - \"chat.completion\",\n \"created\": 1727213882,\n \"model\": \"gpt-4o-2024-05-13\",\n + content: "{\n \"id\": \"chatcmpl-AaqIIsTxhvf75xvuu7gQScIlRSKbW\",\n \"object\": + \"chat.completion\",\n \"created\": 1733344190,\n \"model\": \"gpt-4o-mini-2024-07-18\",\n \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": - \"assistant\",\n \"content\": \"Thought: I now can give a great answer\\nFinal + \"assistant\",\n \"content\": \"I now can give a great answer \\nFinal Answer: Hi\",\n \"refusal\": null\n },\n \"logprobs\": null,\n \ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": - 158,\n \"completion_tokens\": 14,\n \"total_tokens\": 172,\n \"completion_tokens_details\": - {\n \"reasoning_tokens\": 0\n }\n },\n \"system_fingerprint\": \"fp_e375328146\"\n}\n" + 158,\n \"completion_tokens\": 12,\n \"total_tokens\": 170,\n \"prompt_tokens_details\": + {\n \"cached_tokens\": 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": + {\n \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"system_fingerprint\": + \"fp_0705bf87c0\"\n}\n" headers: CF-Cache-Status: - DYNAMIC CF-RAY: - - 8c85eb4f58751cf3-GRU + - 8ece8cfc3b1f4532-ATL Connection: - keep-alive Content-Encoding: @@ -67,7 +70,118 @@ interactions: Content-Type: - application/json Date: - - Tue, 24 Sep 2024 21:38:03 GMT + - Wed, 04 Dec 2024 20:29:50 GMT + Server: + - cloudflare + Set-Cookie: + - __cf_bm=QJZZjZ6eqnVamqUkw.Bx0mj7oBi3a_vGEH1VODcUxlg-1733344190-1.0.1.1-xyN0ekA9xIrSwEhRBmTiWJ3Pt72UYLU5owKfkz5yihVmMTfsr_Qz.ssGPJ5cuft066v1xVjb4zOSTdFmesMSKg; 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ma=86400 openai-organization: - crewai-iuxna1 openai-processing-ms: - - '262' + - '375' openai-version: - '2020-10-01' strict-transport-security: - max-age=31536000; includeSubDomains; preload x-ratelimit-limit-requests: - - '10000' + - '30000' x-ratelimit-limit-tokens: - - '30000000' + - '150000000' x-ratelimit-remaining-requests: - - '9999' + - '29999' x-ratelimit-remaining-tokens: - - '29999817' + - '149999898' x-ratelimit-reset-requests: - - 6ms + - 2ms x-ratelimit-reset-tokens: - 0s x-request-id: - - req_69b1deae1cc3cbf488cee975cd3b04df + - req_be7cb475e0859a82c37ee3f2871ea5ea http_version: HTTP/1.1 status_code: 200 - request: @@ -110,8 +226,10 @@ interactions: is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! 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ma=86400 openai-organization: - crewai-iuxna1 openai-processing-ms: - - '261' + - '484' openai-version: - '2020-10-01' strict-transport-security: - max-age=31536000; includeSubDomains; preload x-ratelimit-limit-requests: - - '10000' + - '30000' x-ratelimit-limit-tokens: - - '30000000' + - '150000000' x-ratelimit-remaining-requests: - - '9999' + - '29999' x-ratelimit-remaining-tokens: - - '29999806' + - '149999793' x-ratelimit-reset-requests: - - 6ms + - 2ms x-ratelimit-reset-tokens: - 0s x-request-id: - - req_11a316792b5f54af94cce0c702aec290 + - req_5bf4a565ad6c2567a1ed204ecac89134 + http_version: HTTP/1.1 + status_code: 200 +- request: + body: '{"messages": [{"role": "system", "content": "Determine if the following + feedback indicates that the user is satisfied or if further changes are needed. + Respond with ''True'' if further changes are needed, or ''False'' if the user + is satisfied. **Important** Do not include any additional commentary outside + of your ''True'' or ''False'' response.\n\nFeedback: \"looks good\""}], "model": + "gpt-4o-mini", "stop": ["\nObservation:"], "stream": false}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '439' + content-type: + - application/json + cookie: + - __cf_bm=QJZZjZ6eqnVamqUkw.Bx0mj7oBi3a_vGEH1VODcUxlg-1733344190-1.0.1.1-xyN0ekA9xIrSwEhRBmTiWJ3Pt72UYLU5owKfkz5yihVmMTfsr_Qz.ssGPJ5cuft066v1xVjb4zOSTdFmesMSKg; + _cfuvid=eCIkP8GVPvpkg19eOhCquWFHm.RTQBQy4yHLGGEAH5c-1733344190334-0.0.1.1-604800000 + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.52.1 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.52.1 + x-stainless-raw-response: + - 'true' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.12.7 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + content: "{\n \"id\": \"chatcmpl-AaqIJqyG8vl9mxj2qDPZgaxyNLLIq\",\n \"object\": + \"chat.completion\",\n \"created\": 1733344191,\n \"model\": \"gpt-4o-mini-2024-07-18\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"False\",\n \"refusal\": null\n + \ },\n \"logprobs\": null,\n \"finish_reason\": \"stop\"\n }\n + \ ],\n \"usage\": {\n \"prompt_tokens\": 73,\n \"completion_tokens\": + 1,\n \"total_tokens\": 74,\n \"prompt_tokens_details\": {\n \"cached_tokens\": + 0,\n \"audio_tokens\": 0\n },\n \"completion_tokens_details\": {\n + \ \"reasoning_tokens\": 0,\n \"audio_tokens\": 0,\n \"accepted_prediction_tokens\": + 0,\n \"rejected_prediction_tokens\": 0\n }\n },\n \"system_fingerprint\": + \"fp_0705bf87c0\"\n}\n" + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 8ece8d0cfdeb4532-ATL + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Wed, 04 Dec 2024 20:29:51 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '341' + openai-version: + - '2020-10-01' + strict-transport-security: + - max-age=31536000; includeSubDomains; preload + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149999902' + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_5554bade8ceda00cf364b76a51b708ff http_version: HTTP/1.1 status_code: 200 version: 1 From b0f1d1fcf0a89f21cf537af211fbb1e3d8f2d9f6 Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Thu, 5 Dec 2024 11:23:20 -0500 Subject: [PATCH 06/11] =?UTF-8?q?New=20docs=20about=20yaml=20crew=20with?= =?UTF-8?q?=20decorators.=20Simplify=20template=20crew=20with=E2=80=A6=20(?= =?UTF-8?q?#1701)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * New docs about yaml crew with decorators. Simplify template crew with links * Fix spelling issues. --- docs/concepts/crews.mdx | 151 +++++++++++++++++- src/crewai/cli/templates/crew/crew.py | 43 ++--- .../flow/crews/poem_crew/poem_crew.py | 62 ++++--- 3 files changed, 203 insertions(+), 53 deletions(-) diff --git a/docs/concepts/crews.mdx b/docs/concepts/crews.mdx index ec0f190de..3225b48b6 100644 --- a/docs/concepts/crews.mdx +++ b/docs/concepts/crews.mdx @@ -41,6 +41,155 @@ A crew in crewAI represents a collaborative group of agents working together to **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it. +## Creating Crews + +There are two ways to create crews in CrewAI: using **YAML configuration (recommended)** or defining them **directly in code**. + +### YAML Configuration (Recommended) + +Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects. + +After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself. + +#### Example Crew Class with Decorators + +```python code +from crewai import Agent, Crew, Task, Process +from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff + + +@CrewBase +class YourCrewName: + """Description of your crew""" + + # Paths to your YAML configuration files + # To see an example agent and task defined in YAML, checkout the following: + # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended + # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended + agents_config = 'config/agents.yaml' + tasks_config = 'config/tasks.yaml' + + @before_kickoff + def prepare_inputs(self, inputs): + # Modify inputs before the crew starts + inputs['additional_data'] = "Some extra information" + return inputs + + @after_kickoff + def process_output(self, output): + # Modify output after the crew finishes + output.raw += "\nProcessed after kickoff." + return output + + @agent + def agent_one(self) -> Agent: + return Agent( + config=self.agents_config['agent_one'], + verbose=True + ) + + @agent + def agent_two(self) -> Agent: + return Agent( + config=self.agents_config['agent_two'], + verbose=True + ) + + @task + def task_one(self) -> Task: + return Task( + config=self.tasks_config['task_one'] + ) + + @task + def task_two(self) -> Task: + return Task( + config=self.tasks_config['task_two'] + ) + + @crew + def crew(self) -> Crew: + return Crew( + agents=self.agents, # Automatically collected by the @agent decorator + tasks=self.tasks, # Automatically collected by the @task decorator. + process=Process.sequential, + verbose=True, + ) +``` + + +Tasks will be executed in the order they are defined. + + +The `CrewBase` class, along with these decorators, automates the collection of agents and tasks, reducing the need for manual management. + +#### Decorators overview from `annotations.py` + +CrewAI provides several decorators in the `annotations.py` file that are used to mark methods within your crew class for special handling: + +- `@CrewBase`: Marks the class as a crew base class. +- `@agent`: Denotes a method that returns an `Agent` object. +- `@task`: Denotes a method that returns a `Task` object. +- `@crew`: Denotes the method that returns the `Crew` object. +- `@before_kickoff`: (Optional) Marks a method to be executed before the crew starts. +- `@after_kickoff`: (Optional) Marks a method to be executed after the crew finishes. + +These decorators help in organizing your crew's structure and automatically collecting agents and tasks without manually listing them. + +### Direct Code Definition (Alternative) + +Alternatively, you can define the crew directly in code without using YAML configuration files. + +```python code +from crewai import Agent, Crew, Task, Process +from crewai_tools import YourCustomTool + +class YourCrewName: + def agent_one(self) -> Agent: + return Agent( + role="Data Analyst", + goal="Analyze data trends in the market", + backstory="An experienced data analyst with a background in economics", + verbose=True, + tools=[YourCustomTool()] + ) + + def agent_two(self) -> Agent: + return Agent( + role="Market Researcher", + goal="Gather information on market dynamics", + backstory="A diligent researcher with a keen eye for detail", + verbose=True + ) + + def task_one(self) -> Task: + return Task( + description="Collect recent market data and identify trends.", + expected_output="A report summarizing key trends in the market.", + agent=self.agent_one() + ) + + def task_two(self) -> Task: + return Task( + description="Research factors affecting market dynamics.", + expected_output="An analysis of factors influencing the market.", + agent=self.agent_two() + ) + + def crew(self) -> Crew: + return Crew( + agents=[self.agent_one(), self.agent_two()], + tasks=[self.task_one(), self.task_two()], + process=Process.sequential, + verbose=True + ) +``` + +In this example: + +- Agents and tasks are defined directly within the class without decorators. +- We manually create and manage the list of agents and tasks. +- This approach provides more control but can be less maintainable for larger projects. ## Crew Output @@ -188,4 +337,4 @@ Then, to replay from a specific task, use: crewai replay -t ``` -These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks. \ No newline at end of file +These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks. diff --git a/src/crewai/cli/templates/crew/crew.py b/src/crewai/cli/templates/crew/crew.py index 0a8c7481a..383bfac48 100644 --- a/src/crewai/cli/templates/crew/crew.py +++ b/src/crewai/cli/templates/crew/crew.py @@ -1,37 +1,26 @@ from crewai import Agent, Crew, Process, Task -from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff -# Uncomment the following line to use an example of a custom tool -# from {{folder_name}}.tools.custom_tool import MyCustomTool -# Uncomment the following line to use an example of a knowledge source -# from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource +from crewai.project import CrewBase, agent, crew, task -# Check our tools documentations for more information on how to use them -# from crewai_tools import SerperDevTool +# If you want to run a snippet of code before or after the crew starts, +# you can use the @before_kickoff and @after_kickoff decorators +# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators @CrewBase class {{crew_name}}(): """{{crew_name}} crew""" + # Learn more about YAML configuration files here: + # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended + # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended agents_config = 'config/agents.yaml' tasks_config = 'config/tasks.yaml' - @before_kickoff # Optional hook to be executed before the crew starts - def pull_data_example(self, inputs): - # Example of pulling data from an external API, dynamically changing the inputs - inputs['extra_data'] = "This is extra data" - return inputs - - @after_kickoff # Optional hook to be executed after the crew has finished - def log_results(self, output): - # Example of logging results, dynamically changing the output - print(f"Results: {output}") - return output - + # If you would like to add tools to your agents, you can learn more about it here: + # https://docs.crewai.com/concepts/agents#agent-tools @agent def researcher(self) -> Agent: return Agent( config=self.agents_config['researcher'], - # tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file verbose=True ) @@ -42,6 +31,9 @@ class {{crew_name}}(): verbose=True ) + # To learn more about structured task outputs, + # task dependencies, and task callbacks, check out the documentation: + # https://docs.crewai.com/concepts/tasks#overview-of-a-task @task def research_task(self) -> Task: return Task( @@ -58,14 +50,8 @@ class {{crew_name}}(): @crew def crew(self) -> Crew: """Creates the {{crew_name}} crew""" - # You can add knowledge sources here - # knowledge_path = "user_preference.txt" - # sources = [ - # TextFileKnowledgeSource( - # file_path="knowledge/user_preference.txt", - # metadata={"preference": "personal"} - # ), - # ] + # To learn how to add knowledge sources to your crew, check out the documentation: + # https://docs.crewai.com/concepts/knowledge#what-is-knowledge return Crew( agents=self.agents, # Automatically created by the @agent decorator @@ -73,5 +59,4 @@ class {{crew_name}}(): process=Process.sequential, verbose=True, # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/ - # knowledge_sources=sources, # In the case you want to add knowledge sources ) diff --git a/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py b/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py index cc3ce7150..5e978d985 100644 --- a/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py +++ b/src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py @@ -1,31 +1,47 @@ from crewai import Agent, Crew, Process, Task from crewai.project import CrewBase, agent, crew, task +# If you want to run a snippet of code before or after the crew starts, +# you can use the @before_kickoff and @after_kickoff decorators +# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators + + @CrewBase -class PoemCrew(): - """Poem Crew""" +class PoemCrew: + """Poem Crew""" - agents_config = 'config/agents.yaml' - tasks_config = 'config/tasks.yaml' + # Learn more about YAML configuration files here: + # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended + # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended + agents_config = "config/agents.yaml" + tasks_config = "config/tasks.yaml" - @agent - def poem_writer(self) -> Agent: - return Agent( - config=self.agents_config['poem_writer'], - ) + # If you would lik to add tools to your crew, you can learn more about it here: + # https://docs.crewai.com/concepts/agents#agent-tools + @agent + def poem_writer(self) -> Agent: + return Agent( + config=self.agents_config["poem_writer"], + ) - @task - def write_poem(self) -> Task: - return Task( - config=self.tasks_config['write_poem'], - ) + # To learn more about structured task outputs, + # task dependencies, and task callbacks, check out the documentation: + # https://docs.crewai.com/concepts/tasks#overview-of-a-task + @task + def write_poem(self) -> Task: + return Task( + config=self.tasks_config["write_poem"], + ) - @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, - ) + @crew + def crew(self) -> Crew: + """Creates the Research Crew""" + # To learn how to add knowledge sources to your crew, check out the documentation: + # https://docs.crewai.com/concepts/knowledge#what-is-knowledge + + return Crew( + agents=self.agents, # Automatically created by the @agent decorator + tasks=self.tasks, # Automatically created by the @task decorator + process=Process.sequential, + verbose=True, + ) From 2c85e8e23a912ef963c9348d31f49e9ad91f33b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Moura?= Date: Thu, 5 Dec 2024 13:51:09 -0300 Subject: [PATCH 07/11] updating tools --- pyproject.toml | 2 +- uv.lock | 9 ++++----- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 86891294c..e82e5e6c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ dependencies = [ "opentelemetry-exporter-otlp-proto-http>=1.22.0", "instructor>=1.3.3", "regex>=2024.9.11", - "crewai-tools>=0.14.0", + "crewai-tools>=0.17.0", "click>=8.1.7", "python-dotenv>=1.0.0", "appdirs>=1.4.4", diff --git a/uv.lock b/uv.lock index 900e730da..bca7a9c03 100644 --- a/uv.lock +++ b/uv.lock @@ -685,7 +685,7 @@ requires-dist = [ { name = "auth0-python", specifier = ">=4.7.1" }, { name = "chromadb", specifier = ">=0.5.18" }, { name = "click", specifier = ">=8.1.7" }, - { name = "crewai-tools", specifier = ">=0.14.0" }, + { name = "crewai-tools", specifier = ">=0.17.0" }, { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" }, { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" }, { name = "instructor", specifier = ">=1.3.3" }, @@ -733,7 +733,7 @@ dev = [ [[package]] name = "crewai-tools" -version = "0.14.0" +version = "0.17.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "beautifulsoup4" }, @@ -742,7 +742,6 @@ dependencies = [ { name = "docx2txt" }, { name = "embedchain" }, { name = "lancedb" }, - { name = "langchain" }, { name = "openai" }, { name = "pydantic" }, { name = "pyright" }, @@ -751,9 +750,9 @@ dependencies = [ { name = "requests" }, { name = "selenium" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/9b/6d/4fa91b481b120f83bb58f365203d8aa8564e8ced1035d79f8aedb7d71e2f/crewai_tools-0.14.0.tar.gz", hash = "sha256:510f3a194bcda4fdae4314bd775521964b5f229ddbe451e5d9e0216cae57f4e3", size = 815892 } +sdist = { url = "https://files.pythonhosted.org/packages/cc/15/365f74e0e8313e7a3399bf01d908aa73575c823275f9196ec14c23159878/crewai_tools-0.17.0.tar.gz", hash = "sha256:2a2986000775c76bad45b9f3a2be857d293cf5daffe5f316abc052e630b1e5ce", size = 818983 } wheels = [ - { url = "https://files.pythonhosted.org/packages/c8/ed/9f4e64e1507062957b0118085332d38b621c1000874baef2d1c4069bfd97/crewai_tools-0.14.0-py3-none-any.whl", hash = "sha256:0a804a828c29869c3af3253f4fc4c3967a3f80f06dab22e9bbe9526608a31564", size = 462980 }, + { url = "https://files.pythonhosted.org/packages/f4/1d/976adc2a4e5237cb03625de412cd051dea7d524084ed442adedfda871526/crewai_tools-0.17.0-py3-none-any.whl", hash = "sha256:85cf15286684ecad579b5a497888c6bf8a079ca443f7dd63a52bf1709655e4a3", size = 467975 }, ] [[package]] From 3daba0c79e3ca377a1f57fde6852793136cbea54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Moura?= Date: Thu, 5 Dec 2024 13:53:10 -0300 Subject: [PATCH 08/11] curting new verson --- pyproject.toml | 2 +- src/crewai/__init__.py | 2 +- src/crewai/cli/templates/crew/pyproject.toml | 2 +- src/crewai/cli/templates/flow/pyproject.toml | 2 +- .../cli/templates/pipeline/pyproject.toml | 17 +++++++++++++++++ src/crewai/cli/templates/tool/pyproject.toml | 2 +- uv.lock | 2 +- 7 files changed, 23 insertions(+), 6 deletions(-) create mode 100644 src/crewai/cli/templates/pipeline/pyproject.toml diff --git a/pyproject.toml b/pyproject.toml index e82e5e6c9..cac8bab9b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "crewai" -version = "0.85.0" +version = "0.86.0" description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." readme = "README.md" requires-python = ">=3.10,<=3.13" diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py index a6ec565e9..0833afd58 100644 --- a/src/crewai/__init__.py +++ b/src/crewai/__init__.py @@ -14,7 +14,7 @@ warnings.filterwarnings( category=UserWarning, module="pydantic.main", ) -__version__ = "0.85.0" +__version__ = "0.86.0" __all__ = [ "Agent", "Crew", diff --git a/src/crewai/cli/templates/crew/pyproject.toml b/src/crewai/cli/templates/crew/pyproject.toml index e45732685..d9ac06cd0 100644 --- a/src/crewai/cli/templates/crew/pyproject.toml +++ b/src/crewai/cli/templates/crew/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<=3.13" dependencies = [ - "crewai[tools]>=0.85.0,<1.0.0" + "crewai[tools]>=0.86.0,<1.0.0" ] [project.scripts] diff --git a/src/crewai/cli/templates/flow/pyproject.toml b/src/crewai/cli/templates/flow/pyproject.toml index d981987c8..ba4f4a823 100644 --- a/src/crewai/cli/templates/flow/pyproject.toml +++ b/src/crewai/cli/templates/flow/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<=3.13" dependencies = [ - "crewai[tools]>=0.85.0,<1.0.0", + "crewai[tools]>=0.86.0,<1.0.0", ] [project.scripts] diff --git a/src/crewai/cli/templates/pipeline/pyproject.toml b/src/crewai/cli/templates/pipeline/pyproject.toml new file mode 100644 index 000000000..97abd41a5 --- /dev/null +++ b/src/crewai/cli/templates/pipeline/pyproject.toml @@ -0,0 +1,17 @@ +[tool.poetry] +name = "{{folder_name}}" +version = "0.1.0" +description = "{{name}} using crewAI" +authors = ["Your Name "] + +[tool.poetry.dependencies] +python = ">=3.10,<=3.13" +crewai = { extras = ["tools"], version = ">=0.86.0,<1.0.0" } +asyncio = "*" + +[tool.poetry.scripts] +{{folder_name}} = "{{folder_name}}.main:main" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/src/crewai/cli/templates/tool/pyproject.toml b/src/crewai/cli/templates/tool/pyproject.toml index 33dabd38e..585bbef38 100644 --- a/src/crewai/cli/templates/tool/pyproject.toml +++ b/src/crewai/cli/templates/tool/pyproject.toml @@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}" readme = "README.md" requires-python = ">=3.10,<=3.13" dependencies = [ - "crewai[tools]>=0.85.0" + "crewai[tools]>=0.86.0" ] diff --git a/uv.lock b/uv.lock index bca7a9c03..42fb38424 100644 --- a/uv.lock +++ b/uv.lock @@ -608,7 +608,7 @@ wheels = [ [[package]] name = "crewai" -version = "0.85.0" +version = "0.86.0" source = { editable = "." } dependencies = [ { name = "appdirs" }, From 7b276e67978b82ae7a160ee0762072da94ead8a2 Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Thu, 5 Dec 2024 12:17:23 -0500 Subject: [PATCH 09/11] Incorporate Stale PRs that have feedback (#1693) * incorporate #1683 * add in --version flag to cli. closes #1679. * Fix env issue * Add in suggestions from @caike to make sure ragstorage doesnt exceed os file limit. Also, included additional checks to support windows. * remove poetry.lock as pointed out by @sanders41 in #1574. * Incorporate feedback from crewai reviewer * Incorporate @lorenzejay feedback --- src/crewai/agent.py | 21 ++++++------------ src/crewai/cli/cli.py | 6 +++++- src/crewai/cli/constants.py | 3 +++ src/crewai/memory/storage/rag_storage.py | 27 ++++++++++++++++++++---- src/crewai/utilities/constants.py | 1 + tests/cli/cli_test.py | 7 ++++++ 6 files changed, 45 insertions(+), 20 deletions(-) diff --git a/src/crewai/agent.py b/src/crewai/agent.py index abe678db1..8c79c6eb8 100644 --- a/src/crewai/agent.py +++ b/src/crewai/agent.py @@ -8,7 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator from crewai.agents import CacheHandler from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.crew_agent_executor import CrewAgentExecutor -from crewai.cli.constants import ENV_VARS +from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context @@ -181,20 +181,11 @@ class Agent(BaseAgent): if key_name and key_name not in unaccepted_attributes: env_value = os.environ.get(key_name) if env_value: - # Map key names containing "API_KEY" to "api_key" - key_name = ( - "api_key" if "API_KEY" in key_name else key_name - ) - # Map key names containing "API_BASE" to "api_base" - key_name = ( - "api_base" if "API_BASE" in key_name else key_name - ) - # Map key names containing "API_VERSION" to "api_version" - key_name = ( - "api_version" - if "API_VERSION" in key_name - else key_name - ) + key_name = key_name.lower() + for pattern in LITELLM_PARAMS: + if pattern in key_name: + key_name = pattern + break llm_params[key_name] = env_value # Check for default values if the environment variable is not set elif env_var.get("default", False): diff --git a/src/crewai/cli/cli.py b/src/crewai/cli/cli.py index 6e8560133..43dc90eed 100644 --- a/src/crewai/cli/cli.py +++ b/src/crewai/cli/cli.py @@ -25,6 +25,7 @@ from .update_crew import update_crew @click.group() +@click.version_option(pkg_resources.get_distribution("crewai").version) def crewai(): """Top-level command group for crewai.""" @@ -50,7 +51,10 @@ def create(type, name, provider, skip_provider=False): ) def version(tools): """Show the installed version of crewai.""" - crewai_version = pkg_resources.get_distribution("crewai").version + try: + crewai_version = pkg_resources.get_distribution("crewai").version + except Exception: + crewai_version = "unknown version" click.echo(f"crewai version: {crewai_version}") if tools: diff --git a/src/crewai/cli/constants.py b/src/crewai/cli/constants.py index e13349155..13279f8d3 100644 --- a/src/crewai/cli/constants.py +++ b/src/crewai/cli/constants.py @@ -159,3 +159,6 @@ MODELS = { } JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" + + +LITELLM_PARAMS = ["api_key", "api_base", "api_version"] diff --git a/src/crewai/memory/storage/rag_storage.py b/src/crewai/memory/storage/rag_storage.py index ded340a19..bf40aee96 100644 --- a/src/crewai/memory/storage/rag_storage.py +++ b/src/crewai/memory/storage/rag_storage.py @@ -4,12 +4,14 @@ import logging import os import shutil import uuid - from typing import Any, Dict, List, Optional + from chromadb.api import ClientAPI + from crewai.memory.storage.base_rag_storage import BaseRAGStorage -from crewai.utilities.paths import db_storage_path from crewai.utilities import EmbeddingConfigurator +from crewai.utilities.constants import MAX_FILE_NAME_LENGTH +from crewai.utilities.paths import db_storage_path @contextlib.contextmanager @@ -37,12 +39,15 @@ class RAGStorage(BaseRAGStorage): app: ClientAPI | None = None - def __init__(self, type, allow_reset=True, embedder_config=None, crew=None, path=None): + def __init__( + self, type, allow_reset=True, embedder_config=None, crew=None, path=None + ): super().__init__(type, allow_reset, embedder_config, crew) agents = crew.agents if crew else [] agents = [self._sanitize_role(agent.role) for agent in agents] agents = "_".join(agents) self.agents = agents + self.storage_file_name = self._build_storage_file_name(type, agents) self.type = type @@ -60,7 +65,7 @@ class RAGStorage(BaseRAGStorage): self._set_embedder_config() chroma_client = chromadb.PersistentClient( - path=self.path if self.path else f"{db_storage_path()}/{self.type}/{self.agents}", + path=self.path if self.path else self.storage_file_name, settings=Settings(allow_reset=self.allow_reset), ) @@ -81,6 +86,20 @@ class RAGStorage(BaseRAGStorage): """ return role.replace("\n", "").replace(" ", "_").replace("/", "_") + def _build_storage_file_name(self, type: str, file_name: str) -> str: + """ + Ensures file name does not exceed max allowed by OS + """ + base_path = f"{db_storage_path()}/{type}" + + if len(file_name) > MAX_FILE_NAME_LENGTH: + logging.warning( + f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters." + ) + file_name = file_name[:MAX_FILE_NAME_LENGTH] + + return f"{base_path}/{file_name}" + def save(self, value: Any, metadata: Dict[str, Any]) -> None: if not hasattr(self, "app") or not hasattr(self, "collection"): self._initialize_app() diff --git a/src/crewai/utilities/constants.py b/src/crewai/utilities/constants.py index 3578b7ece..096bb7c8c 100644 --- a/src/crewai/utilities/constants.py +++ b/src/crewai/utilities/constants.py @@ -3,3 +3,4 @@ TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl" DEFAULT_SCORE_THRESHOLD = 0.35 KNOWLEDGE_DIRECTORY = "knowledge" MAX_LLM_RETRY = 3 +MAX_FILE_NAME_LENGTH = 255 diff --git a/tests/cli/cli_test.py b/tests/cli/cli_test.py index 05e1cf03a..15ed81637 100644 --- a/tests/cli/cli_test.py +++ b/tests/cli/cli_test.py @@ -131,6 +131,13 @@ def test_reset_no_memory_flags(runner): ) +def test_version_flag(runner): + result = runner.invoke(version) + + assert result.exit_code == 0 + assert "crewai version:" in result.output + + def test_version_command(runner): result = runner.invoke(version) From c7c0647dd225d655d928ec9cc96c6b933ad77e0e Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:59:52 -0500 Subject: [PATCH 10/11] drop metadata requirement (#1712) * drop metadata requirement * fix linting * Update docs for new knowledge * more linting * more linting * make save_documents private * update docs to the new way we use knowledge and include clearing memory --- docs/concepts/knowledge.mdx | 67 +++++++------------ src/crewai/knowledge/knowledge.py | 9 +-- .../source/base_file_knowledge_source.py | 9 ++- .../knowledge/source/base_knowledge_source.py | 8 +-- .../knowledge/source/csv_knowledge_source.py | 4 +- .../source/excel_knowledge_source.py | 5 +- .../knowledge/source/json_knowledge_source.py | 4 +- .../knowledge/source/pdf_knowledge_source.py | 4 +- .../source/string_knowledge_source.py | 2 +- .../source/text_file_knowledge_source.py | 4 +- .../knowledge/storage/knowledge_storage.py | 25 ++++--- 11 files changed, 63 insertions(+), 78 deletions(-) diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx index 06c2eb947..96200b356 100644 --- a/docs/concepts/knowledge.mdx +++ b/docs/concepts/knowledge.mdx @@ -48,7 +48,6 @@ from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSourc content = "Users name is John. He is 30 years old and lives in San Francisco." string_source = StringKnowledgeSource( content=content, - metadata={"preference": "personal"} ) # Create an LLM with a temperature of 0 to ensure deterministic outputs @@ -74,10 +73,7 @@ crew = Crew( tasks=[task], verbose=True, process=Process.sequential, - knowledge={ - "sources": [string_source], - "metadata": {"preference": "personal"} - }, # Enable knowledge by adding the sources here. You can also add more sources to the sources list. + knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list. ) result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) @@ -85,17 +81,6 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o ## Knowledge Configuration -### Metadata and Filtering - -Knowledge sources support metadata for better organization and filtering. Metadata is used to filter the knowledge sources when querying the knowledge store. - -```python Code -knowledge_source = StringKnowledgeSource( - content="Users name is John. He is 30 years old and lives in San Francisco.", - metadata={"preference": "personal"} # Metadata is used to filter the knowledge sources -) -``` - ### Chunking Configuration Control how content is split for processing by setting the chunk size and overlap. @@ -116,21 +101,28 @@ You can also configure the embedder for the knowledge store. This is useful if y ... string_source = StringKnowledgeSource( content="Users name is John. He is 30 years old and lives in San Francisco.", - metadata={"preference": "personal"} ) crew = Crew( ... - knowledge={ - "sources": [string_source], - "metadata": {"preference": "personal"}, - "embedder_config": { - "provider": "openai", # Default embedder provider; can be "ollama", "gemini", e.t.c. - "config": {"model": "text-embedding-3-small"} # Default embedder model; can be "mxbai-embed-large", "nomic-embed-tex", e.t.c. - }, + knowledge_sources=[string_source], + embedder={ + "provider": "openai", + "config": {"model": "text-embedding-3-small"}, }, ) ``` +## Clearing Knowledge + +If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option. + +```bash Command +crewai reset-memories --knowledge +``` + +This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information. + + ## Custom Knowledge Sources CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles. @@ -174,12 +166,12 @@ class SpaceNewsKnowledgeSource(BaseKnowledgeSource): formatted = "Space News Articles:\n\n" for article in articles: formatted += f""" -Title: {article['title']} -Published: {article['published_at']} -Summary: {article['summary']} -News Site: {article['news_site']} -URL: {article['url']} --------------------""" + Title: {article['title']} + Published: {article['published_at']} + Summary: {article['summary']} + News Site: {article['news_site']} + URL: {article['url']} + -------------------""" return formatted def add(self) -> None: @@ -189,17 +181,12 @@ URL: {article['url']} chunks = self._chunk_text(text) self.chunks.extend(chunks) - self.save_documents(metadata={ - "source": "space_news_api", - "timestamp": datetime.now().isoformat(), - "article_count": self.limit - }) + self._save_documents() # Create knowledge source recent_news = SpaceNewsKnowledgeSource( api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", limit=10, - metadata={"category": "recent_news", "source": "spaceflight_news"} ) # Create specialized agent @@ -265,7 +252,7 @@ The latest developments in space exploration, based on recent space news article - Implements three key methods: - `load_content()`: Fetches articles from the API - `_format_articles()`: Structures the articles into readable text - - `add()`: Processes and stores the content with metadata + - `add()`: Processes and stores the content 2. **Agent Configuration**: - Specialized role as a Space News Analyst @@ -299,14 +286,12 @@ You can customize the API query by modifying the endpoint URL: recent_news = SpaceNewsKnowledgeSource( api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", limit=20, # Increase the number of articles - metadata={"category": "recent_news"} ) # Add search parameters recent_news = SpaceNewsKnowledgeSource( api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news limit=10, - metadata={"category": "nasa_news"} ) ``` @@ -314,16 +299,14 @@ recent_news = SpaceNewsKnowledgeSource( - - Use descriptive metadata for better filtering - Keep chunk sizes appropriate for your content type - Consider content overlap for context preservation - Organize related information into separate knowledge sources - - Use metadata filtering to narrow search scope - Adjust chunk sizes based on content complexity - Configure appropriate embedding models - Consider using local embedding providers for faster processing - \ No newline at end of file + diff --git a/src/crewai/knowledge/knowledge.py b/src/crewai/knowledge/knowledge.py index 4a1331799..f9f55a517 100644 --- a/src/crewai/knowledge/knowledge.py +++ b/src/crewai/knowledge/knowledge.py @@ -1,11 +1,10 @@ import os +from typing import Any, Dict, List, Optional -from typing import List, Optional, Dict, Any from pydantic import BaseModel, ConfigDict, Field from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage -from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD os.environ["TOKENIZERS_PARALLELISM"] = "false" # removes logging from fastembed @@ -46,9 +45,7 @@ class Knowledge(BaseModel): source.storage = self.storage source.add() - def query( - self, query: List[str], limit: int = 3, preference: Optional[str] = None - ) -> List[Dict[str, Any]]: + def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]: """ Query across all knowledge sources to find the most relevant information. Returns the top_k most relevant chunks. @@ -57,8 +54,6 @@ class Knowledge(BaseModel): results = self.storage.search( query, limit, - filter={"preference": preference} if preference else None, - score_threshold=DEFAULT_SCORE_THRESHOLD, ) return results diff --git a/src/crewai/knowledge/source/base_file_knowledge_source.py b/src/crewai/knowledge/source/base_file_knowledge_source.py index 9cb9ec2a2..7f078b4a4 100644 --- a/src/crewai/knowledge/source/base_file_knowledge_source.py +++ b/src/crewai/knowledge/source/base_file_knowledge_source.py @@ -1,13 +1,13 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Union, List, Dict, Any +from typing import Dict, List, Union from pydantic import Field from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource -from crewai.utilities.logger import Logger from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage from crewai.utilities.constants import KNOWLEDGE_DIRECTORY +from crewai.utilities.logger import Logger class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC): @@ -49,10 +49,9 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC): color="red", ) - def save_documents(self, metadata: Dict[str, Any]): + def _save_documents(self): """Save the documents to the storage.""" - chunk_metadatas = [metadata.copy() for _ in self.chunks] - self.storage.save(self.chunks, chunk_metadatas) + self.storage.save(self.chunks) def convert_to_path(self, path: Union[Path, str]) -> Path: """Convert a path to a Path object.""" diff --git a/src/crewai/knowledge/source/base_knowledge_source.py b/src/crewai/knowledge/source/base_knowledge_source.py index 6be76ca40..07a9a0f25 100644 --- a/src/crewai/knowledge/source/base_knowledge_source.py +++ b/src/crewai/knowledge/source/base_knowledge_source.py @@ -1,5 +1,5 @@ from abc import ABC, abstractmethod -from typing import List, Dict, Any, Optional +from typing import Any, Dict, List, Optional import numpy as np from pydantic import BaseModel, ConfigDict, Field @@ -17,7 +17,7 @@ class BaseKnowledgeSource(BaseModel, ABC): model_config = ConfigDict(arbitrary_types_allowed=True) storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) - metadata: Dict[str, Any] = Field(default_factory=dict) + metadata: Dict[str, Any] = Field(default_factory=dict) # Currently unused collection_name: Optional[str] = Field(default=None) @abstractmethod @@ -41,9 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC): for i in range(0, len(text), self.chunk_size - self.chunk_overlap) ] - def save_documents(self, metadata: Dict[str, Any]): + def _save_documents(self): """ Save the documents to the storage. This method should be called after the chunks and embeddings are generated. """ - self.storage.save(self.chunks, metadata) + self.storage.save(self.chunks) diff --git a/src/crewai/knowledge/source/csv_knowledge_source.py b/src/crewai/knowledge/source/csv_knowledge_source.py index 117cbe32f..3bb0714d9 100644 --- a/src/crewai/knowledge/source/csv_knowledge_source.py +++ b/src/crewai/knowledge/source/csv_knowledge_source.py @@ -1,6 +1,6 @@ import csv -from typing import Dict, List from pathlib import Path +from typing import Dict, List from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource @@ -30,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource): ) new_chunks = self._chunk_text(content_str) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/source/excel_knowledge_source.py b/src/crewai/knowledge/source/excel_knowledge_source.py index 5484e59d5..d379c2763 100644 --- a/src/crewai/knowledge/source/excel_knowledge_source.py +++ b/src/crewai/knowledge/source/excel_knowledge_source.py @@ -1,5 +1,6 @@ -from typing import Dict, List from pathlib import Path +from typing import Dict, List + from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource @@ -44,7 +45,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource): new_chunks = self._chunk_text(content_str) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/source/json_knowledge_source.py b/src/crewai/knowledge/source/json_knowledge_source.py index ed6eec86d..b02d438e6 100644 --- a/src/crewai/knowledge/source/json_knowledge_source.py +++ b/src/crewai/knowledge/source/json_knowledge_source.py @@ -1,6 +1,6 @@ import json -from typing import Any, Dict, List from pathlib import Path +from typing import Any, Dict, List from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource @@ -42,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource): ) new_chunks = self._chunk_text(content_str) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/source/pdf_knowledge_source.py b/src/crewai/knowledge/source/pdf_knowledge_source.py index e6733d78b..38cd67807 100644 --- a/src/crewai/knowledge/source/pdf_knowledge_source.py +++ b/src/crewai/knowledge/source/pdf_knowledge_source.py @@ -1,5 +1,5 @@ -from typing import List, Dict from pathlib import Path +from typing import Dict, List from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource @@ -43,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource): for _, text in self.content.items(): new_chunks = self._chunk_text(text) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/source/string_knowledge_source.py b/src/crewai/knowledge/source/string_knowledge_source.py index 7336fd3ea..1d40ac4b8 100644 --- a/src/crewai/knowledge/source/string_knowledge_source.py +++ b/src/crewai/knowledge/source/string_knowledge_source.py @@ -24,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource): """Add string content to the knowledge source, chunk it, compute embeddings, and save them.""" new_chunks = self._chunk_text(self.content) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/source/text_file_knowledge_source.py b/src/crewai/knowledge/source/text_file_knowledge_source.py index 1ceb02843..ddb1f2516 100644 --- a/src/crewai/knowledge/source/text_file_knowledge_source.py +++ b/src/crewai/knowledge/source/text_file_knowledge_source.py @@ -1,5 +1,5 @@ -from typing import Dict, List from pathlib import Path +from typing import Dict, List from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource @@ -24,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource): for _, text in self.content.items(): new_chunks = self._chunk_text(text) self.chunks.extend(new_chunks) - self.save_documents(metadata=self.metadata) + self._save_documents() def _chunk_text(self, text: str) -> List[str]: """Utility method to split text into chunks.""" diff --git a/src/crewai/knowledge/storage/knowledge_storage.py b/src/crewai/knowledge/storage/knowledge_storage.py index 79aa385a8..e0e36f4a7 100644 --- a/src/crewai/knowledge/storage/knowledge_storage.py +++ b/src/crewai/knowledge/storage/knowledge_storage.py @@ -1,18 +1,20 @@ import contextlib +import hashlib import io import logging -import chromadb import os +from typing import Any, Dict, List, Optional, Union, cast +import chromadb import chromadb.errors -from crewai.utilities.paths import db_storage_path -from typing import Optional, List, Dict, Any, Union -from crewai.utilities import EmbeddingConfigurator -from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage -import hashlib -from chromadb.config import Settings from chromadb.api import ClientAPI +from chromadb.api.types import OneOrMany +from chromadb.config import Settings + +from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage +from crewai.utilities import EmbeddingConfigurator from crewai.utilities.logger import Logger +from crewai.utilities.paths import db_storage_path @contextlib.contextmanager @@ -116,11 +118,16 @@ class KnowledgeStorage(BaseKnowledgeStorage): def save( self, documents: List[str], - metadata: Union[Dict[str, Any], List[Dict[str, Any]]], + metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, ): if self.collection: try: - metadatas = [metadata] if isinstance(metadata, dict) else metadata + if metadata is None: + metadatas: Optional[OneOrMany[chromadb.Metadata]] = None + elif isinstance(metadata, list): + metadatas = [cast(chromadb.Metadata, m) for m in metadata] + else: + metadatas = cast(chromadb.Metadata, metadata) ids = [ hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents From 6930b6848460dc7d9b1ca7d5629286fc559123cd Mon Sep 17 00:00:00 2001 From: "Brandon Hancock (bhancock_ai)" <109994880+bhancockio@users.noreply.github.com> Date: Fri, 6 Dec 2024 13:57:28 -0500 Subject: [PATCH 11/11] add support for langfuse with litellm (#1721) --- src/crewai/llm.py | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/src/crewai/llm.py b/src/crewai/llm.py index bfdaa4418..d860a8907 100644 --- a/src/crewai/llm.py +++ b/src/crewai/llm.py @@ -1,4 +1,5 @@ import logging +import os import sys import threading import warnings @@ -128,6 +129,7 @@ class LLM: litellm.drop_params = True litellm.set_verbose = False self.set_callbacks(callbacks) + self.set_env_callbacks() def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str: with suppress_warnings(): @@ -202,3 +204,39 @@ class LLM: litellm._async_success_callback.remove(callback) litellm.callbacks = callbacks + + def set_env_callbacks(self): + """ + Sets the success and failure callbacks for the LiteLLM library from environment variables. + + This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS` + environment variables, which should contain comma-separated lists of callback names. + It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`, + respectively. + + If the environment variables are not set or are empty, the corresponding callback lists + will be set to empty lists. + + Example: + LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith" + LITELLM_FAILURE_CALLBACKS="langfuse" + + This will set `litellm.success_callback` to ["langfuse", "langsmith"] and + `litellm.failure_callback` to ["langfuse"]. + """ + success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "") + success_callbacks = [] + if success_callbacks_str: + success_callbacks = [ + callback.strip() for callback in success_callbacks_str.split(",") + ] + + failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "") + failure_callbacks = [] + if failure_callbacks_str: + failure_callbacks = [ + callback.strip() for callback in failure_callbacks_str.split(",") + ] + + litellm.success_callback = success_callbacks + litellm.failure_callback = failure_callbacks