diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 45272c3ab..8a71dc247 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -65,7 +65,6 @@ body: - '3.10' - '3.11' - '3.12' - - '3.13' validations: required: true - type: input @@ -113,4 +112,4 @@ body: label: Additional context description: Add any other context about the problem here. validations: - required: true \ No newline at end of file + required: true diff --git a/.github/security.md b/.github/security.md new file mode 100644 index 000000000..5bc967228 --- /dev/null +++ b/.github/security.md @@ -0,0 +1,19 @@ +CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization. +If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below. + + ## Reporting a Vulnerability + Please do not report security vulnerabilities through public GitHub issues. + To report a vulnerability, please email us at security@crewai.com. + Please include the requested information listed below so that we can triage your report more quickly + + - Type of issue (e.g. SQL injection, cross-site scripting, etc.) + - Full paths of source file(s) related to the manifestation of the issue + - The location of the affected source code (tag/branch/commit or direct URL) + - Any special configuration required to reproduce the issue + - Step-by-step instructions to reproduce the issue (please include screenshots if needed) + - Proof-of-concept or exploit code (if possible) + - Impact of the issue, including how an attacker might exploit the issue + + Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue. + + At this time, we are not offering a bug bounty program. Any rewards will be at our discretion. \ No newline at end of file diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml index a7223c506..f9853cf7c 100644 --- a/.github/workflows/linter.yml +++ b/.github/workflows/linter.yml @@ -6,11 +6,11 @@ jobs: lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install Requirements run: | pip install ruff - name: Run Ruff Linter - run: ruff check --exclude "templates","__init__.py" + run: ruff check diff --git a/.github/workflows/mkdocs.yml b/.github/workflows/mkdocs.yml index a15ae0363..84d627db4 100644 --- a/.github/workflows/mkdocs.yml +++ b/.github/workflows/mkdocs.yml @@ -13,10 +13,10 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Setup Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.10' @@ -25,7 +25,7 @@ jobs: run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')" - name: Setup cache - uses: actions/cache@v3 + uses: actions/cache@v4 with: key: mkdocs-material-${{ steps.req-hash.outputs.hash }} path: .cache @@ -42,4 +42,4 @@ jobs: GH_TOKEN: ${{ secrets.GH_TOKEN }} - name: Build and deploy MkDocs - run: mkdocs gh-deploy --force \ No newline at end of file + run: mkdocs gh-deploy --force diff --git a/.github/workflows/security-checker.yml b/.github/workflows/security-checker.yml index d0d309b4c..1588e3ddf 100644 --- a/.github/workflows/security-checker.yml +++ b/.github/workflows/security-checker.yml @@ -11,7 +11,7 @@ jobs: uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: "3.11.9" @@ -19,5 +19,5 @@ jobs: run: pip install bandit - name: Run Bandit - run: bandit -c pyproject.toml -r src/ -lll + run: bandit -c pyproject.toml -r src/ -ll diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 04f6693d0..f655dcc64 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -23,10 +23,10 @@ jobs: - name: Set up Python - run: uv python install 3.11.9 + run: uv python install 3.12.8 - name: Install the project - run: uv sync --dev + run: uv sync --dev --all-extras - name: Run tests - run: uv run pytest tests + run: uv run pytest tests -vv diff --git a/.github/workflows/type-checker.yml b/.github/workflows/type-checker.yml index 72c64dbd3..c694c8cec 100644 --- a/.github/workflows/type-checker.yml +++ b/.github/workflows/type-checker.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v4 - name: Setup Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: "3.11.9" diff --git a/.gitignore b/.gitignore index ad64db4e7..8559a290c 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,7 @@ rc-tests/* temp/* .vscode/* crew_tasks_output.json +.codesight +.mypy_cache +.ruff_cache +.venv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index dfa210d60..291f3f871 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,9 +1,7 @@ repos: - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.4.4 + rev: v0.8.2 hooks: - id: ruff args: ["--fix"] - exclude: "templates" - id: ruff-format - exclude: "templates" diff --git a/.ruff.toml b/.ruff.toml new file mode 100644 index 000000000..acc241bd4 --- /dev/null +++ b/.ruff.toml @@ -0,0 +1,9 @@ +exclude = [ + "templates", + "__init__.py", +] + +[lint] +select = [ + "I", # isort rules +] diff --git a/README.md b/README.md index f79f2a011..b6d950a3b 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ To get started with CrewAI, follow these simple steps: ### 1. Installation -Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. +Ensure you have Python >=3.10 <=3.12 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. First, install CrewAI: @@ -100,7 +100,7 @@ You can now start developing your crew by editing the files in the `src/my_proje #### Example of a simple crew with a sequential process: -Instatiate your crew: +Instantiate your crew: ```shell crewai create crew latest-ai-development @@ -121,7 +121,7 @@ researcher: 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 @@ -205,7 +205,7 @@ class LatestAiDevelopmentCrew(): tasks=self.tasks, # Automatically created by the @task decorator process=Process.sequential, verbose=True, - ) + ) ``` **main.py** @@ -351,13 +351,13 @@ pre-commit install ### Running Tests ```bash -uvx pytest +uv run pytest . ``` ### Running static type checks ```bash -uvx mypy +uvx mypy src ``` ### Packaging @@ -376,7 +376,7 @@ pip install dist/*.tar.gz CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools. -It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. We don't offer a way to disable it now, but we will in the future. +It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy. Users can disable telemetry by setting the environment variable OTEL_SDK_DISABLED to true. Data collected includes: @@ -399,7 +399,7 @@ Data collected includes: - Roles of agents in a crew - Understand high level use cases so we can build better tools, integrations and examples about it - Tools names available - - Understand out of the publically available tools, which ones are being used the most so we can improve them + - Understand out of the publicly available tools, which ones are being used the most so we can improve them Users can opt-in to Further Telemetry, sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share. diff --git a/docs/concepts/agents.mdx b/docs/concepts/agents.mdx index f7e97f186..fda4fc55f 100644 --- a/docs/concepts/agents.mdx +++ b/docs/concepts/agents.mdx @@ -1,159 +1,343 @@ --- title: Agents -description: What are CrewAI Agents and how to use them. +description: Detailed guide on creating and managing agents within the CrewAI framework. icon: robot --- -## What is an agent? +## Overview of an Agent -An agent is an **autonomous unit** programmed to: - +In the CrewAI framework, an `Agent` is an autonomous unit that can: +- Perform specific tasks +- Make decisions based on its role and goal +- Use tools to accomplish objectives +- Communicate and collaborate with other agents +- Maintain memory of interactions +- Delegate tasks when allowed -Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like `Researcher`, `Writer`, or `Customer Support`, each contributing to the overall goal of the crew. +Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content. -## Agent attributes +## Agent Attributes -| Attribute | Parameter | Description | -| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. | -| **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. | -| **Backstory** | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. | -| **LLM** *(optional)* | `llm` | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. | -| **Tools** *(optional)* | `tools` | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. | -| **Function Calling LLM** *(optional)* | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. | -| **Max Iter** *(optional)* | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. | -| **Max RPM** *(optional)* | `max_rpm` | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. | -| **Max Execution Time** *(optional)* | `max_execution_time` | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. | -| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. | -| **Allow Delegation** *(optional)* | `allow_delegation` | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`. -| **Step Callback** *(optional)* | `step_callback` | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. | -| **Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. | -| **System Template** *(optional)* | `system_template` | Specifies the system format for the agent. Default is `None`. | -| **Prompt Template** *(optional)* | `prompt_template` | Specifies the prompt format for the agent. Default is `None`. | -| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. | -| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. | -| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`. -| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. | -| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. | +| Attribute | Parameter | Type | Description | +| :-------------------------------------- | :----------------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- | +| **Role** | `role` | `str` | Defines the agent's function and expertise within the crew. | +| **Goal** | `goal` | `str` | The individual objective that guides the agent's decision-making. | +| **Backstory** | `backstory` | `str` | Provides context and personality to the agent, enriching interactions. | +| **LLM** _(optional)_ | `llm` | `Union[str, LLM, Any]` | Language model that powers the agent. Defaults to the model specified in `OPENAI_MODEL_NAME` or "gpt-4". | +| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | Capabilities or functions available to the agent. Defaults to an empty list. | +| **Function Calling LLM** _(optional)_ | `function_calling_llm` | `Optional[Any]` | Language model for tool calling, overrides crew's LLM if specified. | +| **Max Iterations** _(optional)_ | `max_iter` | `int` | Maximum iterations before the agent must provide its best answer. Default is 20. | +| **Max RPM** _(optional)_ | `max_rpm` | `Optional[int]` | Maximum requests per minute to avoid rate limits. | +| **Max Execution Time** _(optional)_ | `max_execution_time` | `Optional[int]` | Maximum time (in seconds) for task execution. | +| **Memory** _(optional)_ | `memory` | `bool` | Whether the agent should maintain memory of interactions. Default is True. | +| **Verbose** _(optional)_ | `verbose` | `bool` | Enable detailed execution logs for debugging. Default is False. | +| **Allow Delegation** _(optional)_ | `allow_delegation` | `bool` | Allow the agent to delegate tasks to other agents. Default is False. | +| **Step Callback** _(optional)_ | `step_callback` | `Optional[Any]` | Function called after each agent step, overrides crew callback. | +| **Cache** _(optional)_ | `cache` | `bool` | Enable caching for tool usage. Default is True. | +| **System Template** _(optional)_ | `system_template` | `Optional[str]` | Custom system prompt template for the agent. | +| **Prompt Template** _(optional)_ | `prompt_template` | `Optional[str]` | Custom prompt template for the agent. | +| **Response Template** _(optional)_ | `response_template` | `Optional[str]` | Custom response template for the agent. | +| **Allow Code Execution** _(optional)_ | `allow_code_execution` | `Optional[bool]` | Enable code execution for the agent. Default is False. | +| **Max Retry Limit** _(optional)_ | `max_retry_limit` | `int` | Maximum number of retries when an error occurs. Default is 2. | +| **Respect Context Window** _(optional)_ | `respect_context_window` | `bool` | Keep messages under context window size by summarizing. Default is True. | +| **Code Execution Mode** _(optional)_ | `code_execution_mode` | `Literal["safe", "unsafe"]` | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'. | +| **Embedder Config** _(optional)_ | `embedder_config` | `Optional[Dict[str, Any]]` | Configuration for the embedder used by the agent. | +| **Knowledge Sources** _(optional)_ | `knowledge_sources` | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent. | +| **Use System Prompt** _(optional)_ | `use_system_prompt` | `Optional[bool]` | Whether to use system prompt (for o1 model support). Default is True. | -## Creating an agent +## Creating Agents + +There are two ways to create agents 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 agents. We strongly recommend using this approach in your CrewAI projects. + +After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements. - **Agent interaction**: Agents can interact with each other using CrewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew. +Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew: +```python Code +crew.kickoff(inputs={'topic': 'AI Agents'}) +``` -To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes: +Here's an example of how to configure agents using YAML: -```python Code example +```yaml agents.yaml +# src/latest_ai_development/config/agents.yaml +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. +``` + +To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`: + +```python Code +# src/latest_ai_development/crew.py +from crewai import Agent, Crew, Process +from crewai.project import CrewBase, agent, crew +from crewai_tools import SerperDevTool + +@CrewBase +class LatestAiDevelopmentCrew(): + """LatestAiDevelopment crew""" + + @agent + def researcher(self) -> Agent: + return Agent( + config=self.agents_config['researcher'], + verbose=True, + tools=[SerperDevTool()] + ) + + @agent + def reporting_analyst(self) -> Agent: + return Agent( + config=self.agents_config['reporting_analyst'], + verbose=True + ) +``` + + +The names you use in your YAML files (`agents.yaml`) should match the method names in your Python code. + + +### Direct Code Definition + +You can create agents directly in code by instantiating the `Agent` class. Here's a comprehensive example showing all available parameters: + +```python Code from crewai import Agent +from crewai_tools import SerperDevTool +# Create an agent with all available parameters agent = Agent( - role='Data Analyst', - goal='Extract actionable insights', - backstory="""You're a data analyst at a large company. - You're responsible for analyzing data and providing insights - to the business. - You're currently working on a project to analyze the - performance of our marketing campaigns.""", - tools=[my_tool1, my_tool2], # Optional, defaults to an empty list - llm=my_llm, # Optional - function_calling_llm=my_llm, # Optional - max_iter=15, # Optional - max_rpm=None, # Optional - max_execution_time=None, # Optional - verbose=True, # Optional - allow_delegation=False, # Optional - step_callback=my_intermediate_step_callback, # Optional - cache=True, # Optional - system_template=my_system_template, # Optional - prompt_template=my_prompt_template, # Optional - response_template=my_response_template, # Optional - config=my_config, # Optional - crew=my_crew, # Optional - tools_handler=my_tools_handler, # Optional - cache_handler=my_cache_handler, # Optional - callbacks=[callback1, callback2], # Optional - allow_code_execution=True, # Optional - max_retry_limit=2, # Optional - use_system_prompt=True, # Optional - respect_context_window=True, # Optional + role="Senior Data Scientist", + goal="Analyze and interpret complex datasets to provide actionable insights", + backstory="With over 10 years of experience in data science and machine learning, " + "you excel at finding patterns in complex datasets.", + llm="gpt-4", # Default: OPENAI_MODEL_NAME or "gpt-4" + function_calling_llm=None, # Optional: Separate LLM for tool calling + memory=True, # Default: True + verbose=False, # Default: False + allow_delegation=False, # Default: False + max_iter=20, # Default: 20 iterations + max_rpm=None, # Optional: Rate limit for API calls + max_execution_time=None, # Optional: Maximum execution time in seconds + max_retry_limit=2, # Default: 2 retries on error + allow_code_execution=False, # Default: False + code_execution_mode="safe", # Default: "safe" (options: "safe", "unsafe") + respect_context_window=True, # Default: True + use_system_prompt=True, # Default: True + tools=[SerperDevTool()], # Optional: List of tools + knowledge_sources=None, # Optional: List of knowledge sources + embedder_config=None, # Optional: Custom embedder configuration + system_template=None, # Optional: Custom system prompt template + prompt_template=None, # Optional: Custom prompt template + response_template=None, # Optional: Custom response template + step_callback=None, # Optional: Callback function for monitoring ) ``` -## Setting prompt templates +Let's break down some key parameter combinations for common use cases: -Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates: +#### Basic Research Agent +```python Code +research_agent = Agent( + role="Research Analyst", + goal="Find and summarize information about specific topics", + backstory="You are an experienced researcher with attention to detail", + tools=[SerperDevTool()], + verbose=True # Enable logging for debugging +) +``` -```python Code example -agent = Agent( - role="{topic} specialist", - goal="Figure {goal} out", - backstory="I am the master of {role}", - system_template="""<|start_header_id|>system<|end_header_id|> +#### Code Development Agent +```python Code +dev_agent = Agent( + role="Senior Python Developer", + goal="Write and debug Python code", + backstory="Expert Python developer with 10 years of experience", + allow_code_execution=True, + code_execution_mode="safe", # Uses Docker for safety + max_execution_time=300, # 5-minute timeout + max_retry_limit=3 # More retries for complex code tasks +) +``` + +#### Long-Running Analysis Agent +```python Code +analysis_agent = Agent( + role="Data Analyst", + goal="Perform deep analysis of large datasets", + backstory="Specialized in big data analysis and pattern recognition", + memory=True, + respect_context_window=True, + max_rpm=10, # Limit API calls + function_calling_llm="gpt-4o-mini" # Cheaper model for tool calls +) +``` + +#### Custom Template Agent +```python Code +custom_agent = Agent( + role="Customer Service Representative", + goal="Assist customers with their inquiries", + backstory="Experienced in customer support with a focus on satisfaction", + system_template="""<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>""", - prompt_template="""<|start_header_id|>user<|end_header_id|> + prompt_template="""<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>""", - response_template="""<|start_header_id|>assistant<|end_header_id|> + response_template="""<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>""", ) ``` -## Bring your third-party agents +### Parameter Details -Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class. +#### Critical Parameters +- `role`, `goal`, and `backstory` are required and shape the agent's behavior +- `llm` determines the language model used (default: OpenAI's GPT-4) - - **BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew. +#### Memory and Context +- `memory`: Enable to maintain conversation history +- `respect_context_window`: Prevents token limit issues +- `knowledge_sources`: Add domain-specific knowledge bases + +#### Execution Control +- `max_iter`: Maximum attempts before giving best answer +- `max_execution_time`: Timeout in seconds +- `max_rpm`: Rate limiting for API calls +- `max_retry_limit`: Retries on error + +#### Code Execution +- `allow_code_execution`: Must be True to run code +- `code_execution_mode`: + - `"safe"`: Uses Docker (recommended for production) + - `"unsafe"`: Direct execution (use only in trusted environments) + +#### Templates +- `system_template`: Defines agent's core behavior +- `prompt_template`: Structures input format +- `response_template`: Formats agent responses + + +When using custom templates, you can use variables like `{role}`, `{goal}`, and `{input}` in your templates. These will be automatically populated during execution. -CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems. +## Agent Tools -```python Code example -from crewai import Agent, Task, Crew -from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here. +Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from: +- [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) +- [LangChain Tools](https://python.langchain.com/docs/integrations/tools) -from langchain.agents import load_tools +Here's how to add tools to an agent: -langchain_tools = load_tools(["google-serper"], llm=llm) +```python Code +from crewai import Agent +from crewai_tools import SerperDevTool, WikipediaTools -agent1 = CustomAgent( - role="agent role", - goal="who is {input}?", - backstory="agent backstory", - verbose=True, +# Create tools +search_tool = SerperDevTool() +wiki_tool = WikipediaTools() + +# Add tools to agent +researcher = Agent( + role="AI Technology Researcher", + goal="Research the latest AI developments", + tools=[search_tool, wiki_tool], + verbose=True ) - -task1 = Task( - expected_output="a short biography of {input}", - description="a short biography of {input}", - agent=agent1, -) - -agent2 = Agent( - role="agent role", - goal="summarize the short bio for {input} and if needed do more research", - backstory="agent backstory", - verbose=True, -) - -task2 = Task( - description="a tldr summary of the short biography", - expected_output="5 bullet point summary of the biography", - agent=agent2, - context=[task1], -) - -my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2]) -crew = my_crew.kickoff(inputs={"input": "Mark Twain"}) ``` -## Conclusion +## Agent Memory and Context -Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, -you can create sophisticated AI systems that leverage the power of collaborative intelligence. \ No newline at end of file +Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks. + +```python Code +from crewai import Agent + +analyst = Agent( + role="Data Analyst", + goal="Analyze and remember complex data patterns", + memory=True, # Enable memory + verbose=True +) +``` + + +When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks. + + +## Important Considerations and Best Practices + +### Security and Code Execution +- When using `allow_code_execution`, be cautious with user input and always validate it +- Use `code_execution_mode: "safe"` (Docker) in production environments +- Consider setting appropriate `max_execution_time` limits to prevent infinite loops + +### Performance Optimization +- Use `respect_context_window: true` to prevent token limit issues +- Set appropriate `max_rpm` to avoid rate limiting +- Enable `cache: true` to improve performance for repetitive tasks +- Adjust `max_iter` and `max_retry_limit` based on task complexity + +### Memory and Context Management +- Use `memory: true` for tasks requiring historical context +- Leverage `knowledge_sources` for domain-specific information +- Configure `embedder_config` when using custom embedding models +- Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior + +### Agent Collaboration +- Enable `allow_delegation: true` when agents need to work together +- Use `step_callback` to monitor and log agent interactions +- Consider using different LLMs for different purposes: + - Main `llm` for complex reasoning + - `function_calling_llm` for efficient tool usage + +### Model Compatibility +- Set `use_system_prompt: false` for older models that don't support system messages +- Ensure your chosen `llm` supports the features you need (like function calling) + +## Troubleshooting Common Issues + +1. **Rate Limiting**: If you're hitting API rate limits: + - Implement appropriate `max_rpm` + - Use caching for repetitive operations + - Consider batching requests + +2. **Context Window Errors**: If you're exceeding context limits: + - Enable `respect_context_window` + - Use more efficient prompts + - Clear agent memory periodically + +3. **Code Execution Issues**: If code execution fails: + - Verify Docker is installed for safe mode + - Check execution permissions + - Review code sandbox settings + +4. **Memory Issues**: If agent responses seem inconsistent: + - Verify memory is enabled + - Check knowledge source configuration + - Review conversation history management + +Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly. diff --git a/docs/concepts/cli.mdx b/docs/concepts/cli.mdx index 8297ee6aa..ab21232ad 100644 --- a/docs/concepts/cli.mdx +++ b/docs/concepts/cli.mdx @@ -6,7 +6,7 @@ icon: terminal # CrewAI CLI Documentation -The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines. +The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows. ## Installation @@ -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. Version @@ -146,3 +145,34 @@ crewai run Make sure to run these commands from the directory where your CrewAI project is set up. Some commands may require additional configuration or setup within your project structure. + + +### 9. API Keys + +When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one. + +Once you've selected an LLM provider, you will be prompted for API keys. + +#### Initial API key providers + +The CLI will initially prompt for API keys for the following services: + +* OpenAI +* Groq +* Anthropic +* Google Gemini + +When you select a provider, the CLI will prompt you to enter your API key. + +#### Other Options + +If you select option 6, you will be able to select from a list of LiteLLM supported providers. + +When you select a provider, the CLI will prompt you to enter the Key name and the API key. + +See the following link for each provider's key name: + +* [LiteLLM Providers](https://docs.litellm.ai/docs/providers) + + + diff --git a/docs/concepts/crews.mdx b/docs/concepts/crews.mdx index 43451ca4b..58511b07c 100644 --- a/docs/concepts/crews.mdx +++ b/docs/concepts/crews.mdx @@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to | **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. | | **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. | | **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. | -| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`. | +| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). | +| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. | | **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. | | **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. | | **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. | @@ -31,7 +32,6 @@ A crew in crewAI represents a collaborative group of agents working together to | **Share Crew** _(optional)_ | `share_crew` | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. | | **Output Log File** _(optional)_ | `output_log_file` | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. | | **Manager Agent** _(optional)_ | `manager_agent` | `manager` sets a custom agent that will be used as a manager. | -| **Manager Callbacks** _(optional)_ | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. | | **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. | | **Planning** *(optional)* | `planning` | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description. | | **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. | @@ -40,6 +40,155 @@ 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 @@ -187,4 +336,4 @@ 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/docs/concepts/flows.mdx b/docs/concepts/flows.mdx index 65cbb95c2..324118310 100644 --- a/docs/concepts/flows.mdx +++ b/docs/concepts/flows.mdx @@ -23,9 +23,9 @@ Flows allow you to create structured, event-driven workflows. They provide a sea Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task. ```python Code -import asyncio from crewai.flow.flow import Flow, listen, start +from dotenv import load_dotenv from litellm import completion @@ -67,19 +67,19 @@ class ExampleFlow(Flow): return fun_fact -async def main(): - flow = ExampleFlow() - result = await flow.kickoff() - print(f"Generated fun fact: {result}") +flow = ExampleFlow() +result = flow.kickoff() -asyncio.run(main()) +print(f"Generated fun fact: {result}") ``` In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task. When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console. +**Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API. + ### @start() The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started. @@ -119,7 +119,6 @@ Here's how you can access the final output: ```python Code -import asyncio from crewai.flow.flow import Flow, listen, start class OutputExampleFlow(Flow): @@ -131,26 +130,24 @@ class OutputExampleFlow(Flow): def second_method(self, first_output): return f"Second method received: {first_output}" -async def main(): - flow = OutputExampleFlow() - final_output = await flow.kickoff() - print("---- Final Output ----") - print(final_output) -asyncio.run(main()) -``` +flow = OutputExampleFlow() +final_output = flow.kickoff() + +print("---- Final Output ----") +print(final_output) +```` ``` text Output ---- Final Output ---- Second method received: Output from first_method -``` +```` -In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow. +In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow. The `kickoff()` method will return the final output, which is then printed to the console. - #### Accessing and Updating State In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution. @@ -160,7 +157,6 @@ Here's an example of how to update and access the state: ```python Code -import asyncio from crewai.flow.flow import Flow, listen, start from pydantic import BaseModel @@ -181,42 +177,38 @@ class StateExampleFlow(Flow[ExampleState]): self.state.counter += 1 return self.state.message -async def main(): - flow = StateExampleFlow() - final_output = await flow.kickoff() - print(f"Final Output: {final_output}") - print("Final State:") - print(flow.state) - -asyncio.run(main()) +flow = StateExampleFlow() +final_output = flow.kickoff() +print(f"Final Output: {final_output}") +print("Final State:") +print(flow.state) ``` -``` text Output +```text Output Final Output: Hello from first_method - updated by second_method Final State: counter=2 message='Hello from first_method - updated by second_method' ``` + -In this example, the state is updated by both `first_method` and `second_method`. +In this example, the state is updated by both `first_method` and `second_method`. After the Flow has run, you can access the final state to see the updates made by these methods. -By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems, +By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems, while also maintaining and accessing the state throughout the Flow's execution. ## Flow State Management -Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management, +Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management, allowing developers to choose the approach that best fits their application's needs. ### Unstructured State Management -In unstructured state management, all state is stored in the `state` attribute of the `Flow` class. +In unstructured state management, all state is stored in the `state` attribute of the `Flow` class. This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema. ```python Code -import asyncio - from crewai.flow.flow import Flow, listen, start class UntructuredExampleFlow(Flow): @@ -239,12 +231,8 @@ class UntructuredExampleFlow(Flow): print(f"State after third_method: {self.state}") -async def main(): - flow = UntructuredExampleFlow() - await flow.kickoff() - - -asyncio.run(main()) +flow = UntructuredExampleFlow() +flow.kickoff() ``` **Key Points:** @@ -254,12 +242,10 @@ asyncio.run(main()) ### Structured State Management -Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow. +Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow. By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments. ```python Code -import asyncio - from crewai.flow.flow import Flow, listen, start from pydantic import BaseModel @@ -288,12 +274,8 @@ class StructuredExampleFlow(Flow[ExampleState]): print(f"State after third_method: {self.state}") -async def main(): - flow = StructuredExampleFlow() - await flow.kickoff() - - -asyncio.run(main()) +flow = StructuredExampleFlow() +flow.kickoff() ``` **Key Points:** @@ -326,7 +308,6 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger ```python Code -import asyncio from crewai.flow.flow import Flow, listen, or_, start class OrExampleFlow(Flow): @@ -344,22 +325,19 @@ class OrExampleFlow(Flow): print(f"Logger: {result}") -async def main(): - flow = OrExampleFlow() - await flow.kickoff() - -asyncio.run(main()) +flow = OrExampleFlow() +flow.kickoff() ``` -``` text Output +```text Output Logger: Hello from the start method Logger: Hello from the second method ``` -When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`. +When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`. The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output. ### Conditional Logic: `and` @@ -369,7 +347,6 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge ```python Code -import asyncio from crewai.flow.flow import Flow, and_, listen, start class AndExampleFlow(Flow): @@ -387,34 +364,28 @@ class AndExampleFlow(Flow): print("---- Logger ----") print(self.state) - -async def main(): - flow = AndExampleFlow() - await flow.kickoff() - - -asyncio.run(main()) +flow = AndExampleFlow() +flow.kickoff() ``` -``` text Output +```text Output ---- Logger ---- {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'} ``` -When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output. +When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output. The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output. ### Router -The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method. +The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method. You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically. ```python Code -import asyncio import random from crewai.flow.flow import Flow, listen, router, start from pydantic import BaseModel @@ -446,15 +417,11 @@ class RouterFlow(Flow[ExampleState]): print("Fourth method running") -async def main(): - flow = RouterFlow() - await flow.kickoff() - - -asyncio.run(main()) +flow = RouterFlow() +flow.kickoff() ``` -``` text Output +```text Output Starting the structured flow Third method running Fourth method running @@ -462,16 +429,16 @@ Fourth method running -In the above example, the `start_method` generates a random boolean value and sets it in the state. -The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean. -If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`. +In the above example, the `start_method` generates a random boolean value and sets it in the state. +The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean. +If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`. The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value. When you run this Flow, the output will change based on the random boolean value generated by the `start_method`. ## Adding Crews to Flows -Creating a flow with multiple crews in CrewAI is straightforward. +Creating a flow with multiple crews in CrewAI is straightforward. You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command: @@ -485,22 +452,21 @@ This command will generate a new CrewAI project with the necessary folder struct After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following: -| Directory/File | Description | -|:---------------------------------|:------------------------------------------------------------------| -| `name_of_flow/` | Root directory for the flow. | -| ├── `crews/` | Contains directories for specific crews. | -| │ └── `poem_crew/` | Directory for the "poem_crew" with its configurations and scripts.| -| │ ├── `config/` | Configuration files directory for the "poem_crew". | -| │ │ ├── `agents.yaml` | YAML file defining the agents for "poem_crew". | -| │ │ └── `tasks.yaml` | YAML file defining the tasks for "poem_crew". | -| │ ├── `poem_crew.py` | Script for "poem_crew" functionality. | -| ├── `tools/` | Directory for additional tools used in the flow. | -| │ └── `custom_tool.py` | Custom tool implementation. | -| ├── `main.py` | Main script for running the flow. | -| ├── `README.md` | Project description and instructions. | -| ├── `pyproject.toml` | Configuration file for project dependencies and settings. | -| └── `.gitignore` | Specifies files and directories to ignore in version control. | - +| Directory/File | Description | +| :--------------------- | :----------------------------------------------------------------- | +| `name_of_flow/` | Root directory for the flow. | +| ├── `crews/` | Contains directories for specific crews. | +| │ └── `poem_crew/` | Directory for the "poem_crew" with its configurations and scripts. | +| │ ├── `config/` | Configuration files directory for the "poem_crew". | +| │ │ ├── `agents.yaml` | YAML file defining the agents for "poem_crew". | +| │ │ └── `tasks.yaml` | YAML file defining the tasks for "poem_crew". | +| │ ├── `poem_crew.py` | Script for "poem_crew" functionality. | +| ├── `tools/` | Directory for additional tools used in the flow. | +| │ └── `custom_tool.py` | Custom tool implementation. | +| ├── `main.py` | Main script for running the flow. | +| ├── `README.md` | Project description and instructions. | +| ├── `pyproject.toml` | Configuration file for project dependencies and settings. | +| └── `.gitignore` | Specifies files and directories to ignore in version control. | ### Building Your Crews @@ -520,7 +486,6 @@ Here's an example of how you can connect the `poem_crew` in the `main.py` file: ```python Code #!/usr/bin/env python -import asyncio from random import randint from pydantic import BaseModel @@ -536,14 +501,12 @@ class PoemFlow(Flow[PoemState]): @start() def generate_sentence_count(self): print("Generating sentence count") - # Generate a number between 1 and 5 self.state.sentence_count = randint(1, 5) @listen(generate_sentence_count) def generate_poem(self): print("Generating poem") - poem_crew = PoemCrew().crew() - result = poem_crew.kickoff(inputs={"sentence_count": self.state.sentence_count}) + result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count}) print("Poem generated", result.raw) self.state.poem = result.raw @@ -554,18 +517,17 @@ class PoemFlow(Flow[PoemState]): with open("poem.txt", "w") as f: f.write(self.state.poem) -async def run(): - """ - Run the flow. - """ +def kickoff(): poem_flow = PoemFlow() - await poem_flow.kickoff() + poem_flow.kickoff() -def main(): - asyncio.run(run()) + +def plot(): + poem_flow = PoemFlow() + poem_flow.plot() if __name__ == "__main__": - main() + kickoff() ``` In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method. @@ -587,13 +549,13 @@ source .venv/bin/activate After activating the virtual environment, you can run the flow by executing one of the following commands: ```bash -crewai flow run +crewai flow kickoff ``` or ```bash -uv run run_flow +uv run kickoff ``` The flow will execute, and you should see the output in the console. @@ -657,13 +619,13 @@ By exploring these examples, you can gain insights into how to leverage CrewAI F Also, check out our YouTube video on how to use flows in CrewAI below! - \ No newline at end of file + \ No newline at end of file diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx new file mode 100644 index 000000000..8af9b70a2 --- /dev/null +++ b/docs/concepts/knowledge.mdx @@ -0,0 +1,319 @@ +--- +title: Knowledge +description: What is knowledge in CrewAI and how to use it. +icon: book +--- + +# Using Knowledge in CrewAI + +## 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. + + + Key benefits of using Knowledge: + - Enhance agents with domain-specific information + - Support decisions with real-world data + - Maintain context across conversations + - Ground responses in factual information + + +## Supported Knowledge Sources + +CrewAI supports various types of knowledge sources out of the box: + + + + - Raw strings + - Text files (.txt) + - PDF documents + + + - CSV files + - Excel spreadsheets + - JSON documents + + + +## Quick Start + +Here's an example using string-based knowledge: + +```python Code +from crewai import Agent, Task, Crew, Process, LLM +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource + +# Create a knowledge source +content = "Users name is John. He is 30 years old and lives in San Francisco." +string_source = StringKnowledgeSource( + content=content, +) + +# 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, +) + +crew = Crew( + agents=[agent], + tasks=[task], + verbose=True, + process=Process.sequential, + knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list. +) + +result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) +``` + +## Knowledge Configuration + +### Chunking Configuration + +Control how content is split for processing by setting the chunk size and overlap. + +```python Code +knowledge_source = StringKnowledgeSource( + content="Long content...", + chunk_size=4000, # Characters per chunk (default) + chunk_overlap=200 # Overlap between chunks (default) +) +``` + +## Embedder Configuration + +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. + +```python Code +... +string_source = StringKnowledgeSource( + content="Users name is John. He is 30 years old and lives in San Francisco.", +) +crew = Crew( + ... + 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. + +#### 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.""" + + api_endpoint: str = Field(description="API endpoint URL") + limit: int = Field(default=10, description="Number of articles to fetch") + + 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() + for _, text in content.items(): + chunks = self._chunk_text(text) + self.chunks.extend(chunks) + + self._save_documents() + +# Create knowledge source +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", + limit=10, +) + +# 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) +) + +# 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=[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? + + +# Agent: Space News Analyst +## Final Answer: +The latest developments in space exploration, based on recent space news articles, include the following: + +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/) + +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 + +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/docs/), 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 +# Fetch more articles +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles", + limit=20, # Increase the number of articles +) + +# Add search parameters +recent_news = SpaceNewsKnowledgeSource( + api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news + limit=10, +) +``` + +## Best Practices + + + + - Keep chunk sizes appropriate for your content type + - Consider content overlap for context preservation + - Organize related information into separate knowledge sources + + + + - Adjust chunk sizes based on content complexity + - Configure appropriate embedding models + - Consider using local embedding providers for faster processing + + diff --git a/docs/concepts/langchain-tools.mdx b/docs/concepts/langchain-tools.mdx index 538581aee..68a7998a9 100644 --- a/docs/concepts/langchain-tools.mdx +++ b/docs/concepts/langchain-tools.mdx @@ -7,32 +7,45 @@ icon: link ## Using LangChain Tools - CrewAI seamlessly integrates with LangChain’s comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI. + CrewAI seamlessly integrates with LangChain's comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI. ```python Code import os -from crewai import Agent -from langchain.agents import Tool -from langchain.utilities import GoogleSerperAPIWrapper +from dotenv import load_dotenv +from crewai import Agent, Task, Crew +from crewai.tools import BaseTool +from pydantic import Field +from langchain_community.utilities import GoogleSerperAPIWrapper -# Setup API keys -os.environ["SERPER_API_KEY"] = "Your Key" +# Set up your SERPER_API_KEY key in an .env file, eg: +# SERPER_API_KEY= +load_dotenv() search = GoogleSerperAPIWrapper() -# Create and assign the search tool to an agent -serper_tool = Tool( - name="Intermediate Answer", - func=search.run, - description="Useful for search-based queries", -) +class SearchTool(BaseTool): + name: str = "Search" + description: str = "Useful for search-based queries. Use this to find current information about markets, companies, and trends." + search: GoogleSerperAPIWrapper = Field(default_factory=GoogleSerperAPIWrapper) -agent = Agent( - role='Research Analyst', - goal='Provide up-to-date market analysis', - backstory='An expert analyst with a keen eye for market trends.', - tools=[serper_tool] + def _run(self, query: str) -> str: + """Execute the search query and return results""" + try: + return self.search.run(query) + except Exception as e: + return f"Error performing search: {str(e)}" + +# Create Agents +researcher = Agent( + role='Research Analyst', + goal='Gather current market data and trends', + backstory="""You are an expert research analyst with years of experience in + gathering market intelligence. You're known for your ability to find + relevant and up-to-date market information and present it in a clear, + actionable format.""", + tools=[SearchTool()], + verbose=True ) # rest of the code ... @@ -40,6 +53,6 @@ agent = Agent( ## Conclusion -Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. -When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, -and the flexibility of tool arguments to optimize your agents' performance and capabilities. \ No newline at end of file +Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. +When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms, +and the flexibility of tool arguments to optimize your agents' performance and capabilities. diff --git a/docs/concepts/llms.mdx b/docs/concepts/llms.mdx index 728c88c36..8f895ca61 100644 --- a/docs/concepts/llms.mdx +++ b/docs/concepts/llms.mdx @@ -1,158 +1,650 @@ --- -title: LLMs -description: Learn how to configure and optimize LLMs for your CrewAI projects. -icon: microchip-ai +title: 'LLMs' +description: 'A comprehensive guide to configuring and using Large Language Models (LLMs) in your CrewAI projects' +icon: 'microchip-ai' --- -# Large Language Models (LLMs) in CrewAI + + CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects. + -Large Language Models (LLMs) are the backbone of intelligent agents in the CrewAI framework. This guide will help you understand, configure, and optimize LLM usage for your CrewAI projects. +## What are LLMs? -## Key Concepts +Large Language Models (LLMs) are the core intelligence behind CrewAI agents. They enable agents to understand context, make decisions, and generate human-like responses. Here's what you need to know: -- **LLM**: Large Language Model, the AI powering agent intelligence -- **Agent**: A CrewAI entity that uses an LLM to perform tasks -- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers)) + + + Large Language Models are AI systems trained on vast amounts of text data. They power the intelligence of your CrewAI agents, enabling them to understand and generate human-like text. + + + The context window determines how much text an LLM can process at once. Larger windows (e.g., 128K tokens) allow for more context but may be more expensive and slower. + + + Temperature (0.0 to 1.0) controls response randomness. Lower values (e.g., 0.2) produce more focused, deterministic outputs, while higher values (e.g., 0.8) increase creativity and variability. + + + Each LLM provider (e.g., OpenAI, Anthropic, Google) offers different models with varying capabilities, pricing, and features. Choose based on your needs for accuracy, speed, and cost. + + -## Configuring LLMs for Agents +## Available Models and Their Capabilities -CrewAI offers flexible options for setting up LLMs: +Here's a detailed breakdown of supported models and their capabilities, you can compare performance at [lmarena.ai](https://lmarena.ai/): -### 1. Default Configuration + + + | Model | Context Window | Best For | + |-------|---------------|-----------| + | GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning | + | GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis | + | GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing | -By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified: -- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set) -- `OPENAI_API_BASE` -- `OPENAI_API_KEY` + + 1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words. + + + + | Model | Context Window | Best For | + |-------|---------------|-----------| + | Gemini 1.5 Flash | 1M tokens | Balanced multimodal model, good for most tasks | + | Gemini 1.5 Flash 8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks | + | Gemini 1.5 Pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration | -### 2. String Identifier + + Google's Gemini models are all multimodal, supporting audio, images, video and text, supporting context caching, json schema, function calling, etc. + + + + | Model | Context Window | Best For | + |-------|---------------|-----------| + | Llama 3.1 70B/8B | 131,072 tokens | High-performance, large context tasks | + | Llama 3.2 Series | 8,192 tokens | General-purpose tasks | + | Mixtral 8x7B | 32,768 tokens | Balanced performance and context | + | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks | -```python Code -agent = Agent(llm="gpt-4o", ...) -``` + + Groq is known for its fast inference speeds, making it suitable for real-time applications. + + + + | Provider | Context Window | Key Features | + |----------|---------------|--------------| + | Deepseek Chat | 128,000 tokens | Specialized in technical discussions | + | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding | + | Gemini | Varies by model | Multimodal capabilities | -### 3. LLM Instance + + Provider selection should consider factors like: + - API availability in your region + - Pricing structure + - Required features (e.g., streaming, function calling) + - Performance requirements + + + -List of [more providers](https://docs.litellm.ai/docs/providers). +## Setting Up Your LLM -```python Code -from crewai import LLM +There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow: -llm = LLM(model="gpt-4", temperature=0.7) -agent = Agent(llm=llm, ...) -``` + + + The simplest way to get started. Set these variables in your environment: -### 4. Custom LLM Objects + ```bash + # Required: Your API key for authentication + OPENAI_API_KEY= -Pass a custom LLM implementation or object from another library. + # Optional: Default model selection + OPENAI_MODEL_NAME=gpt-4o-mini # Default if not set -## Connecting to OpenAI-Compatible LLMs + # Optional: Organization ID (if applicable) + OPENAI_ORGANIZATION_ID= + ``` -You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class: + + Never commit API keys to version control. Use environment files (.env) or your system's secret management. + + + + Create a YAML file to define your agent configurations. This method is great for version control and team collaboration: -1. Using environment variables: + ```yaml + researcher: + # Agent Definition + role: Research Specialist + goal: Conduct comprehensive research and analysis + backstory: A dedicated research professional with years of experience + verbose: true -```python Code -import os + # Model Selection (uncomment your choice) + + # OpenAI Models - Known for reliability and performance + llm: openai/gpt-4o-mini + # llm: openai/gpt-4 # More accurate but expensive + # llm: openai/gpt-4-turbo # Fast with large context + # llm: openai/gpt-4o # Optimized for longer texts + # llm: openai/o1-preview # Latest features + # llm: openai/o1-mini # Cost-effective -os.environ["OPENAI_API_KEY"] = "your-api-key" -os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1" -``` + # Azure Models - For enterprise deployments + # llm: azure/gpt-4o-mini + # llm: azure/gpt-4 + # llm: azure/gpt-35-turbo -2. Using LLM class attributes: + # Anthropic Models - Strong reasoning capabilities + # llm: anthropic/claude-3-opus-20240229-v1:0 + # llm: anthropic/claude-3-sonnet-20240229-v1:0 + # llm: anthropic/claude-3-haiku-20240307-v1:0 + # llm: anthropic/claude-2.1 + # llm: anthropic/claude-2.0 -```python Code -llm = LLM( - model="custom-model-name", - api_key="your-api-key", - base_url="https://api.your-provider.com/v1" -) -agent = Agent(llm=llm, ...) -``` + # Google Models - Strong reasoning, large cachable context window, multimodal + # llm: gemini/gemini-1.5-pro-latest + # llm: gemini/gemini-1.5-flash-latest + # llm: gemini/gemini-1.5-flash-8b-latest -## LLM Configuration Options + # AWS Bedrock Models - Enterprise-grade + # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0 + # llm: bedrock/anthropic.claude-v2:1 + # llm: bedrock/amazon.titan-text-express-v1 + # llm: bedrock/meta.llama2-70b-chat-v1 -When configuring an LLM for your agent, you have access to a wide range of parameters: + # Mistral Models - Open source alternative + # llm: mistral/mistral-large-latest + # llm: mistral/mistral-medium-latest + # llm: mistral/mistral-small-latest -| Parameter | Type | Description | -|:------------------|:---------------:|:-------------------------------------------------------------------------------------------------| -| **model** | `str` | Name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1"). For more options, visit the providers documentation. | -| **timeout** | `float, int` | Maximum time (in seconds) to wait for a response. | -| **temperature** | `float` | Controls randomness in output (0.0 to 1.0). | -| **top_p** | `float` | Controls diversity of output (0.0 to 1.0). | -| **n** | `int` | Number of completions to generate. | -| **stop** | `str, List[str]` | Sequence(s) where generation should stop. | -| **max_tokens** | `int` | Maximum number of tokens to generate. | -| **presence_penalty** | `float` | Penalizes new tokens based on their presence in prior text. | -| **frequency_penalty**| `float` | Penalizes new tokens based on their frequency in prior text. | -| **logit_bias** | `Dict[int, float]`| Modifies likelihood of specified tokens appearing. | -| **response_format** | `Dict[str, Any]` | Specifies the format of the response (e.g., JSON object). | -| **seed** | `int` | Sets a random seed for deterministic results. | -| **logprobs** | `bool` | Returns log probabilities of output tokens if enabled. | -| **top_logprobs** | `int` | Number of most likely tokens for which to return log probabilities. | -| **base_url** | `str` | The base URL for the API endpoint. | -| **api_version** | `str` | Version of the API to use. | -| **api_key** | `str` | Your API key for authentication. | + # Groq Models - Fast inference + # llm: groq/mixtral-8x7b-32768 + # llm: groq/llama-3.1-70b-versatile + # llm: groq/llama-3.2-90b-text-preview + # llm: groq/gemma2-9b-it + # llm: groq/gemma-7b-it + # IBM watsonx.ai Models - Enterprise features + # llm: watsonx/ibm/granite-13b-chat-v2 + # llm: watsonx/meta-llama/llama-3-1-70b-instruct + # llm: watsonx/bigcode/starcoder2-15b -Example: + # Ollama Models - Local deployment + # llm: ollama/llama3:70b + # llm: ollama/codellama + # llm: ollama/mistral + # llm: ollama/mixtral + # llm: ollama/phi -```python Code -llm = LLM( - model="gpt-4", - temperature=0.8, - max_tokens=150, - top_p=0.9, - frequency_penalty=0.1, - presence_penalty=0.1, - stop=["END"], - seed=42, - base_url="https://api.openai.com/v1", - api_key="your-api-key-here" -) -agent = Agent(llm=llm, ...) -``` -## Using Ollama (Local LLMs) + # Fireworks AI Models - Specialized tasks + # llm: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct + # llm: fireworks_ai/accounts/fireworks/models/mixtral-8x7b + # llm: fireworks_ai/accounts/fireworks/models/zephyr-7b-beta -crewAI supports using Ollama for running open-source models locally: + # Perplexity AI Models - Research focused + # llm: pplx/llama-3.1-sonar-large-128k-online + # llm: pplx/mistral-7b-instruct + # llm: pplx/codellama-34b-instruct + # llm: pplx/mixtral-8x7b-instruct -1. Install Ollama: [ollama.ai](https://ollama.ai/) -2. Run a model: `ollama run llama2` -3. Configure agent: + # Hugging Face Models - Community models + # llm: huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct + # llm: huggingface/mistralai/Mixtral-8x7B-Instruct-v0.1 + # llm: huggingface/tiiuae/falcon-180B-chat + # llm: huggingface/google/gemma-7b-it -```python Code -agent = Agent( - llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"), - ... -) -``` + # Nvidia NIM Models - GPU-optimized + # llm: nvidia_nim/meta/llama3-70b-instruct + # llm: nvidia_nim/mistral/mixtral-8x7b + # llm: nvidia_nim/google/gemma-7b -## Changing the Base API URL + # SambaNova Models - Enterprise AI + # llm: sambanova/Meta-Llama-3.1-8B-Instruct + # llm: sambanova/BioMistral-7B + # llm: sambanova/Falcon-180B + ``` -You can change the base API URL for any LLM provider by setting the `base_url` parameter: + + The YAML configuration allows you to: + - Version control your agent settings + - Easily switch between different models + - Share configurations across team members + - Document model choices and their purposes + + + + For maximum flexibility, configure LLMs directly in your Python code: -```python Code -llm = LLM( - model="custom-model-name", - base_url="https://api.your-provider.com/v1", - api_key="your-api-key" -) -agent = Agent(llm=llm, ...) -``` + ```python + from crewai import LLM -This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider. + # Basic configuration + llm = LLM(model="gpt-4") -## Best Practices + # Advanced configuration with detailed parameters + llm = LLM( + model="gpt-4o-mini", + temperature=0.7, # Higher for more creative outputs + timeout=120, # Seconds to wait for response + max_tokens=4000, # Maximum length of response + top_p=0.9, # Nucleus sampling parameter + frequency_penalty=0.1, # Reduce repetition + presence_penalty=0.1, # Encourage topic diversity + response_format={"type": "json"}, # For structured outputs + seed=42 # For reproducible results + ) + ``` -1. **Choose the right model**: Balance capability and cost. -2. **Optimize prompts**: Clear, concise instructions improve output. -3. **Manage tokens**: Monitor and limit token usage for efficiency. -4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones. -5. **Implement error handling**: Gracefully manage API errors and rate limits. + + Parameter explanations: + - `temperature`: Controls randomness (0.0-1.0) + - `timeout`: Maximum wait time for response + - `max_tokens`: Limits response length + - `top_p`: Alternative to temperature for sampling + - `frequency_penalty`: Reduces word repetition + - `presence_penalty`: Encourages new topics + - `response_format`: Specifies output structure + - `seed`: Ensures consistent outputs + + + -## Troubleshooting +## Advanced Features and Optimization -- **API Errors**: Check your API key, network connection, and rate limits. -- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p. -- **Performance Issues**: Consider using a more powerful model or optimizing your queries. -- **Timeout Errors**: Increase the `timeout` parameter or optimize your input. \ No newline at end of file +Learn how to get the most out of your LLM configuration: + + + + CrewAI includes smart context management features: + + ```python + from crewai import LLM + + # CrewAI automatically handles: + # 1. Token counting and tracking + # 2. Content summarization when needed + # 3. Task splitting for large contexts + + llm = LLM( + model="gpt-4", + max_tokens=4000, # Limit response length + ) + ``` + + + Best practices for context management: + 1. Choose models with appropriate context windows + 2. Pre-process long inputs when possible + 3. Use chunking for large documents + 4. Monitor token usage to optimize costs + + + + + + + Choose the right context window for your task: + - Small tasks (up to 4K tokens): Standard models + - Medium tasks (between 4K-32K): Enhanced models + - Large tasks (over 32K): Large context models + + ```python + # Configure model with appropriate settings + llm = LLM( + model="openai/gpt-4-turbo-preview", + temperature=0.7, # Adjust based on task + max_tokens=4096, # Set based on output needs + timeout=300 # Longer timeout for complex tasks + ) + ``` + + - Lower temperature (0.1 to 0.3) for factual responses + - Higher temperature (0.7 to 0.9) for creative tasks + + + + + 1. Monitor token usage + 2. Implement rate limiting + 3. Use caching when possible + 4. Set appropriate max_tokens limits + + + + + Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance. + + + + +## Provider Configuration Examples + + + + ```python Code + # Required + OPENAI_API_KEY=sk-... + + # Optional + OPENAI_API_BASE= + OPENAI_ORGANIZATION= + ``` + + Example usage: + ```python Code + from crewai import LLM + + llm = LLM( + model="gpt-4", + temperature=0.8, + max_tokens=150, + top_p=0.9, + frequency_penalty=0.1, + presence_penalty=0.1, + stop=["END"], + seed=42 + ) + ``` + + + + ```python Code + ANTHROPIC_API_KEY=sk-ant-... + ``` + + Example usage: + ```python Code + llm = LLM( + model="anthropic/claude-3-sonnet-20240229-v1:0", + temperature=0.7 + ) + ``` + + + + ```python Code + # Option 1. Gemini accessed with an API key. + # https://ai.google.dev/gemini-api/docs/api-key + GEMINI_API_KEY= + + # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden. + # https://cloud.google.com/vertex-ai/generative-ai/docs/overview + ``` + + Example usage: + ```python Code + llm = LLM( + model="gemini/gemini-1.5-pro-latest", + temperature=0.7 + ) + ``` + + + + ```python Code + # Required + AZURE_API_KEY= + AZURE_API_BASE= + AZURE_API_VERSION= + + # Optional + AZURE_AD_TOKEN= + AZURE_API_TYPE= + ``` + + Example usage: + ```python Code + llm = LLM( + model="azure/gpt-4", + api_version="2023-05-15" + ) + ``` + + + + ```python Code + AWS_ACCESS_KEY_ID= + AWS_SECRET_ACCESS_KEY= + AWS_DEFAULT_REGION= + ``` + + Example usage: + ```python Code + llm = LLM( + model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0" + ) + ``` + + + + ```python Code + MISTRAL_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="mistral/mistral-large-latest", + temperature=0.7 + ) + ``` + + + + ```python Code + GROQ_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="groq/llama-3.2-90b-text-preview", + temperature=0.7 + ) + ``` + + + + ```python Code + # Required + WATSONX_URL= + WATSONX_APIKEY= + WATSONX_PROJECT_ID= + + # Optional + WATSONX_TOKEN= + WATSONX_DEPLOYMENT_SPACE_ID= + ``` + + Example usage: + ```python Code + llm = LLM( + model="watsonx/meta-llama/llama-3-1-70b-instruct", + base_url="https://api.watsonx.ai/v1" + ) + ``` + + + + 1. Install Ollama: [ollama.ai](https://ollama.ai/) + 2. Run a model: `ollama run llama2` + 3. Configure: + + ```python Code + llm = LLM( + model="ollama/llama3:70b", + base_url="http://localhost:11434" + ) + ``` + + + + ```python Code + FIREWORKS_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct", + temperature=0.7 + ) + ``` + + + + ```python Code + PERPLEXITY_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="llama-3.1-sonar-large-128k-online", + base_url="https://api.perplexity.ai/" + ) + ``` + + + + ```python Code + HUGGINGFACE_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", + base_url="your_api_endpoint" + ) + ``` + + + + ```python Code + NVIDIA_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="nvidia_nim/meta/llama3-70b-instruct", + temperature=0.7 + ) + ``` + + + + ```python Code + SAMBANOVA_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="sambanova/Meta-Llama-3.1-8B-Instruct", + temperature=0.7 + ) + ``` + + + + ```python Code + # Required + CEREBRAS_API_KEY= + ``` + + Example usage: + ```python Code + llm = LLM( + model="cerebras/llama3.1-70b", + temperature=0.7, + max_tokens=8192 + ) + ``` + + + Cerebras features: + - Fast inference speeds + - Competitive pricing + - Good balance of speed and quality + - Support for long context windows + + + + +## Common Issues and Solutions + + + + + Most authentication issues can be resolved by checking API key format and environment variable names. + + + ```bash + # OpenAI + OPENAI_API_KEY=sk-... + + # Anthropic + ANTHROPIC_API_KEY=sk-ant-... + ``` + + + + Always include the provider prefix in model names + + + ```python + # Correct + llm = LLM(model="openai/gpt-4") + + # Incorrect + llm = LLM(model="gpt-4") + ``` + + + + Use larger context models for extensive tasks + + + ```python + # Large context model + llm = LLM(model="openai/gpt-4o") # 128K tokens + ``` + + + +## Getting Help + +If you need assistance, these resources are available: + + + + Comprehensive documentation for LiteLLM integration and troubleshooting common issues. + + + Report bugs, request features, or browse existing issues for solutions. + + + Connect with other CrewAI users, share experiences, and get help from the community. + + + + + Best Practices for API Key Security: + - Use environment variables or secure vaults + - Never commit keys to version control + - Rotate keys regularly + - Use separate keys for development and production + - Monitor key usage for unusual patterns + diff --git a/docs/concepts/memory.mdx b/docs/concepts/memory.mdx index b07096442..a7677cec1 100644 --- a/docs/concepts/memory.mdx +++ b/docs/concepts/memory.mdx @@ -18,6 +18,7 @@ reason, and learn from past interactions. | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. | | **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. | | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. | +| **User Memory** | Stores user-specific information and preferences, enhancing personalization and user experience. | ## How Memory Systems Empower Agents @@ -92,6 +93,47 @@ my_crew = Crew( ) ``` +## Integrating Mem0 for Enhanced User Memory + +[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. + +To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences. + + +```python Code +import os +from crewai import Crew, Process +from mem0 import MemoryClient + +# Set environment variables for Mem0 +os.environ["MEM0_API_KEY"] = "m0-xx" + +# Step 1: Record preferences based on past conversation or user input +client = MemoryClient() +messages = [ + {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."}, + {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"}, + {"role": "user", "content": "I am more of a beach person than a mountain person."}, + {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"}, + {"role": "user", "content": "I like Airbnb more."}, +] +client.add(messages, user_id="john") + +# Step 2: Create a Crew with User Memory + +crew = Crew( + agents=[...], + tasks=[...], + verbose=True, + process=Process.sequential, + memory=True, + memory_config={ + "provider": "mem0", + "config": {"user_id": "john"}, + }, +) +``` + ## Additional Embedding Providers @@ -105,9 +147,48 @@ my_crew = Crew( process=Process.sequential, memory=True, verbose=True, - embedder=embedding_functions.OpenAIEmbeddingFunction( - api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" - ) + embedder={ + "provider": "openai", + "config": { + "model": 'text-embedding-3-small' + } + } +) +``` +Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter. + +Example: +```python Code +from crewai import Crew, Agent, Task, Process +from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction + +my_crew = Crew( + agents=[...], + tasks=[...], + process=Process.sequential, + memory=True, + verbose=True, + embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"), +) +``` + +### Using Ollama embeddings + +```python Code +from crewai import Crew, Agent, Task, Process + +my_crew = Crew( + agents=[...], + tasks=[...], + process=Process.sequential, + memory=True, + verbose=True, + embedder={ + "provider": "ollama", + "config": { + "model": "mxbai-embed-large" + } + } ) ``` @@ -122,16 +203,20 @@ my_crew = Crew( process=Process.sequential, memory=True, verbose=True, - embedder=embedding_functions.OpenAIEmbeddingFunction( - api_key=os.getenv("OPENAI_API_KEY"), - model_name="text-embedding-ada-002" - ) + embedder={ + "provider": "google", + "config": { + "api_key": "", + "model_name": "" + } + } ) ``` ### Using Azure OpenAI embeddings ```python Code +from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction from crewai import Crew, Agent, Task, Process my_crew = Crew( @@ -140,7 +225,7 @@ my_crew = Crew( process=Process.sequential, memory=True, verbose=True, - embedder=embedding_functions.OpenAIEmbeddingFunction( + embedder=OpenAIEmbeddingFunction( api_key="YOUR_API_KEY", api_base="YOUR_API_BASE_PATH", api_type="azure", @@ -153,6 +238,7 @@ my_crew = Crew( ### Using Vertex AI embeddings ```python Code +from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction from crewai import Crew, Agent, Task, Process my_crew = Crew( @@ -161,7 +247,7 @@ my_crew = Crew( process=Process.sequential, memory=True, verbose=True, - embedder=embedding_functions.GoogleVertexEmbeddingFunction( + embedder=GoogleVertexEmbeddingFunction( project_id="YOUR_PROJECT_ID", region="YOUR_REGION", api_key="YOUR_API_KEY", @@ -181,10 +267,57 @@ my_crew = Crew( process=Process.sequential, memory=True, verbose=True, - embedder=embedding_functions.CohereEmbeddingFunction( - api_key=YOUR_API_KEY, - model_name="" - ) + embedder={ + "provider": "cohere", + "config": { + "api_key": "YOUR_API_KEY", + "model_name": "" + } + } +) +``` +### Using HuggingFace embeddings + +```python Code +from crewai import Crew, Agent, Task, Process + +my_crew = Crew( + agents=[...], + tasks=[...], + process=Process.sequential, + memory=True, + verbose=True, + embedder={ + "provider": "huggingface", + "config": { + "api_url": "", + } + } +) +``` + +### Using Watson embeddings + +```python Code +from crewai import Crew, Agent, Task, Process + +# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work. + +my_crew = Crew( + agents=[...], + tasks=[...], + process=Process.sequential, + memory=True, + verbose=True, + embedder={ + "provider": "watson", + "config": { + "model": "", + "api_url": "", + "api_key": "", + "project_id": "", + } + } ) ``` diff --git a/docs/concepts/tasks.mdx b/docs/concepts/tasks.mdx index 884c8fd27..9ca90fcb5 100644 --- a/docs/concepts/tasks.mdx +++ b/docs/concepts/tasks.mdx @@ -1,6 +1,6 @@ --- title: Tasks -description: Detailed guide on managing and creating tasks within the CrewAI framework, reflecting the latest codebase updates. +description: Detailed guide on managing and creating tasks within the CrewAI framework. icon: list-check --- @@ -8,41 +8,171 @@ icon: list-check In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`. -They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities. - +Tasks provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities. Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency. +### Task Execution Flow + +Tasks can be executed in two ways: +- **Sequential**: Tasks are executed in the order they are defined +- **Hierarchical**: Tasks are assigned to agents based on their roles and expertise + +The execution flow is defined when creating the crew: +```python Code +crew = Crew( + agents=[agent1, agent2], + tasks=[task1, task2], + process=Process.sequential # or Process.hierarchical +) +``` + ## Task Attributes | Attribute | Parameters | Type | Description | | :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- | | **Description** | `description` | `str` | A clear, concise statement of what the task entails. | -| **Agent** | `agent` | `Optional[BaseAgent]` | The agent responsible for the task, assigned either directly or by the crew's process. | | **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. | -| **Tools** _(optional)_ | `tools` | `Optional[List[Any]]` | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list. | -| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False. | -| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Specifies tasks whose outputs are used as context for this task. | -| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. | -| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. | -| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. | -| **Output File** _(optional)_ | `output_file` | `Optional[str]` | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. | -| **Output** _(optional)_ | `output` | `Optional[TaskOutput]` | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details. | -| **Callback** _(optional)_ | `callback` | `Optional[Any]` | A callable that is executed with the task's output upon completion. | -| **Human Input** _(optional)_ | `human_input` | `Optional[bool]` | Indicates if the task should involve human review at the end, useful for tasks needing human oversight. Defaults to False.| -| **Converter Class** _(optional)_ | `converter_cls` | `Optional[Type[Converter]]` | A converter class used to export structured output. Defaults to None. | +| **Name** _(optional)_ | `name` | `Optional[str]` | A name identifier for the task. | +| **Agent** _(optional)_ | `agent` | `Optional[BaseAgent]` | The agent responsible for executing the task. | +| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | The tools/resources the agent is limited to use for this task. | +| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Other tasks whose outputs will be used as context for this task. | +| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | Whether the task should be executed asynchronously. Defaults to False. | +| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Task-specific configuration parameters. | +| **Output File** _(optional)_ | `output_file` | `Optional[str]` | File path for storing the task output. | +| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | A Pydantic model to structure the JSON output. | +| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | A Pydantic model for task output. | +| **Callback** _(optional)_ | `callback` | `Optional[Any]` | Function/object to be executed after task completion. | -## Creating a Task +## Creating Tasks -Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility: +There are two ways to create tasks 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 tasks. We strongly recommend using this approach to define tasks in your CrewAI projects. + +After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements. + + +Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew: ```python Code +crew.kickoff(inputs={'topic': 'AI Agents'}) +``` + + +Here's an example of how to configure tasks using YAML: + +```yaml tasks.yaml +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 + output_file: report.md +``` + +To use this YAML configuration in your code, create a crew class that inherits from `CrewBase`: + +```python crew.py +# src/latest_ai_development/crew.py + +from crewai import Agent, Crew, Process, Task +from crewai.project import CrewBase, agent, crew, task +from crewai_tools import SerperDevTool + +@CrewBase +class LatestAiDevelopmentCrew(): + """LatestAiDevelopment crew""" + + @agent + def researcher(self) -> Agent: + return Agent( + config=self.agents_config['researcher'], + verbose=True, + tools=[SerperDevTool()] + ) + + @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'] + ) + + @crew + def crew(self) -> Crew: + return Crew( + agents=[ + self.researcher(), + self.reporting_analyst() + ], + tasks=[ + self.research_task(), + self.reporting_task() + ], + process=Process.sequential + ) +``` + + +The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code. + + +### Direct Code Definition (Alternative) + +Alternatively, you can define tasks directly in your code without using YAML configuration: + +```python task.py from crewai import Task -task = Task( - description='Find and summarize the latest and most relevant news on AI', - agent=sales_agent, - expected_output='A bullet list summary of the top 5 most important AI news', +research_task = Task( + description=""" + Conduct a thorough research about AI Agents. + 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 AI Agents + """, + agent=researcher +) + +reporting_task = 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, + output_file="report.md" ) ``` @@ -52,6 +182,8 @@ task = Task( ## Task Output +Understanding task outputs is crucial for building effective AI workflows. CrewAI provides a structured way to handle task results through the `TaskOutput` class, which supports multiple output formats and can be easily passed between tasks. + The output of a task in CrewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw output, JSON, and Pydantic models. By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively. @@ -112,6 +244,186 @@ if task_output.pydantic: print(f"Pydantic Output: {task_output.pydantic}") ``` +## Task Dependencies and Context + +Tasks can depend on the output of other tasks using the `context` attribute. For example: + +```python Code +research_task = Task( + description="Research the latest developments in AI", + expected_output="A list of recent AI developments", + agent=researcher +) + +analysis_task = Task( + description="Analyze the research findings and identify key trends", + expected_output="Analysis report of AI trends", + agent=analyst, + context=[research_task] # This task will wait for research_task to complete +) +``` + +## 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. @@ -167,16 +479,16 @@ This is useful when you have a task that depends on the output of another task t # ... research_ai_task = Task( - description='Find and summarize the latest AI news', - expected_output='A bullet list summary of the top 5 most important AI news', + description="Research the latest developments in AI", + expected_output="A list of recent AI developments", async_execution=True, agent=research_agent, tools=[search_tool] ) research_ops_task = Task( - description='Find and summarize the latest AI Ops news', - expected_output='A bullet list summary of the top 5 most important AI Ops news', + description="Research the latest developments in AI Ops", + expected_output="A list of recent AI Ops developments", async_execution=True, agent=research_agent, tools=[search_tool] @@ -184,7 +496,7 @@ research_ops_task = Task( write_blog_task = Task( description="Write a full blog post about the importance of AI and its latest news", - expected_output='Full blog post that is 4 paragraphs long', + expected_output="Full blog post that is 4 paragraphs long", agent=writer_agent, context=[research_ai_task, research_ops_task] ) @@ -320,4 +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. diff --git a/docs/concepts/tools.mdx b/docs/concepts/tools.mdx index 12ab96c6e..8abe0f4e6 100644 --- a/docs/concepts/tools.mdx +++ b/docs/concepts/tools.mdx @@ -5,13 +5,14 @@ icon: screwdriver-wrench --- ## Introduction -CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. + +CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools. ## What is a Tool? -A tool in CrewAI is a skill or function that agents can utilize to perform various actions. -This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), +A tool in CrewAI is a skill or function that agents can utilize to perform various actions. +This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), enabling everything from simple searches to complex interactions and effective teamwork among agents. ## Key Characteristics of Tools @@ -103,57 +104,53 @@ crew.kickoff() Here is a list of the available tools and their descriptions: -| Tool | Description | -| :-------------------------- | :-------------------------------------------------------------------------------------------- | -| **BrowserbaseLoadTool** | A tool for interacting with and extracting data from web browsers. | -| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. | -| **CodeInterpreterTool** | A tool for interpreting python code. | -| **ComposioTool** | Enables use of Composio tools. | -| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. | -| **DALL-E Tool** | A tool for generating images using the DALL-E API. | -| **DirectorySearchTool** | A RAG tool for searching within directories, useful for navigating through file systems. | -| **DOCXSearchTool** | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files. | -| **DirectoryReadTool** | Facilitates reading and processing of directory structures and their contents. | -| **EXASearchTool** | A tool designed for performing exhaustive searches across various data sources. | -| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. | -| **FirecrawlSearchTool** | A tool to search webpages using Firecrawl and return the results. | -| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl. | -| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents. | -| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search.| -| **SerperDevTool** | A specialized tool for development purposes, with specific functionalities under development. | -| **TXTSearchTool** | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data. | -| **JSONSearchTool** | A RAG tool designed for searching within JSON files, catering to structured data handling. | -| **LlamaIndexTool** | Enables the use of LlamaIndex tools. | -| **MDXSearchTool** | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation. | -| **PDFSearchTool** | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents. | -| **PGSearchTool** | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. | -| **Vision Tool** | A tool for generating images using the DALL-E API. | -| **RagTool** | A general-purpose RAG tool capable of handling various data sources and types. | -| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction. | -| **ScrapeWebsiteTool** | Facilitates scraping entire websites, ideal for comprehensive data collection. | -| **WebsiteSearchTool** | A RAG tool for searching website content, optimized for web data extraction. | -| **XMLSearchTool** | A RAG tool designed for searching within XML files, suitable for structured data formats. | -| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis. | -| **YoutubeVideoSearchTool** | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction. | +| Tool | Description | +| :------------------------------- | :--------------------------------------------------------------------------------------------- | +| **BrowserbaseLoadTool** | A tool for interacting with and extracting data from web browsers. | +| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. | +| **CodeInterpreterTool** | A tool for interpreting python code. | +| **ComposioTool** | Enables use of Composio tools. | +| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. | +| **DALL-E Tool** | A tool for generating images using the DALL-E API. | +| **DirectorySearchTool** | A RAG tool for searching within directories, useful for navigating through file systems. | +| **DOCXSearchTool** | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files. | +| **DirectoryReadTool** | Facilitates reading and processing of directory structures and their contents. | +| **EXASearchTool** | A tool designed for performing exhaustive searches across various data sources. | +| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. | +| **FirecrawlSearchTool** | A tool to search webpages using Firecrawl and return the results. | +| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl. | +| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents. | +| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search. | +| **SerperDevTool** | A specialized tool for development purposes, with specific functionalities under development. | +| **TXTSearchTool** | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data. | +| **JSONSearchTool** | A RAG tool designed for searching within JSON files, catering to structured data handling. | +| **LlamaIndexTool** | Enables the use of LlamaIndex tools. | +| **MDXSearchTool** | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation. | +| **PDFSearchTool** | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents. | +| **PGSearchTool** | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. | +| **Vision Tool** | A tool for generating images using the DALL-E API. | +| **RagTool** | A general-purpose RAG tool capable of handling various data sources and types. | +| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction. | +| **ScrapeWebsiteTool** | Facilitates scraping entire websites, ideal for comprehensive data collection. | +| **WebsiteSearchTool** | A RAG tool for searching website content, optimized for web data extraction. | +| **XMLSearchTool** | A RAG tool designed for searching within XML files, suitable for structured data formats. | +| **YoutubeChannelSearchTool** | A RAG tool for searching within YouTube channels, useful for video content analysis. | +| **YoutubeVideoSearchTool** | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction. | ## Creating your own Tools - Developers can craft `custom tools` tailored for their agent’s needs or utilize pre-built options. + Developers can craft `custom tools` tailored for their agent’s needs or + utilize pre-built options. -To create your own CrewAI tools you will need to install our extra tools package: - -```bash -pip install 'crewai[tools]' -``` - -Once you do that there are two main ways for one to create a CrewAI tool: +There are two main ways for one to create a CrewAI tool: ### Subclassing `BaseTool` ```python Code -from crewai_tools import BaseTool +from crewai.tools import BaseTool + class MyCustomTool(BaseTool): name: str = "Name of my tool" @@ -167,7 +164,7 @@ class MyCustomTool(BaseTool): ### Utilizing the `tool` Decorator ```python Code -from crewai_tools import tool +from crewai.tools import tool @tool("Name of my tool") def my_tool(question: str) -> str: """Clear description for what this tool is useful for, your agent will need this information to use it.""" @@ -175,14 +172,58 @@ def my_tool(question: str) -> str: return "Result from your custom tool" ``` +### Structured Tools + +The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities. + +#### Example: +Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example: + +```python +from crewai.tools.structured_tool import CrewStructuredTool +from pydantic import BaseModel + +# Define the schema for the tool's input using Pydantic +class APICallInput(BaseModel): + endpoint: str + parameters: dict + +# Wrapper function to execute the API call +def tool_wrapper(*args, **kwargs): + # Here, you would typically call the API using the parameters + # For demonstration, we'll return a placeholder string + return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}" + +# Create and return the structured tool +def create_structured_tool(): + return CrewStructuredTool.from_function( + name='Wrapper API', + description="A tool to wrap API calls with structured input.", + args_schema=APICallInput, + func=tool_wrapper, + ) + +# Example usage +structured_tool = create_structured_tool() + +# Execute the tool with structured input +result = structured_tool._run(**{ + "endpoint": "https://example.com/api", + "parameters": {"key1": "value1", "key2": "value2"} +}) +print(result) # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'} +``` + ### Custom Caching Mechanism - Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic. + Tools can optionally implement a `cache_function` to fine-tune caching + behavior. This function determines when to cache results based on specific + conditions, offering granular control over caching logic. ```python Code -from crewai_tools import tool +from crewai.tools import tool @tool def multiplication_tool(first_number: int, second_number: int) -> str: @@ -208,6 +249,6 @@ writer1 = Agent( ## Conclusion -Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. -When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, -caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities. \ No newline at end of file +Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. +When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, +caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities. diff --git a/docs/how-to/agentops-observability.mdx b/docs/how-to/agentops-observability.mdx index ce50d1fc5..b6ade320e 100644 --- a/docs/how-to/agentops-observability.mdx +++ b/docs/how-to/agentops-observability.mdx @@ -57,7 +57,7 @@ This feature is useful for debugging and understanding how agents interact with Install AgentOps with: ```bash - pip install crewai[agentops] + pip install 'crewai[agentops]' ``` or ```bash diff --git a/docs/how-to/before-and-after-kickoff-hooks.mdx b/docs/how-to/before-and-after-kickoff-hooks.mdx new file mode 100644 index 000000000..83058ce3c --- /dev/null +++ b/docs/how-to/before-and-after-kickoff-hooks.mdx @@ -0,0 +1,59 @@ +--- +title: Before and After Kickoff Hooks +description: Learn how to use before and after kickoff hooks in CrewAI +--- + +CrewAI provides hooks that allow you to execute code before and after a crew's kickoff. These hooks are useful for preprocessing inputs or post-processing results. + +## Before Kickoff Hook + +The before kickoff hook is executed before the crew starts its tasks. It receives the input dictionary and can modify it before passing it to the crew. You can use this hook to set up your environment, load necessary data, or preprocess your inputs. This is useful in scenarios where the input data might need enrichment or validation before being processed by the crew. + +Here's an example of defining a before kickoff function in your `crew.py`: + +```python +from crewai import CrewBase, before_kickoff + +@CrewBase +class MyCrew: + @before_kickoff + def prepare_data(self, inputs): + # Preprocess or modify inputs + inputs['processed'] = True + return inputs + +#... +``` + +In this example, the prepare_data function modifies the inputs by adding a new key-value pair indicating that the inputs have been processed. + +## After Kickoff Hook + +The after kickoff hook is executed after the crew has completed its tasks. It receives the result object, which contains the outputs of the crew's execution. This hook is ideal for post-processing results, such as logging, data transformation, or further analysis. + +Here's how you can define an after kickoff function in your `crew.py`: + +```python +from crewai import CrewBase, after_kickoff + +@CrewBase +class MyCrew: + @after_kickoff + def log_results(self, result): + # Log or modify the results + print("Crew execution completed with result:", result) + return result + +# ... +``` + + +In the `log_results` function, the results of the crew execution are simply printed out. You can extend this to perform more complex operations such as sending notifications or integrating with other services. + +## Utilizing Both Hooks + +Both hooks can be used together to provide a comprehensive setup and teardown process for your crew's execution. They are particularly useful in maintaining clean code architecture by separating concerns and enhancing the modularity of your CrewAI implementations. + +## Conclusion + +Before and after kickoff hooks in CrewAI offer powerful ways to interact with the lifecycle of a crew's execution. By understanding and utilizing these hooks, you can greatly enhance the robustness and flexibility of your AI agents. diff --git a/docs/how-to/create-custom-tools.mdx b/docs/how-to/create-custom-tools.mdx index 31fd09e3f..e7ee2e9a9 100644 --- a/docs/how-to/create-custom-tools.mdx +++ b/docs/how-to/create-custom-tools.mdx @@ -6,28 +6,27 @@ icon: hammer ## Creating and Utilizing Tools in CrewAI -This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools, -incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, +This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools, +incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, enabling agents to perform a wide range of actions. -### Prerequisites - -Before creating your own tools, ensure you have the crewAI extra tools package installed: - -```bash -pip install 'crewai[tools]' -``` - ### Subclassing `BaseTool` -To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method. +To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method. ```python Code -from crewai_tools import BaseTool +from typing import Type +from crewai.tools import BaseTool +from pydantic import BaseModel, Field + +class MyToolInput(BaseModel): + """Input schema for MyCustomTool.""" + argument: str = Field(..., description="Description of the argument.") class MyCustomTool(BaseTool): name: str = "Name of my tool" description: str = "What this tool does. It's vital for effective utilization." + args_schema: Type[BaseModel] = MyToolInput def _run(self, argument: str) -> str: # Your tool's logic here @@ -40,7 +39,7 @@ Alternatively, you can use the tool decorator `@tool`. This approach allows you offering a concise and efficient way to create specialized tools tailored to your needs. ```python Code -from crewai_tools import tool +from crewai.tools import tool @tool("Tool Name") def my_simple_tool(question: str) -> str: @@ -66,5 +65,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool: cached_tool.cache_function = my_cache_strategy ``` -By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, +By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents. diff --git a/docs/how-to/llm-connections.mdx b/docs/how-to/llm-connections.mdx index 542a9c110..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). @@ -125,10 +126,10 @@ You can connect to OpenAI-compatible LLMs using either environment variables or - ```python Code - llm = LLM( - model="custom-model-name", - api_key="your-api-key", + ```python Code + llm = LLM( + model="custom-model-name", + api_key="your-api-key", base_url="https://api.your-provider.com/v1" ) agent = Agent(llm=llm, ...) @@ -179,4 +180,4 @@ This is particularly useful when working with OpenAI-compatible APIs or when you ## Conclusion -By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options. \ No newline at end of file +By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options. diff --git a/docs/how-to/openlit-observability.mdx b/docs/how-to/openlit-observability.mdx new file mode 100644 index 000000000..e95989e8e --- /dev/null +++ b/docs/how-to/openlit-observability.mdx @@ -0,0 +1,181 @@ +--- +title: Agent Monitoring with OpenLIT +description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry. +icon: magnifying-glass-chart +--- + +# OpenLIT Overview + +[OpenLIT](https://github.com/openlit/openlit?src=crewai-docs) is an open-source tool that makes it simple to monitor the performance of AI agents, LLMs, VectorDBs, and GPUs with just **one** line of code. + +It provides OpenTelemetry-native tracing and metrics to track important parameters like cost, latency, interactions and task sequences. +This setup enables you to track hyperparameters and monitor for performance issues, helping you find ways to enhance and fine-tune your agents over time. + + + Overview Agent usage including cost and tokens + Overview of agent otel traces and metrics + Overview of agent traces in details + + +### Features + +- **Analytics Dashboard**: Monitor your Agents health and performance with detailed dashboards that track metrics, costs, and user interactions. +- **OpenTelemetry-native Observability SDK**: Vendor-neutral SDKs to send traces and metrics to your existing observability tools like Grafana, DataDog and more. +- **Cost Tracking for Custom and Fine-Tuned Models**: Tailor cost estimations for specific models using custom pricing files for precise budgeting. +- **Exceptions Monitoring Dashboard**: Quickly spot and resolve issues by tracking common exceptions and errors with a monitoring dashboard. +- **Compliance and Security**: Detect potential threats such as profanity and PII leaks. +- **Prompt Injection Detection**: Identify potential code injection and secret leaks. +- **API Keys and Secrets Management**: Securely handle your LLM API keys and secrets centrally, avoiding insecure practices. +- **Prompt Management**: Manage and version Agent prompts using PromptHub for consistent and easy access across Agents. +- **Model Playground** Test and compare different models for your CrewAI agents before deployment. + +## Setup Instructions + + + + + + ```shell + git clone git@github.com:openlit/openlit.git + ``` + + + From the root directory of the [OpenLIT Repo](https://github.com/openlit/openlit), Run the below command: + ```shell + docker compose up -d + ``` + + + + + ```shell + pip install openlit + ``` + + + Add the following two lines to your application code: + + + ```python + import openlit + openlit.init(otlp_endpoint="http://127.0.0.1:4318") + ``` + + Example Usage for monitoring a CrewAI Agent: + + ```python + from crewai import Agent, Task, Crew, Process + import openlit + + openlit.init(disable_metrics=True) + # Define your agents + researcher = Agent( + role="Researcher", + goal="Conduct thorough research and analysis on AI and AI agents", + backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.", + allow_delegation=False, + llm='command-r' + ) + + + # Define your task + task = Task( + description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.", + expected_output="5 bullet points, each with a paragraph and accompanying notes.", + ) + + # Define the manager agent + manager = Agent( + role="Project Manager", + goal="Efficiently manage the crew and ensure high-quality task completion", + backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.", + allow_delegation=True, + llm='command-r' + ) + + # Instantiate your crew with a custom manager + crew = Crew( + agents=[researcher], + tasks=[task], + manager_agent=manager, + process=Process.hierarchical, + ) + + # Start the crew's work + result = crew.kickoff() + + print(result) + ``` + + + + Add the following two lines to your application code: + ```python + import openlit + + openlit.init() + ``` + + Run the following command to configure the OTEL export endpoint: + ```shell + export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318" + ``` + + Example Usage for monitoring a CrewAI Async Agent: + + ```python + import asyncio + from crewai import Crew, Agent, Task + import openlit + + openlit.init(otlp_endpoint="http://127.0.0.1:4318") + + # Create an agent with code execution enabled + coding_agent = Agent( + role="Python Data Analyst", + goal="Analyze data and provide insights using Python", + backstory="You are an experienced data analyst with strong Python skills.", + allow_code_execution=True, + llm="command-r" + ) + + # Create a task that requires code execution + data_analysis_task = Task( + description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}", + agent=coding_agent, + expected_output="5 bullet points, each with a paragraph and accompanying notes.", + ) + + # Create a crew and add the task + analysis_crew = Crew( + agents=[coding_agent], + tasks=[data_analysis_task] + ) + + # Async function to kickoff the crew asynchronously + async def async_crew_execution(): + result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]}) + print("Crew Result:", result) + + # Run the async function + asyncio.run(async_crew_execution()) + ``` + + + Refer to OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases. + + + With the Agent Observability data now being collected and sent to OpenLIT, the next step is to visualize and analyze this data to get insights into your Agent's performance, behavior, and identify areas of improvement. + + Just head over to OpenLIT at `127.0.0.1:3000` on your browser to start exploring. You can login using the default credentials + - **Email**: `user@openlit.io` + - **Password**: `openlituser` + + + Overview Agent usage including cost and tokens + Overview of agent otel traces and metrics + + + + + diff --git a/docs/images/openlit1.png b/docs/images/openlit1.png new file mode 100644 index 000000000..4fab0340d Binary files /dev/null and b/docs/images/openlit1.png differ diff --git a/docs/images/openlit2.png b/docs/images/openlit2.png new file mode 100644 index 000000000..d36352847 Binary files /dev/null and b/docs/images/openlit2.png differ diff --git a/docs/images/openlit3.png b/docs/images/openlit3.png new file mode 100644 index 000000000..4a6636a11 Binary files /dev/null and b/docs/images/openlit3.png differ diff --git a/docs/installation.mdx b/docs/installation.mdx index ec3da38b7..ca330e9ca 100644 --- a/docs/installation.mdx +++ b/docs/installation.mdx @@ -1,128 +1,145 @@ --- title: Installation -description: +description: Get started with CrewAI - Install, configure, and build your first AI crew icon: wrench --- -This guide will walk you through the installation process for CrewAI and its dependencies. -CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. -Let's get started! 🚀 + + **Python Version Requirements** + + CrewAI requires `Python >=3.10 and <=3.12`. Here's how to check your version: + ```bash + python3 --version + ``` + + If you need to update Python, visit [python.org/downloads](https://python.org/downloads) + - - Make sure you have `Python >=3.10 <=3.13` installed on your system before you proceed. - +# Installing CrewAI + +CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. +Let's get you set up! 🚀 - Install the main CrewAI package with the following command: - - ```shell Terminal - pip install crewai - ``` - - You can also install the main CrewAI package and the tools package that include a series of helpful tools for your agents: - - ```shell Terminal - pip install 'crewai[tools]' - ``` - - Alternatively, you can also use: - - ```shell Terminal - pip install crewai crewai-tools - ``` - - - - To upgrade CrewAI and CrewAI Tools to the latest version, run the following command - - ```shell Terminal - pip install --upgrade crewai crewai-tools - ``` - - - 1. If you're using an older version of CrewAI, you may receive a warning about using `Poetry` for dependency management. - ![Error from older versions](./images/crewai-run-poetry-error.png) - - 2. In this case, you'll need to run the command below to update your project. - This command will migrate your project to use [UV](https://github.com/astral-sh/uv) and update the necessary files. + Install CrewAI with all recommended tools using either method: ```shell Terminal - crewai update + pip install 'crewai[tools]' + ``` + or + ```shell Terminal + pip install crewai crewai-tools ``` - 3. After running the command above, you should see the following output: - ![Successfully migrated to UV](./images/crewai-update.png) - 4. You're all set! You can now proceed to the next step! 🎉 - + + Both methods install the core package and additional tools needed for most use cases. + - - To verify that `crewai` and `crewai-tools` are installed correctly, run the following command - - ```shell Terminal - pip freeze | grep crewai - ``` - - You should see the version number of `crewai` and `crewai-tools`. - - ```markdown Version - crewai==X.X.X - crewai-tools==X.X.X - ``` - - If you see the version number, then the installation was successful! 🎉 + + + If you have an older version of CrewAI installed, you can upgrade it: + ```shell Terminal + pip install --upgrade crewai crewai-tools + ``` + + + If you see a Poetry-related warning, you'll need to migrate to our new dependency manager: + ```shell Terminal + crewai update + ``` + This will update your project to use [UV](https://github.com/astral-sh/uv), our new faster dependency manager. + + + + Skip this step if you're doing a fresh installation. + + + + + Check your installed versions: + ```shell Terminal + pip freeze | grep crewai + ``` + + You should see something like: + ```markdown Output + crewai==X.X.X + crewai-tools==X.X.X + ``` + Installation successful! You're ready to create your first crew. -## Create a new CrewAI project +# Creating a New Project -The next step is to create a new CrewAI project. -We recommend using the YAML Template scaffolding to get started as it provides a structured approach to defining agents and tasks. + + We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks. + - - To create a new CrewAI project, run the following CLI (Command Line Interface) command: - - ```shell Terminal - crewai create crew - ``` - - This command creates a new project folder with the following structure: - | File/Directory | Description | - |:------------------------|:-------------------------------------------------| - | `my_project/` | Root directory of the project | - | ├── `.gitignore` | Specifies files and directories to ignore in Git | - | ├── `pyproject.toml` | Project configuration and dependencies | - | ├── `README.md` | Project documentation | - | ├── `.env` | Environment variables | - | └── `src/` | Source code directory | - |     └── `my_project/` | Main application package | - |         ├── `__init__.py` | Marks the directory as a Python package | - |         ├── `main.py` | Main application script | - |         ├── `crew.py` | Crew-related functionalities | - |         ├── `tools/` | Custom tools directory | - |         │ ├── `custom_tool.py` | Custom tool implementation | - |         │ └── `__init__.py` | Marks tools directory as a package | - |         └── `config/` | Configuration files directory | - |             ├── `agents.yaml` | Agent configurations | - |             └── `tasks.yaml` | Task configurations | + + Run the CrewAI CLI command: + ```shell Terminal + crewai create crew + ``` - You can now start developing your crew by editing the files in the `src/my_project` folder. - The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, - and the `tasks.yaml` file is where you define your tasks. + This creates a new project with the following structure: + + ``` + my_project/ + ├── .gitignore + ├── pyproject.toml + ├── README.md + ├── .env + └── src/ + └── my_project/ + ├── __init__.py + ├── main.py + ├── crew.py + ├── tools/ + │ ├── custom_tool.py + │ └── __init__.py + └── config/ + ├── agents.yaml + └── tasks.yaml + ``` + - - To customize your project, you can: - - Modify `src/my_project/config/agents.yaml` to define your agents. - - Modify `src/my_project/config/tasks.yaml` to define your tasks. - - Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments. - - Modify `src/my_project/main.py` to add custom inputs for your agents and tasks. - - Add your environment variables into the `.env` file. + + + Your project will contain these essential files: + + | File | Purpose | + | --- | --- | + | `agents.yaml` | Define your AI agents and their roles | + | `tasks.yaml` | Set up agent tasks and workflows | + | `.env` | Store API keys and environment variables | + | `main.py` | Project entry point and execution flow | + | `crew.py` | Crew orchestration and coordination | + | `tools/` | Directory for custom agent tools | + + + Start by editing `agents.yaml` and `tasks.yaml` to define your crew's behavior. + Keep sensitive information like API keys in `.env`. + -## Next steps +## Next Steps -Now that you have installed `crewai` and `crewai-tools`, you're ready to spin up your first crew! - -- 👨‍💻 Build your first agent with CrewAI by following the [Quickstart](/quickstart) guide. -- 💬 Join the [Community](https://community.crewai.com) to get help and share your feedback. + + + Follow our quickstart guide to create your first CrewAI agent and get hands-on experience. + + + Connect with other developers, get help, and share your CrewAI experiences. + + diff --git a/docs/introduction.mdx b/docs/introduction.mdx index d657c9fb2..9aed6cdea 100644 --- a/docs/introduction.mdx +++ b/docs/introduction.mdx @@ -1,49 +1,85 @@ --- title: Introduction -description: Welcome to CrewAI docs! +description: Build AI agent teams that work together to tackle complex tasks icon: handshake --- # What is CrewAI? -**CrewAI is a cutting-edge Python framework for orchestrating role-playing, autonomous AI agents.** +**CrewAI is a cutting-edge framework for orchestrating autonomous AI agents.** -By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. +CrewAI enables you to create AI teams where each agent has specific roles, tools, and goals, working together to accomplish complex tasks. - - CrewAI Mindmap - +Think of it as assembling your dream team - each member (agent) brings unique skills and expertise, collaborating seamlessly to achieve your objectives. -## Why CrewAI? -- 🤼‍♀️ **Role-Playing Agents**: Agents can take on different roles and personas to better understand and interact with complex systems. -- 🤖 **Autonomous Decision Making**: Agents can make decisions autonomously based on the given context and available tools. -- 🤝 **Seamless Collaboration**: Agents can work together seamlessly, sharing information and resources to achieve common goals. -- 🧠 **Complex Task Tackling**: CrewAI is designed to tackle complex tasks, such as multi-step workflows, decision making, and problem solving. +## How CrewAI Works -# Get Started with CrewAI + + Just like a company has departments (Sales, Engineering, Marketing) working together under leadership to achieve business goals, CrewAI helps you create an organization of AI agents with specialized roles collaborating to accomplish complex tasks. + + + + CrewAI Framework Overview + + +| Component | Description | Key Features | +|:----------|:-----------:|:------------| +| **Crew** | The top-level organization | • Manages AI agent teams
• Oversees workflows
• Ensures collaboration
• Delivers outcomes | +| **AI Agents** | Specialized team members | • Have specific roles (researcher, writer)
• Use designated tools
• Can delegate tasks
• Make autonomous decisions | +| **Process** | Workflow management system | • Defines collaboration patterns
• Controls task assignments
• Manages interactions
• Ensures efficient execution | +| **Tasks** | Individual assignments | • Have clear objectives
• Use specific tools
• Feed into larger process
• Produce actionable results | + +### How It All Works Together + +1. The **Crew** organizes the overall operation +2. **AI Agents** work on their specialized tasks +3. The **Process** ensures smooth collaboration +4. **Tasks** get completed to achieve the goal + +## Key Features + + Create specialized agents with defined roles, expertise, and goals - from researchers to analysts to writers + + + Equip agents with custom tools and APIs to interact with external services and data sources + + + Agents work together, sharing insights and coordinating tasks to achieve complex objectives + + + Define sequential or parallel workflows, with agents automatically handling task dependencies + + + +## Why Choose CrewAI? + +- 🧠 **Autonomous Operation**: Agents make intelligent decisions based on their roles and available tools +- 📝 **Natural Interaction**: Agents communicate and collaborate like human team members +- 🛠️ **Extensible Design**: Easy to add new tools, roles, and capabilities +- 🚀 **Production Ready**: Built for reliability and scalability in real-world applications + + - Getting started with CrewAI + Get started with CrewAI in your development environment. + + + Follow our quickstart guide to create your first CrewAI agent and get hands-on experience. - Join the CrewAI community and get help with your project! - - - -## Next Step - -- [Install CrewAI](/installation) to get started with your first agent. - + > + Connect with other developers, get help, and share your CrewAI experiences. + + \ No newline at end of file diff --git a/docs/mint.json b/docs/mint.json index 3ea9f5baf..fad9689b8 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -68,6 +68,7 @@ "concepts/tasks", "concepts/crews", "concepts/flows", + "concepts/knowledge", "concepts/llms", "concepts/processes", "concepts/collaboration", @@ -98,7 +99,8 @@ "how-to/replay-tasks-from-latest-crew-kickoff", "how-to/conditional-tasks", "how-to/agentops-observability", - "how-to/langtrace-observability" + "how-to/langtrace-observability", + "how-to/openlit-observability" ] }, { diff --git a/docs/quickstart.mdx b/docs/quickstart.mdx index 49a690093..9ec3170e1 100644 --- a/docs/quickstart.mdx +++ b/docs/quickstart.mdx @@ -8,7 +8,7 @@ icon: rocket Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject. -Before we proceed, make sure you have `crewai` and `crewai-tools` installed. +Before we proceed, make sure you have `crewai` and `crewai-tools` installed. If you haven't installed them yet, you can do so by following the [installation guide](/installation). Follow the steps below to get crewing! 🚣‍♂️ @@ -23,7 +23,7 @@ Follow the steps below to get crewing! 🚣‍♂️ ``` - + You can also modify the agents as needed to fit your use case or copy and paste as is to your project. Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{topic}` will be replaced by the value of the variable in the `main.py` file. @@ -39,7 +39,7 @@ Follow the steps below to get crewing! 🚣‍♂️ 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 @@ -51,7 +51,7 @@ Follow the steps below to get crewing! 🚣‍♂️ it easy for others to understand and act on the information you provide. ``` - + ```yaml tasks.yaml # src/latest_ai_development/config/tasks.yaml research_task: @@ -73,8 +73,8 @@ Follow the steps below to get crewing! 🚣‍♂️ agent: reporting_analyst output_file: report.md ``` - - + + ```python crew.py # src/latest_ai_development/crew.py from crewai import Agent, Crew, Process, Task @@ -121,10 +121,34 @@ Follow the steps below to get crewing! 🚣‍♂️ tasks=self.tasks, # Automatically created by the @task decorator process=Process.sequential, verbose=True, - ) + ) ``` - + + ```python crew.py + # src/latest_ai_development/crew.py + from crewai import Agent, Crew, Process, Task + from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff + from crewai_tools import SerperDevTool + + @CrewBase + class LatestAiDevelopmentCrew(): + """LatestAiDevelopment crew""" + + @before_kickoff + def before_kickoff_function(self, inputs): + print(f"Before kickoff function with inputs: {inputs}") + return inputs # You can return the inputs or modify them as needed + + @after_kickoff + def after_kickoff_function(self, result): + print(f"After kickoff function with result: {result}") + return result # You can return the result or modify it as needed + + # ... remaining code + ``` + + For example, you can pass the `topic` input to your crew to customize the research and reporting. ```python main.py #!/usr/bin/env python @@ -237,14 +261,14 @@ Follow the steps below to get crewing! 🚣‍♂️ ### Note on Consistency in Naming The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code. -For example, you can reference the agent for specific tasks from `tasks.yaml` file. +For example, you can reference the agent for specific tasks from `tasks.yaml` file. This naming consistency allows CrewAI to automatically link your configurations with your code; otherwise, your task won't recognize the reference properly. #### Example References Note how we use the same name for the agent in the `agents.yaml` (`email_summarizer`) file as the method name in the `crew.py` (`email_summarizer`) file. - + ```yaml agents.yaml email_summarizer: @@ -281,6 +305,8 @@ Use the annotations to properly reference the agent and task in the `crew.py` fi * `@task` * `@crew` * `@tool` +* `@before_kickoff` +* `@after_kickoff` * `@callback` * `@output_json` * `@output_pydantic` @@ -304,7 +330,7 @@ def email_summarizer_task(self) -> Task: In addition to the [sequential process](../how-to/sequential-process), you can use the [hierarchical process](../how-to/hierarchical-process), -which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. +which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. You can learn more about the core concepts [here](/concepts). @@ -323,11 +349,28 @@ Replace `` with the ID of the task you want to replay. If you need to reset the memory of your crew before running it again, you can do so by calling the reset memory feature: ```shell -crewai reset-memory +crewai reset-memories --all ``` This will clear the crew's memory, allowing for a fresh start. ## Deploying Your Project -The easiest way to deploy your crew is through [CrewAI Enterprise](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks. +The easiest way to deploy your crew is through CrewAI Enterprise, where you can deploy your crew in a few clicks. + + + + Get started with CrewAI Enterprise and deploy your crew in a production environment with just a few clicks. + + + Join our open source community to discuss ideas, share your projects, and connect with other CrewAI developers. + + diff --git a/docs/tools/githubsearchtool.mdx b/docs/tools/githubsearchtool.mdx index 6fd0e686a..80b363a3c 100644 --- a/docs/tools/githubsearchtool.mdx +++ b/docs/tools/githubsearchtool.mdx @@ -34,6 +34,7 @@ from crewai_tools import GithubSearchTool # Initialize the tool for semantic searches within a specific GitHub repository tool = GithubSearchTool( github_repo='https://github.com/example/repo', + gh_token='your_github_personal_access_token', content_types=['code', 'issue'] # Options: code, repo, pr, issue ) @@ -41,6 +42,7 @@ tool = GithubSearchTool( # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution tool = GithubSearchTool( + gh_token='your_github_personal_access_token', content_types=['code', 'issue'] # Options: code, repo, pr, issue ) ``` @@ -48,6 +50,7 @@ tool = GithubSearchTool( ## Arguments - `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search. +- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens. - `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. This field is mandatory and allows tailoring the search to specific content types within the GitHub repository. @@ -77,5 +80,4 @@ tool = GithubSearchTool( ), ), ) -) -``` \ No newline at end of file +) \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 1b6c1f959..948744de2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -129,7 +129,6 @@ nav: - Processes: 'core-concepts/Processes.md' - Crews: 'core-concepts/Crews.md' - Collaboration: 'core-concepts/Collaboration.md' - - Pipeline: 'core-concepts/Pipeline.md' - Training: 'core-concepts/Training-Crew.md' - Memory: 'core-concepts/Memory.md' - Planning: 'core-concepts/Planning.md' @@ -152,6 +151,7 @@ nav: - Conditional Tasks: 'how-to/Conditional-Tasks.md' - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md' - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md' + - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md' - Tools Docs: - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md' - 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By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." readme = "README.md" -requires-python = ">=3.10,<=3.13" +requires-python = ">=3.10,<=3.12" authors = [ { name = "Joao Moura", email = "joao@crewai.com" } ] dependencies = [ "pydantic>=2.4.2", - "langchain>=0.2.16", "openai>=1.13.3", "opentelemetry-api>=1.22.0", "opentelemetry-sdk>=1.22.0", "opentelemetry-exporter-otlp-proto-http>=1.22.0", "instructor>=1.3.3", "regex>=2024.9.11", - "crewai-tools>=0.13.1", "click>=8.1.7", "python-dotenv>=1.0.0", "appdirs>=1.4.4", @@ -25,9 +23,13 @@ dependencies = [ "auth0-python>=4.7.1", "litellm>=1.44.22", "pyvis>=0.3.2", - "uv>=0.4.18", + "uv>=0.4.25", "tomli-w>=1.1.0", - "chromadb>=0.4.24", + "tomli>=2.0.2", + "chromadb>=0.5.18", + "pdfplumber>=0.11.4", + "openpyxl>=3.1.5", + "blinker>=1.9.0", ] [project.urls] @@ -36,12 +38,23 @@ Documentation = "https://docs.crewai.com" Repository = "https://github.com/crewAIInc/crewAI" [project.optional-dependencies] -tools = ["crewai-tools>=0.12.1"] +tools = ["crewai-tools>=0.14.0"] agentops = ["agentops>=0.3.0"] +fastembed = ["fastembed>=0.4.1"] +pdfplumber = [ + "pdfplumber>=0.11.4", +] +pandas = [ + "pandas>=2.2.3", +] +openpyxl = [ + "openpyxl>=3.1.5", +] +mem0 = ["mem0ai>=0.1.29"] [tool.uv] dev-dependencies = [ - "ruff>=0.4.10", + "ruff>=0.8.2", "mypy>=1.10.0", "pre-commit>=3.6.0", "mkdocs>=1.4.3", @@ -51,7 +64,7 @@ dev-dependencies = [ "mkdocs-material-extensions>=1.3.1", "pillow>=10.2.0", "cairosvg>=2.7.1", - "crewai-tools>=0.12.1", + "crewai-tools>=0.14.0", "pytest>=8.0.0", "pytest-vcr>=1.0.2", "python-dotenv>=1.0.0", diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py index a72d6e805..0833afd58 100644 --- a/src/crewai/__init__.py +++ b/src/crewai/__init__.py @@ -1,11 +1,11 @@ import warnings + from crewai.agent import Agent from crewai.crew import Crew from crewai.flow.flow import Flow +from crewai.knowledge.knowledge import Knowledge from crewai.llm import LLM -from crewai.pipeline import Pipeline from crewai.process import Process -from crewai.routers import Router from crewai.task import Task warnings.filterwarnings( @@ -14,5 +14,13 @@ warnings.filterwarnings( category=UserWarning, module="pydantic.main", ) -__version__ = "0.74.0" -__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"] +__version__ = "0.86.0" +__all__ = [ + "Agent", + "Crew", + "Process", + "Task", + "LLM", + "Flow", + "Knowledge", +] diff --git a/src/crewai/agent.py b/src/crewai/agent.py index 3f81ece21..8c79c6eb8 100644 --- a/src/crewai/agent.py +++ b/src/crewai/agent.py @@ -1,17 +1,25 @@ import os -from inspect import signature -from typing import Any, List, Optional, Union +import shutil +import subprocess +from typing import Any, Dict, List, Literal, Optional, Union from pydantic import Field, InstanceOf, PrivateAttr, model_validator from crewai.agents import CacheHandler from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.crew_agent_executor import CrewAgentExecutor +from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS +from crewai.knowledge.knowledge import Knowledge +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context from crewai.llm import LLM from crewai.memory.contextual.contextual_memory import ContextualMemory -from crewai.tools.agent_tools import AgentTools +from crewai.task import Task +from crewai.tools import BaseTool +from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.utilities import Converter, Prompts from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE +from crewai.utilities.converter import generate_model_description from crewai.utilities.token_counter_callback import TokenCalcHandler from crewai.utilities.training_handler import CrewTrainingHandler @@ -49,6 +57,7 @@ class Agent(BaseAgent): role: The role of the agent. goal: The objective of the agent. backstory: The backstory of the agent. + knowledge: The knowledge base of the agent. config: Dict representation of agent configuration. llm: The language model that will run the agent. function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm. @@ -59,6 +68,7 @@ class Agent(BaseAgent): allow_delegation: Whether the agent is allowed to delegate tasks to other agents. tools: Tools at agents disposal step_callback: Callback to be executed after each step of the agent execution. + knowledge_sources: Knowledge sources for the agent. """ _times_executed: int = PrivateAttr(default=0) @@ -112,10 +122,31 @@ class Agent(BaseAgent): default=2, description="Maximum number of retries for an agent to execute a task when an error occurs.", ) + code_execution_mode: Literal["safe", "unsafe"] = Field( + default="safe", + description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).", + ) + embedder_config: Optional[Dict[str, Any]] = Field( + default=None, + description="Embedder configuration for the agent.", + ) + knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field( + default=None, + description="Knowledge sources for the agent.", + ) + _knowledge: Optional[Knowledge] = PrivateAttr( + default=None, + ) @model_validator(mode="after") def post_init_setup(self): + self._set_knowledge() self.agent_ops_agent_name = self.role + unaccepted_attributes = [ + "AWS_ACCESS_KEY_ID", + "AWS_SECRET_ACCESS_KEY", + "AWS_REGION_NAME", + ] # Handle different cases for self.llm if isinstance(self.llm, str): @@ -125,8 +156,12 @@ class Agent(BaseAgent): # If it's already an LLM instance, keep it as is pass elif self.llm is None: - # If it's None, use environment variables or default - model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini") + # Determine the model name from environment variables or use default + model_name = ( + os.environ.get("OPENAI_MODEL_NAME") + or os.environ.get("MODEL") + or "gpt-4o-mini" + ) llm_params = {"model": model_name} api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get( @@ -135,9 +170,30 @@ class Agent(BaseAgent): if api_base: llm_params["base_url"] = api_base - api_key = os.environ.get("OPENAI_API_KEY") - if api_key: - llm_params["api_key"] = api_key + set_provider = model_name.split("/")[0] if "/" in model_name else "openai" + + # Iterate over all environment variables to find matching API keys or use defaults + for provider, env_vars in ENV_VARS.items(): + if provider == set_provider: + for env_var in env_vars: + # Check if the environment variable is set + key_name = env_var.get("key_name") + if key_name and key_name not in unaccepted_attributes: + env_value = os.environ.get(key_name) + if env_value: + key_name = key_name.lower() + for pattern in LITELLM_PARAMS: + if pattern in key_name: + key_name = pattern + break + llm_params[key_name] = env_value + # Check for default values if the environment variable is not set + elif env_var.get("default", False): + for key, value in env_var.items(): + if key not in ["prompt", "key_name", "default"]: + # Only add default if the key is already set in os.environ + if key in os.environ: + llm_params[key] = value self.llm = LLM(**llm_params) else: @@ -173,6 +229,9 @@ class Agent(BaseAgent): if not self.agent_executor: self._setup_agent_executor() + if self.allow_code_execution: + self._validate_docker_installation() + return self def _setup_agent_executor(self): @@ -180,11 +239,26 @@ class Agent(BaseAgent): self.cache_handler = CacheHandler() self.set_cache_handler(self.cache_handler) + def _set_knowledge(self): + try: + if self.knowledge_sources: + knowledge_agent_name = f"{self.role.replace(' ', '_')}" + if isinstance(self.knowledge_sources, list) and all( + isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources + ): + self._knowledge = Knowledge( + sources=self.knowledge_sources, + embedder_config=self.embedder_config, + collection_name=knowledge_agent_name, + ) + except (TypeError, ValueError) as e: + raise ValueError(f"Invalid Knowledge Configuration: {str(e)}") + def execute_task( self, - task: Any, + task: Task, context: Optional[str] = None, - tools: Optional[List[Any]] = None, + tools: Optional[List[BaseTool]] = None, ) -> str: """Execute a task with the agent. @@ -201,6 +275,22 @@ class Agent(BaseAgent): task_prompt = task.prompt() + # If the task requires output in JSON or Pydantic format, + # append specific instructions to the task prompt to ensure + # that the final answer does not include any code block markers + if task.output_json or task.output_pydantic: + # Generate the schema based on the output format + if task.output_json: + # schema = json.dumps(task.output_json, indent=2) + schema = generate_model_description(task.output_json) + + elif task.output_pydantic: + schema = generate_model_description(task.output_pydantic) + + task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format( + output_format=schema + ) + if context: task_prompt = self.i18n.slice("task_with_context").format( task=task_prompt, context=context @@ -208,14 +298,32 @@ class Agent(BaseAgent): if self.crew and self.crew.memory: contextual_memory = ContextualMemory( + self.crew.memory_config, self.crew._short_term_memory, self.crew._long_term_memory, self.crew._entity_memory, + self.crew._user_memory, ) memory = contextual_memory.build_context_for_task(task, context) if memory.strip() != "": task_prompt += self.i18n.slice("memory").format(memory=memory) + if self._knowledge: + agent_knowledge_snippets = self._knowledge.query([task.prompt()]) + if agent_knowledge_snippets: + agent_knowledge_context = extract_knowledge_context( + agent_knowledge_snippets + ) + if agent_knowledge_context: + task_prompt += agent_knowledge_context + + if self.crew: + knowledge_snippets = self.crew.query_knowledge([task.prompt()]) + if knowledge_snippets: + crew_knowledge_context = extract_knowledge_context(knowledge_snippets) + if crew_knowledge_context: + task_prompt += crew_knowledge_context + tools = tools or self.tools or [] self.create_agent_executor(tools=tools, task=task) @@ -251,7 +359,9 @@ class Agent(BaseAgent): return result - def create_agent_executor(self, tools=None, task=None) -> None: + def create_agent_executor( + self, tools: Optional[List[BaseTool]] = None, task=None + ) -> None: """Create an agent executor for the agent. Returns: @@ -308,7 +418,9 @@ class Agent(BaseAgent): try: from crewai_tools import CodeInterpreterTool - return [CodeInterpreterTool()] + # Set the unsafe_mode based on the code_execution_mode attribute + unsafe_mode = self.code_execution_mode == "unsafe" + return [CodeInterpreterTool(unsafe_mode=unsafe_mode)] except ModuleNotFoundError: self._logger.log( "info", "Coding tools not available. Install crewai_tools. " @@ -322,11 +434,11 @@ class Agent(BaseAgent): tools_list = [] try: # tentatively try to import from crewai_tools import BaseTool as CrewAITool - from crewai_tools import BaseTool as CrewAITool + from crewai.tools import BaseTool as CrewAITool for tool in tools: if isinstance(tool, CrewAITool): - tools_list.append(tool.to_langchain()) + tools_list.append(tool.to_structured_tool()) else: tools_list.append(tool) except ModuleNotFoundError: @@ -381,33 +493,42 @@ class Agent(BaseAgent): return description - def _render_text_description_and_args(self, tools: List[Any]) -> str: + def _render_text_description_and_args(self, tools: List[BaseTool]) -> str: """Render the tool name, description, and args in plain text. - Output will be in the format of: + Output will be in the format of: - .. code-block:: markdown + .. code-block:: markdown search: This tool is used for search, args: {"query": {"type": "string"}} calculator: This tool is used for math, \ - args: {"expression": {"type": "string"}} + args: {"expression": {"type": "string"}} """ tool_strings = [] for tool in tools: - args_schema = str(tool.args) - if hasattr(tool, "func") and tool.func: - sig = signature(tool.func) - description = ( - f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}" - ) - else: - description = ( - f"Tool Name: {tool.name}\nTool Description: {tool.description}" - ) - tool_strings.append(f"{description}\nTool Arguments: {args_schema}") + tool_strings.append(tool.description) return "\n".join(tool_strings) + def _validate_docker_installation(self) -> None: + """Check if Docker is installed and running.""" + if not shutil.which("docker"): + raise RuntimeError( + f"Docker is not installed. Please install Docker to use code execution with agent: {self.role}" + ) + + try: + subprocess.run( + ["docker", "info"], + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + except subprocess.CalledProcessError: + raise RuntimeError( + f"Docker is not running. Please start Docker to use code execution with agent: {self.role}" + ) + @staticmethod def __tools_names(tools) -> str: return ", ".join([t.name for t in tools]) diff --git a/src/crewai/agents/agent_builder/base_agent.py b/src/crewai/agents/agent_builder/base_agent.py index f42ab3172..207a1769a 100644 --- a/src/crewai/agents/agent_builder/base_agent.py +++ b/src/crewai/agents/agent_builder/base_agent.py @@ -18,6 +18,8 @@ from pydantic_core import PydanticCustomError from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess from crewai.agents.cache.cache_handler import CacheHandler from crewai.agents.tools_handler import ToolsHandler +from crewai.tools import BaseTool +from crewai.tools.base_tool import Tool from crewai.utilities import I18N, Logger, RPMController from crewai.utilities.config import process_config @@ -49,11 +51,11 @@ class BaseAgent(ABC, BaseModel): Methods: - execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None) -> str: + execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str: Abstract method to execute a task. create_agent_executor(tools=None) -> None: Abstract method to create an agent executor. - _parse_tools(tools: List[Any]) -> List[Any]: + _parse_tools(tools: List[BaseTool]) -> List[Any]: Abstract method to parse tools. get_delegation_tools(agents: List["BaseAgent"]): Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew. @@ -134,6 +136,35 @@ class BaseAgent(ABC, BaseModel): def process_model_config(cls, values): return process_config(values, cls) + @field_validator("tools") + @classmethod + def validate_tools(cls, tools: List[Any]) -> List[BaseTool]: + """Validate and process the tools provided to the agent. + + This method ensures that each tool is either an instance of BaseTool + or an object with 'name', 'func', and 'description' attributes. If the + tool meets these criteria, it is processed and added to the list of + tools. Otherwise, a ValueError is raised. + """ + processed_tools = [] + for tool in tools: + if isinstance(tool, BaseTool): + processed_tools.append(tool) + elif ( + hasattr(tool, "name") + and hasattr(tool, "func") + and hasattr(tool, "description") + ): + # Tool has the required attributes, create a Tool instance + processed_tools.append(Tool.from_langchain(tool)) + else: + raise ValueError( + f"Invalid tool type: {type(tool)}. " + "Tool must be an instance of BaseTool or " + "an object with 'name', 'func', and 'description' attributes." + ) + return processed_tools + @model_validator(mode="after") def validate_and_set_attributes(self): # Validate required fields @@ -188,7 +219,7 @@ class BaseAgent(ABC, BaseModel): self, task: Any, context: Optional[str] = None, - tools: Optional[List[Any]] = None, + tools: Optional[List[BaseTool]] = None, ) -> str: pass @@ -197,11 +228,11 @@ class BaseAgent(ABC, BaseModel): pass @abstractmethod - def _parse_tools(self, tools: List[Any]) -> List[Any]: + def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]: pass @abstractmethod - def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]: + def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]: """Set the task tools that init BaseAgenTools class.""" pass 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/agent_builder/utilities/base_token_process.py b/src/crewai/agents/agent_builder/utilities/base_token_process.py index e971d018e..320d34caa 100644 --- a/src/crewai/agents/agent_builder/utilities/base_token_process.py +++ b/src/crewai/agents/agent_builder/utilities/base_token_process.py @@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics class TokenProcess: total_tokens: int = 0 prompt_tokens: int = 0 + cached_prompt_tokens: int = 0 completion_tokens: int = 0 successful_requests: int = 0 @@ -15,6 +16,9 @@ class TokenProcess: self.completion_tokens = self.completion_tokens + tokens self.total_tokens = self.total_tokens + tokens + def sum_cached_prompt_tokens(self, tokens: int): + self.cached_prompt_tokens = self.cached_prompt_tokens + tokens + def sum_successful_requests(self, requests: int): self.successful_requests = self.successful_requests + requests @@ -22,6 +26,7 @@ class TokenProcess: return UsageMetrics( total_tokens=self.total_tokens, prompt_tokens=self.prompt_tokens, + cached_prompt_tokens=self.cached_prompt_tokens, completion_tokens=self.completion_tokens, successful_requests=self.successful_requests, ) diff --git a/src/crewai/agents/crew_agent_executor.py b/src/crewai/agents/crew_agent_executor.py index b901fe132..eb0ff7c5a 100644 --- a/src/crewai/agents/crew_agent_executor.py +++ b/src/crewai/agents/crew_agent_executor.py @@ -1,7 +1,9 @@ import json import re +from dataclasses import dataclass from typing import Any, Dict, List, Union +from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin from crewai.agents.parser import ( FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE, @@ -11,15 +13,21 @@ from crewai.agents.parser import ( OutputParserException, ) 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, ) from crewai.utilities.logger import Logger from crewai.utilities.training_handler import CrewTrainingHandler -from crewai.agents.agent_builder.base_agent import BaseAgent + + +@dataclass +class ToolResult: + result: Any + result_as_answer: bool class CrewAgentExecutor(CrewAgentExecutorMixin): @@ -33,7 +41,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): agent: BaseAgent, prompt: dict[str, str], max_iter: int, - tools: List[Any], + tools: List[BaseTool], tools_names: str, stop_words: List[str], tools_description: str, @@ -70,7 +78,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): self.iterations = 0 self.log_error_after = 3 self.have_forced_answer = False - self.name_to_tool_map = {tool.name: tool for tool in self.tools} + self.tool_name_to_tool_map: Dict[str, BaseTool] = { + tool.name: tool for tool in self.tools + } if self.llm.stop: self.llm.stop = list(set(self.llm.stop + self.stop)) else: @@ -80,7 +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: @@ -93,17 +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} @@ -117,6 +117,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): callbacks=self.callbacks, ) + if answer is None or answer == "": + self._printer.print( + content="Received None or empty response from LLM call.", + color="red", + ) + raise ValueError( + "Invalid response from LLM call - None or empty." + ) + if not self.use_stop_words: try: self._format_answer(answer) @@ -131,30 +140,42 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): formatted_answer = self._format_answer(answer) if isinstance(formatted_answer, AgentAction): - action_result = self._use_tool(formatted_answer) - formatted_answer.text += f"\nObservation: {action_result}" - formatted_answer.result = action_result + tool_result = self._execute_tool_and_check_finality( + formatted_answer + ) + if self.step_callback: + self.step_callback(tool_result) + + formatted_answer.text += f"\nObservation: {tool_result.result}" + formatted_answer.result = tool_result.result + if tool_result.result_as_answer: + return AgentFinish( + thought="", + output=tool_result.result, + text=formatted_answer.text, + ) self._show_logs(formatted_answer) - if self.step_callback: - self.step_callback(formatted_answer) + if self.step_callback: + self.step_callback(formatted_answer) - if self._should_force_answer(): - if self.have_forced_answer: - return AgentFinish( - output=self._i18n.errors( - "force_final_answer_error" - ).format(formatted_answer.text), - text=formatted_answer.text, - ) - else: - formatted_answer.text += ( - f'\n{self._i18n.errors("force_final_answer")}' - ) - self.have_forced_answer = True - self.messages.append( - self._format_msg(formatted_answer.text, role="assistant") - ) + if self._should_force_answer(): + if self.have_forced_answer: + return AgentFinish( + thought="", + output=self._i18n.errors( + "force_final_answer_error" + ).format(formatted_answer.text), + text=formatted_answer.text, + ) + else: + formatted_answer.text += ( + f'\n{self._i18n.errors("force_final_answer")}' + ) + self.have_forced_answer = True + self.messages.append( + self._format_msg(formatted_answer.text, role="assistant") + ) except OutputParserException as e: self.messages.append({"role": "user", "content": e.error}) @@ -229,7 +250,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n" ) - def _use_tool(self, agent_action: AgentAction) -> Any: + def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult: tool_usage = ToolUsage( tools_handler=self.tools_handler, tools=self.tools, @@ -245,19 +266,25 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if isinstance(tool_calling, ToolUsageErrorException): tool_result = tool_calling.message + return ToolResult(result=tool_result, result_as_answer=False) else: if tool_calling.tool_name.casefold().strip() in [ - name.casefold().strip() for name in self.name_to_tool_map + name.casefold().strip() for name in self.tool_name_to_tool_map ] or tool_calling.tool_name.casefold().replace("_", " ") in [ - name.casefold().strip() for name in self.name_to_tool_map + name.casefold().strip() for name in self.tool_name_to_tool_map ]: tool_result = tool_usage.use(tool_calling, agent_action.text) + tool = self.tool_name_to_tool_map.get(tool_calling.tool_name) + if tool: + return ToolResult( + result=tool_result, result_as_answer=tool.result_as_answer + ) else: tool_result = self._i18n.errors("wrong_tool_name").format( tool=tool_calling.tool_name, tools=", ".join([tool.name.casefold() for tool in self.tools]), ) - return tool_result + return ToolResult(result=tool_result, result_as_answer=False) def _summarize_messages(self) -> None: messages_groups = [] @@ -275,7 +302,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): self._i18n.slice("summarizer_system_message"), role="system" ), self._format_msg( - self._i18n.slice("sumamrize_instruction").format(group=group), + self._i18n.slice("summarize_instruction").format(group=group), ), ], callbacks=self.callbacks, @@ -292,16 +319,14 @@ 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( @@ -323,20 +348,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): if self.crew is not None and hasattr(self.crew, "_train_iteration"): train_iteration = self.crew._train_iteration if agent_id in training_data and isinstance(train_iteration, int): - training_data[agent_id][train_iteration]["improved_output"] = ( - result.output - ) + training_data[agent_id][train_iteration][ + "improved_output" + ] = 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", ) @@ -354,15 +377,13 @@ 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", ) @@ -376,4 +397,83 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): return CrewAgentParser(agent=self.agent).parse(answer) 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) + + 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/agents/parser.py b/src/crewai/agents/parser.py index 2286900c1..b4629a8d2 100644 --- a/src/crewai/agents/parser.py +++ b/src/crewai/agents/parser.py @@ -1,5 +1,6 @@ import re from typing import Any, Union + from json_repair import repair_json from crewai.utilities import I18N diff --git a/src/crewai/agents/tools_handler.py b/src/crewai/agents/tools_handler.py index c82f7de8c..fd4bec7ee 100644 --- a/src/crewai/agents/tools_handler.py +++ b/src/crewai/agents/tools_handler.py @@ -1,6 +1,6 @@ from typing import Any, Optional, Union -from ..tools.cache_tools import CacheTools +from ..tools.cache_tools.cache_tools import CacheTools from ..tools.tool_calling import InstructorToolCalling, ToolCalling from .cache.cache_handler import CacheHandler diff --git a/src/crewai/cli/add_crew_to_flow.py b/src/crewai/cli/add_crew_to_flow.py new file mode 100644 index 000000000..ef693a22b --- /dev/null +++ b/src/crewai/cli/add_crew_to_flow.py @@ -0,0 +1,70 @@ +from pathlib import Path + +import click + +from crewai.cli.utils import copy_template + + +def add_crew_to_flow(crew_name: str) -> None: + """Add a new crew to the current flow.""" + # Check if pyproject.toml exists in the current directory + if not Path("pyproject.toml").exists(): + print("This command must be run from the root of a flow project.") + raise click.ClickException( + "This command must be run from the root of a flow project." + ) + + # Determine the flow folder based on the current directory + flow_folder = Path.cwd() + crews_folder = flow_folder / "src" / flow_folder.name / "crews" + + if not crews_folder.exists(): + print("Crews folder does not exist in the current flow.") + raise click.ClickException("Crews folder does not exist in the current flow.") + + # Create the crew within the flow's crews directory + create_embedded_crew(crew_name, parent_folder=crews_folder) + + click.echo( + f"Crew {crew_name} added to the current flow successfully!", + ) + + +def create_embedded_crew(crew_name: str, parent_folder: Path) -> None: + """Create a new crew within an existing flow project.""" + folder_name = crew_name.replace(" ", "_").replace("-", "_").lower() + class_name = crew_name.replace("_", " ").replace("-", " ").title().replace(" ", "") + + crew_folder = parent_folder / folder_name + + if crew_folder.exists(): + if not click.confirm( + f"Crew {folder_name} already exists. Do you want to override it?" + ): + click.secho("Operation cancelled.", fg="yellow") + return + click.secho(f"Overriding crew {folder_name}...", fg="green", bold=True) + else: + click.secho(f"Creating crew {folder_name}...", fg="green", bold=True) + crew_folder.mkdir(parents=True) + + # Create config and crew.py files + config_folder = crew_folder / "config" + config_folder.mkdir(exist_ok=True) + + templates_dir = Path(__file__).parent / "templates" / "crew" + config_template_files = ["agents.yaml", "tasks.yaml"] + crew_template_file = f"{folder_name}.py" # Updated file name + + for file_name in config_template_files: + src_file = templates_dir / "config" / file_name + dst_file = config_folder / file_name + copy_template(src_file, dst_file, crew_name, class_name, folder_name) + + src_file = templates_dir / "crew.py" + dst_file = crew_folder / crew_template_file + copy_template(src_file, dst_file, crew_name, class_name, folder_name) + + click.secho( + f"Crew {crew_name} added to the flow successfully!", fg="green", bold=True + ) diff --git a/src/crewai/cli/authentication/main.py b/src/crewai/cli/authentication/main.py index 331b583e8..5a335e1f2 100644 --- a/src/crewai/cli/authentication/main.py +++ b/src/crewai/cli/authentication/main.py @@ -5,6 +5,8 @@ from typing import Any, Dict import requests from rich.console import Console +from crewai.cli.tools.main import ToolCommand + from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN from .utils import TokenManager, validate_token @@ -34,7 +36,9 @@ class AuthenticationCommand: "scope": "openid", "audience": AUTH0_AUDIENCE, } - response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload) + response = requests.post( + url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20 + ) response.raise_for_status() return response.json() @@ -54,14 +58,31 @@ class AuthenticationCommand: attempts = 0 while True and attempts < 5: - response = requests.post(self.TOKEN_URL, data=token_payload) + response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30) token_data = response.json() if response.status_code == 200: validate_token(token_data["id_token"]) expires_in = 360000 # Token expiration time in seconds self.token_manager.save_tokens(token_data["access_token"], expires_in) - console.print("\nWelcome to CrewAI+ !!", style="green") + + try: + ToolCommand().login() + except Exception: + console.print( + "\n[bold yellow]Warning:[/bold yellow] Authentication with the Tool Repository failed.", + style="yellow", + ) + console.print( + "Other features will work normally, but you may experience limitations " + "with downloading and publishing tools." + "\nRun [bold]crewai login[/bold] to try logging in again.\n", + style="yellow", + ) + + console.print( + "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n" + ) return if token_data["error"] not in ("authorization_pending", "slow_down"): diff --git a/src/crewai/cli/authentication/token.py b/src/crewai/cli/authentication/token.py new file mode 100644 index 000000000..30a33b4ba --- /dev/null +++ b/src/crewai/cli/authentication/token.py @@ -0,0 +1,9 @@ +from .utils import TokenManager + + +def get_auth_token() -> str: + """Get the authentication token.""" + access_token = TokenManager().get_token() + if not access_token: + raise Exception() + return access_token diff --git a/src/crewai/cli/cli.py b/src/crewai/cli/cli.py index 55c8e5b43..43dc90eed 100644 --- a/src/crewai/cli/cli.py +++ b/src/crewai/cli/cli.py @@ -3,9 +3,9 @@ from typing import Optional import click 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, ) @@ -14,36 +14,35 @@ from .authentication.main import AuthenticationCommand from .deploy.main import DeployCommand from .evaluate_crew import evaluate_crew from .install_crew import install_crew +from .kickoff_flow import kickoff_flow from .plot_flow import plot_flow from .replay_from_task import replay_task_command from .reset_memories_command import reset_memories_command from .run_crew import run_crew -from .run_flow import run_flow from .tools.main import ToolCommand from .train_crew import train_crew 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.""" @crewai.command() -@click.argument("type", type=click.Choice(["crew", "pipeline", "flow"])) +@click.argument("type", type=click.Choice(["crew", "flow"])) @click.argument("name") -def create(type, name): - """Create a new crew, pipeline, or flow.""" +@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, or flow.""" if type == "crew": - create_crew(name) - elif type == "pipeline": - create_pipeline(name) + create_crew(name, provider, skip_provider) 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() @@ -52,7 +51,10 @@ def create(type, name): ) 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: @@ -133,6 +135,7 @@ def log_tasks_outputs() -> None: @click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory") @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory") @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory") +@click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage") @click.option( "-k", "--kickoff-outputs", @@ -140,17 +143,24 @@ def log_tasks_outputs() -> None: help="Reset LATEST KICKOFF TASK OUTPUTS", ) @click.option("-a", "--all", is_flag=True, help="Reset ALL memories") -def reset_memories(long, short, entities, kickoff_outputs, all): +def reset_memories( + long: bool, + short: bool, + entities: bool, + knowledge: bool, + kickoff_outputs: bool, + all: bool, +) -> None: """ Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved. """ try: - if not all and not (long or short or entities or kickoff_outputs): + if not all and not (long or short or entities or knowledge or kickoff_outputs): click.echo( "Please specify at least one memory type to reset using the appropriate flags." ) return - reset_memories_command(long, short, entities, kickoff_outputs, all) + reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all) except Exception as e: click.echo(f"An error occurred while resetting memories: {e}", err=True) @@ -176,10 +186,16 @@ def test(n_iterations: int, model: str): evaluate_crew(n_iterations, model) -@crewai.command() -def install(): +@crewai.command( + context_settings=dict( + ignore_unknown_options=True, + allow_extra_args=True, + ) +) +@click.pass_context +def install(context): """Install the Crew.""" - install_crew() + install_crew(context.args) @crewai.command() @@ -304,11 +320,11 @@ def flow(): pass -@flow.command(name="run") +@flow.command(name="kickoff") def flow_run(): - """Run the Flow.""" + """Kickoff the Flow.""" click.echo("Running the Flow") - run_flow() + kickoff_flow() @flow.command(name="plot") @@ -318,5 +334,13 @@ def flow_plot(): plot_flow() +@flow.command(name="add-crew") +@click.argument("crew_name") +def flow_add_crew(crew_name): + """Add a crew to an existing flow.""" + click.echo(f"Adding crew {crew_name} to the flow") + add_crew_to_flow(crew_name) + + if __name__ == "__main__": crewai() diff --git a/src/crewai/cli/command.py b/src/crewai/cli/command.py index f05fe237f..2bef8985d 100644 --- a/src/crewai/cli/command.py +++ b/src/crewai/cli/command.py @@ -1,8 +1,9 @@ import requests from requests.exceptions import JSONDecodeError from rich.console import Console + +from crewai.cli.authentication.token import get_auth_token from crewai.cli.plus_api import PlusAPI -from crewai.cli.utils import get_auth_token from crewai.telemetry.telemetry import Telemetry console = Console() diff --git a/src/crewai/cli/config.py b/src/crewai/cli/config.py new file mode 100644 index 000000000..8e30767ca --- /dev/null +++ b/src/crewai/cli/config.py @@ -0,0 +1,44 @@ +import json +from pathlib import Path +from typing import Optional + +from pydantic import BaseModel, Field + +DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json" + + +class Settings(BaseModel): + tool_repository_username: Optional[str] = Field( + None, description="Username for interacting with the Tool Repository" + ) + tool_repository_password: Optional[str] = Field( + None, description="Password for interacting with the Tool Repository" + ) + config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True) + + def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data): + """Load Settings from config path""" + config_path.parent.mkdir(parents=True, exist_ok=True) + + file_data = {} + if config_path.is_file(): + try: + with config_path.open("r") as f: + file_data = json.load(f) + except json.JSONDecodeError: + file_data = {} + + merged_data = {**file_data, **data} + super().__init__(config_path=config_path, **merged_data) + + def dump(self) -> None: + """Save current settings to settings.json""" + if self.config_path.is_file(): + with self.config_path.open("r") as f: + existing_data = json.load(f) + else: + existing_data = {} + + updated_data = {**existing_data, **self.model_dump(exclude_unset=True)} + with self.config_path.open("w") as f: + json.dump(updated_data, f, indent=4) diff --git a/src/crewai/cli/constants.py b/src/crewai/cli/constants.py index 9a0b36c39..13279f8d3 100644 --- a/src/crewai/cli/constants.py +++ b/src/crewai/cli/constants.py @@ -1,19 +1,164 @@ ENV_VARS = { - 'openai': ['OPENAI_API_KEY'], - 'anthropic': ['ANTHROPIC_API_KEY'], - 'gemini': ['GEMINI_API_KEY'], - 'groq': ['GROQ_API_KEY'], - 'ollama': ['FAKE_KEY'], + "openai": [ + { + "prompt": "Enter your OPENAI API key (press Enter to skip)", + "key_name": "OPENAI_API_KEY", + } + ], + "anthropic": [ + { + "prompt": "Enter your ANTHROPIC API key (press Enter to skip)", + "key_name": "ANTHROPIC_API_KEY", + } + ], + "gemini": [ + { + "prompt": "Enter your GEMINI API key (press Enter to skip)", + "key_name": "GEMINI_API_KEY", + } + ], + "groq": [ + { + "prompt": "Enter your GROQ API key (press Enter to skip)", + "key_name": "GROQ_API_KEY", + } + ], + "watson": [ + { + "prompt": "Enter your WATSONX URL (press Enter to skip)", + "key_name": "WATSONX_URL", + }, + { + "prompt": "Enter your WATSONX API Key (press Enter to skip)", + "key_name": "WATSONX_APIKEY", + }, + { + "prompt": "Enter your WATSONX Project Id (press Enter to skip)", + "key_name": "WATSONX_PROJECT_ID", + }, + ], + "ollama": [ + { + "default": True, + "API_BASE": "http://localhost:11434", + } + ], + "bedrock": [ + { + "prompt": "Enter your AWS Access Key ID (press Enter to skip)", + "key_name": "AWS_ACCESS_KEY_ID", + }, + { + "prompt": "Enter your AWS Secret Access Key (press Enter to skip)", + "key_name": "AWS_SECRET_ACCESS_KEY", + }, + { + "prompt": "Enter your AWS Region Name (press Enter to skip)", + "key_name": "AWS_REGION_NAME", + }, + ], + "azure": [ + { + "prompt": "Enter your Azure deployment name (must start with 'azure/')", + "key_name": "model", + }, + { + "prompt": "Enter your AZURE API key (press Enter to skip)", + "key_name": "AZURE_API_KEY", + }, + { + "prompt": "Enter your AZURE API base URL (press Enter to skip)", + "key_name": "AZURE_API_BASE", + }, + { + "prompt": "Enter your AZURE API version (press Enter to skip)", + "key_name": "AZURE_API_VERSION", + }, + ], + "cerebras": [ + { + "prompt": "Enter your Cerebras model name (must start with 'cerebras/')", + "key_name": "model", + }, + { + "prompt": "Enter your Cerebras API version (press Enter to skip)", + "key_name": "CEREBRAS_API_KEY", + }, + ], } -PROVIDERS = ['openai', 'anthropic', 'gemini', 'groq', 'ollama'] + +PROVIDERS = [ + "openai", + "anthropic", + "gemini", + "groq", + "ollama", + "watson", + "bedrock", + "azure", + "cerebras", +] MODELS = { - 'openai': ['gpt-4', 'gpt-4o', 'gpt-4o-mini', 'o1-mini', 'o1-preview'], - 'anthropic': ['claude-3-5-sonnet-20240620', 'claude-3-sonnet-20240229', 'claude-3-opus-20240229', 'claude-3-haiku-20240307'], - 'gemini': ['gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-gemma-2-9b-it', 'gemini-gemma-2-27b-it'], - 'groq': ['llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama-3.1-405b-reasoning', 'gemma2-9b-it', 'gemma-7b-it'], - 'ollama': ['llama3.1', 'mixtral'], + "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"], + "anthropic": [ + "claude-3-5-sonnet-20240620", + "claude-3-sonnet-20240229", + "claude-3-opus-20240229", + "claude-3-haiku-20240307", + ], + "gemini": [ + "gemini/gemini-1.5-flash", + "gemini/gemini-1.5-pro", + "gemini/gemini-gemma-2-9b-it", + "gemini/gemini-gemma-2-27b-it", + ], + "groq": [ + "groq/llama-3.1-8b-instant", + "groq/llama-3.1-70b-versatile", + "groq/llama-3.1-405b-reasoning", + "groq/gemma2-9b-it", + "groq/gemma-7b-it", + ], + "ollama": ["ollama/llama3.1", "ollama/mixtral"], + "watson": [ + "watsonx/meta-llama/llama-3-1-70b-instruct", + "watsonx/meta-llama/llama-3-1-8b-instruct", + "watsonx/meta-llama/llama-3-2-11b-vision-instruct", + "watsonx/meta-llama/llama-3-2-1b-instruct", + "watsonx/meta-llama/llama-3-2-90b-vision-instruct", + "watsonx/meta-llama/llama-3-405b-instruct", + "watsonx/mistral/mistral-large", + "watsonx/ibm/granite-3-8b-instruct", + ], + "bedrock": [ + "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0", + "bedrock/anthropic.claude-3-sonnet-20240229-v1:0", + "bedrock/anthropic.claude-3-haiku-20240307-v1:0", + "bedrock/anthropic.claude-3-opus-20240229-v1:0", + "bedrock/anthropic.claude-v2:1", + "bedrock/anthropic.claude-v2", + "bedrock/anthropic.claude-instant-v1", + "bedrock/meta.llama3-1-405b-instruct-v1:0", + "bedrock/meta.llama3-1-70b-instruct-v1:0", + "bedrock/meta.llama3-1-8b-instruct-v1:0", + "bedrock/meta.llama3-70b-instruct-v1:0", + "bedrock/meta.llama3-8b-instruct-v1:0", + "bedrock/amazon.titan-text-lite-v1", + "bedrock/amazon.titan-text-express-v1", + "bedrock/cohere.command-text-v14", + "bedrock/ai21.j2-mid-v1", + "bedrock/ai21.j2-ultra-v1", + "bedrock/ai21.jamba-instruct-v1:0", + "bedrock/meta.llama2-13b-chat-v1", + "bedrock/meta.llama2-70b-chat-v1", + "bedrock/mistral.mistral-7b-instruct-v0:2", + "bedrock/mistral.mixtral-8x7b-instruct-v0:1", + ], } -JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" \ No newline at end of file +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/cli/create_crew.py b/src/crewai/cli/create_crew.py index f74331a23..c658b0de1 100644 --- a/src/crewai/cli/create_crew.py +++ b/src/crewai/cli/create_crew.py @@ -1,8 +1,16 @@ +import shutil +import sys from pathlib import Path + import click + +from crewai.cli.constants import ENV_VARS, MODELS +from crewai.cli.provider import ( + get_provider_data, + select_model, + select_provider, +) from crewai.cli.utils import copy_template, load_env_vars, write_env_file -from crewai.cli.provider import get_provider_data, select_provider, PROVIDERS -from crewai.cli.constants import ENV_VARS def create_folder_structure(name, parent_folder=None): @@ -14,24 +22,28 @@ def create_folder_structure(name, parent_folder=None): else: folder_path = Path(folder_name) + if folder_path.exists(): + if not click.confirm( + f"Folder {folder_name} already exists. Do you want to override it?" + ): + click.secho("Operation cancelled.", fg="yellow") + sys.exit(0) + click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True) + shutil.rmtree(folder_path) # Delete the existing folder and its contents + click.secho( f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...", fg="green", bold=True, ) - if not folder_path.exists(): - folder_path.mkdir(parents=True) - (folder_path / "tests").mkdir(exist_ok=True) - if not parent_folder: - (folder_path / "src" / folder_name).mkdir(parents=True) - (folder_path / "src" / folder_name / "tools").mkdir(parents=True) - (folder_path / "src" / folder_name / "config").mkdir(parents=True) - else: - click.secho( - f"\tFolder {folder_name} already exists.", - fg="yellow", - ) + folder_path.mkdir(parents=True) + (folder_path / "tests").mkdir(exist_ok=True) + (folder_path / "knowledge").mkdir(exist_ok=True) + if not parent_folder: + (folder_path / "src" / folder_name).mkdir(parents=True) + (folder_path / "src" / folder_name / "tools").mkdir(parents=True) + (folder_path / "src" / folder_name / "config").mkdir(parents=True) return folder_path, folder_name, class_name @@ -41,7 +53,14 @@ def copy_template_files(folder_path, name, class_name, parent_folder): templates_dir = package_dir / "templates" / "crew" root_template_files = ( - [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else [] + [ + ".gitignore", + "pyproject.toml", + "README.md", + "knowledge/user_preference.txt", + ] + if not parent_folder + else [] ) tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"] config_template_files = ["config/agents.yaml", "config/tasks.yaml"] @@ -70,43 +89,96 @@ def copy_template_files(folder_path, name, class_name, parent_folder): copy_template(src_file, dst_file, name, class_name, folder_path.name) -def create_crew(name, parent_folder=None): +def create_crew(name, provider=None, skip_provider=False, parent_folder=None): folder_path, folder_name, class_name = create_folder_structure(name, parent_folder) env_vars = load_env_vars(folder_path) + if not skip_provider: + if not provider: + provider_models = get_provider_data() + if not provider_models: + return - provider_models = get_provider_data() - if not provider_models: - return + existing_provider = None + for provider, env_keys in ENV_VARS.items(): + if any( + "key_name" in details and details["key_name"] in env_vars + for details in env_keys + ): + existing_provider = provider + break - selected_provider = select_provider(provider_models) - if not selected_provider: - return - provider = selected_provider + if existing_provider: + if not click.confirm( + f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?" + ): + click.secho("Keeping existing provider configuration.", fg="yellow") + return - # selected_model = select_model(provider, provider_models) - # if not selected_model: - # return - # model = selected_model + provider_models = get_provider_data() + if not provider_models: + return - if provider in PROVIDERS: - api_key_var = ENV_VARS[provider][0] - else: - api_key_var = click.prompt( - f"Enter the environment variable name for your {provider.capitalize()} API key", - type=str, - ) + while True: + selected_provider = select_provider(provider_models) + if selected_provider is None: # User typed 'q' + click.secho("Exiting...", fg="yellow") + sys.exit(0) + if selected_provider: # Valid selection + break + click.secho( + "No provider selected. Please try again or press 'q' to exit.", fg="red" + ) - env_vars = {api_key_var: "YOUR_API_KEY_HERE"} - write_env_file(folder_path, env_vars) + # Check if the selected provider has predefined models + if selected_provider in MODELS and MODELS[selected_provider]: + while True: + selected_model = select_model(selected_provider, provider_models) + if selected_model is None: # User typed 'q' + click.secho("Exiting...", fg="yellow") + sys.exit(0) + if selected_model: # Valid selection + break + click.secho( + "No model selected. Please try again or press 'q' to exit.", + fg="red", + ) + env_vars["MODEL"] = selected_model - # env_vars['MODEL'] = model - # click.secho(f"Selected model: {model}", fg="green") + # Check if the selected provider requires API keys + if selected_provider in ENV_VARS: + provider_env_vars = ENV_VARS[selected_provider] + for details in provider_env_vars: + if details.get("default", False): + # Automatically add default key-value pairs + for key, value in details.items(): + if key not in ["prompt", "key_name", "default"]: + env_vars[key] = value + elif "key_name" in details: + # Prompt for non-default key-value pairs + prompt = details["prompt"] + key_name = details["key_name"] + api_key_value = click.prompt(prompt, default="", show_default=False) + + if api_key_value.strip(): + env_vars[key_name] = api_key_value + + if env_vars: + write_env_file(folder_path, env_vars) + click.secho("API keys and model saved to .env file", fg="green") + else: + click.secho( + "No API keys provided. Skipping .env file creation.", fg="yellow" + ) + + click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green") package_dir = Path(__file__).parent templates_dir = package_dir / "templates" / "crew" root_template_files = ( - [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else [] + [".gitignore", "pyproject.toml", "README.md", "knowledge/user_preference.txt"] + if not parent_folder + else [] ) tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"] config_template_files = ["config/agents.yaml", "config/tasks.yaml"] 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/install_crew.py b/src/crewai/cli/install_crew.py index 35206e5eb..d1d0ab9da 100644 --- a/src/crewai/cli/install_crew.py +++ b/src/crewai/cli/install_crew.py @@ -3,12 +3,13 @@ import subprocess import click -def install_crew() -> None: +def install_crew(proxy_options: list[str]) -> None: """ Install the crew by running the UV command to lock and install. """ try: - subprocess.run(["uv", "sync"], check=True, capture_output=False, text=True) + command = ["uv", "sync"] + proxy_options + subprocess.run(command, check=True, capture_output=False, text=True) except subprocess.CalledProcessError as e: click.echo(f"An error occurred while running the crew: {e}", err=True) diff --git a/src/crewai/cli/run_flow.py b/src/crewai/cli/kickoff_flow.py similarity index 78% rename from src/crewai/cli/run_flow.py rename to src/crewai/cli/kickoff_flow.py index 2900e59b3..2123a6c15 100644 --- a/src/crewai/cli/run_flow.py +++ b/src/crewai/cli/kickoff_flow.py @@ -3,11 +3,11 @@ import subprocess import click -def run_flow() -> None: +def kickoff_flow() -> None: """ - Run the flow by running a command in the UV environment. + Kickoff the flow by running a command in the UV environment. """ - command = ["uv", "run", "run_flow"] + command = ["uv", "run", "kickoff"] try: result = subprocess.run(command, capture_output=False, text=True, check=True) diff --git a/src/crewai/cli/plot_flow.py b/src/crewai/cli/plot_flow.py index 2183a02ec..848c55d69 100644 --- a/src/crewai/cli/plot_flow.py +++ b/src/crewai/cli/plot_flow.py @@ -7,7 +7,7 @@ def plot_flow() -> None: """ Plot the flow by running a command in the UV environment. """ - command = ["uv", "run", "plot_flow"] + command = ["uv", "run", "plot"] try: result = subprocess.run(command, capture_output=False, text=True, check=True) diff --git a/src/crewai/cli/plus_api.py b/src/crewai/cli/plus_api.py index 04f6fb8ff..23032ca8f 100644 --- a/src/crewai/cli/plus_api.py +++ b/src/crewai/cli/plus_api.py @@ -1,9 +1,11 @@ -from typing import Optional -import requests from os import getenv -from crewai.cli.utils import get_crewai_version +from typing import Optional from urllib.parse import urljoin +import requests + +from crewai.cli.version import get_crewai_version + class PlusAPI: """ diff --git a/src/crewai/cli/provider.py b/src/crewai/cli/provider.py index f829ca9fd..529ca5e26 100644 --- a/src/crewai/cli/provider.py +++ b/src/crewai/cli/provider.py @@ -1,67 +1,91 @@ import json import time -import requests from collections import defaultdict +from pathlib import Path + import click -from pathlib import Path -from crewai.cli.constants import PROVIDERS, MODELS, JSON_URL +import requests + +from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS + def select_choice(prompt_message, choices): """ Presents a list of choices to the user and prompts them to select one. - + Args: - prompt_message (str): The message to display to the user before presenting the choices. - choices (list): A list of options to present to the user. - + Returns: - - str: The selected choice from the list, or None if the operation is aborted or an invalid selection is made. + - str: The selected choice from the list, or None if the user chooses to quit. """ + + provider_models = get_provider_data() + if not provider_models: + return click.secho(prompt_message, fg="cyan") for idx, choice in enumerate(choices, start=1): click.secho(f"{idx}. {choice}", fg="cyan") - try: - selected_index = click.prompt("Enter the number of your choice", type=int) - 1 - except click.exceptions.Abort: - click.secho("Operation aborted by the user.", fg="red") - return None - if not (0 <= selected_index < len(choices)): - click.secho("Invalid selection.", fg="red") - return None - return choices[selected_index] + click.secho("q. Quit", fg="cyan") + + while True: + choice = click.prompt( + "Enter the number of your choice or 'q' to quit", type=str + ) + + if choice.lower() == "q": + return None + + try: + selected_index = int(choice) - 1 + if 0 <= selected_index < len(choices): + return choices[selected_index] + except ValueError: + pass + + click.secho( + "Invalid selection. Please select a number between 1 and 6 or 'q' to quit.", + fg="red", + ) + def select_provider(provider_models): """ Presents a list of providers to the user and prompts them to select one. - + Args: - provider_models (dict): A dictionary of provider models. - + Returns: - - str: The selected provider, or None if the operation is aborted or an invalid selection is made. + - str: The selected provider + - None: If user explicitly quits """ predefined_providers = [p.lower() for p in PROVIDERS] all_providers = sorted(set(predefined_providers + list(provider_models.keys()))) - provider = select_choice("Select a provider to set up:", predefined_providers + ['other']) - if not provider: + provider = select_choice( + "Select a provider to set up:", predefined_providers + ["other"] + ) + if provider is None: # User typed 'q' return None - provider = provider.lower() - if provider == 'other': + if provider == "other": provider = select_choice("Select a provider from the full list:", all_providers) - if not provider: + if provider is None: # User typed 'q' return None - return provider + + return provider.lower() if provider else False + def select_model(provider, provider_models): """ Presents a list of models for a given provider to the user and prompts them to select one. - + Args: - provider (str): The provider for which to select a model. - provider_models (dict): A dictionary of provider models. - + Returns: - str: The selected model, or None if the operation is aborted or an invalid selection is made. """ @@ -76,37 +100,49 @@ def select_model(provider, provider_models): click.secho(f"No models available for provider '{provider}'.", fg="red") return None - selected_model = select_choice(f"Select a model to use for {provider.capitalize()}:", available_models) + selected_model = select_choice( + f"Select a model to use for {provider.capitalize()}:", available_models + ) return selected_model + def load_provider_data(cache_file, cache_expiry): """ Loads provider data from a cache file if it exists and is not expired. If the cache is expired or corrupted, it fetches the data from the web. - + Args: - cache_file (Path): The path to the cache file. - cache_expiry (int): The cache expiry time in seconds. - + Returns: - dict or None: The loaded provider data or None if the operation fails. """ current_time = time.time() - if cache_file.exists() and (current_time - cache_file.stat().st_mtime) < cache_expiry: + if ( + cache_file.exists() + and (current_time - cache_file.stat().st_mtime) < cache_expiry + ): data = read_cache_file(cache_file) if data: return data - click.secho("Cache is corrupted. Fetching provider data from the web...", fg="yellow") + click.secho( + "Cache is corrupted. Fetching provider data from the web...", fg="yellow" + ) else: - click.secho("Cache expired or not found. Fetching provider data from the web...", fg="cyan") + click.secho( + "Cache expired or not found. Fetching provider data from the web...", + fg="cyan", + ) return fetch_provider_data(cache_file) + def read_cache_file(cache_file): """ Reads and returns the JSON content from a cache file. Returns None if the file contains invalid JSON. - + Args: - cache_file (Path): The path to the cache file. - + Returns: - dict or None: The JSON content of the cache file or None if the JSON is invalid. """ @@ -116,18 +152,19 @@ def read_cache_file(cache_file): except json.JSONDecodeError: return None + def fetch_provider_data(cache_file): """ Fetches provider data from a specified URL and caches it to a file. - + Args: - cache_file (Path): The path to the cache file. - + Returns: - dict or None: The fetched provider data or None if the operation fails. """ try: - response = requests.get(JSON_URL, stream=True, timeout=10) + response = requests.get(JSON_URL, stream=True, timeout=60) response.raise_for_status() data = download_data(response) with open(cache_file, "w") as f: @@ -139,38 +176,42 @@ def fetch_provider_data(cache_file): click.secho("Error parsing provider data. Invalid JSON format.", fg="red") return None + def download_data(response): """ Downloads data from a given HTTP response and returns the JSON content. - + Args: - response (requests.Response): The HTTP response object. - + Returns: - dict: The JSON content of the response. """ - total_size = int(response.headers.get('content-length', 0)) + total_size = int(response.headers.get("content-length", 0)) block_size = 8192 data_chunks = [] - with click.progressbar(length=total_size, label='Downloading', show_pos=True) as progress_bar: + with click.progressbar( + length=total_size, label="Downloading", show_pos=True + ) as progress_bar: for chunk in response.iter_content(block_size): if chunk: data_chunks.append(chunk) progress_bar.update(len(chunk)) - data_content = b''.join(data_chunks) - return json.loads(data_content.decode('utf-8')) + data_content = b"".join(data_chunks) + return json.loads(data_content.decode("utf-8")) + def get_provider_data(): """ Retrieves provider data from a cache file, filters out models based on provider criteria, and returns a dictionary of providers mapped to their models. - + Returns: - dict or None: A dictionary of providers mapped to their models or None if the operation fails. """ - cache_dir = Path.home() / '.crewai' + cache_dir = Path.home() / ".crewai" cache_dir.mkdir(exist_ok=True) - cache_file = cache_dir / 'provider_cache.json' - cache_expiry = 24 * 3600 + cache_file = cache_dir / "provider_cache.json" + cache_expiry = 24 * 3600 data = load_provider_data(cache_file, cache_expiry) if not data: @@ -179,8 +220,8 @@ def get_provider_data(): provider_models = defaultdict(list) for model_name, properties in data.items(): provider = properties.get("litellm_provider", "").strip().lower() - if 'http' in provider or provider == 'other': + if "http" in provider or provider == "other": continue if provider: provider_models[provider].append(model_name) - return provider_models \ No newline at end of file + return provider_models diff --git a/src/crewai/cli/reset_memories_command.py b/src/crewai/cli/reset_memories_command.py index c4808594f..554232f52 100644 --- a/src/crewai/cli/reset_memories_command.py +++ b/src/crewai/cli/reset_memories_command.py @@ -1,13 +1,22 @@ import subprocess + import click +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage 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.utilities.task_output_storage_handler import TaskOutputStorageHandler -def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: +def reset_memories_command( + long, + short, + entity, + knowledge, + kickoff_outputs, + all, +) -> None: """ Reset the crew memories. @@ -17,6 +26,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: entity (bool): Whether to reset the entity memory. kickoff_outputs (bool): Whether to reset the latest kickoff task outputs. all (bool): Whether to reset all memories. + knowledge (bool): Whether to reset the knowledge. """ try: @@ -25,6 +35,7 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: EntityMemory().reset() LongTermMemory().reset() TaskOutputStorageHandler().reset() + KnowledgeStorage().reset() click.echo("All memories have been reset.") else: if long: @@ -40,6 +51,9 @@ def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None: if kickoff_outputs: TaskOutputStorageHandler().reset() click.echo("Latest Kickoff outputs stored has been reset.") + if knowledge: + KnowledgeStorage().reset() + click.echo("Knowledge has been reset.") except subprocess.CalledProcessError as e: click.echo(f"An error occurred while resetting the memories: {e}", err=True) diff --git a/src/crewai/cli/run_crew.py b/src/crewai/cli/run_crew.py index 829d8ed95..95b560109 100644 --- a/src/crewai/cli/run_crew.py +++ b/src/crewai/cli/run_crew.py @@ -1,10 +1,10 @@ import subprocess import click -import tomllib from packaging import version -from crewai.cli.utils import get_crewai_version +from crewai.cli.utils import read_toml +from crewai.cli.version import get_crewai_version def run_crew() -> None: @@ -15,10 +15,9 @@ def run_crew() -> None: crewai_version = get_crewai_version() min_required_version = "0.71.0" - with open("pyproject.toml", "rb") as f: - data = tomllib.load(f) + pyproject_data = read_toml() - if data.get("tool", {}).get("poetry") and ( + if pyproject_data.get("tool", {}).get("poetry") and ( version.parse(crewai_version) < version.parse(min_required_version) ): click.secho( @@ -26,7 +25,6 @@ def run_crew() -> None: f"Please run `crewai update` to update your pyproject.toml to use uv.", fg="red", ) - print() try: subprocess.run(command, capture_output=False, text=True, check=True) @@ -35,10 +33,7 @@ def run_crew() -> None: click.echo(f"An error occurred while running the crew: {e}", err=True) click.echo(e.output, err=True, nl=True) - with open("pyproject.toml", "rb") as f: - data = tomllib.load(f) - - if data.get("tool", {}).get("poetry"): + if pyproject_data.get("tool", {}).get("poetry"): click.secho( "It's possible that you are using an old version of crewAI that uses poetry, please run `crewai update` to update your pyproject.toml to use uv.", fg="yellow", diff --git a/src/crewai/cli/templates/crew/README.md b/src/crewai/cli/templates/crew/README.md index e6f94d95f..ca3cbd3eb 100644 --- a/src/crewai/cli/templates/crew/README.md +++ b/src/crewai/cli/templates/crew/README.md @@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co ## Installation -Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. +Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. First, if you haven't already, install uv: diff --git a/src/crewai/cli/templates/crew/config/tasks.yaml b/src/crewai/cli/templates/crew/config/tasks.yaml index f30820855..e6c0870a2 100644 --- a/src/crewai/cli/templates/crew/config/tasks.yaml +++ b/src/crewai/cli/templates/crew/config/tasks.yaml @@ -12,6 +12,6 @@ reporting_task: Review the context you got and expand each topic into a full section for a report. Make sure the report is detailed and contains any and all relevant information. expected_output: > - A fully fledge reports with the mains topics, each with a full section of information. + A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```' agent: reporting_analyst diff --git a/src/crewai/cli/templates/crew/crew.py b/src/crewai/cli/templates/crew/crew.py index f950d13d4..383bfac48 100644 --- a/src/crewai/cli/templates/crew/crew.py +++ b/src/crewai/cli/templates/crew/crew.py @@ -1,21 +1,26 @@ 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 +# 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(): +class {{crew_name}}(): """{{crew_name}} crew""" + # Learn more about YAML configuration files here: + # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended + # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended + agents_config = 'config/agents.yaml' + tasks_config = 'config/tasks.yaml' + + # If you would like to add tools to your agents, you can learn more about it here: + # https://docs.crewai.com/concepts/agents#agent-tools @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 ) @@ -26,6 +31,9 @@ class {{crew_name}}Crew(): 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( @@ -42,10 +50,13 @@ class {{crew_name}}Crew(): @crew def crew(self) -> Crew: """Creates the {{crew_name}} crew""" + # To learn how to add knowledge sources to your crew, check out the documentation: + # https://docs.crewai.com/concepts/knowledge#what-is-knowledge + 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, # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/ - ) \ No newline at end of file + ) diff --git a/src/crewai/cli/templates/crew/knowledge/user_preference.txt b/src/crewai/cli/templates/crew/knowledge/user_preference.txt new file mode 100644 index 000000000..dd63a17bf --- /dev/null +++ b/src/crewai/cli/templates/crew/knowledge/user_preference.txt @@ -0,0 +1,4 @@ +User name is John Doe. +User is an AI Engineer. +User is interested in AI Agents. +User is based in San Francisco, California. diff --git a/src/crewai/cli/templates/crew/main.py b/src/crewai/cli/templates/crew/main.py index a20125637..d5224edcf 100644 --- a/src/crewai/cli/templates/crew/main.py +++ b/src/crewai/cli/templates/crew/main.py @@ -1,9 +1,13 @@ #!/usr/bin/env python import sys -from {{folder_name}}.crew import {{crew_name}}Crew +import warnings + +from {{folder_name}}.crew import {{crew_name}} + +warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd") # This main file is intended to be a way for you to run your -# crew locally, so refrain from adding necessary logic into this file. +# crew locally, so refrain from adding unnecessary logic into this file. # Replace with inputs you want to test with, it will automatically # interpolate any tasks and agents information @@ -14,7 +18,7 @@ def run(): inputs = { 'topic': 'AI LLMs' } - {{crew_name}}Crew().crew().kickoff(inputs=inputs) + {{crew_name}}().crew().kickoff(inputs=inputs) def train(): @@ -25,7 +29,7 @@ def train(): "topic": "AI LLMs" } try: - {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs) + {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs) except Exception as e: raise Exception(f"An error occurred while training the crew: {e}") @@ -35,7 +39,7 @@ def replay(): Replay the crew execution from a specific task. """ try: - {{crew_name}}Crew().crew().replay(task_id=sys.argv[1]) + {{crew_name}}().crew().replay(task_id=sys.argv[1]) except Exception as e: raise Exception(f"An error occurred while replaying the crew: {e}") @@ -48,7 +52,7 @@ def test(): "topic": "AI LLMs" } try: - {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs) + {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs) except Exception as e: raise Exception(f"An error occurred while replaying the crew: {e}") diff --git a/src/crewai/cli/templates/crew/pyproject.toml b/src/crewai/cli/templates/crew/pyproject.toml index d387443a1..29014881b 100644 --- a/src/crewai/cli/templates/crew/pyproject.toml +++ b/src/crewai/cli/templates/crew/pyproject.toml @@ -3,9 +3,9 @@ name = "{{folder_name}}" version = "0.1.0" description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] -requires-python = ">=3.10,<=3.13" +requires-python = ">=3.10,<=3.12" dependencies = [ - "crewai[tools]>=0.74.0,<1.0.0" + "crewai[tools]>=0.86.0,<1.0.0" ] [project.scripts] diff --git a/src/crewai/cli/templates/crew/tools/custom_tool.py b/src/crewai/cli/templates/crew/tools/custom_tool.py index b12529303..50bffa505 100644 --- a/src/crewai/cli/templates/crew/tools/custom_tool.py +++ b/src/crewai/cli/templates/crew/tools/custom_tool.py @@ -1,11 +1,18 @@ -from crewai_tools import BaseTool +from crewai.tools import BaseTool +from typing import Type +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 diff --git a/src/crewai/cli/templates/flow/README.md b/src/crewai/cli/templates/flow/README.md index 2cde7892b..fead65ece 100644 --- a/src/crewai/cli/templates/flow/README.md +++ b/src/crewai/cli/templates/flow/README.md @@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co ## Installation -Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. +Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. First, if you haven't already, install uv: 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, + ) diff --git a/src/crewai/cli/templates/flow/main.py b/src/crewai/cli/templates/flow/main.py index 38d2d8736..73684051a 100644 --- a/src/crewai/cli/templates/flow/main.py +++ b/src/crewai/cli/templates/flow/main.py @@ -1,65 +1,53 @@ #!/usr/bin/env python -import asyncio from random import randint from pydantic import BaseModel + from crewai.flow.flow import Flow, listen, start + from .crews.poem_crew.poem_crew import PoemCrew + class PoemState(BaseModel): sentence_count: int = 1 poem: str = "" + class PoemFlow(Flow[PoemState]): @start() def generate_sentence_count(self): print("Generating sentence count") - # Generate a number between 1 and 5 - self.state.sentence_count = randint(1, 5) + self.state.sentence_count = randint(1, 5) @listen(generate_sentence_count) def generate_poem(self): print("Generating poem") - print(f"State before poem: {self.state}") - result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count}) - + result = ( + PoemCrew() + .crew() + .kickoff(inputs={"sentence_count": self.state.sentence_count}) + ) + print("Poem generated", result.raw) self.state.poem = result.raw - - print(f"State after generate_poem: {self.state}") @listen(generate_poem) def save_poem(self): print("Saving poem") - print(f"State before save_poem: {self.state}") with open("poem.txt", "w") as f: f.write(self.state.poem) - print(f"State after save_poem: {self.state}") -async def run_flow(): - """ - Run the flow. - """ + +def kickoff(): poem_flow = PoemFlow() - await poem_flow.kickoff() + poem_flow.kickoff() -async def plot_flow(): - """ - Plot the flow. - """ + +def plot(): poem_flow = PoemFlow() poem_flow.plot() -def main(): - asyncio.run(run_flow()) - - -def plot(): - asyncio.run(plot_flow()) - - - if __name__ == "__main__": - main() + kickoff() diff --git a/src/crewai/cli/templates/flow/pyproject.toml b/src/crewai/cli/templates/flow/pyproject.toml index 9db16e2a2..a20653eff 100644 --- a/src/crewai/cli/templates/flow/pyproject.toml +++ b/src/crewai/cli/templates/flow/pyproject.toml @@ -3,16 +3,14 @@ name = "{{folder_name}}" version = "0.1.0" description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] -requires-python = ">=3.10,<=3.13" +requires-python = ">=3.10,<=3.12" dependencies = [ - "crewai[tools]>=0.74.0,<1.0.0", - "asyncio" + "crewai[tools]>=0.86.0,<1.0.0", ] [project.scripts] -{{folder_name}} = "{{folder_name}}.main:main" -run_flow = "{{folder_name}}.main:main" -plot_flow = "{{folder_name}}.main:plot" +kickoff = "{{folder_name}}.main:kickoff" +plot = "{{folder_name}}.main:plot" [build-system] requires = ["hatchling"] diff --git a/src/crewai/cli/templates/flow/tools/custom_tool.py b/src/crewai/cli/templates/flow/tools/custom_tool.py index b12529303..e669b6c3b 100644 --- a/src/crewai/cli/templates/flow/tools/custom_tool.py +++ b/src/crewai/cli/templates/flow/tools/custom_tool.py @@ -1,4 +1,13 @@ -from crewai_tools import BaseTool +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): @@ -6,6 +15,7 @@ class MyCustomTool(BaseTool): 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 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/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/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/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 3eb9b73d1..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 = ">=0.74.0,<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/custom_tool.py b/src/crewai/cli/templates/pipeline/tools/custom_tool.py deleted file mode 100644 index b12529303..000000000 --- a/src/crewai/cli/templates/pipeline/tools/custom_tool.py +++ /dev/null @@ -1,12 +0,0 @@ -from crewai_tools import BaseTool - - -class MyCustomTool(BaseTool): - name: str = "Name of my tool" - description: str = ( - "Clear description for what this tool is useful for, you agent will need this information to use it." - ) - - def _run(self, argument: str) -> str: - # Implementation goes here - return "this is an example of a tool output, ignore it and move along." 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/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/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/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 dabc8d281..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]>=0.74.0,<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/custom_tool.py b/src/crewai/cli/templates/pipeline_router/tools/custom_tool.py deleted file mode 100644 index b12529303..000000000 --- a/src/crewai/cli/templates/pipeline_router/tools/custom_tool.py +++ /dev/null @@ -1,12 +0,0 @@ -from crewai_tools import BaseTool - - -class MyCustomTool(BaseTool): - name: str = "Name of my tool" - description: str = ( - "Clear description for what this tool is useful for, you agent will need this information to use it." - ) - - def _run(self, argument: str) -> str: - # Implementation goes here - return "this is an example of a tool output, ignore it and move along." diff --git a/src/crewai/cli/templates/tool/README.md b/src/crewai/cli/templates/tool/README.md index 43b1d721a..1291ff6a6 100644 --- a/src/crewai/cli/templates/tool/README.md +++ b/src/crewai/cli/templates/tool/README.md @@ -5,7 +5,7 @@ custom tools to power up your crews. ## Installing -Ensure you have Python >=3.10 <=3.13 installed on your system. This project +Ensure you have Python >=3.10 <=3.12 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience. diff --git a/src/crewai/cli/templates/tool/pyproject.toml b/src/crewai/cli/templates/tool/pyproject.toml index 0fef250f5..a1557bd7f 100644 --- a/src/crewai/cli/templates/tool/pyproject.toml +++ b/src/crewai/cli/templates/tool/pyproject.toml @@ -3,8 +3,8 @@ name = "{{folder_name}}" version = "0.1.0" description = "Power up your crews with {{folder_name}}" readme = "README.md" -requires-python = ">=3.10,<=3.13" +requires-python = ">=3.10,<=3.12" dependencies = [ - "crewai[tools]>=0.74.0" + "crewai[tools]>=0.86.0" ] diff --git a/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py b/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py index 63c653a6c..24bc36017 100644 --- a/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py +++ b/src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py @@ -1,4 +1,5 @@ -from crewai_tools import BaseTool +from crewai.tools import BaseTool + class {{class_name}}(BaseTool): name: str = "Name of my tool" diff --git a/src/crewai/cli/tools/main.py b/src/crewai/cli/tools/main.py index b6237c61d..b2d2cec99 100644 --- a/src/crewai/cli/tools/main.py +++ b/src/crewai/cli/tools/main.py @@ -1,17 +1,15 @@ import base64 import os -import platform import subprocess import tempfile from pathlib import Path -from netrc import netrc -import stat import click from rich.console import Console from crewai.cli import git from crewai.cli.command import BaseCommand, PlusAPIMixin +from crewai.cli.config import Settings from crewai.cli.utils import ( get_project_description, get_project_name, @@ -28,8 +26,6 @@ class ToolCommand(BaseCommand, PlusAPIMixin): A class to handle tool repository related operations for CrewAI projects. """ - BASE_URL = "https://app.crewai.com/pypi/" - def __init__(self): BaseCommand.__init__(self) PlusAPIMixin.__init__(self, telemetry=self._telemetry) @@ -121,7 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin): published_handle = publish_response.json()["handle"] console.print( - f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}", + f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}", style="bold green", ) @@ -142,7 +138,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin): self._add_package(get_response.json()) - console.print(f"Succesfully installed {handle}", style="bold green") + console.print(f"Successfully installed {handle}", style="bold green") def login(self): login_response = self.plus_api_client.login_to_tool_repository() @@ -155,39 +151,35 @@ class ToolCommand(BaseCommand, PlusAPIMixin): raise SystemExit login_response_json = login_response.json() - self._set_netrc_credentials(login_response_json["credential"]) + + settings = Settings() + settings.tool_repository_username = login_response_json["credential"]["username"] + settings.tool_repository_password = login_response_json["credential"]["password"] + settings.dump() console.print( "Successfully authenticated to the tool repository.", style="bold green" ) - def _set_netrc_credentials(self, credentials, netrc_path=None): - if not netrc_path: - netrc_filename = "_netrc" if platform.system() == "Windows" else ".netrc" - netrc_path = Path.home() / netrc_filename - netrc_path.touch(mode=stat.S_IRUSR | stat.S_IWUSR, exist_ok=True) - - netrc_instance = netrc(file=netrc_path) - netrc_instance.hosts["app.crewai.com"] = (credentials["username"], "", credentials["password"]) - - with open(netrc_path, 'w') as file: - file.write(str(netrc_instance)) - - console.print(f"Added credentials to {netrc_path}", style="bold green") - def _add_package(self, tool_details): tool_handle = tool_details["handle"] repository_handle = tool_details["repository"]["handle"] + repository_url = tool_details["repository"]["url"] + index = f"{repository_handle}={repository_url}" add_package_command = [ "uv", "add", - "--extra-index-url", - self.BASE_URL + repository_handle, + "--index", + index, tool_handle, ] add_package_result = subprocess.run( - add_package_command, capture_output=False, text=True, check=True + add_package_command, + capture_output=False, + env=self._build_env_with_credentials(repository_handle), + text=True, + check=True ) if add_package_result.stderr: @@ -206,3 +198,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin): "[bold yellow]Tip:[/bold yellow] Navigate to a different directory and try again." ) raise SystemExit + + def _build_env_with_credentials(self, repository_handle: str): + repository_handle = repository_handle.upper().replace("-", "_") + settings = Settings() + + env = os.environ.copy() + env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "") + env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "") + + return env diff --git a/src/crewai/cli/update_crew.py b/src/crewai/cli/update_crew.py index e2d39590d..e7ed69aa1 100644 --- a/src/crewai/cli/update_crew.py +++ b/src/crewai/cli/update_crew.py @@ -1,7 +1,9 @@ +import os import shutil import tomli_w -import tomllib + +from crewai.cli.utils import read_toml def update_crew() -> None: @@ -17,10 +19,9 @@ def migrate_pyproject(input_file, output_file): And it will be used to migrate the pyproject.toml to the new format when uv is used. When the time comes that uv supports the new format, this function will be deprecated. """ - + poetry_data = {} # Read the input pyproject.toml - with open(input_file, "rb") as f: - pyproject = tomllib.load(f) + pyproject_data = read_toml() # Initialize the new project structure new_pyproject = { @@ -29,30 +30,30 @@ def migrate_pyproject(input_file, output_file): } # Migrate project metadata - if "tool" in pyproject and "poetry" in pyproject["tool"]: - poetry = pyproject["tool"]["poetry"] - new_pyproject["project"]["name"] = poetry.get("name") - new_pyproject["project"]["version"] = poetry.get("version") - new_pyproject["project"]["description"] = poetry.get("description") + if "tool" in pyproject_data and "poetry" in pyproject_data["tool"]: + poetry_data = pyproject_data["tool"]["poetry"] + new_pyproject["project"]["name"] = poetry_data.get("name") + new_pyproject["project"]["version"] = poetry_data.get("version") + new_pyproject["project"]["description"] = poetry_data.get("description") new_pyproject["project"]["authors"] = [ { "name": author.split("<")[0].strip(), "email": author.split("<")[1].strip(">").strip(), } - for author in poetry.get("authors", []) + for author in poetry_data.get("authors", []) ] - new_pyproject["project"]["requires-python"] = poetry.get("python") + new_pyproject["project"]["requires-python"] = poetry_data.get("python") else: # If it's already in the new format, just copy the project section - new_pyproject["project"] = pyproject.get("project", {}) + new_pyproject["project"] = pyproject_data.get("project", {}) # Migrate or copy dependencies if "dependencies" in new_pyproject["project"]: # If dependencies are already in the new format, keep them as is pass - elif "dependencies" in poetry: + elif poetry_data and "dependencies" in poetry_data: new_pyproject["project"]["dependencies"] = [] - for dep, version in poetry["dependencies"].items(): + for dep, version in poetry_data["dependencies"].items(): if isinstance(version, dict): # Handle extras extras = ",".join(version.get("extras", [])) new_dep = f"{dep}[{extras}]" @@ -66,10 +67,10 @@ def migrate_pyproject(input_file, output_file): new_pyproject["project"]["dependencies"].append(new_dep) # Migrate or copy scripts - if "scripts" in poetry: - new_pyproject["project"]["scripts"] = poetry["scripts"] - elif "scripts" in pyproject.get("project", {}): - new_pyproject["project"]["scripts"] = pyproject["project"]["scripts"] + if poetry_data and "scripts" in poetry_data: + new_pyproject["project"]["scripts"] = poetry_data["scripts"] + elif pyproject_data.get("project", {}) and "scripts" in pyproject_data["project"]: + new_pyproject["project"]["scripts"] = pyproject_data["project"]["scripts"] else: new_pyproject["project"]["scripts"] = {} @@ -86,14 +87,23 @@ def migrate_pyproject(input_file, output_file): new_pyproject["project"]["scripts"]["run_crew"] = f"{module_name}.main:run" # Migrate optional dependencies - if "extras" in poetry: - new_pyproject["project"]["optional-dependencies"] = poetry["extras"] + if poetry_data and "extras" in poetry_data: + new_pyproject["project"]["optional-dependencies"] = poetry_data["extras"] # Backup the old pyproject.toml backup_file = "pyproject-old.toml" shutil.copy2(input_file, backup_file) print(f"Original pyproject.toml backed up as {backup_file}") + # Rename the poetry.lock file + lock_file = "poetry.lock" + lock_backup = "poetry-old.lock" + if os.path.exists(lock_file): + os.rename(lock_file, lock_backup) + print(f"Original poetry.lock renamed to {lock_backup}") + else: + print("No poetry.lock file found to rename.") + # Write the new pyproject.toml with open(output_file, "wb") as f: tomli_w.dump(new_pyproject, f) diff --git a/src/crewai/cli/utils.py b/src/crewai/cli/utils.py index c32113faa..a385e1f37 100644 --- a/src/crewai/cli/utils.py +++ b/src/crewai/cli/utils.py @@ -1,4 +1,3 @@ -import importlib.metadata import os import shutil import sys @@ -6,9 +5,9 @@ from functools import reduce from typing import Any, Dict, List import click +import tomli from rich.console import Console -from crewai.cli.authentication.utils import TokenManager from crewai.cli.constants import ENV_VARS if sys.version_info >= (3, 11): @@ -34,30 +33,17 @@ def copy_template(src, dst, name, class_name, folder_name): click.secho(f" - Created {dst}", fg="green") -# Drop the simple_toml_parser when we move to python3.11 -def simple_toml_parser(content): - result = {} - current_section = result - for line in content.split("\n"): - line = line.strip() - if line.startswith("[") and line.endswith("]"): - # New section - section = line[1:-1].split(".") - current_section = result - for key in section: - current_section = current_section.setdefault(key, {}) - elif "=" in line: - key, value = line.split("=", 1) - key = key.strip() - value = value.strip().strip('"') - current_section[key] = value - return result +def read_toml(file_path: str = "pyproject.toml"): + """Read the content of a TOML file and return it as a dictionary.""" + with open(file_path, "rb") as f: + toml_dict = tomli.load(f) + return toml_dict def parse_toml(content): if sys.version_info >= (3, 11): return tomllib.loads(content) - return simple_toml_parser(content) + return tomli.loads(content) def get_project_name( @@ -129,11 +115,6 @@ def _get_nested_value(data: Dict[str, Any], keys: List[str]) -> Any: return reduce(dict.__getitem__, keys, data) -def get_crewai_version() -> str: - """Get the version number of CrewAI running the CLI""" - return importlib.metadata.version("crewai") - - def fetch_and_json_env_file(env_file_path: str = ".env") -> dict: """Fetch the environment variables from a .env file and return them as a dictionary.""" try: @@ -158,14 +139,6 @@ def fetch_and_json_env_file(env_file_path: str = ".env") -> dict: return {} -def get_auth_token() -> str: - """Get the authentication token.""" - access_token = TokenManager().get_token() - if not access_token: - raise Exception() - return access_token - - def tree_copy(source, destination): """Copies the entire directory structure from the source to the destination.""" for item in os.listdir(source): diff --git a/src/crewai/cli/version.py b/src/crewai/cli/version.py new file mode 100644 index 000000000..a7c1087a7 --- /dev/null +++ b/src/crewai/cli/version.py @@ -0,0 +1,6 @@ +import importlib.metadata + + +def get_crewai_version() -> str: + """Get the version number of CrewAI running the CLI""" + return importlib.metadata.version("crewai") diff --git a/src/crewai/crew.py b/src/crewai/crew.py index 5c3979cec..f0a502249 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, Dict, List, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union from pydantic import ( UUID4, @@ -23,21 +23,22 @@ 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.memory.user.user_memory import UserMemory from crewai.process import Process from crewai.task import Task from crewai.tasks.conditional_task import ConditionalTask from crewai.tasks.task_output import TaskOutput from crewai.telemetry import Telemetry -from crewai.tools.agent_tools import AgentTools +from crewai.tools.agent_tools.agent_tools import AgentTools from crewai.types.usage_metrics import UsageMetrics from crewai.utilities import I18N, FileHandler, Logger, RPMController -from crewai.utilities.constants import ( - TRAINING_DATA_FILE, -) +from crewai.utilities.constants import TRAINING_DATA_FILE from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator from crewai.utilities.evaluators.task_evaluator import TaskEvaluator from crewai.utilities.formatter import ( @@ -55,8 +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") @@ -71,6 +70,7 @@ class Crew(BaseModel): manager_llm: The language model that will run manager agent. manager_agent: Custom agent that will be used as manager. memory: Whether the crew should use memory to store memories of it's execution. + memory_config: Configuration for the memory to be used for the crew. cache: Whether the crew should use a cache to store the results of the tools execution. function_calling_llm: The language model that will run the tool calling for all the agents. process: The process flow that the crew will follow (e.g., sequential, hierarchical). @@ -94,6 +94,7 @@ class Crew(BaseModel): _short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr() _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr() _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr() + _user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr() _train: Optional[bool] = PrivateAttr(default=False) _train_iteration: Optional[int] = PrivateAttr() _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None) @@ -114,6 +115,10 @@ class Crew(BaseModel): default=False, description="Whether the crew should use memory to store memories of it's execution", ) + memory_config: Optional[Dict[str, Any]] = Field( + default=None, + description="Configuration for the memory to be used for the crew.", + ) short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field( default=None, description="An Instance of the ShortTermMemory to be used by the Crew", @@ -126,7 +131,11 @@ class Crew(BaseModel): default=None, description="An Instance of the EntityMemory to be used by the Crew", ) - embedder: Optional[Any] = Field( + user_memory: Optional[InstanceOf[UserMemory]] = Field( + default=None, + description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.", + ) + embedder: Optional[dict] = Field( default=None, description="Configuration for the embedder to be used for the crew.", ) @@ -154,6 +163,16 @@ class Crew(BaseModel): default=None, description="Callback to be executed after each task for all agents execution.", ) + before_kickoff_callbacks: List[ + Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]] + ] = Field( + default_factory=list, + description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.", + ) + after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field( + default_factory=list, + description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.", + ) max_rpm: Optional[int] = Field( default=None, description="Maximum number of requests per minute for the crew execution to be respected.", @@ -182,6 +201,13 @@ class Crew(BaseModel): default=[], description="List of execution logs for tasks", ) + knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field( + default=None, + description="Knowledge sources for the crew. Add knowledge sources to the knowledge object.", + ) + _knowledge: Optional[Knowledge] = PrivateAttr( + default=None, + ) @field_validator("id", mode="before") @classmethod @@ -238,13 +264,42 @@ class Crew(BaseModel): self._short_term_memory = ( self.short_term_memory if self.short_term_memory - else ShortTermMemory(crew=self, embedder_config=self.embedder) + else ShortTermMemory( + crew=self, + embedder_config=self.embedder, + ) ) self._entity_memory = ( self.entity_memory if self.entity_memory else EntityMemory(crew=self, embedder_config=self.embedder) ) + if hasattr(self, "memory_config") and self.memory_config is not None: + self._user_memory = ( + self.user_memory if self.user_memory else UserMemory(crew=self) + ) + else: + self._user_memory = None + return self + + @model_validator(mode="after") + def create_crew_knowledge(self) -> "Crew": + """Create the knowledge for the crew.""" + if self.knowledge_sources: + try: + if isinstance(self.knowledge_sources, list) and all( + isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources + ): + self._knowledge = Knowledge( + sources=self.knowledge_sources, + embedder_config=self.embedder, + collection_name="crew", + ) + + except Exception as e: + self._logger.log( + "warning", f"Failed to init knowledge: {e}", color="yellow" + ) return self @model_validator(mode="after") @@ -435,27 +490,32 @@ class Crew(BaseModel): self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {} ) -> None: """Trains the crew for a given number of iterations.""" - self._setup_for_training(filename) + train_crew = self.copy() + train_crew._setup_for_training(filename) for n_iteration in range(n_iterations): - self._train_iteration = n_iteration - self.kickoff(inputs=inputs) + train_crew._train_iteration = n_iteration + train_crew.kickoff(inputs=inputs) training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load() - for agent in self.agents: - result = TaskEvaluator(agent).evaluate_training_data( - training_data=training_data, agent_id=str(agent.id) - ) + for agent in train_crew.agents: + if training_data.get(str(agent.id)): + result = TaskEvaluator(agent).evaluate_training_data( + training_data=training_data, agent_id=str(agent.id) + ) - CrewTrainingHandler(filename).save_trained_data( - agent_id=str(agent.role), trained_data=result.model_dump() - ) + CrewTrainingHandler(filename).save_trained_data( + agent_id=str(agent.role), trained_data=result.model_dump() + ) def kickoff( self, inputs: Optional[Dict[str, Any]] = None, ) -> CrewOutput: + for before_callback in self.before_kickoff_callbacks: + inputs = before_callback(inputs) + """Starts the crew to work on its assigned tasks.""" self._execution_span = self._telemetry.crew_execution_span(self, inputs) self._task_output_handler.reset() @@ -498,6 +558,9 @@ class Crew(BaseModel): f"The process '{self.process}' is not implemented yet." ) + for after_callback in self.after_kickoff_callbacks: + result = after_callback(result) + metrics += [agent._token_process.get_summary() for agent in self.agents] self.usage_metrics = UsageMetrics() @@ -892,6 +955,11 @@ class Crew(BaseModel): result = self._execute_tasks(self.tasks, start_index, True) return result + def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]: + if self._knowledge: + return self._knowledge.query(query) + return None + def copy(self): """Create a deep copy of the Crew.""" @@ -988,31 +1056,21 @@ class Crew(BaseModel): inputs: Optional[Dict[str, Any]] = None, ) -> None: """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures.""" - self._test_execution_span = self._telemetry.test_execution_span( - self, + test_crew = self.copy() + + self._test_execution_span = test_crew._telemetry.test_execution_span( + test_crew, n_iterations, inputs, openai_model_name, # type: ignore[arg-type] ) # type: ignore[arg-type] - evaluator = CrewEvaluator(self, openai_model_name) # type: ignore[arg-type] + evaluator = CrewEvaluator(test_crew, openai_model_name) # type: ignore[arg-type] for i in range(1, n_iterations + 1): evaluator.set_iteration(i) - self.kickoff(inputs=inputs) + test_crew.kickoff(inputs=inputs) 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/flow/flow.py b/src/crewai/flow/flow.py index 66fa49659..ccc76dc95 100644 --- a/src/crewai/flow/flow.py +++ b/src/crewai/flow/flow.py @@ -1,11 +1,28 @@ -# flow.py - import asyncio import inspect -from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union +from typing import ( + Any, + Callable, + Dict, + Generic, + List, + Optional, + Set, + Type, + TypeVar, + Union, + cast, +) -from pydantic import BaseModel +from blinker import Signal +from pydantic import BaseModel, ValidationError +from crewai.flow.flow_events import ( + FlowFinishedEvent, + FlowStartedEvent, + MethodExecutionFinishedEvent, + MethodExecutionStartedEvent, +) from crewai.flow.flow_visualizer import plot_flow from crewai.flow.utils import get_possible_return_constants from crewai.telemetry import Telemetry @@ -120,6 +137,7 @@ class FlowMeta(type): methods = attr_value.__trigger_methods__ condition_type = getattr(attr_value, "__condition_type__", "OR") listeners[attr_name] = (condition_type, methods) + elif hasattr(attr_value, "__is_router__"): routers[attr_value.__router_for__] = attr_name possible_returns = get_possible_return_constants(attr_value) @@ -148,6 +166,7 @@ class Flow(Generic[T], metaclass=FlowMeta): _routers: Dict[str, str] = {} _router_paths: Dict[str, List[str]] = {} initial_state: Union[Type[T], T, None] = None + event_emitter = Signal("event_emitter") def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]: class _FlowGeneric(cls): # type: ignore @@ -159,7 +178,7 @@ class Flow(Generic[T], metaclass=FlowMeta): def __init__(self) -> None: self._methods: Dict[str, Callable] = {} self._state: T = self._create_initial_state() - self._completed_methods: Set[str] = set() + self._method_execution_counts: Dict[str, int] = {} self._pending_and_listeners: Dict[str, Set[str]] = {} self._method_outputs: List[Any] = [] # List to store all method outputs @@ -190,7 +209,80 @@ class Flow(Generic[T], metaclass=FlowMeta): """Returns the list of all outputs from executed methods.""" return self._method_outputs - async def kickoff(self) -> Any: + def _initialize_state(self, inputs: Dict[str, Any]) -> None: + """ + Initializes or updates the state with the provided inputs. + + Args: + inputs: Dictionary of inputs to initialize or update the state. + + Raises: + ValueError: If inputs do not match the structured state model. + TypeError: If state is neither a BaseModel instance nor a dictionary. + """ + if isinstance(self._state, BaseModel): + # Structured state management + try: + # Define a function to create the dynamic class + def create_model_with_extra_forbid( + base_model: Type[BaseModel], + ) -> Type[BaseModel]: + class ModelWithExtraForbid(base_model): # type: ignore + model_config = base_model.model_config.copy() + model_config["extra"] = "forbid" + + return ModelWithExtraForbid + + # Create the dynamic class + ModelWithExtraForbid = create_model_with_extra_forbid( + self._state.__class__ + ) + + # Create a new instance using the combined state and inputs + self._state = cast( + T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs}) + ) + + except ValidationError as e: + raise ValueError(f"Invalid inputs for structured state: {e}") from e + elif isinstance(self._state, dict): + # Unstructured state management + self._state.update(inputs) + else: + raise TypeError("State must be a BaseModel instance or a dictionary.") + + def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any: + """ + Starts the execution of the flow synchronously. + + Args: + inputs: Optional dictionary of inputs to initialize or update the state. + + Returns: + The final output from the flow execution. + """ + self.event_emitter.send( + self, + event=FlowStartedEvent( + type="flow_started", + flow_name=self.__class__.__name__, + ), + ) + + if inputs is not None: + self._initialize_state(inputs) + return asyncio.run(self.kickoff_async()) + + async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any: + """ + Starts the execution of the flow asynchronously. + + Args: + inputs: Optional dictionary of inputs to initialize or update the state. + + Returns: + The final output from the flow execution. + """ if not self._start_methods: raise ValueError("No start method defined") @@ -207,23 +299,41 @@ class Flow(Generic[T], metaclass=FlowMeta): # Run all start methods concurrently await asyncio.gather(*tasks) - # Return the final output (from the last executed method) - if self._method_outputs: - return self._method_outputs[-1] - else: - return None # Or raise an exception if no methods were executed + # Determine the final output (from the last executed method) + final_output = self._method_outputs[-1] if self._method_outputs else None - async def _execute_start_method(self, start_method: str) -> None: - result = await self._execute_method(self._methods[start_method]) - await self._execute_listeners(start_method, result) + self.event_emitter.send( + self, + event=FlowFinishedEvent( + type="flow_finished", + flow_name=self.__class__.__name__, + result=final_output, + ), + ) - async def _execute_method(self, method: Callable, *args: Any, **kwargs: Any) -> Any: + return final_output + + async def _execute_start_method(self, start_method_name: str) -> None: + result = await self._execute_method( + start_method_name, self._methods[start_method_name] + ) + await self._execute_listeners(start_method_name, result) + + async def _execute_method( + self, method_name: str, method: Callable, *args: Any, **kwargs: Any + ) -> Any: result = ( await method(*args, **kwargs) if asyncio.iscoroutinefunction(method) else method(*args, **kwargs) ) self._method_outputs.append(result) # Store the output + + # Track method execution counts + self._method_execution_counts[method_name] = ( + self._method_execution_counts.get(method_name, 0) + 1 + ) + return result async def _execute_listeners(self, trigger_method: str, result: Any) -> None: @@ -231,32 +341,49 @@ class Flow(Generic[T], metaclass=FlowMeta): if trigger_method in self._routers: router_method = self._methods[self._routers[trigger_method]] - path = await self._execute_method(router_method) - # Use the path as the new trigger method + path = await self._execute_method( + self._routers[trigger_method], router_method + ) trigger_method = path - for listener, (condition_type, methods) in self._listeners.items(): + for listener_name, (condition_type, methods) in self._listeners.items(): if condition_type == "OR": if trigger_method in methods: + # Schedule the listener without preventing re-execution listener_tasks.append( - self._execute_single_listener(listener, result) + self._execute_single_listener(listener_name, result) ) elif condition_type == "AND": - if listener not in self._pending_and_listeners: - self._pending_and_listeners[listener] = set() - self._pending_and_listeners[listener].add(trigger_method) - if set(methods) == self._pending_and_listeners[listener]: + # Initialize pending methods for this listener if not already done + if listener_name not in self._pending_and_listeners: + self._pending_and_listeners[listener_name] = set(methods) + # Remove the trigger method from pending methods + self._pending_and_listeners[listener_name].discard(trigger_method) + if not self._pending_and_listeners[listener_name]: + # All required methods have been executed listener_tasks.append( - self._execute_single_listener(listener, result) + self._execute_single_listener(listener_name, result) ) - del self._pending_and_listeners[listener] + # Reset pending methods for this listener + self._pending_and_listeners.pop(listener_name, None) # Run all listener tasks concurrently and wait for them to complete - await asyncio.gather(*listener_tasks) + if listener_tasks: + await asyncio.gather(*listener_tasks) - async def _execute_single_listener(self, listener: str, result: Any) -> None: + async def _execute_single_listener(self, listener_name: str, result: Any) -> None: try: - method = self._methods[listener] + method = self._methods[listener_name] + + self.event_emitter.send( + self, + event=MethodExecutionStartedEvent( + type="method_execution_started", + method_name=listener_name, + flow_name=self.__class__.__name__, + ), + ) + sig = inspect.signature(method) params = list(sig.parameters.values()) @@ -265,15 +392,28 @@ class Flow(Generic[T], metaclass=FlowMeta): if method_params: # If listener expects parameters, pass the result - listener_result = await self._execute_method(method, result) + listener_result = await self._execute_method( + listener_name, method, result + ) else: # If listener does not expect parameters, call without arguments - listener_result = await self._execute_method(method) + listener_result = await self._execute_method(listener_name, method) + + self.event_emitter.send( + self, + event=MethodExecutionFinishedEvent( + type="method_execution_finished", + method_name=listener_name, + flow_name=self.__class__.__name__, + ), + ) # Execute listeners of this listener - await self._execute_listeners(listener, listener_result) + await self._execute_listeners(listener_name, listener_result) except Exception as e: - print(f"[Flow._execute_single_listener] Error in method {listener}: {e}") + print( + f"[Flow._execute_single_listener] Error in method {listener_name}: {e}" + ) import traceback traceback.print_exc() diff --git a/src/crewai/flow/flow_events.py b/src/crewai/flow/flow_events.py new file mode 100644 index 000000000..068005ebe --- /dev/null +++ b/src/crewai/flow/flow_events.py @@ -0,0 +1,33 @@ +from dataclasses import dataclass, field +from datetime import datetime +from typing import Any, Optional + + +@dataclass +class Event: + type: str + flow_name: str + timestamp: datetime = field(init=False) + + def __post_init__(self): + self.timestamp = datetime.now() + + +@dataclass +class FlowStartedEvent(Event): + pass + + +@dataclass +class MethodExecutionStartedEvent(Event): + method_name: str + + +@dataclass +class MethodExecutionFinishedEvent(Event): + method_name: str + + +@dataclass +class FlowFinishedEvent(Event): + result: Optional[Any] = None diff --git a/src/crewai/cli/templates/pipeline/__init__.py b/src/crewai/knowledge/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline/__init__.py rename to src/crewai/knowledge/__init__.py diff --git a/src/crewai/cli/templates/pipeline/pipelines/__init__.py b/src/crewai/knowledge/embedder/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline/pipelines/__init__.py rename to src/crewai/knowledge/embedder/__init__.py diff --git a/src/crewai/knowledge/embedder/base_embedder.py b/src/crewai/knowledge/embedder/base_embedder.py new file mode 100644 index 000000000..c3252bf43 --- /dev/null +++ b/src/crewai/knowledge/embedder/base_embedder.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod +from typing import List + +import numpy as np + + +class BaseEmbedder(ABC): + """ + Abstract base class for text embedding models + """ + + @abstractmethod + def embed_chunks(self, chunks: List[str]) -> np.ndarray: + """ + Generate embeddings for a list of text chunks + + Args: + chunks: List of text chunks to embed + + Returns: + Array of embeddings + """ + pass + + @abstractmethod + def embed_texts(self, texts: List[str]) -> np.ndarray: + """ + Generate embeddings for a list of texts + + Args: + texts: List of texts to embed + + Returns: + Array of embeddings + """ + pass + + @abstractmethod + def embed_text(self, text: str) -> np.ndarray: + """ + Generate embedding for a single text + + Args: + text: Text to embed + + Returns: + Embedding array + """ + pass + + @property + @abstractmethod + def dimension(self) -> int: + """Get the dimension of the embeddings""" + pass diff --git a/src/crewai/knowledge/embedder/fastembed.py b/src/crewai/knowledge/embedder/fastembed.py new file mode 100644 index 000000000..54db11643 --- /dev/null +++ b/src/crewai/knowledge/embedder/fastembed.py @@ -0,0 +1,93 @@ +from pathlib import Path +from typing import List, Optional, Union + +import numpy as np + +from .base_embedder import BaseEmbedder + +try: + from fastembed_gpu import TextEmbedding # type: ignore + + FASTEMBED_AVAILABLE = True +except ImportError: + try: + from fastembed import TextEmbedding + + FASTEMBED_AVAILABLE = True + except ImportError: + FASTEMBED_AVAILABLE = False + + +class FastEmbed(BaseEmbedder): + """ + A wrapper class for text embedding models using FastEmbed + """ + + def __init__( + self, + model_name: str = "BAAI/bge-small-en-v1.5", + cache_dir: Optional[Union[str, Path]] = None, + ): + """ + Initialize the embedding model + + Args: + model_name: Name of the model to use + cache_dir: Directory to cache the model + gpu: Whether to use GPU acceleration + """ + if not FASTEMBED_AVAILABLE: + raise ImportError( + "FastEmbed is not installed. Please install it with: " + "uv pip install fastembed or uv pip install fastembed-gpu for GPU support" + ) + + self.model = TextEmbedding( + model_name=model_name, + cache_dir=str(cache_dir) if cache_dir else None, + ) + + def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]: + """ + Generate embeddings for a list of text chunks + + Args: + chunks: List of text chunks to embed + + Returns: + List of embeddings + """ + embeddings = list(self.model.embed(chunks)) + return embeddings + + def embed_texts(self, texts: List[str]) -> List[np.ndarray]: + """ + Generate embeddings for a list of texts + + Args: + texts: List of texts to embed + + Returns: + List of embeddings + """ + embeddings = list(self.model.embed(texts)) + return embeddings + + def embed_text(self, text: str) -> np.ndarray: + """ + Generate embedding for a single text + + Args: + text: Text to embed + + Returns: + Embedding array + """ + return self.embed_texts([text])[0] + + @property + def dimension(self) -> int: + """Get the dimension of the embeddings""" + # Generate a test embedding to get dimensions + test_embed = self.embed_text("test") + return len(test_embed) diff --git a/src/crewai/knowledge/knowledge.py b/src/crewai/knowledge/knowledge.py new file mode 100644 index 000000000..f9f55a517 --- /dev/null +++ b/src/crewai/knowledge/knowledge.py @@ -0,0 +1,63 @@ +import os +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, ConfigDict, Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage + +os.environ["TOKENIZERS_PARALLELISM"] = "false" # removes logging from fastembed + + +class Knowledge(BaseModel): + """ + Knowledge is a collection of sources and setup for the vector store to save and query relevant context. + Args: + sources: List[BaseKnowledgeSource] = Field(default_factory=list) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + embedder_config: Optional[Dict[str, Any]] = None + """ + + sources: List[BaseKnowledgeSource] = Field(default_factory=list) + model_config = ConfigDict(arbitrary_types_allowed=True) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + embedder_config: Optional[Dict[str, Any]] = None + collection_name: Optional[str] = None + + def __init__( + self, + collection_name: str, + sources: List[BaseKnowledgeSource], + embedder_config: Optional[Dict[str, Any]] = None, + storage: Optional[KnowledgeStorage] = None, + **data, + ): + super().__init__(**data) + if storage: + self.storage = storage + else: + self.storage = KnowledgeStorage( + embedder_config=embedder_config, collection_name=collection_name + ) + self.sources = sources + self.storage.initialize_knowledge_storage() + for source in sources: + source.storage = self.storage + source.add() + + 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. + """ + + results = self.storage.search( + query, + limit, + ) + return results + + def _add_sources(self): + for source in self.sources: + source.storage = self.storage + source.add() diff --git a/src/crewai/cli/templates/pipeline/tools/__init__.py b/src/crewai/knowledge/source/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline/tools/__init__.py rename to src/crewai/knowledge/source/__init__.py diff --git a/src/crewai/knowledge/source/base_file_knowledge_source.py b/src/crewai/knowledge/source/base_file_knowledge_source.py new file mode 100644 index 000000000..ae9b3bf71 --- /dev/null +++ b/src/crewai/knowledge/source/base_file_knowledge_source.py @@ -0,0 +1,71 @@ +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Dict, List, Union + +from pydantic import Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +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): + """Base class for knowledge sources that load content from files.""" + + _logger: Logger = Logger(verbose=True) + file_path: Union[Path, List[Path], str, List[str]] = Field( + ..., description="The path to the file" + ) + content: Dict[Path, str] = Field(init=False, default_factory=dict) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + safe_file_paths: List[Path] = Field(default_factory=list) + + def model_post_init(self, _): + """Post-initialization method to load content.""" + self.safe_file_paths = self._process_file_paths() + self.validate_paths() + self.content = self.load_content() + + @abstractmethod + def load_content(self) -> Dict[Path, str]: + """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory.""" + pass + + def validate_paths(self): + """Validate the paths.""" + for path in self.safe_file_paths: + if not path.exists(): + self._logger.log( + "error", + f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.", + color="red", + ) + raise FileNotFoundError(f"File not found: {path}") + if not path.is_file(): + self._logger.log( + "error", + f"Path is not a file: {path}", + color="red", + ) + + def _save_documents(self): + """Save the documents to the storage.""" + self.storage.save(self.chunks) + + def convert_to_path(self, path: Union[Path, str]) -> Path: + """Convert a path to a Path object.""" + return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path + + def _process_file_paths(self) -> List[Path]: + """Convert file_path to a list of Path objects.""" + paths = ( + [self.file_path] + if isinstance(self.file_path, (str, Path)) + else self.file_path + ) + + if not isinstance(paths, list): + raise ValueError("file_path must be a Path, str, or a list of these types") + + return [self.convert_to_path(path) for path in paths] diff --git a/src/crewai/knowledge/source/base_knowledge_source.py b/src/crewai/knowledge/source/base_knowledge_source.py new file mode 100644 index 000000000..07a9a0f25 --- /dev/null +++ b/src/crewai/knowledge/source/base_knowledge_source.py @@ -0,0 +1,49 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + +import numpy as np +from pydantic import BaseModel, ConfigDict, Field + +from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage + + +class BaseKnowledgeSource(BaseModel, ABC): + """Abstract base class for knowledge sources.""" + + chunk_size: int = 4000 + chunk_overlap: int = 200 + chunks: List[str] = Field(default_factory=list) + chunk_embeddings: List[np.ndarray] = Field(default_factory=list) + + model_config = ConfigDict(arbitrary_types_allowed=True) + storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) + metadata: Dict[str, Any] = Field(default_factory=dict) # Currently unused + collection_name: Optional[str] = Field(default=None) + + @abstractmethod + def load_content(self) -> Dict[Any, str]: + """Load and preprocess content from the source.""" + pass + + @abstractmethod + def add(self) -> None: + """Process content, chunk it, compute embeddings, and save them.""" + pass + + def get_embeddings(self) -> List[np.ndarray]: + """Return the list of embeddings for the chunks.""" + return self.chunk_embeddings + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] + + 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) diff --git a/src/crewai/knowledge/source/csv_knowledge_source.py b/src/crewai/knowledge/source/csv_knowledge_source.py new file mode 100644 index 000000000..3bb0714d9 --- /dev/null +++ b/src/crewai/knowledge/source/csv_knowledge_source.py @@ -0,0 +1,40 @@ +import csv +from pathlib import Path +from typing import Dict, List + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class CSVKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries CSV file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess CSV file content.""" + content_dict = {} + for file_path in self.safe_file_paths: + with open(file_path, "r", encoding="utf-8") as csvfile: + reader = csv.reader(csvfile) + content = "" + for row in reader: + content += " ".join(row) + "\n" + content_dict[file_path] = content + return content_dict + + def add(self) -> None: + """ + Add CSV file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + content_str = ( + str(self.content) if isinstance(self.content, dict) else self.content + ) + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self._save_documents() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/excel_knowledge_source.py b/src/crewai/knowledge/source/excel_knowledge_source.py new file mode 100644 index 000000000..d379c2763 --- /dev/null +++ b/src/crewai/knowledge/source/excel_knowledge_source.py @@ -0,0 +1,55 @@ +from pathlib import Path +from typing import Dict, List + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class ExcelKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries Excel file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess Excel file content.""" + pd = self._import_dependencies() + + content_dict = {} + for file_path in self.safe_file_paths: + file_path = self.convert_to_path(file_path) + df = pd.read_excel(file_path) + content = df.to_csv(index=False) + content_dict[file_path] = content + return content_dict + + def _import_dependencies(self): + """Dynamically import dependencies.""" + try: + import openpyxl # noqa + import pandas as pd + + return pd + except ImportError as e: + missing_package = str(e).split()[-1] + raise ImportError( + f"{missing_package} is not installed. Please install it with: pip install {missing_package}" + ) + + def add(self) -> None: + """ + Add Excel file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + # Convert dictionary values to a single string if content is a dictionary + if isinstance(self.content, dict): + content_str = "\n".join(str(value) for value in self.content.values()) + else: + content_str = str(self.content) + + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self._save_documents() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/json_knowledge_source.py b/src/crewai/knowledge/source/json_knowledge_source.py new file mode 100644 index 000000000..b02d438e6 --- /dev/null +++ b/src/crewai/knowledge/source/json_knowledge_source.py @@ -0,0 +1,52 @@ +import json +from pathlib import Path +from typing import Any, Dict, List + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class JSONKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries JSON file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess JSON file content.""" + content: Dict[Path, str] = {} + for path in self.safe_file_paths: + path = self.convert_to_path(path) + with open(path, "r", encoding="utf-8") as json_file: + data = json.load(json_file) + content[path] = self._json_to_text(data) + return content + + def _json_to_text(self, data: Any, level: int = 0) -> str: + """Recursively convert JSON data to a text representation.""" + text = "" + indent = " " * level + if isinstance(data, dict): + for key, value in data.items(): + text += f"{indent}{key}: {self._json_to_text(value, level + 1)}\n" + elif isinstance(data, list): + for item in data: + text += f"{indent}- {self._json_to_text(item, level + 1)}\n" + else: + text += f"{str(data)}" + return text + + def add(self) -> None: + """ + Add JSON file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + content_str = ( + str(self.content) if isinstance(self.content, dict) else self.content + ) + new_chunks = self._chunk_text(content_str) + self.chunks.extend(new_chunks) + self._save_documents() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/pdf_knowledge_source.py b/src/crewai/knowledge/source/pdf_knowledge_source.py new file mode 100644 index 000000000..38cd67807 --- /dev/null +++ b/src/crewai/knowledge/source/pdf_knowledge_source.py @@ -0,0 +1,53 @@ +from pathlib import Path +from typing import Dict, List + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class PDFKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries PDF file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess PDF file content.""" + pdfplumber = self._import_pdfplumber() + + content = {} + + for path in self.safe_file_paths: + text = "" + path = self.convert_to_path(path) + with pdfplumber.open(path) as pdf: + for page in pdf.pages: + page_text = page.extract_text() + if page_text: + text += page_text + "\n" + content[path] = text + return content + + def _import_pdfplumber(self): + """Dynamically import pdfplumber.""" + try: + import pdfplumber + + return pdfplumber + except ImportError: + raise ImportError( + "pdfplumber is not installed. Please install it with: pip install pdfplumber" + ) + + def add(self) -> None: + """ + Add PDF file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + for _, text in self.content.items(): + new_chunks = self._chunk_text(text) + self.chunks.extend(new_chunks) + self._save_documents() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/string_knowledge_source.py b/src/crewai/knowledge/source/string_knowledge_source.py new file mode 100644 index 000000000..1d40ac4b8 --- /dev/null +++ b/src/crewai/knowledge/source/string_knowledge_source.py @@ -0,0 +1,34 @@ +from typing import List, Optional + +from pydantic import Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource + + +class StringKnowledgeSource(BaseKnowledgeSource): + """A knowledge source that stores and queries plain text content using embeddings.""" + + content: str = Field(...) + collection_name: Optional[str] = Field(default=None) + + def model_post_init(self, _): + """Post-initialization method to validate content.""" + self.load_content() + + def load_content(self): + """Validate string content.""" + if not isinstance(self.content, str): + raise ValueError("StringKnowledgeSource only accepts string content") + + def add(self) -> None: + """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() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/knowledge/source/text_file_knowledge_source.py b/src/crewai/knowledge/source/text_file_knowledge_source.py new file mode 100644 index 000000000..ddb1f2516 --- /dev/null +++ b/src/crewai/knowledge/source/text_file_knowledge_source.py @@ -0,0 +1,34 @@ +from pathlib import Path +from typing import Dict, List + +from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource + + +class TextFileKnowledgeSource(BaseFileKnowledgeSource): + """A knowledge source that stores and queries text file content using embeddings.""" + + def load_content(self) -> Dict[Path, str]: + """Load and preprocess text file content.""" + content = {} + for path in self.safe_file_paths: + path = self.convert_to_path(path) + with open(path, "r", encoding="utf-8") as f: + content[path] = f.read() + return content + + def add(self) -> None: + """ + Add text file content to the knowledge source, chunk it, compute embeddings, + and save the embeddings. + """ + for _, text in self.content.items(): + new_chunks = self._chunk_text(text) + self.chunks.extend(new_chunks) + self._save_documents() + + def _chunk_text(self, text: str) -> List[str]: + """Utility method to split text into chunks.""" + return [ + text[i : i + self.chunk_size] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap) + ] diff --git a/src/crewai/cli/templates/pipeline_router/__init__.py b/src/crewai/knowledge/storage/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline_router/__init__.py rename to src/crewai/knowledge/storage/__init__.py diff --git a/src/crewai/knowledge/storage/base_knowledge_storage.py b/src/crewai/knowledge/storage/base_knowledge_storage.py new file mode 100644 index 000000000..d4887e85b --- /dev/null +++ b/src/crewai/knowledge/storage/base_knowledge_storage.py @@ -0,0 +1,29 @@ +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + + +class BaseKnowledgeStorage(ABC): + """Abstract base class for knowledge storage implementations.""" + + @abstractmethod + def search( + self, + query: List[str], + limit: int = 3, + filter: Optional[dict] = None, + score_threshold: float = 0.35, + ) -> List[Dict[str, Any]]: + """Search for documents in the knowledge base.""" + pass + + @abstractmethod + def save( + self, documents: List[str], metadata: Dict[str, Any] | List[Dict[str, Any]] + ) -> None: + """Save documents to the knowledge base.""" + pass + + @abstractmethod + def reset(self) -> None: + """Reset the knowledge base.""" + pass diff --git a/src/crewai/knowledge/storage/knowledge_storage.py b/src/crewai/knowledge/storage/knowledge_storage.py new file mode 100644 index 000000000..7f79877ed --- /dev/null +++ b/src/crewai/knowledge/storage/knowledge_storage.py @@ -0,0 +1,186 @@ +import contextlib +import hashlib +import io +import logging +import os +import shutil +from typing import Any, Dict, List, Optional, Union, cast + +import chromadb +import chromadb.errors +from chromadb.api import ClientAPI +from chromadb.api.types import OneOrMany +from chromadb.config import Settings + +from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage +from crewai.utilities import EmbeddingConfigurator +from crewai.utilities.constants import KNOWLEDGE_DIRECTORY +from crewai.utilities.logger import Logger +from crewai.utilities.paths import db_storage_path + + +@contextlib.contextmanager +def suppress_logging( + logger_name="chromadb.segment.impl.vector.local_persistent_hnsw", + level=logging.ERROR, +): + logger = logging.getLogger(logger_name) + original_level = logger.getEffectiveLevel() + logger.setLevel(level) + with ( + contextlib.redirect_stdout(io.StringIO()), + contextlib.redirect_stderr(io.StringIO()), + contextlib.suppress(UserWarning), + ): + yield + logger.setLevel(original_level) + + +class KnowledgeStorage(BaseKnowledgeStorage): + """ + Extends Storage to handle embeddings for memory entries, improving + search efficiency. + """ + + collection: Optional[chromadb.Collection] = None + collection_name: Optional[str] = "knowledge" + app: Optional[ClientAPI] = None + + def __init__( + self, + embedder_config: Optional[Dict[str, Any]] = None, + collection_name: Optional[str] = None, + ): + self.collection_name = collection_name + self._set_embedder_config(embedder_config) + + def search( + self, + query: List[str], + limit: int = 3, + filter: Optional[dict] = None, + score_threshold: float = 0.35, + ) -> List[Dict[str, Any]]: + with suppress_logging(): + if self.collection: + fetched = self.collection.query( + query_texts=query, + n_results=limit, + where=filter, + ) + results = [] + for i in range(len(fetched["ids"][0])): # type: ignore + result = { + "id": fetched["ids"][0][i], # type: ignore + "metadata": fetched["metadatas"][0][i], # type: ignore + "context": fetched["documents"][0][i], # type: ignore + "score": fetched["distances"][0][i], # type: ignore + } + if result["score"] >= score_threshold: # type: ignore + results.append(result) + return results + else: + raise Exception("Collection not initialized") + + def initialize_knowledge_storage(self): + base_path = os.path.join(db_storage_path(), "knowledge") + chroma_client = chromadb.PersistentClient( + path=base_path, + settings=Settings(allow_reset=True), + ) + + self.app = chroma_client + + try: + collection_name = ( + f"knowledge_{self.collection_name}" + if self.collection_name + else "knowledge" + ) + if self.app: + self.collection = self.app.get_or_create_collection( + name=collection_name, embedding_function=self.embedder_config + ) + else: + raise Exception("Vector Database Client not initialized") + except Exception: + raise Exception("Failed to create or get collection") + + def reset(self): + base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY) + if not self.app: + self.app = chromadb.PersistentClient( + path=base_path, + settings=Settings(allow_reset=True), + ) + + self.app.reset() + shutil.rmtree(base_path) + self.app = None + self.collection = None + + def save( + self, + documents: List[str], + metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, + ): + if self.collection: + try: + if metadata is None: + metadatas: Optional[OneOrMany[chromadb.Metadata]] = None + elif isinstance(metadata, list): + metadatas = [cast(chromadb.Metadata, m) for m in metadata] + else: + metadatas = cast(chromadb.Metadata, metadata) + + ids = [ + hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents + ] + + self.collection.upsert( + documents=documents, + metadatas=metadatas, + ids=ids, + ) + except chromadb.errors.InvalidDimensionException as e: + Logger(verbose=True).log( + "error", + "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`", + "red", + ) + raise ValueError( + "Embedding dimension mismatch. Make sure you're using the same embedding model " + "across all operations with this collection." + "Try resetting the collection using `crewai reset-memories -a`" + ) from e + except Exception as e: + Logger(verbose=True).log( + "error", f"Failed to upsert documents: {e}", "red" + ) + raise + else: + raise Exception("Collection not initialized") + + def _create_default_embedding_function(self): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" + ) + + def _set_embedder_config( + self, embedder_config: Optional[Dict[str, Any]] = None + ) -> None: + """Set the embedding configuration for the knowledge storage. + + Args: + embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder. + If None or empty, defaults to the default embedding function. + """ + self.embedder_config = ( + EmbeddingConfigurator().configure_embedder(embedder_config) + if embedder_config + else self._create_default_embedding_function() + ) diff --git a/src/crewai/knowledge/utils/knowledge_utils.py b/src/crewai/knowledge/utils/knowledge_utils.py new file mode 100644 index 000000000..bdd8b9a4e --- /dev/null +++ b/src/crewai/knowledge/utils/knowledge_utils.py @@ -0,0 +1,12 @@ +from typing import Any, Dict, List + + +def extract_knowledge_context(knowledge_snippets: List[Dict[str, Any]]) -> str: + """Extract knowledge from the task prompt.""" + valid_snippets = [ + result["context"] + for result in knowledge_snippets + if result and result.get("context") + ] + snippet = "\n".join(valid_snippets) + return f"Additional Information: {snippet}" if valid_snippets else "" diff --git a/src/crewai/llm.py b/src/crewai/llm.py index 000bc2509..058589056 100644 --- a/src/crewai/llm.py +++ b/src/crewai/llm.py @@ -1,7 +1,11 @@ +import logging +import os +import sys +import threading +import warnings from contextlib import contextmanager from typing import Any, Dict, List, Optional, Union -import logging -import warnings + import litellm from litellm import get_supported_openai_params @@ -9,20 +13,26 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, ) -import sys -import io +class FilteredStream: + def __init__(self, original_stream): + self._original_stream = original_stream + self._lock = threading.Lock() -class FilteredStream(io.StringIO): - def write(self, s): - if ( - "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new" - in s - or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`" - in s - ): - return - super().write(s) + def write(self, s) -> int: + with self._lock: + if ( + "Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new" + in s + or "LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True`" + in s + ): + return 0 + return self._original_stream.write(s) + + def flush(self): + with self._lock: + return self._original_stream.flush() LLM_CONTEXT_WINDOW_SIZES = { @@ -33,6 +43,10 @@ LLM_CONTEXT_WINDOW_SIZES = { "gpt-4-turbo": 128000, "o1-preview": 128000, "o1-mini": 128000, + # gemini + "gemini-1.5-pro": 2097152, + "gemini-1.5-flash": 1048576, + "gemini-1.5-flash-8b": 1048576, # deepseek "deepseek-chat": 128000, # groq @@ -51,6 +65,9 @@ LLM_CONTEXT_WINDOW_SIZES = { "mixtral-8x7b-32768": 32768, } +DEFAULT_CONTEXT_WINDOW_SIZE = 8192 +CONTEXT_WINDOW_USAGE_RATIO = 0.75 + @contextmanager def suppress_warnings(): @@ -60,8 +77,8 @@ def suppress_warnings(): # Redirect stdout and stderr old_stdout = sys.stdout old_stderr = sys.stderr - sys.stdout = FilteredStream() - sys.stderr = FilteredStream() + sys.stdout = FilteredStream(old_stdout) + sys.stderr = FilteredStream(old_stderr) try: yield @@ -114,16 +131,18 @@ class LLM: self.api_version = api_version self.api_key = api_key self.callbacks = callbacks + self.context_window_size = 0 self.kwargs = kwargs litellm.drop_params = True litellm.set_verbose = False - litellm.callbacks = callbacks + self.set_callbacks(callbacks) + self.set_env_callbacks() def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str: with suppress_warnings(): if callbacks and len(callbacks) > 0: - litellm.callbacks = callbacks + self.set_callbacks(callbacks) try: params = { @@ -180,4 +199,61 @@ class LLM: def get_context_window_size(self) -> int: # Only using 75% of the context window size to avoid cutting the message in the middle - return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75) + if self.context_window_size != 0: + return self.context_window_size + + self.context_window_size = int( + DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO + ) + for key, value in LLM_CONTEXT_WINDOW_SIZES.items(): + if self.model.startswith(key): + self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO) + return self.context_window_size + + def set_callbacks(self, callbacks: List[Any]): + callback_types = [type(callback) for callback in callbacks] + for callback in litellm.success_callback[:]: + if type(callback) in callback_types: + litellm.success_callback.remove(callback) + + for callback in litellm._async_success_callback[:]: + if type(callback) in callback_types: + 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 diff --git a/src/crewai/memory/__init__.py b/src/crewai/memory/__init__.py index 8182bede7..3f7ca2ad6 100644 --- a/src/crewai/memory/__init__.py +++ b/src/crewai/memory/__init__.py @@ -1,5 +1,6 @@ from .entity.entity_memory import EntityMemory from .long_term.long_term_memory import LongTermMemory from .short_term.short_term_memory import ShortTermMemory +from .user.user_memory import UserMemory -__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"] +__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"] diff --git a/src/crewai/memory/contextual/contextual_memory.py b/src/crewai/memory/contextual/contextual_memory.py index 5d91cf47d..cdb9cf836 100644 --- a/src/crewai/memory/contextual/contextual_memory.py +++ b/src/crewai/memory/contextual/contextual_memory.py @@ -1,13 +1,25 @@ -from typing import Optional +from typing import Any, Dict, Optional -from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory +from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory class ContextualMemory: - def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory): + def __init__( + self, + memory_config: Optional[Dict[str, Any]], + stm: ShortTermMemory, + ltm: LongTermMemory, + em: EntityMemory, + um: UserMemory, + ): + if memory_config is not None: + self.memory_provider = memory_config.get("provider") + else: + self.memory_provider = None self.stm = stm self.ltm = ltm self.em = em + self.um = um def build_context_for_task(self, task, context) -> str: """ @@ -23,6 +35,8 @@ class ContextualMemory: context.append(self._fetch_ltm_context(task.description)) context.append(self._fetch_stm_context(query)) context.append(self._fetch_entity_context(query)) + if self.memory_provider == "mem0": + context.append(self._fetch_user_context(query)) return "\n".join(filter(None, context)) def _fetch_stm_context(self, query) -> str: @@ -32,7 +46,10 @@ class ContextualMemory: """ stm_results = self.stm.search(query) formatted_results = "\n".join( - [f"- {result['context']}" for result in stm_results] + [ + f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}" + for result in stm_results + ] ) return f"Recent Insights:\n{formatted_results}" if stm_results else "" @@ -62,6 +79,26 @@ class ContextualMemory: """ em_results = self.em.search(query) formatted_results = "\n".join( - [f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice" + [ + f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}" + for result in em_results + ] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice" ) return f"Entities:\n{formatted_results}" if em_results else "" + + def _fetch_user_context(self, query: str) -> str: + """ + Fetches and formats relevant user information from User Memory. + Args: + query (str): The search query to find relevant user memories. + Returns: + str: Formatted user memories as bullet points, or an empty string if none found. + """ + user_memories = self.um.search(query) + if not user_memories: + return "" + + formatted_memories = "\n".join( + f"- {result['memory']}" for result in user_memories + ) + return f"User memories/preferences:\n{formatted_memories}" diff --git a/src/crewai/memory/entity/entity_memory.py b/src/crewai/memory/entity/entity_memory.py index d4f8d9aae..67c72e927 100644 --- a/src/crewai/memory/entity/entity_memory.py +++ b/src/crewai/memory/entity/entity_memory.py @@ -10,22 +10,45 @@ class EntityMemory(Memory): Inherits from the Memory class. """ - def __init__(self, crew=None, embedder_config=None, storage=None): - storage = ( - storage - if storage - else RAGStorage( - type="entities", - allow_reset=False, - embedder_config=embedder_config, - crew=crew, + def __init__(self, crew=None, embedder_config=None, storage=None, path=None): + if hasattr(crew, "memory_config") and crew.memory_config is not None: + self.memory_provider = crew.memory_config.get("provider") + else: + self.memory_provider = None + + if self.memory_provider == "mem0": + try: + from crewai.memory.storage.mem0_storage import Mem0Storage + except ImportError: + raise ImportError( + "Mem0 is not installed. Please install it with `pip install mem0ai`." + ) + storage = Mem0Storage(type="entities", crew=crew) + else: + storage = ( + storage + if storage + else RAGStorage( + type="entities", + allow_reset=True, + embedder_config=embedder_config, + crew=crew, + path=path, + ) ) - ) super().__init__(storage) def save(self, item: EntityMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory" """Saves an entity item into the SQLite storage.""" - data = f"{item.name}({item.type}): {item.description}" + if self.memory_provider == "mem0": + data = f""" + Remember details about the following entity: + Name: {item.name} + Type: {item.type} + Entity Description: {item.description} + """ + else: + data = f"{item.name}({item.type}): {item.description}" super().save(data, item.metadata) def reset(self) -> None: diff --git a/src/crewai/memory/long_term/long_term_memory.py b/src/crewai/memory/long_term/long_term_memory.py index b9c36bdc9..656709ac9 100644 --- a/src/crewai/memory/long_term/long_term_memory.py +++ b/src/crewai/memory/long_term/long_term_memory.py @@ -14,8 +14,9 @@ class LongTermMemory(Memory): LongTermMemoryItem instances. """ - def __init__(self, storage=None): - storage = storage if storage else LTMSQLiteStorage() + def __init__(self, storage=None, path=None): + if not storage: + storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage() super().__init__(storage) def save(self, item: LongTermMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory" diff --git a/src/crewai/memory/memory.py b/src/crewai/memory/memory.py index d0bcd614f..46af2c04d 100644 --- a/src/crewai/memory/memory.py +++ b/src/crewai/memory/memory.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional, List +from typing import Any, Dict, List, Optional from crewai.memory.storage.rag_storage import RAGStorage @@ -23,5 +23,12 @@ class Memory: self.storage.save(value, metadata) - def search(self, query: str) -> List[Dict[str, Any]]: - return self.storage.search(query) + def search( + self, + query: str, + limit: int = 3, + score_threshold: float = 0.35, + ) -> List[Any]: + return self.storage.search( + query=query, limit=limit, score_threshold=score_threshold + ) diff --git a/src/crewai/memory/short_term/short_term_memory.py b/src/crewai/memory/short_term/short_term_memory.py index 919fb6115..4e5fbbb77 100644 --- a/src/crewai/memory/short_term/short_term_memory.py +++ b/src/crewai/memory/short_term/short_term_memory.py @@ -1,4 +1,5 @@ from typing import Any, Dict, Optional + from crewai.memory.memory import Memory from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem from crewai.memory.storage.rag_storage import RAGStorage @@ -13,14 +14,31 @@ class ShortTermMemory(Memory): MemoryItem instances. """ - def __init__(self, crew=None, embedder_config=None, storage=None): - storage = ( - storage - if storage - else RAGStorage( - type="short_term", embedder_config=embedder_config, crew=crew + def __init__(self, crew=None, embedder_config=None, storage=None, path=None): + if hasattr(crew, "memory_config") and crew.memory_config is not None: + self.memory_provider = crew.memory_config.get("provider") + else: + self.memory_provider = None + + if self.memory_provider == "mem0": + try: + from crewai.memory.storage.mem0_storage import Mem0Storage + except ImportError: + raise ImportError( + "Mem0 is not installed. Please install it with `pip install mem0ai`." + ) + storage = Mem0Storage(type="short_term", crew=crew) + else: + storage = ( + storage + if storage + else RAGStorage( + type="short_term", + embedder_config=embedder_config, + crew=crew, + path=path, + ) ) - ) super().__init__(storage) def save( @@ -30,11 +48,20 @@ class ShortTermMemory(Memory): agent: Optional[str] = None, ) -> None: item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent) + if self.memory_provider == "mem0": + item.data = f"Remember the following insights from Agent run: {item.data}" super().save(value=item.data, metadata=item.metadata, agent=item.agent) - def search(self, query: str, score_threshold: float = 0.35): - return self.storage.search(query=query, score_threshold=score_threshold) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters + def search( + self, + query: str, + limit: int = 3, + score_threshold: float = 0.35, + ): + return self.storage.search( + query=query, limit=limit, score_threshold=score_threshold + ) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters def reset(self) -> None: try: diff --git a/src/crewai/memory/storage/interface.py b/src/crewai/memory/storage/interface.py index 8fbe10b03..8bec9a14f 100644 --- a/src/crewai/memory/storage/interface.py +++ b/src/crewai/memory/storage/interface.py @@ -7,8 +7,10 @@ class Storage: def save(self, value: Any, metadata: Dict[str, Any]) -> None: pass - def search(self, key: str) -> List[Dict[str, Any]]: # type: ignore - pass + def search( + self, query: str, limit: int, score_threshold: float + ) -> Dict[str, Any] | List[Any]: + return {} def reset(self) -> None: pass diff --git a/src/crewai/memory/storage/kickoff_task_outputs_storage.py b/src/crewai/memory/storage/kickoff_task_outputs_storage.py index 57623eef8..26905191c 100644 --- a/src/crewai/memory/storage/kickoff_task_outputs_storage.py +++ b/src/crewai/memory/storage/kickoff_task_outputs_storage.py @@ -70,7 +70,7 @@ class KickoffTaskOutputsSQLiteStorage: task.expected_output, json.dumps(output, cls=CrewJSONEncoder), task_index, - json.dumps(inputs), + json.dumps(inputs, cls=CrewJSONEncoder), was_replayed, ), ) @@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage: else value ) - query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?" + query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?" # nosec values.append(task_index) cursor.execute(query, tuple(values)) diff --git a/src/crewai/memory/storage/ltm_sqlite_storage.py b/src/crewai/memory/storage/ltm_sqlite_storage.py index 7fb388a62..93d993ee6 100644 --- a/src/crewai/memory/storage/ltm_sqlite_storage.py +++ b/src/crewai/memory/storage/ltm_sqlite_storage.py @@ -83,7 +83,7 @@ class LTMSQLiteStorage: WHERE task_description = ? ORDER BY datetime DESC, score ASC LIMIT {latest_n} - """, + """, # nosec (task_description,), ) rows = cursor.fetchall() diff --git a/src/crewai/memory/storage/mem0_storage.py b/src/crewai/memory/storage/mem0_storage.py new file mode 100644 index 000000000..e4e84fab4 --- /dev/null +++ b/src/crewai/memory/storage/mem0_storage.py @@ -0,0 +1,105 @@ +import os +from typing import Any, Dict, List + +from mem0 import MemoryClient + +from crewai.memory.storage.interface import Storage + + +class Mem0Storage(Storage): + """ + Extends Storage to handle embedding and searching across entities using Mem0. + """ + + def __init__(self, type, crew=None): + super().__init__() + + if type not in ["user", "short_term", "long_term", "entities"]: + raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.") + + self.memory_type = type + self.crew = crew + self.memory_config = crew.memory_config + + # User ID is required for user memory type "user" since it's used as a unique identifier for the user. + user_id = self._get_user_id() + if type == "user" and not user_id: + raise ValueError("User ID is required for user memory type") + + # API key in memory config overrides the environment variable + mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv( + "MEM0_API_KEY" + ) + self.memory = MemoryClient(api_key=mem0_api_key) + + def _sanitize_role(self, role: str) -> str: + """ + Sanitizes agent roles to ensure valid directory names. + """ + return role.replace("\n", "").replace(" ", "_").replace("/", "_") + + def save(self, value: Any, metadata: Dict[str, Any]) -> None: + user_id = self._get_user_id() + agent_name = self._get_agent_name() + if self.memory_type == "user": + self.memory.add(value, user_id=user_id, metadata={**metadata}) + elif self.memory_type == "short_term": + agent_name = self._get_agent_name() + self.memory.add( + value, agent_id=agent_name, metadata={"type": "short_term", **metadata} + ) + elif self.memory_type == "long_term": + agent_name = self._get_agent_name() + self.memory.add( + value, + agent_id=agent_name, + infer=False, + metadata={"type": "long_term", **metadata}, + ) + elif self.memory_type == "entities": + entity_name = None + self.memory.add( + value, user_id=entity_name, metadata={"type": "entity", **metadata} + ) + + def search( + self, + query: str, + limit: int = 3, + score_threshold: float = 0.35, + ) -> List[Any]: + params = {"query": query, "limit": limit} + if self.memory_type == "user": + user_id = self._get_user_id() + params["user_id"] = user_id + elif self.memory_type == "short_term": + agent_name = self._get_agent_name() + params["agent_id"] = agent_name + params["metadata"] = {"type": "short_term"} + elif self.memory_type == "long_term": + agent_name = self._get_agent_name() + params["agent_id"] = agent_name + params["metadata"] = {"type": "long_term"} + elif self.memory_type == "entities": + agent_name = self._get_agent_name() + params["agent_id"] = agent_name + params["metadata"] = {"type": "entity"} + + # Discard the filters for now since we create the filters + # automatically when the crew is created. + results = self.memory.search(**params) + return [r for r in results if r["score"] >= score_threshold] + + def _get_user_id(self): + if self.memory_type == "user": + if hasattr(self, "memory_config") and self.memory_config is not None: + return self.memory_config.get("config", {}).get("user_id") + else: + return None + return None + + def _get_agent_name(self): + agents = self.crew.agents if self.crew else [] + agents = [self._sanitize_role(agent.role) for agent in agents] + agents = "_".join(agents) + return agents diff --git a/src/crewai/memory/storage/rag_storage.py b/src/crewai/memory/storage/rag_storage.py index 8d45d9f5a..fd4c77838 100644 --- a/src/crewai/memory/storage/rag_storage.py +++ b/src/crewai/memory/storage/rag_storage.py @@ -5,10 +5,14 @@ import os import shutil import uuid from typing import Any, Dict, List, Optional -from crewai.memory.storage.base_rag_storage import BaseRAGStorage -from crewai.utilities.paths import db_storage_path + from chromadb.api import ClientAPI +from crewai.memory.storage.base_rag_storage import BaseRAGStorage +from crewai.utilities import EmbeddingConfigurator +from crewai.utilities.constants import MAX_FILE_NAME_LENGTH +from crewai.utilities.paths import db_storage_path + @contextlib.contextmanager def suppress_logging( @@ -18,9 +22,11 @@ def suppress_logging( logger = logging.getLogger(logger_name) original_level = logger.getEffectiveLevel() logger.setLevel(level) - with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr( - io.StringIO() - ), contextlib.suppress(UserWarning): + with ( + contextlib.redirect_stdout(io.StringIO()), + contextlib.redirect_stderr(io.StringIO()), + contextlib.suppress(UserWarning), + ): yield logger.setLevel(original_level) @@ -33,24 +39,36 @@ class RAGStorage(BaseRAGStorage): app: ClientAPI | None = None - def __init__(self, type, allow_reset=True, embedder_config=None, crew=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 - self.embedder_config = embedder_config or self._create_embedding_function() + self.allow_reset = allow_reset + self.path = path self._initialize_app() + def _set_embedder_config(self): + configurator = EmbeddingConfigurator() + self.embedder_config = configurator.configure_embedder(self.embedder_config) + def _initialize_app(self): import chromadb + from chromadb.config import Settings + self._set_embedder_config() chroma_client = chromadb.PersistentClient( - path=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), ) + self.app = chroma_client try: @@ -68,6 +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() @@ -118,17 +150,25 @@ class RAGStorage(BaseRAGStorage): def reset(self) -> None: try: - shutil.rmtree(f"{db_storage_path()}/{self.type}") if self.app: self.app.reset() + shutil.rmtree(f"{db_storage_path()}/{self.type}") + self.app = None + self.collection = None except Exception as e: - raise Exception( - f"An error occurred while resetting the {self.type} memory: {e}" - ) + if "attempt to write a readonly database" in str(e): + # Ignore this specific error + pass + else: + raise Exception( + f"An error occurred while resetting the {self.type} memory: {e}" + ) - def _create_embedding_function(self): - import chromadb.utils.embedding_functions as embedding_functions + def _create_default_embedding_function(self): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) - return embedding_functions.OpenAIEmbeddingFunction( + return OpenAIEmbeddingFunction( api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" ) diff --git a/src/crewai/cli/templates/pipeline_router/pipelines/__init__.py b/src/crewai/memory/user/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline_router/pipelines/__init__.py rename to src/crewai/memory/user/__init__.py diff --git a/src/crewai/memory/user/user_memory.py b/src/crewai/memory/user/user_memory.py new file mode 100644 index 000000000..24e5fe035 --- /dev/null +++ b/src/crewai/memory/user/user_memory.py @@ -0,0 +1,45 @@ +from typing import Any, Dict, Optional + +from crewai.memory.memory import Memory + + +class UserMemory(Memory): + """ + UserMemory class for handling user memory storage and retrieval. + Inherits from the Memory class and utilizes an instance of a class that + adheres to the Storage for data storage, specifically working with + MemoryItem instances. + """ + + def __init__(self, crew=None): + try: + from crewai.memory.storage.mem0_storage import Mem0Storage + except ImportError: + raise ImportError( + "Mem0 is not installed. Please install it with `pip install mem0ai`." + ) + storage = Mem0Storage(type="user", crew=crew) + super().__init__(storage) + + def save( + self, + value, + metadata: Optional[Dict[str, Any]] = None, + agent: Optional[str] = None, + ) -> None: + # TODO: Change this function since we want to take care of the case where we save memories for the usr + data = f"Remember the details about the user: {value}" + super().save(data, metadata) + + def search( + self, + query: str, + limit: int = 3, + score_threshold: float = 0.35, + ): + results = self.storage.search( + query=query, + limit=limit, + score_threshold=score_threshold, + ) + return results diff --git a/src/crewai/memory/user/user_memory_item.py b/src/crewai/memory/user/user_memory_item.py new file mode 100644 index 000000000..288c1544a --- /dev/null +++ b/src/crewai/memory/user/user_memory_item.py @@ -0,0 +1,8 @@ +from typing import Any, Dict, Optional + + +class UserMemoryItem: + def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None): + self.data = data + self.user = user + self.metadata = metadata if metadata is not None else {} 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 34759f465..d60212153 100644 --- a/src/crewai/project/__init__.py +++ b/src/crewai/project/__init__.py @@ -1,17 +1,17 @@ from .annotations import ( + after_kickoff, agent, + before_kickoff, cache_handler, callback, crew, llm, output_json, output_pydantic, - pipeline, task, tool, ) from .crew_base import CrewBase -from .pipeline_base import PipelineBase __all__ = [ "agent", @@ -22,8 +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 6c0b9942a..bf0051c4d 100644 --- a/src/crewai/project/annotations.py +++ b/src/crewai/project/annotations.py @@ -5,6 +5,16 @@ from crewai import Crew from crewai.project.utils import memoize +def before_kickoff(func): + func.is_before_kickoff = True + return func + + +def after_kickoff(func): + func.is_after_kickoff = True + return func + + def task(func): func.is_task = True @@ -55,22 +65,9 @@ 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]: + + @wraps(func) def wrapper(self, *args, **kwargs) -> Crew: instantiated_tasks = [] instantiated_agents = [] @@ -99,6 +96,19 @@ def crew(func) -> Callable[..., Crew]: self.agents = instantiated_agents self.tasks = instantiated_tasks - return func(self, *args, **kwargs) + crew = func(self, *args, **kwargs) - return wrapper + def callback_wrapper(callback, instance): + def wrapper(*args, **kwargs): + return callback(instance, *args, **kwargs) + + return wrapper + + for _, callback in self._before_kickoff.items(): + crew.before_kickoff_callbacks.append(callback_wrapper(callback, self)) + for _, callback in self._after_kickoff.items(): + crew.after_kickoff_callbacks.append(callback_wrapper(callback, self)) + + return crew + + return memoize(wrapper) diff --git a/src/crewai/project/crew_base.py b/src/crewai/project/crew_base.py index a420c4dd2..0b43882f2 100644 --- a/src/crewai/project/crew_base.py +++ b/src/crewai/project/crew_base.py @@ -34,18 +34,39 @@ def CrewBase(cls: T) -> T: self.map_all_agent_variables() self.map_all_task_variables() - # Preserve task and agent information - self._original_tasks = { + # Preserve all decorated functions + self._original_functions = { name: method for name, method in cls.__dict__.items() - if hasattr(method, "is_task") and method.is_task - } - self._original_agents = { - name: method - for name, method in cls.__dict__.items() - if hasattr(method, "is_agent") and method.is_agent + if any( + hasattr(method, attr) + for attr in [ + "is_task", + "is_agent", + "is_before_kickoff", + "is_after_kickoff", + "is_kickoff", + ] + ) } + # Store specific function types + self._original_tasks = self._filter_functions( + self._original_functions, "is_task" + ) + self._original_agents = self._filter_functions( + self._original_functions, "is_agent" + ) + self._before_kickoff = self._filter_functions( + self._original_functions, "is_before_kickoff" + ) + self._after_kickoff = self._filter_functions( + self._original_functions, "is_after_kickoff" + ) + self._kickoff = self._filter_functions( + self._original_functions, "is_kickoff" + ) + @staticmethod def load_yaml(config_path: Path): try: @@ -192,4 +213,8 @@ def CrewBase(cls: T) -> T: callback_functions[callback]() for callback in callbacks ] + # Include base class (qual)name in the wrapper class (qual)name. + WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")" + WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")" + return cast(T, WrappedClass) 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/project/utils.py b/src/crewai/project/utils.py index be3f757d9..e8876d941 100644 --- a/src/crewai/project/utils.py +++ b/src/crewai/project/utils.py @@ -1,11 +1,14 @@ +from functools import wraps + + def memoize(func): cache = {} + @wraps(func) def memoized_func(*args, **kwargs): key = (args, tuple(kwargs.items())) if key not in cache: cache[key] = func(*args, **kwargs) return cache[key] - memoized_func.__dict__.update(func.__dict__) return memoized_func 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/src/crewai/task.py b/src/crewai/task.py index 82baa9959..b585e0c69 100644 --- a/src/crewai/task.py +++ b/src/crewai/task.py @@ -1,11 +1,11 @@ import datetime import json -import os import threading import uuid from concurrent.futures import Future from copy import copy from hashlib import md5 +from pathlib import Path from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union from opentelemetry.trace import Span @@ -23,6 +23,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.tasks.output_format import OutputFormat from crewai.tasks.task_output import TaskOutput from crewai.telemetry.telemetry import Telemetry +from crewai.tools.base_tool import BaseTool from crewai.utilities.config import process_config from crewai.utilities.converter import Converter, convert_to_model from crewai.utilities.i18n import I18N @@ -91,7 +92,7 @@ class Task(BaseModel): output: Optional[TaskOutput] = Field( description="Task output, it's final result after being executed", default=None ) - tools: Optional[List[Any]] = Field( + tools: Optional[List[BaseTool]] = Field( default_factory=list, description="Tools the agent is limited to use for this task.", ) @@ -185,7 +186,7 @@ class Task(BaseModel): self, agent: Optional[BaseAgent] = None, context: Optional[str] = None, - tools: Optional[List[Any]] = None, + tools: Optional[List[BaseTool]] = None, ) -> TaskOutput: """Execute the task synchronously.""" return self._execute_core(agent, context, tools) @@ -202,12 +203,14 @@ class Task(BaseModel): self, agent: BaseAgent | None = None, context: Optional[str] = None, - tools: Optional[List[Any]] = None, + tools: Optional[List[BaseTool]] = None, ) -> Future[TaskOutput]: """Execute the task asynchronously.""" future: Future[TaskOutput] = Future() threading.Thread( - target=self._execute_task_async, args=(agent, context, tools, future) + daemon=True, + target=self._execute_task_async, + args=(agent, context, tools, future), ).start() return future @@ -390,12 +393,13 @@ class Task(BaseModel): if self.output_file is None: raise ValueError("output_file is not set.") - directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None" + resolved_path = Path(self.output_file).expanduser().resolve() + directory = resolved_path.parent - if directory and not os.path.exists(directory): - os.makedirs(directory) + if not directory.exists(): + directory.mkdir(parents=True, exist_ok=True) - with open(self.output_file, "w", encoding="utf-8") as file: + with resolved_path.open("w", encoding="utf-8") as file: if isinstance(result, dict): import json diff --git a/src/crewai/telemetry/telemetry.py b/src/crewai/telemetry/telemetry.py index a08ccd96f..aa0f8b9ff 100644 --- a/src/crewai/telemetry/telemetry.py +++ b/src/crewai/telemetry/telemetry.py @@ -21,7 +21,9 @@ with suppress_warnings(): from opentelemetry import trace # noqa: E402 -from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402 +from opentelemetry.exporter.otlp.proto.http.trace_exporter import ( + OTLPSpanExporter, # noqa: E402 +) from opentelemetry.sdk.resources import SERVICE_NAME, Resource # noqa: E402 from opentelemetry.sdk.trace import TracerProvider # noqa: E402 from opentelemetry.sdk.trace.export import BatchSpanProcessor # noqa: E402 @@ -48,6 +50,10 @@ class Telemetry: def __init__(self): self.ready = False self.trace_set = False + + if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true": + return + try: telemetry_endpoint = "https://telemetry.crewai.com:4319" self.resource = Resource( diff --git a/src/crewai/tools/__init__.py b/src/crewai/tools/__init__.py index e69de29bb..41819ccbc 100644 --- a/src/crewai/tools/__init__.py +++ b/src/crewai/tools/__init__.py @@ -0,0 +1 @@ +from .base_tool import BaseTool, tool diff --git a/src/crewai/tools/agent_tools.py b/src/crewai/tools/agent_tools.py deleted file mode 100644 index 7831285cf..000000000 --- a/src/crewai/tools/agent_tools.py +++ /dev/null @@ -1,25 +0,0 @@ -from crewai.agents.agent_builder.utilities.base_agent_tool import BaseAgentTools - - -class AgentTools(BaseAgentTools): - """Default tools around agent delegation""" - - def tools(self): - from langchain.tools import StructuredTool - - coworkers = ", ".join([f"{agent.role}" for agent in self.agents]) - tools = [ - StructuredTool.from_function( - func=self.delegate_work, - name="Delegate work to coworker", - description=self.i18n.tools("delegate_work").format( - coworkers=coworkers - ), - ), - StructuredTool.from_function( - func=self.ask_question, - name="Ask question to coworker", - description=self.i18n.tools("ask_question").format(coworkers=coworkers), - ), - ] - return tools diff --git a/src/crewai/tools/agent_tools/agent_tools.py b/src/crewai/tools/agent_tools/agent_tools.py new file mode 100644 index 000000000..08383c244 --- /dev/null +++ b/src/crewai/tools/agent_tools/agent_tools.py @@ -0,0 +1,32 @@ +from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.tools.base_tool import BaseTool +from crewai.utilities import I18N + +from .ask_question_tool import AskQuestionTool +from .delegate_work_tool import DelegateWorkTool + + +class AgentTools: + """Manager class for agent-related tools""" + + def __init__(self, agents: list[BaseAgent], i18n: I18N = I18N()): + self.agents = agents + self.i18n = i18n + + def tools(self) -> list[BaseTool]: + """Get all available agent tools""" + coworkers = ", ".join([f"{agent.role}" for agent in self.agents]) + + delegate_tool = DelegateWorkTool( + agents=self.agents, + i18n=self.i18n, + description=self.i18n.tools("delegate_work").format(coworkers=coworkers), + ) + + ask_tool = AskQuestionTool( + agents=self.agents, + i18n=self.i18n, + description=self.i18n.tools("ask_question").format(coworkers=coworkers), + ) + + return [delegate_tool, ask_tool] diff --git a/src/crewai/tools/agent_tools/ask_question_tool.py b/src/crewai/tools/agent_tools/ask_question_tool.py new file mode 100644 index 000000000..9294770e5 --- /dev/null +++ b/src/crewai/tools/agent_tools/ask_question_tool.py @@ -0,0 +1,28 @@ +from typing import Optional + +from pydantic import BaseModel, Field + +from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool + + +class AskQuestionToolSchema(BaseModel): + question: str = Field(..., description="The question to ask") + context: str = Field(..., description="The context for the question") + coworker: str = Field(..., description="The role/name of the coworker to ask") + + +class AskQuestionTool(BaseAgentTool): + """Tool for asking questions to coworkers""" + + name: str = "Ask question to coworker" + args_schema: type[BaseModel] = AskQuestionToolSchema + + def _run( + self, + question: str, + context: str, + coworker: Optional[str] = None, + **kwargs, + ) -> str: + coworker = self._get_coworker(coworker, **kwargs) + return self._execute(coworker, question, context) diff --git a/src/crewai/agents/agent_builder/utilities/base_agent_tool.py b/src/crewai/tools/agent_tools/base_agent_tools.py similarity index 64% rename from src/crewai/agents/agent_builder/utilities/base_agent_tool.py rename to src/crewai/tools/agent_tools/base_agent_tools.py index fef4ee9ef..ea63dd51e 100644 --- a/src/crewai/agents/agent_builder/utilities/base_agent_tool.py +++ b/src/crewai/tools/agent_tools/base_agent_tools.py @@ -1,22 +1,20 @@ -from abc import ABC, abstractmethod -from typing import List, Optional, Union +from typing import Optional, Union -from pydantic import BaseModel, Field +from pydantic import Field from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.task import Task +from crewai.tools.base_tool import BaseTool from crewai.utilities import I18N -class BaseAgentTools(BaseModel, ABC): - """Default tools around agent delegation""" +class BaseAgentTool(BaseTool): + """Base class for agent-related tools""" - agents: List[BaseAgent] = Field(description="List of agents in this crew.") - i18n: I18N = Field(default=I18N(), description="Internationalization settings.") - - @abstractmethod - def tools(self): - pass + agents: list[BaseAgent] = Field(description="List of available agents") + i18n: I18N = Field( + default_factory=I18N, description="Internationalization settings" + ) def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]: coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker") @@ -24,27 +22,11 @@ class BaseAgentTools(BaseModel, ABC): is_list = coworker.startswith("[") and coworker.endswith("]") if is_list: coworker = coworker[1:-1].split(",")[0] - return coworker - def delegate_work( - self, task: str, context: str, coworker: Optional[str] = None, **kwargs - ): - """Useful to delegate a specific task to a coworker passing all necessary context and names.""" - coworker = self._get_coworker(coworker, **kwargs) - return self._execute(coworker, task, context) - - def ask_question( - self, question: str, context: str, coworker: Optional[str] = None, **kwargs - ): - """Useful to ask a question, opinion or take from a coworker passing all necessary context and names.""" - coworker = self._get_coworker(coworker, **kwargs) - return self._execute(coworker, question, context) - def _execute( self, agent_name: Union[str, None], task: str, context: Union[str, None] - ): - """Execute the command.""" + ) -> str: try: if agent_name is None: agent_name = "" @@ -57,21 +39,20 @@ class BaseAgentTools(BaseModel, ABC): # when it should look like this: # {"task": "....", "coworker": "...."} agent_name = agent_name.casefold().replace('"', "").replace("\n", "") - agent = [ # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None") available_agent for available_agent in self.agents if available_agent.role.casefold().replace("\n", "") == agent_name ] except Exception as _: - return self.i18n.errors("agent_tool_unexsiting_coworker").format( + return self.i18n.errors("agent_tool_unexisting_coworker").format( coworkers="\n".join( [f"- {agent.role.casefold()}" for agent in self.agents] ) ) if not agent: - return self.i18n.errors("agent_tool_unexsiting_coworker").format( + return self.i18n.errors("agent_tool_unexisting_coworker").format( coworkers="\n".join( [f"- {agent.role.casefold()}" for agent in self.agents] ) diff --git a/src/crewai/tools/agent_tools/delegate_work_tool.py b/src/crewai/tools/agent_tools/delegate_work_tool.py new file mode 100644 index 000000000..9dbf6c920 --- /dev/null +++ b/src/crewai/tools/agent_tools/delegate_work_tool.py @@ -0,0 +1,30 @@ +from typing import Optional + +from pydantic import BaseModel, Field + +from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool + + +class DelegateWorkToolSchema(BaseModel): + task: str = Field(..., description="The task to delegate") + context: str = Field(..., description="The context for the task") + coworker: str = Field( + ..., description="The role/name of the coworker to delegate to" + ) + + +class DelegateWorkTool(BaseAgentTool): + """Tool for delegating work to coworkers""" + + name: str = "Delegate work to coworker" + args_schema: type[BaseModel] = DelegateWorkToolSchema + + def _run( + self, + task: str, + context: str, + coworker: Optional[str] = None, + **kwargs, + ) -> str: + coworker = self._get_coworker(coworker, **kwargs) + return self._execute(coworker, task, context) diff --git a/src/crewai/tools/base_tool.py b/src/crewai/tools/base_tool.py new file mode 100644 index 000000000..c3840d23c --- /dev/null +++ b/src/crewai/tools/base_tool.py @@ -0,0 +1,273 @@ +from abc import ABC, abstractmethod +from inspect import signature +from typing import Any, Callable, Type, get_args, get_origin + +from pydantic import BaseModel, ConfigDict, Field, create_model, validator +from pydantic import BaseModel as PydanticBaseModel + +from crewai.tools.structured_tool import CrewStructuredTool + + +class BaseTool(BaseModel, ABC): + class _ArgsSchemaPlaceholder(PydanticBaseModel): + pass + + model_config = ConfigDict() + + name: str + """The unique name of the tool that clearly communicates its purpose.""" + description: str + """Used to tell the model how/when/why to use the tool.""" + args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder) + """The schema for the arguments that the tool accepts.""" + description_updated: bool = False + """Flag to check if the description has been updated.""" + cache_function: Callable = lambda _args=None, _result=None: True + """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached.""" + result_as_answer: bool = False + """Flag to check if the tool should be the final agent answer.""" + + @validator("args_schema", always=True, pre=True) + def _default_args_schema( + cls, v: Type[PydanticBaseModel] + ) -> Type[PydanticBaseModel]: + if not isinstance(v, cls._ArgsSchemaPlaceholder): + return v + + return type( + f"{cls.__name__}Schema", + (PydanticBaseModel,), + { + "__annotations__": { + k: v for k, v in cls._run.__annotations__.items() if k != "return" + }, + }, + ) + + def model_post_init(self, __context: Any) -> None: + self._generate_description() + + super().model_post_init(__context) + + def run( + self, + *args: Any, + **kwargs: Any, + ) -> Any: + print(f"Using Tool: {self.name}") + return self._run(*args, **kwargs) + + @abstractmethod + def _run( + self, + *args: Any, + **kwargs: Any, + ) -> Any: + """Here goes the actual implementation of the tool.""" + + def to_structured_tool(self) -> CrewStructuredTool: + """Convert this tool to a CrewStructuredTool instance.""" + self._set_args_schema() + return CrewStructuredTool( + name=self.name, + description=self.description, + args_schema=self.args_schema, + func=self._run, + result_as_answer=self.result_as_answer, + ) + + @classmethod + def from_langchain(cls, tool: Any) -> "BaseTool": + """Create a Tool instance from a CrewStructuredTool. + + This method takes a CrewStructuredTool object and converts it into a + Tool instance. It ensures that the provided tool has a callable 'func' + attribute and infers the argument schema if not explicitly provided. + """ + if not hasattr(tool, "func") or not callable(tool.func): + raise ValueError("The provided tool must have a callable 'func' attribute.") + + args_schema = getattr(tool, "args_schema", None) + + if args_schema is None: + # Infer args_schema from the function signature if not provided + func_signature = signature(tool.func) + annotations = func_signature.parameters + args_fields = {} + for name, param in annotations.items(): + if name != "self": + param_annotation = ( + param.annotation if param.annotation != param.empty else Any + ) + field_info = Field( + default=..., + description="", + ) + args_fields[name] = (param_annotation, field_info) + if args_fields: + args_schema = create_model(f"{tool.name}Input", **args_fields) + else: + # Create a default schema with no fields if no parameters are found + args_schema = create_model( + f"{tool.name}Input", __base__=PydanticBaseModel + ) + + return cls( + name=getattr(tool, "name", "Unnamed Tool"), + description=getattr(tool, "description", ""), + func=tool.func, + args_schema=args_schema, + ) + + def _set_args_schema(self): + if self.args_schema is None: + class_name = f"{self.__class__.__name__}Schema" + self.args_schema = type( + class_name, + (PydanticBaseModel,), + { + "__annotations__": { + k: v + for k, v in self._run.__annotations__.items() + if k != "return" + }, + }, + ) + + def _generate_description(self): + args_schema = { + name: { + "description": field.description, + "type": BaseTool._get_arg_annotations(field.annotation), + } + for name, field in self.args_schema.model_fields.items() + } + + self.description = f"Tool Name: {self.name}\nTool Arguments: {args_schema}\nTool Description: {self.description}" + + @staticmethod + def _get_arg_annotations(annotation: type[Any] | None) -> str: + if annotation is None: + return "None" + + origin = get_origin(annotation) + args = get_args(annotation) + + if origin is None: + return ( + annotation.__name__ + if hasattr(annotation, "__name__") + else str(annotation) + ) + + if args: + args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args) + return f"{origin.__name__}[{args_str}]" + + return origin.__name__ + + +class Tool(BaseTool): + """The function that will be executed when the tool is called.""" + + func: Callable + + def _run(self, *args: Any, **kwargs: Any) -> Any: + return self.func(*args, **kwargs) + + @classmethod + def from_langchain(cls, tool: Any) -> "Tool": + """Create a Tool instance from a CrewStructuredTool. + + This method takes a CrewStructuredTool object and converts it into a + Tool instance. It ensures that the provided tool has a callable 'func' + attribute and infers the argument schema if not explicitly provided. + + Args: + tool (Any): The CrewStructuredTool object to be converted. + + Returns: + Tool: A new Tool instance created from the provided CrewStructuredTool. + + Raises: + ValueError: If the provided tool does not have a callable 'func' attribute. + """ + if not hasattr(tool, "func") or not callable(tool.func): + raise ValueError("The provided tool must have a callable 'func' attribute.") + + args_schema = getattr(tool, "args_schema", None) + + if args_schema is None: + # Infer args_schema from the function signature if not provided + func_signature = signature(tool.func) + annotations = func_signature.parameters + args_fields = {} + for name, param in annotations.items(): + if name != "self": + param_annotation = ( + param.annotation if param.annotation != param.empty else Any + ) + field_info = Field( + default=..., + description="", + ) + args_fields[name] = (param_annotation, field_info) + if args_fields: + args_schema = create_model(f"{tool.name}Input", **args_fields) + else: + # Create a default schema with no fields if no parameters are found + args_schema = create_model( + f"{tool.name}Input", __base__=PydanticBaseModel + ) + + return cls( + name=getattr(tool, "name", "Unnamed Tool"), + description=getattr(tool, "description", ""), + func=tool.func, + args_schema=args_schema, + ) + + +def to_langchain( + tools: list[BaseTool | CrewStructuredTool], +) -> list[CrewStructuredTool]: + return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools] + + +def tool(*args): + """ + Decorator to create a tool from a function. + """ + + def _make_with_name(tool_name: str) -> Callable: + def _make_tool(f: Callable) -> BaseTool: + if f.__doc__ is None: + raise ValueError("Function must have a docstring") + if f.__annotations__ is None: + raise ValueError("Function must have type annotations") + + class_name = "".join(tool_name.split()).title() + args_schema = type( + class_name, + (PydanticBaseModel,), + { + "__annotations__": { + k: v for k, v in f.__annotations__.items() if k != "return" + }, + }, + ) + + return Tool( + name=tool_name, + description=f.__doc__, + func=f, + args_schema=args_schema, + ) + + return _make_tool + + if len(args) == 1 and callable(args[0]): + return _make_with_name(args[0].__name__)(args[0]) + if len(args) == 1 and isinstance(args[0], str): + return _make_with_name(args[0]) + raise ValueError("Invalid arguments") diff --git a/src/crewai/cli/templates/pipeline_router/tools/__init__.py b/src/crewai/tools/cache_tools/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline_router/tools/__init__.py rename to src/crewai/tools/cache_tools/__init__.py diff --git a/src/crewai/tools/cache_tools.py b/src/crewai/tools/cache_tools/cache_tools.py similarity index 86% rename from src/crewai/tools/cache_tools.py rename to src/crewai/tools/cache_tools/cache_tools.py index a0bb2dbad..a81ce98cf 100644 --- a/src/crewai/tools/cache_tools.py +++ b/src/crewai/tools/cache_tools/cache_tools.py @@ -1,6 +1,7 @@ from pydantic import BaseModel, Field from crewai.agents.cache import CacheHandler +from crewai.tools.structured_tool import CrewStructuredTool class CacheTools(BaseModel): @@ -13,9 +14,7 @@ class CacheTools(BaseModel): ) def tool(self): - from langchain.tools import StructuredTool - - return StructuredTool.from_function( + return CrewStructuredTool.from_function( func=self.hit_cache, name=self.name, description="Reads directly from the cache", diff --git a/src/crewai/tools/structured_tool.py b/src/crewai/tools/structured_tool.py new file mode 100644 index 000000000..dfd23a9cb --- /dev/null +++ b/src/crewai/tools/structured_tool.py @@ -0,0 +1,246 @@ +from __future__ import annotations + +import inspect +import textwrap +from typing import Any, Callable, Optional, Union, get_type_hints + +from pydantic import BaseModel, Field, create_model + +from crewai.utilities.logger import Logger + + +class CrewStructuredTool: + """A structured tool that can operate on any number of inputs. + + This tool intends to replace StructuredTool with a custom implementation + that integrates better with CrewAI's ecosystem. + """ + + def __init__( + self, + name: str, + description: str, + args_schema: type[BaseModel], + func: Callable[..., Any], + result_as_answer: bool = False, + ) -> None: + """Initialize the structured tool. + + Args: + name: The name of the tool + description: A description of what the tool does + args_schema: The pydantic model for the tool's arguments + func: The function to run when the tool is called + result_as_answer: Whether to return the output directly + """ + self.name = name + self.description = description + self.args_schema = args_schema + self.func = func + self._logger = Logger() + self.result_as_answer = result_as_answer + + # Validate the function signature matches the schema + self._validate_function_signature() + + @classmethod + def from_function( + cls, + func: Callable, + name: Optional[str] = None, + description: Optional[str] = None, + return_direct: bool = False, + args_schema: Optional[type[BaseModel]] = None, + infer_schema: bool = True, + **kwargs: Any, + ) -> CrewStructuredTool: + """Create a tool from a function. + + Args: + func: The function to create a tool from + name: The name of the tool. Defaults to the function name + description: The description of the tool. Defaults to the function docstring + return_direct: Whether to return the output directly + args_schema: Optional schema for the function arguments + infer_schema: Whether to infer the schema from the function signature + **kwargs: Additional arguments to pass to the tool + + Returns: + A CrewStructuredTool instance + + Example: + >>> def add(a: int, b: int) -> int: + ... '''Add two numbers''' + ... return a + b + >>> tool = CrewStructuredTool.from_function(add) + """ + name = name or func.__name__ + description = description or inspect.getdoc(func) + + if description is None: + raise ValueError( + f"Function {name} must have a docstring if description not provided." + ) + + # Clean up the description + description = textwrap.dedent(description).strip() + + if args_schema is not None: + # Use provided schema + schema = args_schema + elif infer_schema: + # Infer schema from function signature + schema = cls._create_schema_from_function(name, func) + else: + raise ValueError( + "Either args_schema must be provided or infer_schema must be True." + ) + + return cls( + name=name, + description=description, + args_schema=schema, + func=func, + result_as_answer=return_direct, + ) + + @staticmethod + def _create_schema_from_function( + name: str, + func: Callable, + ) -> type[BaseModel]: + """Create a Pydantic schema from a function's signature. + + Args: + name: The name to use for the schema + func: The function to create a schema from + + Returns: + A Pydantic model class + """ + # Get function signature + sig = inspect.signature(func) + + # Get type hints + type_hints = get_type_hints(func) + + # Create field definitions + fields = {} + for param_name, param in sig.parameters.items(): + # Skip self/cls for methods + if param_name in ("self", "cls"): + continue + + # Get type annotation + annotation = type_hints.get(param_name, Any) + + # Get default value + default = ... if param.default == param.empty else param.default + + # Add field + fields[param_name] = (annotation, Field(default=default)) + + # Create model + schema_name = f"{name.title()}Schema" + return create_model(schema_name, **fields) + + def _validate_function_signature(self) -> None: + """Validate that the function signature matches the args schema.""" + sig = inspect.signature(self.func) + schema_fields = self.args_schema.model_fields + + # Check required parameters + for param_name, param in sig.parameters.items(): + # Skip self/cls for methods + if param_name in ("self", "cls"): + continue + + # Skip **kwargs parameters + if param.kind in ( + inspect.Parameter.VAR_KEYWORD, + inspect.Parameter.VAR_POSITIONAL, + ): + continue + + # Only validate required parameters without defaults + if param.default == inspect.Parameter.empty: + if param_name not in schema_fields: + raise ValueError( + f"Required function parameter '{param_name}' " + f"not found in args_schema" + ) + + def _parse_args(self, raw_args: Union[str, dict]) -> dict: + """Parse and validate the input arguments against the schema. + + Args: + raw_args: The raw arguments to parse, either as a string or dict + + Returns: + The validated arguments as a dictionary + """ + if isinstance(raw_args, str): + try: + import json + + raw_args = json.loads(raw_args) + except json.JSONDecodeError as e: + raise ValueError(f"Failed to parse arguments as JSON: {e}") + + try: + validated_args = self.args_schema.model_validate(raw_args) + return validated_args.model_dump() + except Exception as e: + raise ValueError(f"Arguments validation failed: {e}") + + async def ainvoke( + self, + input: Union[str, dict], + config: Optional[dict] = None, + **kwargs: Any, + ) -> Any: + """Asynchronously invoke the tool. + + Args: + input: The input arguments + config: Optional configuration + **kwargs: Additional keyword arguments + + Returns: + The result of the tool execution + """ + parsed_args = self._parse_args(input) + + if inspect.iscoroutinefunction(self.func): + return await self.func(**parsed_args, **kwargs) + else: + # Run sync functions in a thread pool + import asyncio + + return await asyncio.get_event_loop().run_in_executor( + None, lambda: self.func(**parsed_args, **kwargs) + ) + + def _run(self, *args, **kwargs) -> Any: + """Legacy method for compatibility.""" + # Convert args/kwargs to our expected format + input_dict = dict(zip(self.args_schema.model_fields.keys(), args)) + input_dict.update(kwargs) + return self.invoke(input_dict) + + def invoke( + self, input: Union[str, dict], config: Optional[dict] = None, **kwargs: Any + ) -> Any: + """Main method for tool execution.""" + parsed_args = self._parse_args(input) + return self.func(**parsed_args, **kwargs) + + @property + def args(self) -> dict: + """Get the tool's input arguments schema.""" + return self.args_schema.model_json_schema()["properties"] + + def __repr__(self) -> str: + return ( + f"CrewStructuredTool(name='{self.name}', description='{self.description}')" + ) diff --git a/src/crewai/tools/tool_usage.py b/src/crewai/tools/tool_usage.py index f75a9443a..e4108c8ba 100644 --- a/src/crewai/tools/tool_usage.py +++ b/src/crewai/tools/tool_usage.py @@ -6,14 +6,14 @@ from difflib import SequenceMatcher from textwrap import dedent from typing import Any, List, Union +import crewai.utilities.events as events from crewai.agents.tools_handler import ToolsHandler from crewai.task import Task from crewai.telemetry import Telemetry +from crewai.tools import BaseTool from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished from crewai.utilities import I18N, Converter, ConverterError, Printer -import crewai.utilities.events as events - agentops = None if os.environ.get("AGENTOPS_API_KEY"): @@ -50,7 +50,7 @@ class ToolUsage: def __init__( self, tools_handler: ToolsHandler, - tools: List[Any], + tools: List[BaseTool], original_tools: List[Any], tools_description: str, tools_names: str, @@ -299,19 +299,7 @@ class ToolUsage: """Render the tool name and description in plain text.""" descriptions = [] for tool in self.tools: - args = { - k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]} - for k, v in tool.args.items() - } - descriptions.append( - "\n".join( - [ - f"Tool Name: {tool.name.lower()}", - f"Tool Description: {tool.description}", - f"Tool Arguments: {args}", - ] - ) - ) + descriptions.append(tool.description) return "\n--\n".join(descriptions) def _function_calling(self, tool_string: str): diff --git a/src/crewai/tools/tool_usage_events.py b/src/crewai/tools/tool_usage_events.py index fea9d3f3c..3c1d16113 100644 --- a/src/crewai/tools/tool_usage_events.py +++ b/src/crewai/tools/tool_usage_events.py @@ -1,6 +1,7 @@ -from typing import Any, Dict -from pydantic import BaseModel from datetime import datetime +from typing import Any, Dict + +from pydantic import BaseModel class ToolUsageEvent(BaseModel): diff --git a/src/crewai/translations/en.json b/src/crewai/translations/en.json index aad49ff5f..2b8fec44b 100644 --- a/src/crewai/translations/en.json +++ b/src/crewai/translations/en.json @@ -11,7 +11,7 @@ "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}", "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n", "no_tools": "\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!", - "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n ", + "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n", "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n", "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n", "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}", @@ -19,14 +19,16 @@ "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}", "getting_input": "This is the agent's final answer: {final_answer}\n\n", "summarizer_system_message": "You are a helpful assistant that summarizes text.", - "sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}", + "summarize_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." + "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.", + "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}", "force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.", - "agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n", + "agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n", "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n", "tool_usage_error": "I encountered an error: {error}", "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.", diff --git a/src/crewai/types/usage_metrics.py b/src/crewai/types/usage_metrics.py index a5cee6a0f..e87a79e33 100644 --- a/src/crewai/types/usage_metrics.py +++ b/src/crewai/types/usage_metrics.py @@ -8,6 +8,7 @@ class UsageMetrics(BaseModel): Attributes: total_tokens: Total number of tokens used. prompt_tokens: Number of tokens used in prompts. + cached_prompt_tokens: Number of cached prompt tokens used. completion_tokens: Number of tokens used in completions. successful_requests: Number of successful requests made. """ @@ -16,6 +17,9 @@ class UsageMetrics(BaseModel): prompt_tokens: int = Field( default=0, description="Number of tokens used in prompts." ) + cached_prompt_tokens: int = Field( + default=0, description="Number of cached prompt tokens used." + ) completion_tokens: int = Field( default=0, description="Number of tokens used in completions." ) @@ -32,5 +36,6 @@ class UsageMetrics(BaseModel): """ self.total_tokens += usage_metrics.total_tokens self.prompt_tokens += usage_metrics.prompt_tokens + self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens self.completion_tokens += usage_metrics.completion_tokens self.successful_requests += usage_metrics.successful_requests diff --git a/src/crewai/utilities/__init__.py b/src/crewai/utilities/__init__.py index 26d35a6cc..dd6d9fa44 100644 --- a/src/crewai/utilities/__init__.py +++ b/src/crewai/utilities/__init__.py @@ -10,6 +10,7 @@ from .rpm_controller import RPMController from .exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, ) +from .embedding_configurator import EmbeddingConfigurator __all__ = [ "Converter", @@ -23,4 +24,5 @@ __all__ = [ "RPMController", "YamlParser", "LLMContextLengthExceededException", + "EmbeddingConfigurator", ] diff --git a/src/crewai/utilities/constants.py b/src/crewai/utilities/constants.py index 22cc2ffbe..096bb7c8c 100644 --- a/src/crewai/utilities/constants.py +++ b/src/crewai/utilities/constants.py @@ -1,2 +1,6 @@ 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 +MAX_FILE_NAME_LENGTH = 255 diff --git a/src/crewai/utilities/converter.py b/src/crewai/utilities/converter.py index 4d0af67ca..ba958ddc6 100644 --- a/src/crewai/utilities/converter.py +++ b/src/crewai/utilities/converter.py @@ -1,6 +1,6 @@ import json import re -from typing import Any, Optional, Type, Union +from typing import Any, Optional, Type, Union, get_args, get_origin from pydantic import BaseModel, ValidationError @@ -214,3 +214,38 @@ def create_converter( raise Exception("No output converter found or set.") return converter + + +def generate_model_description(model: Type[BaseModel]) -> str: + """ + Generate a string description of a Pydantic model's fields and their types. + + This function takes a Pydantic model class and returns a string that describes + the model's fields and their respective types. The description includes handling + of complex types such as `Optional`, `List`, and `Dict`, as well as nested Pydantic + models. + """ + + def describe_field(field_type): + origin = get_origin(field_type) + args = get_args(field_type) + + if origin is Union and type(None) in args: + non_none_args = [arg for arg in args if arg is not type(None)] + return f"Optional[{describe_field(non_none_args[0])}]" + elif origin is list: + return f"List[{describe_field(args[0])}]" + elif origin is dict: + key_type = describe_field(args[0]) + value_type = describe_field(args[1]) + return f"Dict[{key_type}, {value_type}]" + elif isinstance(field_type, type) and issubclass(field_type, BaseModel): + return generate_model_description(field_type) + else: + return field_type.__name__ + + fields = model.__annotations__ + field_descriptions = [ + f'"{name}": {describe_field(type_)}' for name, type_ in fields.items() + ] + return "{\n " + ",\n ".join(field_descriptions) + "\n}" diff --git a/src/crewai/utilities/crew_json_encoder.py b/src/crewai/utilities/crew_json_encoder.py index 3cab07ffc..70c1e9274 100644 --- a/src/crewai/utilities/crew_json_encoder.py +++ b/src/crewai/utilities/crew_json_encoder.py @@ -1,6 +1,8 @@ -from datetime import datetime, date import json +from datetime import date, datetime +from decimal import Decimal from uuid import UUID + from pydantic import BaseModel @@ -8,7 +10,7 @@ class CrewJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, BaseModel): return self._handle_pydantic_model(obj) - elif isinstance(obj, UUID): + elif isinstance(obj, UUID) or isinstance(obj, Decimal): return str(obj) elif isinstance(obj, datetime) or isinstance(obj, date): diff --git a/src/crewai/utilities/crew_pydantic_output_parser.py b/src/crewai/utilities/crew_pydantic_output_parser.py index 5c5fa0d94..c269f3189 100644 --- a/src/crewai/utilities/crew_pydantic_output_parser.py +++ b/src/crewai/utilities/crew_pydantic_output_parser.py @@ -1,10 +1,11 @@ import json -import regex from typing import Any, Type -from crewai.agents.parser import OutputParserException +import regex from pydantic import BaseModel, ValidationError +from crewai.agents.parser import OutputParserException + class CrewPydanticOutputParser: """Parses the text into pydantic models""" diff --git a/src/crewai/utilities/embedding_configurator.py b/src/crewai/utilities/embedding_configurator.py new file mode 100644 index 000000000..44e832ec2 --- /dev/null +++ b/src/crewai/utilities/embedding_configurator.py @@ -0,0 +1,184 @@ +import os +from typing import Any, Dict, cast + +from chromadb import Documents, EmbeddingFunction, Embeddings +from chromadb.api.types import validate_embedding_function + + +class EmbeddingConfigurator: + def __init__(self): + self.embedding_functions = { + "openai": self._configure_openai, + "azure": self._configure_azure, + "ollama": self._configure_ollama, + "vertexai": self._configure_vertexai, + "google": self._configure_google, + "cohere": self._configure_cohere, + "bedrock": self._configure_bedrock, + "huggingface": self._configure_huggingface, + "watson": self._configure_watson, + } + + def configure_embedder( + self, + embedder_config: Dict[str, Any] | None = None, + ) -> EmbeddingFunction: + """Configures and returns an embedding function based on the provided config.""" + if embedder_config is None: + return self._create_default_embedding_function() + + provider = embedder_config.get("provider") + config = embedder_config.get("config", {}) + model_name = config.get("model") + + if isinstance(provider, EmbeddingFunction): + try: + validate_embedding_function(provider) + return provider + except Exception as e: + raise ValueError(f"Invalid custom embedding function: {str(e)}") + + if provider not in self.embedding_functions: + raise Exception( + f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}" + ) + + return self.embedding_functions[provider](config, model_name) + + @staticmethod + def _create_default_embedding_function(): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small" + ) + + @staticmethod + def _configure_openai(config, model_name): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"), + model_name=model_name, + ) + + @staticmethod + def _configure_azure(config, model_name): + from chromadb.utils.embedding_functions.openai_embedding_function import ( + OpenAIEmbeddingFunction, + ) + + return OpenAIEmbeddingFunction( + api_key=config.get("api_key"), + api_base=config.get("api_base"), + api_type=config.get("api_type", "azure"), + api_version=config.get("api_version"), + model_name=model_name, + ) + + @staticmethod + def _configure_ollama(config, model_name): + from chromadb.utils.embedding_functions.ollama_embedding_function import ( + OllamaEmbeddingFunction, + ) + + return OllamaEmbeddingFunction( + url=config.get("url", "http://localhost:11434/api/embeddings"), + model_name=model_name, + ) + + @staticmethod + def _configure_vertexai(config, model_name): + from chromadb.utils.embedding_functions.google_embedding_function import ( + GoogleVertexEmbeddingFunction, + ) + + return GoogleVertexEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_google(config, model_name): + from chromadb.utils.embedding_functions.google_embedding_function import ( + GoogleGenerativeAiEmbeddingFunction, + ) + + return GoogleGenerativeAiEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_cohere(config, model_name): + from chromadb.utils.embedding_functions.cohere_embedding_function import ( + CohereEmbeddingFunction, + ) + + return CohereEmbeddingFunction( + model_name=model_name, + api_key=config.get("api_key"), + ) + + @staticmethod + def _configure_bedrock(config, model_name): + from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import ( + AmazonBedrockEmbeddingFunction, + ) + + return AmazonBedrockEmbeddingFunction( + session=config.get("session"), + ) + + @staticmethod + def _configure_huggingface(config, model_name): + from chromadb.utils.embedding_functions.huggingface_embedding_function import ( + HuggingFaceEmbeddingServer, + ) + + return HuggingFaceEmbeddingServer( + url=config.get("api_url"), + ) + + @staticmethod + def _configure_watson(config, model_name): + try: + import ibm_watsonx_ai.foundation_models as watson_models + from ibm_watsonx_ai import Credentials + from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams + except ImportError as e: + raise ImportError( + "IBM Watson dependencies are not installed. Please install them to use Watson embedding." + ) from e + + class WatsonEmbeddingFunction(EmbeddingFunction): + def __call__(self, input: Documents) -> Embeddings: + if isinstance(input, str): + input = [input] + + embed_params = { + EmbedParams.TRUNCATE_INPUT_TOKENS: 3, + EmbedParams.RETURN_OPTIONS: {"input_text": True}, + } + + embedding = watson_models.Embeddings( + model_id=config.get("model"), + params=embed_params, + credentials=Credentials( + api_key=config.get("api_key"), url=config.get("api_url") + ), + project_id=config.get("project_id"), + ) + + try: + embeddings = embedding.embed_documents(input) + return cast(Embeddings, embeddings) + except Exception as e: + print("Error during Watson embedding:", e) + raise e + + return WatsonEmbeddingFunction() diff --git a/src/crewai/utilities/evaluators/crew_evaluator_handler.py b/src/crewai/utilities/evaluators/crew_evaluator_handler.py index e251f76f2..3387d91b3 100644 --- a/src/crewai/utilities/evaluators/crew_evaluator_handler.py +++ b/src/crewai/utilities/evaluators/crew_evaluator_handler.py @@ -1,13 +1,14 @@ from collections import defaultdict +from pydantic import BaseModel, Field +from rich.box import HEAVY_EDGE +from rich.console import Console +from rich.table import Table + from crewai.agent import Agent from crewai.task import Task from crewai.tasks.task_output import TaskOutput from crewai.telemetry import Telemetry -from pydantic import BaseModel, Field -from rich.box import HEAVY_EDGE -from rich.console import Console -from rich.table import Table class TaskEvaluationPydanticOutput(BaseModel): diff --git a/src/crewai/utilities/events.py b/src/crewai/utilities/events.py index 75425fca2..11175e0d2 100644 --- a/src/crewai/utilities/events.py +++ b/src/crewai/utilities/events.py @@ -1,7 +1,7 @@ -from typing import Any, Callable, Generic, List, Dict, Type, TypeVar from functools import wraps -from pydantic import BaseModel +from typing import Any, Callable, Dict, Generic, List, Type, TypeVar +from pydantic import BaseModel T = TypeVar("T") EVT = TypeVar("EVT", bound=BaseModel) diff --git a/src/crewai/utilities/file_handler.py b/src/crewai/utilities/file_handler.py index 091bd930a..bb97b940f 100644 --- a/src/crewai/utilities/file_handler.py +++ b/src/crewai/utilities/file_handler.py @@ -16,7 +16,11 @@ class FileHandler: def log(self, **kwargs): now = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n" + message = ( + f"{now}: " + + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()]) + + "\n" + ) with open(self._path, "a", encoding="utf-8") as file: file.write(message + "\n") @@ -63,7 +67,7 @@ class PickleHandler: with open(self.file_path, "rb") as file: try: - return pickle.load(file) + return pickle.load(file) # nosec except EOFError: return {} # Return an empty dictionary if the file is empty or corrupted except Exception: diff --git a/src/crewai/utilities/planning_handler.py b/src/crewai/utilities/planning_handler.py index 14a9a50a6..590f42389 100644 --- a/src/crewai/utilities/planning_handler.py +++ b/src/crewai/utilities/planning_handler.py @@ -1,4 +1,5 @@ from typing import Any, List, Optional + from pydantic import BaseModel, Field from crewai.agent import Agent diff --git a/src/crewai/utilities/prompts.py b/src/crewai/utilities/prompts.py index cecd6cb71..66909644f 100644 --- a/src/crewai/utilities/prompts.py +++ b/src/crewai/utilities/prompts.py @@ -1,5 +1,7 @@ -from pydantic import BaseModel, Field from typing import Any, Optional + +from pydantic import BaseModel, Field + from crewai.utilities import I18N diff --git a/src/crewai/utilities/pydantic_schema_parser.py b/src/crewai/utilities/pydantic_schema_parser.py index 073280dd2..f4c8c720f 100644 --- a/src/crewai/utilities/pydantic_schema_parser.py +++ b/src/crewai/utilities/pydantic_schema_parser.py @@ -1,4 +1,4 @@ -from typing import Type, get_args, get_origin, Union +from typing import Type, Union, get_args, get_origin from pydantic import BaseModel diff --git a/src/crewai/utilities/task_output_storage_handler.py b/src/crewai/utilities/task_output_storage_handler.py index 9970f64b4..34cdaccbb 100644 --- a/src/crewai/utilities/task_output_storage_handler.py +++ b/src/crewai/utilities/task_output_storage_handler.py @@ -1,6 +1,8 @@ -from pydantic import BaseModel, Field from datetime import datetime -from typing import Dict, Any, Optional, List +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, Field + from crewai.memory.storage.kickoff_task_outputs_storage import ( KickoffTaskOutputsSQLiteStorage, ) diff --git a/src/crewai/utilities/token_counter_callback.py b/src/crewai/utilities/token_counter_callback.py index 1b6215232..06ad15022 100644 --- a/src/crewai/utilities/token_counter_callback.py +++ b/src/crewai/utilities/token_counter_callback.py @@ -1,4 +1,5 @@ from litellm.integrations.custom_logger import CustomLogger +from litellm.types.utils import Usage from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess @@ -11,8 +12,11 @@ class TokenCalcHandler(CustomLogger): if self.token_cost_process is None: return + usage: Usage = response_obj["usage"] self.token_cost_process.sum_successful_requests(1) - self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens) - self.token_cost_process.sum_completion_tokens( - response_obj["usage"].completion_tokens - ) + self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens) + self.token_cost_process.sum_completion_tokens(usage.completion_tokens) + if usage.prompt_tokens_details: + self.token_cost_process.sum_cached_prompt_tokens( + usage.prompt_tokens_details.cached_tokens + ) diff --git a/tests/agent_test.py b/tests/agent_test.py index 154677ccf..46f6edc29 100644 --- a/tests/agent_test.py +++ b/tests/agent_test.py @@ -5,13 +5,14 @@ from unittest import mock from unittest.mock import patch import pytest -from crewai_tools import tool 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 @@ -277,9 +278,10 @@ def test_cache_hitting(): "multiplier-{'first_number': 12, 'second_number': 3}": 36, } - with patch.object(CacheHandler, "read") as read, patch.object( - Emitter, "emit" - ) as emit: + with ( + patch.object(CacheHandler, "read") as read, + patch.object(Emitter, "emit") as emit, + ): read.return_value = "0" task = Task( description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.", @@ -604,7 +606,7 @@ def test_agent_respect_the_max_rpm_set(capsys): def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys): from unittest.mock import patch - from crewai_tools import tool + from crewai.tools import tool @tool def get_final_answer() -> float: @@ -642,7 +644,7 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys): def test_agent_without_max_rpm_respet_crew_rpm(capsys): from unittest.mock import patch - from crewai_tools import tool + from crewai.tools import tool @tool def get_final_answer() -> float: @@ -696,7 +698,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys): def test_agent_error_on_parsing_tool(capsys): from unittest.mock import patch - from crewai_tools import tool + from crewai.tools import tool @tool def get_final_answer() -> float: @@ -739,7 +741,7 @@ def test_agent_error_on_parsing_tool(capsys): def test_agent_remembers_output_format_after_using_tools_too_many_times(): from unittest.mock import patch - from crewai_tools import tool + from crewai.tools import tool @tool def get_final_answer() -> float: @@ -863,11 +865,16 @@ def test_agent_function_calling_llm(): from crewai.tools.tool_usage import ToolUsage - with patch.object( - instructor, "from_litellm", wraps=instructor.from_litellm - ) as mock_from_litellm, patch.object( - ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception") - ) as mock_original_tool_calling: + with ( + patch.object( + instructor, "from_litellm", wraps=instructor.from_litellm + ) as mock_from_litellm, + patch.object( + ToolUsage, + "_original_tool_calling", + side_effect=Exception("Forced exception"), + ) as mock_original_tool_calling, + ): crew.kickoff() mock_from_litellm.assert_called() mock_original_tool_calling.assert_called() @@ -894,7 +901,7 @@ def test_agent_count_formatting_error(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_tool_result_as_answer_is_the_final_answer_for_the_agent(): - from crewai_tools import BaseTool + from crewai.tools import BaseTool class MyCustomTool(BaseTool): name: str = "Get Greetings" @@ -924,7 +931,7 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_tool_usage_information_is_appended_to_agent(): - from crewai_tools import BaseTool + from crewai.tools import BaseTool class MyCustomTool(BaseTool): name: str = "Decide Greetings" @@ -979,8 +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", @@ -989,6 +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", @@ -996,11 +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(): @@ -1568,3 +1590,42 @@ def test_agent_execute_task_with_ollama(): result = agent.execute_task(task) assert len(result.split(".")) == 2 assert "AI" in result or "artificial intelligence" in result.lower() + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_agent_with_knowledge_sources(): + # Create a knowledge source with some content + content = "Brandon's favorite color is blue and he likes Mexican food." + string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} + ) + + with patch( + "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage" + ) as MockKnowledge: + mock_knowledge_instance = MockKnowledge.return_value + mock_knowledge_instance.sources = [string_source] + mock_knowledge_instance.query.return_value = [ + {"content": content, "metadata": {"preference": "personal"}} + ] + + agent = Agent( + role="Information Agent", + goal="Provide information based on knowledge sources", + backstory="You have access to specific knowledge sources.", + llm=LLM(model="gpt-4o-mini"), + knowledge_sources=[string_source], + ) + + # Create a task that requires the agent to use the knowledge + task = Task( + description="What is Brandon's favorite color?", + expected_output="Brandon's favorite color.", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task]) + result = crew.kickoff() + + # Assert that the agent provides the correct information + assert "blue" in result.raw.lower() diff --git a/tests/agent_tools/lol.py b/tests/agent_tools/lol.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tests/agents/agent_builder/base_agent_test.py b/tests/agents/agent_builder/base_agent_test.py index 4e47f2271..7c0c2f472 100644 --- a/tests/agents/agent_builder/base_agent_test.py +++ b/tests/agents/agent_builder/base_agent_test.py @@ -1,22 +1,24 @@ import hashlib from typing import Any, List, Optional -from crewai.agents.agent_builder.base_agent import BaseAgent from pydantic import BaseModel +from crewai.agents.agent_builder.base_agent import BaseAgent +from crewai.tools.base_tool import BaseTool + class TestAgent(BaseAgent): def execute_task( self, task: Any, context: Optional[str] = None, - tools: Optional[List[Any]] = None, + tools: Optional[List[BaseTool]] = None, ) -> str: return "" def create_agent_executor(self, tools=None) -> None: ... - def _parse_tools(self, tools: List[Any]) -> List[Any]: + def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]: return [] def get_delegation_tools(self, agents: List["BaseAgent"]): ... diff --git a/tests/agents/test_crew_agent_parser.py b/tests/agents/test_crew_agent_parser.py index 6496f7193..4b48e1552 100644 --- a/tests/agents/test_crew_agent_parser.py +++ b/tests/agents/test_crew_agent_parser.py @@ -1,10 +1,11 @@ import pytest -from crewai.agents.parser import CrewAgentParser + from crewai.agents.crew_agent_executor import ( AgentAction, AgentFinish, OutputParserException, ) +from crewai.agents.parser import CrewAgentParser @pytest.fixture diff --git a/tests/cassettes/test_after_crew_modification.yaml b/tests/cassettes/test_after_crew_modification.yaml new file mode 100644 index 000000000..e9d6926b0 --- /dev/null +++ b/tests/cassettes/test_after_crew_modification.yaml @@ -0,0 +1,449 @@ +interactions: +- request: + body: !!binary | + CuMOCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSug4KEgoQY3Jld2FpLnRl + bGVtZXRyeRKSDAoQK+dPhrB8w3HKFlxX60XzYRIIk5aB+A8oCWQqDENyZXcgQ3JlYXRlZDABObix + K+HWrwgYQcBiMeHWrwgYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODAuMEoaCg5weXRob25fdmVy + c2lvbhIICgYzLjExLjdKLgoIY3Jld19rZXkSIgogZjM0NmE5YWQ2ZDczMDYzZTA2NzdiMTdjZTlj + NTAxNzdKMQoHY3Jld19pZBImCiQ3NjRjZWM1YS04NzkxLTRmN2MtOWY0MC1hNTMzMzJmOTk3YzBK + HAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdf + bnVtYmVyX29mX3Rhc2tzEgIYAkobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgCSqwFCgtjcmV3 + X2FnZW50cxKcBQqZBVt7ImtleSI6ICI3M2MzNDljOTNjMTYzYjVkNGRmOThhNjRmYWMxYzQzMCIs + ICJpZCI6ICJjZDgwYjlhNy1hN2QzLTQzNTQtYjUyOC1jMzAyODA0MjA3YzgiLCAicm9sZSI6ICJ7 + dG9waWN9IFNlbmlvciBEYXRhIFJlc2VhcmNoZXJcbiIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4 + X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwg + ImxsbSI6ICJncHQtNG8iLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxsb3dfY29k + ZV9leGVjdXRpb24/IjogZmFsc2UsICJtYXhfcmV0cnlfbGltaXQiOiAyLCAidG9vbHNfbmFtZXMi + OiBbXX0sIHsia2V5IjogImJiMDY4Mzc3YzE2NDFiZTZkN2Q5N2E1MTY1OWRiNjEzIiwgImlkIjog + ImJmZjc3YmUyLWU4MjQtNGEyOS1hZTFlLTQyMWFjMzc2MjY2YyIsICJyb2xlIjogInt0b3BpY30g + UmVwb3J0aW5nIEFuYWx5c3RcbiIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwg + Im1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQt + NG8iLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxsb3dfY29kZV9leGVjdXRpb24/ + IjogZmFsc2UsICJtYXhfcmV0cnlfbGltaXQiOiAyLCAidG9vbHNfbmFtZXMiOiBbXX1dSpMECgpj + cmV3X3Rhc2tzEoQECoEEW3sia2V5IjogIjZhZmM0YjM5NjI1OWZiYjc2ODFmNTZjNzc1NWNjOTM3 + IiwgImlkIjogIjRmNTFlYzM2LTVlMDctNGU4Ni1iYzIxLWU1MTQ0Mzg2YmIyYSIsICJhc3luY19l + eGVjdXRpb24/IjogZmFsc2UsICJodW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAi + e3RvcGljfSBTZW5pb3IgRGF0YSBSZXNlYXJjaGVyXG4iLCAiYWdlbnRfa2V5IjogIjczYzM0OWM5 + M2MxNjNiNWQ0ZGY5OGE2NGZhYzFjNDMwIiwgInRvb2xzX25hbWVzIjogW119LCB7ImtleSI6ICJi + MTdiMTg4ZGJmMTRmOTNhOThlNWI5NWFhZDM2NzU3NyIsICJpZCI6ICIwMGJmZDY5ZC03OWZiLTRj + MjctYTM0Yi02NzBkZWJlMzU0NWYiLCAiYXN5bmNfZXhlY3V0aW9uPyI6IGZhbHNlLCAiaHVtYW5f + aW5wdXQ/IjogZmFsc2UsICJhZ2VudF9yb2xlIjogInt0b3BpY30gUmVwb3J0aW5nIEFuYWx5c3Rc + biIsICJhZ2VudF9rZXkiOiAiYmIwNjgzNzdjMTY0MWJlNmQ3ZDk3YTUxNjU5ZGI2MTMiLCAidG9v + bHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKOAgoQsN5cQC9ZzBr2B0OKBR2WCxII3ULL7Wk965Yq + DFRhc2sgQ3JlYXRlZDABOWB9ROHWrwgYQfg0ReHWrwgYSi4KCGNyZXdfa2V5EiIKIGYzNDZhOWFk + NmQ3MzA2M2UwNjc3YjE3Y2U5YzUwMTc3SjEKB2NyZXdfaWQSJgokNzY0Y2VjNWEtODc5MS00Zjdj + LTlmNDAtYTUzMzMyZjk5N2MwSi4KCHRhc2tfa2V5EiIKIDZhZmM0YjM5NjI1OWZiYjc2ODFmNTZj + Nzc1NWNjOTM3SjEKB3Rhc2tfaWQSJgokNGY1MWVjMzYtNWUwNy00ZTg2LWJjMjEtZTUxNDQzODZi + YjJhegIYAYUBAAEAAA== + headers: + Accept: + - 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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.\n\nYour + personal goal is: Create detailed reports based on Bicycles data analysis and + research findings\n\nTo give my best complete final answer to the task use the + exact following format:\n\nThought: I now can give a great answer\nFinal Answer: + Your final answer must be the great and the most complete as possible, it must + be outcome described.\n\nI MUST use these formats, my job depends on it!"}, + {"role": "user", "content": "\nCurrent Task: Review the context you got and + expand each topic into a full section for a report. Make sure the report is + detailed and contains any and all relevant information.\n\n\nThis is the expect + criteria for your final answer: A fully fledge reports with the mains topics, + each with a full section of information. Formatted as markdown without ''```''\n\nyou + MUST return the actual complete content as the final answer, not a summary.\n\nThis + is the context you''re working with:\n1. **E-Bike Boom**: Electric bikes (e-bikes) + have seen a significant rise in popularity, with industry reports indicating + a projected growth of 60% in sales compared to previous years. Many cities are + paving bike lanes specifically designed for e-bikes to accommodate this surge.\n\n2. + **Sustainability in Manufacturing**: Bicycle manufacturers are increasingly + adopting sustainable practices, such as using recycled materials for frames + and parts, and implementing environmentally friendly production processes. This + shift is driven by consumer demand for greener products.\n\n3. **Smart Bicycles**: + The integration of technology in bicycles has progressed with smart bikes featuring + built-in GPS, automated gear shifting, and performance analytics. These innovations + enhance the cycling experience and cater to data-driven enthusiasts.\n\n4. **Bike + Sharing Programs**: Urban areas are continuing to expand bike-sharing programs, + with some cities introducing electric bike options and introducing smartphone + apps to streamline the renting process, increasing accessibility and convenience + for riders.\n\n5. **Safety Innovations**: Advances in safety technology such + as smart helmets that incorporate lights and indicators, anti-collision systems + using sensor technology, and built-in communication systems to connect with + smartphones are on the rise, aimed at reducing accidents.\n\n6. **Adventure + Cycling Trends**: There is a growing popularity in adventure and gravel cycling, + with more cyclists seeking off-road experiences. This has prompted manufacturers + to develop dedicated bikes that cater to rugged terrains, with features such + as wider tires and durable frames.\n\n7. **Customization and Personalization**: + The market for customizable bicycles is expanding. Consumers are now able to + choose colors, styles, and features that suit their personal preferences, leading + to a more personalized cycling experience.\n\n8. **Communities and Events**: + Cycling communities are thriving globally, with an increase in events such as + group rides, competitive races, and festivals celebrating biking culture. This + fosters social engagement and promotes cycling as a lifestyle.\n\n9. **Cargo + Bikes for Urban Living**: The rise of cargo bikes, particularly in urban environments, + allows for efficient transportation of goods, making them an appealing choice + for small businesses and families. This trend is encouraged by city planners + promoting cycling as an alternative to car deliveries.\n\n10. **Regulatory Changes**: + Governments around the world are increasingly implementing policies to support + cycling infrastructure, such as funding for bike lanes, subsidies for bicycle + purchases, and stricter emissions standards for motor vehicles, making cycling + a more attractive option for commuting.\n\nEach of these points represents the + latest developments in the bicycle industry as we move through 2024, highlighting + advancements in technology, trends in user preferences, and a broader societal + shift towards sustainability and health.\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-mini", "stop": ["\nObservation:"], "stream": + false}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '4457' + content-type: + - application/json + cookie: + - __cf_bm=CkK4UvBd9ukXvn50uJwGambJcz5zERAJfeXJ9xge6H4-1732107842-1.0.1.1-IOK2yVL3RlD75MgmnKzIEyE38HNknwn6I8BBJ1wjGz4jCTd0YWIBPnvUm9gB8D_zLlUA9G7p_wbrfyc4mO_Bmg; + _cfuvid=MmeN9oHWrBLThkEJdaSFHBfWe95JvA8iFnnt7CC92tk-1732107842102-0.0.1.1-604800000 + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.52.1 + x-stainless-arch: + - x64 + 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-AVefbpMLcvelEguI3pyXOpfbaXLGG\",\n \"object\": + \"chat.completion\",\n \"created\": 1732107867,\n \"model\": \"gpt-4o-mini-2024-07-18\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"I now can give a great answer \\nFinal + Answer: \\n\\n# Comprehensive Report on the Latest Developments in the Bicycle + Industry (2024)\\n\\n## 1. E-Bike Boom\\nThe popularity of electric bikes (e-bikes) + has surged dramatically in recent years, with industry reports indicating a + projected growth of 60% in sales compared to previous years. This growth can + be attributed to increasing urbanization, the rising need for more sustainable + modes of transport, and technological advancements that have made e-bikes more + accessible and desirable. Cities worldwide are responding to this boom by developing + dedicated bike lanes specifically designed for e-bikes, which not only promotes + safety but also encourages more individuals to consider cycling as a primary + mode of transportation.\\n\\n## 2. Sustainability in Manufacturing\\nIn line + with global trends towards sustainability, bicycle manufacturers are increasingly + adopting eco-friendlier practices. They are utilizing recycled materials for + frames and components and implementing environmentally friendly production processes. + This shift is not just a response to regulatory pressures but also driven by + consumer demand for greener products. Companies that prioritize sustainability + are seeing a competitive edge in an increasingly eco-conscious market, as consumers + are more likely to align their purchases with their values regarding environmental + responsibility.\\n\\n## 3. Smart Bicycles\\nThe integration of technology in + bicycles has advanced significantly, resulting in the emergence of smart bikes. + These bicycles often feature built-in GPS for navigation, automated gear shifting + for smoother rides, and performance analytics that allow users to track their + cycling metrics. Such innovations enhance the overall cycling experience and + cater to performance-focused cyclists who seek data to optimize their rides. + By merging cycling with technology, manufacturers are not only attracting tech + enthusiasts but also making cycling more mainstream.\\n\\n## 4. Bike Sharing + Programs\\nBike-sharing programs are rapidly expanding, particularly in urban + areas. Many cities have started introducing electric bike options within these + programs to meet the growing demand. The introduction of smartphone apps has + streamlined the renting process, increasing accessibility and convenience for + users. This trend not only promotes a healthier lifestyle but also reduces traffic + congestion and environmental impact in densely populated areas, making cycling + a more viable option for commuting.\\n\\n## 5. Safety Innovations\\nRecent advancements + in safety technology are working towards making cycling safer. Innovations such + as smart helmets equipped with lights and turn indicators, anti-collision systems + utilizing sensor technology, and integrated communication systems linking bicycles + with smartphones are increasingly gaining traction. These developments aim to + minimize accidents and enhance the overall sense of security for cyclists, thereby + encouraging more people to take up cycling as a daily activity.\\n\\n## 6. Adventure + Cycling Trends\\nAdventure and gravel cycling are witnessing a renaissance, + with many cyclists seeking off-road experiences that enable a connection with + nature. This trend has led manufacturers to innovate by developing dedicated + bikes suited for rugged terrains, characterized by features like wider tires + and durable frames. As consumers become more adventurous in their hobbies, manufacturers + are recognizing the need to cater to this niche market, fostering the growth + of adventure cycling as a distinct segment in the industry.\\n\\n## 7. Customization + and Personalization\\nThe demand for customizable bicycles is on the rise, allowing + consumers to choose various aspects of their bikes, including colors, styles, + and features. This trend towards personalization is enhancing the cycling experience, + as riders can tailor their bicycles to their preferences. The flourishing market + for custom bikes reflects a broader societal shift towards individuality and + self-expression, as consumers are no longer content with one-size-fits-all solutions.\\n\\n## + 8. Communities and Events\\nCycling communities are thriving worldwide, reflected + in an increase in events such as group rides, competitive races, and festivals + celebrating biking culture. These gatherings not only foster a sense of camaraderie + among cyclists but also promote cycling as a lifestyle choice to the wider community. + The growth of these events is instrumental in building a culture around cycling, + driving advocacy for cycling infrastructure and safety, and ultimately increasing + the number of people who cycle.\\n\\n## 9. Cargo Bikes for Urban Living\\nThe + rise of cargo bikes, especially in urban settings, represents an innovative + solution for transporting goods efficiently while reducing reliance on motor + vehicles. Such bikes serve as an appealing alternative for small businesses + and families alike, allowing for easy deliveries and shopping. City planners + are increasingly promoting cargo bikes within urban transport strategies, recognizing + them as a sustainable option that aligns with broader goals for reducing carbon + footprints and enhancing urban mobility.\\n\\n## 10. 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Formatted as markdown without ''```''\n\nyou + MUST return the actual complete content as the final answer, not a summary.\n\nThis + is the context you''re working with:\n1. **Electric Bicycles (E-Bikes) Dominate + the Market:** In 2024, e-bikes continue to lead in sales growth globally. Their + popularity is fueled by the advancement in battery technology, offering longer + ranges and shorter charging times, making commuting more efficient and sustainable + in urban environments.\n\n2. **Integration with Smart Technology:** Bicycle + manufacturers are increasingly incorporating IoT technology to enhance user + experience. Features like GPS tracking, fitness data logging, and anti-theft + systems directly linked to smartphones are becoming standard in newer models.\n\n3. + **Sustainable Manufacturing Techniques:** Environmental concerns have pushed + companies to adopt greener manufacturing processes, such as utilizing recycled + materials, reducing carbon footprints in production, and implementing circular + economies within the bicycle industry.\n\n4. **Innovations in Lightweight Materials:** + The development of new composite materials, including carbon and graphene, results + in extremely lightweight and durable frames. This advancement is particularly + noticeable in racing and mountain bikes, enhancing performance and speed.\n\n5. + **Customizable and Modular Bike Designs:** In 2024, there is a notable trend + toward bikes with modular designs that allow riders to customize parts and accessories + easily. This trend caters to diverse consumer needs and promotes longer bike + life cycles by allowing for parts replacement instead of whole bikes.\n\n6. + **Expansion of Urban Cycling Infrastructure:** More cities worldwide are investing + in cycling-friendly infrastructure, such as dedicated bike lanes and bike-sharing + schemes, to encourage eco-friendly commuting and reduce traffic congestion.\n\n7. + **Health and Wellness Benefits:** With growing awareness of health and fitness, + more people are choosing cycling as a daily exercise routine. 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You''re a seasoned researcher with a knack for uncovering the + latest developments in Bicycles. Known for your ability to find the most relevant + information and present it in a clear and concise manner.\n\nYour personal goal + is: Uncover cutting-edge developments in Bicycles\n\nTo give my best complete + final answer to the task use the exact following format:\n\nThought: I now can + give a great answer\nFinal Answer: Your final answer must be the great and the + most complete as possible, it must be outcome described.\n\nI MUST use these + formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: + Conduct a thorough research about Bicycles Make sure you find any interesting + and relevant information given the current year is 2024.\n\n\nThis is the expect + criteria for your final answer: A list with 10 bullet points of the most relevant + information about Bicycles\n\nyou MUST return the actual complete content as + the final answer, not a summary.\n\nBegin! 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This approach not only reduces environmental impact + but also appeals to eco-conscious consumers.\\n\\n4. **Urban Infrastructure + Improvements**: Cities worldwide are investing heavily in improving cycling + infrastructure, including the addition of dedicated bike lanes, bike-sharing + programs, and parking facilities, aiming to promote cycling as a primary mode + of transport and improve safety for cyclists.\\n\\n5. **Global Cycling Tourism + Increase**: Cycling tourism has seen a surge in popularity, with destinations + specifically catering to cyclists emerging across Europe, North America, and + Asia. 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Artificial Intelligence Advancements\\nIn 2024, artificial intelligence (AI) + has undergone remarkable advancements, particularly in the fields of natural + language processing (NLP) and computer vision. AI models are now achieving near-human-level + understanding and interpretation capabilities, enabling more nuanced interactions + between humans and machines. This progression has spurred the development of + sophisticated AI applications across various sectors, from healthcare, where + AI can analyze medical images and assist in diagnostic processes, to finance, + where predictive analytics enhances decision-making and risk management. With + AI being employed in customer service chatbots and personal assistants, the + technology's integration into daily operations significantly improves productivity + and user experience.\\n\\n## 2. Quantum Computing Progress\\nThe capabilities + of quantum computers have expanded significantly, showcasing their potential + to exceed traditional computing power for specific tasks. In 2024, key breakthroughs + have been made in areas such as error correction and qubit coherence, addressing + longstanding challenges in the field. These advancements are not only enhancing + the performance of quantum systems but are also paving the way for practical + applications in cryptography, where quantum encryption could revolutionize data + security protocols, and in complex problem-solving scenarios across scientific + research and logistics. As quantum technology matures, it holds the promise + to solve problems that are currently intractable for classical computers.\\n\\n## + 3. Sustainable Energy Technologies\\nThe global shift towards sustainable energy + sources has gained remarkable momentum in 2024, driven by innovations in solar + panel efficiency and the adoption of hydrogen fuel cells. Advances in photovoltaic + technology have led to the development of more efficient solar panels capable + of capturing a higher percentage of sunlight, reducing reliance on fossil fuels. + Simultaneously, hydrogen fuel cells are emerging as a viable alternative for + energy storage and transportation, particularly in heavy-duty vehicles and public + transport systems. This transformation towards greener energy solutions is critical + in combating climate change while fostering economic growth through new job + creation in the clean technology sector.\\n\\n## 4. Augmented Reality Enhancements\\nAugmented + reality (AR) technologies are becoming increasingly integrated into everyday + applications as of 2024, providing immersive experiences across various industries, + including retail and education. In retail, AR is enhancing consumer engagement + by allowing customers to visualize products in a real-world context before making + a purchase. In education, AR is facilitating interactive learning experiences, + enabling students to engage with complex subjects through visual simulations + and augmented textbooks. These advancements not only improve user engagement + but also foster greater understanding and retention of information.\\n\\n## + 5. 5G Expansion and 6G Development\\nThe global rollout of 5G technology continues + at a rapid pace, significantly enhancing connectivity speeds and reducing latency. + As 2024 progresses, foundational work on the next-generation 6G networks is + also underway, promising even greater improvements in connectivity and the potential + for groundbreaking applications. These advancements are facilitating the emergence + of smart cities, automated industries, and enhanced telecommunications services. + The increased bandwidth provided by 5G and the anticipation surrounding 6G enable + new possibilities in mobile communications, Internet of Things (IoT) implementations, + and real-time data processing.\\n\\n## 6. Data Privacy Regulations\\nAs data + breaches become increasingly sophisticated and pervasive, regulations surrounding + data privacy have intensified globally in 2024. Many countries have enacted + new laws aimed at protecting consumer information and holding businesses accountable + for their data handling practices. This regulatory environment requires organizations + to implement robust data protection measures, maintain transparency, and establish + trust with consumers. The emphasis on data privacy not only safeguards individuals' + personal information but also promotes ethical practices within the tech industry, + thereby fostering greater public confidence in emerging digital services.\\n\\n## + 7. Biotechnology Breakthroughs\\nThe biotechnology sector has experienced significant + developments in 2024, particularly regarding gene editing technologies such + as CRISPR. These advances enable researchers to make precise modifications to + genetic material, paving the way for personalized medicine that targets genetic + diseases at the source. Agricultural improvements are also on the horizon as + genetically modified crops become more resilient to climate change and pests, + addressing global food security challenges. With ongoing research and clinical + trials, the potential applications of biotechnology in healthcare and agriculture + present transformative opportunities to improve quality of life and sustainability.\\n\\n## + 8. Blockchain Applications\\nBeyond its initial use in cryptocurrencies, blockchain + technology is finding diverse applications in areas such as supply chain management + and digital identity verification. In 2024, businesses leverage blockchain's + inherent transparency and security to streamline operations, enhance traceability, + and foster trust among stakeholders. For example, in supply chain management, + blockchain allows for real-time tracking of products, enabling greater accountability + and efficient inventory management. Similarly, digital identity verification + is becoming more secure through decentralized systems, reducing the risk of + identity theft and fraud, thereby enhancing overall trust in digital transactions.\\n\\n## + 9. Mental Health Technology\\nIn recent years, technology's role in mental health + care has expanded dramatically, with innovative solutions such as virtual reality + (VR) therapy and AI-driven mental health applications emerging in 2024. These + technologies offer patients new therapeutic options that enhance accessibility + and treatment personalization. VR environments can simulate therapeutic situations + for exposure therapy while AI algorithms tailor mental health interventions + according to individual needs, improving overall efficacy. This integration + of technology into mental health care has the potential to bridge gaps in traditional + therapy access, providing vital support to those in need.\\n\\n## 10. Transportation + Innovations\\nTransportation has seen significant innovations as of 2024, driven + primarily by advancements in electric vehicles (EVs) and autonomous technology. + Increasing adoption of EVs is correlated with enhanced battery technologies + that extend range and reduce charging time, making them more appealing to consumers. + Simultaneously, pilot programs for autonomous vehicles are indicating promising + results for safe integration into urban environments. These developments present + the opportunity for reduced traffic congestion, lower emissions, and enhanced + mobility solutions, fundamentally reshaping the landscape of transportation + in cities worldwide by 2025. \\n\\nThrough these comprehensive explorations + of emerging technologies, it is clear that 2024 marks a pivotal year for innovation, + shaping future directions in various industries and enhancing societal progress.\",\n + \ \"refusal\": null\n },\n \"logprobs\": null,\n \"finish_reason\": + \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 708,\n \"completion_tokens\": + 1223,\n \"total_tokens\": 1931,\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: + - 8e58a5ec0f936225-GRU + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Wed, 20 Nov 2024 13:05:05 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: + - '15043' + 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: + - '149999013' + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_4bd436f5144121694f8df654ed8514ea + http_version: HTTP/1.1 + status_code: 200 +version: 1 diff --git a/tests/cassettes/test_crew_kickoff_usage_metrics.yaml b/tests/cassettes/test_crew_kickoff_usage_metrics.yaml index cc0863ee4..b51ff6964 100644 --- a/tests/cassettes/test_crew_kickoff_usage_metrics.yaml +++ b/tests/cassettes/test_crew_kickoff_usage_metrics.yaml @@ -10,7 +10,8 @@ interactions: criteria for your final answer: 1 bullet point about dog that''s under 15 words.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! 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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.\n\nYour personal + goal is: Create detailed reports based on plants data analysis and research + findings\n\nTo give my best complete final answer to the task use the exact + following format:\n\nThought: I now can give a great answer\nFinal Answer: Your + final answer must be the great and the most complete as possible, it must be + outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": + "user", "content": "\nCurrent Task: Review the context you got and expand each + topic into a full section for a report. Make sure the report is detailed and + contains any and all relevant information.\n\n\nThis is the expect criteria + for your final answer: A fully fledge reports with the mains topics, each with + a full section of information. Formatted as markdown without ''```''\n\nyou + MUST return the actual complete content as the final answer, not a summary.\n\nThis + is the context you''re working with:\n1. **Vertical Farming Advancements**: + In 2024, vertical farming is set to revolutionize how we grow plants by maximizing + space and resource efficiency. New technologies allow for urban agriculture + with minimal ecological impact, using hydroponics and aeroponics to cultivate + fresh produce in city environments.\n\n2. **CRISPR and Plant Genetics**: CRISPR + technology has advanced significantly, enabling precise genome editing in plants. + This allows for the development of crops that are more resistant to diseases, + pests, and climate change, potentially increasing yield and food security worldwide.\n\n3. + **Biodiversity Conservation**: Efforts to conserve plant biodiversity have gained + traction, with initiatives focusing on seed banks and botanical gardens. 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Researchers are developing + plants specifically engineered to absorb heavy metals and other toxins from + the soil and water, aiding in environmental restoration.\n\n6. **Synthetic Botany**: + This emerging field involves redesigning plant biological processes to create + new functionalities such as biofuels, pharmaceuticals, and other valuable compounds. + This interdisciplinary approach opens new avenues for plant-based technology.\n\n7. + **Climate-Resilient Crops**: With climate change posing significant threats + to agriculture, developing crops that can withstand extreme weather conditions + such as drought and floods is a top priority. 2024 sees breakthroughs in engineering + crops that maintain productivity under these stressors.\n\n8. **Algae Farming + Innovations**: Algae are increasingly used in various industries due to their + efficiency in photosynthesis and rapid growth. 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Research indicates that these + fungi enhance nutrient uptake and enhance plant resilience to environmental + stressors, making them pivotal in sustainable agriculture.\\n\\n7. **Biophilic + Design**: The trend of integrating nature into architecture and urban planning + has expanded, with an emphasis on incorporating plants into building designs + to improve air quality, enhance mental well-being, and reduce overall energy + consumption.\\n\\n8. **Deciduous Trees and Urban Cooling**: Research shows that + strategically planted deciduous trees can significantly lower urban temperatures, + reduce energy consumption for cooling, and improve urban biodiversity, highlighting + the importance of green infrastructure.\\n\\n9. **Carnivorous Plant Studies**: + The understanding of carnivorous plants and their unique adaptations has advanced, + with findings suggesting potential applications in pest control and sustainable + agriculture due to their natural predatory methods.\\n\\n10. **Smart Agriculture + Technology**: The integration of IoT and AI in agriculture is revolutionizing + plant cultivation. 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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.\n\nYour personal + goal is: Create detailed reports based on plants data analysis and research + findings\n\nTo give my best complete final answer to the task use the exact + following format:\n\nThought: I now can give a great answer\nFinal Answer: Your + final answer must be the great and the most complete as possible, it must be + outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": + "user", "content": "\nCurrent Task: Review the context you got and expand each + topic into a full section for a report. Make sure the report is detailed and + contains any and all relevant information.\n\n\nThis is the expect criteria + for your final answer: A fully fledge reports with the mains topics, each with + a full section of information. Formatted as markdown without ''```''\n\nyou + MUST return the actual complete content as the final answer, not a summary.\n\nThis + is the context you''re working with:\n1. **Plant-Based Plastics**: 2024 has + seen significant advancements in bioplastics derived from plants such as corn + and sugarcane, which are being used as sustainable alternatives to petroleum-based + plastics. These innovations aim to reduce plastic waste and reliance on fossil + fuels.\n\n2. **Gene Editing Breakthroughs**: CRISPR technology continues to + revolutionize agricultural practices. Researchers have developed genetically + modified crops that are more resistant to pests and diseases, thereby decreasing + the need for chemical pesticides and increasing food security.\n\n3. **Vertical + Farming Expansion**: Urban agriculture has gained momentum with vertical farming + techniques that utilize less water and space. In 2024, cities worldwide are + adopting these innovative systems to meet the growing food demand while reducing + transportation emissions.\n\n4. **Climate-Resilient Plants**: Scientists are + identifying and breeding plant varieties that can withstand extreme weather + conditions such as droughts, floods, and high temperatures, helping farmers + adapt to climate change and ensuring crop yields remain stable.\n\n5. **Edible + Vaccines in Plants**: Research into producing vaccines in plants has progressed, + with trials showing that certain plants can be engineered to express antigens + that could serve as edible vaccines, offering a low-cost and easy delivery method + for immunizations.\n\n6. **Fungi and Plant Collaborations**: The study of mycorrhizal + fungi and their symbiotic relationships with plants has gained traction. Research + indicates that these fungi enhance nutrient uptake and enhance plant resilience + to environmental stressors, making them pivotal in sustainable agriculture.\n\n7. + **Biophilic Design**: The trend of integrating nature into architecture and + urban planning has expanded, with an emphasis on incorporating plants into building + designs to improve air quality, enhance mental well-being, and reduce overall + energy consumption.\n\n8. **Deciduous Trees and Urban Cooling**: Research shows + that strategically planted deciduous trees can significantly lower urban temperatures, + reduce energy consumption for cooling, and improve urban biodiversity, highlighting + the importance of green infrastructure.\n\n9. **Carnivorous Plant Studies**: + The understanding of carnivorous plants and their unique adaptations has advanced, + with findings suggesting potential applications in pest control and sustainable + agriculture due to their natural predatory methods.\n\n10. **Smart Agriculture + Technology**: The integration of IoT and AI in agriculture is revolutionizing + plant cultivation. Farmers now use sensors and data analytics to monitor plant + health in real-time, optimize water usage, and increase crop yields sustainably.\n\nThese + insights underline the ongoing research and innovations in the plant science + field that can lead to more sustainable and resilient agricultural practices + while addressing critical global challenges.\n\nBegin! 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Plant-Based Plastics\\n2024 has witnessed remarkable progress in the development + of plant-based plastics, particularly those derived from renewable resources + such as corn and sugarcane. Unlike traditional petroleum-based plastics, these + bioplastics are designed to be more environmentally friendly, addressing pressing + concerns regarding plastic waste and the continuous reliance on fossil fuels. + The production processes have been optimized to ensure lower carbon footprints + and reduced greenhouse gas emissions. Moreover, major manufacturers are beginning + to scale up production of these materials, integrating them into a wide array + of products, from packaging to consumer goods, thereby offering sustainable + alternatives and contributing positively to circular economy initiatives.\\n\\n## + 2. Gene Editing Breakthroughs\\nThe introduction of CRISPR technology has emerged + as a game-changer in agricultural methodologies. In 2024, cutting-edge research + has led to the development of genetically modified crops that exhibit enhanced + resistance to pests and diseases. This advancement not only supports increased + food production but also significantly reduces dependency on chemical pesticides, + which can have detrimental effects on both environmental health and biodiversity. + By facilitating the creation of hardier crops, these innovations play a crucial + role in bolstering food security, enabling farmers to achieve higher yields + even under challenging agricultural conditions.\\n\\n## 3. Vertical Farming + Expansion\\nVertical farming techniques have surged in popularity as urban agriculture + continues to evolve in 2024. These innovative systems are engineered to optimize + space and resource usage, utilizing cutting-edge hydroponic and aeroponic methods + to grow food in controlled environments. Cities around the globe are implementing + vertical farms to tackle the increasing demand for fresh produce while simultaneously + minimizing transportation emissions and enhancing food security in urban settings. + By using significantly less water and land compared to traditional farming, + vertical farming represents a sustainable solution to urban food production + challenges in the face of rapid urbanization.\\n\\n## 4. Climate-Resilient Plants\\nIn + response to the unpredictable impacts of climate change, researchers are focused + on identifying and breeding plant varieties capable of enduring extreme weather + conditions, such as droughts, floods, and extreme temperatures. This initiative + aims to empower farmers with viable options to adapt to shifting climate patterns, + ensuring stable crop yields despite adverse environmental conditions. The development + of these climate-resilient plants is vital for maintaining food supply chains + and safeguarding agricultural biodiversity, ultimately contributing to the long-term + sustainability of farming practices.\\n\\n## 5. Edible Vaccines in Plants\\nInnovative + research in the area of edible vaccines is making significant strides in 2024. + Scientists are exploring the potential of genetically engineered plants to express + specific antigens that can serve as immunizations. These advancements hold promising + implications for public health, particularly in developing regions where traditional + vaccine delivery systems may be less feasible. Edible vaccines offer a cost-effective, + accessible, and non-invasive way to promote immunity against various diseases, + highlighting the intersection of agriculture and health science in addressing + global health challenges.\\n\\n## 6. Fungi and Plant Collaborations\\nThe symbiotic + relationships between mycorrhizal fungi and plants have gained increasing attention + in research circles. Studies conducted in 2024 reveal that these fungi enhance + nutrient uptake, improve soil health, and bolster plant resilience when faced + with environmental stressors, such as drought and soil degradation. The incorporation + of these beneficial fungi into sustainable agricultural practices not only promotes + plant health but also contributes to organic farming resiliency, highlighting + the importance of understanding and utilizing biological partnerships in cultivating + healthy crops.\\n\\n## 7. Biophilic Design\\nThe concept of biophilic design + continues to gain traction in architecture and urban planning throughout 2024, + underscoring the importance of integrating nature into built environments. This + approach encourages the incorporation of plants and green spaces into architectural + designs, enhancing air quality and promoting mental well-being. By reducing + reliance on artificial heating and cooling systems, biophilic design offers + opportunities to lower overall energy consumption. As cities recognize the benefits + of urban greenery, significant investments are being made to create healthier, + more sustainable urban ecosystems.\\n\\n## 8. Deciduous Trees and Urban Cooling\\nResearch + conducted this year has demonstrated that strategic planting of deciduous trees + in urban areas can lead to significant temperature reductions. These trees act + as natural air conditioners by providing shade and releasing moisture. The benefits + extend beyond cooling cities\u2014for instance, they contribute to lower energy + consumption for air conditioning, enhance local biodiversity, and improve overall + urban livability. Understanding the role of green infrastructure in combating + urban heat islands is essential for developing climate-responsive cities.\\n\\n## + 9. Carnivorous Plant Studies\\nRecent advancements in the study of carnivorous + plants reveal exciting potential applications in sustainable pest control and + agriculture. By understanding their unique adaptations and natural predatory + methods, researchers envision the use of these plants as biological pest management + tools in crop production. In 2024, interest in harnessing the ecological roles + of these plants may lead to more sustainable farming practices, alleviating + the need for chemical pesticides and promoting healthier ecosystems.\\n\\n## + 10. Smart Agriculture Technology\\nThe integration of Internet of Things (IoT) + and Artificial Intelligence (AI) into agriculture has transformed how farming + is conducted in 2024. Smart agriculture technologies enable farmers to monitor + plant health, soil conditions, and microclimates through advanced sensors and + data analytics. This real-time data helps optimize water usage, elevate crop + yields, and foster sustainable farming practices. As farmers harness these technological + advancements, the sector moves toward a more efficient and environmentally conscious + model of production.\\n\\nIn conclusion, these insights reflect ongoing research + and innovation in the field of plant science, laying the groundwork for more + sustainable and resilient agricultural practices. As the world faces critical + global challenges, these advancements illustrate the potential to leverage plant + science in creating solutions that address both environmental concerns and food + security.\",\n \"refusal\": null\n },\n \"logprobs\": null,\n + \ \"finish_reason\": \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": + 777,\n \"completion_tokens\": 1163,\n \"total_tokens\": 1940,\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: + - 8e58a67f18856225-GRU + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Wed, 20 Nov 2024 13:05:35 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: + - '20960' + 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: + - '149998934' + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_1a11ba8b9c0cb1803e99cd95fa1fb890 + http_version: HTTP/1.1 + status_code: 200 +version: 1 diff --git a/tests/cli/authentication/test_auth_main.py b/tests/cli/authentication/test_auth_main.py index c56968aab..a0b4fa0d9 100644 --- a/tests/cli/authentication/test_auth_main.py +++ b/tests/cli/authentication/test_auth_main.py @@ -2,6 +2,7 @@ import unittest from unittest.mock import MagicMock, patch import requests + from crewai.cli.authentication.main import AuthenticationCommand @@ -43,10 +44,13 @@ class TestAuthenticationCommand(unittest.TestCase): mock_print.assert_any_call("2. Enter the following code: ", "ABCDEF") mock_open.assert_called_once_with("https://example.com") + @patch("crewai.cli.authentication.main.ToolCommand") @patch("crewai.cli.authentication.main.requests.post") @patch("crewai.cli.authentication.main.validate_token") @patch("crewai.cli.authentication.main.console.print") - def test_poll_for_token_success(self, mock_print, mock_validate_token, mock_post): + def test_poll_for_token_success( + self, mock_print, mock_validate_token, mock_post, mock_tool + ): mock_response = MagicMock() mock_response.status_code = 200 mock_response.json.return_value = { @@ -55,10 +59,15 @@ class TestAuthenticationCommand(unittest.TestCase): } mock_post.return_value = mock_response + mock_instance = mock_tool.return_value + mock_instance.login.return_value = None + self.auth_command._poll_for_token({"device_code": "123456"}) mock_validate_token.assert_called_once_with("TOKEN") - mock_print.assert_called_once_with("\nWelcome to CrewAI+ !!", style="green") + mock_print.assert_called_once_with( + "\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n" + ) @patch("crewai.cli.authentication.main.requests.post") @patch("crewai.cli.authentication.main.console.print") diff --git a/tests/cli/authentication/test_utils.py b/tests/cli/authentication/test_utils.py index d634780a8..6da48dcd0 100644 --- a/tests/cli/authentication/test_utils.py +++ b/tests/cli/authentication/test_utils.py @@ -3,9 +3,10 @@ import unittest from datetime import datetime, timedelta from unittest.mock import MagicMock, patch -from crewai.cli.authentication.utils import TokenManager, validate_token from cryptography.fernet import Fernet +from crewai.cli.authentication.utils import TokenManager, validate_token + class TestValidateToken(unittest.TestCase): @patch("crewai.cli.authentication.utils.AsymmetricSignatureVerifier") diff --git a/tests/cli/cli_test.py b/tests/cli/cli_test.py index b2fb8d0e5..15ed81637 100644 --- a/tests/cli/cli_test.py +++ b/tests/cli/cli_test.py @@ -1,7 +1,9 @@ +from pathlib import Path from unittest import mock import pytest from click.testing import CliRunner + from crewai.cli.cli import ( deploy_create, deploy_list, @@ -9,6 +11,7 @@ from crewai.cli.cli import ( deploy_push, deploy_remove, deply_status, + flow_add_crew, reset_memories, signup, test, @@ -128,6 +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) @@ -277,3 +287,42 @@ def test_deploy_remove_no_uuid(command, runner): assert result.exit_code == 0 mock_deploy.remove_crew.assert_called_once_with(uuid=None) + + +@mock.patch("crewai.cli.add_crew_to_flow.create_embedded_crew") +@mock.patch("pathlib.Path.exists", return_value=True) # Mock the existence check +def test_flow_add_crew(mock_path_exists, mock_create_embedded_crew, runner): + crew_name = "new_crew" + result = runner.invoke(flow_add_crew, [crew_name]) + + # Log the output for debugging + print(result.output) + + assert result.exit_code == 0, f"Command failed with output: {result.output}" + assert f"Adding crew {crew_name} to the flow" in result.output + + # Verify that create_embedded_crew was called with the correct arguments + mock_create_embedded_crew.assert_called_once() + call_args, call_kwargs = mock_create_embedded_crew.call_args + assert call_args[0] == crew_name + assert "parent_folder" in call_kwargs + assert isinstance(call_kwargs["parent_folder"], Path) + + +def test_add_crew_to_flow_not_in_root(runner): + # Simulate not being in the root of a flow project + with mock.patch("pathlib.Path.exists", autospec=True) as mock_exists: + # Mock Path.exists to return False when checking for pyproject.toml + def exists_side_effect(self): + if self.name == "pyproject.toml": + return False # Simulate that pyproject.toml does not exist + return True # All other paths exist + + mock_exists.side_effect = exists_side_effect + + result = runner.invoke(flow_add_crew, ["new_crew"]) + + assert result.exit_code != 0 + assert "This command must be run from the root of a flow project." in str( + result.output + ) diff --git a/tests/cli/config_test.py b/tests/cli/config_test.py new file mode 100644 index 000000000..95a38c06b --- /dev/null +++ b/tests/cli/config_test.py @@ -0,0 +1,107 @@ +import json +import shutil +import tempfile +import unittest +from pathlib import Path + +from crewai.cli.config import Settings + + +class TestSettings(unittest.TestCase): + def setUp(self): + self.test_dir = Path(tempfile.mkdtemp()) + self.config_path = self.test_dir / "settings.json" + + def tearDown(self): + shutil.rmtree(self.test_dir) + + def test_empty_initialization(self): + settings = Settings(config_path=self.config_path) + self.assertIsNone(settings.tool_repository_username) + self.assertIsNone(settings.tool_repository_password) + + def test_initialization_with_data(self): + settings = Settings( + config_path=self.config_path, tool_repository_username="user1" + ) + self.assertEqual(settings.tool_repository_username, "user1") + self.assertIsNone(settings.tool_repository_password) + + def test_initialization_with_existing_file(self): + self.config_path.parent.mkdir(parents=True, exist_ok=True) + with self.config_path.open("w") as f: + json.dump({"tool_repository_username": "file_user"}, f) + + settings = Settings(config_path=self.config_path) + self.assertEqual(settings.tool_repository_username, "file_user") + + def test_merge_file_and_input_data(self): + self.config_path.parent.mkdir(parents=True, exist_ok=True) + with self.config_path.open("w") as f: + json.dump( + { + "tool_repository_username": "file_user", + "tool_repository_password": "file_pass", + }, + f, + ) + + settings = Settings( + config_path=self.config_path, tool_repository_username="new_user" + ) + self.assertEqual(settings.tool_repository_username, "new_user") + self.assertEqual(settings.tool_repository_password, "file_pass") + + def test_dump_new_settings(self): + settings = Settings( + config_path=self.config_path, tool_repository_username="user1" + ) + settings.dump() + + with self.config_path.open("r") as f: + saved_data = json.load(f) + + self.assertEqual(saved_data["tool_repository_username"], "user1") + + def test_update_existing_settings(self): + self.config_path.parent.mkdir(parents=True, exist_ok=True) + with self.config_path.open("w") as f: + json.dump({"existing_setting": "value"}, f) + + settings = Settings( + config_path=self.config_path, tool_repository_username="user1" + ) + settings.dump() + + with self.config_path.open("r") as f: + saved_data = json.load(f) + + self.assertEqual(saved_data["existing_setting"], "value") + self.assertEqual(saved_data["tool_repository_username"], "user1") + + def test_none_values(self): + settings = Settings(config_path=self.config_path, tool_repository_username=None) + settings.dump() + + with self.config_path.open("r") as f: + saved_data = json.load(f) + + self.assertIsNone(saved_data.get("tool_repository_username")) + + def test_invalid_json_in_config(self): + self.config_path.parent.mkdir(parents=True, exist_ok=True) + with self.config_path.open("w") as f: + f.write("invalid json") + + try: + settings = Settings(config_path=self.config_path) + self.assertIsNone(settings.tool_repository_username) + except json.JSONDecodeError: + self.fail("Settings initialization should handle invalid JSON") + + def test_empty_config_file(self): + self.config_path.parent.mkdir(parents=True, exist_ok=True) + self.config_path.touch() + + settings = Settings(config_path=self.config_path) + self.assertIsNone(settings.tool_repository_username) diff --git a/tests/cli/deploy/test_deploy_main.py b/tests/cli/deploy/test_deploy_main.py index 385dbb8a5..00e355a62 100644 --- a/tests/cli/deploy/test_deploy_main.py +++ b/tests/cli/deploy/test_deploy_main.py @@ -228,17 +228,18 @@ class TestDeployCommand(unittest.TestCase): "builtins.open", new_callable=unittest.mock.mock_open, read_data=""" - [project] - name = "test_project" - version = "0.1.0" - requires-python = ">=3.10,<=3.13" - dependencies = ["crewai"] - """, + [project] + name = "test_project" + version = "0.1.0" + requires-python = ">=3.10,<=3.12" + dependencies = ["crewai"] + """, ) def test_get_project_name_python_310(self, mock_open): from crewai.cli.utils import get_project_name project_name = get_project_name() + print("project_name", project_name) self.assertEqual(project_name, "test_project") @unittest.skipIf(sys.version_info < (3, 11), "Requires Python 3.11+") @@ -249,7 +250,7 @@ class TestDeployCommand(unittest.TestCase): [project] name = "test_project" version = "0.1.0" - requires-python = ">=3.10,<=3.13" + requires-python = ">=3.10,<=3.12" dependencies = ["crewai"] """, ) @@ -260,6 +261,6 @@ class TestDeployCommand(unittest.TestCase): self.assertEqual(project_name, "test_project") def test_get_crewai_version(self): - from crewai.cli.utils import get_crewai_version + from crewai.cli.version import get_crewai_version assert isinstance(get_crewai_version(), str) diff --git a/tests/cli/test_git.py b/tests/cli/test_git.py index 1fb1296cd..ccf8f0539 100644 --- a/tests/cli/test_git.py +++ b/tests/cli/test_git.py @@ -1,6 +1,7 @@ -from crewai.cli.git import Repository import pytest +from crewai.cli.git import Repository + @pytest.fixture() def repository(fp): diff --git a/tests/cli/test_plus_api.py b/tests/cli/test_plus_api.py index e0acf22e3..daefeee42 100644 --- a/tests/cli/test_plus_api.py +++ b/tests/cli/test_plus_api.py @@ -1,6 +1,7 @@ import os import unittest from unittest.mock import MagicMock, patch + from crewai.cli.plus_api import PlusAPI diff --git a/tests/cli/test_utils.py b/tests/cli/test_utils.py index 616fb3d2f..0270b12fc 100644 --- a/tests/cli/test_utils.py +++ b/tests/cli/test_utils.py @@ -1,7 +1,9 @@ -import pytest +import os import shutil import tempfile -import os + +import pytest + from crewai.cli import utils diff --git a/tests/cli/tools/test_main.py b/tests/cli/tools/test_main.py index fda833a85..10c29b920 100644 --- a/tests/cli/tools/test_main.py +++ b/tests/cli/tools/test_main.py @@ -75,16 +75,17 @@ def test_install_success(mock_get, mock_subprocess_run): [ "uv", "add", - "--extra-index-url", - "https://app.crewai.com/pypi/sample-repo", + "--index", + "sample-repo=https://example.com/repo", "sample-tool", ], capture_output=False, text=True, check=True, + env=unittest.mock.ANY ) - assert "Succesfully installed sample-tool" in output + assert "Successfully installed sample-tool" in output @patch("crewai.cli.plus_api.PlusAPI.get_tool") diff --git a/src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml b/tests/config/agents.yaml similarity index 95% rename from src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml rename to tests/config/agents.yaml index f8cf1f5c1..84e8ef3cc 100644 --- a/src/crewai/cli/templates/pipeline/crews/research_crew/config/agents.yaml +++ b/tests/config/agents.yaml @@ -7,6 +7,7 @@ researcher: 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. + verbose: true reporting_analyst: role: > @@ -17,3 +18,4 @@ reporting_analyst: 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. + verbose: true \ No newline at end of file diff --git a/src/crewai/cli/templates/pipeline_router/config/tasks.yaml b/tests/config/tasks.yaml similarity index 100% rename from src/crewai/cli/templates/pipeline_router/config/tasks.yaml rename to tests/config/tasks.yaml diff --git a/tests/crew_test.py b/tests/crew_test.py index c01e84f80..caecf4524 100644 --- a/tests/crew_test.py +++ b/tests/crew_test.py @@ -9,6 +9,7 @@ from unittest.mock import MagicMock, patch import instructor import pydantic_core import pytest + from crewai.agent import Agent from crewai.agents.cache import CacheHandler from crewai.crew import Crew @@ -455,7 +456,7 @@ def test_crew_verbose_output(capsys): def test_cache_hitting_between_agents(): from unittest.mock import call, patch - from crewai_tools import tool + from crewai.tools import tool @tool def multiplier(first_number: int, second_number: int) -> float: @@ -497,7 +498,8 @@ def test_cache_hitting_between_agents(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_api_calls_throttling(capsys): from unittest.mock import patch - from crewai_tools import tool + + from crewai.tools import tool @tool def get_final_answer() -> float: @@ -562,6 +564,7 @@ def test_crew_kickoff_usage_metrics(): assert result.token_usage.prompt_tokens > 0 assert result.token_usage.completion_tokens > 0 assert result.token_usage.successful_requests > 0 + assert result.token_usage.cached_prompt_tokens == 0 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set(): @@ -779,11 +782,14 @@ def test_async_task_execution_call_count(): list_important_history.output = mock_task_output write_article.output = mock_task_output - with patch.object( - Task, "execute_sync", return_value=mock_task_output - ) as mock_execute_sync, patch.object( - Task, "execute_async", return_value=mock_future - ) as mock_execute_async: + with ( + patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync, + patch.object( + Task, "execute_async", return_value=mock_future + ) as mock_execute_async, + ): crew.kickoff() assert mock_execute_async.call_count == 2 @@ -1105,7 +1111,8 @@ def test_dont_set_agents_step_callback_if_already_set(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_function_calling_llm(): from unittest.mock import patch - from crewai_tools import tool + + from crewai.tools import tool llm = "gpt-4o" @@ -1140,7 +1147,7 @@ def test_crew_function_calling_llm(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_task_with_no_arguments(): - from crewai_tools import tool + from crewai.tools import tool @tool def return_data() -> str: @@ -1274,10 +1281,11 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process(): assert result.raw == "Howdy!" assert result.token_usage == UsageMetrics( - total_tokens=2626, - prompt_tokens=2482, - completion_tokens=144, - successful_requests=5, + total_tokens=1673, + prompt_tokens=1562, + completion_tokens=111, + successful_requests=3, + cached_prompt_tokens=0, ) @@ -1303,8 +1311,9 @@ def test_hierarchical_crew_creation_tasks_with_agents(): assert crew.manager_agent is not None assert crew.manager_agent.tools is not None - assert crew.manager_agent.tools[0].description.startswith( - "Delegate a specific task to one of the following coworkers: Senior Writer" + assert ( + "Delegate a specific task to one of the following coworkers: Senior Writer\n" + in crew.manager_agent.tools[0].description ) @@ -1331,8 +1340,9 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(): crew.kickoff() assert crew.manager_agent is not None assert crew.manager_agent.tools is not None - assert crew.manager_agent.tools[0].description.startswith( + assert ( "Delegate a specific task to one of the following coworkers: Senior Writer\n" + in crew.manager_agent.tools[0].description ) @@ -1364,8 +1374,9 @@ def test_hierarchical_crew_creation_tasks_with_sync_last(): crew.kickoff() assert crew.manager_agent is not None assert crew.manager_agent.tools is not None - assert crew.manager_agent.tools[0].description.startswith( + assert ( "Delegate a specific task to one of the following coworkers: Senior Writer, Researcher, CEO\n" + in crew.manager_agent.tools[0].description ) @@ -1448,52 +1459,6 @@ def test_crew_does_not_interpolate_without_inputs(): interpolate_task_inputs.assert_not_called() -# def test_crew_partial_inputs(): -# agent = Agent( -# role="{topic} Researcher", -# goal="Express hot takes on {topic}.", -# backstory="You have a lot of experience with {topic}.", -# ) - -# task = Task( -# description="Give me an analysis around {topic}.", -# expected_output="{points} bullet points about {topic}.", -# ) - -# crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI"}) -# inputs = {"topic": "AI"} -# crew._interpolate_inputs(inputs=inputs) # Manual call for now - -# assert crew.tasks[0].description == "Give me an analysis around AI." -# assert crew.tasks[0].expected_output == "{points} bullet points about AI." -# assert crew.agents[0].role == "AI Researcher" -# assert crew.agents[0].goal == "Express hot takes on AI." -# assert crew.agents[0].backstory == "You have a lot of experience with AI." - - -# def test_crew_invalid_inputs(): -# agent = Agent( -# role="{topic} Researcher", -# goal="Express hot takes on {topic}.", -# backstory="You have a lot of experience with {topic}.", -# ) - -# task = Task( -# description="Give me an analysis around {topic}.", -# expected_output="{points} bullet points about {topic}.", -# ) - -# crew = Crew(agents=[agent], tasks=[task], inputs={"subject": "AI"}) -# inputs = {"subject": "AI"} -# crew._interpolate_inputs(inputs=inputs) # Manual call for now - -# assert crew.tasks[0].description == "Give me an analysis around {topic}." -# assert crew.tasks[0].expected_output == "{points} bullet points about {topic}." -# assert crew.agents[0].role == "{topic} Researcher" -# assert crew.agents[0].goal == "Express hot takes on {topic}." -# assert crew.agents[0].backstory == "You have a lot of experience with {topic}." - - def test_task_callback_on_crew(): from unittest.mock import MagicMock, patch @@ -1534,7 +1499,7 @@ def test_task_callback_on_crew(): def test_tools_with_custom_caching(): from unittest.mock import patch - from crewai_tools import tool + from crewai.tools import tool @tool def multiplcation_tool(first_number: int, second_number: int) -> int: @@ -1736,7 +1701,7 @@ def test_manager_agent_in_agents_raises_exception(): def test_manager_agent_with_tools_raises_exception(): - from crewai_tools import tool + from crewai.tools import tool @tool def testing_tool(first_number: int, second_number: int) -> int: @@ -1770,7 +1735,10 @@ def test_manager_agent_with_tools_raises_exception(): @patch("crewai.crew.Crew.kickoff") @patch("crewai.crew.CrewTrainingHandler") @patch("crewai.crew.TaskEvaluator") -def test_crew_train_success(task_evaluator, crew_training_handler, kickoff): +@patch("crewai.crew.Crew.copy") +def test_crew_train_success( + copy_mock, task_evaluator, crew_training_handler, kickoff_mock +): task = Task( description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", expected_output="5 bullet points with a paragraph for each idea.", @@ -1781,9 +1749,19 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff): agents=[researcher, writer], tasks=[task], ) + + # Create a mock for the copied crew + copy_mock.return_value = crew + crew.train( n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl" ) + + # Ensure kickoff is called on the copied crew + kickoff_mock.assert_has_calls( + [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] + ) + task_evaluator.assert_has_calls( [ mock.call(researcher), @@ -1801,31 +1779,25 @@ def test_crew_train_success(task_evaluator, crew_training_handler, kickoff): ] ) - crew_training_handler.assert_has_calls( + crew_training_handler.assert_any_call("training_data.pkl") + crew_training_handler().load.assert_called() + + crew_training_handler.assert_any_call("trained_agents_data.pkl") + crew_training_handler().load.assert_called() + + crew_training_handler().save_trained_data.assert_has_calls( [ - mock.call("training_data.pkl"), - mock.call().load(), - mock.call("trained_agents_data.pkl"), - mock.call().save_trained_data( + mock.call( agent_id="Researcher", trained_data=task_evaluator().evaluate_training_data().model_dump(), ), - mock.call("trained_agents_data.pkl"), - mock.call().save_trained_data( + mock.call( agent_id="Senior Writer", trained_data=task_evaluator().evaluate_training_data().model_dump(), ), - mock.call(), - mock.call().load(), - mock.call(), - mock.call().load(), ] ) - kickoff.assert_has_calls( - [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] - ) - def test_crew_train_error(): task = Task( @@ -1840,7 +1812,7 @@ def test_crew_train_error(): ) with pytest.raises(TypeError) as e: - crew.train() + crew.train() # type: ignore purposefully throwing err assert "train() missing 1 required positional argument: 'n_iterations'" in str( e ) @@ -2536,8 +2508,9 @@ def test_conditional_should_execute(): @mock.patch("crewai.crew.CrewEvaluator") +@mock.patch("crewai.crew.Crew.copy") @mock.patch("crewai.crew.Crew.kickoff") -def test_crew_testing_function(mock_kickoff, crew_evaluator): +def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator): task = Task( description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", expected_output="5 bullet points with a paragraph for each idea.", @@ -2548,11 +2521,15 @@ def test_crew_testing_function(mock_kickoff, crew_evaluator): agents=[researcher], tasks=[task], ) + + # Create a mock for the copied crew + copy_mock.return_value = crew + n_iterations = 2 crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"}) - assert len(mock_kickoff.mock_calls) == n_iterations - mock_kickoff.assert_has_calls( + # Ensure kickoff is called on the copied crew + kickoff_mock.assert_has_calls( [mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})] ) diff --git a/tests/flow_test.py b/tests/flow_test.py new file mode 100644 index 000000000..2e2020361 --- /dev/null +++ b/tests/flow_test.py @@ -0,0 +1,265 @@ +"""Test Flow creation and execution basic functionality.""" + +import asyncio + +import pytest + +from crewai.flow.flow import Flow, and_, listen, or_, router, start + + +def test_simple_sequential_flow(): + """Test a simple flow with two steps called sequentially.""" + execution_order = [] + + class SimpleFlow(Flow): + @start() + def step_1(self): + execution_order.append("step_1") + + @listen(step_1) + def step_2(self): + execution_order.append("step_2") + + flow = SimpleFlow() + flow.kickoff() + + assert execution_order == ["step_1", "step_2"] + + +def test_flow_with_multiple_starts(): + """Test a flow with multiple start methods.""" + execution_order = [] + + class MultiStartFlow(Flow): + @start() + def step_a(self): + execution_order.append("step_a") + + @start() + def step_b(self): + execution_order.append("step_b") + + @listen(step_a) + def step_c(self): + execution_order.append("step_c") + + @listen(step_b) + def step_d(self): + execution_order.append("step_d") + + flow = MultiStartFlow() + flow.kickoff() + + assert "step_a" in execution_order + assert "step_b" in execution_order + assert "step_c" in execution_order + assert "step_d" in execution_order + assert execution_order.index("step_c") > execution_order.index("step_a") + assert execution_order.index("step_d") > execution_order.index("step_b") + + +def test_cyclic_flow(): + """Test a cyclic flow that runs a finite number of iterations.""" + execution_order = [] + + class CyclicFlow(Flow): + iteration = 0 + max_iterations = 3 + + @start("loop") + def step_1(self): + if self.iteration >= self.max_iterations: + return # Do not proceed further + execution_order.append(f"step_1_{self.iteration}") + + @listen(step_1) + def step_2(self): + execution_order.append(f"step_2_{self.iteration}") + + @router(step_2) + def step_3(self): + execution_order.append(f"step_3_{self.iteration}") + self.iteration += 1 + if self.iteration < self.max_iterations: + return "loop" + + return "exit" + + flow = CyclicFlow() + flow.kickoff() + + expected_order = [] + for i in range(flow.max_iterations): + expected_order.extend([f"step_1_{i}", f"step_2_{i}", f"step_3_{i}"]) + + assert execution_order == expected_order + + +def test_flow_with_and_condition(): + """Test a flow where a step waits for multiple other steps to complete.""" + execution_order = [] + + class AndConditionFlow(Flow): + @start() + def step_1(self): + execution_order.append("step_1") + + @start() + def step_2(self): + execution_order.append("step_2") + + @listen(and_(step_1, step_2)) + def step_3(self): + execution_order.append("step_3") + + flow = AndConditionFlow() + flow.kickoff() + + assert "step_1" in execution_order + assert "step_2" in execution_order + assert execution_order[-1] == "step_3" + assert execution_order.index("step_3") > execution_order.index("step_1") + assert execution_order.index("step_3") > execution_order.index("step_2") + + +def test_flow_with_or_condition(): + """Test a flow where a step is triggered when any of multiple steps complete.""" + execution_order = [] + + class OrConditionFlow(Flow): + @start() + def step_a(self): + execution_order.append("step_a") + + @start() + def step_b(self): + execution_order.append("step_b") + + @listen(or_(step_a, step_b)) + def step_c(self): + execution_order.append("step_c") + + flow = OrConditionFlow() + flow.kickoff() + + assert "step_a" in execution_order or "step_b" in execution_order + assert "step_c" in execution_order + assert execution_order.index("step_c") > min( + execution_order.index("step_a"), execution_order.index("step_b") + ) + + +def test_flow_with_router(): + """Test a flow that uses a router method to determine the next step.""" + execution_order = [] + + class RouterFlow(Flow): + @start() + def start_method(self): + execution_order.append("start_method") + + @router(start_method) + def router(self): + execution_order.append("router") + # Ensure the condition is set to True to follow the "step_if_true" path + condition = True + return "step_if_true" if condition else "step_if_false" + + @listen("step_if_true") + def truthy(self): + execution_order.append("step_if_true") + + @listen("step_if_false") + def falsy(self): + execution_order.append("step_if_false") + + flow = RouterFlow() + flow.kickoff() + + assert execution_order == ["start_method", "router", "step_if_true"] + + +def test_async_flow(): + """Test an asynchronous flow.""" + execution_order = [] + + class AsyncFlow(Flow): + @start() + async def step_1(self): + execution_order.append("step_1") + await asyncio.sleep(0.1) + + @listen(step_1) + async def step_2(self): + execution_order.append("step_2") + await asyncio.sleep(0.1) + + flow = AsyncFlow() + asyncio.run(flow.kickoff_async()) + + assert execution_order == ["step_1", "step_2"] + + +def test_flow_with_exceptions(): + """Test flow behavior when exceptions occur in steps.""" + execution_order = [] + + class ExceptionFlow(Flow): + @start() + def step_1(self): + execution_order.append("step_1") + raise ValueError("An error occurred in step_1") + + @listen(step_1) + def step_2(self): + execution_order.append("step_2") + + flow = ExceptionFlow() + + with pytest.raises(ValueError): + flow.kickoff() + + # Ensure step_2 did not execute + assert execution_order == ["step_1"] + + +def test_flow_restart(): + """Test restarting a flow after it has completed.""" + execution_order = [] + + class RestartableFlow(Flow): + @start() + def step_1(self): + execution_order.append("step_1") + + @listen(step_1) + def step_2(self): + execution_order.append("step_2") + + flow = RestartableFlow() + flow.kickoff() + flow.kickoff() # Restart the flow + + assert execution_order == ["step_1", "step_2", "step_1", "step_2"] + + +def test_flow_with_custom_state(): + """Test a flow that maintains and modifies internal state.""" + + class StateFlow(Flow): + def __init__(self): + super().__init__() + self.counter = 0 + + @start() + def step_1(self): + self.counter += 1 + + @listen(step_1) + def step_2(self): + self.counter *= 2 + assert self.counter == 2 + + flow = StateFlow() + flow.kickoff() + assert flow.counter == 2 diff --git a/tests/agent_tools/__init__.py b/tests/knowledge/__init__.py similarity index 100% rename from tests/agent_tools/__init__.py rename to tests/knowledge/__init__.py diff --git a/tests/knowledge/crewai_quickstart.pdf b/tests/knowledge/crewai_quickstart.pdf new file mode 100644 index 000000000..671baf782 Binary files /dev/null and b/tests/knowledge/crewai_quickstart.pdf differ diff --git a/tests/knowledge/knowledge_test.py b/tests/knowledge/knowledge_test.py new file mode 100644 index 000000000..201ddea12 --- /dev/null +++ b/tests/knowledge/knowledge_test.py @@ -0,0 +1,545 @@ +"""Test Knowledge creation and querying functionality.""" + +from pathlib import Path +from unittest.mock import patch + +import pytest + +from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource +from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource +from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource +from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource +from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource + + +@pytest.fixture(autouse=True) +def mock_vector_db(): + """Mock vector database operations.""" + with patch("crewai.knowledge.storage.knowledge_storage.KnowledgeStorage") as mock: + # Mock the query method to return a predefined response + instance = mock.return_value + instance.query.return_value = [ + { + "context": "Brandon's favorite color is blue and he likes Mexican food.", + "score": 0.9, + } + ] + instance.reset.return_value = None + yield instance + + +@pytest.fixture(autouse=True) +def reset_knowledge_storage(mock_vector_db): + """Fixture to reset knowledge storage before each test.""" + yield + + +def test_single_short_string(mock_vector_db): + # Create a knowledge base with a single short string + content = "Brandon's favorite color is blue and he likes Mexican food." + string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [string_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What is Brandon's favorite color?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("blue" in result["context"].lower() for result in results) + # Verify the mock was called + mock_vector_db.query.assert_called_once() + + +# @pytest.mark.vcr(filter_headers=["authorization"]) +def test_single_2k_character_string(mock_vector_db): + # Create a 2k character string with various facts about Brandon + content = ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + string_source = StringKnowledgeSource( + content=content, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [string_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite movie?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("inception" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_short_strings(mock_vector_db): + # Create multiple short string sources + contents = [ + "Brandon loves hiking.", + "Brandon has a dog named Max.", + "Brandon enjoys painting landscapes.", + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in contents + ] + + # Mock the vector db query response + mock_vector_db.query.return_value = [ + {"context": "Brandon has a dog named Max.", "score": 0.9} + ] + + mock_vector_db.sources = string_sources + + # Perform a query + query = "What is the name of Brandon's pet?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("max" in result["context"].lower() for result in results) + # Verify the mock was called + mock_vector_db.query.assert_called_once() + + +def test_multiple_2k_character_strings(mock_vector_db): + # Create multiple 2k character strings with various facts about Brandon + contents = [ + ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + * 2, # Repeat to ensure it's 2k characters + ( + "Brandon loves traveling and has visited over 20 countries. " + "He is fluent in Spanish and often practices with his friends. " + "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. " + "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. " + "Brandon is an avid cyclist and participates in local cycling events. " + "He is also a photographer and enjoys capturing landscapes and cityscapes. " + "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. " + "He is also a fan of virtual reality and owns a VR headset. " + "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. " + "He enjoys watching documentaries and learning about history and science. " + "Brandon is a coffee lover and has a collection of coffee mugs from different countries. " + "He is also a fan of jazz music and often attends live performances. " + "Brandon is a member of a local running club and participates in marathons. " + "He is also a volunteer at a local animal shelter and helps with dog walking. " + "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. " + "He is also a fan of classic movies and has a collection of DVDs. " + "Brandon is a mentor for young professionals and enjoys giving career advice. " + "He is also a fan of puzzles and enjoys solving them in his free time. " + "Brandon's favorite sport is soccer, and he often plays with his friends. " + "He is also a fan of FC Barcelona and enjoys watching their matches. " + ) + * 2, # Repeat to ensure it's 2k characters + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in contents + ] + + mock_vector_db.sources = string_sources + mock_vector_db.query.return_value = [{"context": contents[1], "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "the hitchhiker's guide to the galaxy" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +def test_single_short_file(mock_vector_db, tmpdir): + # Create a single short text file + content = "Brandon's favorite sport is basketball." + file_path = Path(tmpdir.join("short_file.txt")) + with open(file_path, "w") as f: + f.write(content) + + file_source = TextFileKnowledgeSource( + file_path=file_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [file_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What sport does Brandon like?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("basketball" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_single_2k_character_file(mock_vector_db, tmpdir): + # Create a single 2k character text file with various facts about Brandon + content = ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) * 2 # Repeat to ensure it's 2k characters + file_path = Path(tmpdir.join("long_file.txt")) + with open(file_path, "w") as f: + f.write(content) + + file_source = TextFileKnowledgeSource( + file_path=file_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [file_source] + mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] + # Perform a query + query = "What is Brandon's favorite movie?" + results = mock_vector_db.query(query) + + # Assert that the results contain the expected information + assert any("inception" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_short_files(mock_vector_db, tmpdir): + # Create multiple short text files + contents = [ + { + "content": "Brandon works as a software engineer.", + "metadata": {"category": "profession", "source": "occupation"}, + }, + { + "content": "Brandon lives in New York.", + "metadata": {"category": "city", "source": "personal"}, + }, + { + "content": "Brandon enjoys cooking Italian food.", + "metadata": {"category": "hobby", "source": "personal"}, + }, + ] + file_paths = [] + for i, item in enumerate(contents): + file_path = Path(tmpdir.join(f"file_{i}.txt")) + with open(file_path, "w") as f: + f.write(item["content"]) + file_paths.append((file_path, item["metadata"])) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata=metadata) + for path, metadata in file_paths + ] + mock_vector_db.sources = file_sources + mock_vector_db.query.return_value = [ + {"context": "Brandon lives in New York.", "score": 0.9} + ] + # Perform a query + query = "What city does he reside in?" + results = mock_vector_db.query(query) + # Assert that the correct information is retrieved + assert any("new york" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_2k_character_files(mock_vector_db, tmpdir): + # Create multiple 2k character text files with various facts about Brandon + contents = [ + ( + "Brandon loves traveling and has visited over 20 countries. " + "He is fluent in Spanish and often practices with his friends. " + "Brandon's favorite city is Barcelona, where he enjoys the architecture and culture. " + "He is a foodie and loves trying new cuisines, with a particular fondness for sushi. " + "Brandon is an avid cyclist and participates in local cycling events. " + "He is also a photographer and enjoys capturing landscapes and cityscapes. " + "Brandon is a tech enthusiast and follows the latest trends in gadgets and software. " + "He is also a fan of virtual reality and owns a VR headset. " + "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'. " + "He enjoys watching documentaries and learning about history and science. " + "Brandon is a coffee lover and has a collection of coffee mugs from different countries. " + "He is also a fan of jazz music and often attends live performances. " + "Brandon is a member of a local running club and participates in marathons. " + "He is also a volunteer at a local animal shelter and helps with dog walking. " + "Brandon's favorite holiday is Christmas, and he enjoys decorating his home. " + "He is also a fan of classic movies and has a collection of DVDs. " + "Brandon is a mentor for young professionals and enjoys giving career advice. " + "He is also a fan of puzzles and enjoys solving them in his free time. " + "Brandon's favorite sport is soccer, and he often plays with his friends. " + "He is also a fan of FC Barcelona and enjoys watching their matches. " + ) + * 2, # Repeat to ensure it's 2k characters + ( + "Brandon is a software engineer who lives in San Francisco. " + "He enjoys hiking and often visits the trails in the Bay Area. " + "Brandon has a pet dog named Max, who is a golden retriever. " + "He loves reading science fiction books, and his favorite author is Isaac Asimov. " + "Brandon's favorite movie is Inception, and he enjoys watching it with his friends. " + "He is also a fan of Mexican cuisine, especially tacos and burritos. " + "Brandon plays the guitar and often performs at local open mic nights. " + "He is learning French and plans to visit Paris next year. " + "Brandon is passionate about technology and often attends tech meetups in the city. " + "He is also interested in AI and machine learning, and he is currently working on a project related to natural language processing. " + "Brandon's favorite color is blue, and he often wears blue shirts. " + "He enjoys cooking and often tries new recipes on weekends. " + "Brandon is a morning person and likes to start his day with a run in the park. " + "He is also a coffee enthusiast and enjoys trying different coffee blends. " + "Brandon is a member of a local book club and enjoys discussing books with fellow members. " + "He is also a fan of board games and often hosts game nights at his place. " + "Brandon is an advocate for environmental conservation and volunteers for local clean-up drives. " + "He is also a mentor for aspiring software developers and enjoys sharing his knowledge with others. " + "Brandon's favorite sport is basketball, and he often plays with his friends on weekends. " + "He is also a fan of the Golden State Warriors and enjoys watching their games. " + ) + * 2, # Repeat to ensure it's 2k characters + ] + file_paths = [] + for i, content in enumerate(contents): + file_path = Path(tmpdir.join(f"long_file_{i}.txt")) + with open(file_path, "w") as f: + f.write(content) + file_paths.append(file_path) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + for path in file_paths + ] + mock_vector_db.sources = file_sources + mock_vector_db.query.return_value = [ + { + "context": "Brandon's favorite book is 'The Hitchhiker's Guide to the Galaxy'.", + "score": 0.9, + } + ] + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "the hitchhiker's guide to the galaxy" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_hybrid_string_and_files(mock_vector_db, tmpdir): + # Create string sources + string_contents = [ + "Brandon is learning French.", + "Brandon visited Paris last summer.", + ] + string_sources = [ + StringKnowledgeSource(content=content, metadata={"preference": "personal"}) + for content in string_contents + ] + + # Create file sources + file_contents = [ + "Brandon prefers tea over coffee.", + "Brandon's favorite book is 'The Alchemist'.", + ] + file_paths = [] + for i, content in enumerate(file_contents): + file_path = Path(tmpdir.join(f"file_{i}.txt")) + with open(file_path, "w") as f: + f.write(content) + file_paths.append(file_path) + + file_sources = [ + TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + for path in file_paths + ] + + # Combine string and file sources + mock_vector_db.sources = string_sources + file_sources + mock_vector_db.query.return_value = [{"context": file_contents[1], "score": 0.9}] + + # Perform a query + query = "What is Brandon's favorite book?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("the alchemist" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_pdf_knowledge_source(mock_vector_db): + # Get the directory of the current file + current_dir = Path(__file__).parent + # Construct the path to the PDF file + pdf_path = current_dir / "crewai_quickstart.pdf" + + # Create a PDFKnowledgeSource + pdf_source = PDFKnowledgeSource( + file_path=pdf_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [pdf_source] + mock_vector_db.query.return_value = [ + {"context": "crewai create crew latest-ai-development", "score": 0.9} + ] + + # Perform a query + query = "How do you create a crew?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any( + "crewai create crew latest-ai-development" in result["context"].lower() + for result in results + ) + mock_vector_db.query.assert_called_once() + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_csv_knowledge_source(mock_vector_db, tmpdir): + """Test CSVKnowledgeSource with a simple CSV file.""" + + # Create a CSV file with sample data + csv_content = [ + ["Name", "Age", "City"], + ["Brandon", "30", "New York"], + ["Alice", "25", "Los Angeles"], + ["Bob", "35", "Chicago"], + ] + csv_path = Path(tmpdir.join("data.csv")) + with open(csv_path, "w", encoding="utf-8") as f: + for row in csv_content: + f.write(",".join(row) + "\n") + + # Create a CSVKnowledgeSource + csv_source = CSVKnowledgeSource( + file_path=csv_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [csv_source] + mock_vector_db.query.return_value = [ + {"context": "Brandon is 30 years old.", "score": 0.9} + ] + + # Perform a query + query = "How old is Brandon?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("30" in result["context"] for result in results) + mock_vector_db.query.assert_called_once() + + +def test_json_knowledge_source(mock_vector_db, tmpdir): + """Test JSONKnowledgeSource with a simple JSON file.""" + + # Create a JSON file with sample data + json_data = { + "people": [ + {"name": "Brandon", "age": 30, "city": "New York"}, + {"name": "Alice", "age": 25, "city": "Los Angeles"}, + {"name": "Bob", "age": 35, "city": "Chicago"}, + ] + } + json_path = Path(tmpdir.join("data.json")) + with open(json_path, "w", encoding="utf-8") as f: + import json + + json.dump(json_data, f) + + # Create a JSONKnowledgeSource + json_source = JSONKnowledgeSource( + file_path=json_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [json_source] + mock_vector_db.query.return_value = [ + {"context": "Alice lives in Los Angeles.", "score": 0.9} + ] + + # Perform a query + query = "Where does Alice reside?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("los angeles" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_excel_knowledge_source(mock_vector_db, tmpdir): + """Test ExcelKnowledgeSource with a simple Excel file.""" + + # Create an Excel file with sample data + import pandas as pd + + excel_data = { + "Name": ["Brandon", "Alice", "Bob"], + "Age": [30, 25, 35], + "City": ["New York", "Los Angeles", "Chicago"], + } + df = pd.DataFrame(excel_data) + excel_path = Path(tmpdir.join("data.xlsx")) + df.to_excel(excel_path, index=False) + + # Create an ExcelKnowledgeSource + excel_source = ExcelKnowledgeSource( + file_path=excel_path, metadata={"preference": "personal"} + ) + mock_vector_db.sources = [excel_source] + mock_vector_db.query.return_value = [ + {"context": "Brandon is 30 years old.", "score": 0.9} + ] + + # Perform a query + query = "What is Brandon's age?" + results = mock_vector_db.query(query) + + # Assert that the correct information is retrieved + assert any("30" in result["context"] for result in results) + mock_vector_db.query.assert_called_once() diff --git a/tests/llm_test.py b/tests/llm_test.py new file mode 100644 index 000000000..e824d54c9 --- /dev/null +++ b/tests/llm_test.py @@ -0,0 +1,30 @@ +import pytest + +from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess +from crewai.llm import LLM +from crewai.utilities.token_counter_callback import TokenCalcHandler + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_llm_callback_replacement(): + llm = LLM(model="gpt-4o-mini") + + calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess()) + calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess()) + + llm.call( + messages=[{"role": "user", "content": "Hello, world!"}], + callbacks=[calc_handler_1], + ) + usage_metrics_1 = calc_handler_1.token_cost_process.get_summary() + + llm.call( + messages=[{"role": "user", "content": "Hello, world from another agent!"}], + callbacks=[calc_handler_2], + ) + usage_metrics_2 = calc_handler_2.token_cost_process.get_summary() + + # The first handler should not have been updated + assert usage_metrics_1.successful_requests == 1 + assert usage_metrics_2.successful_requests == 1 + assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary() diff --git a/tests/memory/cassettes/test_save_and_search_with_provider.yaml b/tests/memory/cassettes/test_save_and_search_with_provider.yaml new file mode 100644 index 000000000..c30f3f065 --- /dev/null +++ b/tests/memory/cassettes/test_save_and_search_with_provider.yaml @@ -0,0 +1,270 @@ +interactions: +- request: + body: '' + headers: + accept: + - '*/*' + accept-encoding: + - gzip, deflate + connection: + - keep-alive + host: + - api.mem0.ai + user-agent: + - python-httpx/0.27.0 + method: GET + uri: https://api.mem0.ai/v1/memories/?user_id=test + response: + body: + string: '[]' + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 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posthog-python/3.5.0 + method: POST + uri: https://us.i.posthog.com/batch/ + response: + body: + string: '{"status":"Ok"}' + headers: + Connection: + - keep-alive + Content-Length: + - '15' + Content-Type: + - application/json + Date: + - Sat, 17 Aug 2024 06:00:13 GMT + access-control-allow-credentials: + - 'true' + server: + - envoy + vary: + - origin, access-control-request-method, access-control-request-headers + x-envoy-upstream-service-time: + - '33' + status: + code: 200 + message: OK +version: 1 diff --git a/tests/memory/short_term_memory_test.py b/tests/memory/short_term_memory_test.py index c09b22691..6cde2a044 100644 --- a/tests/memory/short_term_memory_test.py +++ b/tests/memory/short_term_memory_test.py @@ -1,5 +1,7 @@ -import pytest from unittest.mock import patch + +import pytest + from crewai.agent import Agent from crewai.crew import Crew from crewai.memory.short_term.short_term_memory import ShortTermMemory diff --git a/tests/pipeline/test_pipeline.py b/tests/pipeline/test_pipeline.py deleted file mode 100644 index a08f185c8..000000000 --- a/tests/pipeline/test_pipeline.py +++ /dev/null @@ -1,919 +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 - - 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 - - # 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" diff --git a/tests/project_test.py b/tests/project_test.py index 188ba024d..6c68f4993 100644 --- a/tests/project_test.py +++ b/tests/project_test.py @@ -1,5 +1,8 @@ +import pytest + from crewai.agent import Agent -from crewai.project import agent, task +from crewai.crew import Crew +from crewai.project import CrewBase, after_kickoff, agent, before_kickoff, crew, task from crewai.task import Task @@ -23,6 +26,43 @@ class SimpleCrew: ) +@CrewBase +class TestCrew: + agents_config = "config/agents.yaml" + tasks_config = "config/tasks.yaml" + + @agent + def researcher(self): + return Agent(config=self.agents_config["researcher"]) + + @agent + def reporting_analyst(self): + return Agent(config=self.agents_config["reporting_analyst"]) + + @task + def research_task(self): + return Task(config=self.tasks_config["research_task"]) + + @task + def reporting_task(self): + return Task(config=self.tasks_config["reporting_task"]) + + @before_kickoff + def modify_inputs(self, inputs): + if inputs: + inputs["topic"] = "Bicycles" + return inputs + + @after_kickoff + def modify_outputs(self, outputs): + outputs.raw = outputs.raw + " post processed" + return outputs + + @crew + def crew(self): + return Crew(agents=self.agents, tasks=self.tasks, verbose=True) + + def test_agent_memoization(): crew = SimpleCrew() first_call_result = crew.simple_agent() @@ -43,6 +83,16 @@ def test_task_memoization(): ), "Task memoization is not working as expected" +def test_crew_memoization(): + crew = TestCrew() + first_call_result = crew.crew() + second_call_result = crew.crew() + + assert ( + first_call_result is second_call_result + ), "Crew references should point to the same object" + + def test_task_name(): simple_task = SimpleCrew().simple_task() assert ( @@ -53,3 +103,84 @@ def test_task_name(): assert ( custom_named_task.name == "Custom" ), "Custom task name is not being set as expected" + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_before_kickoff_modification(): + crew = TestCrew() + inputs = {"topic": "LLMs"} + result = crew.crew().kickoff(inputs=inputs) + assert "bicycles" in result.raw, "Before kickoff function did not modify inputs" + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_after_kickoff_modification(): + crew = TestCrew() + # Assuming the crew execution returns a dict + result = crew.crew().kickoff({"topic": "LLMs"}) + + assert ( + "post processed" in result.raw + ), "After kickoff function did not modify outputs" + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_before_kickoff_with_none_input(): + crew = TestCrew() + crew.crew().kickoff(None) + # Test should pass without raising exceptions + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_multiple_before_after_kickoff(): + @CrewBase + class MultipleHooksCrew: + agents_config = "config/agents.yaml" + tasks_config = "config/tasks.yaml" + + @agent + def researcher(self): + return Agent(config=self.agents_config["researcher"]) + + @agent + def reporting_analyst(self): + return Agent(config=self.agents_config["reporting_analyst"]) + + @task + def research_task(self): + return Task(config=self.tasks_config["research_task"]) + + @task + def reporting_task(self): + return Task(config=self.tasks_config["reporting_task"]) + + @before_kickoff + def first_before(self, inputs): + inputs["topic"] = "Bicycles" + return inputs + + @before_kickoff + def second_before(self, inputs): + inputs["topic"] = "plants" + return inputs + + @after_kickoff + def first_after(self, outputs): + outputs.raw = outputs.raw + " processed first" + return outputs + + @after_kickoff + def second_after(self, outputs): + outputs.raw = outputs.raw + " processed second" + return outputs + + @crew + def crew(self): + return Crew(agents=self.agents, tasks=self.tasks, verbose=True) + + crew = MultipleHooksCrew() + result = crew.crew().kickoff({"topic": "LLMs"}) + + assert "plants" in result.raw, "First before_kickoff not executed" + assert "processed first" in result.raw, "First after_kickoff not executed" + assert "processed second" in result.raw, "Second after_kickoff not executed" diff --git a/tests/task_test.py b/tests/task_test.py index 1e20c9491..b38b95996 100644 --- a/tests/task_test.py +++ b/tests/task_test.py @@ -6,16 +6,17 @@ import os from unittest.mock import MagicMock, patch import pytest +from pydantic import BaseModel +from pydantic_core import ValidationError + from crewai import Agent, Crew, Process, Task from crewai.tasks.conditional_task import ConditionalTask from crewai.tasks.task_output import TaskOutput from crewai.utilities.converter import Converter -from pydantic import BaseModel -from pydantic_core import ValidationError def test_task_tool_reflect_agent_tools(): - from crewai_tools import tool + from crewai.tools import tool @tool def fake_tool() -> None: @@ -39,7 +40,7 @@ def test_task_tool_reflect_agent_tools(): def test_task_tool_takes_precedence_over_agent_tools(): - from crewai_tools import tool + from crewai.tools import tool @tool def fake_tool() -> None: @@ -656,7 +657,7 @@ def test_increment_delegations_for_sequential_process(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_increment_tool_errors(): - from crewai_tools import tool + from crewai.tools import tool @tool def scoring_examples() -> None: diff --git a/src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/agents.yaml b/tests/tools/agent_tools/__init__.py similarity index 100% rename from src/crewai/cli/templates/pipeline/crews/write_linkedin_crew/config/agents.yaml rename to tests/tools/agent_tools/__init__.py diff --git a/tests/agent_tools/agent_tools_test.py b/tests/tools/agent_tools/agent_tools_test.py similarity index 95% rename from tests/agent_tools/agent_tools_test.py rename to tests/tools/agent_tools/agent_tools_test.py index 8d9345b46..9aea7b4bc 100644 --- a/tests/agent_tools/agent_tools_test.py +++ b/tests/tools/agent_tools/agent_tools_test.py @@ -3,7 +3,7 @@ import pytest from crewai.agent import Agent -from crewai.tools.agent_tools import AgentTools +from crewai.tools.agent_tools.agent_tools import AgentTools researcher = Agent( role="researcher", @@ -11,12 +11,14 @@ researcher = Agent( backstory="You're an expert researcher, specialized in technology", allow_delegation=False, ) -tools = AgentTools(agents=[researcher]) +tools = AgentTools(agents=[researcher]).tools() +delegate_tool = tools[0] +ask_tool = tools[1] @pytest.mark.vcr(filter_headers=["authorization"]) def test_delegate_work(): - result = tools.delegate_work( + result = delegate_tool.run( coworker="researcher", task="share your take on AI Agents", context="I heard you hate them", @@ -30,8 +32,8 @@ def test_delegate_work(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_delegate_work_with_wrong_co_worker_variable(): - result = tools.delegate_work( - co_worker="researcher", + result = delegate_tool.run( + coworker="researcher", task="share your take on AI Agents", context="I heard you hate them", ) @@ -44,7 +46,7 @@ def test_delegate_work_with_wrong_co_worker_variable(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_ask_question(): - result = tools.ask_question( + result = ask_tool.run( coworker="researcher", question="do you hate AI Agents?", context="I heard you LOVE them", @@ -58,8 +60,8 @@ def test_ask_question(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_ask_question_with_wrong_co_worker_variable(): - result = tools.ask_question( - co_worker="researcher", + result = ask_tool.run( + coworker="researcher", question="do you hate AI Agents?", context="I heard you LOVE them", ) @@ -72,8 +74,8 @@ def test_ask_question_with_wrong_co_worker_variable(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_delegate_work_withwith_coworker_as_array(): - result = tools.delegate_work( - co_worker="[researcher]", + result = delegate_tool.run( + coworker="[researcher]", task="share your take on AI Agents", context="I heard you hate them", ) @@ -86,8 +88,8 @@ def test_delegate_work_withwith_coworker_as_array(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_ask_question_with_coworker_as_array(): - result = tools.ask_question( - co_worker="[researcher]", + result = ask_tool.run( + coworker="[researcher]", question="do you hate AI Agents?", context="I heard you LOVE them", ) @@ -99,7 +101,7 @@ def test_ask_question_with_coworker_as_array(): def test_delegate_work_to_wrong_agent(): - result = tools.ask_question( + result = ask_tool.run( coworker="writer", question="share your take on AI Agents", context="I heard you hate them", @@ -112,7 +114,7 @@ def test_delegate_work_to_wrong_agent(): def test_ask_question_to_wrong_agent(): - result = tools.ask_question( + result = ask_tool.run( coworker="writer", question="do you hate AI Agents?", context="I heard you LOVE them", diff --git a/tests/agent_tools/cassettes/test_ask_question.yaml b/tests/tools/agent_tools/cassettes/test_ask_question.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_ask_question.yaml rename to tests/tools/agent_tools/cassettes/test_ask_question.yaml diff --git a/tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml b/tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml rename to tests/tools/agent_tools/cassettes/test_ask_question_with_coworker_as_array.yaml diff --git a/tests/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml b/tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml rename to tests/tools/agent_tools/cassettes/test_ask_question_with_wrong_co_worker_variable.yaml diff --git a/tests/agent_tools/cassettes/test_delegate_work.yaml b/tests/tools/agent_tools/cassettes/test_delegate_work.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_delegate_work.yaml rename to tests/tools/agent_tools/cassettes/test_delegate_work.yaml diff --git a/tests/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml b/tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml rename to tests/tools/agent_tools/cassettes/test_delegate_work_with_wrong_co_worker_variable.yaml diff --git a/tests/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml b/tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml similarity index 100% rename from tests/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml rename to tests/tools/agent_tools/cassettes/test_delegate_work_withwith_coworker_as_array.yaml diff --git a/tests/tools/test_base_tool.py b/tests/tools/test_base_tool.py new file mode 100644 index 000000000..cd4b53caf --- /dev/null +++ b/tests/tools/test_base_tool.py @@ -0,0 +1,102 @@ +from typing import Callable + +from crewai.tools import BaseTool, tool + + +def test_creating_a_tool_using_annotation(): + @tool("Name of my tool") + def my_tool(question: str) -> str: + """Clear description for what this tool is useful for, you agent will need this information to use it.""" + return question + + # Assert all the right attributes were defined + assert my_tool.name == "Name of my tool" + assert ( + my_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert my_tool.args_schema.schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + my_tool.func("What is the meaning of life?") == "What is the meaning of life?" + ) + + converted_tool = my_tool.to_structured_tool() + assert converted_tool.name == "Name of my tool" + + assert ( + converted_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert converted_tool.args_schema.schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + converted_tool.func("What is the meaning of life?") + == "What is the meaning of life?" + ) + + +def test_creating_a_tool_using_baseclass(): + class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = "Clear description for what this tool is useful for, you agent will need this information to use it." + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert my_tool.name == "Name of my tool" + + assert ( + my_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert my_tool.args_schema.schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?" + + converted_tool = my_tool.to_structured_tool() + assert converted_tool.name == "Name of my tool" + + assert ( + converted_tool.description + == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it." + ) + assert converted_tool.args_schema.schema()["properties"] == { + "question": {"title": "Question", "type": "string"} + } + assert ( + converted_tool._run("What is the meaning of life?") + == "What is the meaning of life?" + ) + + +def test_setting_cache_function(): + 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." + cache_function: Callable = lambda: False + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert not my_tool.cache_function() + + +def test_default_cache_function_is_true(): + class MyCustomTool(BaseTool): + name: str = "Name of my tool" + description: str = "Clear description for what this tool is useful for, you agent will need this information to use it." + + def _run(self, question: str) -> str: + return question + + my_tool = MyCustomTool() + # Assert all the right attributes were defined + assert my_tool.cache_function() diff --git a/tests/tools/test_structured_tool.py b/tests/tools/test_structured_tool.py new file mode 100644 index 000000000..32ebd805b --- /dev/null +++ b/tests/tools/test_structured_tool.py @@ -0,0 +1,146 @@ +from typing import Optional + +import pytest +from pydantic import BaseModel, Field + +from crewai.tools.structured_tool import CrewStructuredTool + + +# Test fixtures +@pytest.fixture +def basic_function(): + def test_func(param1: str, param2: int = 0) -> str: + """Test function with basic params.""" + return f"{param1} {param2}" + + return test_func + + +@pytest.fixture +def schema_class(): + class TestSchema(BaseModel): + param1: str + param2: int = Field(default=0) + + return TestSchema + + +class TestCrewStructuredTool: + def test_initialization(self, basic_function, schema_class): + """Test basic initialization of CrewStructuredTool""" + tool = CrewStructuredTool( + name="test_tool", + description="Test tool description", + func=basic_function, + args_schema=schema_class, + ) + + assert tool.name == "test_tool" + assert tool.description == "Test tool description" + assert tool.func == basic_function + assert tool.args_schema == schema_class + + def test_from_function(self, basic_function): + """Test creating tool from function""" + tool = CrewStructuredTool.from_function( + func=basic_function, name="test_tool", description="Test description" + ) + + assert tool.name == "test_tool" + assert tool.description == "Test description" + assert tool.func == basic_function + assert isinstance(tool.args_schema, type(BaseModel)) + + def test_validate_function_signature(self, basic_function, schema_class): + """Test function signature validation""" + tool = CrewStructuredTool( + name="test_tool", + description="Test tool", + func=basic_function, + args_schema=schema_class, + ) + + # Should not raise any exceptions + tool._validate_function_signature() + + @pytest.mark.asyncio + async def test_ainvoke(self, basic_function): + """Test asynchronous invocation""" + tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool") + + result = await tool.ainvoke(input={"param1": "test"}) + assert result == "test 0" + + def test_parse_args_dict(self, basic_function): + """Test parsing dictionary arguments""" + tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool") + + parsed = tool._parse_args({"param1": "test", "param2": 42}) + assert parsed["param1"] == "test" + assert parsed["param2"] == 42 + + def test_parse_args_string(self, basic_function): + """Test parsing string arguments""" + tool = CrewStructuredTool.from_function(func=basic_function, name="test_tool") + + parsed = tool._parse_args('{"param1": "test", "param2": 42}') + assert parsed["param1"] == "test" + assert parsed["param2"] == 42 + + def test_complex_types(self): + """Test handling of complex parameter types""" + + def complex_func(nested: dict, items: list) -> str: + """Process complex types.""" + return f"Processed {len(items)} items with {len(nested)} nested keys" + + tool = CrewStructuredTool.from_function( + func=complex_func, name="test_tool", description="Test complex types" + ) + result = tool.invoke({"nested": {"key": "value"}, "items": [1, 2, 3]}) + assert result == "Processed 3 items with 1 nested keys" + + def test_schema_inheritance(self): + """Test tool creation with inherited schema""" + + def extended_func(base_param: str, extra_param: int) -> str: + """Test function with inherited schema.""" + return f"{base_param} {extra_param}" + + class BaseSchema(BaseModel): + base_param: str + + class ExtendedSchema(BaseSchema): + extra_param: int + + tool = CrewStructuredTool.from_function( + func=extended_func, name="test_tool", args_schema=ExtendedSchema + ) + + result = tool.invoke({"base_param": "test", "extra_param": 42}) + assert result == "test 42" + + def test_default_values_in_schema(self): + """Test handling of default values in schema""" + + def default_func( + required_param: str, + optional_param: str = "default", + nullable_param: Optional[int] = None, + ) -> str: + """Test function with default values.""" + return f"{required_param} {optional_param} {nullable_param}" + + tool = CrewStructuredTool.from_function( + func=default_func, name="test_tool", description="Test defaults" + ) + + # Test with minimal parameters + result = tool.invoke({"required_param": "test"}) + assert result == "test default None" + + # Test with all parameters + result = tool.invoke( + {"required_param": "test", "optional_param": "custom", "nullable_param": 42} + ) + assert result == "test custom 42" diff --git a/tests/tools/test_tool_usage.py b/tests/tools/test_tool_usage.py new file mode 100644 index 000000000..05b9b23af --- /dev/null +++ b/tests/tools/test_tool_usage.py @@ -0,0 +1,123 @@ +import json +import random +from unittest.mock import MagicMock + +import pytest +from pydantic import BaseModel, Field + +from crewai import Agent, Task +from crewai.tools import BaseTool +from crewai.tools.tool_usage import ToolUsage + + +class RandomNumberToolInput(BaseModel): + min_value: int = Field( + ..., description="The minimum value of the range (inclusive)" + ) + max_value: int = Field( + ..., description="The maximum value of the range (inclusive)" + ) + + +class RandomNumberTool(BaseTool): + name: str = "Random Number Generator" + description: str = "Generates a random number within a specified range" + args_schema: type[BaseModel] = RandomNumberToolInput + + def _run(self, min_value: int, max_value: int) -> int: + return random.randint(min_value, max_value) + + +# Example agent and task +example_agent = Agent( + role="Number Generator", + goal="Generate random numbers for various purposes", + backstory="You are an AI agent specialized in generating random numbers within specified ranges.", + tools=[RandomNumberTool()], + verbose=True, +) + +example_task = Task( + description="Generate a random number between 1 and 100", + expected_output="A random number between 1 and 100", + agent=example_agent, +) + + +def test_random_number_tool_range(): + tool = RandomNumberTool() + result = tool._run(1, 10) + assert 1 <= result <= 10 + + +def test_random_number_tool_invalid_range(): + tool = RandomNumberTool() + with pytest.raises(ValueError): + tool._run(10, 1) # min_value > max_value + + +def test_random_number_tool_schema(): + tool = RandomNumberTool() + + # Get the schema using model_json_schema() + schema = tool.args_schema.model_json_schema() + + # Convert the schema to a string + schema_str = json.dumps(schema) + + # Check if the schema string contains the expected fields + assert "min_value" in schema_str + assert "max_value" in schema_str + + # Parse the schema string back to a dictionary + schema_dict = json.loads(schema_str) + + # Check if the schema contains the correct field types + assert schema_dict["properties"]["min_value"]["type"] == "integer" + assert schema_dict["properties"]["max_value"]["type"] == "integer" + + # Check if the schema contains the field descriptions + assert ( + "minimum value" in schema_dict["properties"]["min_value"]["description"].lower() + ) + assert ( + "maximum value" in schema_dict["properties"]["max_value"]["description"].lower() + ) + + +def test_tool_usage_render(): + tool = RandomNumberTool() + + tool_usage = ToolUsage( + tools_handler=MagicMock(), + tools=[tool], + original_tools=[tool], + tools_description="Sample tool for testing", + tools_names="random_number_generator", + task=MagicMock(), + function_calling_llm=MagicMock(), + agent=MagicMock(), + action=MagicMock(), + ) + + rendered = tool_usage._render() + + # Updated checks to match the actual output + assert "Tool Name: Random Number Generator" in rendered + assert "Tool Arguments:" in rendered + assert ( + "'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}" + in rendered + ) + assert ( + "'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}" + in rendered + ) + assert ( + "Tool Description: Generates a random number within a specified range" + in rendered + ) + assert ( + "Tool Name: Random Number Generator\nTool Arguments: {'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}, 'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}}\nTool Description: Generates a random number within a specified range" + in rendered + ) diff --git a/tests/utilities/evaluators/test_crew_evaluator_handler.py b/tests/utilities/evaluators/test_crew_evaluator_handler.py index 6c92ffdbc..649c25998 100644 --- a/tests/utilities/evaluators/test_crew_evaluator_handler.py +++ b/tests/utilities/evaluators/test_crew_evaluator_handler.py @@ -1,6 +1,7 @@ from unittest import mock import pytest + from crewai.agent import Agent from crewai.crew import Crew from crewai.task import Task diff --git a/tests/utilities/prompts.json b/tests/utilities/prompts.json index 55bbfae8f..a51203dbc 100644 --- a/tests/utilities/prompts.json +++ b/tests/utilities/prompts.json @@ -26,7 +26,7 @@ }, "errors": { "force_final_answer": "Lorem ipsum dolor sit amet", - "agent_tool_unexsiting_coworker": "Lorem ipsum dolor sit amet", + "agent_tool_unexisting_coworker": "Lorem ipsum dolor sit amet", "task_repeated_usage": "Lorem ipsum dolor sit amet", "tool_usage_error": "Lorem ipsum dolor sit amet", "tool_arguments_error": "Lorem ipsum dolor sit amet", diff --git a/tests/utilities/test_converter.py b/tests/utilities/test_converter.py index 0bb6b7263..c63d6dba3 100644 --- a/tests/utilities/test_converter.py +++ b/tests/utilities/test_converter.py @@ -1,7 +1,10 @@ import json +from typing import Dict, List, Optional from unittest.mock import MagicMock, Mock, patch import pytest +from pydantic import BaseModel + from crewai.llm import LLM from crewai.utilities.converter import ( Converter, @@ -9,12 +12,11 @@ from crewai.utilities.converter import ( convert_to_model, convert_with_instructions, create_converter, + generate_model_description, get_conversion_instructions, handle_partial_json, validate_model, ) -from pydantic import BaseModel - from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser @@ -269,3 +271,45 @@ def test_create_converter_fails_without_agent_or_converter_cls(): create_converter( llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert" ) + + +def test_generate_model_description_simple_model(): + description = generate_model_description(SimpleModel) + expected_description = '{\n "name": str,\n "age": int\n}' + assert description == expected_description + + +def test_generate_model_description_nested_model(): + description = generate_model_description(NestedModel) + expected_description = ( + '{\n "id": int,\n "data": {\n "name": str,\n "age": int\n}\n}' + ) + assert description == expected_description + + +def test_generate_model_description_optional_field(): + class ModelWithOptionalField(BaseModel): + name: Optional[str] + age: int + + description = generate_model_description(ModelWithOptionalField) + expected_description = '{\n "name": Optional[str],\n "age": int\n}' + assert description == expected_description + + +def test_generate_model_description_list_field(): + class ModelWithListField(BaseModel): + items: List[int] + + description = generate_model_description(ModelWithListField) + expected_description = '{\n "items": List[int]\n}' + assert description == expected_description + + +def test_generate_model_description_dict_field(): + class ModelWithDictField(BaseModel): + attributes: Dict[str, int] + + description = generate_model_description(ModelWithDictField) + expected_description = '{\n "attributes": Dict[str, int]\n}' + assert description == expected_description diff --git a/uv.lock b/uv.lock index b0416fa72..915111e8c 100644 --- a/uv.lock +++ b/uv.lock @@ -1,17 +1,14 @@ version = 1 -requires-python = ">=3.10, <=3.13" +requires-python = ">=3.10, <=3.12" resolution-markers = [ - "python_full_version < '3.11' and platform_python_implementation == 'PyPy'", - "python_full_version < '3.11' and platform_python_implementation != 'PyPy'", + "python_full_version < '3.11'", "python_full_version == '3.11.*'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.13'", + "python_full_version >= '3.12'", ] [[package]] name = "agentops" -version = "0.3.13" +version = "0.3.14" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "packaging" }, @@ -20,9 +17,9 @@ dependencies = [ { name = "requests" }, { name = "termcolor" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/35/11/fb06b4cee96285a5f745809d0f4efddef70d2a82112a633ed53834d6fc64/agentops-0.3.13.tar.gz", hash = "sha256:319b7325fb79004ce996191aa21f0982489be22cc1acc2f3f6d02cdff1db2429", size = 48559 } +sdist = { url = "https://files.pythonhosted.org/packages/46/cb/183fdaf40ae97ac1806ba91f6f23d55dc0a1a5cdf0881a5c834c8ca7175a/agentops-0.3.14.tar.gz", hash = "sha256:fcb515e5743d73efee851b687692bed74797dc88e29a8327b2bbfb21d73a7447", size = 48548 } wheels = [ - { url = "https://files.pythonhosted.org/packages/68/ef/a3b8adc0de2e7daa1e6e2734af9a0e37c90e3346b8a804e3fdc322c82b6c/agentops-0.3.13-py3-none-any.whl", hash = "sha256:81bfdfedd990fbc3064ee42a67422ddbee07b6cd96c5fca7e124eb8c1e0cebdc", size = 50813 }, + { url = "https://files.pythonhosted.org/packages/1c/27/75ab5bf99341a6a02775e3858f54a18cbcda0f35b5c6c0f114a829d62b8e/agentops-0.3.14-py3-none-any.whl", hash = "sha256:f4a2fcf1a7caf1d5383bfb66d8a9d567f3cb88fc7495cfd81ade167b0c06a4ea", size = 50825 }, ] [[package]] @@ -36,7 +33,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.10.8" +version = "3.10.10" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -47,68 +44,53 @@ dependencies = [ { name = "multidict" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/4e/05/da5ff89c85444a6ade9079e73580fb3f78c6ba0e170a2472f15400d03e02/aiohttp-3.10.8.tar.gz", hash = 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