diff --git a/.gitignore b/.gitignore index 8559a290c..947b99b7b 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,4 @@ crew_tasks_output.json .mypy_cache .ruff_cache .venv +agentops.log \ No newline at end of file diff --git a/README.md b/README.md index bf1287d4d..edcbb6f51 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,6 @@ First, install CrewAI: ```shell pip install crewai ``` - If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: ```shell @@ -93,6 +92,22 @@ pip install 'crewai[tools]' ``` The command above installs the basic package and also adds extra components which require more dependencies to function. +### Troubleshooting Dependencies + +If you encounter issues during installation or usage, here are some common solutions: + +#### Common Issues + +1. **ModuleNotFoundError: No module named 'tiktoken'** + - Install tiktoken explicitly: `pip install 'crewai[embeddings]'` + - If using embedchain or other tools: `pip install 'crewai[tools]'` + +2. **Failed building wheel for tiktoken** + - Ensure Rust compiler is installed (see installation steps above) + - For Windows: Verify Visual C++ Build Tools are installed + - Try upgrading pip: `pip install --upgrade pip` + - If issues persist, use a pre-built wheel: `pip install tiktoken --prefer-binary` + ### 2. Setting Up Your Crew with the YAML Configuration To create a new CrewAI project, run the following CLI (Command Line Interface) command: diff --git a/docs/concepts/flows.mdx b/docs/concepts/flows.mdx index 324118310..cf9d20f63 100644 --- a/docs/concepts/flows.mdx +++ b/docs/concepts/flows.mdx @@ -138,7 +138,7 @@ print("---- Final Output ----") print(final_output) ```` -``` text Output +```text Output ---- Final Output ---- Second method received: Output from first_method ```` diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx index 8a777833e..91110e19f 100644 --- a/docs/concepts/knowledge.mdx +++ b/docs/concepts/knowledge.mdx @@ -4,8 +4,6 @@ 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. @@ -36,7 +34,20 @@ CrewAI supports various types of knowledge sources out of the box: -## Quick Start +## Supported Knowledge Parameters + +| Parameter | Type | Required | Description | +| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- | +| `sources` | **List[BaseKnowledgeSource]** | Yes | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content. | +| `collection_name` | **str** | No | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided. | +| `storage` | **Optional[KnowledgeStorage]** | No | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. | + +## Quickstart Example + + +For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. +Also, use relative paths from the `knowledge` directory when creating the source. + Here's an example using string-based knowledge: @@ -80,7 +91,8 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o ``` -Here's another example with the `CrewDoclingSource` +Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. + ```python Code from crewai import LLM, Agent, Crew, Process, Task from crewai.knowledge.source.crew_docling_source import CrewDoclingSource @@ -128,39 +140,217 @@ result = crew.kickoff( ) ``` +## More Examples + +Here are examples of how to use different types of knowledge sources: + +### Text File Knowledge Source +```python +from crewai.knowledge.source.crew_docling_source import CrewDoclingSource + +# Create a text file knowledge source +text_source = CrewDoclingSource( + file_paths=["document.txt", "another.txt"] +) + +# Create crew with text file source on agents or crew level +agent = Agent( + ... + knowledge_sources=[text_source] +) + +crew = Crew( + ... + knowledge_sources=[text_source] +) +``` + +### PDF Knowledge Source +```python +from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource + +# Create a PDF knowledge source +pdf_source = PDFKnowledgeSource( + file_paths=["document.pdf", "another.pdf"] +) + +# Create crew with PDF knowledge source on agents or crew level +agent = Agent( + ... + knowledge_sources=[pdf_source] +) + +crew = Crew( + ... + knowledge_sources=[pdf_source] +) +``` + +### CSV Knowledge Source +```python +from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource + +# Create a CSV knowledge source +csv_source = CSVKnowledgeSource( + file_paths=["data.csv"] +) + +# Create crew with CSV knowledge source or on agent level +agent = Agent( + ... + knowledge_sources=[csv_source] +) + +crew = Crew( + ... + knowledge_sources=[csv_source] +) +``` + +### Excel Knowledge Source +```python +from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource + +# Create an Excel knowledge source +excel_source = ExcelKnowledgeSource( + file_paths=["spreadsheet.xlsx"] +) + +# Create crew with Excel knowledge source on agents or crew level +agent = Agent( + ... + knowledge_sources=[excel_source] +) + +crew = Crew( + ... + knowledge_sources=[excel_source] +) +``` + +### JSON Knowledge Source +```python +from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource + +# Create a JSON knowledge source +json_source = JSONKnowledgeSource( + file_paths=["data.json"] +) + +# Create crew with JSON knowledge source on agents or crew level +agent = Agent( + ... + knowledge_sources=[json_source] +) + +crew = Crew( + ... + knowledge_sources=[json_source] +) +``` + ## Knowledge Configuration ### Chunking Configuration -Control how content is split for processing by setting the chunk size and overlap. +Knowledge sources automatically chunk content for better processing. +You can configure chunking behavior in your knowledge sources: -```python Code -knowledge_source = StringKnowledgeSource( - content="Long content...", - chunk_size=4000, # Characters per chunk (default) - chunk_overlap=200 # Overlap between chunks (default) +```python +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource + +source = StringKnowledgeSource( + content="Your content here", + chunk_size=4000, # Maximum size of each chunk (default: 4000) + chunk_overlap=200 # Overlap between chunks (default: 200) ) ``` -## Embedder Configuration +The chunking configuration helps in: +- Breaking down large documents into manageable pieces +- Maintaining context through chunk overlap +- Optimizing retrieval accuracy -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. +### Embeddings Configuration -```python Code -... +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. +The `embedder` parameter supports various embedding model providers that include: +- `openai`: OpenAI's embedding models +- `google`: Google's text embedding models +- `azure`: Azure OpenAI embeddings +- `ollama`: Local embeddings with Ollama +- `vertexai`: Google Cloud VertexAI embeddings +- `cohere`: Cohere's embedding models +- `bedrock`: AWS Bedrock embeddings +- `huggingface`: Hugging Face models +- `watson`: IBM Watson embeddings + +Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model: + +```python Example +from crewai import Agent, Task, Crew, Process, LLM +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource +import os + +# Get the GEMINI API key +GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY") + +# Create a knowledge source +content = "Users name is John. He is 30 years old and lives in San Francisco." string_source = StringKnowledgeSource( - content="Users name is John. He is 30 years old and lives in San Francisco.", + content=content, ) + +# Create an LLM with a temperature of 0 to ensure deterministic outputs +gemini_llm = LLM( + model="gemini/gemini-1.5-pro-002", + api_key=GEMINI_API_KEY, + 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=gemini_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], embedder={ - "provider": "openai", - "config": {"model": "text-embedding-3-small"}, - }, + "provider": "google", + "config": { + "model": "models/text-embedding-004", + "api_key": GEMINI_API_KEY, + } + } ) -``` +result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) +``` +```text Output +# Agent: About User +## Task: Answer the following questions about the user: What city does John live in and how old is he? + +# Agent: About User +## Final Answer: +John is 30 years old and lives in San Francisco. +``` + ## Clearing Knowledge If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option. diff --git a/docs/how-to/Portkey-Observability-and-Guardrails.md b/docs/how-to/portkey-observability-and-guardrails.mdx similarity index 100% rename from docs/how-to/Portkey-Observability-and-Guardrails.md rename to docs/how-to/portkey-observability-and-guardrails.mdx diff --git a/docs/how-to/portkey-observability.mdx b/docs/how-to/portkey-observability.mdx new file mode 100644 index 000000000..4002323a5 --- /dev/null +++ b/docs/how-to/portkey-observability.mdx @@ -0,0 +1,202 @@ +--- +title: Portkey Observability and Guardrails +description: How to use Portkey with CrewAI +icon: key +--- + +Portkey CrewAI Header Image + + +[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast. + +Portkey adds 4 core production capabilities to any CrewAI agent: +1. Routing to **200+ LLMs** +2. Making each LLM call more robust +3. Full-stack tracing & cost, performance analytics +4. Real-time guardrails to enforce behavior + +## Getting Started + + + + ```bash + pip install -qU crewai portkey-ai + ``` + + + To build CrewAI Agents with Portkey, you'll need two keys: + - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key + - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault + + ```python + from crewai import LLM + from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL + + gpt_llm = LLM( + model="gpt-4", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", # We are using Virtual key + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey + ) + ) + ``` + + + ```python + from crewai import Agent, Task, Crew + + # Define your agents with roles and goals + coder = Agent( + role='Software developer', + goal='Write clear, concise code on demand', + backstory='An expert coder with a keen eye for software trends.', + llm=gpt_llm + ) + + # Create tasks for your agents + task1 = Task( + description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!", + expected_output="A clear and concise HTML code", + agent=coder + ) + + # Instantiate your crew + crew = Crew( + agents=[coder], + tasks=[task1], + ) + + result = crew.kickoff() + print(result) + ``` + + + +## Key Features + +| Feature | Description | +|:--------|:------------| +| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface | +| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks | +| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata | +| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs | +| 🚧 Security Controls | Set budget limits and implement role-based access control | +| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement | +| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching | + + +## Production Features with Portkey Configs + +All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference. + + + + + + +### 1. Use 250+ LLMs +Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api) + + +Easily switch between different LLM providers: + +```python +# Anthropic Configuration +anthropic_llm = LLM( + model="claude-3-5-sonnet-latest", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys + trace_id="anthropic_agent" + ) +) + +# Azure OpenAI Configuration +azure_llm = LLM( + model="gpt-4", + base_url=PORTKEY_GATEWAY_URL, + api_key="dummy", + extra_headers=createHeaders( + api_key="YOUR_PORTKEY_API_KEY", + virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys + trace_id="azure_agent" + ) +) +``` + + +### 2. Caching +Improve response times and reduce costs with two powerful caching modes: +- **Simple Cache**: Perfect for exact matches +- **Semantic Cache**: Matches responses for requests that are semantically similar +[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic) + +```py +config = { + "cache": { + "mode": "semantic", # or "simple" for exact matching + } +} +``` + +### 3. Production Reliability +Portkey provides comprehensive reliability features: +- **Automatic Retries**: Handle temporary failures gracefully +- **Request Timeouts**: Prevent hanging operations +- **Conditional Routing**: Route requests based on specific conditions +- **Fallbacks**: Set up automatic provider failovers +- **Load Balancing**: Distribute requests efficiently + +[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/) + + + +### 4. Metrics + +Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need. + + +- Cost per agent interaction +- Response times and latency +- Token usage and efficiency +- Success/failure rates +- Cache hit rates + +Portkey Dashboard + +### 5. Detailed Logging +Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes. + + +Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata. + +
+ Traces + Portkey Traces +
+ +
+ Logs + Portkey Logs +
+ +### 6. Enterprise Security Features +- Set budget limit and rate limts per Virtual Key (disposable API keys) +- Implement role-based access control +- Track system changes with audit logs +- Configure data retention policies + + + +For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs). + +## Resources + +- [📘 Portkey Documentation](https://docs.portkey.ai) +- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) +- [🐦 Twitter](https://twitter.com/portkeyai) +- [💬 Discord Community](https://discord.gg/DD7vgKK299) diff --git a/docs/mint.json b/docs/mint.json index fad9689b8..9103434b4 100644 --- a/docs/mint.json +++ b/docs/mint.json @@ -100,7 +100,8 @@ "how-to/conditional-tasks", "how-to/agentops-observability", "how-to/langtrace-observability", - "how-to/openlit-observability" + "how-to/openlit-observability", + "how-to/portkey-observability" ] }, { diff --git a/pyproject.toml b/pyproject.toml index 3f10c1a87..fd1798d36 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "crewai" -version = "0.86.0" +version = "0.95.0" description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks." readme = "README.md" requires-python = ">=3.10,<3.13" @@ -8,28 +8,39 @@ authors = [ { name = "Joao Moura", email = "joao@crewai.com" } ] dependencies = [ + # Core Dependencies "pydantic>=2.4.2", "openai>=1.13.3", + "litellm>=1.44.22", + "instructor>=1.3.3", + + # Text Processing + "pdfplumber>=0.11.4", + "regex>=2024.9.11", + + # Telemetry and Monitoring "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", - "click>=8.1.7", + + # Data Handling + "chromadb>=0.5.23", + "openpyxl>=3.1.5", + "pyvis>=0.3.2", + + # Authentication and Security + "auth0-python>=4.7.1", "python-dotenv>=1.0.0", + + # Configuration and Utils + "click>=8.1.7", "appdirs>=1.4.4", "jsonref>=1.1.0", "json-repair>=0.25.2", - "auth0-python>=4.7.1", - "litellm>=1.44.22", - "pyvis>=0.3.2", "uv>=0.4.25", "tomli-w>=1.1.0", "tomli>=2.0.2", - "chromadb>=0.5.23", - "pdfplumber>=0.11.4", - "openpyxl>=3.1.5", - "blinker>=1.9.0", + "blinker>=1.9.0" ] [project.urls] @@ -38,7 +49,10 @@ Documentation = "https://docs.crewai.com" Repository = "https://github.com/crewAIInc/crewAI" [project.optional-dependencies] -tools = ["crewai-tools>=0.17.0"] +tools = ["crewai-tools>=0.25.5"] +embeddings = [ + "tiktoken~=0.7.0" +] agentops = ["agentops>=0.3.0"] fastembed = ["fastembed>=0.4.1"] pdfplumber = [ diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py index 0833afd58..f0ba35a38 100644 --- a/src/crewai/__init__.py +++ b/src/crewai/__init__.py @@ -14,7 +14,7 @@ warnings.filterwarnings( category=UserWarning, module="pydantic.main", ) -__version__ = "0.86.0" +__version__ = "0.95.0" __all__ = [ "Agent", "Crew", diff --git a/src/crewai/cli/templates/crew/pyproject.toml b/src/crewai/cli/templates/crew/pyproject.toml index 5ea8194c8..bd2d871c5 100644 --- a/src/crewai/cli/templates/crew/pyproject.toml +++ b/src/crewai/cli/templates/crew/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<3.13" dependencies = [ - "crewai[tools]>=0.86.0,<1.0.0" + "crewai[tools]>=0.95.0,<1.0.0" ] [project.scripts] diff --git a/src/crewai/cli/templates/flow/pyproject.toml b/src/crewai/cli/templates/flow/pyproject.toml index 8a523d2ed..6ca589497 100644 --- a/src/crewai/cli/templates/flow/pyproject.toml +++ b/src/crewai/cli/templates/flow/pyproject.toml @@ -5,7 +5,7 @@ description = "{{name}} using crewAI" authors = [{ name = "Your Name", email = "you@example.com" }] requires-python = ">=3.10,<3.13" dependencies = [ - "crewai[tools]>=0.86.0,<1.0.0", + "crewai[tools]>=0.95.0,<1.0.0", ] [project.scripts] diff --git a/src/crewai/cli/templates/tool/pyproject.toml b/src/crewai/cli/templates/tool/pyproject.toml index 69b8d355f..0c4b88e9c 100644 --- a/src/crewai/cli/templates/tool/pyproject.toml +++ b/src/crewai/cli/templates/tool/pyproject.toml @@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}" readme = "README.md" requires-python = ">=3.10,<3.13" dependencies = [ - "crewai[tools]>=0.86.0" + "crewai[tools]>=0.95.0" ] [tool.crewai] diff --git a/src/crewai/crew.py b/src/crewai/crew.py index d488783ea..c01a89280 100644 --- a/src/crewai/crew.py +++ b/src/crewai/crew.py @@ -726,11 +726,7 @@ class Crew(BaseModel): # Determine which tools to use - task tools take precedence over agent tools tools_for_task = task.tools or agent_to_use.tools or [] - tools_for_task = self._prepare_tools( - agent_to_use, - task, - tools_for_task - ) + tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task) self._log_task_start(task, agent_to_use.role) @@ -797,14 +793,18 @@ class Crew(BaseModel): return skipped_task_output return None - def _prepare_tools(self, agent: BaseAgent, task: Task, tools: List[Tool]) -> List[Tool]: + def _prepare_tools( + self, agent: BaseAgent, task: Task, tools: List[Tool] + ) -> List[Tool]: # Add delegation tools if agent allows delegation if agent.allow_delegation: if self.process == Process.hierarchical: if self.manager_agent: tools = self._update_manager_tools(task, tools) else: - raise ValueError("Manager agent is required for hierarchical process.") + raise ValueError( + "Manager agent is required for hierarchical process." + ) elif agent and agent.allow_delegation: tools = self._add_delegation_tools(task, tools) @@ -823,7 +823,9 @@ class Crew(BaseModel): return self.manager_agent return task.agent - def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> List[Tool]: + def _merge_tools( + self, existing_tools: List[Tool], new_tools: List[Tool] + ) -> List[Tool]: """Merge new tools into existing tools list, avoiding duplicates by tool name.""" if not new_tools: return existing_tools @@ -839,7 +841,9 @@ class Crew(BaseModel): return tools - def _inject_delegation_tools(self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]): + def _inject_delegation_tools( + self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent] + ): delegation_tools = task_agent.get_delegation_tools(agents) return self._merge_tools(tools, delegation_tools) @@ -856,7 +860,9 @@ class Crew(BaseModel): if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent: if not tools: tools = [] - tools = self._inject_delegation_tools(tools, task.agent, agents_for_delegation) + tools = self._inject_delegation_tools( + tools, task.agent, agents_for_delegation + ) return tools def _log_task_start(self, task: Task, role: str = "None"): @@ -870,7 +876,9 @@ class Crew(BaseModel): if task.agent: tools = self._inject_delegation_tools(tools, task.agent, [task.agent]) else: - tools = self._inject_delegation_tools(tools, self.manager_agent, self.agents) + tools = self._inject_delegation_tools( + tools, self.manager_agent, self.agents + ) return tools def _get_context(self, task: Task, task_outputs: List[TaskOutput]): diff --git a/src/crewai/flow/flow.py b/src/crewai/flow/flow.py index 4a6361cce..dc46aa6d8 100644 --- a/src/crewai/flow/flow.py +++ b/src/crewai/flow/flow.py @@ -30,7 +30,47 @@ from crewai.telemetry import Telemetry T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]]) -def start(condition=None): +def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable: + """ + Marks a method as a flow's starting point. + + This decorator designates a method as an entry point for the flow execution. + It can optionally specify conditions that trigger the start based on other + method executions. + + Parameters + ---------- + condition : Optional[Union[str, dict, Callable]], optional + Defines when the start method should execute. Can be: + - str: Name of a method that triggers this start + - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers) + - Callable: A method reference that triggers this start + Default is None, meaning unconditional start. + + Returns + ------- + Callable + A decorator function that marks the method as a flow start point. + + Raises + ------ + ValueError + If the condition format is invalid. + + Examples + -------- + >>> @start() # Unconditional start + >>> def begin_flow(self): + ... pass + + >>> @start("method_name") # Start after specific method + >>> def conditional_start(self): + ... pass + + >>> @start(and_("method1", "method2")) # Start after multiple methods + >>> def complex_start(self): + ... pass + """ def decorator(func): func.__is_start_method__ = True if condition is not None: @@ -55,8 +95,42 @@ def start(condition=None): return decorator +def listen(condition: Union[str, dict, Callable]) -> Callable: + """ + Creates a listener that executes when specified conditions are met. -def listen(condition): + This decorator sets up a method to execute in response to other method + executions in the flow. It supports both simple and complex triggering + conditions. + + Parameters + ---------- + condition : Union[str, dict, Callable] + Specifies when the listener should execute. Can be: + - str: Name of a method that triggers this listener + - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers) + - Callable: A method reference that triggers this listener + + Returns + ------- + Callable + A decorator function that sets up the method as a listener. + + Raises + ------ + ValueError + If the condition format is invalid. + + Examples + -------- + >>> @listen("process_data") # Listen to single method + >>> def handle_processed_data(self): + ... pass + + >>> @listen(or_("success", "failure")) # Listen to multiple methods + >>> def handle_completion(self): + ... pass + """ def decorator(func): if isinstance(condition, str): func.__trigger_methods__ = [condition] @@ -80,10 +154,49 @@ def listen(condition): return decorator -def router(condition): +def router(condition: Union[str, dict, Callable]) -> Callable: + """ + Creates a routing method that directs flow execution based on conditions. + + This decorator marks a method as a router, which can dynamically determine + the next steps in the flow based on its return value. Routers are triggered + by specified conditions and can return constants that determine which path + the flow should take. + + Parameters + ---------- + condition : Union[str, dict, Callable] + Specifies when the router should execute. Can be: + - str: Name of a method that triggers this router + - dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers) + - Callable: A method reference that triggers this router + + Returns + ------- + Callable + A decorator function that sets up the method as a router. + + Raises + ------ + ValueError + If the condition format is invalid. + + Examples + -------- + >>> @router("check_status") + >>> def route_based_on_status(self): + ... if self.state.status == "success": + ... return SUCCESS + ... return FAILURE + + >>> @router(and_("validate", "process")) + >>> def complex_routing(self): + ... if all([self.state.valid, self.state.processed]): + ... return CONTINUE + ... return STOP + """ def decorator(func): func.__is_router__ = True - # Handle conditions like listen/start if isinstance(condition, str): func.__trigger_methods__ = [condition] func.__condition_type__ = "OR" @@ -105,8 +218,39 @@ def router(condition): return decorator +def or_(*conditions: Union[str, dict, Callable]) -> dict: + """ + Combines multiple conditions with OR logic for flow control. -def or_(*conditions): + Creates a condition that is satisfied when any of the specified conditions + are met. This is used with @start, @listen, or @router decorators to create + complex triggering conditions. + + Parameters + ---------- + *conditions : Union[str, dict, Callable] + Variable number of conditions that can be: + - str: Method names + - dict: Existing condition dictionaries + - Callable: Method references + + Returns + ------- + dict + A condition dictionary with format: + {"type": "OR", "methods": list_of_method_names} + + Raises + ------ + ValueError + If any condition is invalid. + + Examples + -------- + >>> @listen(or_("success", "timeout")) + >>> def handle_completion(self): + ... pass + """ methods = [] for condition in conditions: if isinstance(condition, dict) and "methods" in condition: @@ -120,7 +264,39 @@ def or_(*conditions): return {"type": "OR", "methods": methods} -def and_(*conditions): +def and_(*conditions: Union[str, dict, Callable]) -> dict: + """ + Combines multiple conditions with AND logic for flow control. + + Creates a condition that is satisfied only when all specified conditions + are met. This is used with @start, @listen, or @router decorators to create + complex triggering conditions. + + Parameters + ---------- + *conditions : Union[str, dict, Callable] + Variable number of conditions that can be: + - str: Method names + - dict: Existing condition dictionaries + - Callable: Method references + + Returns + ------- + dict + A condition dictionary with format: + {"type": "AND", "methods": list_of_method_names} + + Raises + ------ + ValueError + If any condition is invalid. + + Examples + -------- + >>> @listen(and_("validated", "processed")) + >>> def handle_complete_data(self): + ... pass + """ methods = [] for condition in conditions: if isinstance(condition, dict) and "methods" in condition: @@ -286,6 +462,23 @@ class Flow(Generic[T], metaclass=FlowMeta): return final_output async def _execute_start_method(self, start_method_name: str) -> None: + """ + Executes a flow's start method and its triggered listeners. + + This internal method handles the execution of methods marked with @start + decorator and manages the subsequent chain of listener executions. + + Parameters + ---------- + start_method_name : str + The name of the start method to execute. + + Notes + ----- + - Executes the start method and captures its result + - Triggers execution of any listeners waiting on this start method + - Part of the flow's initialization sequence + """ result = await self._execute_method( start_method_name, self._methods[start_method_name] ) @@ -306,6 +499,28 @@ class Flow(Generic[T], metaclass=FlowMeta): return result async def _execute_listeners(self, trigger_method: str, result: Any) -> None: + """ + Executes all listeners and routers triggered by a method completion. + + This internal method manages the execution flow by: + 1. First executing all triggered routers sequentially + 2. Then executing all triggered listeners in parallel + + Parameters + ---------- + trigger_method : str + The name of the method that triggered these listeners. + result : Any + The result from the triggering method, passed to listeners + that accept parameters. + + Notes + ----- + - Routers are executed sequentially to maintain flow control + - Each router's result becomes the new trigger_method + - Normal listeners are executed in parallel for efficiency + - Listeners can receive the trigger method's result as a parameter + """ # First, handle routers repeatedly until no router triggers anymore while True: routers_triggered = self._find_triggered_methods( @@ -335,6 +550,33 @@ class Flow(Generic[T], metaclass=FlowMeta): def _find_triggered_methods( self, trigger_method: str, router_only: bool ) -> List[str]: + """ + Finds all methods that should be triggered based on conditions. + + This internal method evaluates both OR and AND conditions to determine + which methods should be executed next in the flow. + + Parameters + ---------- + trigger_method : str + The name of the method that just completed execution. + router_only : bool + If True, only consider router methods. + If False, only consider non-router methods. + + Returns + ------- + List[str] + Names of methods that should be triggered. + + Notes + ----- + - Handles both OR and AND conditions: + * OR: Triggers if any condition is met + * AND: Triggers only when all conditions are met + - Maintains state for AND conditions using _pending_and_listeners + - Separates router and normal listener evaluation + """ triggered = [] for listener_name, (condition_type, methods) in self._listeners.items(): is_router = listener_name in self._routers @@ -363,6 +605,33 @@ class Flow(Generic[T], metaclass=FlowMeta): return triggered async def _execute_single_listener(self, listener_name: str, result: Any) -> None: + """ + Executes a single listener method with proper event handling. + + This internal method manages the execution of an individual listener, + including parameter inspection, event emission, and error handling. + + Parameters + ---------- + listener_name : str + The name of the listener method to execute. + result : Any + The result from the triggering method, which may be passed + to the listener if it accepts parameters. + + Notes + ----- + - Inspects method signature to determine if it accepts the trigger result + - Emits events for method execution start and finish + - Handles errors gracefully with detailed logging + - Recursively triggers listeners of this listener + - Supports both parameterized and parameter-less listeners + + Error Handling + ------------- + Catches and logs any exceptions during execution, preventing + individual listener failures from breaking the entire flow. + """ try: method = self._methods[listener_name] diff --git a/src/crewai/flow/flow_visualizer.py b/src/crewai/flow/flow_visualizer.py index 988f27919..a70e91a18 100644 --- a/src/crewai/flow/flow_visualizer.py +++ b/src/crewai/flow/flow_visualizer.py @@ -1,12 +1,14 @@ # flow_visualizer.py import os +from pathlib import Path from pyvis.network import Network from crewai.flow.config import COLORS, NODE_STYLES from crewai.flow.html_template_handler import HTMLTemplateHandler from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items +from crewai.flow.path_utils import safe_path_join, validate_path_exists from crewai.flow.utils import calculate_node_levels from crewai.flow.visualization_utils import ( add_edges, @@ -16,89 +18,209 @@ from crewai.flow.visualization_utils import ( class FlowPlot: + """Handles the creation and rendering of flow visualization diagrams.""" + def __init__(self, flow): + """ + Initialize FlowPlot with a flow object. + + Parameters + ---------- + flow : Flow + A Flow instance to visualize. + + Raises + ------ + ValueError + If flow object is invalid or missing required attributes. + """ + if not hasattr(flow, '_methods'): + raise ValueError("Invalid flow object: missing '_methods' attribute") + if not hasattr(flow, '_listeners'): + raise ValueError("Invalid flow object: missing '_listeners' attribute") + if not hasattr(flow, '_start_methods'): + raise ValueError("Invalid flow object: missing '_start_methods' attribute") + self.flow = flow self.colors = COLORS self.node_styles = NODE_STYLES def plot(self, filename): - net = Network( - directed=True, - height="750px", - width="100%", - bgcolor=self.colors["bg"], - layout=None, - ) - - # Set options to disable physics - net.set_options( - """ - var options = { - "nodes": { - "font": { - "multi": "html" - } - }, - "physics": { - "enabled": false - } - } """ - ) + Generate and save an HTML visualization of the flow. - # Calculate levels for nodes - node_levels = calculate_node_levels(self.flow) + Parameters + ---------- + filename : str + Name of the output file (without extension). - # Compute positions - node_positions = compute_positions(self.flow, node_levels) + Raises + ------ + ValueError + If filename is invalid or network generation fails. + IOError + If file operations fail or visualization cannot be generated. + RuntimeError + If network visualization generation fails. + """ + if not filename or not isinstance(filename, str): + raise ValueError("Filename must be a non-empty string") + + try: + # Initialize network + net = Network( + directed=True, + height="750px", + width="100%", + bgcolor=self.colors["bg"], + layout=None, + ) - # Add nodes to the network - add_nodes_to_network(net, self.flow, node_positions, self.node_styles) + # Set options to disable physics + net.set_options( + """ + var options = { + "nodes": { + "font": { + "multi": "html" + } + }, + "physics": { + "enabled": false + } + } + """ + ) - # Add edges to the network - add_edges(net, self.flow, node_positions, self.colors) + # Calculate levels for nodes + try: + node_levels = calculate_node_levels(self.flow) + except Exception as e: + raise ValueError(f"Failed to calculate node levels: {str(e)}") - network_html = net.generate_html() - final_html_content = self._generate_final_html(network_html) + # Compute positions + try: + node_positions = compute_positions(self.flow, node_levels) + except Exception as e: + raise ValueError(f"Failed to compute node positions: {str(e)}") - # Save the final HTML content to the file - with open(f"{filename}.html", "w", encoding="utf-8") as f: - f.write(final_html_content) - print(f"Plot saved as {filename}.html") + # Add nodes to the network + try: + add_nodes_to_network(net, self.flow, node_positions, self.node_styles) + except Exception as e: + raise RuntimeError(f"Failed to add nodes to network: {str(e)}") - self._cleanup_pyvis_lib() + # Add edges to the network + try: + add_edges(net, self.flow, node_positions, self.colors) + except Exception as e: + raise RuntimeError(f"Failed to add edges to network: {str(e)}") + + # Generate HTML + try: + network_html = net.generate_html() + final_html_content = self._generate_final_html(network_html) + except Exception as e: + raise RuntimeError(f"Failed to generate network visualization: {str(e)}") + + # Save the final HTML content to the file + try: + with open(f"{filename}.html", "w", encoding="utf-8") as f: + f.write(final_html_content) + print(f"Plot saved as {filename}.html") + except IOError as e: + raise IOError(f"Failed to save flow visualization to {filename}.html: {str(e)}") + + except (ValueError, RuntimeError, IOError) as e: + raise e + except Exception as e: + raise RuntimeError(f"Unexpected error during flow visualization: {str(e)}") + finally: + self._cleanup_pyvis_lib() def _generate_final_html(self, network_html): - # Extract just the body content from the generated HTML - current_dir = os.path.dirname(__file__) - template_path = os.path.join( - current_dir, "assets", "crewai_flow_visual_template.html" - ) - logo_path = os.path.join(current_dir, "assets", "crewai_logo.svg") + """ + Generate the final HTML content with network visualization and legend. - html_handler = HTMLTemplateHandler(template_path, logo_path) - network_body = html_handler.extract_body_content(network_html) + Parameters + ---------- + network_html : str + HTML content generated by pyvis Network. - # Generate the legend items HTML - legend_items = get_legend_items(self.colors) - legend_items_html = generate_legend_items_html(legend_items) - final_html_content = html_handler.generate_final_html( - network_body, legend_items_html - ) - return final_html_content + Returns + ------- + str + Complete HTML content with styling and legend. + + Raises + ------ + IOError + If template or logo files cannot be accessed. + ValueError + If network_html is invalid. + """ + if not network_html: + raise ValueError("Invalid network HTML content") + + try: + # Extract just the body content from the generated HTML + current_dir = os.path.dirname(__file__) + template_path = safe_path_join("assets", "crewai_flow_visual_template.html", root=current_dir) + logo_path = safe_path_join("assets", "crewai_logo.svg", root=current_dir) + + if not os.path.exists(template_path): + raise IOError(f"Template file not found: {template_path}") + if not os.path.exists(logo_path): + raise IOError(f"Logo file not found: {logo_path}") + + html_handler = HTMLTemplateHandler(template_path, logo_path) + network_body = html_handler.extract_body_content(network_html) + + # Generate the legend items HTML + legend_items = get_legend_items(self.colors) + legend_items_html = generate_legend_items_html(legend_items) + final_html_content = html_handler.generate_final_html( + network_body, legend_items_html + ) + return final_html_content + except Exception as e: + raise IOError(f"Failed to generate visualization HTML: {str(e)}") def _cleanup_pyvis_lib(self): - # Clean up the generated lib folder - lib_folder = os.path.join(os.getcwd(), "lib") + """ + Clean up the generated lib folder from pyvis. + + This method safely removes the temporary lib directory created by pyvis + during network visualization generation. + """ try: + lib_folder = safe_path_join("lib", root=os.getcwd()) if os.path.exists(lib_folder) and os.path.isdir(lib_folder): import shutil - shutil.rmtree(lib_folder) + except ValueError as e: + print(f"Error validating lib folder path: {e}") except Exception as e: - print(f"Error cleaning up {lib_folder}: {e}") + print(f"Error cleaning up lib folder: {e}") def plot_flow(flow, filename="flow_plot"): + """ + Convenience function to create and save a flow visualization. + + Parameters + ---------- + flow : Flow + Flow instance to visualize. + filename : str, optional + Output filename without extension, by default "flow_plot". + + Raises + ------ + ValueError + If flow object or filename is invalid. + IOError + If file operations fail. + """ visualizer = FlowPlot(flow) visualizer.plot(filename) diff --git a/src/crewai/flow/html_template_handler.py b/src/crewai/flow/html_template_handler.py index d521d8cf8..f0d2d89ad 100644 --- a/src/crewai/flow/html_template_handler.py +++ b/src/crewai/flow/html_template_handler.py @@ -1,26 +1,53 @@ import base64 import re +from pathlib import Path + +from crewai.flow.path_utils import safe_path_join, validate_path_exists class HTMLTemplateHandler: + """Handles HTML template processing and generation for flow visualization diagrams.""" + def __init__(self, template_path, logo_path): - self.template_path = template_path - self.logo_path = logo_path + """ + Initialize HTMLTemplateHandler with validated template and logo paths. + + Parameters + ---------- + template_path : str + Path to the HTML template file. + logo_path : str + Path to the logo image file. + + Raises + ------ + ValueError + If template or logo paths are invalid or files don't exist. + """ + try: + self.template_path = validate_path_exists(template_path, "file") + self.logo_path = validate_path_exists(logo_path, "file") + except ValueError as e: + raise ValueError(f"Invalid template or logo path: {e}") def read_template(self): + """Read and return the HTML template file contents.""" with open(self.template_path, "r", encoding="utf-8") as f: return f.read() def encode_logo(self): + """Convert the logo SVG file to base64 encoded string.""" with open(self.logo_path, "rb") as logo_file: logo_svg_data = logo_file.read() return base64.b64encode(logo_svg_data).decode("utf-8") def extract_body_content(self, html): + """Extract and return content between body tags from HTML string.""" match = re.search("(.*?)", html, re.DOTALL) return match.group(1) if match else "" def generate_legend_items_html(self, legend_items): + """Generate HTML markup for the legend items.""" legend_items_html = "" for item in legend_items: if "border" in item: @@ -48,6 +75,7 @@ class HTMLTemplateHandler: return legend_items_html def generate_final_html(self, network_body, legend_items_html, title="Flow Plot"): + """Combine all components into final HTML document with network visualization.""" html_template = self.read_template() logo_svg_base64 = self.encode_logo() diff --git a/src/crewai/flow/legend_generator.py b/src/crewai/flow/legend_generator.py index fb3d5cfd6..f250dec20 100644 --- a/src/crewai/flow/legend_generator.py +++ b/src/crewai/flow/legend_generator.py @@ -1,3 +1,4 @@ + def get_legend_items(colors): return [ {"label": "Start Method", "color": colors["start"]}, diff --git a/src/crewai/flow/path_utils.py b/src/crewai/flow/path_utils.py new file mode 100644 index 000000000..09ae8cd3d --- /dev/null +++ b/src/crewai/flow/path_utils.py @@ -0,0 +1,135 @@ +""" +Path utilities for secure file operations in CrewAI flow module. + +This module provides utilities for secure path handling to prevent directory +traversal attacks and ensure paths remain within allowed boundaries. +""" + +import os +from pathlib import Path +from typing import List, Union + + +def safe_path_join(*parts: str, root: Union[str, Path, None] = None) -> str: + """ + Safely join path components and ensure the result is within allowed boundaries. + + Parameters + ---------- + *parts : str + Variable number of path components to join. + root : Union[str, Path, None], optional + Root directory to use as base. If None, uses current working directory. + + Returns + ------- + str + String representation of the resolved path. + + Raises + ------ + ValueError + If the resulting path would be outside the root directory + or if any path component is invalid. + """ + if not parts: + raise ValueError("No path components provided") + + try: + # Convert all parts to strings and clean them + clean_parts = [str(part).strip() for part in parts if part] + if not clean_parts: + raise ValueError("No valid path components provided") + + # Establish root directory + root_path = Path(root).resolve() if root else Path.cwd() + + # Join and resolve the full path + full_path = Path(root_path, *clean_parts).resolve() + + # Check if the resolved path is within root + if not str(full_path).startswith(str(root_path)): + raise ValueError( + f"Invalid path: Potential directory traversal. Path must be within {root_path}" + ) + + return str(full_path) + + except Exception as e: + if isinstance(e, ValueError): + raise + raise ValueError(f"Invalid path components: {str(e)}") + + +def validate_path_exists(path: Union[str, Path], file_type: str = "file") -> str: + """ + Validate that a path exists and is of the expected type. + + Parameters + ---------- + path : Union[str, Path] + Path to validate. + file_type : str, optional + Expected type ('file' or 'directory'), by default 'file'. + + Returns + ------- + str + Validated path as string. + + Raises + ------ + ValueError + If path doesn't exist or is not of expected type. + """ + try: + path_obj = Path(path).resolve() + + if not path_obj.exists(): + raise ValueError(f"Path does not exist: {path}") + + if file_type == "file" and not path_obj.is_file(): + raise ValueError(f"Path is not a file: {path}") + elif file_type == "directory" and not path_obj.is_dir(): + raise ValueError(f"Path is not a directory: {path}") + + return str(path_obj) + + except Exception as e: + if isinstance(e, ValueError): + raise + raise ValueError(f"Invalid path: {str(e)}") + + +def list_files(directory: Union[str, Path], pattern: str = "*") -> List[str]: + """ + Safely list files in a directory matching a pattern. + + Parameters + ---------- + directory : Union[str, Path] + Directory to search in. + pattern : str, optional + Glob pattern to match files against, by default "*". + + Returns + ------- + List[str] + List of matching file paths. + + Raises + ------ + ValueError + If directory is invalid or inaccessible. + """ + try: + dir_path = Path(directory).resolve() + if not dir_path.is_dir(): + raise ValueError(f"Not a directory: {directory}") + + return [str(p) for p in dir_path.glob(pattern) if p.is_file()] + + except Exception as e: + if isinstance(e, ValueError): + raise + raise ValueError(f"Error listing files: {str(e)}") diff --git a/src/crewai/flow/utils.py b/src/crewai/flow/utils.py index dc1f611fb..c0686222f 100644 --- a/src/crewai/flow/utils.py +++ b/src/crewai/flow/utils.py @@ -1,9 +1,25 @@ +""" +Utility functions for flow visualization and dependency analysis. + +This module provides core functionality for analyzing and manipulating flow structures, +including node level calculation, ancestor tracking, and return value analysis. +Functions in this module are primarily used by the visualization system to create +accurate and informative flow diagrams. + +Example +------- +>>> flow = Flow() +>>> node_levels = calculate_node_levels(flow) +>>> ancestors = build_ancestor_dict(flow) +""" + import ast import inspect import textwrap +from typing import Any, Dict, List, Optional, Set, Union -def get_possible_return_constants(function): +def get_possible_return_constants(function: Any) -> Optional[List[str]]: try: source = inspect.getsource(function) except OSError: @@ -77,11 +93,34 @@ def get_possible_return_constants(function): return list(return_values) if return_values else None -def calculate_node_levels(flow): - levels = {} - queue = [] - visited = set() - pending_and_listeners = {} +def calculate_node_levels(flow: Any) -> Dict[str, int]: + """ + Calculate the hierarchical level of each node in the flow. + + Performs a breadth-first traversal of the flow graph to assign levels + to nodes, starting with start methods at level 0. + + Parameters + ---------- + flow : Any + The flow instance containing methods, listeners, and router configurations. + + Returns + ------- + Dict[str, int] + Dictionary mapping method names to their hierarchical levels. + + Notes + ----- + - Start methods are assigned level 0 + - Each subsequent connected node is assigned level = parent_level + 1 + - Handles both OR and AND conditions for listeners + - Processes router paths separately + """ + levels: Dict[str, int] = {} + queue: List[str] = [] + visited: Set[str] = set() + pending_and_listeners: Dict[str, Set[str]] = {} # Make all start methods at level 0 for method_name, method in flow._methods.items(): @@ -140,7 +179,20 @@ def calculate_node_levels(flow): return levels -def count_outgoing_edges(flow): +def count_outgoing_edges(flow: Any) -> Dict[str, int]: + """ + Count the number of outgoing edges for each method in the flow. + + Parameters + ---------- + flow : Any + The flow instance to analyze. + + Returns + ------- + Dict[str, int] + Dictionary mapping method names to their outgoing edge count. + """ counts = {} for method_name in flow._methods: counts[method_name] = 0 @@ -152,16 +204,53 @@ def count_outgoing_edges(flow): return counts -def build_ancestor_dict(flow): - ancestors = {node: set() for node in flow._methods} - visited = set() +def build_ancestor_dict(flow: Any) -> Dict[str, Set[str]]: + """ + Build a dictionary mapping each node to its ancestor nodes. + + Parameters + ---------- + flow : Any + The flow instance to analyze. + + Returns + ------- + Dict[str, Set[str]] + Dictionary mapping each node to a set of its ancestor nodes. + """ + ancestors: Dict[str, Set[str]] = {node: set() for node in flow._methods} + visited: Set[str] = set() for node in flow._methods: if node not in visited: dfs_ancestors(node, ancestors, visited, flow) return ancestors -def dfs_ancestors(node, ancestors, visited, flow): +def dfs_ancestors( + node: str, + ancestors: Dict[str, Set[str]], + visited: Set[str], + flow: Any +) -> None: + """ + Perform depth-first search to build ancestor relationships. + + Parameters + ---------- + node : str + Current node being processed. + ancestors : Dict[str, Set[str]] + Dictionary tracking ancestor relationships. + visited : Set[str] + Set of already visited nodes. + flow : Any + The flow instance being analyzed. + + Notes + ----- + This function modifies the ancestors dictionary in-place to build + the complete ancestor graph. + """ if node in visited: return visited.add(node) @@ -185,12 +274,48 @@ def dfs_ancestors(node, ancestors, visited, flow): dfs_ancestors(listener_name, ancestors, visited, flow) -def is_ancestor(node, ancestor_candidate, ancestors): +def is_ancestor(node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]) -> bool: + """ + Check if one node is an ancestor of another. + + Parameters + ---------- + node : str + The node to check ancestors for. + ancestor_candidate : str + The potential ancestor node. + ancestors : Dict[str, Set[str]] + Dictionary containing ancestor relationships. + + Returns + ------- + bool + True if ancestor_candidate is an ancestor of node, False otherwise. + """ return ancestor_candidate in ancestors.get(node, set()) -def build_parent_children_dict(flow): - parent_children = {} +def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]: + """ + Build a dictionary mapping parent nodes to their children. + + Parameters + ---------- + flow : Any + The flow instance to analyze. + + Returns + ------- + Dict[str, List[str]] + Dictionary mapping parent method names to lists of their child method names. + + Notes + ----- + - Maps listeners to their trigger methods + - Maps router methods to their paths and listeners + - Children lists are sorted for consistent ordering + """ + parent_children: Dict[str, List[str]] = {} # Map listeners to their trigger methods for listener_name, (_, trigger_methods) in flow._listeners.items(): @@ -214,7 +339,24 @@ def build_parent_children_dict(flow): return parent_children -def get_child_index(parent, child, parent_children): +def get_child_index(parent: str, child: str, parent_children: Dict[str, List[str]]) -> int: + """ + Get the index of a child node in its parent's sorted children list. + + Parameters + ---------- + parent : str + The parent node name. + child : str + The child node name to find the index for. + parent_children : Dict[str, List[str]] + Dictionary mapping parents to their children lists. + + Returns + ------- + int + Zero-based index of the child in its parent's sorted children list. + """ children = parent_children.get(parent, []) children.sort() return children.index(child) diff --git a/src/crewai/flow/visualization_utils.py b/src/crewai/flow/visualization_utils.py index 321f63344..781677276 100644 --- a/src/crewai/flow/visualization_utils.py +++ b/src/crewai/flow/visualization_utils.py @@ -1,5 +1,23 @@ +""" +Utilities for creating visual representations of flow structures. + +This module provides functions for generating network visualizations of flows, +including node placement, edge creation, and visual styling. It handles the +conversion of flow structures into visual network graphs with appropriate +styling and layout. + +Example +------- +>>> flow = Flow() +>>> net = Network(directed=True) +>>> node_positions = compute_positions(flow, node_levels) +>>> add_nodes_to_network(net, flow, node_positions, node_styles) +>>> add_edges(net, flow, node_positions, colors) +""" + import ast import inspect +from typing import Any, Dict, List, Optional, Tuple, Union from .utils import ( build_ancestor_dict, @@ -9,8 +27,25 @@ from .utils import ( ) -def method_calls_crew(method): - """Check if the method calls `.crew()`.""" +def method_calls_crew(method: Any) -> bool: + """ + Check if the method contains a call to `.crew()`. + + Parameters + ---------- + method : Any + The method to analyze for crew() calls. + + Returns + ------- + bool + True if the method calls .crew(), False otherwise. + + Notes + ----- + Uses AST analysis to detect method calls, specifically looking for + attribute access of 'crew'. + """ try: source = inspect.getsource(method) source = inspect.cleandoc(source) @@ -20,6 +55,7 @@ def method_calls_crew(method): return False class CrewCallVisitor(ast.NodeVisitor): + """AST visitor to detect .crew() method calls.""" def __init__(self): self.found = False @@ -34,7 +70,34 @@ def method_calls_crew(method): return visitor.found -def add_nodes_to_network(net, flow, node_positions, node_styles): +def add_nodes_to_network( + net: Any, + flow: Any, + node_positions: Dict[str, Tuple[float, float]], + node_styles: Dict[str, Dict[str, Any]] +) -> None: + """ + Add nodes to the network visualization with appropriate styling. + + Parameters + ---------- + net : Any + The pyvis Network instance to add nodes to. + flow : Any + The flow instance containing method information. + node_positions : Dict[str, Tuple[float, float]] + Dictionary mapping node names to their (x, y) positions. + node_styles : Dict[str, Dict[str, Any]] + Dictionary containing style configurations for different node types. + + Notes + ----- + Node types include: + - Start methods + - Router methods + - Crew methods + - Regular methods + """ def human_friendly_label(method_name): return method_name.replace("_", " ").title() @@ -73,9 +136,33 @@ def add_nodes_to_network(net, flow, node_positions, node_styles): ) -def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150): - level_nodes = {} - node_positions = {} +def compute_positions( + flow: Any, + node_levels: Dict[str, int], + y_spacing: float = 150, + x_spacing: float = 150 +) -> Dict[str, Tuple[float, float]]: + """ + Compute the (x, y) positions for each node in the flow graph. + + Parameters + ---------- + flow : Any + The flow instance to compute positions for. + node_levels : Dict[str, int] + Dictionary mapping node names to their hierarchical levels. + y_spacing : float, optional + Vertical spacing between levels, by default 150. + x_spacing : float, optional + Horizontal spacing between nodes, by default 150. + + Returns + ------- + Dict[str, Tuple[float, float]] + Dictionary mapping node names to their (x, y) coordinates. + """ + level_nodes: Dict[int, List[str]] = {} + node_positions: Dict[str, Tuple[float, float]] = {} for method_name, level in node_levels.items(): level_nodes.setdefault(level, []).append(method_name) @@ -90,7 +177,33 @@ def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150): return node_positions -def add_edges(net, flow, node_positions, colors): +def add_edges( + net: Any, + flow: Any, + node_positions: Dict[str, Tuple[float, float]], + colors: Dict[str, str] +) -> None: + edge_smooth: Dict[str, Union[str, float]] = {"type": "continuous"} # Default value + """ + Add edges to the network visualization with appropriate styling. + + Parameters + ---------- + net : Any + The pyvis Network instance to add edges to. + flow : Any + The flow instance containing edge information. + node_positions : Dict[str, Tuple[float, float]] + Dictionary mapping node names to their positions. + colors : Dict[str, str] + Dictionary mapping edge types to their colors. + + Notes + ----- + - Handles both normal listener edges and router edges + - Applies appropriate styling (color, dashes) based on edge type + - Adds curvature to edges when needed (cycles or multiple children) + """ ancestors = build_ancestor_dict(flow) parent_children = build_parent_children_dict(flow) @@ -126,7 +239,7 @@ def add_edges(net, flow, node_positions, colors): else: edge_smooth = {"type": "cubicBezier"} else: - edge_smooth = False + edge_smooth.update({"type": "continuous"}) edge_style = { "color": edge_color, @@ -189,7 +302,7 @@ def add_edges(net, flow, node_positions, colors): else: edge_smooth = {"type": "cubicBezier"} else: - edge_smooth = False + edge_smooth.update({"type": "continuous"}) edge_style = { "color": colors["router_edge"], diff --git a/src/crewai/knowledge/source/crew_docling_source.py b/src/crewai/knowledge/source/crew_docling_source.py index 8b197168b..bbfcf9b92 100644 --- a/src/crewai/knowledge/source/crew_docling_source.py +++ b/src/crewai/knowledge/source/crew_docling_source.py @@ -2,11 +2,16 @@ from pathlib import Path from typing import Iterator, List, Optional, Union from urllib.parse import urlparse -from docling.datamodel.base_models import InputFormat -from docling.document_converter import DocumentConverter -from docling.exceptions import ConversionError -from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker -from docling_core.types.doc.document import DoclingDocument +try: + from docling.datamodel.base_models import InputFormat + from docling.document_converter import DocumentConverter + from docling.exceptions import ConversionError + from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker + from docling_core.types.doc.document import DoclingDocument + DOCLING_AVAILABLE = True +except ImportError: + DOCLING_AVAILABLE = False + from pydantic import Field from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource @@ -19,6 +24,14 @@ class CrewDoclingSource(BaseKnowledgeSource): This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth. """ + def __init__(self, *args, **kwargs): + if not DOCLING_AVAILABLE: + raise ImportError( + "The docling package is required to use CrewDoclingSource. " + "Please install it using: uv add docling" + ) + super().__init__(*args, **kwargs) + _logger: Logger = Logger(verbose=True) file_path: Optional[List[Union[Path, str]]] = Field(default=None) diff --git a/src/crewai/llm.py b/src/crewai/llm.py index 5d6a0ccf5..bb1167bd8 100644 --- a/src/crewai/llm.py +++ b/src/crewai/llm.py @@ -4,10 +4,13 @@ import sys import threading import warnings from contextlib import contextmanager +from importlib import resources from typing import Any, Dict, List, Optional, Union -import litellm -from litellm import get_supported_openai_params +with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + import litellm + from litellm import get_supported_openai_params from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, @@ -76,6 +79,7 @@ CONTEXT_WINDOW_USAGE_RATIO = 0.75 def suppress_warnings(): with warnings.catch_warnings(): warnings.filterwarnings("ignore") + warnings.filterwarnings("ignore", message="open_text is deprecated*", category=DeprecationWarning) # Redirect stdout and stderr old_stdout = sys.stdout @@ -138,7 +142,7 @@ class LLM: self.kwargs = kwargs litellm.drop_params = True - litellm.set_verbose = False + self.set_callbacks(callbacks) self.set_env_callbacks() @@ -214,16 +218,17 @@ class LLM: 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) + with suppress_warnings(): + 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) + for callback in litellm._async_success_callback[:]: + if type(callback) in callback_types: + litellm._async_success_callback.remove(callback) - litellm.callbacks = callbacks + litellm.callbacks = callbacks def set_env_callbacks(self): """ @@ -244,19 +249,20 @@ class LLM: 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(",") - ] + with suppress_warnings(): + 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(",") - ] + 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 + litellm.success_callback = success_callbacks + litellm.failure_callback = failure_callbacks diff --git a/src/crewai/project/annotations.py b/src/crewai/project/annotations.py index bf0051c4d..d7c636ccf 100644 --- a/src/crewai/project/annotations.py +++ b/src/crewai/project/annotations.py @@ -4,18 +4,23 @@ from typing import Callable from crewai import Crew from crewai.project.utils import memoize +"""Decorators for defining crew components and their behaviors.""" + def before_kickoff(func): + """Marks a method to execute before crew kickoff.""" func.is_before_kickoff = True return func def after_kickoff(func): + """Marks a method to execute after crew kickoff.""" func.is_after_kickoff = True return func def task(func): + """Marks a method as a crew task.""" func.is_task = True @wraps(func) @@ -29,43 +34,51 @@ def task(func): def agent(func): + """Marks a method as a crew agent.""" func.is_agent = True func = memoize(func) return func def llm(func): + """Marks a method as an LLM provider.""" func.is_llm = True func = memoize(func) return func def output_json(cls): + """Marks a class as JSON output format.""" cls.is_output_json = True return cls def output_pydantic(cls): + """Marks a class as Pydantic output format.""" cls.is_output_pydantic = True return cls def tool(func): + """Marks a method as a crew tool.""" func.is_tool = True return memoize(func) def callback(func): + """Marks a method as a crew callback.""" func.is_callback = True return memoize(func) def cache_handler(func): + """Marks a method as a cache handler.""" func.is_cache_handler = True return memoize(func) def crew(func) -> Callable[..., Crew]: + """Marks a method as the main crew execution point.""" @wraps(func) def wrapper(self, *args, **kwargs) -> Crew: diff --git a/src/crewai/project/crew_base.py b/src/crewai/project/crew_base.py index 0b43882f2..d4f2286fa 100644 --- a/src/crewai/project/crew_base.py +++ b/src/crewai/project/crew_base.py @@ -9,8 +9,10 @@ load_dotenv() T = TypeVar("T", bound=type) +"""Base decorator for creating crew classes with configuration and function management.""" def CrewBase(cls: T) -> T: + """Wraps a class with crew functionality and configuration management.""" class WrappedClass(cls): # type: ignore is_crew_class: bool = True # type: ignore @@ -216,5 +218,5 @@ def CrewBase(cls: T) -> T: # 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/task.py b/src/crewai/task.py index 30ab79c00..78538ebee 100644 --- a/src/crewai/task.py +++ b/src/crewai/task.py @@ -41,6 +41,7 @@ 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 +from crewai.utilities.printer import Printer class Task(BaseModel): @@ -127,38 +128,40 @@ class Task(BaseModel): processed_by_agents: Set[str] = Field(default_factory=set) guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field( default=None, - description="Function to validate task output before proceeding to next task" + description="Function to validate task output before proceeding to next task", ) max_retries: int = Field( - default=3, - description="Maximum number of retries when guardrail fails" + default=3, description="Maximum number of retries when guardrail fails" ) - retry_count: int = Field( - default=0, - description="Current number of retries" + retry_count: int = Field(default=0, description="Current number of retries") + start_time: Optional[datetime.datetime] = Field( + default=None, description="Start time of the task execution" + ) + end_time: Optional[datetime.datetime] = Field( + default=None, description="End time of the task execution" ) @field_validator("guardrail") @classmethod def validate_guardrail_function(cls, v: Optional[Callable]) -> Optional[Callable]: """Validate that the guardrail function has the correct signature and behavior. - + While type hints provide static checking, this validator ensures runtime safety by: 1. Verifying the function accepts exactly one parameter (the TaskOutput) 2. Checking return type annotations match Tuple[bool, Any] if present 3. Providing clear, immediate error messages for debugging - + This runtime validation is crucial because: - Type hints are optional and can be ignored at runtime - Function signatures need immediate validation before task execution - Clear error messages help users debug guardrail implementation issues - + Args: v: The guardrail function to validate - + Returns: The validated guardrail function - + Raises: ValueError: If the function signature is invalid or return annotation doesn't match Tuple[bool, Any] @@ -171,16 +174,21 @@ class Task(BaseModel): # Check return annotation if present, but don't require it return_annotation = sig.return_annotation if return_annotation != inspect.Signature.empty: - if not (return_annotation == Tuple[bool, Any] or str(return_annotation) == 'Tuple[bool, Any]'): - raise ValueError("If return type is annotated, it must be Tuple[bool, Any]") + if not ( + return_annotation == Tuple[bool, Any] + or str(return_annotation) == "Tuple[bool, Any]" + ): + raise ValueError( + "If return type is annotated, it must be Tuple[bool, Any]" + ) return v _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry) _execution_span: Optional[Span] = PrivateAttr(default=None) _original_description: Optional[str] = PrivateAttr(default=None) _original_expected_output: Optional[str] = PrivateAttr(default=None) + _original_output_file: Optional[str] = PrivateAttr(default=None) _thread: Optional[threading.Thread] = PrivateAttr(default=None) - _execution_time: Optional[float] = PrivateAttr(default=None) @model_validator(mode="before") @classmethod @@ -205,16 +213,54 @@ class Task(BaseModel): "may_not_set_field", "This field is not to be set by the user.", {} ) - def _set_start_execution_time(self) -> float: - return datetime.datetime.now().timestamp() - - def _set_end_execution_time(self, start_time: float) -> None: - self._execution_time = datetime.datetime.now().timestamp() - start_time - @field_validator("output_file") @classmethod - def output_file_validation(cls, value: str) -> str: - """Validate the output file path by removing the / from the beginning of the path.""" + def output_file_validation(cls, value: Optional[str]) -> Optional[str]: + """Validate the output file path. + + Args: + value: The output file path to validate. Can be None or a string. + If the path contains template variables (e.g. {var}), leading slashes are preserved. + For regular paths, leading slashes are stripped. + + Returns: + The validated and potentially modified path, or None if no path was provided. + + Raises: + ValueError: If the path contains invalid characters, path traversal attempts, + or other security concerns. + """ + if value is None: + return None + + # Basic security checks + if ".." in value: + raise ValueError( + "Path traversal attempts are not allowed in output_file paths" + ) + + # Check for shell expansion first + if value.startswith("~") or value.startswith("$"): + raise ValueError( + "Shell expansion characters are not allowed in output_file paths" + ) + + # Then check other shell special characters + if any(char in value for char in ["|", ">", "<", "&", ";"]): + raise ValueError( + "Shell special characters are not allowed in output_file paths" + ) + + # Don't strip leading slash if it's a template path with variables + if "{" in value or "}" in value: + # Validate template variable format + template_vars = [part.split("}")[0] for part in value.split("{")[1:]] + for var in template_vars: + if not var.isidentifier(): + raise ValueError(f"Invalid template variable name: {var}") + return value + + # Strip leading slash for regular paths if value.startswith("/"): return value[1:] return value @@ -263,6 +309,12 @@ class Task(BaseModel): return md5("|".join(source).encode(), usedforsecurity=False).hexdigest() + @property + def execution_duration(self) -> float | None: + if not self.start_time or not self.end_time: + return None + return (self.end_time - self.start_time).total_seconds() + def execute_async( self, agent: BaseAgent | None = None, @@ -303,7 +355,7 @@ class Task(BaseModel): f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical." ) - start_time = self._set_start_execution_time() + self.start_time = datetime.datetime.now() self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self) self.prompt_context = context @@ -339,10 +391,14 @@ class Task(BaseModel): ) self.retry_count += 1 - context = ( - f"### Previous attempt failed validation: {guardrail_result.error}\n\n\n" - f"### Previous result:\n{task_output.raw}\n\n\n" - "Try again, making sure to address the validation error." + context = self.i18n.errors("validation_error").format( + guardrail_result_error=guardrail_result.error, + task_output=task_output.raw + ) + printer = Printer() + printer.print( + content=f"Guardrail blocked, retrying, due to:{guardrail_result.error}\n", + color="yellow", ) return self._execute_core(agent, context, tools) @@ -353,15 +409,17 @@ class Task(BaseModel): if isinstance(guardrail_result.result, str): task_output.raw = guardrail_result.result - pydantic_output, json_output = self._export_output(guardrail_result.result) + pydantic_output, json_output = self._export_output( + guardrail_result.result + ) task_output.pydantic = pydantic_output task_output.json_dict = json_output elif isinstance(guardrail_result.result, TaskOutput): task_output = guardrail_result.result self.output = task_output + self.end_time = datetime.datetime.now() - self._set_end_execution_time(start_time) if self.callback: self.callback(self.output) @@ -373,7 +431,9 @@ class Task(BaseModel): content = ( json_output if json_output - else pydantic_output.model_dump_json() if pydantic_output else result + else pydantic_output.model_dump_json() + if pydantic_output + else result ) self._save_file(content) @@ -393,27 +453,101 @@ class Task(BaseModel): tasks_slices = [self.description, output] return "\n".join(tasks_slices) - def interpolate_inputs(self, inputs: Dict[str, Any]) -> None: - """Interpolate inputs into the task description and expected output.""" + def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float]]) -> None: + """Interpolate inputs into the task description, expected output, and output file path. + + Args: + inputs: Dictionary mapping template variables to their values. + Supported value types are strings, integers, and floats. + + Raises: + ValueError: If a required template variable is missing from inputs. + """ if self._original_description is None: self._original_description = self.description if self._original_expected_output is None: self._original_expected_output = self.expected_output + if self.output_file is not None and self._original_output_file is None: + self._original_output_file = self.output_file - if inputs: + if not inputs: + return + + try: self.description = self._original_description.format(**inputs) + except KeyError as e: + raise ValueError( + f"Missing required template variable '{e.args[0]}' in description" + ) from e + except ValueError as e: + raise ValueError(f"Error interpolating description: {str(e)}") from e + + try: self.expected_output = self.interpolate_only( input_string=self._original_expected_output, inputs=inputs ) + except (KeyError, ValueError) as e: + raise ValueError(f"Error interpolating expected_output: {str(e)}") from e - def interpolate_only(self, input_string: str, inputs: Dict[str, Any]) -> str: - """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.""" - escaped_string = input_string.replace("{", "{{").replace("}", "}}") + if self.output_file is not None: + try: + self.output_file = self.interpolate_only( + input_string=self._original_output_file, inputs=inputs + ) + except (KeyError, ValueError) as e: + raise ValueError( + f"Error interpolating output_file path: {str(e)}" + ) from e - for key in inputs.keys(): - escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}") + def interpolate_only( + self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]] + ) -> str: + """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched. - return escaped_string.format(**inputs) + Args: + input_string: The string containing template variables to interpolate. + Can be None or empty, in which case an empty string is returned. + inputs: Dictionary mapping template variables to their values. + Supported value types are strings, integers, and floats. + If input_string is empty or has no placeholders, inputs can be empty. + + Returns: + The interpolated string with all template variables replaced with their values. + Empty string if input_string is None or empty. + + Raises: + ValueError: If a required template variable is missing from inputs. + KeyError: If a template variable is not found in the inputs dictionary. + """ + if input_string is None or not input_string: + return "" + if "{" not in input_string and "}" not in input_string: + return input_string + if not inputs: + raise ValueError( + "Inputs dictionary cannot be empty when interpolating variables" + ) + + try: + # Validate input types + for key, value in inputs.items(): + if not isinstance(value, (str, int, float)): + raise ValueError( + f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}" + ) + + escaped_string = input_string.replace("{", "{{").replace("}", "}}") + + for key in inputs.keys(): + escaped_string = escaped_string.replace(f"{{{{{key}}}}}", f"{{{key}}}") + + return escaped_string.format(**inputs) + except KeyError as e: + raise KeyError( + f"Template variable '{e.args[0]}' not found in inputs dictionary" + ) from e + except ValueError as e: + raise ValueError(f"Error during string interpolation: {str(e)}") from e def increment_tools_errors(self) -> None: """Increment the tools errors counter.""" @@ -496,10 +630,10 @@ class Task(BaseModel): def _save_file(self, result: Any) -> None: """Save task output to a file. - + Args: result: The result to save to the file. Can be a dict or any stringifiable object. - + Raises: ValueError: If output_file is not set RuntimeError: If there is an error writing to the file @@ -517,6 +651,7 @@ class Task(BaseModel): with resolved_path.open("w", encoding="utf-8") as file: if isinstance(result, dict): import json + json.dump(result, file, ensure_ascii=False, indent=2) else: file.write(str(result)) diff --git a/src/crewai/tools/agent_tools/base_agent_tools.py b/src/crewai/tools/agent_tools/base_agent_tools.py index ea63dd51e..b00fbb7b5 100644 --- a/src/crewai/tools/agent_tools/base_agent_tools.py +++ b/src/crewai/tools/agent_tools/base_agent_tools.py @@ -1,4 +1,5 @@ -from typing import Optional, Union +import logging +from typing import Optional from pydantic import Field @@ -7,6 +8,8 @@ from crewai.task import Task from crewai.tools.base_tool import BaseTool from crewai.utilities import I18N +logger = logging.getLogger(__name__) + class BaseAgentTool(BaseTool): """Base class for agent-related tools""" @@ -16,6 +19,25 @@ class BaseAgentTool(BaseTool): default_factory=I18N, description="Internationalization settings" ) + def sanitize_agent_name(self, name: str) -> str: + """ + Sanitize agent role name by normalizing whitespace and setting to lowercase. + Converts all whitespace (including newlines) to single spaces and removes quotes. + + Args: + name (str): The agent role name to sanitize + + Returns: + str: The sanitized agent role name, with whitespace normalized, + converted to lowercase, and quotes removed + """ + if not name: + return "" + # Normalize all whitespace (including newlines) to single spaces + normalized = " ".join(name.split()) + # Remove quotes and convert to lowercase + return normalized.replace('"', "").casefold() + def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]: coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker") if coworker: @@ -25,11 +47,27 @@ class BaseAgentTool(BaseTool): return coworker def _execute( - self, agent_name: Union[str, None], task: str, context: Union[str, None] + self, + agent_name: Optional[str], + task: str, + context: Optional[str] = None ) -> str: + """ + Execute delegation to an agent with case-insensitive and whitespace-tolerant matching. + + Args: + agent_name: Name/role of the agent to delegate to (case-insensitive) + task: The specific question or task to delegate + context: Optional additional context for the task execution + + Returns: + str: The execution result from the delegated agent or an error message + if the agent cannot be found + """ try: if agent_name is None: agent_name = "" + logger.debug("No agent name provided, using empty string") # It is important to remove the quotes from the agent name. # The reason we have to do this is because less-powerful LLM's @@ -38,31 +76,49 @@ class BaseAgentTool(BaseTool): # {"task": "....", "coworker": ".... # when it should look like this: # {"task": "....", "coworker": "...."} - agent_name = agent_name.casefold().replace('"', "").replace("\n", "") + sanitized_name = self.sanitize_agent_name(agent_name) + logger.debug(f"Sanitized agent name from '{agent_name}' to '{sanitized_name}'") + + available_agents = [agent.role for agent in self.agents] + logger.debug(f"Available agents: {available_agents}") + 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 + if self.sanitize_agent_name(available_agent.role) == sanitized_name ] - except Exception as _: + logger.debug(f"Found {len(agent)} matching agents for role '{sanitized_name}'") + except (AttributeError, ValueError) as e: + # Handle specific exceptions that might occur during role name processing return self.i18n.errors("agent_tool_unexisting_coworker").format( coworkers="\n".join( - [f"- {agent.role.casefold()}" for agent in self.agents] - ) + [f"- {self.sanitize_agent_name(agent.role)}" for agent in self.agents] + ), + error=str(e) ) if not agent: + # No matching agent found after sanitization return self.i18n.errors("agent_tool_unexisting_coworker").format( coworkers="\n".join( - [f"- {agent.role.casefold()}" for agent in self.agents] - ) + [f"- {self.sanitize_agent_name(agent.role)}" for agent in self.agents] + ), + error=f"No agent found with role '{sanitized_name}'" ) agent = agent[0] - task_with_assigned_agent = Task( # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str") - description=task, - agent=agent, - expected_output=agent.i18n.slice("manager_request"), - i18n=agent.i18n, - ) - return agent.execute_task(task_with_assigned_agent, context) + try: + task_with_assigned_agent = Task( + description=task, + agent=agent, + expected_output=agent.i18n.slice("manager_request"), + i18n=agent.i18n, + ) + logger.debug(f"Created task for agent '{self.sanitize_agent_name(agent.role)}': {task}") + return agent.execute_task(task_with_assigned_agent, context) + except Exception as e: + # Handle task creation or execution errors + return self.i18n.errors("agent_tool_execution_error").format( + agent_role=self.sanitize_agent_name(agent.role), + error=str(e) + ) diff --git a/src/crewai/tools/base_tool.py b/src/crewai/tools/base_tool.py index c3840d23c..b3c0f997c 100644 --- a/src/crewai/tools/base_tool.py +++ b/src/crewai/tools/base_tool.py @@ -1,12 +1,23 @@ +import warnings 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, + ConfigDict, + Field, + PydanticDeprecatedSince20, + create_model, + validator, +) from pydantic import BaseModel as PydanticBaseModel from crewai.tools.structured_tool import CrewStructuredTool +# Ignore all "PydanticDeprecatedSince20" warnings globally +warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20) + class BaseTool(BaseModel, ABC): class _ArgsSchemaPlaceholder(PydanticBaseModel): diff --git a/src/crewai/translations/en.json b/src/crewai/translations/en.json index 12850c9e2..3cee10aa6 100644 --- a/src/crewai/translations/en.json +++ b/src/crewai/translations/en.json @@ -33,7 +33,9 @@ "tool_usage_error": "I encountered an error: {error}", "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.", "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.", - "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}" + "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts these inputs: {tool_inputs}", + "agent_tool_execution_error": "Error executing task with agent '{agent_role}'. Error: {error}", + "validation_error": "### Previous attempt failed validation: {guardrail_result_error}\n\n\n### Previous result:\n{task_output}\n\n\nTry again, making sure to address the validation error." }, "tools": { "delegate_work": "Delegate a specific task to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.", diff --git a/src/crewai/utilities/crew_json_encoder.py b/src/crewai/utilities/crew_json_encoder.py index 298c9681a..6e667431d 100644 --- a/src/crewai/utilities/crew_json_encoder.py +++ b/src/crewai/utilities/crew_json_encoder.py @@ -1,3 +1,5 @@ +"""JSON encoder for handling CrewAI specific types.""" + import json from datetime import date, datetime from decimal import Decimal @@ -8,6 +10,7 @@ from pydantic import BaseModel class CrewJSONEncoder(json.JSONEncoder): + """Custom JSON encoder for CrewAI objects and special types.""" def default(self, obj): if isinstance(obj, BaseModel): return self._handle_pydantic_model(obj) diff --git a/src/crewai/utilities/crew_pydantic_output_parser.py b/src/crewai/utilities/crew_pydantic_output_parser.py index c269f3189..d0dbfae06 100644 --- a/src/crewai/utilities/crew_pydantic_output_parser.py +++ b/src/crewai/utilities/crew_pydantic_output_parser.py @@ -6,9 +6,10 @@ from pydantic import BaseModel, ValidationError from crewai.agents.parser import OutputParserException +"""Parser for converting text outputs into Pydantic models.""" class CrewPydanticOutputParser: - """Parses the text into pydantic models""" + """Parses text outputs into specified Pydantic models.""" pydantic_object: Type[BaseModel] diff --git a/src/crewai/utilities/evaluators/crew_evaluator_handler.py b/src/crewai/utilities/evaluators/crew_evaluator_handler.py index 3387d91b3..ef9b908e1 100644 --- a/src/crewai/utilities/evaluators/crew_evaluator_handler.py +++ b/src/crewai/utilities/evaluators/crew_evaluator_handler.py @@ -180,12 +180,12 @@ class CrewEvaluator: self._test_result_span = self._telemetry.individual_test_result_span( self.crew, evaluation_result.pydantic.quality, - current_task._execution_time, + current_task.execution_duration, self.openai_model_name, ) self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality) self.run_execution_times[self.iteration].append( - current_task._execution_time + current_task.execution_duration ) else: raise ValueError("Evaluation result is not in the expected format") diff --git a/src/crewai/utilities/i18n.py b/src/crewai/utilities/i18n.py index ebf1abcda..f2540e455 100644 --- a/src/crewai/utilities/i18n.py +++ b/src/crewai/utilities/i18n.py @@ -4,8 +4,10 @@ from typing import Dict, Optional, Union from pydantic import BaseModel, Field, PrivateAttr, model_validator +"""Internationalization support for CrewAI prompts and messages.""" class I18N(BaseModel): + """Handles loading and retrieving internationalized prompts.""" _prompts: Dict[str, Dict[str, str]] = PrivateAttr() prompt_file: Optional[str] = Field( default=None, diff --git a/src/crewai/utilities/internal_instructor.py b/src/crewai/utilities/internal_instructor.py index 13fe5a19f..65a05a61f 100644 --- a/src/crewai/utilities/internal_instructor.py +++ b/src/crewai/utilities/internal_instructor.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Optional, Type @@ -25,14 +26,15 @@ class InternalInstructor: if self.agent and not self.llm: self.llm = self.agent.function_calling_llm or self.agent.llm - # Lazy import - import instructor - from litellm import completion + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + import instructor + from litellm import completion - self._client = instructor.from_litellm( - completion, - mode=instructor.Mode.TOOLS, - ) + self._client = instructor.from_litellm( + completion, + mode=instructor.Mode.TOOLS, + ) def to_json(self): model = self.to_pydantic() diff --git a/src/crewai/utilities/paths.py b/src/crewai/utilities/paths.py index 51cf8b4e4..9bf167ee6 100644 --- a/src/crewai/utilities/paths.py +++ b/src/crewai/utilities/paths.py @@ -3,8 +3,10 @@ from pathlib import Path import appdirs +"""Path management utilities for CrewAI storage and configuration.""" def db_storage_path(): + """Returns the path for database storage.""" app_name = get_project_directory_name() app_author = "CrewAI" @@ -14,6 +16,7 @@ def db_storage_path(): def get_project_directory_name(): + """Returns the current project directory name.""" project_directory_name = os.environ.get("CREWAI_STORAGE_DIR") if project_directory_name: diff --git a/src/crewai/utilities/planning_handler.py b/src/crewai/utilities/planning_handler.py index 590f42389..6ce74f236 100644 --- a/src/crewai/utilities/planning_handler.py +++ b/src/crewai/utilities/planning_handler.py @@ -1,3 +1,4 @@ +import logging from typing import Any, List, Optional from pydantic import BaseModel, Field @@ -5,8 +6,11 @@ from pydantic import BaseModel, Field from crewai.agent import Agent from crewai.task import Task +"""Handles planning and coordination of crew tasks.""" +logger = logging.getLogger(__name__) class PlanPerTask(BaseModel): + """Represents a plan for a specific task.""" task: str = Field(..., description="The task for which the plan is created") plan: str = Field( ..., @@ -15,6 +19,7 @@ class PlanPerTask(BaseModel): class PlannerTaskPydanticOutput(BaseModel): + """Output format for task planning results.""" list_of_plans_per_task: List[PlanPerTask] = Field( ..., description="Step by step plan on how the agents can execute their tasks using the available tools with mastery", @@ -22,6 +27,7 @@ class PlannerTaskPydanticOutput(BaseModel): class CrewPlanner: + """Plans and coordinates the execution of crew tasks.""" def __init__(self, tasks: List[Task], planning_agent_llm: Optional[Any] = None): self.tasks = tasks @@ -68,19 +74,39 @@ class CrewPlanner: output_pydantic=PlannerTaskPydanticOutput, ) + def _get_agent_knowledge(self, task: Task) -> List[str]: + """ + Safely retrieve knowledge source content from the task's agent. + + Args: + task: The task containing an agent with potential knowledge sources + + Returns: + List[str]: A list of knowledge source strings + """ + try: + if task.agent and task.agent.knowledge_sources: + return [source.content for source in task.agent.knowledge_sources] + except AttributeError: + logger.warning("Error accessing agent knowledge sources") + return [] + def _create_tasks_summary(self) -> str: """Creates a summary of all tasks.""" tasks_summary = [] for idx, task in enumerate(self.tasks): - tasks_summary.append( - f""" + knowledge_list = self._get_agent_knowledge(task) + task_summary = f""" Task Number {idx + 1} - {task.description} "task_description": {task.description} "task_expected_output": {task.expected_output} "agent": {task.agent.role if task.agent else "None"} "agent_goal": {task.agent.goal if task.agent else "None"} "task_tools": {task.tools} - "agent_tools": {task.agent.tools if task.agent else "None"} - """ - ) + "agent_tools": %s%s""" % ( + f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"', + f',\n "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else "" + ) + + tasks_summary.append(task_summary) return " ".join(tasks_summary) diff --git a/src/crewai/utilities/printer.py b/src/crewai/utilities/printer.py index edb339c29..abebf6aae 100644 --- a/src/crewai/utilities/printer.py +++ b/src/crewai/utilities/printer.py @@ -1,7 +1,11 @@ +"""Utility for colored console output.""" + from typing import Optional class Printer: + """Handles colored console output formatting.""" + def print(self, content: str, color: Optional[str] = None): if color == "purple": self._print_purple(content) diff --git a/src/crewai/utilities/rpm_controller.py b/src/crewai/utilities/rpm_controller.py index 5ee054c5f..f2d90615c 100644 --- a/src/crewai/utilities/rpm_controller.py +++ b/src/crewai/utilities/rpm_controller.py @@ -6,8 +6,10 @@ from pydantic import BaseModel, Field, PrivateAttr, model_validator from crewai.utilities.logger import Logger +"""Controls request rate limiting for API calls.""" class RPMController(BaseModel): + """Manages requests per minute limiting.""" max_rpm: Optional[int] = Field(default=None) logger: Logger = Field(default_factory=lambda: Logger(verbose=False)) _current_rpm: int = PrivateAttr(default=0) diff --git a/src/crewai/utilities/task_output_storage_handler.py b/src/crewai/utilities/task_output_storage_handler.py index 34cdaccbb..80e749bee 100644 --- a/src/crewai/utilities/task_output_storage_handler.py +++ b/src/crewai/utilities/task_output_storage_handler.py @@ -8,8 +8,10 @@ from crewai.memory.storage.kickoff_task_outputs_storage import ( ) from crewai.task import Task +"""Handles storage and retrieval of task execution outputs.""" class ExecutionLog(BaseModel): + """Represents a log entry for task execution.""" task_id: str expected_output: Optional[str] = None output: Dict[str, Any] @@ -22,6 +24,8 @@ class ExecutionLog(BaseModel): return getattr(self, key) +"""Manages storage and retrieval of task outputs.""" + class TaskOutputStorageHandler: def __init__(self) -> None: self.storage = KickoffTaskOutputsSQLiteStorage() diff --git a/src/crewai/utilities/token_counter_callback.py b/src/crewai/utilities/token_counter_callback.py index 06ad15022..46c7c68f9 100644 --- a/src/crewai/utilities/token_counter_callback.py +++ b/src/crewai/utilities/token_counter_callback.py @@ -1,3 +1,5 @@ +import warnings + from litellm.integrations.custom_logger import CustomLogger from litellm.types.utils import Usage @@ -12,11 +14,13 @@ 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(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 - ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + usage: Usage = response_obj["usage"] + self.token_cost_process.sum_successful_requests(1) + 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 6879a4519..c490a5e13 100644 --- a/tests/agent_test.py +++ b/tests/agent_test.py @@ -1445,44 +1445,43 @@ def test_llm_call_with_all_attributes(): @pytest.mark.vcr(filter_headers=["authorization"]) -def test_agent_with_ollama_gemma(): +def test_agent_with_ollama_llama3(): agent = Agent( role="test role", goal="test goal", backstory="test backstory", - llm=LLM( - model="ollama/gemma2:latest", - base_url="http://localhost:8080", - ), + llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"), ) assert isinstance(agent.llm, LLM) - assert agent.llm.model == "ollama/gemma2:latest" - assert agent.llm.base_url == "http://localhost:8080" + assert agent.llm.model == "ollama/llama3.2:3b" + assert agent.llm.base_url == "http://localhost:11434" - task = "Respond in 20 words. Who are you?" + task = "Respond in 20 words. Which model are you?" response = agent.llm.call([{"role": "user", "content": task}]) assert response assert len(response.split()) <= 25 # Allow a little flexibility in word count - assert "Gemma" in response or "AI" in response or "language model" in response + assert "Llama3" in response or "AI" in response or "language model" in response @pytest.mark.vcr(filter_headers=["authorization"]) -def test_llm_call_with_ollama_gemma(): +def test_llm_call_with_ollama_llama3(): llm = LLM( - model="ollama/gemma2:latest", - base_url="http://localhost:8080", + model="ollama/llama3.2:3b", + base_url="http://localhost:11434", temperature=0.7, max_tokens=30, ) - messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}] + messages = [ + {"role": "user", "content": "Respond in 20 words. Which model are you?"} + ] response = llm.call(messages) assert response assert len(response.split()) <= 25 # Allow a little flexibility in word count - assert "Gemma" in response or "AI" in response or "language model" in response + assert "Llama3" in response or "AI" in response or "language model" in response @pytest.mark.vcr(filter_headers=["authorization"]) @@ -1578,7 +1577,7 @@ def test_agent_execute_task_with_ollama(): role="test role", goal="test goal", backstory="test backstory", - llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"), + llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"), ) task = Task( diff --git a/tests/agents/agent_builder/base_agent_test.py b/tests/agents/agent_builder/base_agent_test.py index 7c0c2f472..99f66dcc4 100644 --- a/tests/agents/agent_builder/base_agent_test.py +++ b/tests/agents/agent_builder/base_agent_test.py @@ -7,7 +7,7 @@ from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.tools.base_tool import BaseTool -class TestAgent(BaseAgent): +class MockAgent(BaseAgent): def execute_task( self, task: Any, @@ -29,7 +29,7 @@ class TestAgent(BaseAgent): def test_key(): - agent = TestAgent( + agent = MockAgent( role="test role", goal="test goal", backstory="test backstory", diff --git a/tests/cassettes/test_agent_execute_task_with_ollama.yaml b/tests/cassettes/test_agent_execute_task_with_ollama.yaml index 62f1fe37f..d8ecb4dde 100644 --- a/tests/cassettes/test_agent_execute_task_with_ollama.yaml +++ b/tests/cassettes/test_agent_execute_task_with_ollama.yaml @@ -1,42 +1,6 @@ interactions: - request: - body: !!binary | - CrcCCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkSjgIKEgoQY3Jld2FpLnRl - bGVtZXRyeRJoChA/Q8UW5bidCRtKvri5fOaNEgh5qLzvLvZJkioQVG9vbCBVc2FnZSBFcnJvcjAB - OYjFVQr1TPgXQXCXhwr1TPgXShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuNjEuMHoCGAGFAQABAAAS - jQEKEChQTWQ07t26ELkZmP5RresSCHEivRGBpsP7KgpUb29sIFVzYWdlMAE5sKkbC/VM+BdB8MIc - C/VM+BdKGgoOY3Jld2FpX3ZlcnNpb24SCAoGMC42MS4wShkKCXRvb2xfbmFtZRIMCgpkdW1teV90 - b29sSg4KCGF0dGVtcHRzEgIYAXoCGAGFAQABAAA= - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '314' - Content-Type: - - application/x-protobuf - User-Agent: - - OTel-OTLP-Exporter-Python/1.27.0 - method: POST - uri: https://telemetry.crewai.com:4319/v1/traces - response: - body: - string: "\n\0" - headers: - Content-Length: - - '2' - Content-Type: - - application/x-protobuf - Date: - - Tue, 24 Sep 2024 21:57:54 GMT - status: - code: 200 - message: OK -- request: - body: '{"model": "gemma2:latest", "prompt": "### System:\nYou are test role. test + body: '{"model": "llama3.2:3b", "prompt": "### System:\nYou are test role. test backstory\nYour personal goal is: test goal\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 @@ -46,7 +10,7 @@ interactions: explanation of AI\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:\n\n", - "options": {}, "stream": false}' + "options": {"stop": ["\nObservation:"]}, "stream": false}' headers: Accept: - '*/*' @@ -55,26 +19,26 @@ interactions: Connection: - keep-alive Content-Length: - - '815' + - '839' Content-Type: - application/json User-Agent: - - python-requests/2.31.0 + - python-requests/2.32.3 method: POST - uri: http://localhost:8080/api/generate + uri: http://localhost:11434/api/generate response: body: - string: '{"model":"gemma2:latest","created_at":"2024-09-24T21:57:55.835715Z","response":"Thought: - I can explain AI in one sentence. \n\nFinal Answer: Artificial intelligence - (AI) is the ability of computer systems to perform tasks that typically require - human intelligence, such as learning, problem-solving, and decision-making. \n","done":true,"done_reason":"stop","context":[106,1645,108,6176,1479,235292,108,2045,708,2121,4731,235265,2121,135147,108,6922,3749,6789,603,235292,2121,6789,108,1469,2734,970,1963,3407,2048,3448,577,573,6911,1281,573,5463,2412,5920,235292,109,65366,235292,590,1490,798,2734,476,1775,3448,108,11263,10358,235292,3883,2048,3448,2004,614,573,1775,578,573,1546,3407,685,3077,235269,665,2004,614,17526,6547,235265,109,235285,44472,1281,1450,32808,235269,970,3356,12014,611,665,235341,109,6176,4926,235292,109,6846,12297,235292,36576,1212,16481,603,575,974,13060,109,1596,603,573,5246,12830,604,861,2048,3448,235292,586,974,235290,47366,15844,576,16481,108,4747,44472,2203,573,5579,3407,3381,685,573,2048,3448,235269,780,476,13367,235265,109,12694,235341,1417,603,50471,2845,577,692,235269,1281,573,8112,2506,578,2734,861,1963,14124,10358,235269,861,3356,12014,611,665,235341,109,65366,235292,109,107,108,106,2516,108,65366,235292,590,798,10200,16481,575,974,13060,235265,235248,109,11263,10358,235292,42456,17273,591,11716,235275,603,573,7374,576,6875,5188,577,3114,13333,674,15976,2817,3515,17273,235269,1582,685,6044,235269,3210,235290,60495,235269,578,4530,235290,14577,235265,139,108],"total_duration":3370959792,"load_duration":20611750,"prompt_eval_count":173,"prompt_eval_duration":688036000,"eval_count":51,"eval_duration":2660291000}' + string: '{"model":"llama3.2:3b","created_at":"2025-01-02T20:05:52.24992Z","response":"Final + Answer: Artificial Intelligence (AI) refers to the development of computer + systems capable of performing tasks that typically require human intelligence, + such as learning, problem-solving, decision-making, and perception.","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,2675,527,1296,3560,13,1296,93371,198,7927,4443,5915,374,25,1296,5915,198,1271,3041,856,1888,4686,1620,4320,311,279,3465,1005,279,4839,2768,3645,1473,85269,25,358,1457,649,3041,264,2294,4320,198,19918,22559,25,4718,1620,4320,2011,387,279,2294,323,279,1455,4686,439,3284,11,433,2011,387,15632,7633,382,40,28832,1005,1521,20447,11,856,2683,14117,389,433,2268,14711,2724,1473,5520,5546,25,83017,1148,15592,374,304,832,11914,271,2028,374,279,1755,13186,369,701,1620,4320,25,362,832,1355,18886,16540,315,15592,198,9514,28832,471,279,5150,4686,2262,439,279,1620,4320,11,539,264,12399,382,11382,0,1115,374,48174,3062,311,499,11,1005,279,7526,2561,323,3041,701,1888,13321,22559,11,701,2683,14117,389,433,2268,85269,1473,128009,128006,78191,128007,271,19918,22559,25,59294,22107,320,15836,8,19813,311,279,4500,315,6500,6067,13171,315,16785,9256,430,11383,1397,3823,11478,11,1778,439,6975,11,3575,99246,11,5597,28846,11,323,21063,13],"total_duration":1461909875,"load_duration":39886208,"prompt_eval_count":181,"prompt_eval_duration":701000000,"eval_count":39,"eval_duration":719000000}' headers: Content-Length: - 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AI has a profound impact on various sectors, enhancing efficiency, + improving decision-making, and leading to groundbreaking innovations. This report + highlights three key points regarding the significance and implications of AI + technology.\\n\\n## Key Point 1: Transformative Potential in Various Industries\\nAI's + transformative potential is evident across multiple industries, including healthcare, + finance, transportation, and agriculture. In healthcare, AI algorithms can analyze + complex medical data, leading to improved diagnostics, personalized medicine, + and predictive analytics, thereby enhancing patient outcomes. The financial + sector employs AI for risk management, fraud detection, and automated trading, + which increases operational efficiency and minimizes human error. In transportation, + AI is integral to the development of autonomous vehicles and smart traffic systems, + optimizing routes and reducing congestion. 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You''re + an expert at writing structured reports.\nYour personal goal is: Create properly + formatted reports\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: Write a report about AI with exactly + 3 key points.\n\nThis is the expect criteria for your final answer: A properly + formatted report\nyou MUST return the actual complete content as the final answer, + not a summary.\n\nThis is the context you''re working with:\n### Previous attempt + failed validation: Output must start with ''REPORT:'' no formatting, just the + word REPORT\n\n\n### Previous result:\n# Report on Artificial Intelligence (AI)\n\n## + Introduction\nArtificial Intelligence (AI) is a revolutionary technology designed + to simulate human intelligence processes, enabling machines to perform tasks + that typically require human cognition. 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Organizations are encouraged + to develop stringent guidelines and regulatory measures aimed at minimizing + bias and ensuring that AI systems uphold values of transparency and accountability. + Additionally, the implications of job displacement necessitate strategies for + workforce retraining and educational reforms to adequately prepare the workforce + for an economy increasingly shaped by AI technologies.\\n\\n## Key Point 3: + Future Directions and Developments\\nThe future of AI is poised for remarkable + advancements, with trends indicating a growing integration into daily life and + widespread applications. The emergence of explainable AI (XAI) aims to enhance + the transparency and interpretability of AI decision-making processes, fostering + trust and understanding among users. Improvements in natural language processing + (NLP) are likely to lead to more seamless and intuitive human-computer interactions. + Furthermore, AI's potential to address global challenges, including climate + change and disparities in healthcare access, is becoming increasingly significant. + Collaborative efforts among stakeholders will be vital to ensuring that AI advancements + are ethical and responsive to societal needs, paving the way for a responsible + and equitable AI landscape.\\n\\n## Conclusion\\nAI technology is at the forefront + of innovation, with the capacity to transform industries and tackle pressing + global issues. As we navigate through the complexities and ethical challenges + posed by AI, it is crucial to prioritize responsible development and implementation. + By harnessing AI's transformative capabilities with a focus on equity and transparency, + we can pave the way for a promising future that benefits all.\\n\\nEND REPORT\",\n + \ \"refusal\": null\n },\n \"logprobs\": null,\n \"finish_reason\": + \"stop\"\n }\n ],\n \"usage\": {\n \"prompt_tokens\": 774,\n \"completion_tokens\": + 574,\n \"total_tokens\": 1348,\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_0aa8d3e20b\"\n}\n" + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 8fcd9928eaa40133-GRU + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Sat, 04 Jan 2025 19:22:46 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: + - '9767' + 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: + - '149998862' + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_d3d0e47180363d07d988cb5ab639597c + http_version: HTTP/1.1 + status_code: 200 +version: 1 diff --git a/tests/cassettes/test_llm_call_with_ollama_gemma.yaml b/tests/cassettes/test_llm_call_with_ollama_gemma.yaml deleted file mode 100644 index 9735ae23e..000000000 --- a/tests/cassettes/test_llm_call_with_ollama_gemma.yaml +++ /dev/null @@ -1,35 +0,0 @@ -interactions: -- request: - body: '{"model": "gemma2:latest", "prompt": "### User:\nRespond in 20 words. 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Investigate + algorithms that enable AI agents to recognize and respond to human emotions, + enhancing user experience in sectors such as therapy, customer service, and + education. This idea is unique as it blends psychology with artificial intelligence, + presenting a new frontier for AI applications.\\n\\n- **AI Agents in Problem-Solving + for Climate Change**: Analyze how AI agents can contribute to developing innovative + solutions for climate change challenges. Focus on their role in predicting climate + patterns, optimizing energy consumption, and managing resources more efficiently. + This topic is unique because it highlights the practical impact of AI on one + of the most pressing global issues.\\n\\n- **The Ethics of Autonomous Decision-Making + AI**: Delve into the ethical implications surrounding AI agents that make autonomous + decisions, especially in critical areas like healthcare, transportation, and + law enforcement. 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mock_response.json.return_value = {"name": "TestCrew", "status": "active"} + mock_response.json.return_value = {"name": "InternalCrew", "status": "active"} self.mock_client.crew_status_by_name.return_value = mock_response with patch("sys.stdout", new=StringIO()) as fake_out: self.deploy_command.get_crew_status() - self.assertIn("TestCrew", fake_out.getvalue()) + self.assertIn("InternalCrew", fake_out.getvalue()) self.assertIn("active", fake_out.getvalue()) def test_get_crew_logs(self): diff --git a/tests/cli/tools/test_main.py b/tests/cli/tools/test_main.py index 10c29b920..b06c0b28c 100644 --- a/tests/cli/tools/test_main.py +++ b/tests/cli/tools/test_main.py @@ -28,9 +28,10 @@ def test_create_success(mock_subprocess): with in_temp_dir(): tool_command = ToolCommand() - with patch.object(tool_command, "login") as mock_login, patch( - "sys.stdout", new=StringIO() - ) as fake_out: + with ( + patch.object(tool_command, "login") as mock_login, + patch("sys.stdout", new=StringIO()) as fake_out, + ): tool_command.create("test-tool") output = fake_out.getvalue() @@ -82,7 +83,7 @@ def test_install_success(mock_get, mock_subprocess_run): capture_output=False, text=True, check=True, - env=unittest.mock.ANY + env=unittest.mock.ANY, ) assert "Successfully installed sample-tool" in output diff --git a/tests/crew_test.py b/tests/crew_test.py index 2003ddada..3505cc701 100644 --- a/tests/crew_test.py +++ b/tests/crew_test.py @@ -333,16 +333,16 @@ def test_manager_agent_delegating_to_assigned_task_agent(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Because we are mocking execute_sync, we never hit the underlying _execute_core # which sets the output attribute of the task task.output = mock_task_output - with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Verify execute_sync was called once @@ -350,12 +350,20 @@ def test_manager_agent_delegating_to_assigned_task_agent(): # Get the tools argument from the call _, kwargs = mock_execute_sync.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] # Verify the delegation tools were passed correctly assert len(tools) == 2 - assert any("Delegate a specific task to one of the following coworkers: Researcher" in tool.description for tool in tools) - assert any("Ask a specific question to one of the following coworkers: Researcher" in tool.description for tool in tools) + assert any( + "Delegate a specific task to one of the following coworkers: Researcher" + in tool.description + for tool in tools + ) + assert any( + "Ask a specific question to one of the following coworkers: Researcher" + in tool.description + for tool in tools + ) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -391,6 +399,83 @@ def test_manager_agent_delegating_to_all_agents(): ) +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_manager_agent_delegates_with_varied_role_cases(): + """ + Test that the manager agent can delegate to agents regardless of case or whitespace variations in role names. + This test verifies the fix for issue #1503 where role matching was too strict. + """ + # Create agents with varied case and whitespace in roles + researcher_spaced = Agent( + role=" Researcher ", # Extra spaces + goal="Research with spaces in role", + backstory="A researcher with spaces in role name", + allow_delegation=False, + ) + + writer_caps = Agent( + role="SENIOR WRITER", # All caps + goal="Write with caps in role", + backstory="A writer with caps in role name", + allow_delegation=False, + ) + + task = Task( + description="Research and write about AI. The researcher should do the research, and the writer should write it up.", + expected_output="A well-researched article about AI.", + agent=researcher_spaced, # Assign to researcher with spaces + ) + + crew = Crew( + agents=[researcher_spaced, writer_caps], + process=Process.hierarchical, + manager_llm="gpt-4o", + tasks=[task], + ) + + mock_task_output = TaskOutput( + description="Mock description", raw="mocked output", agent="mocked agent" + ) + task.output = mock_task_output + + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: + crew.kickoff() + + # Verify execute_sync was called once + mock_execute_sync.assert_called_once() + + # Get the tools argument from the call + _, kwargs = mock_execute_sync.call_args + tools = kwargs["tools"] + + # Verify the delegation tools were passed correctly and can handle case/whitespace variations + assert len(tools) == 2 + + # Check delegation tool descriptions (should work despite case/whitespace differences) + delegation_tool = tools[0] + question_tool = tools[1] + + assert ( + "Delegate a specific task to one of the following coworkers:" + in delegation_tool.description + ) + assert ( + " Researcher " in delegation_tool.description + or "SENIOR WRITER" in delegation_tool.description + ) + + assert ( + "Ask a specific question to one of the following coworkers:" + in question_tool.description + ) + assert ( + " Researcher " in question_tool.description + or "SENIOR WRITER" in question_tool.description + ) + + @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_with_delegating_agents(): tasks = [ @@ -414,6 +499,7 @@ def test_crew_with_delegating_agents(): == "In the rapidly evolving landscape of technology, AI agents have emerged as formidable tools, revolutionizing how we interact with data and automate tasks. These sophisticated systems leverage machine learning and natural language processing to perform a myriad of functions, from virtual personal assistants to complex decision-making companions in industries such as finance, healthcare, and education. By mimicking human intelligence, AI agents can analyze massive data sets at unparalleled speeds, enabling businesses to uncover valuable insights, enhance productivity, and elevate user experiences to unprecedented levels.\n\nOne of the most striking aspects of AI agents is their adaptability; they learn from their interactions and continuously improve their performance over time. This feature is particularly valuable in customer service where AI agents can address inquiries, resolve issues, and provide personalized recommendations without the limitations of human fatigue. Moreover, with intuitive interfaces, AI agents enhance user interactions, making technology more accessible and user-friendly, thereby breaking down barriers that have historically hindered digital engagement.\n\nDespite their immense potential, the deployment of AI agents raises important ethical and practical considerations. Issues related to privacy, data security, and the potential for job displacement necessitate thoughtful dialogue and proactive measures. Striking a balance between technological innovation and societal impact will be crucial as organizations integrate these agents into their operations. Additionally, ensuring transparency in AI decision-making processes is vital to maintain public trust as AI agents become an integral part of daily life.\n\nLooking ahead, the future of AI agents appears bright, with ongoing advancements promising even greater capabilities. As we continue to harness the power of AI, we can expect these agents to play a transformative role in shaping various sectors—streamlining workflows, enabling smarter decision-making, and fostering more personalized experiences. Embracing this technology responsibly can lead to a future where AI agents not only augment human effort but also inspire creativity and efficiency across the board, ultimately redefining our interaction with the digital world." ) + @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_with_delegating_agents_should_not_override_task_tools(): from typing import Type @@ -424,6 +510,7 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(): class TestToolInput(BaseModel): """Input schema for TestTool.""" + query: str = Field(..., description="Query to process") class TestTool(BaseTool): @@ -451,24 +538,29 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Because we are mocking execute_sync, we never hit the underlying _execute_core # which sets the output attribute of the task tasks[0].output = mock_task_output - with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Execute the task and verify both tools are present _, kwargs = mock_execute_sync.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] + + assert any( + isinstance(tool, TestTool) for tool in tools + ), "TestTool should be present" + assert any( + "delegate" in tool.name.lower() for tool in tools + ), "Delegation tool should be present" - assert any(isinstance(tool, TestTool) for tool in tools), "TestTool should be present" - assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present" @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_with_delegating_agents_should_not_override_agent_tools(): @@ -480,6 +572,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(): class TestToolInput(BaseModel): """Input schema for TestTool.""" + query: str = Field(..., description="Query to process") class TestTool(BaseTool): @@ -498,7 +591,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(): Task( description="Produce and amazing 1 paragraph draft of an article about AI Agents.", expected_output="A 4 paragraph article about AI.", - agent=new_ceo + agent=new_ceo, ) ] @@ -509,24 +602,29 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Because we are mocking execute_sync, we never hit the underlying _execute_core # which sets the output attribute of the task tasks[0].output = mock_task_output - with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Execute the task and verify both tools are present _, kwargs = mock_execute_sync.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] + + assert any( + isinstance(tool, TestTool) for tool in new_ceo.tools + ), "TestTool should be present" + assert any( + "delegate" in tool.name.lower() for tool in tools + ), "Delegation tool should be present" - assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), "TestTool should be present" - assert any("delegate" in tool.name.lower() for tool in tools), "Delegation tool should be present" @pytest.mark.vcr(filter_headers=["authorization"]) def test_task_tools_override_agent_tools(): @@ -538,6 +636,7 @@ def test_task_tools_override_agent_tools(): class TestToolInput(BaseModel): """Input schema for TestTool.""" + query: str = Field(..., description="Query to process") class TestTool(BaseTool): @@ -565,14 +664,10 @@ def test_task_tools_override_agent_tools(): description="Write a test task", expected_output="Test output", agent=new_researcher, - tools=[AnotherTestTool()] + tools=[AnotherTestTool()], ) - crew = Crew( - agents=[new_researcher], - tasks=[task], - process=Process.sequential - ) + crew = Crew(agents=[new_researcher], tasks=[task], process=Process.sequential) crew.kickoff() @@ -585,6 +680,7 @@ def test_task_tools_override_agent_tools(): assert len(new_researcher.tools) == 1 assert isinstance(new_researcher.tools[0], TestTool) + @pytest.mark.vcr(filter_headers=["authorization"]) def test_task_tools_override_agent_tools_with_allow_delegation(): """ @@ -637,13 +733,13 @@ def test_task_tools_override_agent_tools_with_allow_delegation(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # We mock execute_sync to verify which tools get used at runtime - with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Inspect the call kwargs to verify the actual tools passed to execution @@ -651,16 +747,23 @@ def test_task_tools_override_agent_tools_with_allow_delegation(): used_tools = kwargs["tools"] # Confirm AnotherTestTool is present but TestTool is not - assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), "AnotherTestTool should be present" - assert not any(isinstance(tool, TestTool) for tool in used_tools), "TestTool should not be present among used tools" + assert any( + isinstance(tool, AnotherTestTool) for tool in used_tools + ), "AnotherTestTool should be present" + assert not any( + isinstance(tool, TestTool) for tool in used_tools + ), "TestTool should not be present among used tools" # Confirm delegation tool(s) are present - assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present" + assert any( + "delegate" in tool.name.lower() for tool in used_tools + ), "Delegation tool should be present" # Finally, make sure the agent's original tools remain unchanged assert len(researcher_with_delegation.tools) == 1 assert isinstance(researcher_with_delegation.tools[0], TestTool) + @pytest.mark.vcr(filter_headers=["authorization"]) def test_crew_verbose_output(capsys): tasks = [ @@ -947,8 +1050,8 @@ def test_three_task_with_async_execution(): ) -@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.asyncio +@pytest.mark.vcr(filter_headers=["authorization"]) async def test_crew_async_kickoff(): inputs = [ {"topic": "dog"}, @@ -995,8 +1098,9 @@ async def test_crew_async_kickoff(): assert result[0].token_usage.successful_requests > 0 # type: ignore +@pytest.mark.asyncio @pytest.mark.vcr(filter_headers=["authorization"]) -def test_async_task_execution_call_count(): +async def test_async_task_execution_call_count(): from unittest.mock import MagicMock, patch list_ideas = Task( @@ -1123,7 +1227,6 @@ def test_kickoff_for_each_empty_input(): assert results == [] -@pytest.mark.vcr(filter_headers=["authorization"]) def test_kickoff_for_each_invalid_input(): """Tests if kickoff_for_each raises TypeError for invalid input types.""" @@ -1146,7 +1249,6 @@ def test_kickoff_for_each_invalid_input(): crew.kickoff_for_each("invalid input") -@pytest.mark.vcr(filter_headers=["authorization"]) def test_kickoff_for_each_error_handling(): """Tests error handling in kickoff_for_each when kickoff raises an error.""" from unittest.mock import patch @@ -1183,7 +1285,6 @@ def test_kickoff_for_each_error_handling(): crew.kickoff_for_each(inputs=inputs) -@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.asyncio async def test_kickoff_async_basic_functionality_and_output(): """Tests the basic functionality and output of kickoff_async.""" @@ -1218,7 +1319,6 @@ async def test_kickoff_async_basic_functionality_and_output(): mock_kickoff.assert_called_once_with(inputs) -@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.asyncio async def test_async_kickoff_for_each_async_basic_functionality_and_output(): """Tests the basic functionality and output of kickoff_for_each_async.""" @@ -1265,7 +1365,6 @@ async def test_async_kickoff_for_each_async_basic_functionality_and_output(): mock_kickoff_async.assert_any_call(inputs=input_data) -@pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.asyncio async def test_async_kickoff_for_each_async_empty_input(): """Tests if akickoff_for_each_async handles an empty input list.""" @@ -1449,12 +1548,12 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff(): crew = Crew(agents=[programmer], tasks=[task]) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) - with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Get the tools that were actually used in execution @@ -1463,7 +1562,10 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff(): # Verify that exactly one tool was used and it was a CodeInterpreterTool assert len(used_tools) == 1, "Should have exactly one tool" - assert isinstance(used_tools[0], CodeInterpreterTool), "Tool should be CodeInterpreterTool" + assert isinstance( + used_tools[0], CodeInterpreterTool + ), "Tool should be CodeInterpreterTool" + @pytest.mark.vcr(filter_headers=["authorization"]) def test_delegation_is_not_enabled_if_there_are_only_one_agent(): @@ -1574,16 +1676,16 @@ def test_hierarchical_crew_creation_tasks_with_agents(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Because we are mocking execute_sync, we never hit the underlying _execute_core # which sets the output attribute of the task task.output = mock_task_output - with patch.object(Task, 'execute_sync', return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Verify execute_sync was called once @@ -1591,12 +1693,20 @@ def test_hierarchical_crew_creation_tasks_with_agents(): # Get the tools argument from the call _, kwargs = mock_execute_sync.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] # Verify the delegation tools were passed correctly assert len(tools) == 2 - assert any("Delegate a specific task to one of the following coworkers: Senior Writer" in tool.description for tool in tools) - assert any("Ask a specific question to one of the following coworkers: Senior Writer" in tool.description for tool in tools) + assert any( + "Delegate a specific task to one of the following coworkers: Senior Writer" + in tool.description + for tool in tools + ) + assert any( + "Ask a specific question to one of the following coworkers: Senior Writer" + in tool.description + for tool in tools + ) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -1619,9 +1729,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Create a mock Future that returns our TaskOutput @@ -1632,7 +1740,9 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(): # which sets the output attribute of the task task.output = mock_task_output - with patch.object(Task, 'execute_async', return_value=mock_future) as mock_execute_async: + with patch.object( + Task, "execute_async", return_value=mock_future + ) as mock_execute_async: crew.kickoff() # Verify execute_async was called once @@ -1640,12 +1750,20 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(): # Get the tools argument from the call _, kwargs = mock_execute_async.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] # Verify the delegation tools were passed correctly assert len(tools) == 2 - assert any("Delegate a specific task to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools) - assert any("Ask a specific question to one of the following coworkers: Senior Writer\n" in tool.description for tool in tools) + assert any( + "Delegate a specific task to one of the following coworkers: Senior Writer\n" + in tool.description + for tool in tools + ) + assert any( + "Ask a specific question to one of the following coworkers: Senior Writer\n" + in tool.description + for tool in tools + ) @pytest.mark.vcr(filter_headers=["authorization"]) @@ -1942,6 +2060,88 @@ def test_crew_log_file_output(tmp_path): @pytest.mark.vcr(filter_headers=["authorization"]) +def test_crew_output_file_end_to_end(tmp_path): + """Test output file functionality in a full crew context.""" + # Create an agent + agent = Agent( + role="Researcher", + goal="Analyze AI topics", + backstory="You have extensive AI research experience.", + allow_delegation=False, + ) + + # Create a task with dynamic output file path + dynamic_path = tmp_path / "output_{topic}.txt" + task = Task( + description="Explain the advantages of {topic}.", + expected_output="A summary of the main advantages, bullet points recommended.", + agent=agent, + output_file=str(dynamic_path), + ) + + # Create and run the crew + crew = Crew( + agents=[agent], + tasks=[task], + process=Process.sequential, + ) + crew.kickoff(inputs={"topic": "AI"}) + + # Verify file creation and cleanup + expected_file = tmp_path / "output_AI.txt" + assert expected_file.exists(), f"Output file {expected_file} was not created" + + +def test_crew_output_file_validation_failures(): + """Test output file validation failures in a crew context.""" + agent = Agent( + role="Researcher", + goal="Analyze data", + backstory="You analyze data files.", + allow_delegation=False, + ) + + # Test path traversal + with pytest.raises(ValueError, match="Path traversal"): + task = Task( + description="Analyze data", + expected_output="Analysis results", + agent=agent, + output_file="../output.txt", + ) + Crew(agents=[agent], tasks=[task]).kickoff() + + # Test shell special characters + with pytest.raises(ValueError, match="Shell special characters"): + task = Task( + description="Analyze data", + expected_output="Analysis results", + agent=agent, + output_file="output.txt | rm -rf /", + ) + Crew(agents=[agent], tasks=[task]).kickoff() + + # Test shell expansion + with pytest.raises(ValueError, match="Shell expansion"): + task = Task( + description="Analyze data", + expected_output="Analysis results", + agent=agent, + output_file="~/output.txt", + ) + Crew(agents=[agent], tasks=[task]).kickoff() + + # Test invalid template variable + with pytest.raises(ValueError, match="Invalid template variable"): + task = Task( + description="Analyze data", + expected_output="Analysis results", + agent=agent, + output_file="{invalid-name}/output.txt", + ) + Crew(agents=[agent], tasks=[task]).kickoff() + + def test_manager_agent(): from unittest.mock import patch @@ -2900,6 +3100,7 @@ def test_task_tools_preserve_code_execution_tools(): class TestToolInput(BaseModel): """Input schema for TestTool.""" + query: str = Field(..., description="Query to process") class TestTool(BaseTool): @@ -2933,7 +3134,7 @@ def test_task_tools_preserve_code_execution_tools(): description="Write a program to calculate fibonacci numbers.", expected_output="A working fibonacci calculator.", agent=programmer, - tools=[TestTool()] + tools=[TestTool()], ) crew = Crew( @@ -2943,12 +3144,12 @@ def test_task_tools_preserve_code_execution_tools(): ) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) - with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Get the tools that were actually used in execution @@ -2956,12 +3157,21 @@ def test_task_tools_preserve_code_execution_tools(): used_tools = kwargs["tools"] # Verify all expected tools are present - assert any(isinstance(tool, TestTool) for tool in used_tools), "Task's TestTool should be present" - assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), "CodeInterpreterTool should be present" - assert any("delegate" in tool.name.lower() for tool in used_tools), "Delegation tool should be present" + assert any( + isinstance(tool, TestTool) for tool in used_tools + ), "Task's TestTool should be present" + assert any( + isinstance(tool, CodeInterpreterTool) for tool in used_tools + ), "CodeInterpreterTool should be present" + assert any( + "delegate" in tool.name.lower() for tool in used_tools + ), "Delegation tool should be present" # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools) - assert len(used_tools) == 4, "Should have TestTool, CodeInterpreter, and 2 delegation tools" + assert ( + len(used_tools) == 4 + ), "Should have TestTool, CodeInterpreter, and 2 delegation tools" + @pytest.mark.vcr(filter_headers=["authorization"]) def test_multimodal_flag_adds_multimodal_tools(): @@ -2990,13 +3200,13 @@ def test_multimodal_flag_adds_multimodal_tools(): crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential) mock_task_output = TaskOutput( - description="Mock description", - raw="mocked output", - agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent" ) # Mock execute_sync to verify the tools passed at runtime - with patch.object(Task, "execute_sync", return_value=mock_task_output) as mock_execute_sync: + with patch.object( + Task, "execute_sync", return_value=mock_task_output + ) as mock_execute_sync: crew.kickoff() # Get the tools that were actually used in execution @@ -3004,13 +3214,14 @@ def test_multimodal_flag_adds_multimodal_tools(): used_tools = kwargs["tools"] # Check that the multimodal tool was added - assert any(isinstance(tool, AddImageTool) for tool in used_tools), ( - "AddImageTool should be present when agent is multimodal" - ) + assert any( + isinstance(tool, AddImageTool) for tool in used_tools + ), "AddImageTool should be present when agent is multimodal" # Verify we have exactly one tool (just the AddImageTool) assert len(used_tools) == 1, "Should only have the AddImageTool" + @pytest.mark.vcr(filter_headers=["authorization"]) def test_multimodal_agent_image_tool_handling(): """ @@ -3052,10 +3263,10 @@ def test_multimodal_agent_image_tool_handling(): mock_task_output = TaskOutput( description="Mock description", raw="A detailed analysis of the image", - agent="Image Analyst" + agent="Image Analyst", ) - with patch.object(Task, 'execute_sync') as mock_execute_sync: + with patch.object(Task, "execute_sync") as mock_execute_sync: # Set up the mock to return our task output mock_execute_sync.return_value = mock_task_output @@ -3064,7 +3275,7 @@ def test_multimodal_agent_image_tool_handling(): # Get the tools that were passed to execute_sync _, kwargs = mock_execute_sync.call_args - tools = kwargs['tools'] + tools = kwargs["tools"] # Verify the AddImageTool is present and properly configured image_tools = [tool for tool in tools if tool.name == "Add image to content"] @@ -3074,7 +3285,7 @@ def test_multimodal_agent_image_tool_handling(): image_tool = image_tools[0] result = image_tool._run( image_url="https://example.com/test-image.jpg", - action="Please analyze this image" + action="Please analyze this image", ) # Verify the tool returns the expected format @@ -3084,6 +3295,7 @@ def test_multimodal_agent_image_tool_handling(): assert result["content"][0]["type"] == "text" assert result["content"][1]["type"] == "image_url" + @pytest.mark.vcr(filter_headers=["authorization"]) def test_multimodal_agent_live_image_analysis(): """ @@ -3097,7 +3309,7 @@ def test_multimodal_agent_live_image_analysis(): allow_delegation=False, multimodal=True, verbose=True, - llm="gpt-4o" + llm="gpt-4o", ) # Create a task for image analysis @@ -3108,21 +3320,127 @@ def test_multimodal_agent_live_image_analysis(): Image: {image_url} """, expected_output="A comprehensive description of the image contents.", - agent=image_analyst + agent=image_analyst, ) # Create and run the crew - crew = Crew( - agents=[image_analyst], - tasks=[analyze_image] - ) + crew = Crew(agents=[image_analyst], tasks=[analyze_image]) # Execute with an image URL - result = crew.kickoff(inputs={ - "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k=" - }) + result = crew.kickoff( + inputs={ + "image_url": "https://media.istockphoto.com/id/946087016/photo/aerial-view-of-lower-manhattan-new-york.jpg?s=612x612&w=0&k=20&c=viLiMRznQ8v5LzKTt_LvtfPFUVl1oiyiemVdSlm29_k=" + } + ) # Verify we got a meaningful response assert isinstance(result.raw, str) assert len(result.raw) > 100 # Expecting a detailed analysis - assert "error" not in result.raw.lower() # No error messages in response \ No newline at end of file + assert "error" not in result.raw.lower() # No error messages in response + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_crew_with_failing_task_guardrails(): + """Test that crew properly handles failing guardrails and retries with validation feedback.""" + + def strict_format_guardrail(result: TaskOutput): + """Validates that the output follows a strict format: + - Must start with 'REPORT:' + - Must end with 'END REPORT' + """ + content = result.raw.strip() + + if not ('REPORT:' in content or '**REPORT:**' in content): + return (False, "Output must start with 'REPORT:' no formatting, just the word REPORT") + + if not ('END REPORT' in content or '**END REPORT**' in content): + return (False, "Output must end with 'END REPORT' no formatting, just the word END REPORT") + + return (True, content) + + researcher = Agent( + role="Report Writer", + goal="Create properly formatted reports", + backstory="You're an expert at writing structured reports.", + ) + + task = Task( + description="""Write a report about AI with exactly 3 key points.""", + expected_output="A properly formatted report", + agent=researcher, + guardrail=strict_format_guardrail, + max_retries=3 + ) + + crew = Crew( + agents=[researcher], + tasks=[task], + ) + + result = crew.kickoff() + + # Verify the final output meets all format requirements + content = result.raw.strip() + assert content.startswith('REPORT:'), "Output should start with 'REPORT:'" + assert content.endswith('END REPORT'), "Output should end with 'END REPORT'" + + # Verify task output + task_output = result.tasks_output[0] + assert isinstance(task_output, TaskOutput) + assert task_output.raw == result.raw + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_crew_guardrail_feedback_in_context(): + """Test that guardrail feedback is properly appended to task context for retries.""" + + def format_guardrail(result: TaskOutput): + """Validates that the output contains a specific keyword.""" + if "IMPORTANT" not in result.raw: + return (False, "Output must contain the keyword 'IMPORTANT'") + return (True, result.raw) + + # Create execution contexts list to track contexts + execution_contexts = [] + + researcher = Agent( + role="Writer", + goal="Write content with specific keywords", + backstory="You're an expert at following specific writing requirements.", + allow_delegation=False + ) + + task = Task( + description="Write a short response.", + expected_output="A response containing the keyword 'IMPORTANT'", + agent=researcher, + guardrail=format_guardrail, + max_retries=2 + ) + + crew = Crew(agents=[researcher], tasks=[task]) + + with patch.object(Agent, "execute_task") as mock_execute_task: + # Define side_effect to capture context and return different responses + def side_effect(task, context=None, tools=None): + execution_contexts.append(context if context else "") + if len(execution_contexts) == 1: + return "This is a test response" + return "This is an IMPORTANT test response" + + mock_execute_task.side_effect = side_effect + + result = crew.kickoff() + + # Verify that we had multiple executions + assert len(execution_contexts) > 1, "Task should have been executed multiple times" + + # Verify that the second execution included the guardrail feedback + assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], \ + "Guardrail feedback should be included in retry context" + + # Verify final output meets guardrail requirements + assert "IMPORTANT" in result.raw, "Final output should contain required keyword" + + # Verify task retry count + assert task.retry_count == 1, "Task should have been retried once" diff --git a/tests/knowledge/knowledge_test.py b/tests/knowledge/knowledge_test.py index 6704d3031..fad2d2513 100644 --- a/tests/knowledge/knowledge_test.py +++ b/tests/knowledge/knowledge_test.py @@ -578,14 +578,6 @@ def test_multiple_docling_sources(): assert docling_source.content is not None -def test_docling_source_with_local_file(): - current_dir = Path(__file__).parent - pdf_path = current_dir / "crewai_quickstart.pdf" - docling_source = CrewDoclingSource(file_paths=[pdf_path]) - assert docling_source.file_paths == [pdf_path] - assert docling_source.content is not None - - def test_file_path_validation(): """Test file path validation for knowledge sources.""" current_dir = Path(__file__).parent @@ -606,6 +598,6 @@ def test_file_path_validation(): # Test neither file_path nor file_paths provided with pytest.raises( ValueError, - match="file_path/file_paths must be a Path, str, or a list of these types" + match="file_path/file_paths must be a Path, str, or a list of these types", ): PDFKnowledgeSource() diff --git a/tests/project_test.py b/tests/project_test.py index 6c68f4993..ed9d86f2f 100644 --- a/tests/project_test.py +++ b/tests/project_test.py @@ -27,7 +27,7 @@ class SimpleCrew: @CrewBase -class TestCrew: +class InternalCrew: agents_config = "config/agents.yaml" tasks_config = "config/tasks.yaml" @@ -84,7 +84,7 @@ def test_task_memoization(): def test_crew_memoization(): - crew = TestCrew() + crew = InternalCrew() first_call_result = crew.crew() second_call_result = crew.crew() @@ -107,7 +107,7 @@ def test_task_name(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_before_kickoff_modification(): - crew = TestCrew() + crew = InternalCrew() inputs = {"topic": "LLMs"} result = crew.crew().kickoff(inputs=inputs) assert "bicycles" in result.raw, "Before kickoff function did not modify inputs" @@ -115,7 +115,7 @@ def test_before_kickoff_modification(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_after_kickoff_modification(): - crew = TestCrew() + crew = InternalCrew() # Assuming the crew execution returns a dict result = crew.crew().kickoff({"topic": "LLMs"}) @@ -126,7 +126,7 @@ def test_after_kickoff_modification(): @pytest.mark.vcr(filter_headers=["authorization"]) def test_before_kickoff_with_none_input(): - crew = TestCrew() + crew = InternalCrew() crew.crew().kickoff(None) # Test should pass without raising exceptions diff --git a/tests/task_test.py b/tests/task_test.py index 40eb98e54..794d42c47 100644 --- a/tests/task_test.py +++ b/tests/task_test.py @@ -719,61 +719,58 @@ def test_interpolate_inputs(): task = Task( description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.", expected_output="Bullet point list of 5 interesting ideas about {topic}.", + output_file="/tmp/{topic}/output_{date}.txt", ) - task.interpolate_inputs(inputs={"topic": "AI"}) + task.interpolate_inputs(inputs={"topic": "AI", "date": "2024"}) assert ( task.description == "Give me a list of 5 interesting ideas about AI to explore for an article, what makes them unique and interesting." ) assert task.expected_output == "Bullet point list of 5 interesting ideas about AI." + assert task.output_file == "/tmp/AI/output_2024.txt" - task.interpolate_inputs(inputs={"topic": "ML"}) + task.interpolate_inputs(inputs={"topic": "ML", "date": "2025"}) assert ( task.description == "Give me a list of 5 interesting ideas about ML to explore for an article, what makes them unique and interesting." ) assert task.expected_output == "Bullet point list of 5 interesting ideas about ML." + assert task.output_file == "/tmp/ML/output_2025.txt" def test_interpolate_only(): """Test the interpolate_only method for various scenarios including JSON structure preservation.""" task = Task( - description="Unused in this test", - expected_output="Unused in this test" + description="Unused in this test", expected_output="Unused in this test" ) - + # Test JSON structure preservation json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}' result = task.interpolate_only( input_string=json_string, - inputs={"placeholder": "the data", "nestedVal": "something else"} + inputs={"placeholder": "the data", "nestedVal": "something else"}, ) assert '"info": "Look at the data"' in result assert '"val": "something else"' in result assert "{placeholder}" not in result assert "{nestedVal}" not in result - + # Test normal string interpolation normal_string = "Hello {name}, welcome to {place}!" result = task.interpolate_only( - input_string=normal_string, - inputs={"name": "John", "place": "CrewAI"} + input_string=normal_string, inputs={"name": "John", "place": "CrewAI"} ) assert result == "Hello John, welcome to CrewAI!" - + # Test empty string - result = task.interpolate_only( - input_string="", - inputs={"unused": "value"} - ) + result = task.interpolate_only(input_string="", inputs={"unused": "value"}) assert result == "" - + # Test string with no placeholders no_placeholders = "Hello, this is a test" result = task.interpolate_only( - input_string=no_placeholders, - inputs={"unused": "value"} + input_string=no_placeholders, inputs={"unused": "value"} ) assert result == no_placeholders @@ -872,3 +869,96 @@ def test_key(): assert ( task.key == hash ), "The key should be the hash of the non-interpolated description." + + +def test_output_file_validation(): + """Test output file path validation.""" + # Valid paths + assert ( + Task( + description="Test task", + expected_output="Test output", + output_file="output.txt", + ).output_file + == "output.txt" + ) + assert ( + Task( + description="Test task", + expected_output="Test output", + output_file="/tmp/output.txt", + ).output_file + == "tmp/output.txt" + ) + assert ( + Task( + description="Test task", + expected_output="Test output", + output_file="{dir}/output_{date}.txt", + ).output_file + == "{dir}/output_{date}.txt" + ) + + # Invalid paths + with pytest.raises(ValueError, match="Path traversal"): + Task( + description="Test task", + expected_output="Test output", + output_file="../output.txt", + ) + with pytest.raises(ValueError, match="Path traversal"): + Task( + description="Test task", + expected_output="Test output", + output_file="folder/../output.txt", + ) + with pytest.raises(ValueError, match="Shell special characters"): + Task( + description="Test task", + expected_output="Test output", + output_file="output.txt | rm -rf /", + ) + with pytest.raises(ValueError, match="Shell expansion"): + Task( + description="Test task", + expected_output="Test output", + output_file="~/output.txt", + ) + with pytest.raises(ValueError, match="Shell expansion"): + Task( + description="Test task", + expected_output="Test output", + output_file="$HOME/output.txt", + ) + with pytest.raises(ValueError, match="Invalid template variable"): + Task( + description="Test task", + expected_output="Test output", + output_file="{invalid-name}/output.txt", + ) + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_task_execution_times(): + researcher = Agent( + role="Researcher", + goal="Make the best research and analysis on content about AI and AI agents", + backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.", + allow_delegation=False, + ) + + task = Task( + description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.", + expected_output="Bullet point list of 5 interesting ideas.", + agent=researcher, + ) + + assert task.start_time is None + assert task.end_time is None + assert task.execution_duration is None + + task.execute_sync(agent=researcher) + + assert task.start_time is not None + assert task.end_time is not None + assert task.execution_duration == (task.end_time - task.start_time).total_seconds() diff --git a/tests/test_manager_llm_delegation.py b/tests/test_manager_llm_delegation.py new file mode 100644 index 000000000..6b1b97b9a --- /dev/null +++ b/tests/test_manager_llm_delegation.py @@ -0,0 +1,56 @@ +from unittest.mock import MagicMock + +import pytest + +from crewai import Agent, Task +from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool + + +class InternalAgentTool(BaseAgentTool): + """Concrete implementation of BaseAgentTool for testing.""" + + def _run(self, *args, **kwargs): + """Implement required _run method.""" + return "Test response" + + +@pytest.mark.parametrize( + "role_name,should_match", + [ + ("Futel Official Infopoint", True), # exact match + (' "Futel Official Infopoint" ', True), # extra quotes and spaces + ("Futel Official Infopoint\n", True), # trailing newline + ('"Futel Official Infopoint"', True), # embedded quotes + (" FUTEL\nOFFICIAL INFOPOINT ", True), # multiple whitespace and newline + ("futel official infopoint", True), # lowercase + ("FUTEL OFFICIAL INFOPOINT", True), # uppercase + ("Non Existent Agent", False), # non-existent agent + (None, False), # None agent name + ], +) +def test_agent_tool_role_matching(role_name, should_match): + """Test that agent tools can match roles regardless of case, whitespace, and special characters.""" + # Create test agent + test_agent = Agent( + role="Futel Official Infopoint", + goal="Answer questions about Futel", + backstory="Futel Football Club info", + allow_delegation=False, + ) + + # Create test agent tool + agent_tool = InternalAgentTool( + name="test_tool", description="Test tool", agents=[test_agent] + ) + + # Test role matching + result = agent_tool._execute(agent_name=role_name, task="Test task", context=None) + + if should_match: + assert ( + "coworker mentioned not found" not in result.lower() + ), f"Should find agent with role name: {role_name}" + else: + assert ( + "coworker mentioned not found" in result.lower() + ), f"Should not find agent with role name: {role_name}" diff --git a/tests/test_task_guardrails.py b/tests/test_task_guardrails.py index dc96cb878..e22e76234 100644 --- a/tests/test_task_guardrails.py +++ b/tests/test_task_guardrails.py @@ -15,10 +15,7 @@ def test_task_without_guardrail(): agent.execute_task.return_value = "test result" agent.crew = None - task = Task( - description="Test task", - expected_output="Output" - ) + task = Task(description="Test task", expected_output="Output") result = task.execute_sync(agent=agent) assert isinstance(result, TaskOutput) @@ -27,6 +24,7 @@ def test_task_without_guardrail(): def test_task_with_successful_guardrail(): """Test that successful guardrail validation passes transformed result.""" + def guardrail(result: TaskOutput): return (True, result.raw.upper()) @@ -35,11 +33,7 @@ def test_task_with_successful_guardrail(): agent.execute_task.return_value = "test result" agent.crew = None - task = Task( - description="Test task", - expected_output="Output", - guardrail=guardrail - ) + task = Task(description="Test task", expected_output="Output", guardrail=guardrail) result = task.execute_sync(agent=agent) assert isinstance(result, TaskOutput) @@ -48,22 +42,20 @@ def test_task_with_successful_guardrail(): def test_task_with_failing_guardrail(): """Test that failing guardrail triggers retry with error context.""" + def guardrail(result: TaskOutput): return (False, "Invalid format") agent = Mock() agent.role = "test_agent" - agent.execute_task.side_effect = [ - "bad result", - "good result" - ] + agent.execute_task.side_effect = ["bad result", "good result"] agent.crew = None task = Task( description="Test task", expected_output="Output", guardrail=guardrail, - max_retries=1 + max_retries=1, ) # First execution fails guardrail, second succeeds @@ -77,6 +69,7 @@ def test_task_with_failing_guardrail(): def test_task_with_guardrail_retries(): """Test that guardrail respects max_retries configuration.""" + def guardrail(result: TaskOutput): return (False, "Invalid format") @@ -89,7 +82,7 @@ def test_task_with_guardrail_retries(): description="Test task", expected_output="Output", guardrail=guardrail, - max_retries=2 + max_retries=2, ) with pytest.raises(Exception) as exc_info: @@ -102,6 +95,7 @@ def test_task_with_guardrail_retries(): def test_guardrail_error_in_context(): """Test that guardrail error is passed in context for retry.""" + def guardrail(result: TaskOutput): return (False, "Expected JSON, got string") @@ -113,11 +107,12 @@ def test_guardrail_error_in_context(): description="Test task", expected_output="Output", guardrail=guardrail, - max_retries=1 + max_retries=1, ) # Mock execute_task to succeed on second attempt first_call = True + def execute_task(task, context, tools): nonlocal first_call if first_call: diff --git a/tests/tools/test_base_tool.py b/tests/tools/test_base_tool.py index 9f46b13ba..51eb05b75 100644 --- a/tests/tools/test_base_tool.py +++ b/tests/tools/test_base_tool.py @@ -15,7 +15,7 @@ def test_creating_a_tool_using_annotation(): 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, your agent will need this information to use it." ) - assert my_tool.args_schema.schema()["properties"] == { + assert my_tool.args_schema.model_json_schema()["properties"] == { "question": {"title": "Question", "type": "string"} } assert ( @@ -29,7 +29,7 @@ def test_creating_a_tool_using_annotation(): 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, your agent will need this information to use it." ) - assert converted_tool.args_schema.schema()["properties"] == { + assert converted_tool.args_schema.model_json_schema()["properties"] == { "question": {"title": "Question", "type": "string"} } assert ( @@ -54,7 +54,7 @@ def test_creating_a_tool_using_baseclass(): 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, your agent will need this information to use it." ) - assert my_tool.args_schema.schema()["properties"] == { + assert my_tool.args_schema.model_json_schema()["properties"] == { "question": {"title": "Question", "type": "string"} } assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?" @@ -66,7 +66,7 @@ def test_creating_a_tool_using_baseclass(): 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, your agent will need this information to use it." ) - assert converted_tool.args_schema.schema()["properties"] == { + assert converted_tool.args_schema.model_json_schema()["properties"] == { "question": {"title": "Question", "type": "string"} } assert ( diff --git a/tests/tools/test_structured_tool.py b/tests/tools/test_structured_tool.py index 32ebd805b..333220486 100644 --- a/tests/tools/test_structured_tool.py +++ b/tests/tools/test_structured_tool.py @@ -25,7 +25,7 @@ def schema_class(): return TestSchema -class TestCrewStructuredTool: +class InternalCrewStructuredTool: def test_initialization(self, basic_function, schema_class): """Test basic initialization of CrewStructuredTool""" tool = CrewStructuredTool( diff --git a/tests/utilities/evaluators/test_crew_evaluator_handler.py b/tests/utilities/evaluators/test_crew_evaluator_handler.py index 649c25998..4fbe2b2d4 100644 --- a/tests/utilities/evaluators/test_crew_evaluator_handler.py +++ b/tests/utilities/evaluators/test_crew_evaluator_handler.py @@ -12,7 +12,7 @@ from crewai.utilities.evaluators.crew_evaluator_handler import ( ) -class TestCrewEvaluator: +class InternalCrewEvaluator: @pytest.fixture def crew_planner(self): agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1") diff --git a/tests/utilities/test_knowledge_planning.py b/tests/utilities/test_knowledge_planning.py new file mode 100644 index 000000000..37b6df69f --- /dev/null +++ b/tests/utilities/test_knowledge_planning.py @@ -0,0 +1,84 @@ +""" +Tests for verifying the integration of knowledge sources in the planning process. +This module ensures that agent knowledge is properly included during task planning. +""" + +from unittest.mock import patch + +import pytest + +from crewai.agent import Agent +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource +from crewai.task import Task +from crewai.utilities.planning_handler import CrewPlanner + + +@pytest.fixture +def mock_knowledge_source(): + """ + Create a mock knowledge source with test content. + Returns: + StringKnowledgeSource: + A knowledge source containing AI-related test content + """ + content = """ + Important context about AI: + 1. AI systems use machine learning algorithms + 2. Neural networks are a key component + 3. Training data is essential for good performance + """ + return StringKnowledgeSource(content=content) + +@patch('crewai.knowledge.storage.knowledge_storage.chromadb') +def test_knowledge_included_in_planning(mock_chroma): + """Test that verifies knowledge sources are properly included in planning.""" + # Mock ChromaDB collection + mock_collection = mock_chroma.return_value.get_or_create_collection.return_value + mock_collection.add.return_value = None + + # Create an agent with knowledge + agent = Agent( + role="AI Researcher", + goal="Research and explain AI concepts", + backstory="Expert in artificial intelligence", + knowledge_sources=[ + StringKnowledgeSource( + content="AI systems require careful training and validation." + ) + ] + ) + + # Create a task for the agent + task = Task( + description="Explain the basics of AI systems", + expected_output="A clear explanation of AI fundamentals", + agent=agent + ) + + # Create a crew planner + planner = CrewPlanner([task], None) + + # Get the task summary + task_summary = planner._create_tasks_summary() + + # Verify that knowledge is included in planning when present + assert "AI systems require careful training" in task_summary, \ + "Knowledge content should be present in task summary when knowledge exists" + assert '"agent_knowledge"' in task_summary, \ + "agent_knowledge field should be present in task summary when knowledge exists" + + # Verify that knowledge is properly formatted + assert isinstance(task.agent.knowledge_sources, list), \ + "Knowledge sources should be stored in a list" + assert len(task.agent.knowledge_sources) > 0, \ + "At least one knowledge source should be present" + assert task.agent.knowledge_sources[0].content in task_summary, \ + "Knowledge source content should be included in task summary" + + # Verify that other expected components are still present + assert task.description in task_summary, \ + "Task description should be present in task summary" + assert task.expected_output in task_summary, \ + "Expected output should be present in task summary" + assert agent.role in task_summary, \ + "Agent role should be present in task summary" diff --git a/tests/utilities/test_planning_handler.py b/tests/utilities/test_planning_handler.py index 85101606d..e1c27c341 100644 --- a/tests/utilities/test_planning_handler.py +++ b/tests/utilities/test_planning_handler.py @@ -1,10 +1,14 @@ -from unittest.mock import patch +from typing import Optional +from unittest.mock import MagicMock, patch import pytest +from pydantic import BaseModel from crewai.agent import Agent +from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource from crewai.task import Task from crewai.tasks.task_output import TaskOutput +from crewai.tools.base_tool import BaseTool from crewai.utilities.planning_handler import ( CrewPlanner, PlannerTaskPydanticOutput, @@ -12,7 +16,7 @@ from crewai.utilities.planning_handler import ( ) -class TestCrewPlanner: +class InternalCrewPlanner: @pytest.fixture def crew_planner(self): tasks = [ @@ -92,7 +96,72 @@ class TestCrewPlanner: tasks_summary = crew_planner._create_tasks_summary() assert isinstance(tasks_summary, str) assert tasks_summary.startswith("\n Task Number 1 - Task 1") - assert tasks_summary.endswith('"agent_tools": []\n ') + assert '"agent_tools": "agent has no tools"' in tasks_summary + # Knowledge field should not be present when empty + assert '"agent_knowledge"' not in tasks_summary + + @patch('crewai.knowledge.storage.knowledge_storage.chromadb') + def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma): + """Test task summary generation with both knowledge and tools present.""" + # Mock ChromaDB collection + mock_collection = mock_chroma.return_value.get_or_create_collection.return_value + mock_collection.add.return_value = None + + # Create mock tools with proper string descriptions and structured tool support + class MockTool(BaseTool): + name: str + description: str + + def __init__(self, name: str, description: str): + tool_data = {"name": name, "description": description} + super().__init__(**tool_data) + + def __str__(self): + return self.name + + def __repr__(self): + return self.name + + def to_structured_tool(self): + return self + + def _run(self, *args, **kwargs): + pass + + def _generate_description(self) -> str: + """Override _generate_description to avoid args_schema handling.""" + return self.description + + tool1 = MockTool("tool1", "Tool 1 description") + tool2 = MockTool("tool2", "Tool 2 description") + + # Create a task with knowledge and tools + task = Task( + description="Task with knowledge and tools", + expected_output="Expected output", + agent=Agent( + role="Test Agent", + goal="Test Goal", + backstory="Test Backstory", + tools=[tool1, tool2], + knowledge_sources=[ + StringKnowledgeSource(content="Test knowledge content") + ] + ) + ) + + # Create planner with the new task + planner = CrewPlanner([task], None) + tasks_summary = planner._create_tasks_summary() + + # Verify task summary content + assert isinstance(tasks_summary, str) + assert task.description in tasks_summary + assert task.expected_output in tasks_summary + assert '"agent_tools": [tool1, tool2]' in tasks_summary + assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary + assert task.agent.role in tasks_summary + assert task.agent.goal in tasks_summary def test_handle_crew_planning_different_llm(self, crew_planner_different_llm): with patch.object(Task, "execute_sync") as execute: diff --git a/tests/utilities/test_training_handler.py b/tests/utilities/test_training_handler.py index a4da5a3dc..17a1ceb54 100644 --- a/tests/utilities/test_training_handler.py +++ b/tests/utilities/test_training_handler.py @@ -4,7 +4,7 @@ import unittest from crewai.utilities.training_handler import CrewTrainingHandler -class TestCrewTrainingHandler(unittest.TestCase): +class InternalCrewTrainingHandler(unittest.TestCase): def setUp(self): self.handler = CrewTrainingHandler("trained_data.pkl") diff --git a/uv.lock b/uv.lock index c37a1fa4e..6c2387dd2 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,42 @@ version = 1 requires-python = ">=3.10, <3.13" resolution-markers = [ - "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'", + "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'", + "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'", + "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 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