Merge branch 'main' into DOCS/poetry.toml_update

README.md

@@ -284,3 +284,39 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 ## License
 
 CrewAI is released under the MIT License.
+
+## Frequently Asked Questions (FAQ)
+
+### Q: What is CrewAI?
+A: CrewAI is a cutting-edge framework for orchestrating role-playing, autonomous AI agents. It enables agents to work together seamlessly, tackling complex tasks through collaborative intelligence.
+
+### Q: How do I install CrewAI?
+A: You can install CrewAI using pip:
+```shell
+pip install crewai
+```
+For additional tools, use:
+```shell
+pip install 'crewai[tools]'
+```
+
+### Q: Can I use CrewAI with local models?
+A: Yes, CrewAI supports various LLMs, including local models. You can configure your agents to use local models via tools like Ollama & LM Studio. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
+
+### Q: What are the key features of CrewAI?
+A: Key features include role-based agent design, autonomous inter-agent delegation, flexible task management, process-driven execution, output saving as files, and compatibility with both open-source and proprietary models.
+
+### Q: How does CrewAI compare to other AI orchestration tools?
+A: CrewAI is designed with production in mind, offering flexibility similar to Autogen's conversational agents and structured processes like ChatDev, but with more adaptability for real-world applications.
+
+### Q: Is CrewAI open-source?
+A: Yes, CrewAI is open-source and welcomes contributions from the community.
+
+### Q: Does CrewAI collect any data?
+A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.
+
+### Q: Where can I find examples of CrewAI in action?
+A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
+
+### Q: How can I contribute to CrewAI?
+A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

docs/how-to/Langtrace-Observability.md

@@ -7,10 +7,14 @@ description: How to monitor cost, latency, and performance of CrewAI Agents usin
 
 Langtrace is an open-source, external tool that helps you set up observability and evaluations for Large Language Models (LLMs), LLM frameworks, and Vector Databases. While not built directly into CrewAI, Langtrace can be used alongside CrewAI to gain deep visibility into the cost, latency, and performance of your CrewAI Agents. This integration allows you to log hyperparameters, monitor performance regressions, and establish a process for continuous improvement of your Agents.
 
+![Overview of a select series of agent session runs](..%2Fassets%2Flangtrace1.png)
+![Overview of agent traces](..%2Fassets%2Flangtrace2.png)
+![Overview of llm traces in details](..%2Fassets%2Flangtrace3.png)
+
 ## Setup Instructions
 
 1. Sign up for [Langtrace](https://langtrace.ai/) by visiting [https://langtrace.ai/signup](https://langtrace.ai/signup).
-2. Create a project and generate an API key.
+2. Create a project, set the project type to crewAI & generate an API key.
 3. Install Langtrace in your CrewAI project using the following commands:
 
 ```bash
@@ -32,58 +36,29 @@ langtrace.init(api_key='<LANGTRACE_API_KEY>')
 from crewai import Agent, Task, Crew
 ```
 
-2. Create your CrewAI agents and tasks as usual.
-
-3. Use Langtrace's tracking functions to monitor your CrewAI operations. For example:
-
-```python
-with langtrace.trace("CrewAI Task Execution"):
-    result = crew.kickoff()
-```
-
 ### Features and Their Application to CrewAI
 
 1. **LLM Token and Cost Tracking**
+
    - Monitor the token usage and associated costs for each CrewAI agent interaction.
-   - Example:
-     ```python
-     with langtrace.trace("Agent Interaction"):
-         agent_response = agent.execute(task)
-     ```
 
 2. **Trace Graph for Execution Steps**
+
    - Visualize the execution flow of your CrewAI tasks, including latency and logs.
    - Useful for identifying bottlenecks in your agent workflows.
 
 3. **Dataset Curation with Manual Annotation**
+
    - Create datasets from your CrewAI task outputs for future training or evaluation.
-   - Example:
-     ```python
-     langtrace.log_dataset_item(task_input, agent_output, {"task_type": "research"})
-     ```
 
 4. **Prompt Versioning and Management**
+
    - Keep track of different versions of prompts used in your CrewAI agents.
    - Useful for A/B testing and optimizing agent performance.
 
 5. **Prompt Playground with Model Comparisons**
+
    - Test and compare different prompts and models for your CrewAI agents before deployment.
 
 6. **Testing and Evaluations**
    - Set up automated tests for your CrewAI agents and tasks.
-   - Example:
-     ```python
-     langtrace.evaluate(agent_output, expected_output, "accuracy")
-     ```
-
-## Monitoring New CrewAI Features
-
-CrewAI has introduced several new features that can be monitored using Langtrace:
-
-1. **Code Execution**: Monitor the performance and output of code executed by agents.
-   ```python
-   with langtrace.trace("Agent Code Execution"):
-       code_output = agent.execute_code(code_snippet)
-   ```
-
-2. **Third-party Agent Integration**: Track interactions with LlamaIndex, LangChain, and Autogen agents.

src/crewai/crew.py

@@ -584,7 +584,10 @@ def _create_manager_agent(self):
             self.manager_agent.allow_delegation = True
             manager = self.manager_agent
             if manager.tools is not None and len(manager.tools) > 0:
-                raise Exception("Manager agent should not have tools")
+                self._logger.log(
+                    "warning", "Manager agent should not have tools", color="orange"
+                )
+                manager.tools = []
             manager.tools = self.manager_agent.get_delegation_tools(self.agents)
         else:
             manager = Agent(

src/crewai/project/annotations.py

@@ -103,7 +103,8 @@ def wrapper(self, *args, **kwargs):
         for task_name in sorted_task_names:
             task_instance = tasks[task_name]()
             instantiated_tasks.append(task_instance)
-            if hasattr(task_instance, "agent"):
+            agent_instance = getattr(task_instance, "agent", None)
+            if agent_instance is not None:
                 agent_instance = task_instance.agent
                 if agent_instance.role not in agent_roles:
                     instantiated_agents.append(agent_instance)

src/crewai/task.py

@@ -6,7 +6,7 @@
 from concurrent.futures import Future
 from copy import copy
 from hashlib import md5
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
+from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
 
 from opentelemetry.trace import Span
 from pydantic import (
@@ -108,6 +108,7 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
+    processed_by_agents: Set[str] = Field(default_factory=set)
 
     _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
     _execution_span: Optional[Span] = PrivateAttr(default=None)
@@ -241,6 +242,8 @@ def _execute_core(
         self.prompt_context = context
         tools = tools or self.tools or []
 
+        self.processed_by_agents.add(agent.role)
+
         result = agent.execute_task(
             task=self,
             context=context,
@@ -273,9 +276,7 @@ def _execute_core(
             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)
 
@@ -310,8 +311,10 @@ def increment_tools_errors(self) -> None:
         """Increment the tools errors counter."""
         self.tools_errors += 1
 
-    def increment_delegations(self) -> None:
+    def increment_delegations(self, agent_name: Optional[str]) -> None:
         """Increment the delegations counter."""
+        if agent_name:
+            self.processed_by_agents.add(agent_name)
         self.delegations += 1
 
     def copy(self, agents: List["BaseAgent"]) -> "Task":

src/crewai/tools/tool_calling.py

@@ -1,8 +1,8 @@
 from typing import Any, Dict, Optional
 
+from pydantic import BaseModel, Field
 from pydantic import BaseModel as PydanticBaseModel
 from pydantic import Field as PydanticField
-from pydantic.v1 import BaseModel, Field
 
 
 class ToolCalling(BaseModel):

src/crewai/tools/tool_output_parser.py

@@ -5,7 +5,7 @@
 from langchain.output_parsers import PydanticOutputParser
 from langchain_core.exceptions import OutputParserException
 from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import ValidationError
+from pydantic import ValidationError
 
 
 class ToolOutputParser(PydanticOutputParser):

src/crewai/tools/tool_usage.py

@@ -8,6 +8,7 @@
 from langchain_openai import ChatOpenAI
 
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.utilities import I18N, Converter, ConverterError, Printer
@@ -51,7 +52,7 @@ def __init__(
         original_tools: List[Any],
         tools_description: str,
         tools_names: str,
-        task: Any,
+        task: Task,
         function_calling_llm: Any,
         agent: Any,
         action: Any,
@@ -154,7 +155,10 @@ def _use(
                     "Delegate work to coworker",
                     "Ask question to coworker",
                 ]:
-                    self.task.increment_delegations()
+                    coworker = (
+                        calling.arguments.get("coworker") if calling.arguments else None
+                    )
+                    self.task.increment_delegations(coworker)
 
                 if calling.arguments:
                     try:
@@ -241,7 +245,7 @@ def _format_result(self, result: Any) -> None:
             result = self._remember_format(result=result)  # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
         return result
 
-    def _should_remember_format(self) -> None:
+    def _should_remember_format(self) -> bool:
         return self.task.used_tools % self._remember_format_after_usages == 0
 
     def _remember_format(self, result: str) -> None:
@@ -353,10 +357,10 @@ def _tool_calling(
                     return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
                         f'{self._i18n.errors("tool_arguments_error")}'
                     )
-                calling = ToolCalling(  # type: ignore # Unexpected keyword argument "log" for "ToolCalling"
+                calling = ToolCalling(
                     tool_name=tool.name,
                     arguments=arguments,
-                    log=tool_string,
+                    log=tool_string,  # type: ignore
                 )
         except Exception as e:
             self._run_attempts += 1
@@ -404,19 +408,19 @@ def _validate_tool_input(self, tool_input: str) -> str:
                         '"' + value.replace('"', '\\"') + '"'
                     )  # Re-encapsulate with double quotes
                 elif value.isdigit():  # Check if value is a digit, hence integer
-                    formatted_value = value
+                    value = value
                 elif value.lower() in [
                     "true",
                     "false",
                     "null",
                 ]:  # Check for boolean and null values
-                    formatted_value = value.lower()
+                    value = value.lower()
                 else:
                     # Assume the value is a string and needs quotes
-                    formatted_value = '"' + value.replace('"', '\\"') + '"'
+                    value = '"' + value.replace('"', '\\"') + '"'
 
                 # Rebuild the entry with proper quoting
-                formatted_entry = f'"{key}": {formatted_value}'
+                formatted_entry = f'"{key}": {value}'
                 formatted_entries.append(formatted_entry)
 
             # Reconstruct the JSON string

src/crewai/utilities/config.py

@@ -23,17 +23,16 @@ def process_config(
     # Copy values from config (originally from YAML) to the model's attributes.
     # Only copy if the attribute isn't already set, preserving any explicitly defined values.
     for key, value in config.items():
-        if key not in model_class.model_fields:
+        if key not in model_class.model_fields or values.get(key) is not None:
             continue
-        if values.get(key) is not None:
-            continue
-        if isinstance(value, (str, int, float, bool, list)):
-            values[key] = value
-        elif isinstance(value, dict):
+
+        if isinstance(value, dict):
             if isinstance(values.get(key), dict):
                 values[key].update(value)
             else:
                 values[key] = value
+        else:
+            values[key] = value
 
     # Remove the config from values to avoid duplicate processing
     values.pop("config", None)

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -5,8 +5,7 @@
 from langchain.output_parsers import PydanticOutputParser
 from langchain_core.exceptions import OutputParserException
 from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import ValidationError
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 
 class CrewPydanticOutputParser(PydanticOutputParser):