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feature: human input per task (#395)

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* feature: human input per task

* Update executor.py

* Update executor.py

* Update executor.py

* Update executor.py

* Update executor.py

* feat: change human input for unit testing
added documentation and unit test

* Create test_agent_human_input.yaml

add yaml for test

---------

Co-authored-by: João Moura <joaomdmoura@gmail.com>
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GabeKoga
2024-04-01 10:04:56 -03:00
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parent 22ab99cbd6
commit bcf701b287
7 changed files with 449 additions and 12 deletions
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docs/core-concepts/Tasks.md
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@@ -23,6 +23,7 @@ Tasks in CrewAI can be designed to require collaboration between agents. For exa
| **Output Pydantic** *(optional)* | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output File** *(optional)* | Takes a file path and saves the output of the task on it. |
| **Callback** *(optional)* | A function to be executed after the task is completed. |
| **Human Input** *(optional)* | Indicates whether the agent should ask for feedback at the end of the task |
## Creating a Task
@@ -224,4 +225,4 @@ These validations help in maintaining the consistency and reliability of task ex
## Conclusion
Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.