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- Add parallel process type - Implement parallel flow execution - Add tests for parallel flows - Update documentation Fixes #2129 Co-Authored-By: Joe Moura <joao@crewai.com>
88 lines
4.6 KiB
Plaintext
88 lines
4.6 KiB
Plaintext
---
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title: Processes
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description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
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icon: bars-staggered
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---
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## Understanding Processes
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<Tip>
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Processes orchestrate the execution of tasks by agents, akin to project management in human teams.
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These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
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</Tip>
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## Process Implementations
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- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
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- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
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- **Parallel**: Enables concurrent execution of multiple flows, allowing transitions from one flow to multiple parallel flows for improved task parallelization. Parallel execution is automatically handled using asyncio for optimal performance.
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- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
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## The Role of Processes in Teamwork
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Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
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## Assigning Processes to a Crew
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To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
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```python
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from crewai import Crew, Process
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# Example: Creating a crew with a sequential process
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crew = Crew(
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agents=my_agents,
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tasks=my_tasks,
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process=Process.sequential
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)
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# Example: Creating a crew with a hierarchical process
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# Ensure to provide a manager_llm or manager_agent
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crew = Crew(
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agents=my_agents,
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tasks=my_tasks,
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process=Process.hierarchical,
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manager_llm="gpt-4o"
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# or
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# manager_agent=my_manager_agent
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)
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```
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**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.
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## Sequential Process
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This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
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To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
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## Hierarchical Process
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Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent or automatically creates one, requiring the specification of a manager language model (`manager_llm`). This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
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## Process Class: Detailed Overview
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The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`, `parallel`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
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## Parallel Process
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The parallel process type enables concurrent execution of multiple flows, leveraging Python's asyncio for efficient task parallelization. When using parallel execution:
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- Multiple start methods are executed concurrently
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- Listeners can run in parallel when triggered by the same method
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- State consistency is maintained through thread-safe operations
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- Execution timing and order are preserved where necessary
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Example of parallel flow execution:
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```python
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from crewai import Crew, Process
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# Create a crew with parallel process
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crew = Crew(
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agents=my_agents,
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tasks=my_tasks,
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process=Process.parallel
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)
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```
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## Conclusion
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The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents.
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This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.
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