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# Swarms 5.8.1 Feature Documentation
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## 1. Enhanced Command Line Interface (CLI)
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### 1.1 Integrated Onboarding Process
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```bash
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$ swarms onboarding
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```
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### 1.2 Run Agents Command
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```bash
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$ swarms run-agents --yaml-file agents.yaml
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```
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This command allows users to execute multiple agents defined in a YAML file. Here's the process:
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1. The command reads the specified YAML file (`agents.yaml` in this case).
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2. It parses the YAML content, extracting the configuration for each agent.
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3. For each agent defined in the YAML:
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- It creates an instance of the agent with the specified parameters.
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- It sets up the agent's environment (model, temperature, max tokens, etc.).
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- It assigns the given task to the agent.
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- It executes the agent, respecting parameters like `max_loops`, `autosave`, and `verbose`.
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4. The results from all agents are collected and presented to the user.
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The YAML file structure allows users to define multiple agents with different configurations, making it easy to run complex, multi-agent tasks from the command line.
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### 1.3 Generate Prompt Feature
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```bash
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$ swarms generate-prompt --prompt "Create a marketing strategy for a new product launch"
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```
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This feature leverages Swarms' language model to generate expanded or refined prompts:
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1. The command takes the user's input prompt as a starting point.
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2. It likely sends this prompt to a pre-configured language model (possibly GPT-4 or a similar model).
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3. The model then generates an expanded or more detailed version of the prompt.
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4. The generated prompt is returned to the user, possibly with options to further refine or save it.
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This feature can help users create more effective prompts for their agents or other AI tasks.
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## 2. New Prompt Management System
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### 2.1 Prompt Class
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The new `Prompt` class provides a robust system for managing and versioning prompts:
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```python
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from swarms import Prompt
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marketing_prompt = Prompt(content="Initial marketing strategy draft", autosave=True)
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print(marketing_prompt.get_prompt())
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```
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Key features of the `Prompt` class:
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1. **Initialization**: The class is initialized with initial content and an `autosave` option.
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2. **Editing**:
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```python
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marketing_prompt.edit_prompt("Updated marketing strategy with social media focus")
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```
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This method updates the prompt content and, if `autosave` is True, automatically saves the new version.
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3. **Retrieval**:
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```python
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current_content = marketing_prompt.get_prompt()
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```
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This method returns the current content of the prompt.
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4. **Version History**:
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```python
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print(f"Edit history: {marketing_prompt.edit_history}")
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```
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The class maintains a history of edits, allowing users to track changes over time.
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5. **Rollback**:
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```python
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marketing_prompt.rollback(1)
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```
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This feature allows users to revert to a previous version of the prompt.
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6. **Duplicate Prevention**:
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The class includes logic to prevent duplicate edits, raising a `ValueError` if an attempt is made to save the same content twice in a row.
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This system provides a powerful way to manage prompts, especially for complex projects where prompt engineering and iteration are crucial.
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## 3. Upcoming Features Preview
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### 3.1 Enhanced Agent Execution Capabilities
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The preview code demonstrates planned enhancements for agent execution:
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```python
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from swarms import Agent, ExecutionEnvironment
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my_agent = Agent(name="data_processor")
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cpu_env = ExecutionEnvironment(type="cpu", cores=4)
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my_agent.run(environment=cpu_env)
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gpu_env = ExecutionEnvironment(type="gpu", device_id=0)
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my_agent.run(environment=gpu_env)
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fractional_env = ExecutionEnvironment(type="fractional", cpu_fraction=0.5, gpu_fraction=0.3)
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my_agent.run(environment=fractional_env)
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```
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This upcoming feature will allow for more fine-grained control over the execution environment:
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1. **CPU Execution**: Users can specify the number of CPU cores to use.
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2. **GPU Execution**: Allows selection of a specific GPU device.
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3. **Fractionalized Execution**: Enables partial allocation of CPU and GPU resources.
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These features will provide users with greater flexibility in resource allocation, potentially improving performance and allowing for more efficient use of available hardware.
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