Kye Gomez
6 months ago
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# Swarms Framework: Integrating and Customizing Agent Libraries
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Agent-based systems have emerged as a powerful paradigm for solving complex problems and automating tasks.
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The swarms framework offers a flexible and extensible approach to working with various agent libraries, allowing developers to create custom agents and integrate them seamlessly into their projects.
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In this comprehensive guide, we'll explore the swarms framework, discuss agent handling, and demonstrate how to build custom agents using swarms. We'll also cover the integration of popular agent libraries such as Langchain, Griptape, CrewAI, and Autogen.
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## Table of Contents
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1. [Introduction to the Swarms Framework](#introduction-to-the-swarms-framework)
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2. [The Need for Wrappers](#the-need-for-wrappers)
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3. [Building Custom Agents with Swarms](#building-custom-agents-with-swarms)
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4. [Integrating Third-Party Agent Libraries](#integrating-third-party-agent-libraries)
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- [Griptape Integration](#griptape-integration)
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- [Langchain Integration](#langchain-integration)
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- [CrewAI Integration](#crewai-integration)
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- [Autogen Integration](#autogen-integration)
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5. [Advanced Agent Handling Techniques](#advanced-agent-handling-techniques)
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6. [Best Practices for Custom Agent Development](#best-practices-for-custom-agent-development)
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7. [Future Directions and Challenges](#future-directions-and-challenges)
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8. [Conclusion](#conclusion)
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## 1. Introduction to the Swarms Framework
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The swarms framework is a powerful and flexible system designed to facilitate the creation, management, and coordination of multiple AI agents. It provides a standardized interface for working with various agent types, allowing developers to leverage the strengths of different agent libraries while maintaining a consistent programming model.
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At its core, the swarms framework is built around the concept of a parent `Agent` class, which serves as a foundation for creating custom agents and integrating third-party agent libraries. This approach offers several benefits:
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1. **Consistency**: By wrapping different agent implementations with a common interface, developers can work with a unified API across various agent types.
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2. **Extensibility**: The framework makes it easy to add new agent types or customize existing ones without affecting the overall system architecture.
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3. **Interoperability**: Agents from different libraries can communicate and collaborate seamlessly within the swarms ecosystem.
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4. **Scalability**: The standardized approach allows for easier scaling of agent-based systems, from simple single-agent applications to complex multi-agent swarms.
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## 2. The Need for Wrappers
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As the field of AI and agent-based systems continues to grow, numerous libraries and frameworks have emerged, each with its own strengths and specialized features. While this diversity offers developers a wide range of tools to choose from, it also presents challenges in terms of integration and interoperability.
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This is where the concept of wrappers becomes crucial. By creating wrappers around different agent libraries, we can:
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1. **Unify interfaces**: Standardize the way we interact with agents, regardless of their underlying implementation.
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2. **Simplify integration**: Make it easier to incorporate new agent libraries into existing projects.
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3. **Enable composition**: Allow for the creation of complex agent systems that leverage the strengths of multiple libraries.
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4. **Facilitate maintenance**: Centralize the management of agent-related code and reduce the impact of library-specific changes.
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In the context of the swarms framework, wrappers take the form of custom classes that inherit from the parent `Agent` class. These wrapper classes encapsulate the functionality of specific agent libraries while exposing a consistent interface that aligns with the swarms framework.
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## 3. Building Custom Agents with Swarms
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To illustrate the process of building custom agents using the swarms framework, let's start with a basic example of creating a custom agent class:
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```python
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from swarms import Agent
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class MyCustomAgent(Agent):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Custom initialization logic
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def custom_method(self, *args, **kwargs):
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# Implement custom logic here
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pass
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def run(self, task, *args, **kwargs):
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# Customize the run method
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response = super().run(task, *args, **kwargs)
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# Additional custom logic
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return response
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```
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This example demonstrates the fundamental structure of a custom agent class within the swarms framework. Let's break down the key components:
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1. **Inheritance**: The class inherits from the `Agent` parent class, ensuring it adheres to the swarms framework's interface.
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2. **Initialization**: The `__init__` method calls the parent class's initializer and can include additional custom initialization logic.
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3. **Custom methods**: You can add any number of custom methods to extend the agent's functionality.
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4. **Run method**: The `run` method is a key component of the agent interface. By overriding this method, you can customize how the agent processes tasks while still leveraging the parent class's functionality.
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To create more sophisticated custom agents, you can expand on this basic structure by adding features such as:
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- **State management**: Implement methods to manage the agent's internal state.
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- **Communication protocols**: Define how the agent interacts with other agents in the swarm.
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- **Learning capabilities**: Incorporate machine learning models or adaptive behaviors.
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- **Specialized task handling**: Create methods for dealing with specific types of tasks or domains.
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By leveraging these custom agent classes, developers can create highly specialized and adaptive agents tailored to their specific use cases while still benefiting from the standardized interface provided by the swarms framework.
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## 4. Integrating Third-Party Agent Libraries
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One of the key strengths of the swarms framework is its ability to integrate with various third-party agent libraries. In this section, we'll explore how to create wrappers for popular agent libraries, including Griptape, Langchain, CrewAI, and Autogen.
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### Griptape Integration
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Griptape is a powerful library for building AI agents with a focus on composability and tool use. Let's create a wrapper for a Griptape agent:
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```python
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from typing import List, Optional
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from griptape.structures import Agent as GriptapeAgent
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from griptape.tools import FileManager, TaskMemoryClient, WebScraper
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from swarms import Agent
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class GriptapeAgentWrapper(Agent):
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"""
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A wrapper class for the GriptapeAgent from the griptape library.
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"""
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def __init__(self, name: str, tools: Optional[List] = None, *args, **kwargs):
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"""
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Initialize the GriptapeAgentWrapper.
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Parameters:
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- name: The name of the agent.
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- tools: A list of tools to be used by the agent. If not provided, default tools will be used.
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- *args, **kwargs: Additional arguments to be passed to the parent class constructor.
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"""
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super().__init__(*args, **kwargs)
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self.name = name
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self.tools = tools or [
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WebScraper(off_prompt=True),
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TaskMemoryClient(off_prompt=True),
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FileManager()
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]
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self.griptape_agent = GriptapeAgent(
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input=f"I am {name}, an AI assistant. How can I help you?",
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tools=self.tools
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)
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def run(self, task: str, *args, **kwargs) -> str:
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"""
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Run a task using the GriptapeAgent.
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Parameters:
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- task: The task to be performed by the agent.
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Returns:
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- The response from the GriptapeAgent as a string.
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"""
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response = self.griptape_agent.run(task, *args, **kwargs)
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return str(response)
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def add_tool(self, tool) -> None:
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"""
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Add a tool to the agent.
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Parameters:
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- tool: The tool to be added.
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"""
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self.tools.append(tool)
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self.griptape_agent = GriptapeAgent(
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input=f"I am {self.name}, an AI assistant. How can I help you?",
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tools=self.tools
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)
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# Usage example
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griptape_wrapper = GriptapeAgentWrapper("GriptapeAssistant")
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result = griptape_wrapper.run("Load https://example.com, summarize it, and store it in a file called example_summary.txt.")
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print(result)
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```
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This wrapper encapsulates the functionality of a Griptape agent while exposing it through the swarms framework's interface. It allows for easy customization of tools and provides a simple way to execute tasks using the Griptape agent.
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### Langchain Integration
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Langchain is a popular framework for developing applications powered by language models. Here's an example of how we can create a wrapper for a Langchain agent:
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```python
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from typing import List, Optional
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from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
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from langchain.chains import LLMChain
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from langchain.llms import OpenAI
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from langchain.prompts import StringPromptTemplate
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from langchain.tools import DuckDuckGoSearchRun
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from swarms import Agent
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class LangchainAgentWrapper(Agent):
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"""
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Initialize the LangchainAgentWrapper.
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Args:
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name (str): The name of the agent.
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tools (List[Tool]): The list of tools available to the agent.
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llm (Optional[OpenAI], optional): The OpenAI language model to use. Defaults to None.
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"""
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def __init__(
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self,
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name: str,
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tools: List[Tool],
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llm: Optional[OpenAI] = None,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.name = name
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self.tools = tools
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self.llm = llm or OpenAI(temperature=0)
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prompt = StringPromptTemplate.from_template(
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"You are {name}, an AI assistant. Answer the following question: {question}"
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)
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llm_chain = LLMChain(llm=self.llm, prompt=prompt)
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tool_names = [tool.name for tool in self.tools]
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self.agent = LLMSingleActionAgent(
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llm_chain=llm_chain,
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output_parser=None,
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stop=["\nObservation:"],
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allowed_tools=tool_names,
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)
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self.agent_executor = AgentExecutor.from_agent_and_tools(
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agent=self.agent, tools=self.tools, verbose=True
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)
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def run(self, task: str, *args, **kwargs):
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"""
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Run the agent with the given task.
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Args:
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task (str): The task to be performed by the agent.
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Returns:
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Any: The result of the agent's execution.
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"""
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try:
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return self.agent_executor.run(task)
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except Exception as e:
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print(f"An error occurred: {e}")
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# Usage example
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search_tool = DuckDuckGoSearchRun()
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tools = [
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Tool(
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name="Search",
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func=search_tool.run,
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description="Useful for searching the internet",
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)
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]
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langchain_wrapper = LangchainAgentWrapper("LangchainAssistant", tools)
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result = langchain_wrapper.run("What is the capital of France?")
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print(result)
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```
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This wrapper integrates a Langchain agent into the swarms framework, allowing for easy use of Langchain's powerful features such as tool use and multi-step reasoning.
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### CrewAI Integration
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CrewAI is a library focused on creating and managing teams of AI agents. Let's create a wrapper for a CrewAI agent:
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```python
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from swarms import Agent
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from crewai import Agent as CrewAIAgent
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from crewai import Task, Crew, Process
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class CrewAIAgentWrapper(Agent):
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def __init__(self, name, role, goal, backstory, tools=None, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.name = name
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self.crewai_agent = CrewAIAgent(
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role=role,
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goal=goal,
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backstory=backstory,
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verbose=True,
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allow_delegation=False,
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tools=tools or []
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)
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def run(self, task, *args, **kwargs):
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crew_task = Task(
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description=task,
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agent=self.crewai_agent
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)
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crew = Crew(
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agents=[self.crewai_agent],
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tasks=[crew_task],
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process=Process.sequential
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)
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result = crew.kickoff()
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return result
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# Usage example
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from crewai_tools import SerperDevTool
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search_tool = SerperDevTool()
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crewai_wrapper = CrewAIAgentWrapper(
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"ResearchAnalyst",
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role='Senior Research Analyst',
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goal='Uncover cutting-edge developments in AI and data science',
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backstory="""You work at a leading tech think tank.
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Your expertise lies in identifying emerging trends.
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You have a knack for dissecting complex data and presenting actionable insights.""",
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tools=[search_tool]
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)
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result = crewai_wrapper.run("Analyze the latest trends in quantum computing and summarize the key findings.")
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print(result)
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```
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This wrapper allows us to use CrewAI agents within the swarms framework, leveraging CrewAI's focus on role-based agents and collaborative task execution.
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### Autogen Integration
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Autogen is a framework for building conversational AI agents. Here's how we can create a wrapper for an Autogen agent:
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```python
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from swarms import Agent
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from autogen import ConversableAgent
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class AutogenAgentWrapper(Agent):
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def __init__(self, name, llm_config, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.name = name
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self.autogen_agent = ConversableAgent(
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name=name,
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llm_config=llm_config,
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code_execution_config=False,
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function_map=None,
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human_input_mode="NEVER"
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)
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def run(self, task, *args, **kwargs):
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messages = [{"content": task, "role": "user"}]
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response = self.autogen_agent.generate_reply(messages)
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return response
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# Usage example
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import os
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llm_config = {
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"config_list": [{"model": "gpt-4", "api_key": os.environ.get("OPENAI_API_KEY")}]
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}
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autogen_wrapper = AutogenAgentWrapper("AutogenAssistant", llm_config)
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result = autogen_wrapper.run("Tell me a joke about programming.")
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print(result)
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```
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This wrapper integrates Autogen's ConversableAgent into the swarms framework, allowing for easy use of Autogen's conversational AI capabilities.
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By creating these wrappers, we can seamlessly integrate agents from various libraries into the swarms framework, allowing for a unified approach to agent management and task execution.
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## 5. Advanced Agent Handling Techniques
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As you build more complex systems using the swarms framework and integrated agent libraries, you'll need to employ advanced techniques for agent handling. Here are some strategies to consider:
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### 1. Dynamic Agent Creation
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Implement a factory pattern to create agents dynamically based on task requirements:
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```python
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class AgentFactory:
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@staticmethod
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def create_agent(agent_type, *args, **kwargs):
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if agent_type == "griptape":
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return GriptapeAgentWrapper(*args, **kwargs)
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elif agent_type == "langchain":
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return LangchainAgentWrapper(*args, **kwargs)
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elif agent_type == "crewai":
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return CrewAIAgentWrapper(*args, **kwargs)
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elif agent_type == "autogen":
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return AutogenAgentWrapper(*args, **kwargs)
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else:
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raise ValueError(f"Unknown agent type: {agent_type}")
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# Usage
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agent = AgentFactory.create_agent("griptape", "DynamicGriptapeAgent")
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```
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### 2. Agent Pooling
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Implement an agent pool to manage and reuse agents efficiently:
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```python
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from queue import Queue
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class AgentPool:
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def __init__(self, pool_size=5):
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self.pool = Queue(maxsize=pool_size)
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self.pool_size = pool_size
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def get_agent(self, agent_type, *args, **kwargs):
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if not self.pool.empty():
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return self.pool.get()
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else:
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return AgentFactory.create_agent(agent_type, *args, **kwargs)
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def release_agent(self, agent):
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if self.pool.qsize() < self.pool_size:
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self.pool.put(agent)
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# Usage
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pool = AgentPool()
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agent = pool.get_agent("langchain", "PooledLangchainAgent")
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result = agent.run("Perform a task")
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pool.release_agent(agent)
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```
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### 3. Agent Composition
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Create composite agents that combine the capabilities of multiple agent types:
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```python
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class CompositeAgent(Agent):
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def __init__(self, name, agents):
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super().__init__()
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self.name = name
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self.agents = agents
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def run(self, task):
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results = []
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for agent in self.agents:
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results.append(agent.run(task))
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return self.aggregate_results(results)
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def aggregate_results(self, results):
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# Implement your own logic to combine results
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return "\n".join(results)
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# Usage
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griptape_agent = GriptapeAgentWrapper("GriptapeComponent")
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langchain_agent = LangchainAgentWrapper("LangchainComponent", [])
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composite_agent = CompositeAgent("CompositeAssistant", [griptape_agent, langchain_agent])
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result = composite_agent.run("Analyze the pros and cons of quantum computing")
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```
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### 4. Agent Specialization
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Create specialized agents for specific domains or tasks:
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```python
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class DataAnalysisAgent(Agent):
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def __init__(self, name, analysis_tools):
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super().__init__()
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self.name = name
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self.analysis_tools = analysis_tools
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def run(self, data):
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results = {}
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for tool in self.analysis_tools:
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results[tool.name] = tool.analyze(data)
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return results
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# Usage
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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class AnalysisTool:
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def __init__(self, name, func):
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self.name = name
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self.func = func
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def analyze(self, data):
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return self.func(data)
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tools = [
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AnalysisTool("Descriptive Stats", lambda data: data.describe()),
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AnalysisTool("Correlation", lambda data: data.corr()),
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AnalysisTool("PCA", lambda data: PCA().fit_transform(StandardScaler().fit_transform(data)))
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]
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data_agent = DataAnalysisAgent("DataAnalyst", tools)
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df = pd.read_csv("sample_data.csv")
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analysis_results = data_agent.run(df)
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```
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### 5. Agent Monitoring and Logging
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Implement a monitoring system to track agent performance and log their activities:
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```python
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import logging
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||||
from functools import wraps
|
||||
|
||||
def log_agent_activity(func):
|
||||
@wraps(func)
|
||||
def wrapper(self, *args, **kwargs):
|
||||
logging.info(f"Agent {self.name} started task: {args[0]}")
|
||||
result = func(self, *args, **kwargs)
|
||||
logging.info(f"Agent {self.name} completed task. Result length: {len(str(result))}")
|
||||
return result
|
||||
return wrapper
|
||||
|
||||
class MonitoredAgent(Agent):
|
||||
def __init__(self, name, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
|
||||
@log_agent_activity
|
||||
def run(self, task, *args, **kwargs):
|
||||
return super().run(task, *args, **kwargs)
|
||||
|
||||
# Usage
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
monitored_agent = MonitoredAgent("MonitoredGriptapeAgent")
|
||||
result = monitored_agent.run("Summarize the latest AI research papers")
|
||||
```
|
||||
|
||||
## 6. Best Practices for Custom Agent Development
|
||||
|
||||
When developing custom agents using the swarms framework, consider the following best practices:
|
||||
|
||||
1. **Modular Design**: Design your agents with modularity in mind. Break down complex functionality into smaller, reusable components.
|
||||
|
||||
2. **Consistent Interfaces**: Maintain consistent interfaces across your custom agents to ensure interoperability within the swarms framework.
|
||||
|
||||
3. **Error Handling**: Implement robust error handling and graceful degradation in your agents to ensure system stability.
|
||||
|
||||
4. **Performance Optimization**: Optimize your agents for performance, especially when dealing with resource-intensive tasks or large-scale deployments.
|
||||
|
||||
5. **Testing and Validation**: Develop comprehensive test suites for your custom agents to ensure their reliability and correctness.
|
||||
|
||||
6. **Documentation**: Provide clear and detailed documentation for your custom agents, including their capabilities, limitations, and usage examples.
|
||||
|
||||
7. **Versioning**: Implement proper versioning for your custom agents to manage updates and maintain backwards compatibility.
|
||||
|
||||
8. **Security Considerations**: Implement security best practices, especially when dealing with sensitive data or integrating with external services.
|
||||
|
||||
Here's an example that incorporates some of these best practices:
|
||||
|
||||
```python
|
||||
import logging
|
||||
from typing import Dict, Any
|
||||
from swarms import Agent
|
||||
|
||||
class SecureCustomAgent(Agent):
|
||||
def __init__(self, name: str, api_key: str, version: str = "1.0.0", *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
self._api_key = api_key # Store sensitive data securely
|
||||
self.version = version
|
||||
self.logger = logging.getLogger(f"{self.__class__.__name__}.{self.name}")
|
||||
|
||||
def run(self, task: str, *args, **kwargs) -> Dict[str, Any]:
|
||||
try:
|
||||
self.logger.info(f"Agent {self.name} (v{self.version}) starting task: {task}")
|
||||
result = self._process_task(task)
|
||||
self.logger.info(f"Agent {self.name} completed task successfully")
|
||||
return {"status": "success", "result": result}
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error in agent {self.name}: {str(e)}")
|
||||
return {"status": "error", "message": str(e)}
|
||||
|
||||
def _process_task(self, task: str) -> str:
|
||||
# Implement the core logic of your agent here
|
||||
# This is a placeholder implementation
|
||||
return f"Processed task: {task}"
|
||||
|
||||
@property
|
||||
def api_key(self) -> str:
|
||||
# Provide a secure way to access the API key
|
||||
return self._api_key
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<{self.__class__.__name__} name='{self.name}' version='{self.version}'>"
|
||||
|
||||
# Usage
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
secure_agent = SecureCustomAgent("SecureAgent", api_key="your-api-key-here")
|
||||
result = secure_agent.run("Perform a secure operation")
|
||||
print(result)
|
||||
```
|
||||
|
||||
This example demonstrates several best practices:
|
||||
- Modular design with separate methods for initialization and task processing
|
||||
- Consistent interface adhering to the swarms framework
|
||||
- Error handling and logging
|
||||
- Secure storage of sensitive data (API key)
|
||||
- Version tracking
|
||||
- Type hinting for improved code readability and maintainability
|
||||
- Informative string representation of the agent
|
||||
|
||||
## 7. Future Directions and Challenges
|
||||
|
||||
As the field of AI and agent-based systems continues to evolve, the swarms framework and its ecosystem of integrated agent libraries will face new opportunities and challenges. Some potential future directions and areas of focus include:
|
||||
|
||||
1. **Enhanced Interoperability**: Developing more sophisticated protocols for agent communication and collaboration across different libraries and frameworks.
|
||||
|
||||
2. **Scalability**: Improving the framework's ability to handle large-scale swarms of agents, potentially leveraging distributed computing techniques.
|
||||
|
||||
3. **Adaptive Learning**: Incorporating more advanced machine learning techniques to allow agents to adapt and improve their performance over time.
|
||||
|
||||
4. **Ethical AI**: Integrating ethical considerations and safeguards into the agent development process to ensure responsible AI deployment.
|
||||
|
||||
5. **Human-AI Collaboration**: Exploring new paradigms for human-AI interaction and collaboration within the swarms framework.
|
||||
|
||||
6. **Domain-Specific Optimizations**: Developing specialized agent types and tools for specific industries or problem domains.
|
||||
|
||||
7. **Explainability and Transparency**: Improving the ability to understand and explain agent decision-making processes.
|
||||
|
||||
8. **Security and Privacy**: Enhancing the framework's security features to protect against potential vulnerabilities and ensure data privacy.
|
||||
|
||||
As these areas develop, developers working with the swarms framework will need to stay informed about new advancements and be prepared to adapt their agent implementations accordingly.
|
||||
|
||||
## 8. Conclusion
|
||||
|
||||
The swarms framework provides a powerful and flexible foundation for building custom agents and integrating various agent libraries. By leveraging the techniques and best practices discussed in this guide, developers can create sophisticated, efficient, and scalable agent-based systems.
|
||||
|
||||
The ability to seamlessly integrate agents from libraries like Griptape, Langchain, CrewAI, and Autogen opens up a world of possibilities for creating diverse and specialized AI applications. Whether you're building a complex multi-agent system for data analysis, a conversational AI platform, or a collaborative problem-solving environment, the swarms framework offers the tools and flexibility to bring your vision to life.
|
||||
|
||||
As you embark on your journey with the swarms framework, remember that the field of AI and agent-based systems is rapidly evolving. Stay curious, keep experimenting, and don't hesitate to push the boundaries of what's possible with custom agents and integrated libraries.
|
||||
|
||||
By embracing the power of the swarms framework and the ecosystem of agent libraries it supports, you're well-positioned to create the next generation of intelligent, adaptive, and collaborative AI systems. Happy agent building!
|
||||
@ -0,0 +1,205 @@
|
||||
# Swarm Architectures
|
||||
|
||||
### Hierarchical Swarm
|
||||
|
||||
**Overview:**
|
||||
A Hierarchical Swarm architecture organizes the agents in a tree-like structure. Higher-level agents delegate tasks to lower-level agents, which can further divide tasks among themselves. This structure allows for efficient task distribution and scalability.
|
||||
|
||||
**Use-Cases:**
|
||||
- Complex decision-making processes where tasks can be broken down into subtasks.
|
||||
- Multi-stage workflows such as data processing pipelines or hierarchical reinforcement learning.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Root Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
B1 --> C1[Sub-Agent 1.1]
|
||||
B1 --> C2[Sub-Agent 1.2]
|
||||
B2 --> C3[Sub-Agent 2.1]
|
||||
B2 --> C4[Sub-Agent 2.2]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Parallel Swarm
|
||||
|
||||
**Overview:**
|
||||
In a Parallel Swarm architecture, multiple agents operate independently and simultaneously on different tasks. Each agent works on its own task without dependencies on the others.
|
||||
|
||||
**Use-Cases:**
|
||||
- Tasks that can be processed independently, such as parallel data analysis.
|
||||
- Large-scale simulations where multiple scenarios are run in parallel.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph LR
|
||||
A[Coordinator Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
A --> B3[Sub-Agent 3]
|
||||
A --> B4[Sub-Agent 4]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Sequential Swarm
|
||||
|
||||
**Overview:**
|
||||
A Sequential Swarm architecture processes tasks in a linear sequence. Each agent completes its task before passing the result to the next agent in the chain. This architecture ensures orderly processing and is useful when tasks have dependencies.
|
||||
|
||||
**Use-Cases:**
|
||||
- Workflows where each step depends on the previous one, such as assembly lines or sequential data processing.
|
||||
- Scenarios requiring strict order of operations.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[First Agent] --> B[Second Agent]
|
||||
B --> C[Third Agent]
|
||||
C --> D[Fourth Agent]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Round Robin Swarm
|
||||
|
||||
**Overview:**
|
||||
In a Round Robin Swarm architecture, tasks are distributed cyclically among a set of agents. Each agent takes turns handling tasks in a rotating order, ensuring even distribution of workload.
|
||||
|
||||
**Use-Cases:**
|
||||
- Load balancing in distributed systems.
|
||||
- Scenarios requiring fair distribution of tasks to avoid overloading any single agent.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Coordinator Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
A --> B3[Sub-Agent 3]
|
||||
A --> B4[Sub-Agent 4]
|
||||
B1 --> A
|
||||
B2 --> A
|
||||
B3 --> A
|
||||
B4 --> A
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Federated Swarm
|
||||
|
||||
**Overview:**
|
||||
A Federated Swarm architecture involves multiple independent swarms collaborating to complete a task. Each swarm operates autonomously but can share information and results with other swarms.
|
||||
|
||||
**Use-Cases:**
|
||||
- Distributed learning systems where data is processed across multiple nodes.
|
||||
- Scenarios requiring collaboration between different teams or departments.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Central Coordinator]
|
||||
subgraph Swarm1
|
||||
B1[Agent 1.1] --> B2[Agent 1.2]
|
||||
B2 --> B3[Agent 1.3]
|
||||
end
|
||||
subgraph Swarm2
|
||||
C1[Agent 2.1] --> C2[Agent 2.2]
|
||||
C2 --> C3[Agent 2.3]
|
||||
end
|
||||
subgraph Swarm3
|
||||
D1[Agent 3.1] --> D2[Agent 3.2]
|
||||
D2 --> D3[Agent 3.3]
|
||||
end
|
||||
B1 --> A
|
||||
C1 --> A
|
||||
D1 --> A
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Star Swarm
|
||||
|
||||
**Overview:**
|
||||
A Star Swarm architecture features a central agent that coordinates the activities of several peripheral agents. The central agent assigns tasks to the peripheral agents and aggregates their results.
|
||||
|
||||
**Use-Cases:**
|
||||
- Centralized decision-making processes.
|
||||
- Scenarios requiring a central authority to coordinate multiple workers.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Central Agent] --> B1[Peripheral Agent 1]
|
||||
A --> B2[Peripheral Agent 2]
|
||||
A --> B3[Peripheral Agent 3]
|
||||
A --> B4[Peripheral Agent 4]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Mesh Swarm
|
||||
|
||||
**Overview:**
|
||||
A Mesh Swarm architecture allows for a fully connected network of agents where each agent can communicate with any other agent. This setup provides high flexibility and redundancy.
|
||||
|
||||
**Use-Cases:**
|
||||
- Complex systems requiring high fault tolerance and redundancy.
|
||||
- Scenarios involving dynamic and frequent communication between agents.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A1[Agent 1] --> A2[Agent 2]
|
||||
A1 --> A3[Agent 3]
|
||||
A1 --> A4[Agent 4]
|
||||
A2 --> A3
|
||||
A2 --> A4
|
||||
A3 --> A4
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Cascade Swarm
|
||||
|
||||
**Overview:**
|
||||
A Cascade Swarm architecture involves a chain of agents where each agent triggers the next one in a cascade effect. This is useful for scenarios where tasks need to be processed in stages, and each stage initiates the next.
|
||||
|
||||
**Use-Cases:**
|
||||
- Multi-stage processing tasks such as data transformation pipelines.
|
||||
- Event-driven architectures where one event triggers subsequent actions.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Trigger Agent] --> B[Agent 1]
|
||||
B --> C[Agent 2]
|
||||
C --> D[Agent 3]
|
||||
D --> E[Agent 4]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Hybrid Swarm
|
||||
|
||||
**Overview:**
|
||||
A Hybrid Swarm architecture combines elements of various architectures to suit specific needs. It might integrate hierarchical and parallel components, or mix sequential and round robin patterns.
|
||||
|
||||
**Use-Cases:**
|
||||
- Complex workflows requiring a mix of different processing strategies.
|
||||
- Custom scenarios tailored to specific operational requirements.
|
||||
|
||||
**Mermaid Graph:**
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Root Agent] --> B1[Sub-Agent 1]
|
||||
A --> B2[Sub-Agent 2]
|
||||
B1 --> C1[Parallel Agent 1]
|
||||
B1 --> C2[Parallel Agent 2]
|
||||
B2 --> C3[Sequential Agent 1]
|
||||
C3 --> C4[Sequential Agent 2]
|
||||
C3 --> C5[Sequential Agent 3]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
These swarm architectures provide different models for organizing and orchestrating large language models (LLMs) to perform various tasks efficiently. Depending on the specific requirements of your project, you can choose the appropriate architecture or even combine elements from multiple architectures to create a hybrid solution.
|
||||
@ -0,0 +1,71 @@
|
||||
import os
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from autogen import ConversableAgent
|
||||
from loguru import logger
|
||||
|
||||
from swarms import Agent
|
||||
|
||||
|
||||
class AutogenAgentWrapper(Agent):
|
||||
"""
|
||||
Wrapper class for the ConversableAgent that provides additional functionality.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
llm_config: Dict[str, Any],
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
):
|
||||
"""
|
||||
Initialize the AutogenAgentWrapper.
|
||||
|
||||
Args:
|
||||
name (str): The name of the agent.
|
||||
llm_config (Dict[str, Any]): The configuration for the ConversableAgent.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
"""
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
self.autogen_agent = ConversableAgent(
|
||||
name=name,
|
||||
llm_config=llm_config,
|
||||
code_execution_config=False,
|
||||
function_map=None,
|
||||
human_input_mode="NEVER",
|
||||
)
|
||||
|
||||
def run(self, task: str, *args: Any, **kwargs: Any) -> Optional[str]:
|
||||
"""
|
||||
Run the AutogenAgentWrapper.
|
||||
|
||||
Args:
|
||||
task (str): The task to be performed by the agent.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
Optional[str]: The response generated by the agent, or None if an error occurred.
|
||||
"""
|
||||
try:
|
||||
messages = [{"content": task, "role": "user"}]
|
||||
response = self.autogen_agent.generate_reply(messages)
|
||||
logger.info("Task: %s, Response: %s", task, response)
|
||||
return response
|
||||
except Exception as e:
|
||||
logger.error("An error occurred: %s", str(e))
|
||||
return None
|
||||
|
||||
|
||||
llm_config = {
|
||||
"config_list": [
|
||||
{"model": "gpt-4", "api_key": os.environ.get("OPENAI_API_KEY")}
|
||||
]
|
||||
}
|
||||
|
||||
autogen_wrapper = AutogenAgentWrapper("AutogenAssistant", llm_config)
|
||||
result = autogen_wrapper.run("Tell me a joke about programming.")
|
||||
print(result)
|
||||
@ -0,0 +1,92 @@
|
||||
from typing import List, Optional
|
||||
|
||||
from crewai import Agent as CrewAIAgent
|
||||
from crewai import Crew, Process, Task
|
||||
from crewai_tools import SerperDevTool
|
||||
from loguru import logger
|
||||
|
||||
from swarms import Agent
|
||||
|
||||
|
||||
class CrewAIAgentWrapper(Agent):
|
||||
"""
|
||||
Initialize the CrewAIAgentWrapper.
|
||||
|
||||
Args:
|
||||
name (str): The name of the agent.
|
||||
role (str): The role of the agent.
|
||||
goal (str): The goal of the agent.
|
||||
backstory (str): The backstory of the agent.
|
||||
tools (Optional[List]): The tools used by the agent (default: None).
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
role: str,
|
||||
goal: str,
|
||||
backstory: str,
|
||||
tools: Optional[List] = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
self.crewai_agent = CrewAIAgent(
|
||||
role=role,
|
||||
goal=goal,
|
||||
backstory=backstory,
|
||||
verbose=True,
|
||||
allow_delegation=False,
|
||||
tools=tools or [],
|
||||
*args,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Run the agent's task.
|
||||
|
||||
Args:
|
||||
task (str): The task to be performed by the agent.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Returns:
|
||||
Any: The result of the task execution.
|
||||
"""
|
||||
try:
|
||||
crew_task = Task(
|
||||
description=task, agent=self.crewai_agent, *args, **kwargs
|
||||
)
|
||||
crew = Crew(
|
||||
agents=[self.crewai_agent],
|
||||
tasks=[crew_task],
|
||||
process=Process.sequential,
|
||||
)
|
||||
result = crew.kickoff()
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"An error occurred: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# Usage example
|
||||
search_tool = SerperDevTool()
|
||||
|
||||
crewai_wrapper = CrewAIAgentWrapper(
|
||||
name="ResearchAnalyst",
|
||||
role="Senior Research Analyst",
|
||||
goal="Uncover cutting-edge developments in AI and data science",
|
||||
backstory="""You work at a leading tech think tank.
|
||||
Your expertise lies in identifying emerging trends.
|
||||
You have a knack for dissecting complex data and presenting actionable insights.""",
|
||||
tools=[search_tool],
|
||||
)
|
||||
|
||||
result = crewai_wrapper.run(
|
||||
"Analyze the latest trends in quantum computing and summarize the key findings."
|
||||
)
|
||||
print(result)
|
||||
@ -0,0 +1,69 @@
|
||||
from typing import List, Optional
|
||||
|
||||
from griptape.structures import Agent as GriptapeAgent
|
||||
from griptape.tools import FileManager, TaskMemoryClient, WebScraper
|
||||
|
||||
from swarms import Agent
|
||||
|
||||
|
||||
class GriptapeAgentWrapper(Agent):
|
||||
"""
|
||||
A wrapper class for the GriptapeAgent from the griptape library.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, name: str, tools: Optional[List] = None, *args, **kwargs
|
||||
):
|
||||
"""
|
||||
Initialize the GriptapeAgentWrapper.
|
||||
|
||||
Parameters:
|
||||
- name: The name of the agent.
|
||||
- tools: A list of tools to be used by the agent. If not provided, default tools will be used.
|
||||
- *args, **kwargs: Additional arguments to be passed to the parent class constructor.
|
||||
"""
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
self.tools = tools or [
|
||||
WebScraper(off_prompt=True),
|
||||
TaskMemoryClient(off_prompt=True),
|
||||
FileManager(),
|
||||
]
|
||||
self.griptape_agent = GriptapeAgent(
|
||||
input=f"I am {name}, an AI assistant. How can I help you?",
|
||||
tools=self.tools,
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs) -> str:
|
||||
"""
|
||||
Run a task using the GriptapeAgent.
|
||||
|
||||
Parameters:
|
||||
- task: The task to be performed by the agent.
|
||||
|
||||
Returns:
|
||||
- The response from the GriptapeAgent as a string.
|
||||
"""
|
||||
response = self.griptape_agent.run(task, *args, **kwargs)
|
||||
return str(response)
|
||||
|
||||
def add_tool(self, tool) -> None:
|
||||
"""
|
||||
Add a tool to the agent.
|
||||
|
||||
Parameters:
|
||||
- tool: The tool to be added.
|
||||
"""
|
||||
self.tools.append(tool)
|
||||
self.griptape_agent = GriptapeAgent(
|
||||
input=f"I am {self.name}, an AI assistant. How can I help you?",
|
||||
tools=self.tools,
|
||||
)
|
||||
|
||||
|
||||
# Usage example
|
||||
griptape_wrapper = GriptapeAgentWrapper("GriptapeAssistant")
|
||||
result = griptape_wrapper.run(
|
||||
"Load https://example.com, summarize it, and store it in a file called example_summary.txt."
|
||||
)
|
||||
print(result)
|
||||
@ -0,0 +1,82 @@
|
||||
from typing import List, Optional
|
||||
|
||||
from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.llms import OpenAI
|
||||
from langchain.prompts import StringPromptTemplate
|
||||
from langchain.tools import DuckDuckGoSearchRun
|
||||
|
||||
from swarms import Agent
|
||||
|
||||
|
||||
class LangchainAgentWrapper(Agent):
|
||||
"""
|
||||
Initialize the LangchainAgentWrapper.
|
||||
|
||||
Args:
|
||||
name (str): The name of the agent.
|
||||
tools (List[Tool]): The list of tools available to the agent.
|
||||
llm (Optional[OpenAI], optional): The OpenAI language model to use. Defaults to None.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
tools: List[Tool],
|
||||
llm: Optional[OpenAI] = None,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.name = name
|
||||
self.tools = tools
|
||||
self.llm = llm or OpenAI(temperature=0)
|
||||
|
||||
prompt = StringPromptTemplate.from_template(
|
||||
"You are {name}, an AI assistant. Answer the following question: {question}"
|
||||
)
|
||||
|
||||
llm_chain = LLMChain(llm=self.llm, prompt=prompt)
|
||||
tool_names = [tool.name for tool in self.tools]
|
||||
|
||||
self.agent = LLMSingleActionAgent(
|
||||
llm_chain=llm_chain,
|
||||
output_parser=None,
|
||||
stop=["\nObservation:"],
|
||||
allowed_tools=tool_names,
|
||||
)
|
||||
|
||||
self.agent_executor = AgentExecutor.from_agent_and_tools(
|
||||
agent=self.agent, tools=self.tools, verbose=True
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Run the agent with the given task.
|
||||
|
||||
Args:
|
||||
task (str): The task to be performed by the agent.
|
||||
|
||||
Returns:
|
||||
Any: The result of the agent's execution.
|
||||
"""
|
||||
try:
|
||||
return self.agent_executor.run(task)
|
||||
except Exception as e:
|
||||
print(f"An error occurred: {e}")
|
||||
|
||||
|
||||
# Usage example
|
||||
|
||||
search_tool = DuckDuckGoSearchRun()
|
||||
tools = [
|
||||
Tool(
|
||||
name="Search",
|
||||
func=search_tool.run,
|
||||
description="Useful for searching the internet",
|
||||
)
|
||||
]
|
||||
|
||||
langchain_wrapper = LangchainAgentWrapper("LangchainAssistant", tools)
|
||||
result = langchain_wrapper.run("What is the capital of France?")
|
||||
print(result)
|
||||
Loading…
Reference in new issue