Welcome to Swarms - the future of AI, where we leverage the power of autonomous agents to create 'swarms' of Language Models (LLM) that work together, creating a dynamic and interactive AI system.
## Vision
In the world of AI and machine learning, individual models have made significant strides in understanding and generating human-like text. But imagine the possibilities when these models are no longer solitary units, but part of a cooperative and communicative swarm. This is the future we envision.
Just as a swarm of bees works together, communicating and coordinating their actions for the betterment of the hive, swarming LLM agents can work together to create richer, more nuanced outputs. By harnessing the strengths of individual agents and combining them through a swarming architecture, we can unlock a new level of performance and responsiveness in AI systems. We envision swarms of LLM agents revolutionizing fields like customer support, content creation, research, and much more.
This `AutoAgent` is used to create the `MultiModalVisualAgent`, an autonomous agent that can process tasks in a multi-modal environment, like dealing with both text and visual data.
To use this agent, you need to import the agent and instantiate it. Here is a brief guide:
```python
from swarms.agents.auto_agent import MultiModalVisualAgent
# Initialize the agent
multimodal_agent = MultiModalVisualAgent()
```
### Working with MultiModalVisualAgentTool
The `MultiModalVisualAgentTool` class is a tool wrapper around the `MultiModalVisualAgent`. It simplifies working with the agent by encapsulating agent-related logic within its methods. Here's a brief guide on how to use it:
```python
from swarms.agents.auto_agent import MultiModalVisualAgent, MultiModalVisualAgentTool
# Now, you can use the agent tool to perform tasks. The run method is one of them.
result = multimodal_agent_tool.run('Your text here')
```
## Note
- The `AutoAgent` makes use of several helper tools and context managers for tasks such as processing CSV files, browsing web pages, and querying web pages. For the best use of this agent, understanding these tools is crucial.
- Additionally, the agent uses the ChatOpenAI, a language learning model (LLM), to perform its tasks. You need to provide an OpenAI API key to make use of it.
- Detailed knowledge of FAISS, a library for efficient similarity search and clustering of dense vectors, is also essential as it's used for memory storage and retrieval.
Here are three examples of swarming architectures that could be applied in this context.
1.**Hierarchical Swarms**: In this architecture, a 'lead' agent coordinates the efforts of other agents, distributing tasks based on each agent's unique strengths. The lead agent might be equipped with additional functionality or decision-making capabilities to effectively manage the swarm.
2.**Collaborative Swarms**: Here, each agent in the swarm works in parallel, potentially on different aspects of a task. They then collectively determine the best output, often through a voting or consensus mechanism.
3.**Competitive Swarms**: In this setup, multiple agents work on the same task independently. The output from the agent which produces the highest confidence or quality result is then selected. This can often lead to more robust outputs, as the competition drives each agent to perform at its best.
4.**Multi-Agent Debate**: Here, multiple agents debate a topic. The output from the agent which produces the highest confidence or quality result is then selected. This can lead to more robust outputs, as the competition drives each agent to perform it's best.
If you love what we're building here, please consider sharing our project with your friends and colleagues! You can use the following buttons to share on social media.
We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our [Contributing Guidelines](./CONTRIBUTING.md).
