Swarms is brought to you by Agora, the open source AI research organization. Join Agora and Help create swarms and or recieve support to advance Humanity.

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.

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.

1. Installation
2. Usage
3. Sharing

git clone https://github.com/kyegomez/swarms.git
cd swarms
pip install -r requirements.txt

The primary agent in this repository is the AutoAgent from ./swarms/agents/workers/auto_agent.py.

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:

from swarms.agents.auto_agent import MultiModalVisualAgent

# Initialize the agent
multimodal_agent = MultiModalVisualAgent()

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:

from swarms.agents.auto_agent import MultiModalVisualAgent, MultiModalVisualAgentTool

# Initialize the agent
multimodal_agent = MultiModalVisualAgent()

# Initialize the tool with the agent
multimodal_agent_tool = MultiModalVisualAgentTool(multimodal_agent)

# 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')

• 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.

Share on Twitter:

Share on Facebook:

Share on LinkedIn:

Share on Reddit:

Share on Hacker News:

Share on Pinterest:

Share on WhatsApp:

We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our Contributing Guidelines.

Thank you for being a part of our project!

A swarm, particularly in the context of distributed computing, refers to a large number of coordinated agents or nodes that work together to solve a problem. The specific requirements of a swarm might vary depending on the task at hand, but some of the general requirements include:

1. Distributed Nature: The swarm should consist of multiple individual units or nodes, each capable of functioning independently.

2. Coordination: The nodes in the swarm need to coordinate with each other to ensure they're working together effectively. This might involve communication between nodes, or it could be achieved through a central orchestrator.

3. Scalability: A well-designed swarm system should be able to scale up or down as needed, adding or removing nodes based on the task load.

4. Resilience: If a node in the swarm fails, it shouldn't bring down the entire system. Instead, other nodes should be able to pick up the slack.

5. Load Balancing: Tasks should be distributed evenly across the nodes in the swarm to avoid overloading any single node.

6. Interoperability: Each node should be able to interact with others, regardless of differences in underlying hardware or software.

Integrating these requirements with Large Language Models (LLMs) can be done as follows:

1. Distributed Nature: Each LLM agent can be considered as a node in the swarm. These agents can be distributed across multiple servers or even geographically dispersed data centers.

2. Coordination: An orchestrator can manage the LLM agents, assigning tasks, coordinating responses, and ensuring effective collaboration between agents.

3. Scalability: As the demand for processing power increases or decreases, the number of LLM agents can be adjusted accordingly.

4. Resilience: If an LLM agent goes offline or fails, the orchestrator can assign its tasks to other agents, ensuring the swarm continues functioning smoothly.

5. Load Balancing: The orchestrator can also handle load balancing, ensuring tasks are evenly distributed amongst the LLM agents.

6. Interoperability: By standardizing the input and output formats of the LLM agents, they can effectively communicate and collaborate, regardless of the specific model or configuration of each agent.

In terms of architecture, the swarm might look something like this:

                                           (Orchestrator)
                                             /        \
           (LLM Agent)---(Communication Layer)       (Communication Layer)---(LLM Agent)
              /                  |                                           |                 \
(Task Assignment)      (Task Completion)                    (Task Assignment)       (Task Completion)

Each LLM agent communicates with the orchestrator through a dedicated communication layer. The orchestrator assigns tasks to each LLM agent, which the agents then complete and return. This setup allows for a high degree of flexibility, scalability, and robustness.

Communication layers play a critical role in distributed systems, enabling interaction between different nodes (agents) and the orchestrator. Here are three potential communication layers for a distributed system, including their strengths and weaknesses:

1. Message Queuing Systems (like RabbitMQ, Kafka):

   • Strengths: They are highly scalable, reliable, and designed for high-throughput systems. They also ensure delivery of messages and can persist them if necessary. Furthermore, they support various messaging patterns like publish/subscribe, which can be highly beneficial in a distributed system. They also have robust community support.

   • Weaknesses: They can add complexity to the system, including maintenance of the message broker. Moreover, they require careful configuration to perform optimally, and handling failures can sometimes be challenging.

2. RESTful APIs:

   • Strengths: REST is widely adopted, and most programming languages have libraries to easily create RESTful APIs. They leverage standard HTTP(S) protocols and methods and are straightforward to use. Also, they can be stateless, meaning each request contains all the necessary information, enabling scalability.

   • Weaknesses: For real-time applications, REST may not be the best fit due to its synchronous nature. Additionally, handling a large number of API requests can put a strain on the system, causing slowdowns or timeouts.

3. gRPC (Google Remote Procedure Call):

   • Strengths: gRPC uses Protocol Buffers as its interface definition language, leading to smaller payloads and faster serialization/deserialization compared to JSON (commonly used in RESTful APIs). It supports bidirectional streaming and can use HTTP/2 features, making it excellent for real-time applications.

   • Weaknesses: gRPC is more complex to set up compared to REST. Protocol Buffers' binary format can be more challenging to debug than JSON. It's also not as widely adopted as REST, so tooling and support might be limited in some environments.

In the context of swarm LLMs, one could consider an Omni-Vector Embedding Database for communication. This database could store and manage the high-dimensional vectors produced by each LLM agent.

• Strengths: This approach would allow for similarity-based lookup and matching of LLM-generated vectors, which can be particularly useful for tasks that involve finding similar outputs or recognizing patterns.

• Weaknesses: An Omni-Vector Embedding Database might add complexity to the system in terms of setup and maintenance. It might also require significant computational resources, depending on the volume of data being handled and the complexity of the vectors. The handling and transmission of high-dimensional vectors could also pose challenges in terms of network load.

• Integrate Multi Agent debate

• Integrate Multi agent2 debate

• Integrate meta prompting into all worker agents

• Create 1 main swarms class swarms('Increase sales by 40$', workers=4)

• Integrate Jarvis as worker nodes

• Integrate guidance and token healing

• Add text to speech whisper x, youtube script and text to speech code models as tools