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| Introduction to Agents in Swarms | |
| ================================ | |
| Welcome to the revolutionary world of Agents in Swarms. I'm a big believer in simplicity, modularity, and the power of open collaboration. The same principles apply here. | |
| Agents are the individual building blocks in a swarm. They are the worker bees, each with a specific task, but all working together towards a common goal. In our case, an agent is a combination of a Language Model (LLM), Long Term Memory, and Tools. | |
| In other words, an agent is: | |
| `LLM => Long Term Memory => Tools` | |
| That's it. That's as simple as it can get. | |
| Why does this work? Because each component has a specific, well-defined role. The Language Model is the driving force, generating text based on a given prompt. The Long Term Memory stores information that the agent can draw upon to make its output more coherent and contextually relevant. The Tools provide additional capabilities, such as the ability to parse text, search the web, or interact with APIs. | |
| But the real beauty of this system is not just in the individual components, but in how they work together. The output of one becomes the input of another, creating a feedback loop of continuous learning and improvement. | |
| And the best part? Our Agent classes are designed to be as simple as humanely possible. They are plug-and-play with any of our language model classes, vector stores, and tools. This means you can easily swap out one component for another, allowing for endless customization and flexibility. | |
| The file structure is equally straightforward: | |
| ``` | |
| * memory | |
| * models | |
| * tools | |
| * utils | |
| ``` | |
| Each directory contains different components of the swarm. The `models` directory contains the language models, the `memory` directory contains the long-term memory, the `tools` directory contains the tools, the `utils` directory contains various utility functions. | |
| Let's see how simple it is to use these components with some examples: | |
| ```python | |
| # Import the necessary classes | |
| from swarms.agents import Anthropic, HuggingFaceLLM | |
| # Create an instance of the Anthropic class | |
| anthropic = Anthropic(model="claude-2", max_tokens_to_sample=100, temperature=0.8) | |
| # Use the Anthropic instance to generate text | |
| prompt = "Once upon a time" | |
| stop = ["The end"] | |
| print("Anthropic output:") | |
| print(anthropic.generate(prompt, stop)) | |
| # Create an instance of the HuggingFaceLLM class | |
| huggingface = HuggingFaceLLM(model_id="gpt2", device="cpu", max_length=50) | |
| # Use the HuggingFaceLLM instance to generate text | |
| prompt = "Once upon a time" | |
| print("\nHuggingFaceLLM output:") | |
| print(huggingface.generate(prompt)) | |
| ``` | |
| And to build an agent: | |
| ```python | |
| from swarms.agents import vectorstore, tool, Agent | |
| # Create an instance of the Agent class | |
| agent = Agent( | |
| llm=huggingface, | |
| memory=vectorstore, | |
| tools=tool, | |
| ) | |
| agent.run("Make me an instagram clone") | |
| ``` | |
| In conclusion, the Agents in Swarms represent a new way of thinking about AI. They are simple, modular, and highly customizable, allowing you to create powerful AI systems that are more than the sum of their parts. And as always, we're just getting started. There's always room for improvement, for simplification, for making things even better. That's the spirit of open collaboration. That's the spirit of Swarms. | |
| Thanks for becoming an alpha build user, email kye@apac.ai with all complaints. | |