Swarm Agent is a powerful autonomous agent framework designed to connect Language Models (LLMs) with various tools and long-term memory. This framework provides the ability to ingest and process various types of documents such as PDFs, text files, Markdown files, JSON files, and more. The Swarm Agent offers a wide range of features to enhance the capabilities of LLMs and facilitate efficient task execution.
Swarm Agent is a powerful autonomous agent framework designed to connect Language Models (LLMs) with various tools and long-term memory. This framework provides the ability to ingest and process various types of documents such as PDFs, text files, Markdown files, JSON files, and more. The Swarm Agent offers a wide range of features to enhance the capabilities of LLMs and facilitate efficient task execution.
The `Agent` class serves several key purposes:
1. **Conversational Loop**: It establishes a conversational loop with a language model. This means it allows you to interact with the model in a back-and-forth manner, taking turns in the conversation.
1. **Conversational Loop**: It establishes a conversational loop with a language model. This means it allows you to interact with the model in a back-and-forth manner, taking turns in the conversation.
2. **Feedback Collection**: The class allows users to provide feedback on the responses generated by the model. This feedback can be valuable for training and improving the model's responses over time.
2. **Feedback Collection**: The class allows users to provide feedback on the responses generated by the model. This feedback can be valuable for training and improving the model's responses over time.
@ -128,15 +125,13 @@ The `Agent` class serves several key purposes:
## Getting Started
## Getting Started
To get started with the Swarm Agent, follow these steps:
First run the following:
1. Install the required dependencies by running `pip install swarms`.
```bash
2. Import the necessary modules and classes from the `swarms` package.
pip3 install swarms
3. Initialize an instance of the `Agent` class, providing the necessary configuration parameters such as the language model, system prompt, tools, and memory components.
```
4. Define the tasks you want the agent to perform and pass them to the `run()` method.
5. Optionally, customize the agent's behavior by adjusting parameters such as the maximum number of loops, stopping conditions, and tool integration.
Here's a basic example to illustrate the usage of the Swarm Agent:
And, then now you can get started with the following:
```python
```python
from swarms.models import OpenAIChat
from swarms.models import OpenAIChat
@ -274,5 +269,3 @@ tasks = [
responses = agent.bulk_run(tasks)
responses = agent.bulk_run(tasks)
print(responses)
print(responses)
```
```
These examples demonstrate just a few of the many features and capabilities offered by the Swarm Agent framework. For more detailed information and additional examples, please refer to the comprehensive documentation provided within the codebase.
Kye Gomez
7 months ago