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192 lines
7.4 KiB
192 lines
7.4 KiB
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<div align="center">
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Swarms is a modular framework that enables reliable and useful multi-agent collaboration at scale to automate real-world tasks.
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[](https://github.com/kyegomez/swarms/issues) [](https://github.com/kyegomez/swarms/network) [](https://github.com/kyegomez/swarms/stargazers) [](https://github.com/kyegomez/swarms/blob/main/LICENSE)[](https://star-history.com/#kyegomez/swarms)[](https://libraries.io/github/kyegomez/swarms) [](https://pepy.tech/project/swarms)
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### Share on Social Media
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[](https://twitter.com/intent/tweet?text=Check%20out%20this%20amazing%20AI%20project:%20&url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms) [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms) [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms&title=&summary=&source=)
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[](https://www.reddit.com/submit?url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms&title=Swarms%20-%20the%20future%20of%20AI) [](https://news.ycombinator.com/submitlink?u=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms&t=Swarms%20-%20the%20future%20of%20AI) [](https://pinterest.com/pin/create/button/?url=https%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms&media=https%3A%2F%2Fexample.com%2Fimage.jpg&description=Swarms%20-%20the%20future%20of%20AI) [](https://api.whatsapp.com/send?text=Check%20out%20Swarms%20-%20the%20future%20of%20AI%20%23swarms%20%23AI%0A%0Ahttps%3A%2F%2Fgithub.com%2Fkyegomez%2Fswarms)
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</div>
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[](https://github.com/users/kyegomez/projects/1)
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## Vision
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At Swarms, we're transforming the landscape of AI from siloed AI agents to a unified 'swarm' of intelligence. Through relentless iteration and the power of collective insight from our 1500+ Agora researchers, we're developing a groundbreaking framework for AI collaboration. Our mission is to catalyze a paradigm shift, advancing Humanity with the power of unified autonomous AI agent swarms.
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-----
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## 🤝 Schedule a 1-on-1 Session
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Book a [1-on-1 Session with Kye](https://calendly.com/swarm-corp/30min), the Creator, to discuss any issues, provide feedback, or explore how we can improve Swarms for you.
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----------
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## Installation
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`pip3 install --upgrade swarms`
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---
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## Usage
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We have a small gallery of examples to run here, [for more check out the docs to build your own agent and or swarms!](https://docs.apac.ai)
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### `Flow` Example
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- The `Flow` is a superior iteratioin of the `LLMChain` from Langchain, our intent with `Flow` is to create the most reliable loop structure that gives the agents their "autonomy" through 3 main methods of interaction, one through user specified loops, then dynamic where the agent parses a <DONE> token, and or an interactive human input verison, or a mix of all 3.
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```python
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from swarms.models import OpenAIChat
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from swarms.structs import Flow
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api_key = ""
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# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
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llm = OpenAIChat(
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# model_name="gpt-4"
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openai_api_key=api_key,
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temperature=0.5,
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# max_tokens=100,
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)
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## Initialize the workflow
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flow = Flow(
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llm=llm,
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max_loops=2,
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dashboard=True,
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# stopping_condition=None, # You can define a stopping condition as needed.
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# loop_interval=1,
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# retry_attempts=3,
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# retry_interval=1,
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# interactive=False, # Set to 'True' for interactive mode.
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# dynamic_temperature=False, # Set to 'True' for dynamic temperature handling.
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)
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# out = flow.load_state("flow_state.json")
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# temp = flow.dynamic_temperature()
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# filter = flow.add_response_filter("Trump")
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out = flow.run("Generate a 10,000 word blog on health and wellness.")
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# out = flow.validate_response(out)
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# out = flow.analyze_feedback(out)
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# out = flow.print_history_and_memory()
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# # out = flow.save_state("flow_state.json")
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# print(out)
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```
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## `GodMode`
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- A powerful tool for concurrent execution of tasks using multiple Language Model (LLM) instances.
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```python
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from swarms.swarms import GodMode
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from swarms.models import OpenAIChat
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api_key = ""
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llm = OpenAIChat(
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openai_api_key=api_key
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)
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llms = [
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llm,
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llm,
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llm
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]
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god_mode = GodMode(llms)
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task = 'Generate a 10,000 word blog on health and wellness.'
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out = god_mode.run(task)
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god_mode.print_responses(task)
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```
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------
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### `SequentialWorkflow`
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- Execute tasks step by step by passing in an LLM and the task description!
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- Pass in flows with various LLMs
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- Save and restore Workflow states!
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```python
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from swarms.models import OpenAIChat
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from swarms.structs import Flow
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from swarms.structs.sequential_workflow import SequentialWorkflow
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# Example usage
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api_key = (
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"" # Your actual API key here
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)
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# Initialize the language flow
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llm = OpenAIChat(
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openai_api_key=api_key,
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temperature=0.5,
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max_tokens=3000,
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)
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# Initialize the Flow with the language flow
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flow1 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create another Flow for a different task
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flow2 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create the workflow
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workflow = SequentialWorkflow(max_loops=1)
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# Add tasks to the workflow
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workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)
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# Suppose the next task takes the output of the first task as input
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workflow.add("Summarize the generated blog", flow2)
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# Run the workflow
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workflow.run()
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# Output the results
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for task in workflow.tasks:
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print(f"Task: {task.description}, Result: {task.result}")
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```
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### `OmniModalAgent`
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- OmniModal Agent is an LLM that access to 10+ multi-modal encoders and diffusers! It can generate images, videos, speech, music and so much more, get started with:
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```python
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from swarms.models import OpenAIChat
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from swarms.agents import OmniModalAgent
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api_key = "SK-"
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llm = OpenAIChat(model_name="gpt-4", openai_api_key=api_key)
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agent = OmniModalAgent(llm)
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agent.run("Create a video of a swarm of fish")
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```
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---
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## Documentation
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- For documentation, go here, [swarms.apac.ai](https://swarms.apac.ai)
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## Contribute
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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) and our [contributing board](https://github.com/users/kyegomez/projects/1)
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# License
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MIT
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