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@ -1,178 +1,225 @@
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The Swarms framework provides developers with the ability to create AI systems that operate across two dimensions: **predictability** and **creativity**.
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# Swarms
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For **predictability**, Swarms enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Swarms safely prompts LLMs with tools and short-term memory connecting them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case.
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<div align="center">
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Swarms not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Swarms maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities.
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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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Swarms’s design philosophy is based on the following tenets:
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1. **Modularity and composability**: All framework primitives are useful and usable on their own in addition to being easy to plug into each other.
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2. **Technology-agnostic**: Swarms is designed to work with any capable LLM, data store, and backend through the abstraction of drivers.
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3. **Keep data off prompt by default**: When working with data through loaders and tools, Swarms aims to keep it off prompt by default, making it easy to work with big data securely and with low latency.
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4. **Minimal prompt engineering**: It’s much easier to reason about code written in Python, not natural languages. Swarms aims to default to Python in most cases unless absolutely necessary.
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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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## Installation
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### Share on Social Media
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There are 2 methods, one is through `git clone` and the other is by `pip install swarms`. Check out the [DOCUMENTATION](DOCS/DOCUMENTATION.md) for more information on the classes.
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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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* Pip install `pip3 install swarms`
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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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* Create new python file and unleash superintelligence
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</div>
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```python
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<!-- [](https://github.com/users/kyegomez/projects/1) -->
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from swarms import Worker
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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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node = Worker(
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openai_api_key="",
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ai_name="Optimus Prime",
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)
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## 🤝 Schedule a 1-on-1 Session
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task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
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response = node.run(task)
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print(response)
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```
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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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# Documentation
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For documentation, go here, [the docs folder in the root diectory](https://swarms.apac.ai)
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----------
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**NOTE: We need help building the documentation**
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## Installation
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`pip3 install --upgrade swarms`
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-----
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---
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# Docker Setup
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The docker file is located in the docker folder in the `infra` folder, [click here and navigate here in your environment](/infra/Docker)
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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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* Build the Docker image
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### `Flow` Example
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- Reliable Structure that provides LLMS autonomy
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- Extremely Customizeable with stopping conditions, interactivity, dynamical temperature, loop intervals, and so much more
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- Enterprise Grade + Production Grade: `Flow` is designed and optimized for automating real-world tasks at scale!
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* You can build the Docker image using the provided Dockerfile. Navigate to the infra/Docker directory where the Dockerfiles are located.
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```python
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* For the CPU version, use:
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from swarms.models import OpenAIChat
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from swarms.structs import Flow
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```bash
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docker build -t swarms-api:latest -f Dockerfile.cpu .
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```
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For the GPU version, use:
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api_key = ""
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```bash
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docker build -t swarms-api:gpu -f Dockerfile.gpu .
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```
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### Run the Docker container
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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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After building the Docker image, you can run the Swarms API in a Docker container. Replace your_redis_host and your_redis_port with your actual Redis host and port.
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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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For the CPU version:
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```bash
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docker run -p 8000:8000 -e REDIS_HOST=your_redis_host -e REDIS_PORT=your_redis_port swarms-api:latest
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```
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## For the GPU version:
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```bash
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docker run --gpus all -p 8000:8000 -e REDIS_HOST=your_redis_host -e REDIS_PORT=your_redis_port swarms-api:gpu
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```
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## Access the Swarms API
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------
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* The Swarms API will be accessible at http://localhost:8000. You can use tools like curl or Postman to send requests to the API.
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### `SequentialWorkflow`
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- A Sequential swarm of autonomous agents where each agent's outputs are fed into the next agent
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- Save and Restore Workflow states!
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- Integrate Flow's with various LLMs and Multi-Modality Models
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Here's an example curl command to send a POST request to the /chat endpoint:
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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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```bash
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curl -X POST -H "Content-Type: application/json" -d '{"api_key": "your_openai_api_key", "objective": "your_objective"}' http://localhost:8000/chat
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```
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Replace your_openai_api_key and your_objective with your actual OpenAI API key and objective.
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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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----
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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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agent1 = Flow(llm=llm, max_loops=1, dashboard=False)
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# ✨ Features
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* Easy to use Base LLMs, `OpenAI` `Palm` `Anthropic` `HuggingFace`
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* Enterprise Grade, Production Ready with robust Error Handling
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* Multi-Modality Native with Multi-Modal LLMs as tools
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* Infinite Memory Processing: Store infinite sequences of infinite Multi-Modal data, text, images, videos, audio
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* Usability: Extreme emphasis on useability, code is at it's theortical minimum simplicity factor to use
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* Reliability: Outputs that accomplish tasks and activities you wish to execute.
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* Fluidity: A seamless all-around experience to build production grade workflows
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* Speed: Lower the time to automate tasks by 90%.
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* Simplicity: Swarms is extremely simple to use, if not thee simplest agent framework of all time
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* Powerful: Swarms is capable of building entire software apps, to large scale data analysis, and handling chaotic situations
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# Create another Flow for a different task
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agent2 = Flow(llm=llm, max_loops=1, dashboard=False)
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agent3 = Flow(llm=llm, max_loops=1, dashboard=False)
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---
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# Roadmap
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# Create the workflow
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workflow = SequentialWorkflow(max_loops=1)
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Please checkout our [Roadmap](DOCS/ROADMAP.md) and consider contributing to make the dream of Swarms real to advance Humanity.
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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.", agent1)
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## Optimization Priorities
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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", agent2)
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1. **Reliability**: Increase the reliability of the swarm - obtaining the desired output with a basic and un-detailed input.
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workflow.add("Create a references sheet of materials for the curriculm", agent3)
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2. **Speed**: Reduce the time it takes for the swarm to accomplish tasks by improving the communication layer, critiquing, and self-alignment with meta prompting.
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# Run the workflow
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workflow.run()
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3. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.
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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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Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise.
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```
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---
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# Bounty Program
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# Features 🤖
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The Swarms framework is designed with a strong emphasis on reliability, performance, and production-grade readiness.
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Below are the key features that make Swarms an ideal choice for enterprise-level AI deployments.
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Our bounty program is an exciting opportunity for contributors to help us build the future of Swarms. By participating, you can earn rewards while contributing to a project that aims to revolutionize digital activity.
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## 🚀 Production-Grade Readiness
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- **Scalable Architecture**: Built to scale effortlessly with your growing business needs.
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- **Enterprise-Level Security**: Incorporates top-notch security features to safeguard your data and operations.
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- **Containerization and Microservices**: Easily deployable in containerized environments, supporting microservices architecture.
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Here's how it works:
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## ⚙️ Reliability and Robustness
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- **Fault Tolerance**: Designed to handle failures gracefully, ensuring uninterrupted operations.
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- **Consistent Performance**: Maintains high performance even under heavy loads or complex computational demands.
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- **Automated Backup and Recovery**: Features automatic backup and recovery processes, reducing the risk of data loss.
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1. **Check out our Roadmap**: We've shared our roadmap detailing our short and long-term goals. These are the areas where we're seeking contributions.
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## 💡 Advanced AI Capabilities
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2. **Pick a Task**: Choose a task from the roadmap that aligns with your skills and interests. If you're unsure, you can reach out to our team for guidance.
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The Swarms framework is equipped with a suite of advanced AI capabilities designed to cater to a wide range of applications and scenarios, ensuring versatility and cutting-edge performance.
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3. **Get to Work**: Once you've chosen a task, start working on it. Remember, quality is key. We're looking for contributions that truly make a difference.
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### Multi-Modal Autonomous Agents
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- **Versatile Model Support**: Seamlessly works with various AI models, including NLP, computer vision, and more, for comprehensive multi-modal capabilities.
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- **Context-Aware Processing**: Employs context-aware processing techniques to ensure relevant and accurate responses from agents.
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4. **Submit your Contribution**: Once your work is complete, submit it for review. We'll evaluate your contribution based on its quality, relevance, and the value it brings to Swarms.
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### Function Calling Models for API Execution
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- **Automated API Interactions**: Function calling models that can autonomously execute API calls, enabling seamless integration with external services and data sources.
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- **Dynamic Response Handling**: Capable of processing and adapting to responses from APIs for real-time decision making.
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5. **Earn Rewards**: If your contribution is approved, you'll earn a bounty. The amount of the bounty depends on the complexity of the task, the quality of your work, and the value it brings to Swarms.
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### Varied Architectures of Swarms
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- **Flexible Configuration**: Supports multiple swarm architectures, from centralized to decentralized, for diverse application needs.
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- **Customizable Agent Roles**: Allows customization of agent roles and behaviors within the swarm to optimize performance and efficiency.
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---
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## The Plan
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### Phase 1: Building the Foundation
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In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you.
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### Generative Models
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- **Advanced Generative Capabilities**: Incorporates state-of-the-art generative models to create content, simulate scenarios, or predict outcomes.
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- **Creative Problem Solving**: Utilizes generative AI for innovative problem-solving approaches and idea generation.
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### Phase 2: Optimizing the System
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In the second phase, we'll focus on optimizng Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you.
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### Enhanced Decision-Making
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- **AI-Powered Decision Algorithms**: Employs advanced algorithms for swift and effective decision-making in complex scenarios.
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- **Risk Assessment and Management**: Capable of assessing risks and managing uncertain situations with AI-driven insights.
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### Phase 3: Towards Super-Intelligence
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The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you.
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### Real-Time Adaptation and Learning
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- **Continuous Learning**: Agents can continuously learn and adapt from new data, improving their performance and accuracy over time.
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- **Environment Adaptability**: Designed to adapt to different operational environments, enhancing robustness and reliability.
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Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms.
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---
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## 🔄 Efficient Workflow Automation
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- **Streamlined Task Management**: Simplifies complex tasks with automated workflows, reducing manual intervention.
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- **Customizable Workflows**: Offers customizable workflow options to fit specific business needs and requirements.
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- **Real-Time Analytics and Reporting**: Provides real-time insights into agent performance and system health.
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# EcoSystem
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## 🌐 Wide-Ranging Integration
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- **API-First Design**: Easily integrates with existing systems and third-party applications via robust APIs.
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- **Cloud Compatibility**: Fully compatible with major cloud platforms for flexible deployment options.
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- **Continuous Integration/Continuous Deployment (CI/CD)**: Supports CI/CD practices for seamless updates and deployment.
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* [The-Compiler, compile natural language into serene, reliable, and secure programs](https://github.com/kyegomez/the-compiler)
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## 📊 Performance Optimization
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- **Resource Management**: Efficiently manages computational resources for optimal performance.
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- **Load Balancing**: Automatically balances workloads to maintain system stability and responsiveness.
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- **Performance Monitoring Tools**: Includes comprehensive monitoring tools for tracking and optimizing performance.
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*[The Replicator, an autonomous swarm that conducts Multi-Modal AI research by creating new underlying mathematical operations and models](https://github.com/kyegomez/The-Replicator)
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## 🛡️ Security and Compliance
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- **Data Encryption**: Implements end-to-end encryption for data at rest and in transit.
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- **Compliance Standards Adherence**: Adheres to major compliance standards ensuring legal and ethical usage.
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- **Regular Security Updates**: Regular updates to address emerging security threats and vulnerabilities.
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* Make a swarm that checks arxviv for papers -> checks if there is a github link -> then implements them and checks them
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## 💬 Community and Support
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- **Extensive Documentation**: Detailed documentation for easy implementation and troubleshooting.
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- **Active Developer Community**: A vibrant community for sharing ideas, solutions, and best practices.
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- **Professional Support**: Access to professional support for enterprise-level assistance and guidance.
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* [SwarmLogic, where a swarm is your API, database, and backend!](https://github.com/kyegomez/SwarmLogic)
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Swarms framework is not just a tool but a robust, scalable, and secure partner in your AI journey, ready to tackle the challenges of modern AI applications in a business environment.
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---
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# Demos
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## Documentation
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- For documentation, go here, [swarms.apac.ai](https://swarms.apac.ai)
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## Swarm Video Demo {Click for more}
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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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[](https://youtu.be/Br62cDMYXgc)
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## Community
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- [Join the Swarms community here on Discord!](https://discord.gg/AJazBmhKnr)
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---
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# Contact
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For enterprise and production ready deployments, allow us to discover more about you and your story, [book a call with us here](https://www.apac.ai/Setup-Call)
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# License
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MIT
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Kye
1 year ago