docs/old-docs/corp/ROADMAP.md

## The Plan

### Phase 1: Building the Foundation
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.

### Phase 2: Optimizing the System
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.

### Phase 3: Towards Super-Intelligence
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.

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.


## Optimization Priorities

1. **Reliability**: Increase the reliability of the swarm - obtaining the desired output with a basic and un-detailed input.

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.

3. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.

Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise.

# Open Source Roadmap

Here is the detailed roadmap of our priorities and planned features for the near term:

## TODO

* Create a non-langchain worker and swarm class and compare

* Create extensive documentation

* Make sure that the boss agent successfully calls the worker agent if when it's finished makinng a plan

* Make sure the worker agent can access tools like web browser, terminal, and code editor, and multi-modal agents

* Make sure inputs and outputs from boss to worker are well defined and are collaborating if not then readjust prompt

* Create a tool that creates other tools with access to write code, debug, and an architectural argent that creates the architecture and then another agent that creates the code[Architecter(with code examples), code generator (with access to writing code and terminalrools)] -- The Compiler?

* Create a screenshot tool that takes a screen shot and then passes it to a worker multi-modal agent for visual context.

* API endroute in FASTAPI

* Develop Conversational UI with Gradio

* Integrate omni agent as a worker tool

* Integrate Ocean Database as primary vectorstore

* Integrate visual agent

* Integrate quantized hf models as base models with langchain huggingface

1. **Multi-Agent Debate Integration**: Integrate multi-agent debate frameworks ([Multi Agent debate](https://github.com/Skytliang/Multi-Agents-Debate) and [Multi agent2 debate](https://github.com/composable-models/llm_multiagent_debate)) to improve decision-making.

2. **Meta Prompting Integration**: Include meta prompting across all worker agents to guide their actions.

3. **Swarms Class**: Create a main swarms class `swarms('Increase sales by 40$', workers=4)` for managing and coordinating multiple worker nodes.

4. **Integration of Additional Tools**: Integrate [Jarvis](https://github.com/microsoft/JARVIS) as worker nodes, add text to speech and text to script tools ([whisper x](https://github.com/kyegomez/youtubeURL-to-text)), and integrate Hugging Face agents and other external tools.

5. **Task Completion and Evaluation Logic**: Include task completion logic with meta prompting, and evaluate task completion on a scale from 0.0 to 1.0.

7. **Ocean Integration**: Use the [Ocean](https://github.com/kyegomez/Ocean) vector database as the main embedding database for all the agents, both boss and worker.

8. **Improved Communication**: Develop a universal vector database that is only used when a task is completed in this format `[TASK][COMPLETED]`.

9. **Testing and Evaluation**: Create unit tests, benchmarks, and evaluations for performance monitoring and continuous improvement.

10. **Worker Swarm Class**: Create a class for self-scaling worker swarms. If they need help, they can spawn an entirely new worker and more workers if needed.

## Documentation

1. **Examples**: Create extensive and useful examples for a variety of use cases.

2. **README**: Update the README to include the examples and usage instructions.


# Mid-Long term
Here are some potential middle-to-long-term improvements to consider for this project:

1. **Modular Design**: Aim to design a more modular and scalable framework, making it easy for developers to plug-and-play various components.

2. **Interactive User Interface**: Develop a more interactive, user-friendly GUI that allows users to interact with the system without needing to understand the underlying code.

3. **Advanced Error Handling**: Implement advanced error handling and debugging capabilities to make it easier for developers to diagnose and fix issues.

4. **Optimized Resource Utilization**: Improve the efficiency of resource use, aiming to reduce memory consumption and improve speed without sacrificing accuracy.

5. **Collaborative Learning**: Integrate more sophisticated techniques for collaborative learning among the swarm, allowing them to share knowledge and learn from each other's successes and failures.

6. **Autonomous Self-Improvement**: Implement mechanisms that allow the swarm to autonomously learn from its past experiences and improve its performance over time.

7. **Security Enhancements**: Include robust security measures to protect sensitive data and prevent unauthorized access.

8. **Privacy-Preserving Techniques**: Consider incorporating privacy-preserving techniques such as differential privacy to ensure the confidentiality of user data.

9. **Support for More Languages**: Expand language support to allow the system to cater to a more global audience.

10. **Robustness and Resilience**: Improve the system's robustness and resilience, ensuring that it can operate effectively even in the face of hardware or software failures.

11. **Continual Learning**: Implement continual learning techniques to allow the system to adapt and evolve as new data comes in.

12. **More Contextual Understanding**: Enhance the system's capability to understand context better, making it more effective in handling real-world, complex tasks.

13. **Dynamic Task Prioritization**: Develop advanced algorithms for dynamic task prioritization, ensuring that the most important tasks are addressed first.

14. **Expanding the Swarm's Skills**: Train the swarm on a wider range of tasks, gradually expanding their skill set and problem-solving capabilities.

15. **Real-World Deployment**: Test and refine the system in real-world settings, learning from these experiences to further improve and adapt the system. 

Remember, these are potential improvements. It's important to revisit your priorities regularly and adjust them based on project needs, feedback, and learning from both successes and failures.