Attributions and Acknowledgments
Context engineering methodology and AI development credits for this project.
This project incorporates concepts, methodologies, and inspiration from various sources in the AI and software development community. We believe in giving credit where credit is due and acknowledging the foundational work that makes projects like this possible.
Context Engineering Methodology
Anthropic's Context Engineering Research
This project implements context management strategies inspired by research and best practices in AI context engineering, including concepts discussed by Anthropic.
Source: Anthropic's research on effective context management for large language models
Reference: Anthropic Blog - Context Windows & Engineering
Nature: Methodological approach and best practices (not proprietary code)
Our Implementation: We implement these concepts using our own code with open-source
models We are grateful to Anthropic for their contributions to the field of AI safety and effective context management strategies.
Development Assistance
Claude AI by Anthropic
This project was designed and bootstrapped with assistance from Claude (Claude.ai), Anthropic's AI assistant.
Contributions:
- System architecture design
- Multi-agent orchestration patterns
- Code structure and implementation guidance
- Documentation and specifications
- Best practices recommendations
Attribution: While Claude provided design guidance and code examples, all final code in this
project is original work, uses open-source models (Qwen, etc.), and is independently licensed.
License Note: Our use of Claude.ai for development assistance does not impose any licensing
restrictions on our project, which uses entirely open-source models for its runtime operations. We thank Anthropic and the Claude team for creating a powerful tool that accelerates software development and architectural design.
Open Source Models
Qwen Models by Alibaba Cloud
This project uses models from the Qwen family for its AI operations:
Models Used:
- Qwen 2.5 (7B, 14B) - Analysis and orchestration
- Qwen 3 Coder (30B) - Code generation
License: Apache License 2.0
Provider: Alibaba Cloud / Qwen Team
Repository: https://github.com/QwenLM
Why We Chose Qwen:
- Exceptional performance and capabilities
- Apache 2.0 license (commercial-friendly)
- Large context windows
- Strong multilingual support
- Excellent code generation abilities
We are deeply appreciative of the Qwen team for releasing these powerful models under a permissive open-source license.
Runtime Infrastructure
Ollama
This project uses Ollama to run large language models locally.
License: MIT License
Repository: https://github.com/ollama/ollama
Website: https://ollama.com
Ollama makes it simple to run large language models locally, enabling privacy-preserving AI applications. We thank the Ollama team for their excellent work.
Research & Academic Foundations
Agentic Context Engineering (ACE)
Our multi-agent architecture draws inspiration from research in agentic AI systems and context management.
Relevant Research:
- Academic papers on agentic AI architectures
- Research on context window management strategies
- Studies on multi-agent coordination
Note: We implement these concepts independently using our own code and design decisions.
Programming Languages & Frameworks
This project builds upon the work of countless open-source contributors:
- Python - Python Software Foundation
- JavaScript/Node.js - OpenJS Foundation
- Various open-source libraries (see requirements.txt and package.json )
Inspiration & Community
We are grateful to the broader AI and open-source communities for:
- Sharing knowledge and best practices
- Creating and maintaining open-source tools
- Publishing research and documentation
- Building communities around AI development
Our Commitment
Open Source Philosophy
While this project was developed with assistance from Claude.ai, we are committed to:
- Using only open-source, commercially-licensed models in production
- Providing clear attribution for all influences and tools
- Contributing back to the community when possible
- Maintaining transparency about our dependencies
- Respecting all licenses and intellectual property
Privacy & Local-First
This project is designed to run entirely locally using open-source models, ensuring:
- Your data never leaves your machine
- No API dependencies for core functionality
- Full control over your AI infrastructure
- Commercial use without per-query costs
License Compliance Summary
| Component | License | Commercial Use | Attribution Required |
|---|---|---|---|
| Qwen Models | Apache 2.0 | Yes | Yes (provided) |
| Ollama | MIT | Yes | Yes (provided) |
| This Project | MIT/Apache 2.0 | Yes | Yes |
| Context Engineering Concepts | Methodology | Yes | Acknowledged |
| Claude.ai (dev assistance) | N/A | Yes | Acknowledged |
Contact & Questions
If you have questions about attributions, licensing, or want to discuss this project:
- Issues: [Your GitHub Issues Link]
- Email: [Your Contact Email]
- License: See LICENSE file in repository
Updates to This Document
This attributions document will be updated as:
- New dependencies are added
- New models or tools are integrated
- Community feedback is received
- License terms are clarified
Last Updated: November 9, 2025
Thank You
To everyone who contributes to open-source AI, shares knowledge freely, and builds tools that empower developers - thank you. This project stands on the shoulders of giants.
Special thanks to:
- The Anthropic team for Claude and context engineering research
- The Qwen/Alibaba team for outstanding open models
- The Ollama team for making local AI accessible
- The broader open-source AI community
Together, we're building a future where powerful AI tools are accessible to everyone. "If I have seen further, it is by standing on the shoulders of giants." - Isaac Newton