Summary
Tokalator is an open-source context engineering toolkit for AI coding assistants. It makes token usage explicit, measurable, and optimisable so teams can control cost and quality across coding workflows.
Contribution
- Token-aware context management: real-time token budgeting and tab relevance scoring
- Output-quality models: Cobb-Douglas style models relating context quality to output quality
- Caching break-even analysis: when caching pays off across 17 supported LLMs
- Shipped as a VS Code extension (500+ installs) and a web platform
- MIT licensed, open source
Adoption
- 500+ VS Code extension installs
- 17 LLM models supported for token economics
- 58 wiki articles on context engineering
- Programme lead, Perplexity AI Business Fellowship
References & Links
- arXiv: https://arxiv.org/abs/2604.08290
- Website: https://tokalator.wiki
- VS Code Marketplace: vfaraji89.tokalator
- GitHub: github.com/vfaraji89/tokalator
- Author: Vahid Faraji, Applied AI Specialist
- Related paper: Multi-Agent Position Classification with Tool Orchestration
- Related paper: Leveraging LLMs For Turkish Skill Extraction
Cite this work
@article{faraji2026tokalator,
title = {Tokalator: A Context Engineering Toolkit for AI Coding Assistants},
author = {Faraji, Vahid},
journal = {arXiv preprint arXiv:2604.08290},
year = {2026}
}