Artifacto
You're not alone. On large projects, token limits run out faster than expected — even on the highest plans. Here's why. The real problemEvery new chat session forces the agent to rediscover your project from scratch. It re-reads files it already read. It re-learns architecture it already understood. It re-finds patterns it already knew. By the time it's ready to do useful work, a significant portion of your context window is already gone. This happens every time you:
Each switch resets everything. Each reset costs tokens. On a large project with frequent resets, you can exhaust a maximum-tier plan without finishing a single feature. The common workarounds — opening a new account, upgrading mid-month, splitting work across multiple sessions — are expensive and slow. What Artifacto does insteadArtifacto builds a local project intelligence layer that persists across sessions, agents, and models. Before any agent starts working, it reads a compact structured map of your project — not the project itself. It immediately knows:
Instead of reading 40 files to find 3, the agent reads 3. This reduces context consumption, preserves more of the token budget for actual work, and makes every new session — with any model — faster to get productive. About the files-read reportArtifacto shows which files the agent opened and estimates the reading cost in tokens. Note: this is an estimate of context consumed for reading, not a count of tokens spent on generation or reasoning. Full token accounting requires model-level data that most plans don't expose. What Artifacto shows is still the most useful part — because reading is where most of the waste happens. Everything stays local
Works with your existing agentsArtifacto is infrastructure, not another agent.
Completely freeAll features are available to everyone at no cost.
Buy me a coffee ☕Artifacto is free and always will be. If it saves you time and tokens, consider buying me a coffee — it helps keep the project alive. ☕ Buy me a coffee via PayPal — Quick start
Requirements
Most effective on large projects with frequent agent restarts. On small projects, the difference is smaller. |