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Eggshell
Local memory for AI agents to reuse work, spend fewer tokens
4 followers
Local memory for AI agents to reuse work, spend fewer tokens
4 followers
Eggshell carries useful work across AI agent chats. It stores results and evidence locally, retrieves relevant memory, and helps reduce repeated investigation—without LLM calls to organize that memory.
Eggshell
Eggshell
Hi Product Hunt! I'm Momo, the maker of Eggshell.
When you open a new AI coding chat, useful work from the previous conversation can get left behind. The agent may search the same files, trace the same code, and spend tokens rebuilding context.
I built Eggshell with GPT-6 Astra to help carry that work forward. It saves tool results, findings, and their evidence in a local .egg graph, then brings relevant work into a later chat. It does not call an LLM to summarize or organize the memory.
Eggshell is open source and written in Lean. You can try it through the Codex plugin and inspect the memory that actually reached the next chat. Separate experimental adapters are also available for Claude Code, Gemini CLI, Cursor, and OpenCode.
In our published LLVM follow-up experiment, it used about 82% fewer input-plus-output tokens than a saved fresh-chat reference. Nine of the ten answers needed no substantive correction in our review. This measured one task with existing prior work using Luna; it is not an Astra benchmark or a general savings guarantee. The full measurements and review limits are in the README.
I'd love feedback from people who return to related work across coding sessions. Try the two-chat example and tell me what carried over, what still had to be checked, and where setup was difficult.
Source, installation, and evidence: https://github.com/momonpya/eggs...