Local memory for AI agents to reuse work, spend fewer tokens
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.
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.
“Local memory for AI agents to reuse work, spend fewer tokens”
Eggshell launched on Product Hunt on September 18th, 2026 and earned 74 upvotes and 3 comments, placing #25 on the daily leaderboard. 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.
On the analytics side, Eggshell competes within Open Source, Developer Tools, Artificial Intelligence and OpenAI Day — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Eggshell performed against the three products that launched closest to it on the same day.
Who hunted Eggshell?
Eggshell was hunted by Momo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Eggshell including community comment highlights and product details, visit the product overview.
Momo
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...