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Etch
Trace, replay, and verify every AI agent decision.
Every AI agent decision (prompts, tool calls, edits, multi-agent handoffs) recorded into a tamper-evident, verifiable history. Debug why the agent did what it did without grepping text logs. When customers or auditors ask what your agent did on Tuesday, prove it in one link. Sits underneath whichever agent stack or context graph you already use. Works with Claude Code, Cursor, Codex, Continue, and any tool that emits webhook events.
Hey everyone, Saravanan here.
Started building world-model-mcp a while back to help coding agents maintain context across sessions and avoid repeating mistakes. Focus was on giving agents a structured memory they could actually use reliably.
While using it on real projects, it became clear that memory alone was not the full problem. Even when the agent made good decisions, there was no clean way to later understand or prove why a particular decision was made, what context was used, or what constraints were considered.
That gap became especially noticeable when I needed to debug something after the fact, review a multi-agent handoff, or answer a straight question about what an agent actually did. Text logs go stale, in-memory state is gone.
That is what led to Etch. It records every agent decision into a verifiable history, so you can trace, replay, and validate what happened long after the run.
Free to try today. If you build with AI agents and have run into any of this, would love to hear how you are handling it currently.
world-model-mcp is Apache-2.0 on GitHub for anyone who wants to look at the memory layer side.
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About Etch on Product Hunt
“Trace, replay, and verify every AI agent decision.”
Etch was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #55 on the daily leaderboard. Every AI agent decision (prompts, tool calls, edits, multi-agent handoffs) recorded into a tamper-evident, verifiable history. Debug why the agent did what it did without grepping text logs. When customers or auditors ask what your agent did on Tuesday, prove it in one link. Sits underneath whichever agent stack or context graph you already use. Works with Claude Code, Cursor, Codex, Continue, and any tool that emits webhook events.
Etch was featured in SaaS (43.2k followers), Developer Tools (516.2k followers) and Artificial Intelligence (474.3k followers) on Product Hunt. Together, these topics include over 236k products, making this a competitive space to launch in.
Who hunted Etch?
Etch was hunted by Saravanan Jaichandaran. 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.
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Saravanan Jaichandaran