This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
LEVH is a local-first memory layer for AI agents and human. It captures context from conversations, projects, files, email, calendars, and MCP tools; builds an inspectable knowledge graph; surfaces trust and conflict signals; and lets stale details fade—without cloud accounts or telemetry.
I built LEVH because AI tools keep losing the context that makes work coherent: decisions, commitments, people, project conventions, and information that changed last week.
LEVH is a local-first memory layer for AI agents and workflows. It captures and connects context from conversations, projects, files, email, calendars, and MCP-compatible tools—then makes it inspectable through a knowledge graph, trust signals, reviewable conflict candidates, and memory decay.
The goal is not another transcript archive. It is durable, self-curating context that stays on your machine: no cloud account, telemetry, Redis, or managed database required.
Quick start:
pip install levh
levh setup --demo --client claude --profile work
levh serve
I’d love feedback from anyone working with AI assistants, personal knowledge workflows, MCP tools, or multi-agent systems: what context does your AI forget most often?
Thanks so much to everyone checking out LEVH today! 🙏 Building this has been a labor of love — if you try the quick start, I'd genuinely love to hear what breaks, what surprises you, or what memory problem you wish it solved. Every comment and upvote means a lot on launch day!
About Levh on Product Hunt
“Local-first memory for AI agents and workflows”
Levh was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #105 on the daily leaderboard. LEVH is a local-first memory layer for AI agents and human. It captures context from conversations, projects, files, email, calendars, and MCP tools; builds an inspectable knowledge graph; surfaces trust and conflict signals; and lets stale details fade—without cloud accounts or telemetry.
Levh was featured in Productivity (658.2k followers), Open Source (68.7k followers), Artificial Intelligence (475.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 307.1k products, making this a competitive space to launch in.
Who hunted Levh?
Levh was hunted by Ali Ulu. 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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Ali Ulu