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Recall
Long-term memory for AI agents, visible to humans
Recall is a local-first memory layer for AI agents: Markdown for humans, SQLite for search, MCP for agents. Agents write structured durable facts through CLI/MCP/API. Humans browse, search, inspect, and audit those memories through a read/view-first UI. The source of truth stays as Markdown on your machine; SQLite is a rebuildable local index. Agents write. Humans inspect. Recall indexes. MCP retrieves.
Hey Product Hunt 👋
I built Recall because I was tired of losing context when switching between laptops, projects, or different AI agents.
Every tool seemed to store memory differently, and most of the time I had no clear way to see what was saved, edit it, or understand what the AI would remember later.
Recall is a shared memory layer for both humans and LLMs. It includes an MCP server, so compatible AI tools and agents can store, search, and update long-lived memories through a standard interface.
humans can review and search memories in one UI
AI agents can use MCP or CLI to access memory across sessions
context stays portable instead of being locked inside one chat, laptop, or tool
The goal is simple: make AI memory visible and reusable.
Would love your feedback, especially from people building with multiple agents, MCP tools, or multi-device workflows.
About Recall on Product Hunt
“Long-term memory for AI agents, visible to humans”
Recall was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #130 on the daily leaderboard. Recall is a local-first memory layer for AI agents: Markdown for humans, SQLite for search, MCP for agents. Agents write structured durable facts through CLI/MCP/API. Humans browse, search, inspect, and audit those memories through a read/view-first UI. The source of truth stays as Markdown on your machine; SQLite is a rebuildable local index. Agents write. Humans inspect. Recall indexes. MCP retrieves.
On the analytics side, Recall competes within Artificial Intelligence and GitHub — topics that collectively have 514.4k followers on Product Hunt. The dashboard above tracks how Recall performed against the three products that launched closest to it on the same day.
Who hunted Recall?
Recall was hunted by Guram Sanikidze. 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 Recall including community comment highlights and product details, visit the product overview.
Guram Sanikidze