Memory Store is a shared memory platform for teams and agents that turns conversations, snippets, and decisions from tools like Gmail, Slack, Granola, Claude, and Codex into connected memory. The homepage positions the product as a way to keep the work your team already does available as reusable context instead of leaving it trapped in isolated apps.
The site makes the product feel like an operational layer for knowledge, not just a note-taking app. It talks about syncing from every tool, organizing information in the background, and making playbooks available wherever people work. That gives Memory Store a clear identity as a company brain for modern teams that use both humans and AI agents in the same workflow.
Key Features
- Sync conversations, snippets, and decisions from tools your team already uses.
- Turn messages and work artifacts into connected memory for teammates and agents.
- Work with sources such as Gmail, Slack, Granola, Claude, Codex, ChatGPT, Cursor, Linear, Fathom, and OpenCode.
- Organize memory in the background by project, person, and decision.
- Keep living playbooks and context available inside everyday workflows.
- Support team-wide adoption with guides, pricing, blog, and demo pages.
Use Cases
- Teams can preserve decisions that would otherwise disappear in chat threads.
- Agents can pull from connected memory instead of starting every task from scratch.
- Operators can keep project context available across tools and meetings.
- Organizations can turn their existing work history into reusable knowledge.
Rebrand Signal
The old Julep domain redirects to memory.store, and the destination page clearly describes the same product family under the new brand. That makes the current resource a rebrand case rather than a dead project, which is why the URL needs to point at the new home before publication.
Why It Matters
Memory Store stands out because it tries to solve a real AI-era problem: the work is happening across too many tools, and the useful context gets lost between them. By syncing conversations and decisions into a shared memory, the product gives teams and agents something they can return to later. That matters for speed, because repetitive context gathering is one of the biggest hidden costs in modern workflows. It also matters for quality, because better memory leads to fewer repeated mistakes and more consistent decisions. The result is a product that feels like infrastructure for working with AI at team scale. It is especially relevant when multiple people and multiple agents need the same context without rebuilding it from scratch every time.
