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Hyper gives your AI agents a shared, persistent memory so they always know what your team knows. If you’re new to Hyper — or just curious about the details — the answers below cover the most common questions we hear from teams evaluating or already using the product.
Hyper sits between your data sources (email, calendar, documents, and more) and your AI agents. When your agents call remember or observe, Hyper stores a structured fact in your workspace’s memory. When they call ask, Hyper searches that memory and returns the most relevant facts. Over time, your agents accumulate a rich, searchable knowledge base that travels with them across every conversation and task — no more re-explaining context from scratch.
No. Hyper is designed to integrate with any AI agent or LLM stack, and you can disconnect any integration from Settings → Connections at any time. The facts stored in Hyper are your data — you can export them or delete them whenever you choose. We don’t build artificial switching costs into the product; if Hyper stops being useful, you should be able to leave without friction.
Hyper is priced per seat per month. All plans include every product feature — the only difference is how many tokens your workspace can consume each month. Recording a fact costs 2,000 tokens; retrieving a fact costs 500 tokens. Seats in a team workspace each contribute their token budget to a shared pool. You can find a full breakdown on the Plans page and dive into token math on the Usage page.
Hyper currently ships connectors for Gmail, Google Drive, Google Calendar, and Slack, with more integrations on the way. Beyond first-party connectors, any AI agent or LLM that can make API calls can use Hyper’s remember, observe, and ask tools — so if your preferred tool supports tool-calling or function-calling, it very likely works with Hyper today. Check Settings → Connections in the app for the current list of available integrations.
Hyper stores facts as discrete, structured entries rather than dumping raw documents into a vector store. This means the memory layer stays searchable and precise even as it grows. When agents record new facts that conflict with or supersede older ones, Hyper surfaces that tension so you can resolve it. You can also manually review, edit, or delete specific memories from the app — giving you fine-grained control over what your agents actually know.
Hyper is built for AI-powered teams where multiple agents — or multiple people working alongside agents — need to share context without constantly re-briefing each other. It’s especially valuable for teams running automated workflows, customer-facing AI assistants, or research-heavy processes where institutional knowledge compounds over time. If your team is already using AI heavily and you’re frustrated by how much context gets lost between sessions, Hyper is designed exactly for that problem.
Absolutely. Personal workspaces are billed as a single seat, and every plan tier is available to individual users. Solo developers building AI products often use Hyper to give their agents long-term memory without building and maintaining their own memory infrastructure. The free trial gives you three days of unlimited access to see whether it fits your workflow before committing.
The most direct signal is whether your agents stop asking you to re-explain things they’ve already been told. Beyond that, Hyper’s desktop app shows you the facts your agents have recorded over time, so you can audit what the memory layer actually contains. You can also measure the reduction in prompt length over time — teams typically see agents require significantly less upfront context per conversation after a few weeks of active memory use.
Tools like Glean focus on enterprise search across documents and wikis — they’re great for helping humans find information, but they’re not designed to be a live memory layer that AI agents read from and write to during task execution. Mem0 and Letta are closer in spirit, but Hyper’s design prioritizes team-level shared memory (not just per-user memory) and deep integration with the connectors your team already uses. Hyper is built for the workflow where agents and humans collaborate together, not just for a single-user AI assistant.
AI agents are only as useful as the context they carry. Right now, most teams are solving that problem by pasting the same briefing documents into every conversation — which doesn’t scale. Hyper is purpose-built for the next stage of AI adoption, where agents need persistent, shared, team-level memory to do real work autonomously. The founders are deeply focused on this problem and are shipping fast. If you believe AI agents are going to run more and more of your team’s workflows, a reliable memory layer isn’t optional — it’s the foundation.