Hemory

Hemory — the name combines Hear and Memory — is an always-on listening app that turns everything you live through into a private, searchable memory your AI agents can actually use. It is explicitly not meeting transcription: rather than capturing scheduled calls, Hemory listens on the phone or Apple Watch you already own and keeps every conversation it hears as memory. Your day is automatically split into moments with speaker labels, and those moments settle into a private memory store. Hemory then connects to your agents over MCP, so your AI finally has real context to revisit what you heard and build on it. It is built for people who want their agents grounded in what actually happened. The problem Hemory addresses is that AI agents are powerful but blind to your actual life. They can reason, write, and generate, yet they have no source material about what was said in a Tuesday sync, what a customer described in an interview, or what was decided in a brainstorm — unless a human reconstructs it by hand. Meeting transcription, by definition, only covers meetings: a ninety-minute release review may be captured, but the lunch that followed, the voice note recorded on the walk back, and the small promises made in passing usually vanish. That gap matters because the details that move work forward — who committed to what, and by when — are exactly the details people forget. Hemory exists to close that gap by listening continuously and keeping what it hears as durable, searchable memory. Listening is designed to be controlled rather than intrusive, and Hemory gives you two ways to run it. Manual mode turns listening on only when you need it, so the privacy boundary stays yours. Schedule mode runs listening inside defined windows — for example, weekdays from 9:00 to 18:00 — with the app nudged right on time, which the product describes as what always-on actually feels like. Underneath both modes sits voice-activity detection, or VAD: always-on listening sounds expensive, but it is not, because VAD only counts the moments when someone is actually speaking, while silence, gaps, and background noise never touch your quota. The interface makes the state visible with a listening timer, so you can see at a glance that Hemory has been capturing, for example, 480 minutes. Once Hemory is listening, your day is automatically split into moments instead of being left as one long recording. Each moment carries a speaker label, so a session can appear as an entry such as "App 2.0 release review — scope, search latency and ship date" with its participants listed, a lunch with a colleague becomes its own moment, and a short voice note on the walk back is captured as a separate work block. The timeline arranges these moments across the day with their start and end times and durations, alongside home, timeline, outputs, and agent sections in the app. Everything then settles into a private, searchable memory — the memory palace you build over time — that your agents can query on your behalf instead of you. Connecting Hemory to an AI agent takes seconds, and it happens over MCP. Hemory is added to mainstream agents on the command line — commands such as "codex mcp add hemory" or "claude mcp add hemory" confirm "connected" and list the available tool, search_memory. The clients explicitly supported include Claude Code, Codex, Gemini, Cursor, VS Code, OpenClaw, Hermes, and more standard MCP clients. Because the memory is exposed as a tool rather than another dashboard, you do not have to learn a new workflow: you simply chat with your agent about the memories Hemory has heard, and the agent reaches for search_memory when it needs to check what was really said. Hemory's overall approach is a two-step loop: start listening in Hemory, then connect Hemory to your agent via MCP. Everything downstream is grounded in what actually happened, which is what makes the outputs usable — the memory is ready to answer questions or generate any document you need. In practice this shows up as generation, not just retrieval. A single prompt can ask an agent to build a monthly report deck as a web page for an upcoming management meeting, complete with progress, key takeaways, and next month's plan. Another can ask for a nightly journal entry, written every evening at 22:00, capturing the interesting things from your life that were heard that day. A third can turn recent customer interviews and internal brainstorm sessions into a complete PRD. The agent can also write files directly, as in a follow-up draft produced as a twelve-line follow-up.md that is ready to send. Privacy is a first-class part of that approach. Hemory states that your audio is never stored in the cloud: it is processed as a stream and destroyed the moment processing ends, which the product calls zero audio retention. Raw audio is stored only on the listening device and is never synced across devices, so the recording stays where it was made. A self-host option is coming soon, letting you optionally run Hemory entirely on your own infrastructure. Together these choices mean the benefit of an always-on assistant — an agent that remembers your day — without handing over a permanent archive of your voice, and VAD keeps the cost of continuous listening low by counting only actual speech. The moments Hemory captures are what make its use cases concrete. You can ask an agent what someone promised you in last Tuesday's sync and get an answer drawn from three matching sessions — for example, that a colleague committed to the final launch budget by Thursday and that his team owns the App Store screenshots. You can ask the agent to draft a follow-up for Thursday's check-in and receive a finished file ready to send. You can ask for a monthly work report deck assembled from the past month of work you heard, delivered as a web page for a management meeting. You can set up a nightly journal at 22:00 so small moments from your day become one entry every night. And you can ask for a complete PRD built from recent smart-ring customer interviews plus internal brainstorms. Hemory is aimed at people who live and work through conversation and who already use AI agents. That includes professionals who want their everyday work captured and searchable, users who want all-day listening under a fair-use cap, and developers and teams working with coding and general agents such as Claude Code, Codex, Gemini, Cursor, VS Code, OpenClaw, and Hermes. It is available for iOS and Android today, with macOS, Windows, Linux, Web, and self-host listed as coming soon. Pricing starts with a Free Trial at $0 — no payment, 10 hours one-time with no renewal — then Starter at $10 per month for 20 hours, Pro at $20 per month for 60 hours (marked most popular), and Max at $50 per month for unlimited listening under a 24-hour-per-day cap and fair use. All listening quotas are measured with VAD. In short, Hemory is a memory layer for your AI agents: always-on listening on hardware you already own, a private and searchable record of your day split into speaker-labeled moments, and an MCP connection that lets Claude, Codex, Cursor, or any compatible agent search that memory and generate from it. From today on, every real conversation becomes material you can generate from tomorrow.