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CatchMe
CatchMe is an always-on activity-capture project that records windows, keystrokes, mouse activity, screenshots, clipboard content, notifications, and file activity, then organizes that history for vectorless retrieval.
The repository says raw activity stays on the user's machine and can be processed with local models. If a cloud model is used for summaries or retrieval, private activity may be sent to that provider. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
Always-on activity memory
CatchMe runs background recorders that turn detailed computer activity into a searchable hierarchy for later recall. It is a context and memory layer rather than a finished assistant.
Why it stands out
Vectorless retrieval framing
CatchMe uses tree-based retrieval instead of embeddings and vector storage.
Availability
Local storage with local or cloud models
The project stores raw screenshots, keystrokes, and activity trees locally. It supports offline local models, while cloud-model use can expose private activity during summarization or retrieval.
Why it matters
What makes it useful
CatchMe lets readers compare two very different memory designs: an always-on record of computer activity organized for tree-based recall, or a workflow built around embeddings and vector storage.
What to know
Where it fits
Read it as part of the context and agent-memory layer rather than the chatbot layer. It is more relevant to readers comparing retrieval and memory patterns than to readers looking for a ready-made assistant.
Notable points
What stands out
CatchMe keeps raw activity in local storage and offers offline model options. Its README warns that cloud APIs used for summarization can receive private activity, so the model endpoint is part of the privacy decision.
Before using
What to review
Whether you are comfortable recording windows, keystrokes, mouse activity, screenshots, clipboard content, notifications, and file activity on the machine where CatchMe runs.
Whether summaries and retrieval will use a fully local model or a cloud endpoint that may receive private activity.
Who or what can access the local history, and how you will protect, retain, and delete that sensitive archive.
The operating-system permissions involved: macOS asks for Accessibility, Input Monitoring, and Screen Recording access; on Windows, global input monitoring requires running CatchMe as Administrator.
Storage, model, context-window, and usage-cost requirements for an always-on capture workflow.
Reader fit
Who may find it relevant
Readers comparing agent-memory and retrieval approaches.
Builders interested in context systems beyond standard vector-database patterns.
Less relevant for readers mainly looking for a consumer chat interface.
Editorial note
Why LifeHubber lists it
LifeHubber lists CatchMe because it makes a very different memory tradeoff easy to see: detailed, always-on activity capture can support vectorless recall, but the value depends on whether that sensitive history stays with a local model or reaches a cloud provider.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
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