Contextberg brings local AI agent memory to macOS and Windows. It captures screens, browser history, and agent conversations into a private, searchable archive, then serves relevant context to Codex, Claude Code, Cursor, and other agents over MCP. This launch adds native macOS capture, OCR screenshot search, source exclusions, and flexible model routing: use your existing Codex sign-in, a Gemini/OpenRouter API key, Contextberg Cloud, or a fully local model.
Hey Product Hunt 👋
I’m Tiger, the solo founder of Contextberg.
I built it because I was tired of re-explaining my work to AI agents. After every reset, task switch, or weekend away, the context already existed—in my screens, browser research, and previous agent conversations—but I had to reconstruct it manually.
Contextberg turns that work into local, reusable memory and serves the relevant context to Codex, Claude Code, Cursor, and other agents over MCP.
What’s new in this launch:
🍎 Native macOS app, alongside Windows
🔎 OCR search across captured screens
🛡️ App and source exclusions
🧠 Short-term, daily, and long-term memory
🔀 Use your Codex sign-in, Gemini/OpenRouter key, Contextberg Cloud, or a local model
Your archive stays on-device. Only the context you choose to use is sent to the model provider you select.
I’m building the memory layer under the agent—not another agent that locks you into one model.
What part of your workflow does your AI agent forget most often? I’d love to hear how you currently reconstruct context.
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About Contextberg on Product Hunt
“Local AI agent memory served via MCP”
Contextberg launched on Product Hunt on September 22nd, 2026 and earned 70 upvotes and 1 comments, placing #29 on the daily leaderboard. Contextberg brings local AI agent memory to macOS and Windows. It captures screens, browser history, and agent conversations into a private, searchable archive, then serves relevant context to Codex, Claude Code, Cursor, and other agents over MCP. This launch adds native macOS capture, OCR screenshot search, source exclusions, and flexible model routing: use your existing Codex sign-in, a Gemini/OpenRouter API key, Contextberg Cloud, or a fully local model.
Contextberg was featured in Productivity (661.4k followers), Developer Tools (520k followers) and Artificial Intelligence (479.3k followers) on Product Hunt. Together, these topics include over 370.5k products, making this a competitive space to launch in.
Who hunted Contextberg?
Contextberg was hunted by Tiger. 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.
Want to see how Contextberg stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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