Contextberg

About

Contextberg is a local-first memory layer for people who want their agent to remember what actually happened during work, not just what was typed into a chat box. It runs in the background, watches multiple surfaces, and tries to make that passive context usable by coding agents later.

Why It Is Hot Now

The recent Product Hunt launch brought attention because the product takes a stronger stance than most memory tools: instead of waiting for users to manually save context, it captures work continuously and makes that history available through MCP.

Key Features

• Passively captures screen activity, browsing context, and agent conversations on the local machine.
• Serves that memory back to tools like Claude Code and Cursor through MCP.
• Focuses on local operation, which matters for users uncomfortable with sending raw work history to a remote SaaS.

Real Use Cases

• Helping coding agents remember what was researched, tested, or decided across task switches.
• Reconstructing interrupted work without manually maintaining a notes file for the agent.
• Giving personal agents a broader memory surface than prompt history alone.

Community Pulse

The product's most interesting feedback is also its biggest tension. Builders like the passive capture idea because it removes friction, but they also see the obvious tradeoff: if a tool watches screens and history in the background, users will want very clear boundaries, filters, and pause controls.

Limits and Risks

Contextberg's value depends on how much trust a user is willing to place in background capture. Even when everything stays local, teams still need to think about private tabs, credentials, personal messages, and noisy data that can pollute memory quality.

Alternatives

Users will likely compare it with Agentmemory, Mem0-style memory layers, local knowledge-base workflows, and manual context notebooks maintained inside the repo.

FAQ

• Who is Contextberg best for? It fits heavy users of coding agents who often task-switch and want memory capture without constant manual tagging.
• What should be evaluated before rollout? Privacy controls, pause behavior, data retention, and how well the captured context stays relevant instead of becoming noise.