Contextberg

Product Introduction

1. Definition: Contextberg is a local-first, background monitoring application specifically designed as a memory and context provider for AI-powered coding agents and Large Language Models (LLMs). It functions as a persistent local MCP (Model Context Protocol) server.
2. Core Value Proposition: Contextberg exists to eliminate the manual context-recapitulation bottleneck in human-AI collaboration. It automatically provides AI coding assistants like Claude Code and Cursor with continuous, rich context about the user's screen activity, browser history, and past agent conversations, enabling true "memory" for AI agents and enhancing developer productivity.

Main Features

1. Automated Context Capture via MCP: Contextberg operates silently in the background, capturing a comprehensive stream of user activity data. This includes periodic screenshots of all application windows, detailed browser history and navigation, and full transcripts of interactions with connected AI agents (like Claude Code, Cursor, GitHub Copilot, OpenClaw). It packages this data and serves it directly to compatible AI tools through its built-in MCP server, requiring no complex configuration files.
2. Structured, Multi-Layer Memory Generation: The application processes raw activity data to automatically construct three distinct, LLM-optimized memory layers. Activity Memory provides fine-grained logs of recent actions. Daily Memory aggregates and summarizes progress on a per-day basis. Long-term Memory distills frequently used tools, work patterns, and behavioral tendencies over time, creating a persistent knowledge base about the user's workflow.
3. Strict Local-First & Offline Operation: All data processing occurs locally on the user's machine. Screen captures, activity logs, memory synthesis, and context serving via MCP never leave the local PC. This architecture enables full offline functionality and is designed to integrate seamlessly with local LLM runners like LM Studio for a completely private, end-to-end local AI workflow.
4. Intelligent Session Resumption ("Remember"): Upon user return or session start, Contextberg analyzes the most recent activity memory, browser history, and agent logs to generate a concise summary of "what you were doing before." This allows users and their AI agents to immediately resume work from the exact previous context, and the summary can be used as a launch point for further chat-based inquiry.

Problems Solved

1. Pain Point: The constant need to manually re-explain project state, open files, browser tabs, and recent actions to AI coding assistants breaks flow and reduces efficiency. Traditional AI agents lack persistent, cross-session memory.
2. Target Audience: Software Developers and Engineers who regularly use AI coding assistants (Claude Code, Cursor, GitHub Copilot); Power Users of Local LLMs (e.g., LM Studio, Oobabooga) seeking private, context-aware workflows; Technical Teams prioritizing data privacy and offline capability for AI-augmented development.
3. Use Cases: Resuming Complex Coding Tasks after a break or overnight, where the AI assistant needs full context of recent changes and browser research. Conducting Private, Local-Only Development with sensitive codebases, using LM Studio and local models fueled by personal activity context. Onboarding an AI Agent to a Large, Ongoing Project by providing it with automatically generated summaries of recent work and long-term development patterns.

Unique Advantages

1. Differentiation: Unlike simple clipboard managers or note-taking apps, Contextberg is a dedicated, automated system for AI context provisioning. Compared to cloud-based activity trackers, its strict local-first policy ensures no sensitive screen or code data is ever transmitted externally, a critical advantage for developers.
2. Key Innovation: The integration of automated, multi-source context capture (screen+browser+agent chats) with a structured, multi-layer memory system that is directly exposed to AI agents via the emerging MCP standard. This creates a persistent "workflow memory" that is both immediately useful for session resumption and increasingly valuable as a long-term knowledge base for AI reasoning.

Frequently Asked Questions (FAQ)

1. Is Contextberg safe? Does it send my screen data to the cloud? No, Contextberg is a strictly local-first application. All data—including screenshots, browser history, and conversation logs—is processed and stored exclusively on your computer. It does not send any information to external servers.
2. How does Contextberg work with AI agents like Claude Code or Cursor? Contextberg runs a local MCP (Model Context Protocol) server in the background. Compatible AI agents can connect to this server to receive a real-time stream of your activity context and memory summaries, allowing them to "remember" what you've been working on without manual input.
3. Can I use Contextberg completely offline with local LLMs? Yes. Contextberg's local-first design is ideal for offline use. You can pair it with a local LLM runner like LM Studio, where Contextberg provides the context and memory, and your chosen local model (e.g., Llama, Gemma) performs the reasoning, creating a fully private, offline AI assistant setup.
4. What operating systems does Contextberg support? Currently, Contextberg offers a native download for Windows 10 and 11 (64-bit). Support for macOS and Linux is planned for future releases, as indicated on their website.
5. What is MCP (Model Context Protocol) and why is it important for Contextberg? MCP is a protocol developed by Anthropic that allows tools to provide context and capabilities to LLMs in a standardized way. Contextberg's built-in MCP server is key because it lets it seamlessly feed context to any MCP-compatible agent (like Claude Desktop, Cursor) without requiring custom integrations for each tool.