mksglu/context-mode

Context Mode is a context window optimization tool for AI coding agents that reduces tool output bloat and maintains session continuity across multiple platforms.

The problem it addresses is that tool calls from Model Context Protocol servers dump raw, uncompressed data into the agent's context window, rapidly consuming available tokens. A single Playwright snapshot or access log can consume tens of kilobytes, and after extended sessions, a significant portion of context is lost. When agents compact conversations to reclaim space, they lose track of editing state, task progress, and user intent. Additionally, agents generate verbose filler and explanations that waste output tokens. Context Mode solves this through sandboxing tool output to achieve substantial size reduction, persisting session memory across interactions, and enforcing routing across multiple platforms via MCP servers and hooks.

The tool is designed for teams building AI coding agents who need to maximize effective context window usage. It suits projects where agents interact with external tools frequently and where maintaining coherent session state across long interactions is critical. The project explicitly positions itself as addressing "the other half of the context problem," suggesting it complements existing context management approaches rather than replacing them wholesale.

The project shows active development with ongoing refinement of its core optimization mechanisms. The codebase is maintained in TypeScript and integrates with a broad ecosystem of AI coding platforms and tools through MCP and hook-based routing. The tool has been adopted across multiple organizations, indicating real-world validation of its approach to context efficiency.