The author argues that owning the context layer (unified memory, business logic) separate from AI harnesses enables true portability, using MCP servers to swap between tools like Claude Code and Codex with a one-line config change.
Models are commoditizing fast. Harnesses already have. What I care about is my context: my research, notes, and domain knowledge, not the harness. My biggest issue was that my skills were locked into the Claude Claude ecosystem, such as their agents and workflows logic. Even if I would transition to an open-source harness such as Pi, Hermes, OpenCode, I would have the same issue. The context layer is not portable until you design it to be. So my point is that "Free" open-source harnesses don't make you free. What you want to own is the context layer. Here is what I learnt about building two personal assistants from scratch: Scrabble (as an LLM Wiki over my Obsidian, Readwise, Notion, and Google) and Tree (as an MCP server over a knowledge graph). Here's how I designed my context layer for true freedom: I pulled my memory out of the harness. A context layer is 3 parts: a unified memory, the business logic and a serving layer. While the harness on top is now disposable. Keep the memory as simple as possible. Start with a file-based system, move to a single database that supports text, vector and graph search (such as MongoDB) and only if truly necessary use multiple specialized databases. I wrap memory and business logic behind an MCP server (or skills). In Tree they're MCP tools, so "swap the harness, keep the memory" is a one-line config change. I moved Claude Code to Codex by re-pointing one config entry, and my memory came along. It gets smarter the more I use it. I give agents high-level primitives: Tree has 6 tools, 3 to search and 3 to write. A hook auto-ingests the conversation every ~10 turns. With this design, the data from the context layer is easily portable between harnesses. My biggest issue is with skills glued to a harness's conventions. The only solutions I see are to either make the skills super generic (losing some functionality) or to move everything to an MCP server, which adds complexity. At the moment, some of my skills are still coupled to Claude Code's workflows and agents' logic. Curious how you make your skills more portable between harnesses? TL;DR: Own the context layer, not the harness. A unified memory served over MCP tools (or skills) lets you swap Claude Code for Codex with a one-line config change and keep everything. A ~10-turn ingest hook makes it smarter as you go.
The author questions the efficiency of monolithic agentic coding tools like Claude Code, suggesting that a custom harness with per-phase model routing could cut costs without sacrificing quality, and asks the community for their experiences and recommendations.
A developer is building an external memory tool (MCP connector) for AI coding agents like Claude, allowing them to persist project context across sessions and models, preventing amnesia from context window limits.
A developer describes building a multi-engine agentic coding harness that routes sessions across Claude Code, Codex, and Ollama, selecting from twelve models per session based on task value.
A developer shares their struggle with losing context when switching between Cursor, Claude Code, and Codex, and how they solved it using MEMMY, a tool that syncs chat histories into a shared memory hub for all agents.