Hit Your Claude Code Limit Mid-Feature? How to Pick Up in Codex or Cursor Without Re-Explaining Anything

"Claude Code usage limit, switch to Codex" describes what you do when Claude Code stops you partway through a task because you've used up your plan's allowance, and you carry on in another AI coding agent, such as Codex or Cursor, without losing the context you'd built up. The hard part isn't opening the other tool. The hard part is that the new tool knows nothing: what you decided, what you already tried, which fix worked and which conventions the project follows. This guide covers why that happens, what it costs you in tokens, and how to make the switch without explaining anything again.

Most developers who use AI coding agents have been here. It's late afternoon and you're three hours into a feature. Claude Code knows you dropped Redis for SQLite because the deployment target is one small VM. It knows you always use pnpm, never npm. It knows the flaky auth test fails because of a timezone assumption in the fixture, not because of the code under test. Then the usage limit message appears.

You still have Codex, Cursor or Antigravity open. Switching tools takes seconds, but rebuilding context takes much longer. Every session starts from zero, and no tool knows what the others learned. So you either:

re-explain the project from memory and hope you don't leave out the decision that matters,

All three cost time. The last two also cost tokens, and tokens are the reason you got cut off in the first place.

Usage limits on Claude, Cursor and Codex come down to how much the model reads and writes. When you rebuild context by pasting history or letting an agent crawl the repo, you spend a large share of your budget on information the model already had in the last session. Then you hit the next tool's limit sooner, and you go round again.

At Open Agentic Brain & Memory (OpenAB), we measured how big that gap is. Loading context from a store of 10,000 memories with OpenAB takes 1,325 tokens. Putting the raw history in front of the model instead would take 1,131,367 tokens. On the LoCoMo long-conversation benchmark, OpenAB keeps 97% of the full-conversation score while reading 18% of the tokens. Every tool gets a briefing capped at 2,000 tokens, so the cost of starting a session is small and predictable.

In practice, fewer tokens per session means you reach your Claude, Cursor and Codex limits later. When you do reach one, the next tool starts already up to date.

The fix is to stop keeping project knowledge inside one tool's chat history. Keep it in a shared memory that every tool reads from. That's why we built OpenAB, a free, self-hosted memory layer shared by Claude Code, Cursor, Codex, Antigravity and Kilo Code (Kilo Code support is in beta). Tell one tool and every tool knows.

During your Claude Code session, OpenAB records the decisions, fixes and preferences that come up, such as "SQLite instead of Redis," "always pnpm" or "auth test failure is a fixture timezone bug." Each one becomes a memory tagged with its project. OpenAB works out the project by walking up from your working folder to the nearest.git directory, so memories from different repos stay separate without any setup.

Memories go into a local SQLite database on your machine. OpenAB doesn't keep a raw transcript. It distills what matters and ranks it, using hybrid search (keywords plus meaning) and outcome scores, so fixes that actually worked rank above dead ends.

When you open Codex or Cursor in the same repo, a start-up hook injects a briefing of at most 2,000 tokens, in 84 ms at p50. That briefing holds what Claude Code learned an hour ago. You don't have to paste or summarize anything, and you don't spend the first ten minutes re-teaching the agent your stack.

If the new agent needs more detail mid-task, it can query the memory directly over MCP with tools such as brain_recall, brain_remember, brain_context and load-my-brain. Recall over 10,000 memories takes 28 ms at p50. The agent pulls the one decision it needs instead of reading everything.

So your switch looks like this: Claude Code tells you you've hit the limit, you open Codex in the same folder, and Codex already knows about the Redis decision, the pnpm rule and the flaky test. You type "continue the export feature" and it does.

Say that on Monday you're working in Claude Code on a small internal service. You and the agent agree on three things:

Drop Redis and use SQLite for the job queue, because the service runs on one machine and Redis was operational overhead with no real benefit.

You hit your usage limit before finishing the queue worker. On Tuesday you open Cursor on a different laptop. Without shared memory, Cursor might suggest adding a Redis client, run npm install and create a lockfile you don't want, or generate an ORM migration. With OpenAB, Cursor's session starts with a briefing that already includes all three decisions, so the first thing it proposes fits the project.

Usage limits often come with a change of setting: you finish on a home laptop, or hand off to a work machine. OpenAB syncs across machines. At start-up it merges in what your other laptops learned, so the briefing is the same everywhere. The decision you made on your desktop is in the briefing on your laptop the next morning.

When you open a folder, OpenAB gives you that repo's history first, because that's usually what matters. Projects aren't sealed off from each other, though. A map of up to 20 of your projects lives in the global CLAUDE.md, AGENTS.md and GEMINI.md files, so any agent can see what else you work on. Recall can search one project or all of them. That's useful when you fixed the same Docker networking problem in another repo six weeks ago and want the agent to find it.

A shared memory across several AI vendors raises an obvious question: where does it live? With OpenAB, it lives with you.

No account and no OpenAB server. Nothing goes through infrastructure we run.

That's why OpenAB is a privacy-first, owner-held alternative to hosted memory APIs. Your project decisions don't become another vendor's data just because you switched from one coding agent to another.

Shared memory is only useful if you trust it. OpenAB includes a local control panel where you can read, edit, pin or delete every memory. Pin the decisions that should always appear in the briefing, such as "always pnpm." Delete the ones that no longer apply, for example after you bring Redis back. When you set it up, you can start from your past AI work or start fresh.

If you want evidence that recall is pulling the right things, brain eval scores recall against your own history, so you can measure it on your projects instead of relying on our numbers.

Keep project decisions in a shared memory, not in one tool's chat.

If you want the background behind this approach, the OpenAB blog has guides on AI memory layers, context engineering, long context vs RAG vs memory, agent hooks and self-hosted memory privacy. The source code is in the GitHub repository rohithhn/Open-Agentic-Brain-and-Memory.

Open Agentic Brain & Memory (OpenAB) is free and open source under the Apache-2.0 license, built by Rohith, Chandan and Sushmita. If you've ever hit a Claude Code usage limit mid-feature and spent the next twenty minutes catching Codex up, you can install OpenAB from the project site or the GitHub repository above. The next time you switch tools or machines, the new agent will already know your project.