I was deep in a refactor in Claude Code when it hit rate limits. Twenty minutes of context — the task list, the decisions I'd made about the approach, the governance state of the API changes — all trapped in a conversation I couldn't continue.
I opened Codex to keep working. It had no idea what I was doing. I spent 15 minutes re-explaining the task, the constraints, the decisions already made. Then Codex hit its own limit, and I opened Gemini CLI. Same story. Start over. Re-explain. Lose momentum.
This happens every day to anyone using more than one AI coding assistant.
The problem is architectural
Each AI assistant stores context in its own conversation window. When that window ends — rate limit, crash, session timeout, or just opening a different tool — the context is gone. There's no shared layer.
It's like having three developers on a team with no issue tracker, no shared docs, and no standups. Each one knows what they worked on. None of them know what the others did.
Shared state on disk
Delimit stores everything in ~/.delimit/ — plain files on your local machine, not in any model's context window. Task ledger, governance rules, memory, deliberation history. When you switch from Claude Code to Codex to Gemini CLI, they all read and write the same state.
Here's what that looks like in practice:
# In Claude Code:
> "add to ledger: refactor auth module"
LED-042 created
# Claude hits rate limit. Switch to Codex:
> "what's on the ledger?"
LED-042: refactor auth module [open]
# Need a second opinion. Open Gemini CLI:
> "review LED-042 approach — JWT vs session tokens?"
Reading LED-042 context... recommending JWT with refresh tokens
Three assistants. One shared workspace. No re-explaining.
What carries across models
Task ledger — Every open item, its priority, notes, and completion status. Any assistant can create, update, or close tasks. The ledger is the source of truth, not any single conversation.
Governance state — API policies, breaking change history, security audit results. If Claude Code ran a lint check and found issues, Codex can see those results without re-running the check.
Memory — Project decisions, architectural notes, user preferences. Stored once, accessible from every assistant. You tell Claude "we use PostgreSQL, not MySQL" and Gemini CLI knows it too.
Deliberation history — When you ask multiple AI models to debate a decision (JWT vs sessions, monorepo vs polyrepo, REST vs GraphQL), the transcript and consensus are stored. Any assistant can reference past deliberations.
No extra API cost
This is important: Delimit doesn't proxy your requests or add another API in the middle. If you pay for Claude Pro, Codex, or Gemini, Delimit uses your existing CLI installations. It's a local layer that coordinates between them — not a service that sits in the request path.
The only exception is deliberation, where Delimit calls multiple model APIs to get consensus on a decision. That uses the xAI API for Grok (the others run through their native CLIs).
Setup is one command
npx delimit-cli setup
This detects which AI assistants you have installed, configures MCP (Model Context Protocol) for each one, and sets up the shared workspace. Takes about 30 seconds.
After setup, every assistant you open has access to the same tools — delimit_ledger_list, delimit_memory_search, delimit_lint, and about 100 others. They all read and write the same local state.
Why this matters beyond convenience
The convenience of not re-explaining your task is nice. But the real value is that governance and decisions persist across model switches.
If Claude Code ran a security audit and flagged a vulnerability, that finding doesn't disappear when you switch to Codex. The evidence is stored, the ledger item is open, and any assistant you open next will see it.
If three models deliberated on an architecture decision and reached consensus, that decision is recorded. Six months later, when a different assistant (or a different developer) asks "why did we choose JWT?", the answer is in the deliberation history.
Context continuity isn't just about productivity. It's about institutional memory that survives model switches, rate limits, and team changes.
Try it
npx delimit-cli setup
Works with Claude Code, Codex, Cursor, and Gemini CLI. Free tier includes governance, breaking change detection, and the shared workspace. Pro ($10/mo) adds deliberation, persistent memory, and deploy tracking.
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