The Multi-LLM Reality
You're using Cursor with Claude for refactoring. But for creative brainstorming, you prefer ChatGPT. For code review, maybe Gemini. This multi-LLM workflow is becoming the norm.
The problem? Each model is an island.
"When I switched to Claude for refactoring, it lacked any context regarding what mini had discovered, necessitating a complete re-explanation." — Developer on Reddit, November 2025
This isn't an edge case. It's the daily reality for developers who use the best tool for each task. The ability to transfer AI context between tools is now essential.
Why Model Switching Breaks
Context loss is a fundamental problemContext loss is a fundamental problem/cursor/blog/cursor-context-loss-killing-productivity in AI-assisted development. Each LLM maintains its own context window. When you:
- Start a new chat (even with the same model)
- Switch to a different provider
- Hit token limits and start fresh
- Open a different interface (web vs. API)
...all accumulated context vanishes.
The new model doesn't know:
- What you're building
- What constraints exist
- What you've already tried
- What decisions you've made
You're back to "I'm building a Next.js app with..."
The Re-Explanation Tax
Every model switch costs time:
| Scenario | Re-explanation Time |
|----------|-------------------|
| Simple context | 5-10 minutes |
| Medium project | 15-25 minutes |
| Complex architecture | 30-45 minutes |
Multiply by 4-8 switches per day. That's 1-4 hours daily just briefing AI.
Current "Solutions" (And Why They Fail)
Manual summary: Write a project brief, copy-paste to each model.
- Problem: Gets stale immediately
- Problem: Misses decisions made in chat
- Problem: Tedious to maintain
Copy entire conversation: Paste the whole chat history.
- Problem: Hits token limits fast
- Problem: Includes irrelevant tangents
- Problem: No structure or priority
Start from docs: Point AI to README and docs.
- Problem: Docs don't capture decisions
- Problem: Missing the "why" behind choices
- Problem: No awareness of what was tried/failed
The Portable Context Approach
What if AI context portability was solved? What if context could travel with you — automatically?
With RL4 v2.0, there are now two ways to transfer context between LLMs:
Method 1: MCP Native (zero friction)
RL4 exposes an MCP server that works across Cursor, Claude Code, Codex, and Gemini CLI. When you switch tools, your context is already there:
# In Claude Code CLI
Use search_context to find what I was working on
→ Returns your full Cursor development history with citationsRL4 auto-detects Claude Code (`~/.claude/projects/`) and injects context into `CLAUDE.md`. No manual transfer needed.
Method 2: Snapshot paste (universal)
For LLMs that don't support MCP (ChatGPT, Gemini web), generate a snapshot and paste:
Learn the exact steps in our 5-minute tutorialLearn the exact steps in our 5-minute tutorial/cursor/blog/create-first-ai-snapshot-tutorial. Here's what this looks like in practice:
Before (switching from Cursor to Claude):
"I'm building a Next.js app with authentication. We're using
Supabase for the backend. The auth flow should support Google
OAuth and magic links. We decided to use server actions instead
of API routes because... [continues for 10 minutes]"After (with portable context):
[Paste RL4 snapshot]
"Continue from where we left off. Next task: implement the
refresh token rotation."The AI immediately understands your project, constraints, and current state. No re-explanation.
What Gets Captured
A good portable context includes:
Topics with weights:
- What you've been working on
- Relative importance of each area
- Time spent per topic
Key decisions:
- Choices made and rationale
- Rejected alternatives
- Trade-offs considered
Active constraints:
- Requirements that must be respected
- Limitations discovered
- Non-negotiable rules
Current state:
- What's implemented
- What's in progress
- What's blocked
Learned patterns:
- What works in your codebase
- What to avoid
- Conventions established
The Workflow
Here's the practical flow for multi-LLM work:
1. Work in Cursor (or any primary tool)
- Build context naturally through conversation
- Make decisions, discover constraints
- Let the AI learn your codebase
2. Generate snapshot before switching
- Capture current state
- Compress conversations (10-100x)
- Extract key decisions and constraints
3. Paste snapshot in new LLM
- Claude, ChatGPT, Gemini—any model
- Context transfers instantly
- Continue without re-explaining
4. Update snapshot when done
- Capture new decisions
- Ready for next switch
Real-World Example
Scenario: You've been working in Cursor on authentication. Now you want Claude's help with a complex refactor.
Without portable context:
You: "I need help refactoring the auth module. We're using Supabase..."
Claude: "Sure! What's your current auth flow? Are you using JWTs or sessions?"
You: "We went through this. We chose JWT because [re-explains everything]"
Claude: "Got it. What about refresh tokens?"
You: "[sighs] We decided to use rotation because [more re-explanation]"With portable context:
You: [paste snapshot] "Help me refactor the auth module. Focus on
the refresh token rotation we discussed in D4."
Claude: "Based on your snapshot, I see you're using JWT with rotation
(decision D4), Supabase backend (D2), and server actions (D7). For
the refactor, I'd suggest..."One paste. Full context. No re-explanation.
Best Practices for Multi-LLM Workflows
Choose the right model for each task:
- Cursor Claude: Deep code work, long sessions
- ChatGPT: Creative brainstorming, explanations
- Claude (direct): Complex reasoning, refactoring
- Gemini: Large file analysis, documentation
Snapshot at transition points:
- Before switching models
- End of work session
- Before major decisions
Keep snapshots focused:
- One project per snapshot
- Capture decisions, not transcripts
- Update regularly, don't let them stale
Native MCP Support Across Tools
RL4 v2.0 provides native MCP integration for multiple LLM tools:
| Tool | Integration | How |
|------|-------------|-----|
| Cursor IDE | VSIX extension + MCP | Auto-configured via `.cursor/mcp.json` |
| Claude Code CLI | MCP + CLAUDE.md | Auto-detected, context injected |
| Codex CLI | MCP registration | `codex mcp add rl4` |
| Gemini CLI | MCP via env config | `~/.rl4/mcp.env` |
| ChatGPT / Gemini web | Snapshot paste | Manual but fast |
The MCP approach means zero-friction switching between Cursor and Claude Code. Your `search_context`, `rl4_ask`, and `get_evidence` tools work identically in both.
The Future of AI Context
Model switching friction is decreasing now. MCP (Model Context Protocol) is becoming the standard for tool-to-tool context sharing.
The developers who solve context portability first will:
- Move faster between tools
- Maintain continuity across sessions
- Onboard teammates instantly
- Never lose accumulated knowledge
Start Switching Without Friction
Ready to use multiple LLMs without the re-explanation tax? Whether you're going cursor to Claude, ChatGPT, or Gemini, the process is the same.
First, export your Cursor chat historyexport your Cursor chat history/cursor/blog/export-cursor-chat-history-complete-guide if you need a backup. Then generate a portable snapshot.
**Try RL4 Snapshot**Try RL4 Snapshot/cursor/form — generate portable context from your Cursor history in 2 minutes. Switch models freely, transfer AI context instantly.
Your AI knowledge should travel with you. Multi-LLM workflow mastered.