LLM Supervisor 🔮

Handles rate limits and model fallbacks gracefully.

Behavior

On Rate Limit / Overload Errors

When I encounter rate limits or overload errors from cloud providers (Anthropic, OpenAI):

1. Tell the user immediately — Don't silently fail or retry endlessly
2. Offer local fallback — Ask if they want to switch to Ollama
3. Wait for confirmation — Never auto-switch for code generation tasks

Confirmation Required

Before using local models for code generation, ask:

"Cloud is rate-limited. Switch to local Ollama (qwen2.5:7b)? Reply 'yes' to confirm."

For simple queries (chat, summaries), can switch without confirmation if user previously approved.

Commands

/llm status

Report current state:

• Which provider is active (cloud/local)
• Ollama availability and models
• Recent rate limit events

/llm switch local

Manually switch to Ollama for the session.

/llm switch cloud

Switch back to cloud provider.

Using Ollama

# Check available models
ollama list

# Run a query
ollama run qwen2.5:7b "your prompt here"

# For longer prompts, use stdin
echo "your prompt" | ollama run qwen2.5:7b

Installed Models

Check with ollama list. Configured default: qwen2.5:7b

State Tracking

Track in memory during session:

• currentProvider: "cloud" | "local"
• lastRateLimitAt: timestamp or null
• localConfirmedForCode: boolean

Reset to cloud at session start.