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):

Tell the user immediately — Don't silently fail or retry endlessly
Offer local fallback — Ask if they want to switch to Ollama
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.

LLM Supervisor