llm-supervisorGraceful rate limit handling with Ollama fallback. Notifies on rate limits, offers local model switch with confirmation for code tasks.
Install via ClawdBot CLI:
clawdbot install dhardie/llm-supervisorGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
http://127.0.0.1:11434Audited Apr 17, 2026 · audit v1.0
Generated Mar 1, 2026
A development team uses the LLM Supervisor to handle API rate limits from cloud providers like OpenAI during code generation tasks. When rate limits are hit, the agent notifies the team and offers a fallback to a local Ollama model, ensuring uninterrupted workflow without compromising on code quality by requiring confirmation for switches.
Researchers rely on cloud LLMs for data analysis and summarization but face rate limits during peak usage. The LLM Supervisor automatically switches to local Ollama for non-code tasks like text summarization after user approval, maintaining productivity while managing costs and API constraints effectively.
A company uses AI agents for customer support chats, which depend on cloud LLMs for responses. During high traffic, rate limits can disrupt service. The LLM Supervisor detects limits, notifies operators, and can switch to local models for simple queries, ensuring continuous support with minimal downtime.
Content creators use AI for generating articles and social media posts, often hitting API rate limits. The LLM Supervisor offers a fallback to local Ollama models for non-code tasks like drafting, allowing the agency to maintain output without extra costs or delays, with manual switches for code-related content.
Offer the LLM Supervisor as a cloud-based service with tiered pricing based on usage limits and features like advanced monitoring. Revenue comes from monthly subscriptions, targeting businesses that rely heavily on AI APIs and need reliable fallback options to avoid disruptions.
Sell perpetual licenses or annual contracts to large organizations for on-premise deployment, including customization and support services. This model caters to industries with strict data privacy requirements, ensuring local fallback without external dependencies.
Provide a free version with basic rate limit handling and Ollama integration, then charge for advanced features like detailed analytics, multi-provider support, and priority support. This attracts individual developers and small teams, converting them to paid plans as needs grow.
💬 Integration Tip
Ensure Ollama is installed and configured with default models like qwen2.5:7b before deployment, and use the /llm status command to monitor provider states during integration testing.
Scored Apr 19, 2026
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