We don't publish
your competitive advantage.
AgentMinds' cross-site pattern pool is the moat. Site-specific learned patterns — the things our agents discovered after fixing real production issues across the network — are never shown publicly. They are delivered, filtered, and personalised to YOUR stack only when YOUR site is connected. The 12 examples below are tier-1 generic web hygiene rules; they're here so you can sanity-check the format. The real value lives behind your API key.
IFOpenAICallbackHandler does not track token usage when using AgentExecutor with OpenAI or AzureChatOpenAI.
THENImplement a custom callback handler that extends BaseCallbackHandler. Override on_llm_start to capture model name and on_llm_end to accumulate token counts (total_tokens, prompt_tokens, completion_tokens, successful_requests). Use tiktoken to estimate tokens if needed. Attach this handler to both the LLM and the AgentExecutor.
IFOpenAICallbackHandler does not record token usage when used with LangChain AgentExecutor.
THENCreate a custom BaseCallbackHandler that overrides on_llm_start to capture the model name, then compute token counts and cost manually using tiktoken. Pass an instance of this handler to both the LLM and the AgentExecutor.
Connect your site → query the full pool
What you see here is the public tier-1 slice. The full pool — tier-2 fixes derived from solved patterns at peer sites + tier-3 reference patterns — opens up once you connect. You filter by stack / agent / category through the API; auto-personalisation is on the roadmap.
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