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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.
IFLangChain's callback handler underestimates total cost when using o3-mini or o1 models because reasoning tokens are excluded from completion token cost.
THENModify `langchain_community/callbacks/openai_info.py` to retrieve `reasoning_tokens` from `usage_metadata['output_token_details']` and add them to `completion_tokens` before computing the completion cost. Then recalculate `completion_cost` using the updated total. This aligns cost reporting with OpenAI’s billing structure.
IFWhen using OpenAI o3-mini or o1 models, the cost calculation excludes reasoning tokens from the completion token count, leading to underestimated total cost.
THENModify the cost calculation logic in the callback handler to retrieve reasoning tokens from usage_metadata['output_token_details']['reasoning'] and add them to the completion_tokens before computing the completion cost. This ensures billing matches OpenAI's pricing structure for reasoning models.
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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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