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.
IFGemini models fail with 400 error when using pydantic-ai's tool-calling with TypedDict schemas containing optional fields or unsupported parameter formats.
THENImplement an alternative mode (like instructor's MD_JSON) that guides the LLM to output structured JSON via prompt engineering and parses the raw text response, bypassing tool-calling APIs for broader model compatibility.
IFUsing a Pydantic model as response_format (or with_structured_output) with deepseek-chat via LiteLLM or LangChain causes BadRequestError because Deepseek does not support json_schema.
THENReplace response_format with {'type': 'json_object'} and add instructions in the system message to output JSON matching the expected schema. Alternatively, use a model like Llama 3.3 that supports json_schema.
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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