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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.
IFWhen using LangChain with vLLM's OpenAI-compatible API and Llama 3.1, tool call responses contain a '<|python_tag|>' prefix and are not parsed into actual function calls.
THENImplement a custom parser that strips the '<|python_tag|>' prefix from the AIMessage content and attempts to parse the remaining JSON into a ToolCall object with name and parameters.
IFUnhandled tool invocations with Llama 3.1 when using LangChain and vLLM's OpenAI-compatible API: the model returns <|python_tag|> metadata but does not produce a proper function call.
THENImplement a custom parser that strips the '<|python_tag|>' prefix from the assistant message content, attempts to parse the remainder as JSON containing 'name' and 'parameters', and constructs a ToolCall object to pass to the LangChain chain. This workaround bridges the gap until vLLM fully supports native tool calls for Llama 3.1.
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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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