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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.
IFAgent continues to throw 'An output parsing error occurred' despite setting handle_parsing_errors=True in the AgentExecutor.
THENModify the prompt template to enforce that every 'Thought:' is immediately followed by either an 'Action:' plus 'Action Input:' or a 'Final Answer:'. This can be done by appending an instruction like 'IMPORTANT: After each Thought you MUST output exactly one Action/Action Input or Final Answer. Do not include any other text.' to the system or user prompt. Alternatively, reinstall the correct version of LangChain to ensure the feature works as expected.
IFLLM output for a ReAct agent contains both a final answer and a parseable action (e.g., 'Final Answer:' and 'Action:'), triggering OutputParserException.
THENSet the stop token to 'Observation:' when initializing the language model used with the agent. This prevents the LLM from continuing to generate text beyond the tool observation, ensuring it only produces one action or final answer per step. Example: if using ChatOpenAI, pass stop=["Observation:"] in the model constructor or call. For other LLMs, use the appropriate stop parameter.
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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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