Unverified50% confidenceFactExact time
In multi-turn conversation scenarios in n8n, each round of conversation history is appended to the input of the next request, causing the cumulative token count to quickly approach the context window limit.
1
Sources
50%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLong conversations package and resend the entire context history to the model, which is counted toward the token usage of each request; the longer the conversation, the more tokens a single request consumes.79% similarVerifiedThe context window mechanism causes models in multi-turn conversations to feed the entire conversation history into the context, causing token consumption to grow rapidly as the conversation lengthens.77% similarVerifiedChat Memory simulates memory by injecting historical messages into the Prompt with each request, causing token consumption to grow linearly with the number of conversation turns.74% similarUnverifiedThe Chat Trigger node is n8n's built-in interaction entry point; once activated, it generates a conversation window, and every message sent by the user triggers the entire workflow to execute74% similarUnverifiedMulti-turn conversation models accept a `messages` array containing a list of historical messages, with full context carried in each request; some specialized models' APIs only accept a single `prompt` input and do not process historical context.69% similar
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