Unverified50% confidenceSolutionExact time
Early Agent frameworks often concatenated the entire conversation history losslessly, and as the number of rounds increased, input Tokens would grow quadratically; Hermes Agent retains only fragments relevant to the current task through strategies such as summary compression and on-demand retrieval, making the per-round cost stable during long-term operation.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Hermes Agent Practical Tutorial: An AI Framework That Saves More Tokens Than Open Cloud
bilibili研究院张Q10/15/2026
Related Entities
Related Claims
UnverifiedAgentExecutor每次工具调用前会重新拼接完整对话历史进Prompt，导致Token消耗随任务步骤线性增长78% similarUnverifiedIn agentic scenarios, an Agent often initiates multiple rounds of model calls within a single user request, with each round carrying an increasingly longer context, causing costs to grow nonlinearly with conversation depth.75% similarUnverified多线程为Agent提供全新上下文，可避免长对话中的上下文稀释问题，从而使Agent在更优状态下运行75% similarUnverifiedIn agent scenarios, the context window accumulates continuously with each conversation turn, causing the input tokens for each subsequent turn to grow persistently.74% similarUnverifiedThe longer an Agent runs and the more tool results and history accumulate, the more likely that the original task requirements and operational constraints will be lost during truncation and compression if everything is stuffed into the context; key constraints must be readable at every round of execution.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042999API
curl https://kongchang.com/api/v1/knowledge/claims/1042999MCP
get_claim(id=1042999)