Taming Agent Context Inflation: Integrating OpenViking for Cache-Aligned RAG and Token Optimization

说明与用途

将检索到的知识直接拼接到每个代理提示中导致令牌膨胀和缓存失效。引入 OpenViking 将上下文结构化为层级树，分离静态知识和动态会话状态，降低了令牌使用并恢复了缓存命中率。

问题

Naive concatenation of retrieved knowledge into every agent prompt caused massive token inflation, cache misses, and a $600 API quota drain in 42 minutes.

结果

Token usage per turn dropped below the $600 quota threshold, cache hit rates improved to ~85%, and the agent pipeline stabilized without catastrophic token inflation.

工作流

• 1. Restructure knowledge into OpenViking’s tiered context tree (static docs + dynamic session nodes).
• 2. Fetch immutable knowledge chunks via OpenViking’s query_knowledge API and concatenate into a deterministic prefix.
• 3. Retrieve evolving session state from OpenViking’s get_session_memory API.
• 4. Assemble prompt messages with static prefix, session state, and user query.
• 5. Send assembled messages to the model gateway with proper headers and payload.
• 6. Process model response and update session memory in OpenViking.

证据

Every single reasoning hop re-serialized 48,000 raw tokens of vector search chunks, document metadata, and ephemeral scratchpad state directly into the prompt prefix

来源

OpenViking restructures this data into a tiered context tree

来源