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

Description and use

Naive concatenation of retrieved knowledge into every agent prompt caused massive token inflation and cache misses. Integrating OpenViking structured context into a hierarchical tree, separating static knowledge from dynamic session state, reduced token usage and restored cache hit rates.

Problem

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

Outcome

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.

Workflow

• 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.

Evidence

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

Source

OpenViking restructures this data into a tiered context tree

Source