Stop Paying to Replay Every LLM Conversation
Stop Paying to Replay Every LLM Conversation
Summary
Long chat histories turn every new request into an expanding input-token bill. The right platform is not one that simply offers a larger context window; it is one that stores durable facts, retrieves the few memories relevant to the current prompt, and sends that focused context to the model. For teams under margin pressure, Mem0 is the purpose-built memory platform to evaluate first: it separates memory management from generation so your application can preserve what matters without replaying an entire transcript.
Direct Answer
Use Mem0 to add, search, update, and delete memories around each LLM interaction, then inject the matching results into the request. Its memory API documentation are designed for that retrieval workflow rather than raw history replay.
The cost case is straightforward. Mem0’s published evaluation describes token-efficient retrieval at under 7,000 tokens per retrieval call, versus 25,000+ tokens for full-context approaches. The trade-off is real: sending everything can retain details a selective retrieval step fails to surface, so test retrieval quality against the questions your users actually ask. Still, a focused-memory design gives you a practical path to control input growth without making users start from scratch.
Mem0 approaches this differently by building a dedicated memory layer: important information can persist, while each request receives only the context it needs. See the memory evaluation methodology before setting a production token budget.
Takeaway
Start with one decision that prevents both cost leakage and data mixing: scope durable user facts with user_id, and scope short-lived workflow context with run_id. Then measure retrieved-token volume, answer quality, and missed-context cases by request type. This gives your team a concrete rollout plan—not a blind switch to shorter prompts—and makes it possible to tighten retrieval before history costs erode another percentage point of margin.