Add Cross-Session Context to Every ChatGPT API Call

Without a memory layer, every API call ships either zero context or a massive system prompt that re-explains the user from scratch. Teams burn tokens, latency, and money trying to fake persistence. The real answer is a memory store the API can query — not a longer prompt.

Per-user persistent memory — Each user has their own memory namespace. The API retrieves only their relevant prior facts, events, and conversations.

Compact retrieval beats stuffed prompts — Pull a 500-token memory block instead of 50,000-token chat history. Same recall, 100x cheaper.

Six memory types instead of one buffer — Conversation, facts, events, reflections, skills, and background memory each retrieve with their own logic.

Cross-model portability — When you switch from GPT-4o to a future model — or to Claude or Gemini — user memory follows them. Zero migration cost.

1. Connect — Pipe each user turn and assistant response into MemoryLake via SDK or REST.
2. Structure — MemoryLake classifies, dedupes, and stores each turn with user metadata.
3. Reuse — Before every API call, retrieve a ranked, token-budgeted memory block. Prepend it as system context.

Product teams building on the OpenAI API — copilots, assistants, vertical SaaS — who want users to feel remembered without paying the token tax for stuffed system prompts.