Add Cross-Session Context to Every ChatGPT API Call
The ChatGPT API is stateless. Every call starts blank unless you stuff context into the system prompt — which inflates tokens, bloats latency, and still loses fidelity. MemoryLake adds a cross-session memory layer to the ChatGPT API, so each call retrieves only the context that matters.
Add Cross-Session Context to Every ChatGPT API Call
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The problem: the ChatGPT API forgets between every request
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.
How MemoryLake solves cross-session context for the ChatGPT API
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.
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How it works for the ChatGPT API
- Connect — Pipe each user turn and assistant response into MemoryLake via SDK or REST.
- Structure — MemoryLake classifies, dedupes, and stores each turn with user metadata.
- Reuse — Before every API call, retrieve a ranked, token-budgeted memory block. Prepend it as system context.
Before vs. after: ChatGPT API context handling
| Without MemoryLake | With MemoryLake | |
|---|---|---|
| Returning user request | Empty system prompt | Personalized memory injected |
| Token usage for context | 30k+ per call | <2k per call |
| Latency from huge prompts | Slow first token | Compact context, fast response |
| Switching to GPT-5 or Claude | Migrate everything | Memory follows the user |
Who this is for
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.
Try this use case on your own account
The chat API starts blank on every call, so an app built on it either sends no context or pastes the user's history into every prompt. Here a tutor app keeps its call and adds one flag: each learner's boundary, a saved scope over their own project and actor. MemoryRouter attaches the memories that matter for the message, and the demo measures the reply, the prompt tokens and what leaks.
What you will see
- Two learners (Japanese and German), three past sessions each, stored as conversations; one project, one actor and one boundary per learner (
boundary create --projects … --human-actor …) - The same message through
memorylake router chatthree ways: no boundary mentions 0 of 30 learner details over 5 runs; each learner's boundary brings their own (keigo, Osaka, dative, football) and never the other's (0 of 15 leaks) - The token bill: Mina's three pasted sessions cost 767 prompt tokens, her boundary 286 to 399. Off-topic messages still got some memory attached in 3 of 5 runs; the demo prints that too
- The same boundary through
router messages --model claude-haiku-4-5, and tonight's session piped in like every other one: the next call moves from N4 to N3 (5/5), while "drop the romaji" lost to an older pinned preference once (4/5). Plusaskand a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/chatgpt-api-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
How is this different from OpenAI's built-in memory feature?
How is this different from OpenAI's built-in memory feature?
OpenAI's memory is product-specific to ChatGPT, opaque, and not portable. MemoryLake is developer-controlled, structured, exportable, and works with any model.
Does it support streaming responses?
Does it support streaming responses?
Yes. Retrieval happens before the streaming call. The memory block is just part of your system prompt.
What's the latency impact?
What's the latency impact?
Single-digit millisecond retrieval. Negligible next to model latency.