“I already told it that” is an easy experience to recognise, and it usually has a specific cause rather than one general failure. An assistant answers from what the system gives it for that particular run: the current conversation, any memory the product has saved, and anything an application retrieves and adds. If a detail is in none of those places, the assistant cannot use it for that answer, even if you can still see it in an older chat. This article explains which layer is usually responsible, how ChatGPT’s controls differ, and why “OmniMemory” refers to several unrelated projects rather than one product.
What an assistant can draw on in a single run
Current session context
Current session context is the set of recent messages supplied to the run in progress. It lets the assistant follow the exchange in front of it. By itself, it does not make anything available in a future chat.
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Persistent or cross-session memory
Persistent memory is information saved or derived for later use. It is usually a selection rather than a transcript: a fact, a preference, or a summary. Products may also let the assistant draw on past chats, but that is a separate mechanism, and it is selective too.
Retrieval and context assembly
Retrieval is the step that decides which stored items are relevant to a new run and supplies them to the model. One concrete pattern, documented in TanStack AI’s middleware, recalls relevant memory before model execution and saves the turn afterward. That documentation also states that memory is unnecessary merely to pass the last few messages of the same request, which is a useful reminder that recent context and persistent memory do different jobs.
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Why did my AI assistant forget what I told it?
The same symptom can come from several places. Work through them in roughly this order:
- A session boundary. The detail was said in a different chat, and the current chat does not carry it. Check the chat where you said it and confirm you are asking in a new one.
- Nothing was saved. The detail never became a saved memory. If your account shows a memory summary, look for the item there.
- Selective formation. The system chose not to keep the detail. OpenAI’s documentation says Memory does not keep every detail, so state important facts explicitly and ask the assistant to remember them.
- A retrieval miss. The item is stored but was not selected for this answer. Ask the assistant what it remembers about the topic and compare its answer with the stored items.
- Changed settings or availability. Memory or chat-history reference may be off, or your plan, region, platform, or workspace may not offer the feature. Check your Settings.
- A source that is gone. The original chat was deleted, or the data it held was removed. Check whether the chat still appears in your history.
The symptom alone cannot tell you which of these applies. Judge it by checking your own account’s controls, not by assuming a cause.
Why the vocabulary is confusing
The survey preprint Memory in the Age of AI Agents notes that terminology is fragmented. It distinguishes agent memory from retrieval-augmented generation (RAG) and from context engineering. It offers three lenses for comparing memory systems, and they are useful for asking what a system stores, what the memory is for, and how it changes.
| Lens | Categories named in the survey | Question it helps answer |
|---|---|---|
| Forms | Token-level, parametric, latent | In what representation is the memory held? |
| Functions | Factual, experiential, working | What is the memory for? |
| Dynamics | Formation, evolution, retrieval | How does memory come into being, change, and get used? |
Treat this as a taxonomy from a preprint, not a settled standard.
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How ChatGPT’s memory behaves (checked October 7, 2026)
OpenAI’s Help Center article “Memory in ChatGPT,” updated in 2026, states: “Memory does not retain every detail from every conversation.” According to the same page, ChatGPT may use saved memories and, depending on your experience and controls, information from past chats and other available sources. Availability and controls vary by plan, region, platform, and workspace, so the switches in your account may differ from another person’s.
What the main controls change
Saved memories are separate from chat history. Chat-history reference is selective: OpenAI says it does not search history for every response, and it recommends saved memory for information you want kept in mind.
| Action | Effect on chats | Effect on remembered information |
|---|---|---|
| Delete the original chat | That chat is removed | A separately saved memory is not automatically deleted |
| Turn off Memory | Existing chats are not deleted | Not stated (OpenAI Help Center, 2026) |
| Turn off chat-history reference | Original chats remain unless you delete them | Information remembered from past chats is scheduled for deletion within 30 days |
These controls do not all have identical deletion effects, so do not assume that one switch erases everything related to a fact.
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Checks to run in your own account
- Open Settings and find the Memory controls. Labels and location vary by platform and plan, so use what your account shows.
- If a memory summary and Sources are displayed, review them to see what was saved and where it came from.
- Ask ChatGPT what it remembers about the topic, and compare its answer with the summary.
- Correct any wrong item. If you do not want an item raised again, use “Don’t mention this again,” and remember that it does not necessarily erase the underlying information.
- If the controls are missing, check whether your plan, region, platform, or workspace restricts them.
Chat history, memory, and retention are three different things
Temporary Chat shows why these terms should not be treated as synonyms. According to OpenAI’s Help Center, a Temporary Chat does not create or update memories while it is active. It can use existing memory if you chose personalization before starting it. It may also be retained for up to 30 days for safety purposes, so “temporary” does not mean removed from every system immediately.
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- ONE-TAP RECORDING FOR REAL-LIFE MOMENTS: Capture meetings, phone calls, and in-person conversations instantly with a simple tap, no typing, no interruptions, just effortless note-taking anywhere you go.
- SMART AI INSIGHTS & ORGANIZATION: Pocket automatically turns recordings into clear summaries, key action items and structured conversation maps so you can quickly review what matters without digging through audio.
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Keep the three terms apart. Chat history is the transcript of conversations. Memory is the selected set of items kept for later use. Retention is how long a provider keeps data, and for which purposes. Each setting changes only one of these, which is why the controls table above lists different effects.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.OmniMemory is several unrelated projects
“OmniMemory” is not one standard or one uniquely identifiable product. Several unrelated software projects and a paper use similar names. Their claims come from different places, and none of them shows that the others share code or authors. In this article, “OmniMem” means the richarvey self-hosted project, and the others are named explicitly.
| Name in use | What it is | Where its claims come from |
|---|---|---|
| OmniMem (richarvey/OmniMem) | Self-hosted MCP server aimed at coding agents | Project repository description, not independent validation |
| OmniMemory (OmniNode-ai) | Persistence, recall, semantic retrieval, and lifecycle management for the OmniNode platform, with integrations including Qdrant, Memgraph, Valkey, and Kreuzberg | Repository description; its architecture does not transfer to the richarvey project |
| OmniMemory (omnirexflora-labs) | Python framework with asynchronous dual-agent synthesis and multi-tenant context | Repository README; the documented conversation-analysis path expects a default ten-message batch and supports an optional session identifier |
| O-Mem | Framework described in the paper Omni Memory System for Personalized, Long Horizon, Self-Evolving Agents | Paper; not evidence of shared authorship or code with the projects above |
| OmniMemory-OS | Local-first repository that separates source evidence, memory assertions, and memory-use outcomes | Repository README, which describes user review, correction, revocation, and export controls |
OmniMem as a self-hosted example
OmniMem is a self-hosted MCP server aimed at coding agents. Its repository describes memory that persists across sessions, projects, and machines, organised into episodic, project, knowledge, preference, and skill namespaces. Retrieval ranks items by a combination of similarity, surface, recency, and experience. Each item moves through a lifecycle from ACTIVE to DEPRIORITISED to ARCHIVED to DELETED. The project also describes contradiction checks, duplicate handling, provenance tracking, a web UI, and local storage in Valkey.
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The hardware question
The project says it runs on your own hardware and lists Raspberry Pi support. That makes a Raspberry Pi one optional host for this implementation. It does not mean a Raspberry Pi is needed for AI memory in general, and no specific board model is identified.
What the O-Mem benchmark numbers show
The O-Mem paper, posted to arXiv on November 17, 2025, reports 51.76% on LoCoMo and 62.99% on PERSONAMEM. The authors state that these results improve on the baselines the paper names for each benchmark. Treat them as author-reported results for that framework on those tasks. They have not been independently replicated, and they say nothing about how ChatGPT or any other OmniMemory-named project performs. No reliable public figure on how often consumer assistants forget is available to cite, so this article offers none.
How to compare memory systems
When two systems both claim to “remember,” compare them on these axes:
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- Memory contents: recent turns, explicit saved facts, inferred preferences, episodes or decisions, external documents, or a mix.
- Formation: user-directed save, automatic extraction, summarisation, synthesis, or import.
- Retrieval: semantic relevance, structured filters, recency, importance, scopes, and how retrieved material is shown to the model.
- Change management: update, merge, contradiction detection, expiry, archive, deprioritisation, and deletion.
- User control and provenance: whether you can inspect source messages, correct or contest an inferred item, revoke it, and export it.
- Privacy and portability: where data is stored, which provider or app can access it, whether it can move to another system, and what deletion means across its sources.
- Evaluation: which benchmark and task are reported, the baseline used, and whether the results are independent.
Limits to plan around
- Stored items can be wrong or stale. A memory is a saved claim, and it may no longer match your situation.
- Continuity is not guaranteed. A saved fact does not ensure the assistant applies it in every later answer.
- Exports are limited by the platform. An importer cannot recover data a platform did not export, and provider-controlled threads and memory can differ in portability, deletion, and accuracy.
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