A coding agent that brings up an old code pattern can feel as if it remembers—and judges—you. That does not show that it formed a human-like opinion or kept a permanent record of your mistakes. It may be using conversation history, workspace files, project instructions, or a saved memory, and those sources have different scopes and controls.
What “memory” can mean in a coding agent
There is no single, universal agent-memory mechanism. Depending on the product, a response may draw on the current conversation, earlier chat history, files in an open workspace, tool outputs, custom instructions, or persistent memory. Microsoft’s Visual Studio Code documentation describes these as distinct context sources, rather than implying that every agent permanently stores every interaction.
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That distinction matters when an agent refers to something you wrote before. The detail could have come from an earlier turn in the current session, a project file it can currently see, an instruction, or a persistent memory. Without checking the source, the response alone does not establish which one it was.
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Memory has different scopes
Session and workspace context in VS Code
VS Code documents three memory scopes: session memory for the current conversation, repository memory associated with the current workspace, and user memory for preferences that apply across workspaces. Its documentation says these scopes are stored locally. Session context is not the same thing as a persistent preference, and repository memory is not automatically a team-approved record.
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Microsoft recommends verifying stable project knowledge and moving team-critical conventions into source-controlled documentation or custom instructions. That gives teammates a reviewable source of truth instead of leaving important decisions in local memory alone. Review such changes through the project’s normal repository process.
Repository facts and user preferences in GitHub Copilot
GitHub describes Copilot Memory as holding both repository-level facts and user-level preferences. Repository facts can include coding conventions, architecture decisions, build commands, and project-specific rules. GitHub says those facts carry citations to supporting code and are checked against the current branch before use; Copilot uses only validated facts. User preferences have a different scope: GitHub says they are not applied in code review, which uses repository-level facts only.
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GitHub’s documentation says unused facts or preferences are automatically deleted after 28 days. The timer may reset when Copilot successfully validates and uses an entry. This is a documented Copilot behavior, not a general retention rule for coding agents.
How to find out what context was involved
If an agent brings up an old pattern and you want to understand why, trace the likely source before assuming it has a permanent record. Products do not all expose identical source tracing, so this is a practical checklist, not a guarantee that every agent can identify the origin of every detail.
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- Ask the agent what context it used. Treat the answer as a clue, then verify it in the product rather than assuming the explanation is complete.
- Inspect the active workspace. Look for relevant code, project notes, instruction files, or tool outputs the agent could currently access.
- Review the product’s memory and chat controls. Check whether the detail appears in saved memory, past-chat history, or a repository-level record.
- Correct or remove the relevant item and source if you choose. A saved-memory deletion may not erase the chat, file, or other source it came from.
- Put team rules in reviewed project documentation. Use version-controlled guidance for conventions that need to be consistent across a team.
What the controls do in different products
| Product | Documented controls and behavior | Important scope or deletion detail |
|---|---|---|
| ChatGPT | Memory controls are under Settings > Personalization > Memory. Depending on plan, region, platform, and workspace, users may be able to review or correct memory, ask ChatGPT not to mention something, or delete a saved memory. | Turning Memory off does not delete past chats; deleting a chat alone may not remove a separately saved memory. OpenAI says removing remembered information may require deleting both the saved memory and the original chat, as well as other sources such as Library files or connected apps. See OpenAI’s Memory in ChatGPT help page. |
| Claude | Anthropic documents controls to view and edit memory, turn off memory or past-chat search, and use incognito chats. | Anthropic says incognito chats are not saved to memory or chat history. Names and effects of controls differ from other products. See Claude’s chat search and memory help page. |
| GitHub Copilot | Users can review and delete user-level preferences; repository owners can review and manually delete repository facts. Enterprise and organization administrators have additional management options. | Copilot Memory is enabled per user, and policy behavior differs between individual and managed plans. Check current account or organization settings rather than assuming one default. See GitHub’s Copilot Memory documentation. |
Deleting a memory may not delete its source
A memory item and the material that led to it are not necessarily the same thing. OpenAI explicitly cautions that deleting a saved memory does not necessarily delete the chat or other source containing the information. If you want information removed from ChatGPT, check the saved memory, the original chat, and other relevant sources such as Library files or connected apps; turning memory off or deleting only one source may not do all of that.
Deletion and retention behavior is product-specific. Use the current controls documented for the service and, where applicable, the policies set by your organization. Features and settings can also vary by plan, region, platform, or workspace.
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Make lasting coding guidance reviewable
Local or personal memory can be convenient for an individual, but it is a weak place to keep a rule the whole team must follow. For stable conventions, document the rule in a source-controlled project file or custom instructions, then review it like other code changes. That makes the guidance visible, editable, and accountable to the people who maintain the project.
So if an agent seems to remember your old code, first ask what source it may be using. A surfaced pattern is evidence of context, not evidence of a human-like verdict about you.
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