AI assistant keeps giving outdated answers after codebase refactoring, how to update its learned knowledge?

TL;DR

• A refactor changes the code; the assistant's notes about that code stay as they were. Whether it notices depends on two things: does a note point at the code it is about, and is that pointer checked before the note is used?
• GitHub Copilot Memory documents both: facts cite code, and the citations are checked against the current branch before use. Claude Code dates its notes and tells the model to verify older ones against current code when it uses them. Cursor advises rules that reference files instead of copying them.
• Devin and Cline tie memory to the session it came from or to markdown files that you or the agent keep current. Memory layers (Zep, Mem0, Supermemory, Cognee) trace a fact to a conversation or document, not to code.
• After a refactor: keep out of memory what the code already says, point the rest at files and date it, run the checks that exist, replace a stale note instead of appending a correction, and send changed documents through an update path.

A renamed function is easy to find in the repository. It can be much harder to get out of an assistant's memory.

GitHub's engineering post on Copilot's memory says the core challenge is not retrieval but "ensuring that any stored knowledge remains valid as code evolves across branches and time" (GitHub blog, 2026-01-15). Persistence is not the problem. Persistence with no connection to current code is.

A stale note is a stored claim that describes an earlier state of the code and was not revised or invalidated when that state changed.

Code-anchored memory is a stored claim tied to code locations (files, symbols, tests) that can be checked against the current repository before the claim is used.

This is a reading of each vendor's published documentation and shipped files, first read on 2026-10-06 and re-read on 2026-10-10. It is not a hands-on test of the products. Where a page does not describe a behaviour, this article says so rather than claiming the product cannot do it.

Why a refactor leaves the assistant on the old code

A saved note and a repository file have separate lifecycles unless something connects them.

Say an assistant noted that authentication goes through validateSession(). A refactor replaces that function. The repository now holds the new name, but the old note can still be retrieved, because nothing ties it to the changed file and nothing checks it before use. Anthropic describes the same failure for an index of the code: it "returns a function the team renamed two weeks ago, or references a module that was deleted in the last sprint, with no indication that either is out of date" (Anthropic, 2026-05-14).

So two questions matter more than whether a product "has memory":

1. What does the note point to? Current code, a file, the session it was learned in, a conversation, or a document.
2. What checks or updates it? A check when it is used, a code change, a scheduled cleanup, a command you run, or nothing that is described.

Copilot Memory, Claude Code, Cursor, Devin and Cline compared

Read from each vendor's documentation and shipped files on 2026-10-10.

Assistant	What a note points to	What signals or checks a stale note	Remember or re-read
GitHub Copilot Memory	Facts with citations to the code that supports them	Citations checked against the current branch before use; unused entries deleted after 28 days	Remembers, then re-reads the cited code
Claude Code	One auto memory directory per git repository, on one machine	Last-modified time on notes; from two full days old, an age note and an instruction to verify against current code	Skips what it can derive from the codebase
Cursor	Version-controlled .mdc rules that can reference files	You edit a rule, or Agent does when you tag @cursor on a GitHub issue or PR	Rules point at files; search index stays on your machine
Devin	The session where the entry was learned, with the date it was added	Session Insights flags Knowledge items (now moving to Skills) written for a previous version of the codebase	Does not save what is cheap to rediscover
Cline	Markdown Memory Bank files in your project	Update occasions named in the recommended instructions, one of them your update memory bank	Reads the Memory Bank after each reset, per the recommended instructions

GitHub Copilot Memory checks cited code against the current branch

In the Copilot Memory docs, Copilot "checks those citations against the current branch to confirm the information is still accurate. Only validated facts are used." The engineering post says how: "the agent is prompted to verify its accuracy and relevance by checking the cited code locations." So the check is an instruction to the agent when it reads a fact, not a rewrite on every commit. When the code disagrees, the agent "is encouraged to store a corrected version of the memory".

The docs add three details that matter after a refactor. A fact captured from a pull request closed without merging has no effect unless the current codebase still supports it. Unused facts are deleted after 28 days, and "The 28-day timer may reset whenever Copilot successfully validates and uses an entry." Repository owners can review and delete repository-level facts by hand. Memory serves the Copilot cloud agent, code review, the CLI and agentic autofix, and, since 2026-08-11, agent chat in JetBrains (GitHub changelog). It "is currently in public preview and is subject to change."

Claude Code auto memory dates notes and warns when they are used

The memory page: "Auto memory is machine-local." All worktrees and subdirectories of one git repository share one directory on that machine, and the files "stay until you or Claude edits or deletes them." Notes have carried timestamps since 2.1.75 (2026-03-13) and an ISO modified write time in their frontmatter since 2.1.214 (2026-07-18) (changelog). The /memory command gives "access to the auto memory folder and the auto memory toggle" (debug your config), so you can read and edit the notes yourself.

The program shipped as Claude Code 2.1.291 adds an age note to any memory two full days old or older, followed by "Verify against current code before asserting as fact." That acts when a note is used, not when the code changes. A link from a note to a file or commit, and a re-check triggered by a code change, are not described on the Claude Code documentation read on 2026-10-10.

Claude Code's first defence is upstream: auto memory skips "anything it can derive from the codebase". Function names, paths and signatures are cheaper to re-read than to keep as prose. For instruction files, /doctor prompt-audit (since 2.1.283, 2026-09-25) reports references to missing files or commands and contradicting files, and changes nothing until you ask. Its default list covers CLAUDE.md, CLAUDE.local.md, AGENTS.md and the rules, skills, commands, subagents and output styles under .claude/ and ~/.claude/. It does not list the auto memory directory, though you can pass a path. The best practices put it in one line: "Treat CLAUDE.md like code".

Cursor rules should reference files, not copy them

Cursor's rules page says it directly: "Reference files instead of copying their contents. This keeps rules short and prevents them from becoming stale as code changes." Rules are .mdc files in .cursor/rules, under version control. You edit them, or Agent does when you tag @cursor on a GitHub issue or PR.

The difference is concrete. A rule that says "follow the interface in src/auth/session.ts" sends the assistant to the current file. A rule that pastes the old interface becomes a second specification, and after the refactor it is the wrong one. Cursor's search builds its index on your machine and "does not store embeddings of your codebase for search"; that is a statement about search, not about rules.

Devin memory links each entry to the session it came from

In Devin's Memory, each entry "links back to the session where it was learned" and is dated when added. That records origin; it points at a past session, not at current code. Session Insights gives as one reason knowledge misled Devin: "Knowledge was written for a previous version of your codebase", listed under Misleading Knowledge so you can update or remove it. Dreaming "reviews your recent sessions alongside your existing memory" and removes "stale notes that no session has used". A check of a note against changed code is not described on the Devin Memory page read on 2026-10-10. The older Knowledge feature "is deprecated"; Devin's release notes date its move into Skills to 2026-09-18.

The Agent Memory Repo specification that this memory is built on recommends a source key, "a link to the agent session where the information was learned", and an added date. A key tying an entry to a code file is not described in the specification, though "Keys are open."

Cline Memory Bank is documentation you keep current

Cline calls its Memory Bank "a documentation methodology". The recommended custom instructions on that page name four occasions to update it, among them "After implementing significant changes" and your own update memory bank. The same instructions say: "After each reset, I rely ENTIRELY on my Memory Bank". These are recommended instructions, not built-in behaviour, and they make a stale Memory Bank expensive. Separately, the extension's source tracks outside edits to files read in the current task "to prevent stale context"; that covers the current task, not the Memory Bank.

Memory layers trace a fact to a conversation or document, not to code

If your agent's memory sits in a separate memory layer, the anchor is the episode, conversation or document the fact came from. Zep ties facts to episodes and says plainly that this "records where information came from. It does not establish that the source content or a derived fact is true." Mem0 keeps in a memory's history "The conversation input that led to this memory change". Supermemory's MCP returns "source document IDs", and its GitHub connector syncs pushed documentation "within approximately 10 minutes", removes deleted files, and excludes code files by default.

What matters after a refactor is what each layer does with a changed document:

• Cognee: "add() creates documents; it never updates one." (add). Its update() replaces what was derived from the old content and by default re-ingests "only the chunks the edit touched"; when it rebuilds a document instead, entities that other documents in the same dataset also reference are kept.
• Supermemory: "Content changes trigger full reprocessing" (document operations), and "facts supported only by the previous source can disappear after reprocessing" (v5 document updates).
• Zep: "Reingesting the same file creates new episodes, even when its contents are unchanged" (ingestion); each extracted fact is then compared with existing ones, and "duplicates merge, and contradictions invalidate older facts rather than leaving conflicting truths side by side." (graph creation).

The lesson is the same in all three: a changed document goes through the update path. Adding it again can leave both versions in memory.

Remember or re-read: what the October 2026 preprints measured

Remembering saves rediscovering. Re-reading avoids trusting yesterday's summary. Anthropic's post explains why Claude Code searches the live code without an index, and names the price: "it works best when Claude has enough starting context to know where to look." Copilot keeps memory and re-reads the cited code.

Three arXiv preprints from October 2026 study this trade-off. None studies refactoring, none is peer-reviewed, and every number below is the authors' own measurement on their own setup.

• arXiv 2610.03902 (Wenhui Chu, v1, 2026-10-02) asks, when supporting documents are revoked or replaced, "should it repair memory or re-read current evidence?" On ICU medical records with two 7B models, local repair used 5 to 10 times fewer revision tokens than rebuilding on short records, yet every memory pipeline cost at least twice full re-reading in held-out conditions; in a small development sweep that padded records to about 10,000 tokens with documents the task does not use, memory became cheaper than full re-reading after 2 to 14 uses, "while source-filtered re-reading remained cheapest." The author's own limit: "none of the four primary confirmatory tests reached statistical significance." No code or data are published yet. Its example fits code too: "The raw record is marked revoked; the extracted claim and the summary are not".
• MemTrace, arXiv 2610.04838 (Hongming Xu and others; v1 2026-10-04, v2 2026-10-08 with the same results) anchors a coding agent's records to "files, symbols, tests" and checks them "against the current repository state"; with nothing applicable, "the agent reads the current file or symbol, or reruns the required test." The authors: "Persistence alone, however, does not establish current validity". In 84 archived coding-task runs, 57.0% of 9,226 eligible file-bound records met a later change to at least one bound file, which is exposure, not error: "Exposure measures file changes; Trace Validation assesses whether historical evidence remains applicable." The performance runs used one model; sessions are left to future work; no refactoring. The code is published.
• StateWise, arXiv 2610.05241 (Yongyuan Peng and others, v1, 2026-10-04) planted one wrong operational record in an agent's persistent memory in each of 150 cases. Even with an explicit reminder to refresh, "refresh remains selective and recovery is limited". Removing the wrong record "reduces harm but leaves the agent without information needed to complete the task." The records were about environment and repository policy, not code after a refactor, and developer responses were simulated.

Together they support a design question, not a product ranking: which notes are worth repairing, and which should be read again from the source.

Replace the stale note, do not append a correction

A correction added beside the old note leaves two notes that disagree, and the old one can still win retrieval. An illustration of the pattern to avoid:

- Session tokens are validated by `validateSession()` in src/auth/session.ts.
- UPDATE 2026-10-10: `validateSession()` was removed; use `verifyToken()`.

Several systems tell the agent to fix the note in place instead. Letta's reflection prompt: "fix the stale entry at the source. Do not append the new version alongside the old." The Agent Memory Repo skill: "Update or remove entries that are out of date instead of adding a contradicting entry." Kevin Liao's essay (version of 2026-10-05): "Facts should be rewritten when truth changes. No appending." Whether a model actually does this varies: Letta's evaluation of 2026-07-28 found that "weaker models make minimal edits that leave stale and contradictory information behind." What a store does with an older record that a newer one contradicts is compared in Agent memory consolidation compared.

A rating moves a note down, it does not say why

Several products take a rating on a memory or an answer, and none of them turns a thumbs-down into the corrected fact. Cognee can fold answer ratings into graph search, at a default influence of 0.0 when self-hosted, which its demonstration raises to 0.2; there a rating lifted both contradicting facts equally, and Cognee concludes that "a rating steers attention, it does not decide what is true." Mem0 takes POSITIVE, NEGATIVE and VERY_NEGATIVE ratings and says "This feedback is used to improve the accuracy of the memories and search results." (feedback); how NEGATIVE lowers a memory is not described on that page. Supermemory down-weights facts its engine inferred "until someone confirms them": once approved, such a fact "Ranks like a stated fact"; once declined, it is "Removed from search entirely" (memory review). In Mnemoverse (the author's company), "Ratings re-rank what comes back next." (agent memory).

A rating can push a stale note down without saying that it is stale or what replaced it. After a refactor, fix the note.

What to do after a refactor

1. Keep out of memory what the code already says. Function names, paths, signatures and behaviour are for the agent to read from the code; Claude Code skips what it can derive from the codebase, Devin skips what is cheap to rediscover. A prompt such as "Read src/auth/session.ts and confirm the current exports before writing the tests" does more than any remembered summary of that file.
2. Point what you keep at files, and date it. Cursor's rules advice and Copilot's citations both point at code; Claude Code records write times; the Agent Memory Repo recommends source and added keys. A session link tells you where a note came from, a file reference gives the agent something current to check.
3. Run the checks that exist. Copilot validates cited facts on its own; repository owners can delete stale ones. In Claude Code, open /memory and read the notes about the code you moved, and run /doctor prompt-audit on your instruction files. In Cursor, update the affected rules. In Devin, review the Misleading Knowledge list in Session Insights (it covers Knowledge items, now moving to Skills). In Cline, run update memory bank.
4. Replace a stale note instead of adding one beside it (Letta, the Agent Memory Repo). Copilot's agent is encouraged to store a corrected version; GitHub's pages do not say the old fact is removed, and repository owners can delete it by hand.
5. Send a changed document down the update path, not the add path: Cognee's update(), not add(); in Supermemory, "update the canonical document when facts change" (v5 memory forgetting).

Rules files age with the code too: a rules file is text in a context window. Forgetting over time is a separate question, as are outdated library patterns after a correction.

Common questions

AI assistant keeps giving outdated answers after codebase refactoring, how to update its learned knowledge?

Remove from memory what the code already says, point what you keep at files and date it, run the checks your assistant documents, replace a stale note instead of appending a correction beside it, and send changed documents through an update path. Whether the assistant notices a refactor on its own depends on whether its notes point at code and whether that pointer is checked before use; as of 2026-10-10, GitHub Copilot Memory documents both.

Why does my AI coding assistant still suggest a function I renamed?

Something it reads was written before the rename. If that note points only to an earlier session, conversation or document, nothing in the rename touches it. GitHub Copilot Memory checks a fact's code citations against the current branch before use; Claude Code tells the model to verify notes two full days old or older against current code. Otherwise, rewrite or delete the note, or let the agent read the code again.

Does Claude Code update its memory after a refactor?

Its memory files "stay until you or Claude edits or deletes them." (memory page). The Claude Code documentation read on 2026-10-10 does not describe a re-check of a note when code changes. The program shipped as Claude Code 2.1.291 dates notes and, when a note two full days old or older is used, tells the model to verify it against current code.

How does GitHub Copilot Memory keep facts from going stale?

Facts carry citations to code, and before a fact is used the agent is prompted to check those citations against the current branch; in GitHub's docs, "Only validated facts are used." Unused entries are deleted after 28 days, and the timer may reset on validated use. Repository owners can also delete facts by hand. The feature is in public preview.

Can a thumbs-down rating fix an outdated memory?

No. A rating can move a note down in retrieval where a product documents that, but it does not say why the note is wrong or what replaced it. Cognee's own demo concludes that "a rating steers attention, it does not decide what is true." Correct or remove the note itself.

Related

• Rules files are text in a context window
• Why agents need to forget
• Evidence vs policy in agent memory
• Stop an agent repeating mistakes you already fixed
• Agent memory consolidation compared
• Provenance in agent memory
• Reference: agent memory, REST API

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Sources and method. Each vendor's pages were first read on 2026-10-06; every quotation on this page was read again on 2026-10-10 and is contiguous text of the source linked beside it (for arXiv papers, the full text behind the abstract page; for Claude Code's shipped instructions, the npm package named below), with the vendor's punctuation. Where a vendor describes an instruction to the model rather than a mechanism, this page says so. Claude Code: memory, best practices, changelog, debug your config, and the shipped program in the npm package @anthropic-ai/claude-code-linux-x64 2.1.291, searched byte by byte; Anthropic, How Claude Code works in large codebases (2026-05-14). GitHub: Copilot Memory, Building an agentic memory system for GitHub Copilot (2026-01-15), JetBrains changelog (2026-08-11). Cursor: rules, search. Devin: memory, Session Insights, Knowledge, release notes. Agent Memory Repo: SPEC.md, SKILL.md. Cline: Memory Bank, FileContextTracker.ts. Letta: reflection.md, Evaluating memory in production agents (2026-07-28). Mem0: memory history, feedback. Zep: source traceability, preparing data for ingestion, how graph creation works. Cognee: update, add, contradiction handling. Supermemory: document operations, GitHub connector, MCP, memory review, v5 document updates, v5 memory forgetting. Research: arXiv 2610.03902 (v1), arXiv 2610.04838 (v1 and v2), arXiv 2610.05241 (v1). Discussion: Kevin Liao's essay (version of 2026-10-05). Mnemoverse: agent memory. No score or performance number for any product appears on this page.

Edward Izgorodin · Mnemoverse · 2026-10-10

Mnemoverse is a persistent-memory API for AI agents. Free key: console.mnemoverse.com · Plans and limits · Docs: Getting Started