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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA code review agent should remember verified, reusable knowledge about a repository—not blindly recycle old comments. Start with the repository’s own code, tests, documentation and agent instructions; add a small, repository-scoped memory layer only for context those files do not reliably preserve. Every remembered claim needs provenance and a check against the current code before it can influence a finding.
What a code review agent should remember
Persistent memory is useful when it preserves knowledge that would otherwise be lost between tasks: a convention established during earlier work, an operational discovery, a completed task’s outcome, or a review rule that maintainers accepted. It is not a replacement for reading the repository, and it should not become an archive of every comment the agent has seen.
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Separate information by how it behaves over time. A durable repository fact might describe a local convention; an episode records what happened in one task; reviewer feedback expresses a proposed or accepted rule. These categories have different lifetimes and authority. A one-off request to rename a variable, for example, should not become a permanent style rule.
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|---|---|---|
| Repository knowledge | Verified conventions, architecture notes or operational facts that help with future work. | Scope to one repository, attach evidence, and revalidate against the current code. |
| Task episode | A concise account of a completed task, its outcome and any discovery likely to matter later. | Keep it as historical context, not as an instruction or proof that the code still behaves the same way. |
| Reviewer feedback | A generalizable rule derived from an authoritative review interaction. | Distinguish accepted guidance from a suggestion, preserve its origin and let maintainers correct or remove it. |
The repository remains the primary source of truth. Read applicable agent instructions, README and contribution guidance, code, tests, CI configuration, relevant pull-request descriptions and commits before relying on external memory. GitHub documents several instruction surfaces, including repository-wide Copilot instructions, path-specific instructions, agent-agnostic AGENTS.md files and task-specific skills.
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How to build the memory loop
A practical design has six stages. It can use a database or a simpler store; the essential properties are scope, evidence, selective retrieval and safe failure behavior.
- Establish current repository context. Identify the repository and branch, load the applicable instructions, and inspect files relevant to the change. This gives the agent a current baseline before it consults older knowledge.
- Collect candidate learnings. Consider completed task summaries, codebase conventions, operational discoveries and review feedback. Separate durable, reusable guidance from task-specific preferences and unaccepted suggestions.
- Gate and record writes. Store only concise candidates with enough context to audit them. A record should identify its repository, state one fact or rule, link it to its origin (such as a pull request or code location), record when or at which revision it was observed, and state how it will be reviewed for validity.
- Retrieve selectively. Load relevant repository knowledge before analysis, rather than injecting the whole archive. A second targeted lookup can help assess draft findings, but retrieved items should remain bounded so they do not crowd out the code evidence.
- Verify before use. Check each memory’s cited file, lines or other evidence against the current branch. If the evidence has changed, is missing, or conflicts with current repository materials, do not treat the memory as a valid rule.
- Update after authoritative outcomes. Once a review interaction is complete, determine whether accepted feedback expresses a reusable rule and whether the resulting code supports that interpretation. Preserve the source and allow maintainers to inspect, revise or delete the record.
Google describes a workflow that analyzes review interactions after a pull request is merged, then generalizes applicable feedback into rules. Waiting for a completed interaction can help distinguish a durable lesson from an initial suggestion; it does not make every comment suitable for memory.
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What to put in a memory record
Keep records short enough to retrieve and inspect. A useful record tells the reviewer what is being claimed, where the claim came from and what would make it stale. For example, a conceptual record might contain:
Repository: example-service
Kind: repository knowledge
Claim: Request handlers use the shared validation helper before writing to storage.
Evidence: Pull request 184; src/handlers/orders.ts at revision 8f31c2a
Observed: 2026-03-12
Validity: Recheck the cited implementation and current tests before applying.This is an illustrative format, not a universal schema. The source reference and revision make the claim inspectable; the validity instruction prevents an old observation from silently becoming permanent policy. If a fact has no credible origin, store it as an unverified hypothesis—or do not store it as guidance.
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Make failure and stale information safe
Memory is fallible. Code changes, branches diverge, and two observations may conflict. A stored error can be repeated with growing confidence if the system treats prior agent output as evidence of correctness. GitHub has described just-in-time verification of code-location citations as a way to reduce stale-memory risk; checking evidence is central to keeping memory useful as code evolves.
- Scope records by repository. Do not allow one project’s conventions to leak into another project’s review.
- Attach provenance. Preserve the originating interaction or code location, observed revision and time.
- Re-ground claims. Treat current code, tests and maintained instructions as stronger evidence than a memory that disagrees with them.
- Review and expire. Give maintainers a way to inspect, correct and remove records, and define a review or expiry policy for claims likely to change.
- Keep reads and writes fail-open. If a memory service is unavailable or returns nothing, continue with repository exploration and report the limitation. An outage should not block a review.
An AWS sample architecture describes fail-open reads and writes, retries and cold-start behavior. That is an example design, not a standard or proof that a particular implementation is reliable.
Protect memory from poisoned inputs
Pull-request comments, issue text and tool outputs can contain incorrect or malicious instructions. Treat them as untrusted data, not as authority to change the agent’s rules. A memory system can otherwise preserve a poisoned comment, crystallize a hallucination into supposed repository knowledge, or create an error feedback loop in which later reviews cite earlier mistakes.
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Require a trusted path for turning feedback into a durable rule. Preserve who or what supplied it, distinguish accepted guidance from raw suggestions, and require current-code support before retrieval can affect a finding. Repository scoping, maintainer review and deletion controls reduce risk, but they do not eliminate the need to inspect how the agent reached a conclusion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure whether memory improves reviews
Compare the memory-enabled agent with the same agent operating without memory. Use the same review task mix and evaluation process where practical, and track regressions as well as gains. Useful measures include:
- Reviewer-rated usefulness of comments and recurrence of rejected or duplicate findings.
- Whether accepted rules are applied consistently, and whether the agent misses issues it should detect.
- False positives, stale memories used, and conflicting guidance retrieved.
- Review latency and operating cost, including the time spent maintaining memory.
GitHub reported results for its own systems in 2026: pull-request merge rates of 90% with memories versus 83% without, and positive feedback on code-review comments of 77% versus 75%. GitHub reported p-values below 0.00001 for both increases. These are vendor-reported outcomes for GitHub’s systems, not independent measurements or a forecast of the effect a custom agent will achieve. A custom system needs its own baseline and evaluation.
Build or adopt: what to check
Whether you build a memory layer or use a managed review workflow, assess the controls that determine whether it fits your repository and risk model. GitHub says Copilot code review can use relevant MCP servers and agent skills; Google describes a managed GitHub review workflow that derives rules from pull-request feedback. Product capabilities, plan requirements and rollout conditions can change, so verify current documentation before deciding.
- Who owns memories, what their scope is, and who can inspect or delete them.
- Whether each stored claim has source evidence and is checked against current code.
- How the system connects to your code host, documentation, issue tracker and other relevant sources.
- Whether you can control extraction, retrieval and review policy.
- Where data is handled and which users or services can access it.
- How the system behaves during outages, and whether quality, latency and cost are observable.
Keep a human accountable for the review
Memory can help an agent apply repository-specific context, but it does not establish that a finding is correct or that a review is complete. GitHub’s code-review guidance cautions that Copilot may miss issues or make mistakes and tells users to validate its feedback and supplement it with human review. A reviewer should be able to trace an agent’s claim to current code and understand when remembered context influenced it.
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