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What belongs in an LLM wiki?
Durable memory has a different job from conversation history. OpenAI’s Agents SDK distinguishes reusable lessons saved in memory files from the record of messages in a session. A wiki should preserve information that can guide later work; it should not become a transcript archive.
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Use these events as prompts to evaluate a candidate, not as automatic permission to store it:
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A correction or stable preference
When a user corrects an answer or states a preference likely to matter again, capture the guidance in an appropriate scope. For example, a recurring preference about response format may help future tasks; a one-off request for a particular answer does not necessarily define a lasting preference. OpenAI identifies corrections and preferences as reusable guidance, and Microsoft’s Azure SRE Agent documentation includes team preferences among user-memory candidates.
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A decision and the reason behind it
Record a decision when its rationale will constrain future work. The reason is often more useful than the outcome alone: it lets the agent recognize when the decision still applies and when changed circumstances warrant revisiting it. This is a practical application of the project lessons and operating rules described in official memory guidance, not a claim that every memory product has a dedicated decision trigger.
A reusable project fact or constraint
Save non-obvious architecture details, configuration, dependencies, and constraints that are likely to matter again. Microsoft specifically identifies problem constraints, configuration details, and non-obvious dependencies as useful persistent knowledge. Label the project or environment so an agent does not mistake local context for a universal rule.
The outcome of a difficult task
After a challenging task, preserve the useful lesson: the root cause, steps that worked, pitfalls, or failed approaches that could prevent repeated exploration. A bare task summary is less actionable than a concise record of what happened and what to do differently next time. OpenAI lists task summaries and project-specific lessons; Microsoft lists symptoms, successful steps, root causes, and pitfalls.
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An explicit request to remember
Honor a direct request to save information, subject to the system’s privacy and scope rules. Cloudflare documents explicit memory additions, and Microsoft documents explicit save commands. A save request is a capture trigger, not a reason to ignore restrictions on sensitive or inappropriate data.
Run a quality check before saving
For each candidate, ask these questions before creating or editing a wiki entry:
- Is it in scope? Check the configured topics and identify the owner: a person, project, team, tenant, agent, or environment. A fact that is true for one user or project should not silently become global guidance.
- Will it help a future task? Prefer information with a plausible future payoff. If it is only relevant to the current exchange, leave it in session history.
- Is it appropriate to retain? Do not save sensitive information simply because it is useful. Google Cloud cautions that sensitive-data exclusion is not infallible, so filtering should not be treated as a guarantee.
- Does an existing note already cover it? Update the relevant page instead of adding a duplicate fragment.
- Does it conflict with or replace an existing fact? Resolve the contradiction, revise the older note, or record the change with enough context to make the current state clear.
Google Cloud’s Memory Bank describes filtering against configured topics and checking for duplicates and contradictions during consolidation. Microsoft describes updating existing files and removing stale or incorrect content. These are useful design practices even when a wiki is implemented without those products.
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Organize memory so the agent can find the right note
Use a small orientation layer—such as a summary or index—that points to focused topic pages. Keep detailed notes out of the default context until a task makes them relevant. OpenAI documents an injected memory summary, a searchable index, and detailed summaries retrieved on demand; Microsoft describes an overview file linking to topic files; Anthropic describes just-in-time retrieval from memory files.
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Keep operational facts that may change easy to locate and revise. The documentation supports repairing stale memory and consolidating duplicates or contradictions, but it does not establish a universal expiry period or best time-to-live. Set review or expiry rules according to the system’s needs rather than presenting one interval as a general standard.
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Choose capture and storage controls deliberately
There is no evidence here for a universal winning design or a single threshold for saving information. Compare implementations on the controls that matter to your use case:
| Design axis | What to decide | Documented examples |
|---|---|---|
| Capture mode | Should memory be written explicitly, extracted automatically, or handled with a hybrid? | Cloudflare documents automatic extraction and explicit additions; Google Cloud documents triggered generation and continuous event ingestion. |
| Scope and isolation | How are user, project, team, tenant, agent, or environment boundaries represented? | Cloudflare documents separate profiles and namespaces for different entities. |
| Storage and control | Are notes workspace files, application-controlled files, or held in a managed memory service? | OpenAI documents workspace memory files; Anthropic describes client-side file operations with data remaining in the application; Cloudflare documents a managed service. |
| Review and consolidation | Does the system filter by topic, detect duplicates or contradictions, and allow edits or deletion? | Google Cloud documents topic matching and consolidation; Cloudflare lists operations to add, list, recall, and delete memories. |
| Retrieval strategy | Is a short summary always available, with details loaded selectively when needed? | OpenAI, Anthropic, and Microsoft document selective or progressive access patterns. |
Availability can change: Cloudflare’s Agent Memory documentation, last updated June 2, 2026, labels the service private beta. Check current product documentation before relying on that service or any other product’s present status.
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A memory entry proves that information was recorded earlier; it does not prove the information remains true. OpenAI warns that memory can become stale, and Google Cloud and Microsoft describe updating or removing outdated, duplicate, or contradictory material. Design for revision and deletion, and make it possible for the agent to express uncertainty when a note may no longer apply.
The practical rule is simple: a trigger makes information eligible for review, not automatically permanent. Save concise, scoped information when it has future value and passes the quality check; otherwise, keep it in the conversation or discard it.
Quick Recap
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