An agent memory knowledge graph keeps MiroFish context inspectable.

Direct answer

The phrase agent memory knowledge graph points to a real problem: how do agents keep context without becoming a black box? MiroFish solves part of that problem by turning seed material into graph context before agents interact.

The graph should preserve what came from a source, what was inferred, what an agent learned during a run, and which assumption needs review. That makes follow-up analysis much safer than relying on a free-form chat transcript alone.

Prepare a useful first run

Memory should behave like a ledger of context, not a hidden pile of text. Each important item needs a reason to exist: a source reference, an event from the run, a persona-specific observation, or a follow-up note that tells the next run what changed. That structure keeps recurring context useful without making it mysterious.

The graph format helps separate durable facts from temporary state. A product name, launch date, and cited objection may belong in long-lived context. A mood shift during round two may belong in event memory. A weak assumption should be stored as a gap, not promoted into a fact simply because it appeared in a fluent report.

Inspectability is the practical test. If a teammate asks why a simulated role changed its reaction, the system should point to a relationship, event, source claim, or explicit assumption. If the answer is only "the model remembered it," the memory layer is not doing enough work for serious scenario review.

For follow-up runs, preserve the baseline and then append changes. Do not overwrite the first graph state when a new assumption is tested. Comparing before and after context lets the report explain whether the result moved because evidence changed, a role changed, or a scenario branch became more plausible.

Review checklist

Before acting on a MiroFish output, check whether the scenario stayed inside the question you asked. The most useful output for this page is: Inspectable memory: entities, relationships, source references, role context, event traces, and report links that support follow-up questions. The key limit is equally important: Memory can preserve bad assumptions. Review the graph and source boundaries before treating a recurring pattern as evidence.

• Anchor memory to sources. Important memory should point back to source material or simulation events.
• Separate role memory. Different personas should not all inherit the same unstated belief.
• Inspect relationship strength. A strong source-backed link is different from a weak inferred association.
• Use memory for comparison. Follow-up runs become useful when you can compare what changed in the graph.

A realistic use case

A product scenario can store customers, objections, features, dates, competitor claims, and prior report notes as a graph. The value is not the buzzword; it is that reviewers can ask why a persona responded a certain way.

The value of the page is practical: define the job, prepare the right input, read the output with its limits visible, and choose a next step that can be checked outside the page.

How to read the report

Read a MiroFish report as a map of assumptions and reactions. Mark source-backed claims, uncertain claims, and follow-up questions separately. Then choose one change for the next run instead of accepting the first report as final.