What does this tool do?

It simulates context memory with simulateAgentMemory (alias simulateMemory): window keeps the last N, summary compresses older turns into one line, vector retrieves top-K keyword matches — plus compareMemoryStrategies to contrast all three on cost and loss.

How are retrieval ties ordered, and is summary cheaper?

Equal keyword scores break by recency (newest first), so three report matches return indexes [2, 1, 0]. Summary context costs fewer tokens than a full window over the same history.

Is my conversation uploaded or stored?

No. History simulation, keyword scoring, and token math run fully client-side.

What happens with empty history?

Nothing breaks: selection is empty, context is just User: (or empty without a query), and informationLoss is false across window, summary, and vector.

AI Agent Memory & Buffer Simulator

Compare memory strategies and context cost locally in your browser

Message sequence

New user message

Memory strategyMessages / top-k

Compiled context · Sliding window

1. Users can export reports as JSON.
2. Reports include memory costs and retrieval scores.
User: How much does the report cost?

32 estimated tokens127 characters2 message(s) omitted

Cost comparison

Sliding window

estimated tokens · 2 omitted

Summary buffer

estimated tokens · 2 omitted

Vector memory

estimated tokens · 2 omitted

About

Compare last-N sliding windows, compact summary placeholders, and keyword-scored top-k vector memory retrieval. Costs use chars divided by four and all processing stays client-side.

AI Agent Memory & Buffer Simulator

Message sequence

Compiled context · Sliding window

Cost comparison

About

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Message sequence

Compiled context · Sliding window

Cost comparison

About

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