# How do you test an agent that has memory?

You test it for whether the right fact is still in play, not whether a fact got stored. Mem0's own testing write-up gives the gap directly: a test can assert `memory.search("diet") == "vegan"` and pass cleanly while the agent still recommends a cheese plate to a user who went vegan six weeks earlier.

Four checks catch what that single assertion misses: whether an old preference still surfaces after an update (a stale fact), whether the old and new values both come back with nothing marking which is current (a contradiction), whether a fact set early stays retrievable as the conversation grows (drift), and whether one user's memory ever turns up in another user's results (a leak). None of those show up in a five-turn test; Mem0 ran its checks across several contradiction rates and several random seeds to tell a consistent failure from one bad roll.

That's the same discipline [a quality shift after a change needs generally](/answers/regression-detection/is-every-quality-shift-a-regression): a rate measured against a baseline, across enough runs to mean something, not a verdict from whichever run you happened to look at.

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Sources:
- Aashi Dutt, "How to Test AI Agent Memory with Mem0: A Practical Memory Simulation Guide" (Mem0, October 13, 2026): https://mem0.ai/library/integrations/how-to-test-ai-agent-memory-with-mem0-a-practical-memory-simulation-guide (fetched 2026-10-19)

Source: https://tessary.ai/answers/agent-memory/how-do-you-test-an-agent-that-has-memory
More on Agent memory: https://tessary.ai/answers/agent-memory
From Tessary, agent reliability for AI agents in production: https://tessary.ai