Crystal Memory is a small open-source memory tool for coding agents built on one idea: a stored lesson should be delivered when the agent is about to do the thing the lesson concerns, instead of sitting in a database until someone thinks to search for it. Its author, Tom Jones, reports real usage numbers from his own setup, and he is explicit that those numbers show notes arriving, not work improving. Everything below comes from his September 17, 2026 article on DEV Community, so it is the project author’s account, not an independent evaluation.
What a “crystal” is
A crystal is a short piece of knowledge bound to an action rather than a topic. Each note carries a trigger rule. When a coding agent is about to run a matching shell command, write a file, or make a commit, the note’s marked essence can be injected into the agent’s context just before it acts.
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The author’s example: a note triggers on shell commands that pipe into tail. Its message is that the exit status you see belongs to tail, not to the command before it, so a failed build can look like a success. The agent never has to suspect this problem and search for it; the warning shows up at the moment the risky command is about to run.
Push versus pull
Ordinary agent memory is “pull”: the agent (or you) runs a search and gets results. That works when the agent already knows what question to ask. Crystal Memory adds “push”: delivery keyed to what the agent is doing. The author presents the two as complements. Push can surface a mistake the agent did not know to look for; pull answers a question the agent already has.
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| Axis | Pull retrieval (search) | Crystal Memory delivery (push) |
|---|---|---|
| When notes appear | When a query is made | When a matching action is about to happen |
| How relevance is chosen | Typically ranking or embeddings | Comma-separated literal substrings checked against the action text; no embeddings, no model judging relevance |
| Main risk | The agent never asks the right question | Irrelevant or excess notes spending context and attention |
| Inspectability | Depends on the system | High: you can read the trigger list and see why a note fired |
| Measured benefit | Not covered by the article | Not yet established with a credible control (see below) |
The article does not benchmark against named competing products, so this comparison describes design differences only, not measured outcomes.
Why literal matching
The matcher is intentionally plain. A substring either appears in the command or file action or it does not. The trade-off is clear: you give up fuzzy recall (a note will not fire on a paraphrase of its trigger), and in exchange you get predictability. When a note shows up or fails to show up, you can work out why by reading the rule.
The context-window cost
Pushed notes consume context, so delivery is capped. The author reports a shared budget of 4,000 characters per action; matching notes split that allowance rather than each getting their own. This also matters for experiments, because suppressing one note can free budget for others.
Implementation and maturity
According to the author, the delivery half is five files of standard-library Python, runs locally with no network or service, and is licensed Apache 2.0. The code is at github.com/tjonesit/crystal-memory. At the time of writing he described it as public and still in testing, and said nobody outside his team had installed it. Current repository contents, release status and compatibility with particular coding agents are not established by the article, so check the repository directly before relying on it. No hardware or paid product is involved.
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Rank #3
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The numbers the author reports
All of these are the author’s own operational counts from his setup, not audited results:
- 266 crystals registered as of 2026-09-17.
- 14,375 deliveries over 60 days, 2026-07-19 to 2026-09-17.
- 387 blocked lookups over 94 days starting 2026-06-15.
- 19 suppressions since the withholding experiment began on 2026-09-17.
- Filing-system “hunting” fell from 22 to 7.5 instances per thousand notes delivered between the two halves of the period the author compares.
The hunting drop is suggestive, but the author notes the two periods involved different projects and growing familiarity with the codebase, so it cannot be credited to Crystal Memory.
Rank #4
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.“Counting deliveries measures how often a crystal showed up. Whether the crystal helped is a separate question, and that count is silent on it.” — Tom Jones, project author
What is known about whether it works
Very little yet, and the author says so. He describes two small task measurements that point in a favorable direction but calls them weak evidence. The stronger test is a withholding experiment begun 2026-09-17: the system randomly holds back 10% of otherwise deliverable crystals so outcomes with and without a note can be compared. He plans to stop at 100 units or on 2026-12-17, whichever comes first, and to publish a null result if no effect appears. The article reports the start of that experiment, not its outcome, and no result had been established as of its publication.
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- One operator working in one repository, so results may not generalize.
- The system watches shell commands but not file reads, so some relevant moments are invisible to it.
- Withholding one note can free shared context budget for other notes, which muddies the comparison.
If you want to try it
Treat it as an experiment you can inspect, not a proven productivity tool. Start from the repository’s own instructions, write a few notes for mistakes you have actually seen (the tail exit-status trap is a good model), keep trigger substrings specific so notes do not crowd the 4,000-character budget, and judge value by whether the mistakes stop recurring, not by how often notes fire.
Quick Recap
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