One command to install, a binary to download, or an installer for Windows.
Seven crates are on crates.io, and the web page ships inside the binary crate, so cargo install cyberbrain needs Rust but no node toolchain. If you would rather not build it, the v0.4.0 release carries binaries for Linux and Windows with a checksum file to verify them against, and a Windows installer that adds a Start menu entry instead of a terminal.
Cyberbrainmemory that shows its work
Cited, trust-tiered, local-first memory for AI coding agents. Every retrieved statement carries an identifier that resolves to its source block, every result lists what was not checked, and the whole thing is one native binary that needs nothing else on the machine.
r0-5b2e0c17a4f3 ring 0 invariants.md
Deploy only from a tagged release.
r2-867ef2a8cd01 ring 2 notes/2026-08-deploy.md
Hotfix shipped from main on 12 Aug; tagged afterwards.
caveats
! semantic search skipped: no embedder configured
! contradiction check skipped: no inference model is configuredThe shape of a result, not a transcript: two hits with their citations and rings, then the two caveats a fresh install prints on every query until a model is configured.
It says what it did not check
Most retrieval tools return a ranked list and stop. Cyberbrain returns the list and a second list: everything it could not do for this query. A fresh install with no model configured answers every query with two lines it is not allowed to omit:
That is the design, not an error state. An agent reading the result knows the hits came from keyword search alone and that nobody looked for contradicting notes. And an answer that arrives without a citation is treated as a bug.
What it does
Cited, every time
Each retrieved statement carries an identifier like r2-867ef2a8cd01. It resolves back to the exact source block, so an agent can quote a note and a human can check the quote. A result without one is a bug, not a shortcut.
Trust rings 0 to 4
Notes live in rings. Ring 0 holds the operator's invariants and overrides everything. Rings 0 and 1 are injected into every session, under a size cap so they cannot crowd out the actual work.
Contradictions are reported, not resolved
When two blocks disagree, the lower ring wins and the conflict is named in the result. Nothing is quietly merged on your behalf.
Plain Markdown, and it stays yours
Notes are Markdown files with YAML frontmatter. You can read, edit and grep them without the tool. The index is a disposable cache; the audit log is a separate, append-only file.
One binary, nothing at runtime
Linux and Windows. No system SQLite, no OpenSSL, no model server, no Node. Copy the file and run it.
Hybrid retrieval, all local
BM25 over SQLite FTS5, fused with cosine similarity over static embeddings computed on your machine. No embedding API is called.
How the rings work
Trust is a property of the note, not of the query. Five rings, one short rule: when two blocks contradict each other, the lower ring wins.
Lower ring wins. The conflict is reported in the result, not resolved silently.
Search on day one, and after
A landing page that hides this produces a bad first run, so here it is: semantic search needs a model artefact you place yourself. Nothing is downloaded without your explicit consent.
Lexical, always
BM25 over SQLite FTS5 is built in and works the moment notes exist. It needs no model and no network.
Semantic, once you add a model
Cosine similarity over static embeddings, computed locally. Until the model artefact is in place, search stays lexical, and every result says so in the caveats.
Fused
With both available, the two rankings are fused into one list, and the semantic caveat goes away because there is nothing left unchecked on that axis.
Optional local inference
Some checks need a language model; the contradiction check is one. Cyberbrain talks to any OpenAI-compatible endpoint and contains no vendor-specific code. Anything that speaks that API is a valid backend.
http://127.0.0.1:11434/v1The usual local servers, at the default address or wherever you point the endpoint.
Released 3 September 2026 under Apache-2.0. It spreads local inference across RTX 20-series and newer, DGX Spark and Apple M4 and later machines, discovered over mDNS and paired with mTLS. Cyberbrain has no PAIR-specific code; it reaches PAIR because PAIR speaks the same API. We have not tested against a PAIR setup ourselves, and say so rather than let the logo imply it.
Inference is optional. Without it, the checks that need a model are skipped, and the result names which ones. When it runs on hardware you control, there is no processor and no third-country transfer to document.
What hardware this needs
A laptop. There is no large model to host here. Semantic search uses static embeddings: a table, not a forward pass through a network. A word is looked up, the words of a block are averaged, and 256 numbers come out. That is why one CPU core is enough, and why the file on disk is larger than the work it does — what takes up the space is vocabulary, not computation.
- 1your question
- 2look each word up
- 3average them
- 4256 numbers
No forward pass, no GPU, no model server, no warm-up. The same four steps ran over every block when the index was built, which is why the search itself is a dot product and nothing more.
Install nothing
Lexical search over SQLite FTS5, rings, citations, the audit log. Everything except semantic search.
- 14 MB
- memory per call
- 10 ms
- a search across 4,031 blocks
- 0.7 s
- to rebuild the whole index
Drop in the embedding model
Semantic search: a question in the words you would use finds the note written in someone else's.
- 1.55 GB
- peak while a search runs
- 2.3 s
- a search, most of it loading the model
- 507 MB
- on disk, and 4.0 s to rebuild the index
Point it at an inference endpoint
The contradiction check: hits are held against each other instead of merely listed next to each other.
- 126 s
- one check with a 7B model on this CPU-only server
- 3 s
- the budget, after which hits come back unchecked and say so
- 0 GB
- in Cyberbrain itself — the model runs in its own process
Measured on 6 September 2026 on a server with no GPU (AMD EPYC-Milan, 12 vCPU, 23 GB RAM) against a corpus of 1,004 notes and 4,031 blocks. Your numbers will differ with your corpus; the point is the order of magnitude. Step 2 is the only one that wants better hardware, and it is the only one that is optional twice over: without it, the check is skipped and the result says it was.
Compliance is a subsystem
Not a page in the docs that asserts things about the code. These are commands and data structures. They can be enumerated, run and tested.
Every path by which data can leave the process, as an enumerable list. Today it has two entries and ends with the line "Telemetry does not exist."
Removes the note, its blocks, its vectors, its index rows and every derivative in one transaction. Nothing is left in a cache to resurface later.
A subject-access export for Art. 15 requests.
A write that appears to contain personal data is held for the operator's decision. It is not redacted behind your back.
A separate file with a blake3 hash chain. verify walks the chain, names the first altered row and exits non-zero, so a nightly check fails instead of quietly passing.
Applied on request, per note, rather than as a global sweep.
What the active profile claims the law says, line by line, each with the article it rests on and how sure the author is of it. On the compliance screen and from the command line.
Profiles: eu, ch, off
Switzerland is a separate profile rather than "EU minus something". The revised FADP differs from the GDPR in substance, and folding the two into one profile produces claims that are wrong in one of the two countries.
euGDPRchrevised FADPoffcompliance behaviour switched offEvidence, not assurance
The GDPR applies today. That is what most of this is for: erasure, subject access, a record of what was processed, and the ability to show what actually happened rather than what the manual says should have happened. What a tool can contribute is artefacts, and these are the ones it produces.
An audit log you can hand over
Append-only, one row per operation, blake3-chained. verify walks the chain, names the first altered row and exits non-zero, so a nightly check fails instead of quietly passing.
Erasure with a receipt
The note, its blocks, its vectors, its index rows and its derivatives in one transaction, and two audit rows: that it was asked for, and that it happened.
A subject access answer
Everything the store holds about an identifier, with citations, in a form that can be handed to the person who asked. The search itself is audited.
Data flows you can enumerate
The egress register is closed at compile time and printable. Two entries today, and the last line reads: Telemetry does not exist.
A model card
Identity, source, licence, hash, intended use and limits of every model in play. What a technical file needs, and what an endpoint model cannot tell you about itself.
The AI Act moved. Under the Digital Omnibus (Regulation (EU) 2026/1744, in force 27 July 2026) the high-risk obligations for standalone Annex III systems shifted to 2 December 2027 and for Annex I products to 2 August 2028; the Article 50 transparency duties have applied since 2 August 2026. Nothing about the GDPR moved. So what you have to show today, you have to show because of the GDPR — and what you will have to show in 2027 is easier to collect from today than to reconstruct then.
What this is not: a conformity assessment, a CE mark, a risk management system or a certification. Using it makes nobody compliant. And the artefacts describe Cyberbrain itself — what it retrieved, what it sent to which model, what it erased. They are the part of your file that covers the memory, not the file.
Surfaces
Four ways in, all served by the same binary.
Everything is reachable from the command line.
Fire on agent lifecycle events. Measured p99 of 4 ms including process start, against a 15 ms budget.
For any client that speaks the Model Context Protocol.
Served from the binary, in English and German. There is no separate frontend to install.
Where it stands
The numbers a landing page usually leaves out.
- It has been run against one operator's real corpus: 1,086 notes across five projects. Not much else. Expect edges.
- Semantic search needs a model artefact you place yourself. Nothing is downloaded without your explicit consent. Until then search is lexical, and says so.
- The contradiction check needs an inference endpoint. Without one it is skipped, and says so. It also has a time budget: on a machine where one check takes two minutes, the hits come back anyway and the result says the check did not run.
- Binaries for Linux and Windows hang on the release with a checksum file, and cargo install cyberbrain builds it from source on either.
Three other ways to have it
Each of these was announced on this page while it was still a plan, and each is out now. They are kept here rather than folded into the feature list because none of them is Cyberbrain itself: one is a smaller product, one is a different way into the same one, and one is what happens when a team rather than a person has to show what its agents did.
Cyberbrain Light
out nowThe same citations, the same rings, the same store, without the model and without the compliance subsystem. Keyword search only, one 5 MB binary, nothing to place and no network code in it at all. Its own product in its own repository, and nothing was taken out of Cyberbrain to make it.
Cyberbrain LightA desktop application for Windows
out nowAn installer and a Start menu entry instead of a terminal. It asks once which project to open, starts the server on a port Windows picks, opens your browser at it and waits in the notification area — right-click for the project folder, a different project, or quit. The page and the API are the ones the command line serves; this is the way in, not a second product.
Cyberbrain for WindowsOne machine collects what the others did
out now, needs a licenceA team keeps its notes where it always did — on each person's machine, in each person's store. What one machine collects is the audit trail: which machine did what kind of thing, when, in a chain that cannot be edited afterwards without it showing. It has no notes, no index and no search, and what a note said never leaves the machine that holds it. On Windows it runs as a service and the installer registers it; its own page shows the licence, the seats in use and which machines are reporting, with what is wrong first. What it collects crosses the network encrypted: it makes its own certificate where there is no authority to ask for one, and each machine is invited with the fingerprint of the certificate it should expect — so a delivery reaches that collector or none at all. Reading somebody's activity is a separate procedure that needs a second person to approve it, modelled on how works agreements handle access to video recordings.
How a team runs itLicence
FSL-1.1-ALv2. The source is available, and you may use it for anything except building a competing product. Two years after a release ships, that release becomes Apache-2.0.
Provenance
Cyberbrain is an independent work, written from scratch under a clean-room discipline. It shares no source code with any other memory tool and carries its own licence.
Krynex Labs
Built by Krynex Labs
Krynex Labs builds AI agents, automations and LLM systems for companies that measure them in revenue. Cyberbrain comes out of that work, and the corpus it has been run against is ours.
What we do →FAQ
What is Cyberbrain?
A memory for AI coding agents: notes in plain Markdown, a disposable index, and retrieval that returns cited statements with trust rings and a list of what was not checked. One native binary for Linux and Windows, with nothing else needed at runtime.
Can I install it?
Yes. cargo install cyberbrain builds it from crates.io, or take a binary for Linux or Windows from the v0.4.0 release on GitHub and check it against the SHA256SUMS file that ships with it. On Windows there is also an installer on that release, which adds a Start menu entry that opens the page without a terminal. Nothing else is needed at runtime.
Can a team use it?
Yes, and the interesting half of the answer is what does not move. Each person keeps their notes on their own machine, in their own store, exactly as before. One machine can be licensed to collect the audit trail of the others: which machine did what kind of thing, when, in a hash chain that cannot be edited afterwards without it showing. That machine has no notes, no index and no search — what a note said never leaves the machine that holds it. It runs as a Windows service or a systemd unit, and its own page shows the licence, the seats in use and which machines are reporting. Reading one person's activity is a separate procedure with a named reason that somebody else has to countersign, modelled on how works agreements handle access to video recordings; the request, the approval and the disclosure are themselves entries in a chain.
What hardware does it need?
A laptop. Without the embedding model it uses 14 MB of memory and answers a search across 4,031 blocks in 10 milliseconds. With the model, a search peaks at 1.55 GB and takes 2.3 seconds, most of that loading the model, and the whole index rebuilds in 4 seconds. No GPU: the embeddings are static, a table lookup and an average rather than a forward pass. Only the optional contradiction check wants better hardware, and it says so when it is skipped. Measured on a server with no GPU against 1,004 notes.
Does it need a model?
No. Lexical search over SQLite FTS5 works without one. Semantic search needs a static-embedding model artefact you place yourself, and the contradiction check needs an inference endpoint. Nothing is downloaded without your consent, and every result names what was skipped.
Does anything leave my machine?
The egress register lists every path out of the process; today it has two entries and ends with the line "Telemetry does not exist." Inference, if you enable it, goes to the OpenAI-compatible endpoint you configure, by default one on 127.0.0.1.
What does "trust-tiered" mean?
Notes live in rings 0 to 4. Ring 0 is the operator's invariants and overrides everything; rings 0 and 1 are injected into every session under a size cap. When two blocks contradict, the lower ring wins and the conflict is reported instead of silently resolved.
How does it connect to an agent?
Through six agent hooks, an MCP server over stdio, the CLI, and a web UI served from the binary. The hooks were measured at a p99 of 4 ms including process start, against a 15 ms budget.
What licence is it under?
FSL-1.1-ALv2. Source-available; use it for anything except building a competing product. Each release becomes Apache-2.0 two years after it ships.
Try it, or say what is missing
It is out: seven crates on crates.io, binaries on the release page. If you have a corpus you would like it run against, or something your case needs and it does not do, use the contact form and mention Cyberbrain.
The form is in German. Writing in English is fine.