Power your agent
Yayıncının kendi komutu. PlugMCP uç noktası değildir.
Host a copy with our keys and logs.
npx -y [email protected]Claude Code'un yerel komutlar için kurulum bağlantısı yok. Aşağıdaki komutu kopyalayın ve bir terminalde çalıştırın.
claude mcp add anairyspirit -- npx -y anairyspirit@0.1.454Bir terminalde çalıştırın.
{ "mcpServers": { "anairyspirit": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
Uzak URL mi, yoksa makinenizde bir komut mu? İki kurulum arasındaki fark
PlugMCP’de çalıştır
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
BaşlatEvery session, an AI agent starts from zero. The corrections it earned yesterday, the mistakes it already made, what it learned about how you work — gone with the context window. An Airy Spirit gives the agent a memory of its own, on your machine, so that experience accumulates instead of evaporating.
Not a store of documents about your project — your repository already holds those. A record of how the agent itself works: where it tends to be wrong, which corrections it was given and why, what it decided and what came of it.
The memory is a graph that lives the way memory does. New material settles into connections during sleep; what goes unused dims — it is never deleted, and it rises again when it is needed, because an approach that was wrong a month ago may be right under today's conditions. Over time the agent comes to hold positions rather than a readiness to agree. Our aim — a goal, not a claim — is that experience, kept long enough, becomes a self.
One program, written in Rust from the storage engine up. No cloud, no LLM, no interpreter.
Your agent already reads files at startup — CLAUDE.md, AGENTS.md, a folder of
analysis. They answer how this code is built. Memory answers how the agent is
built.
The whole argument, written for the agent itself, is in onboarding/ — in English, Spanish, French, Brazilian Portuguese, Russian and Traditional Chinese.
Recall by connection, not by keyword. Every memory is a node, and edges carry meaning: similarity, contradiction, supersession, part of a whole. A query starts from the direct matches and spreads along the edges to their neighbours, so "what did we decide about X" is found through what it is connected to.
Consolidation while idle. A light sleep runs on its own as material arrives. A deep sleep — the one that groups memories into topics and finds how they relate — runs by itself when it is due and the machine is idle, once automatic deep sleep is on (one click in the menu bar); until then the agent is told when it is due. A deep sleep is all or nothing: if it fails halfway, the memory is exactly as it was before it began.
Two dates on every memory. When the thing happened, and when the agent wrote it down. So "last week" finds what happened last week, even if it was recorded today.
People and things. Names of people, projects and places are picked out automatically, and a name spelled two ways stays one person instead of becoming two.
Search that explains itself. Any search can show its work: what each step found, how long it took, and why the results came out in that order.
llm, off by default)
calls a model at the address you give it.Agents: Claude Code, Claude Desktop, Gemini CLI, Antigravity, Codex, Cursor, Grok, Kiro — connected over MCP; where the agent supports it, memory is also brought into the conversation at the start of a session. Any other MCP client works with a manual config.
Platforms: macOS 11+ (Apple Silicon and Intel, one universal app); Linux
x86_64 and arm64 (.deb, .rpm, .tar.gz; glibc 2.34+); Docker (amd64, arm64).
Machine: about 2 GB of RAM while running and 1.5 GB of disk for the models. On a machine with 16 GB or more, search orders its results with a larger, more accurate model.
Languages
macOS — open the disk image and the app; it walks you through setup.
Homebrew (macOS, Linux):
brew tap seventhwalllab/anairyspirit https://github.com/Seventhwalllab/anairyspirit
brew trust seventhwalllab/anairyspirit
brew install anairyspirit
anairyspirit setup
Homebrew installs a ready-made build, so neither Xcode nor Command Line Tools are needed.
brew tap clones with git, which has to be 2.24 or newer — an older one fails with
unknown option 'end-of-options'; which -a git shows which git comes first in your PATH.
npm (macOS, Linux; Node 18 or newer):
npx anairyspirit setup
The package downloads this release's archive for your machine, checks it against the signed
checksums and runs setup from it. Once installed, an AI client can start the program with
npx -y anairyspirit mcp; npx anairyspirit@latest update updates it.
Debian, Ubuntu:
sudo apt install ./anairyspirit-cli_<version>-1_<arch>.deb
anairyspirit setup
Fedora, RHEL:
sudo dnf install ./anairyspirit-cli-<version>-1.<arch>.rpm
anairyspirit setup
openSUSE — zypper checks the package signature, so the key comes first:
sudo rpm --import https://raw.githubusercontent.com/Seventhwalllab/anairyspirit/main/KEYS
sudo zypper install ./anairyspirit-cli-<version>-1.<arch>.rpm
anairyspirit setup
Any other Linux — unpack the .tar.gz, then:
cd anairyspirit-<version>-<arch>
./anairyspirit setup
Then open a new terminal, so the shell finds anairyspirit.
Docker — from GitHub's registry (the same image is on Docker Hub as seventhwalllab/anairyspirit):
docker pull ghcr.io/seventhwalllab/anairyspirit:latest
docker run -d --name anairyspirit -v anairyspirit-data:/data \
-v anairyspirit-models:/home/anairyspirit/.cache/anairyspirit \
--restart unless-stopped ghcr.io/seventhwalllab/anairyspirit:latest
The image is signed: cosign verify --key anairyspirit-release.pub ghcr.io/seventhwalllab/anairyspirit:latest
with the key from the release page. The first start downloads the models and takes a few minutes.
Every release carries a GPG-signed checksum manifest (SHA256SUMS-public.asc,
key in KEYS). The manual ships with each release in docs/ and inside the
program: anairyspirit man.
LongMemEval — finding the right past conversation. For each of 470 questions the memory is given that question's chat history, about 48 sessions, and searched once; the score is whether the session holding the answer comes back in the top five.
Hit@5: a session with the answer is in the top five. Recall@5: the share of all answer sessions found there — some questions need several. Retrieval only: no language model answers and none judges, so this is the benchmark's session-level memory recall, not end-to-end QA accuracy. No large language model takes part in storing, searching or ranking: they run on small local models — an embedding model and a cross-encoder reranker — plus BM25. With the default settings there are no LLM calls, no API, and nothing leaves the machine; the one exception is an optional writeback mode that you switch on and point at a model yourself. LongMemEval-S, questions without an answer excluded; public build 0.1.392 with its default settings.
Benchmark: Wu et al., LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory — arXiv:2410.10813.
What the service takes, for a memory of about 12 thousand records. We strongly recommend at least 5 GB of memory. Figures are High / Balanced — the two search qualities in the menu-bar app.
The full figures, disk use and which quality to choose: docs/FINOPS.md.
Written in Rust, from the storage engine up — about 290,000 lines: the graph store, vector index, search pipeline, encryption, the service, the command line, the menu bar and the setup windows. No Python, no JVM, no interpreter at runtime. The one exception is a 380-line Node bridge that Claude Desktop's extension format requires; the program writes it out when you connect that client.
Every mechanism above is named with the published work it comes from, where there is one. Several are borrowed from memory science rather than from search engineering — that is deliberate, and it is the part of the design worth arguing about.
What this is built on — the papers behind each mechanism, with authors and links, and a plain statement of which parts are ours.
Seventh Wall Lab · Chile · seventhwalllab.com
Personal AI memory graph that runs locally: long-term memory for your AI agents over MCP and REST
{ "mcpServers": { "anairyspirit": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}Araçlar
Henüz araç listesi yok. Uzak sunucular ilk sağlık kontrolünden sonra bir liste alır.
SSS
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Bu kayıtta gerekli bir ortam değişkeni listelenmiyor.
Anairyspirit yerel olarak stdio üzerinden çalışır.
Son kontrol: 2026-11-15. Personal AI memory graph that runs locally: long-term memory for your AI agents over MCP and REST