Sunbeam Memory MCP server
Commit 27. Aug. 2026Bietet einen persönlichen semantischen Memory Server, mit dem KI-Assistenten Fakten, Code und Dokumente mittels Vector Embeddings speichern und durchsuchen können.
Auch bekannt als sunbeamdotpt/memory
- Stars
- 23
- Tools
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- Downloads, 30 Tage
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- Neueste Version
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- Letzter Commit
- 27. Aug. 2026
- Lizenz
- MIT
Was ist Sunbeam Memory MCP?
Sunbeam Memory ist ein MCP server. Bietet einen persönlichen semantischen Memory Server, mit dem KI-Assistenten Fakten, Code und Dokumente mittels Vector Embeddings speichern und durchsuchen können.
Zusammenfassung generiert aus dem README des Projekts
Über Sunbeam Memory MCP server
Sunbeam Memory bietet eine semantische Memory-Schicht für KI-Assistenten, um Informationen mithilfe von Vector Embeddings zu speichern und abzurufen. Es ermöglicht Nutzern, Fakten, Code-Snippets, Notizen und Dokumente zu sichern, wodurch Suchen basierend auf der Bedeutung statt nur auf einfachem Keyword-Matching möglich werden. Der Server kann entweder als lokaler stdio-Prozess für Claude Desktop ohne Konfiguration oder als remote HTTP-Server für den Zugriff über verschiedene Rechner hinweg bereitgestellt werden.
Entwickler können den Server nutzen, um Daten automatisch zu importieren, indem sie Verzeichnisse, Git-Repositories oder spezifische Dateien wie PDFs und Textdokumente überwachen. Er verfügt über einen integrierten Indexer, der Änderungen in Git-Branches verfolgt und die Fakten entsprechend aktualisiert. Für Remote-Setups unterstützt der Server die Authentifizierung über einen einfachen Bearer Token oder über OIDC-Provider wie Keycloak oder Auth0. Die Daten werden in einer SQLite-Datenbank verwaltet, und das System nutzt das BGE Embedding-Modell zur Verarbeitung der Informationen.
Was du damit machen kannst
- 1Semantische InformationsabfrageBitte deinen KI-Assistenten, spezifische Informationen basierend auf dem Konzept statt auf der exakten Wortwahl zu finden.
- 2Automatisierte ProjektsynchronisierungHalte das Gedächtnis deiner KI automatisch aktuell, indem du sie auf deine lokalen Code-Repositories verweist.
- 3Remote Memory AccessHoste deine persönliche Wissensdatenbank auf einem zentralen Server, um sie überall mit jedem MCP-Client zu nutzen.
- 4Tracking strukturierter DatenBehalte dank eindeutiger Identifikatoren stets den Überblick darüber, woher jede gespeicherte Information stammt.
Geschrieben aus dem README des Projekts. Jeder Use Case wurde gegen ein Zitat daraus geprüft.
Konfiguration
MCP_AUTH_TOKENMCP_MEMORY_BASE_DIRMCP_OIDC_AUDIENCEMCP_OIDC_ISSUER
| Name | Beschreibung | Typ |
|---|---|---|
| MCP_AUTH_TOKEN | — | |
| MCP_MEMORY_BASE_DIR | — | |
| MCP_OIDC_AUDIENCE | — | |
| MCP_OIDC_ISSUER | — |
Automatisierte Checks
Geluften am 14. Nov. 2026Vollständiger Bericht →Noch nicht verifiziert. Ein Server benötigt einen Score von 80, keine kritischen Befunde und genug Daten für unsere Prüfung. Wie die Checks funktionieren · Was du für mehr Punkte verbessern kannst
- Source Repository verknüpft und erreichbarsunbeamdotpt/memory is public and reachablegithub.com/sunbeamdotpt/memory ↗Bestanden
- Paket nennt dieses Repository als QuelleNicht geprüft
- Build-Provenienz (Sigstore-Attestierung)Nicht geprüft
- Open Source Lizenz (OSI)MITspdx.org/licenses/MIT.html ↗Bestanden
- Commit oder Release im letzten JahrLast commit or release 2026-08-27github.com/sunbeamdotpt/memory ↗Bestanden
- OpenSSF ScorecardNicht geprüft
- Package-Name ähnelt keinem populären PackageNicht geprüft
- Keine kritischen Advisories im Paket oder seinen AbhängigkeitenNicht geprüft
- Keine versteckten Anweisungen an das Modell0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersBestanden
- Destruktive Tools sind markiertNicht geprüft
- Remote-Endpoint beantwortet den MCP-HandshakeNicht geprüft
README
Auf GitHub ansehentitle: Sunbeam Memory
sunbeam-memory
A personal semantic memory server for AI assistants. Store facts, code snippets, notes, and documents with vector embeddings — then let your AI search them by meaning, not just keywords.
Also watches directories and auto-ingests files (code, PDFs, text) so your memory stays in sync with your projects.
Works as a local stdio MCP server (zero config, Claude Desktop) or as a remote HTTP server so you can access your memory from any machine.
Built on the official rmcp Rust SDK for native MCP protocol support. Also exposes a ConnectRPC (protobuf over HTTP/2) API for programmatic access from scripts and services.
Install
Requirements: Rust 1.75+ — the first build downloads the BGE embedding model (~130 MB).
git clone https://github.com/your-org/sunbeam-memory
cd sunbeam-memory/mcp-server
cargo build --release
# binary at target/release/sunbeam-memory
Or run directly without a permanent binary:
cargo run --release -- http --port 3456
Local use with Claude Desktop
The simplest setup: Claude Desktop talks to the server over stdio. No network, no auth.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"memory": {
"command": "/path/to/sunbeam-memory"
}
}
}
The server stores data in the platform data directory by default:
- macOS:
~/Library/Application Support/sunbeam/memory - Linux:
~/.local/share/sunbeam/memory - Windows:
%APPDATA%/sunbeam/memory
Set MCP_MEMORY_BASE_DIR to change it:
{
"mcpServers": {
"memory": {
"command": "/path/to/sunbeam-memory",
"env": {
"MCP_MEMORY_BASE_DIR": "/Users/you/.local/share/sunbeam-memory"
}
}
}
}
Remote use (HTTP mode)
Run the server on a VPS or home server so you can access your memory from any machine or AI client.
1. Generate a token:
openssl rand -hex 32
# e.g. a3f8c2e1b4d7...
2. Start the server with the token:
MCP_AUTH_TOKEN=a3f8c2e1b4d7... cargo run --release -- http --port 3456
With MCP_AUTH_TOKEN set, the server binds to 0.0.0.0 and requires Authorization: Bearer <token> on every request.
3. Configure Claude Desktop (or any MCP client) to use the remote server:
{
"mcpServers": {
"memory": {
"type": "http",
"url": "http://your-server:3456/mcp",
"headers": {
"Authorization": "Bearer a3f8c2e1b4d7..."
}
}
}
}
Tip: Put a reverse proxy (nginx, Caddy) in front with TLS so your token travels over HTTPS.
Run as a systemd user daemon (Kimi Code / local HTTP)
You can keep the HTTP server running in the background as a user-scoped systemd service. This is the easiest way to connect Kimi Code (or any local MCP client) over HTTP without managing a terminal window on Linux.
1. Create the service file
~/.config/systemd/user/sunbeam-memory.service:
[Unit]
Description=Sunbeam Memory MCP Server
After=network.target
[Service]
Type=simple
ExecStart=/usr/local/bin/sunbeam-memory http --port 3456
Restart=on-failure
Environment="MCP_MEMORY_BASE_DIR=%h/.local/share/sunbeam/memory"
[Install]
WantedBy=default.target
Adjust ExecStart to the path of your sunbeam-memory binary (for example, ExecStart=%h/development/sunbeam/memory/target/release/sunbeam-memory http --port 3456 if you are running from a local build).
2. Start and enable the service
systemctl --user daemon-reload
systemctl --user enable sunbeam-memory.service
systemctl --user start sunbeam-memory.service
systemctl --user status sunbeam-memory.service
3. Configure Kimi Code
Add the server to ~/.kimi-code/mcp.json:
{
"mcpServers": {
"memory": {
"url": "http://127.0.0.1:3456/mcp"
}
}
}
Because no MCP_AUTH_TOKEN is set, the server binds to 127.0.0.1 only and does not require authentication, which is safe for a local daemon.
OIDC / OAuth2 authentication
If you already have an OIDC provider (Keycloak, Auth0, Dex, Kratos+Hydra, etc.), you can use it instead of a raw token. The server fetches the JWKS at startup and validates RS256/ES256 JWTs on every request.
MCP_OIDC_ISSUER=https://auth.example.com \
MCP_OIDC_AUDIENCE=sunbeam-memory \ # optional — leave out to skip aud check
cargo run --release -- http --port 3456
Your MCP client then gets a token from the provider and passes it as a Bearer token:
{
"mcpServers": {
"memory": {
"type": "http",
"url": "http://your-server:3456/mcp",
"headers": {
"Authorization": "Bearer <access_token>"
}
}
}
}
OIDC takes priority over MCP_AUTH_TOKEN if both are set.
Environment variables
| Variable | Default | Description |
|---|---|---|
MCP_MEMORY_BASE_DIR |
platform data dir | Where the SQLite database and model cache are stored |
MCP_AUTH_TOKEN |
(unset) | Simple bearer token for remote hosting. Unset = localhost-only |
MCP_OIDC_ISSUER |
(unset) | OIDC issuer URL. When set, validates JWT bearer tokens via JWKS |
MCP_OIDC_AUDIENCE |
(unset) | Expected aud claim. Leave unset to skip audience validation |
MCP_STDIO_KEEPALIVE_SECONDS |
30 |
Interval between MCP ping requests over stdio; 0 disables |
MCP_SSE_KEEPALIVE_SECONDS |
15 |
Interval between SSE keep-alive comments in HTTP mode; 0 disables |
MCP_SESSION_KEEPALIVE_SECONDS |
300 |
Idle timeout before an inactive HTTP session is closed; 0 disables |
Tools
Memory tools
store_fact
Embed and store a piece of text. Returns the fact ID.
content (required) Text to store
namespace (optional) Logical group — e.g. "code", "notes", "docs". Default: "default"
source (optional) smem URN identifying where this came from (see below)
search_facts
Semantic search — finds content by meaning, not exact words.
query (required) What you're looking for
limit (optional) Max results. Default: 10
namespace (optional) Restrict search to one namespace
update_fact
Update an existing fact in place. Keeps the same ID, re-embeds the new content.
id (required) Fact ID from store_fact or search_facts
content (required) New text content
source (optional) New smem URN
delete_fact
Delete a fact by ID.
id (required) Fact ID
list_facts
List facts in a namespace, newest first. Supports date filtering.
namespace (optional) Namespace to list. Default: "default"
limit (optional) Max results. Default: 50
from (optional) Only show facts stored on or after this time (RFC 3339 or Unix timestamp)
to (optional) Only show facts stored on or before this time
Indexer tools (file watching & ingestion)
Point the server at directories, git repos, or individual files and it will automatically ingest them into searchable memory. PDFs are extracted page-by-page. Binary files are skipped. Git state is tracked so edits on the same branch update facts in place, while new branches create new facts.
add_watch_target
Start watching a path. Supports globs (* ? [...]). Respects .gitignore when scanning git repositories.
path (required) Directory, file, or glob pattern to watch
namespace (optional) Namespace for ingested facts. Default: "default"
type (optional) "file" | "directory" | "git_repo". Default: inferred from path
Examples:
add_watch_targetwithpath: "/home/me/project/src"add_watch_targetwithpath: "/home/me/docs/**/*.pdf",namespace: "docs"add_watch_targetwithpath: "/home/me/notes/*.{md,txt}",namespace: "notes"add_watch_targetwithpath: "/home/me/project",type: "git_repo",namespace: "project"
remove_watch_target
Stop watching a target by ID.
target_id (required) ID returned by add_watch_target
list_watch_targets
List all active watch targets. No inputs.
sync_watch_target
Force a full re-scan of a target right now.
target_id (required) ID of the target to sync
get_index_progress
Check ingestion progress for a target: files pending, completed, failed, and current file.
target_id (required) ID of the target
restore_stale_fact
Restore a stale (soft-deleted) fact so it appears in search again.
id (required) Fact ID to restore
URN tools
build_source_urn
Build a valid smem URN from components. Use this before passing source to store_fact.
content_type (required) code | doc | web | data | note | conf
origin (required) git | fs | https | http | db | api | manual
locator (required) Origin-specific path (see describe_urn_schema)
fragment (optional) Line reference: L42 or L10-L30
parse_source_urn
Parse and validate a smem URN. Returns structured components or an error.
urn (required) The URN to parse, e.g. urn:smem:code:fs:/path/to/file.rs#L10
describe_urn_schema
Returns the full smem URN taxonomy: content types, origins, locator shapes, and examples. No inputs.
Observability tools
get_recent_errors
List recent ingestion or system errors so you can be informed of failures.
component (optional) Filter to a specific component (e.g. "indexer", "extractor")
limit (optional) Max errors to return. Default: 10
resolve_error
Mark a logged error as resolved so it no longer appears in get_recent_errors.
error_id (required) ID of the error to resolve
Source URNs
Every fact can carry a source URN that records where it came from:
urn:smem:<type>:<origin>:<locator>[#<fragment>]
Types: code doc web data note conf
Origins and locator shapes:
| Origin | Locator | Example |
|---|---|---|
fs |
[hostname:]<absolute-path> |
urn:smem:code:fs:/home/me/project/main.rs#L10-L30 |
git |
<host>:<org>:<repo>:<branch>:<path> |
urn:smem:code:git:github.com:org:repo:main:src/lib.rs |
https |
<host>/<path> |
urn:smem:doc:https:docs.example.com/guide |
db |
<driver>/<host>/<db>/<table>/<pk> |
urn:smem:data:db:postgres/localhost/app/users/42 |
api |
<host>/<path> |
urn:smem:data:api:api.example.com/v1/items/99 |
manual |
<label> |
urn:smem:note:manual:meeting-2026-03-04 |
Use build_source_urn to construct one without memorising the format. Use describe_urn_schema for the full spec.
Data
Facts are stored in a SQLite database (semantic.db) in MCP_MEMORY_BASE_DIR. The embedding model is cached by fastembed on first run.
To back up your memory: copy the semantic.db file. It's self-contained.
Architecture
┌─────────────────┐ ┌─────────────────┐
│ MCP client │ │ gRPC/HTTP │
│ (stdio / HTTP) │ │ client │
└────────┬────────┘ └────────┬────────┘
│ │
▼ ▼
mcp/server.rs connect/service.rs
(rmcp tools) (ConnectRPC proto)
│ │
└───────────┬───────────┘
▼
core/service.rs ← validation, orchestration
│
memory/service.rs ← embed, search, CRUD
│
┌───────────┴───────────┐
▼ ▼
semantic/store.rs indexer/
(HNSW + SQLite) (watcher, scanner, PDF)
MCP Protocol: MCP 2025-06-18 via rmcp (stdio + Streamable HTTP).
Programmatic API: ConnectRPC (sunbeam.memory.v1.MemoryService) via protobuf + HTTP/2.
Embeddings: BGE-Base-English-v1.5 via fastembed, 768 dimensions, ~130 MB model download on first run.
Vector search: usearch HNSW index with cosine similarity, persisted as a blob inside SQLite.
Search strategy: Fused BM25 + vector search via Reciprocal Rank Fusion (RRF).
Sunbeam Memory MCP Fragen
Wie geht der Server mit Datei-Updates um?
Der Server überwacht Verzeichnisse und importiert automatisch Dateien wie Code, PDFs und Text. Bei Git-Repositories wird der Status getrackt, sodass Änderungen im selben Branch bestehende Fakten direkt aktualisieren.
Kann ich auf meine Memory von verschiedenen Rechnern aus zugreifen?
Ja, du kannst den Server im HTTP-Modus auf einem VPS oder Home-Server laufen lassen, um von jedem Rechner oder KI-Client auf dein Gedächtnis zuzugreifen.
Wie sichere ich meine gespeicherten Daten?
Da die Daten in einer eigenständigen SQLite-Datenbank gespeichert werden, kannst du sie einfach sichern, indem du die Datei semantic.db kopierst.
Wird Authentifizierung für die Remote-Nutzung unterstützt?
Ja, du kannst einen einfachen Bearer Token via MCP_AUTH_TOKEN nutzen oder dich mit einem OIDC-Provider wie Keycloak oder Auth0 verbinden.