Sunbeam Memory MCP server
Commit Aug 27, 2026Provides a personal semantic memory server that allows AI assistants to store and search facts, code, and documents using vector embeddings.
Also known as sunbeamdotpt/memory
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- MIT
What is Sunbeam Memory MCP?
Sunbeam Memory provides a personal semantic memory server that allows AI assistants to store and search facts, code, and documents using vector embeddings.
Summary generated from the project's README
About Sunbeam Memory MCP server
Sunbeam Memory provides a semantic memory layer for AI assistants to store and retrieve information using vector embeddings. It allows users to save facts, code snippets, notes, and documents, enabling searches based on meaning rather than simple keyword matching. The server can be deployed as a local stdio process for Claude Desktop with zero configuration or as a remote HTTP server for access across different machines.
Developers can use the server to automatically ingest data by watching directories, git repositories, or specific files like PDFs and text documents. It features a built-in indexer that tracks changes in git branches and updates facts accordingly. For remote setups, the server supports authentication via a simple bearer token or through OIDC providers like Keycloak or Auth0. Data is managed within a SQLite database, and the system uses the BGE embedding model to process information.
What you can do with it
- 1Semantic information retrievalAsk your AI assistant to find specific information based on the concept rather than exact wording.
- 2Automated project synchronizationAutomatically keep your AI's memory updated by pointing it at your local code repositories.
- 3Remote memory accessHost your personal knowledge base on a central server to use it with any MCP client anywhere.
- 4Structured data trackingMaintain a clear record of where every piece of stored information originated using unique identifiers.
Written from the project's README. Every use case is checked against a quote from it.
Configuration
MCP_AUTH_TOKENMCP_MEMORY_BASE_DIRMCP_OIDC_AUDIENCEMCP_OIDC_ISSUER
| Name | Description | |
|---|---|---|
| MCP_AUTH_TOKEN | — | |
| MCP_MEMORY_BASE_DIR | — | |
| MCP_OIDC_AUDIENCE | — | |
| MCP_OIDC_ISSUER | — |
Automated checks
Ran Nov 14, 2026Full report →Not verified yet. A server needs a score of 80, no serious finding, and enough data for us to check. How checks work · What to fix for more points
- Source repository linked and reachablesunbeamdotpt/memory is public and reachablegithub.com/sunbeamdotpt/memory ↗Pass
- Package names this repository as its sourceNot checked
- Build provenance (Sigstore attestation)Not checked
- Open source license (OSI)MITspdx.org/licenses/MIT.html ↗Pass
- Commit or release in the last yearLast commit or release 2026-08-27github.com/sunbeamdotpt/memory ↗Pass
- OpenSSF ScorecardNot checked
- Package name not close to a popular packageNot checked
- No critical advisory in the package or its dependenciesNot checked
- No hidden instructions to the model0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersPass
- Destructive tools are labelledNot checked
- Remote endpoint answers the MCP handshakeNot checked
README
View on GitHubtitle: 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 questions
How does the server handle file updates?
The server watches directories and automatically ingests files like code, PDFs, and text. For git repositories, it tracks state so edits on the same branch update existing facts in place.
Can I access my memory from different machines?
Yes, you can run the server in HTTP mode on a VPS or home server to access your memory from any machine or AI client.
How do I back up my stored data?
Since the data is stored in a self-contained SQLite database, you can back it up simply by copying the semantic.db file.
Does it support authentication for remote use?
Yes, you can use a simple bearer token via MCP_AUTH_TOKEN or integrate with an OIDC provider like Keycloak or Auth0.