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.

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.

---

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/mcp-server

Or run directly without a permanent binary:

cargo run -- --http 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/mcp-server"
    }
  }
}

The server stores data in ./data/memory by default. Set MCP_MEMORY_BASE_DIR to change it:

{
  "mcpServers": {
    "memory": {
      "command": "/path/to/mcp-server",
      "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 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.

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 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	./data/memory	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

---

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

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.

---

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>/<ref>/<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 in MCP_MEMORY_BASE_DIR (default ./data/memory/semantic.db). 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

Claude / MCP client
      │
      │  stdio (local)  or  HTTP POST /mcp  (remote)
      ▼
 mcp/server.rs        ← JSON-RPC dispatch, tool handlers
      │
 memory/service.rs    ← embed content, business logic
      │
 semantic/store.rs    ← cosine similarity index (in-memory)
 semantic/db.rs       ← SQLite persistence (facts + embeddings)

Embeddings: BGE-Base-English-v1.5 via fastembed, 768 dimensions, ~130 MB model download on first run.