Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.
SaferSkills independently audited Amber (MCP Server) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Long-term memory for AI assistants.
Amber is an MCP server that gives any AI assistant persistent, searchable memory across conversations. Your AI remembers preferences, decisions, project context, and personal details - without you doing anything special.
Just talk normally. Amber stores what matters and finds it when relevant.
One command. Works with any MCP-compatible client.
claude mcp add --transport http --scope user amber https://mcp.ambermem.comAdd to ~/.cursor/mcp.json (or %USERPROFILE%\.cursor\mcp.json on Windows):
{
"mcpServers": {
"amber": {
"url": "https://mcp.ambermem.com"
}
}
}Settings → Connectors → Create → URL: https://mcp.ambermem.com
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"amber": {
"serverUrl": "https://mcp.ambermem.com"
}
}
}Add to .vscode/mcp.json:
{
"servers": {
"amber": {
"type": "http",
"url": "https://mcp.ambermem.com"
}
}
}URL: https://mcp.ambermem.com | Transport: Streamable HTTP | Auth: OAuth 2.1 (auto-discovered)
No configuration. No tagging. No manual organization.
| Feature | Basic memory servers | Amber |
|---|---|---|
| Storage | One embedding per memory | Multiple semantic variants per fact |
| Search | Single vector lookup | Hybrid: vector + keyword + RRF fusion |
| Queries | Exact match only | Auto-expanded (synonyms, paraphrases) |
| Input | Stored as-is | LLM-chunked into atomic facts |
| Topics | Manual tags or none | Auto-categorized by LLM |
| Time | No temporal awareness | Natural language time parsing ("last week", "3 days ago") |
Amber runs on Cloudflare Workers (zero cold starts, global edge deployment) with Turso databases (one per user, full isolation). LLM processing uses Gemini Flash for chunking/expansion and OpenAI for embeddings.
For full technical documentation: ambermem.com/llms.txt
https://mcp.ambermem.com[email protected] or use the amber_send_feedback_to_developer toolWill it slow my AI down? No. Storage is async (background). Search adds <1 second.
What if Amber shuts down? Export all your data as JSON anytime. Your data is always yours.
Do I need a PayPal account? Currently yes. PayPal handles both identity and billing. More login options coming soon.
Is my data safe? Each user gets a completely isolated database. No data is shared between users. Amber has no access to your PayPal payment details.
Can I self-host? Not currently. Amber is a managed service. We handle the infrastructure, scaling, and LLM costs so you don't have to.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.