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Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.
About
Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.
Remote endpoints: streamable-http: https://mcp.ambermem.com
Security Report
This is a well-structured MCP server introspection stub for Amber, a long-term memory service. The code is clean, uses proper input validation with Zod schemas, implements appropriate rate limiting documentation, and includes security-conscious design patterns like confirmation gates for destructive operations. All tools are documented with clear descriptions of permissions and limitations. No malicious patterns, credential handling issues, or dangerous code execution risks detected. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity).
3 files analyzed · 5 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
Amber
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.
Quick Install
One command. Works with any MCP-compatible client.
Claude Code / Claude Desktop
claude mcp add --transport http --scope user amber https://mcp.ambermem.com
Cursor
Add to ~/.cursor/mcp.json (or %USERPROFILE%\.cursor\mcp.json on Windows):
{
"mcpServers": {
"amber": {
"url": "https://mcp.ambermem.com"
}
}
}
ChatGPT
Settings → Connectors → Create → URL: https://mcp.ambermem.com
Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"amber": {
"serverUrl": "https://mcp.ambermem.com"
}
}
}
VS Code (GitHub Copilot)
Add to .vscode/mcp.json:
{
"servers": {
"amber": {
"type": "http",
"url": "https://mcp.ambermem.com"
}
}
}
Any MCP client
URL: https://mcp.ambermem.com | Transport: Streamable HTTP | Auth: OAuth 2.1 (auto-discovered)
How It Works
- You talk to your AI normally. Amber stores important facts in the background.
- Next conversation, your AI searches Amber automatically when context would help.
- Memory improves over time. The more you use it, the better it gets.
No configuration. No tagging. No manual organization.
What Makes Amber Different
| 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") |
Technical Details
- 18 MCP tools (9 memory, 7 account, 2 feedback)
- Hybrid retrieval pipeline: vector search + full-text search + Reciprocal Rank Fusion
- LLM-powered chunking: text → atomic facts, each independently searchable
- Multi-variant embeddings: each fact stored with ~4 paraphrases for higher recall
- Query expansion: searches are auto-rephrased to find semantically related memories
- Automatic topic categorization: memories grouped by LLM-generated topics
- Temporal parsing: "what did I say last week?" just works
- Async processing: storage completes in 10-30s background, never blocks your conversation
Pricing
- 60-day free trial - no charge, cancel anytime
- $2.99/month after trial, via PayPal
- Cancel instantly - ask your AI to cancel, or cancel through PayPal directly
- No lock-in - export all your data as JSON anytime
Privacy
- No email collected
- No marketing, no spam
- Data isolated per user (separate database)
- PayPal handles all payment info
- Full export + account deletion available
- GDPR compliant (data minimization by design)
Architecture
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
Links
- Website: ambermem.com
- MCP endpoint:
https://mcp.ambermem.com - Privacy policy: ambermem.com/privacy
- Terms of service: ambermem.com/terms
- Technical docs (for AI): ambermem.com/llms.txt
- Support:
support@ambermem.comor use theamber_send_feedback_to_developertool
FAQ
Will 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.
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