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

1. You talk to your AI normally. Amber stores important facts in the background.
2. Next conversation, your AI searches Amber automatically when context would help.
3. 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: [email protected] or use the amber_send_feedback_to_developer tool

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.