Odel
Amber

Amber

@ambermem1JavaScriptMITUpdated 1w ago

Long-term memory for AI assistants. Hybrid retrieval, query expansion, auto-topics.

Server endpointStreamable HTTPOAuthProbed

This is the third-party server itself — Odel doesn't run it. Hitting this URL directly talks straight to the upstream server with no auth or proxying. Connect through Odel to front it with managed auth.

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

FeatureBasic memory serversAmber
StorageOne embedding per memoryMultiple semantic variants per fact
SearchSingle vector lookupHybrid: vector + keyword + RRF fusion
QueriesExact match onlyMany phrasings per fact, matched semantically
InputStored as-isChunked into atomic facts, each independently searchable
TopicsManual tags or noneAuto-grouped, matched semantically at search time
PrivacyVaries by serverNo generative model reads your memories
TimeNo temporal awarenessNatural language time parsing ("last week", "3 days ago")

Technical Details

  • 25 MCP tools (14 memory, 9 account, 2 feedback/notification)
  • Hybrid retrieval pipeline: vector search + full-text search + Reciprocal Rank Fusion
  • Multi-variant embeddings: every fact is stored with several paraphrases — at least 5, and a store is rejected below that — which is the main thing that makes it findable later
  • No generative model reads your memories: your assistant does the chunking, the topics and the phrasings. Memory text is sent only to an embedding endpoint (a vector model, not a chat model), so no chat model is ever shown what you store
  • Atomic facts: a conversation becomes individually searchable facts rather than one blob, each with its own subjects, topics and dates
  • Temporal search: "what did I say last week?" resolves to a real date range rather than a keyword match
  • Automatic topic grouping: memories are grouped by topic, and a search for "work" also finds "career" and "job"
  • Async processing: storage completes in the background, never blocking 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

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.