@rembr/client

Client configuration package for REMBR - semantic memory for AI agents and assistants.

What is REMBR?

REMBR is a hosted Model Context Protocol (MCP) server that provides persistent, searchable semantic memory for AI agents. It's designed for:

- GitHub Copilot Agents - Give your agents long-term memory
- Cursor - Persistent context for Cursor AI
- Windsurf - Cascade flow coordination with shared memory
- Claude Desktop - Enhanced context across conversations
- Recursive Learning Machines (RLMs) - Context management for recursive decomposition
- Multi-Agent Systems - Shared knowledge base across agent teams

Features

✨ 19 MCP Tools for comprehensive memory management:
- Core Memory: store, search, update, delete, list
- Advanced Search: phrase search, semantic search, metadata filtering
- Discovery: find similar memories, get embedding stats
- RLM Support: contexts, snapshots, memory graphs
- Analytics: usage stats, contradiction detection, insights

🔍 4 Search Modes:
- Hybrid (default) - 0.7 semantic + 0.3 text matching
- Semantic - Conceptual similarity (finds "OAuth" when you search "authentication")
- Text - Fast fuzzy keyword matching
- Phrase - Multi-word exact matching ("rate limiting" not "limit the rate")

🎯 Built for RLMs: Task isolation, metadata filtering, progressive refinement

💰 Affordable: From free (1,000 memories) to £99/mo (1M memories)

Installation

``bash
npm install -D @rembr/client
` configure:
1. MCP server connections for VS Code, Cursor, Windsurf, and Claude Desktop
2. Agent instructions for each tool
3. Project-specific configuration files

Or run setup manually:
`bash
npx rembr setup
`

What Gets Configured

$3

Adds REMBR MCP server to:
- .vscode/mcp.json - VS Code + GitHub Copilot
- .cursor/mcp.json - Cursor
- .windsurf/mcp.json - Windsurf
- ~/Library/Application Support/Claude/claude_desktop_config.json - Claude Desktop (global)

`json
{
"servers": {
"rembr": {
"url": "https://rembr.ai/mcp",
"type": "http"
}
}
}
`

$3

Creates tool-specific instruction files:

1. GitHub Copilot (.github/agents/recursive-analyst.agent.md)
- Formal agent definition with tool access
- RLM pattern implementation
- Structured subagent protocol

2. Cursor (.cursorrules)
- Cursor-specific REMBR integration patterns
- Memory management examples
- Subtask coordyour tool**

VS Code / GitHub Copilot:
- Open Settings (Cmd+, or Ctrl+,) → Search "MCP"
- Add to .vscode/settings.json:
`json
{
"mcp.servers.rembr.env": {
"REMBR_API_KEY": "rembr_live_xxxxxxxxxxxx"
}
}
`
- Reload window: Cmd+Shift+P → "Developer: Reload Window"
- Test: @Recursive-Analyst what tasks have I worked on?

Cursor:
- Settings → MCP → rembr → Environment Variables
- Add REMBR_API_KEY=rembr_live_xxxxxxxxxxxx
- Restart Cursor
- Test: Ask Cursor to "search REMBR for authentication patterns"

Windsurf:
- Settings → MCP → rembr → Environment Variables
- Add REMBR_API_KEY=rembr_live_xxxxxxxxxxxx
- Restart Windsurf
- Test: Use REMBR tools in a Cascade flow

Claude Desktop:
- Edit ~/Library/Application Support/Claude/claude_desktop_config.json
- Add under mcpServers.rembr:
`json
{
"mcpServers": {
"rembr": {
"url": "https://rembr.ai/mcp",
"type": "http",
"env": {
"REMBR_API_KEY": "rembr_live_xxxxxxxxxxxx"
}
}
}
}
`
- Restart Claude Desktop
- Test: "Use REMBR to remember that I prefer TypeScript"

Aider:
- Export in your shell: export REMBR_API_KEY=rembr_live_xxxxxxxxxxxx
- Use the bash aliases from .aider.conf.yml
- Test: rembr-query "recent changes"
- Go to Dashboard → Settings → API Keys
- Create a new key

3. Configure VS Code
- Open VS Code Settings (Cmd+, or Ctrl+,)
- Search for "MCP"
- Add environment variable: REMBR_API_KEY=your_key_here

Or add to .vscode/settings.json:
``json
{
"mcp.servers.rembr.env": {
"REMBR_API_KEY": "rembr_live_xxxxxxxxxxxx"
}
}
`

4. Reload VS Code
- Cmd+Shift+P → "Developer: Reload Window"

5. Test the connection
- Open GitHub Copilot Chat
- Try: @Recursive-Analyst what tasks have I worked on recently?

Available MCP Tools

Once configured, these tools are available to all agents:

$3


- store_memory - Store new memories with categories and metadata
- search_memory - Hybrid semantic + text search
- list_memories - List recent memories by category
- get_memory - Retrieve specific memory by ID
- delete_memory - Remove a memory

$3

- create_context - Create workspace for related memories
- add_memory_to_context - Link memories to contexts
- search_context - Scoped search within a context
In GitHub Copilot / Cursor / Windsurf / Claude Desktop:

`javascript
store_memory({
category: "facts",
content: "The payment API uses Stripe webhooks for event processing",
metadata: {
area: "payments",
file: "src/webhooks/stripe.ts"
}
})
`

In Aider (via bash alias):
In MCP-enabled tools:
`javascript
search_memory({
query: "how do we handle Stripe webhooks",
category: "facts",
limit: 5
})
`

In Aider:
`bash
rembr-query "how do we handle Stripe webhooks" Memory Categories

Organize memories using semantic categories:

- facts - Concrete information and data points
- preferences - User preferences and settings
- conversations - Conversation history and context
- projects - Project-specific information
- learning - Knowledge and insights learned
- goals - Objectives and targets
- context - Situational context
- reminders - Future actions and reminders

Example Usage

$3

`javascript
// In GitHub Copilot Chat or agent
store_memory({
category: "facts",
content: "The payment API uses Stripe webhooks for event processing",
metadata: {
area: "payments",
file: "src/webhooks/stripe.ts"
}
})
`/Flow Context

GitHub Copilot (Subagents):
`javascript
// Parent agent retrieves context for subagent
const context = search_memory({
query: "authentication middleware patterns",
category: "facts",
limit: 10
})

// Spawn subagent with this context
// Subagent stores findings with taskId metadata
// Parent retrieves subagent findings later
`

Windsurf (Cascade Flows):
`javascript
// Before creating a flow, retrieve relevant context
const context = search_memory({
query: "API rate limiting patterns",
category: "facts"
})

// Create flow with this context
// Flow stores findings for other flows to use
`

Cursor / Claude Desktop:
`javascript
// Before breaking down a task, query REMBR
const priorWork = search_memory({
query: "database migration strategies",
limit: 5
})

// Use context to inform implementation
// Store new insights back to REMBR

`javascript
// Parent agent retrieves context for subagent
const context = search_memory({
query: "authentication middleware patterns",
category: "facts",
limit: 10
})

// Spawn subagent with this context
// Subagent stores findings with taskId metadata
// Parent retrieves subagent findings later
``

Pricing

- Free Dev Tier: 100 memories, 1,000 searches/day
- Pro: £10/month - 10,000 memories, 100,000 searches
- Team: £30/month - 100,000 memories, 1M searches
- Enterprise: £100/month - 1M memories, unlimited searches

All tiers include:
- Hybrid semantic + text search
- Multi-tenant isolation
- OAuth support for Claude Desktop
- Full API access

Documentation

- Website: https://rembr.ai
- API Docs: https://rembr.ai/docs
- MCP Spec: https://modelcontextprotocol.io
- RLM Paper: https://github.com/alexzhang13/rlm

Support

- Email: support@rembr.ai
- Issues: https://github.com/radicalgeek/rembr-client/issues
- Slack: https://rembr.ai/slack

License

MIT