reflect-memory

reflect-memory

Privacy-first AI memory system with MCP server for managing user-authored memories, enabling AI agents to read/write memories with fine-grained vendor access control.

Category
访问服务器

README

Reflect Memory

npm: reflect-memory-sdk npm: reflect-memory-mcp npm: n8n-nodes-reflect-memory GitHub stars

Privacy-first AI memory system. All memory is explicitly user-authored, structured, editable, and deletable. The AI model is stateless -- it sees only what you choose to show it.

Requirements

  • Node.js >= 20.0.0 (LTS)
  • An OpenAI-compatible API key (OpenAI, local model via ollama, etc.)

Setup

npm install

Environment Variables

Required:

export RM_API_KEY="your-secret-api-key"        # User key -- full access to all endpoints
export RM_MODEL_API_KEY="sk-..."               # Your OpenAI (or compatible) API key
export RM_MODEL_NAME="gpt-4o-mini"             # Model identifier

Optional:

export RM_PORT=3000                            # HTTP port (default: 3000)
export RM_DB_PATH="/data/reflect-memory.db"     # SQLite file path (default on Railway)
export RM_MODEL_BASE_URL="https://api.openai.com/v1"  # Model API base URL
export RM_MODEL_TEMPERATURE=0.7                # Temperature (default: 0.7)
export RM_MODEL_MAX_TOKENS=1024                # Max tokens (default: 1024)
export RM_SYSTEM_PROMPT="Your custom prompt"   # System prompt for AI queries

Agent keys (per-vendor, optional):

export RM_AGENT_KEY_CHATGPT="agent-key-for-chatgpt"   # Registers vendor "chatgpt"
export RM_AGENT_KEY_CLAUDE="agent-key-for-claude"      # Registers vendor "claude"
# RM_AGENT_KEY_<NAME> -- any env var matching this pattern registers a vendor

Dashboard multi-user auth (required for dashboard deployment):

export RM_DASHBOARD_SERVICE_KEY="..."   # Shared with dashboard. Generate: openssl rand -hex 32
export RM_DASHBOARD_JWT_SECRET="..."    # Must match dashboard AUTH_SECRET. Minimum 32 characters.

Multi-vendor chat (dashboard Chat tab -- enables GPT, Claude, Gemini, Perplexity, Grok):

export RM_CHAT_OPENAI_KEY="sk-..."        # Defaults to RM_MODEL_API_KEY if omitted
export RM_CHAT_ANTHROPIC_KEY="sk-ant-..." # Claude (console.anthropic.com)
export RM_CHAT_GOOGLE_KEY="..."           # Gemini (aistudio.google.com)
export RM_CHAT_PERPLEXITY_KEY="..."       # Perplexity (perplexity.ai/settings/api)
export RM_CHAT_XAI_KEY="..."             # Grok (x.ai)

Each agent key gives the vendor scoped access:

  • Can write memories via POST /agent/memories
  • Can query via POST /query (sees only memories with allowed_vendors containing "*" or their vendor name)
  • Can check identity via GET /whoami
  • Cannot access user endpoints (POST /memories, GET /memories/:id, PUT /memories/:id, DELETE /memories/:id, POST /memories/list)

Run

Development (with hot reload via tsx):

npm run dev

Production:

npm run build
npm start

API

All requests (except /health) require the Authorization header:

Authorization: Bearer your-secret-api-key

Health check (no auth required)

curl -s https://api.reflectmemory.com/health | jq

Who am I? (identity debugging)

curl -s https://api.reflectmemory.com/whoami \
  -H "Authorization: Bearer your-secret-api-key" | jq

Response:

{ "role": "user", "vendor": null }

With an agent key:

{ "role": "agent", "vendor": "chatgpt" }

Create a memory (user path)

curl -s -X POST http://localhost:3000/memories \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Project deadline",
    "content": "The API migration must be completed by end of Q3 2026.",
    "tags": ["work", "deadlines"]
  }' | jq

allowed_vendors is optional for user writes. If omitted, defaults to ["*"] (all vendors can see it). To restrict:

curl -s -X POST http://localhost:3000/memories \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Private note",
    "content": "Only Claude should see this.",
    "tags": ["private"],
    "allowed_vendors": ["claude"]
  }' | jq

Create a memory (agent path)

Agents must use POST /agent/memories. The origin field is set server-side from the agent's key -- it cannot be self-reported. allowed_vendors is required.

curl -s -X POST http://localhost:3000/agent/memories \
  -H "Authorization: Bearer agent-key-for-chatgpt" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "ChatGPT learned this",
    "content": "User prefers bullet points over paragraphs.",
    "tags": ["preference"],
    "allowed_vendors": ["chatgpt"]
  }' | jq

Response (201):

{
  "id": "a1b2c3d4-...",
  "user_id": "...",
  "title": "ChatGPT learned this",
  "content": "User prefers bullet points over paragraphs.",
  "tags": ["preference"],
  "origin": "chatgpt",
  "allowed_vendors": ["chatgpt"],
  "created_at": "2026-02-08T...",
  "updated_at": "2026-02-08T..."
}

Read a memory by ID

curl -s http://localhost:3000/memories/MEMORY_ID \
  -H "Authorization: Bearer your-secret-api-key" | jq

List memories (explicit filter required)

All memories:

curl -s -X POST http://localhost:3000/memories/list \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{ "filter": { "by": "all" } }' | jq

By tags:

curl -s -X POST http://localhost:3000/memories/list \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{ "filter": { "by": "tags", "tags": ["work"] } }' | jq

Update a memory (full replacement)

Now requires allowed_vendors in the body (full replacement -- all fields required).

curl -s -X PUT http://localhost:3000/memories/MEMORY_ID \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Project deadline (revised)",
    "content": "The API migration deadline has been extended to Q4 2026.",
    "tags": ["work", "deadlines", "revised"],
    "allowed_vendors": ["*"]
  }' | jq

Delete a memory

curl -s -X DELETE http://localhost:3000/memories/MEMORY_ID \
  -H "Authorization: Bearer your-secret-api-key" -w "\nHTTP %{http_code}\n"

Returns 204 No Content on success. The row is gone.

Query the AI (with memory context)

User key sees all memories matching the filter:

curl -s -X POST http://localhost:3000/query \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "When is the API migration deadline?",
    "memory_filter": { "by": "tags", "tags": ["deadlines"] }
  }' | jq

Agent key sees only memories where allowed_vendors contains "*" or the agent's vendor name:

curl -s -X POST http://localhost:3000/query \
  -H "Authorization: Bearer agent-key-for-chatgpt" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the user preferences?",
    "memory_filter": { "by": "all" }
  }' | jq

The vendor_filter field in the receipt shows which vendor filter was applied (null for users, vendor name for agents).

MCP Server

Reflect Memory includes a built-in MCP (Model Context Protocol) server for native integration with Claude, Cursor, and other MCP-compatible tools.

Endpoint: /mcp (proxied through the main API — single port, no extra config)

Transport: Streamable HTTP (MCP clients must use streamable-http / streamableHttp)

Enabling MCP: Set at least one agent key environment variable. Agent keys serve double duty: they tell the server to start the MCP endpoint and authenticate requests against it.

export RM_AGENT_KEY_CURSOR="your-cursor-key"
export RM_AGENT_KEY_CLAUDE="your-claude-key"

Without any RM_AGENT_KEY_* variables set, the /mcp endpoint returns 404.

Cursor — create .cursor/mcp.json in your project:

{
  "mcpServers": {
    "reflect-memory": {
      "type": "streamable-http",
      "url": "https://api.reflectmemory.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_AGENT_KEY"
      }
    }
  }
}

Claude — go to Claude.ai Settings > Connectors, click +, paste https://api.reflectmemory.com/mcp. Claude handles OAuth automatically.

Tools (9): read_memories, get_memory_by_id, get_latest_memory, browse_memories, search_memories, get_memories_by_tag, write_memory, read_team_memories, share_memory.

See integrations/cursor/README.md and integrations/claude/README.md for detailed setup guides.

Team Memories

Team Memories let multiple users share context through a shared pool. Any team member can share personal memories with the team, and all members can read them from any connected tool.

# Create a team
curl -s -X POST http://localhost:3000/teams \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{"name": "My Team"}' | jq

# Invite a member
curl -s -X POST http://localhost:3000/teams/TEAM_ID/invite \
  -H "Authorization: Bearer your-secret-api-key" \
  -H "Content-Type: application/json" \
  -d '{"email": "teammate@example.com"}' | jq

Team tools (read_team_memories, share_memory) are available in all MCP clients once the user belongs to a team. The team API endpoints (/teams, /teams/:id/invite, etc.) use standard Bearer token auth.

Docker Quick Start (Private Deploy)

Run Reflect Memory locally with Docker Compose. Data stays on your machine.

Upgrading? The container runs as a non-root user. If you have an existing /data volume with root-owned files, run docker compose down && docker compose --profile isolated-hosted up --build to rebuild. If the database fails to open, fix volume permissions: docker run --rm -v rm_data_isolated:/data node:20-bookworm-slim chown -R 65534:65534 /data

  1. Clone the repo and create a .env file:
git clone https://github.com/van-reflect/Reflect-Memory.git
cd Reflect-Memory
# .env
RM_API_KEY=your-api-key
RM_MODEL_API_KEY=sk-...
RM_MODEL_NAME=gpt-4o-mini

# MCP — at least one agent key is required to enable /mcp
RM_AGENT_KEY_CURSOR=pick-any-strong-secret
RM_AGENT_KEY_CLAUDE=pick-any-strong-secret
  1. Build and start:
docker compose --profile isolated-hosted up --build -d
  1. Verify:
curl -s http://localhost:3000/health | jq
# → { "service": "reflect-memory", "status": "ok", ... }

curl -s http://localhost:3000/whoami \
  -H "Authorization: Bearer your-api-key" | jq
# → { "role": "user", "vendor": null }
  1. Connect Cursor to your local instance:
{
  "mcpServers": {
    "reflect-memory": {
      "type": "streamable-http",
      "url": "http://localhost:3000/mcp",
      "headers": {
        "Authorization": "Bearer your-RM_AGENT_KEY_CURSOR-value"
      }
    }
  }
}

Important: The /mcp endpoint uses agent keys for auth, not RM_API_KEY. Your RM_API_KEY works for REST/curl calls, but MCP clients must use the corresponding RM_AGENT_KEY_* value.

Deploy to Railway

1. Environment variables

Set these in the Railway service's Variables tab:

Variable Required Value
RM_API_KEY Yes A strong random string (your user API key)
RM_MODEL_API_KEY Yes Your OpenAI API key (sk-...)
RM_MODEL_NAME Yes gpt-4o-mini or any OpenAI model
RM_DB_PATH No Defaults to /data/reflect-memory.db
RM_AGENT_KEY_CHATGPT No Agent key for ChatGPT integration
RM_AGENT_KEY_CLAUDE No Agent key for Claude integration

Railway sets PORT automatically -- the app picks it up.

2. Attach a volume (persistent storage)

Without a volume, Railway containers are ephemeral -- the SQLite database resets on every deploy or restart. To persist data:

  1. Click on the Reflect-Memory service in Railway
  2. Go to the Volumes section (or Settings > Volumes)
  3. Click "Add Volume"
  4. Set Mount Path to /data
  5. Save

Railway will mount a persistent disk at /data. The app creates the database file at /data/reflect-memory.db by default. This survives restarts, redeploys, and container replacements.

3. Build and start commands

Railway should auto-detect these from package.json:

  • Build: npm run build
  • Start: npm start

4. Custom domain

To use api.reflectmemory.com:

  1. In Railway: Service → Settings → Networking → Custom Domain → add api.reflectmemory.com
  2. In your DNS provider: add the CNAME and TXT records Railway shows you
  3. Wait for the green checkmark

Verification Checklist

Whoami

# User key
curl -s https://api.reflectmemory.com/whoami \
  -H "Authorization: Bearer YOUR_USER_KEY" | jq
# → { "role": "user", "vendor": null }

# Agent key (ChatGPT)
curl -s https://api.reflectmemory.com/whoami \
  -H "Authorization: Bearer YOUR_CHATGPT_AGENT_KEY" | jq
# → { "role": "agent", "vendor": "chatgpt" }

Agent write

curl -s -X POST https://api.reflectmemory.com/agent/memories \
  -H "Authorization: Bearer YOUR_CHATGPT_AGENT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Agent test",
    "content": "Written by chatgpt agent.",
    "tags": ["agent-test"],
    "allowed_vendors": ["chatgpt"]
  }' | jq
# → origin: "chatgpt", allowed_vendors: ["chatgpt"]

Agent query scoping

# Agent sees only memories with allowed_vendors containing "*" or "chatgpt"
curl -s -X POST https://api.reflectmemory.com/query \
  -H "Authorization: Bearer YOUR_CHATGPT_AGENT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What do you know?",
    "memory_filter": { "by": "all" }
  }' | jq '.memories_used | length'
# → vendor_filter: "chatgpt" in receipt

User sees all

# User sees every memory regardless of allowed_vendors
curl -s -X POST https://api.reflectmemory.com/memories/list \
  -H "Authorization: Bearer YOUR_USER_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "filter": { "by": "all" } }' | jq '.memories | length'

Agent route restriction

# Agent cannot hit user-only endpoints
curl -s -X POST https://api.reflectmemory.com/memories \
  -H "Authorization: Bearer YOUR_CHATGPT_AGENT_KEY" \
  -H "Content-Type: application/json" \
  -d '{"title":"x","content":"x","tags":["x"]}' | jq
# → { "error": "Agent keys cannot access this endpoint" } (403)

Persistence (data survives redeploy)

  1. Create a memory, note the ID
  2. Trigger a redeploy in Railway
  3. Read the memory by ID -- should still exist

Architecture

User key  → POST /memories        → Memory Service → SQLite (origin: "user")
          → GET  /memories/:id     → Memory Service → SQLite
          → POST /memories/list    → Memory Service → SQLite
          → PUT  /memories/:id     → Memory Service → SQLite
          → DELETE /memories/:id   → Memory Service → SQLite

Agent key → POST /agent/memories   → Memory Service → SQLite (origin: vendor)
          → POST /query            → Memory Service (vendor-filtered read)
                                   → Context Builder → Model Gateway → QueryReceipt

Both      → GET /health           (no auth)
          → GET /whoami           (returns role + vendor)
          → POST /query           (vendor filter from key, not body)

Hard Invariants

  1. Explicit Intent -- No defaults, no inferred behavior. Every request declares exactly what it wants.
  2. Hard Deletion -- Delete means delete. One row, one table, gone. No soft deletes.
  3. Pure Context Builder -- No I/O. Same inputs, same output. Always.
  4. No AI Write Path -- The model cannot create, modify, or delete memories. One-directional data flow.
  5. Deterministic Visibility -- Every query response includes the full receipt: memories used, prompt sent, model config, vendor filter.

Hard Security Constraints

  1. /agent/memories must never accept origin in the body. If present, hard 400 (enforced by additionalProperties: false in the schema).
  2. Agent keys must never be allowed to call user endpoints. Agents can only hit /agent/*, /query, /whoami, /health. Everything else returns 403.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选