经 AI Skill Hub 精选评估,CRMy 获评「推荐使用」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。
CRMy 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
CRMy 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/crmy-ai/crmy
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"crmy": {
"command": "npx",
"args": ["-y", "crmy"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 CRMy 执行以下任务... Claude: [自动调用 CRMy MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"crmy": {
"command": "npx",
"args": ["-y", "crmy"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Customer memory for AI sales agents.
Before an AI sales agent sends a follow-up, prepares a meeting brief, updates an opportunity, reviews a renewal, or writes back to a system of record, it needs trusted customer context.
It needs to know what is true, what is stale, what is inferred, what requires approval, and which system owns the record.
A briefing_get call returns typed customer context across accounts, contacts, opportunities, activities, risks, commitments, next steps, evidence, stale warnings, and open handoffs.
MCP-native, with CLI, REST API, and Web UI access. PostgreSQL-backed. Open source.
If you are building agents that need operational customer memory, star CRMy and keep reading.
---
CRMy does not replace your systems of record. Your CRM, warehouse, support desk, mailbox, calendar, and other tools remain where work happens and state is stored.
CRMy makes that state agent-operable.
It turns messy customer context into typed operational Memory, gives agents scoped tools, and governs the path from recommendation to human review to system-of-record writeback.
Raw Context -> Signals -> Memory -> Handoffs / Writeback
Before an agent acts on a customer, CRMy can tell it what is known, what is stale, what is inferred, what is approved, what action is allowed, what system owns the record, and what proof or audit trail will exist afterward.
Use CRMy when you want agents that can:
You need Node.js 20+ and PostgreSQL. For local development, pgvector is recommended but not required.
Start Postgres:
docker run --name crmy-postgres \
-e POSTGRES_USER=postgres \
-e POSTGRES_PASSWORD=postgres \
-e POSTGRES_DB=crmy \
-p 5432:5432 \
-d pgvector/pgvector:pg16
Initialize CRMy:
export DATABASE_URL=postgresql://postgres:postgres@localhost:5432/crmy
export CRMY_ADMIN_EMAIL=admin@example.com
export CRMY_ADMIN_PASSWORD=change-me-please-123
npx -y @crmy/cli init --yes
npx -y @crmy/cli doctor
npx -y @crmy/cli server
Open:
Web UI http://localhost:3000/app
REST http://localhost:3000/api/v1
MCP http://localhost:3000/mcp
Health http://localhost:3000/health
What init --yes does:
Prefer interactive setup?
npx -y @crmy/cli init
Prefer a global install?
npm install -g @crmy/cli
crmy init
crmy doctor
crmy server
The seeded demo shows the full source-to-action loop:
npx -y @crmy/cli briefing "account:Northstar Labs"
npx -y @crmy/cli context raw-sources
npx -y @crmy/cli context signals
npx -y @crmy/cli context lineage --subject "account:Northstar Labs"
npx -y @crmy/cli hitl list
npx -y @crmy/cli agent-smoke
You should see:
Demo users:
Admin sample.admin@crmy.local / crmy-demo-123
Manager sample.manager@crmy.local / crmy-demo-123
Rep sample.rep@crmy.local / crmy-demo-123
Peer sample.peer@crmy.local / crmy-demo-123
The CLI accepts friendly record references, so you usually do not need IDs:
account:Northstar Labs
contact:Maya Patel
opportunity:Agent Context Rollout
use_case:Production Rollout
IDs are still used for system artifacts such as Handoffs, raw-source receipts, sync runs, and writeback requests.
| Variable | Required | Purpose |
|---|---|---|
DATABASE_URL | Yes | PostgreSQL connection string. |
JWT_SECRET | Production | JWT signing secret. Required for hardened production deployments. |
CRMY_ADMIN_EMAIL | Optional | Auto-create the first owner account. |
CRMY_ADMIN_PASSWORD | Optional | Password for the first owner account. |
CRMY_SEED_DEMO | Optional | Seed demo data on startup when set to true. |
ENABLE_PGVECTOR | Optional | Enable pgvector migrations and semantic retrieval support. |
EMBEDDING_PROVIDER | Optional | Embedding provider for semantic retrieval. |
EMBEDDING_API_KEY | Optional | Embedding provider API key. |
LLM_TIMEOUT_MS | Optional | General Workspace Agent and background LLM timeout. |
CONTEXT_EXTRACTION_LLM_TIMEOUT_MS | Optional | Raw Context extraction timeout. |
See .env.example for the full reference.
crmy init # setup wizard
crmy doctor # local health check
crmy server # start API, Web UI, REST, and MCP HTTP
crmy seed-demo --reset # reset and seed demo data
crmy briefing "account:Northstar Labs"
crmy context ingest --file call.txt
crmy context signals
crmy context lineage --subject "account:Northstar Labs"
crmy activities meetings
crmy emails messages
crmy hitl list
crmy systems list
crmy workflows list
crmy sequences list
The CLI is curated for setup, demos, Raw Context ingestion, activity/email review, systems, workflows, and operational QA. MCP is the complete agent-facing surface.
REST endpoints live at:
http://localhost:3000/api/v1
Use REST for integrations that cannot run MCP or for custom web tooling.
Authentication:
Authorization: Bearer <jwt-token> # human login
Authorization: Bearer crmy_<api-key> # agent or integration
Create scoped API keys for agents and integrations:
POST /auth/api-keys
{ "label": "my-agent", "scopes": ["contacts:read", "activities:write"] }
CRMy是一个有潜力的MCP工具,值得关注
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
AI Skill Hub 点评:CRMy 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | crmy |
| 原始描述 | 开源MCP工具:Deploy CRMy alongside your AI agents to give them operational customer context a。⭐7 · TypeScript |
| Topics | mcpagentic-aiagentstypescript |
| GitHub | https://github.com/crmy-ai/crmy |
| License | Apache-2.0 |
| 语言 | TypeScript |
收录时间:2026-05-31 · 更新时间:2026-06-01 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端