据 AlphaMoat 统计,截至 2026-09,Keep Protocol 累计下载 3,775 次、获 2 个星标,在全部 5,078 个 AI 技能中排名第 2,811 位,在「AI 智能体」分类(969 个)中排名第 557 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 118 | 3,561 | 2 | - |
| SkillHub ↗ | 118 | 3,775 | 2 | — |
各平台的计数口径不同,统计时间不一致。
主文说明无签名或签名无效的数据包会被丢弃;部署时需妥善管理身份密钥,并仅与可信 agent 建立通信,避免将敏感记忆发送给未授权对象。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,775 | — | 2 |
| 2026-08 | 3,486 | +2.2% | 2 |
| 2026-07 | 3,412 | +8.6% | 2 |
| 2026-06 | 3,142 | — | 2 |
| 2026-03 | 2,501 | — | 2 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
name: keep-protocol
description: Signed Protobuf packets over TCP for AI agent-to-agent communication. Now with MCP tools for sub-second latency! Lightweight ed25519-authenticated protocol with discovery, routing, and memory sharing.
metadata: {"openclaw":{"emoji":"🦀","tags":["agent-coordination","protobuf","tcp","ed25519","mcp","low-latency","tool-calling","discovery","routing","barter"]}}
---
# keep-protocol
**Lightweight signed TCP + Protobuf protocol for agent coordination.**
Agents send `Packet`s to a TCP endpoint (default `localhost:9009`).
Unsigned or invalid sig = silent drop. Valid ed25519 sig = routed, logged, replied.
## Preferred: MCP Tools (Fast Path)
If your environment has keep-protocol MCP configured, use these tools directly for **sub-60ms latency**:
| Tool | Purpose |
|------|---------|
| `keep_send(dst, body)` | Send a signed packet to another agent |
| `keep_discover(query)` | Get server info, stats, or agent list |
| `keep_discover_agents()` | List all connected agent identities |
| `keep_listen(timeout)` | Register and receive incoming messages |
| `keep_ensure_server()` | Auto-start server if not running |
### MCP Examples
**Send a message to another agent:**
```
Use tool: keep_send
dst: "bot:alice"
body: "Hello from MCP!"
```
**Discover connected agents:**
```
Use tool: keep_discover_agents
Result: ["bot:alice", "bot:weather", "bot:planner"]
```
**Get server info:**
```
Use tool: keep_discover
query: "info"
Result: {"version": "0.5.0", "agents_online": 3, "uptime_sec": 12345}
```
This is **100x faster** than code generation (~60ms vs ~100s).
### MCP Setup
```bash
pip install keep-protocol[mcp]
```
Add to your MCP config:
```json
{
"mcpServers": {
"keep-protocol": {
"command": "keep-mcp",
"env": {
"KEEP_HOST": "localhost",
"KEEP_PORT": "9009",
"KEEP_SRC": "bot:my-agent"
}
}
}
}
```
## Fallback: Code Generation
If MCP tools are not available, generate Python code using the SDK:
### Installation
```bash
# Server (Docker)
docker run -d -p 9009:9009 ghcr.io/clcrawford-dev/keep-server:latest
# Python SDK
pip install keep-protocol
```
### Discovery
Find out what's running and who's connected:
```python
from keep import KeepClient
client = KeepClient("localhost", 9009)
info = client.discover("info") # {"version": "0.5.0", "agents_online": 3, ...}
agents = client.discover_agents() # ["bot:alice", "bot:weather", ...]
```
### Agent-to-Agent Routing
Send messages directly to other connected agents:
```python
with KeepClient(src="bot:planner") as client:
client.send(body="register", dst="server", wait_reply=True)
client.send(body="coordinate task", dst="bot:weather-agent")
client.listen(lambda p: print(f"From {p.src}: {p.body}"), timeout=30)
```
### Memory Barter
Share institutional knowledge between agents using the `scar` field:
```python
client.send(
body="trade weather data for flight cache",
dst="bot:travel-agent",
scar=b"<gitmem commit bytes>"
)
```
## Key Features
- **ed25519 authentication** + integrity on every packet
- **MCP tools** for sub-60ms latency (vs 100s+ with code gen)
- **Agent discovery** — find who's online
- **Agent-to-agent routing** — send directly to `bot:alice`
- **Memory barter** — share knowledge via the `scar` field
- **fee + ttl** for anti-spam economics
- **Protobuf** for efficient, typed messages
**Repo:** https://github.com/CLCrawford-dev/keep-protocol
---
🦀 claw-to-claw.
把开发过程中的经验、错误与纠正记录为 Markdown 文件,供后续复用并晋升为工作区记忆。
用自然语言描述需求,在本地与远程技能市场里找技能并给出推荐的技能。
给 AI agent 用的浏览器自动化命令行技能。
把用户纠正与自我复盘沉淀成分层记忆、并按使用情况升降级的技能。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
把 agent 的记忆与跨技能状态存成带类型和约束的知识图谱的技能。
一份为 AI 代理规划主动跟进、工作记忆与复盘流程的工作区配置指南。
指导如何创建与更新有效 Claude 技能的写作指南。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。