據 AlphaMoat 統計,截至 2026-09,Keep Protocol 累計下載 3,775 次、獲 2 個星標,在全部 5,078 個 AI 技能中排名第 2,811 位,在「AI 智慧體」分類(969 個)中排名第 557 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 118 | 3561 | 2 | - |
| SkillHub ↗ | 118 | 3775 | 2 | — |
各平台的計數口徑不同,統計時間不一致。
主文說明無簽名或簽名無效的資料包會被丟棄;部署時需妥善管理身份金鑰,並僅與可信 agent 建立通訊,避免將敏感記憶傳送給未授權物件。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 3775 | — | 2 |
| 2026-08 | 3486 | +2.2% | 2 |
| 2026-07 | 3412 | +8.6% | 2 |
| 2026-06 | 3142 | — | 2 |
| 2026-03 | 2501 | — | 2 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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.
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。