MemClawz Connect

该 Skill 提供一套基于 curl 与 HTTP API 的共享记忆协议：任务开始前按关键词检索已有记忆，任务完成后按 fact、decision、procedure、event 等类型写回结果，并提供健康检查、统计、列出 Agent 和查询记忆等接口，可通过环境变量切换本地或远程地址。

yoniassia

热度 53.93

最近更新 2026-05-18 12:25:03

分类：知识依赖：第三方 API / 代码执行应用场景：笔记整理 / 工作流 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add memclawz-connect

Metadata

name: memclawz-connect
description: Connect any AI agent to the MemClawz shared memory bus. Gives agents read-before-act and write-after-complete patterns via a simple HTTP API. Use when an agent needs fleet memory, shared memory, cross-agent memory, or long-term recall across sessions. Triggers on: 'connect to memory', 'fleet memory', 'shared memory', 'memclawz', 'remember this across sessions', 'search memory'.

MemClawz Connect

One skill. Any agent. Shared memory.

Setup

export MEMCLAWZ_URL="http://localhost:3500"   # or remote: http://YOUR_SERVER:3500
export MEMCLAWZ_AGENT_ID="my-agent"           # unique per agent

No API key required for default installs. If auth is enabled, also set MEMCLAWZ_API_KEY.

Health Check

curl -s "$MEMCLAWZ_URL/health"
# {"status":"ok","version":"...","qdrant":"connected"}

Agent Protocol

Before ANY Task — Search First

curl -s "$MEMCLAWZ_URL/api/v1/search?q=TOPIC&limit=5"

Response:

{"results": [{"content": "...", "agent_id": "quant-dev", "memory_type": "decision", "score": 0.92}]}

Use results as context before starting work. Avoids re-discovering what's already known.

After Completing Work — Write Back

curl -s -X POST "$MEMCLAWZ_URL/api/v1/add" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Deployed v2.0 — fixed auth race condition with mutex on refresh",
    "agent_id": "'"$MEMCLAWZ_AGENT_ID"'",
    "memory_type": "event"
  }'

Memory Types

Type	When
`fact`	Discovered info (endpoints, versions, configs)
`decision`	Choices made (architecture, approach, tool selection)
`procedure`	How something was done (deploy steps, build process)
`event`	What happened (deployed X, fixed Y, shipped Z)
`insight`	Lessons learned (what worked, what didn't)
`intention`	Planned actions
`commitment`	Promises made
`action`	Actions taken
`outcome`	Results of actions

Stats

curl -s "$MEMCLAWZ_URL/api/v1/stats"

List Agents

curl -s "$MEMCLAWZ_URL/api/v1/agents"

Get Memories

curl -s "$MEMCLAWZ_URL/api/v1/memories?agent_id=$MEMCLAWZ_AGENT_ID&limit=50"

AGENTS.md Integration

Append to your agent's AGENTS.md:

## MemClawz Shared Memory

Fleet memory API: $MEMCLAWZ_URL/api/v1

### Before ANY task:
Search shared memory for relevant context:
curl -s "$MEMCLAWZ_URL/api/v1/search?q=<task keywords>&limit=5"

### After completing ANY significant work:
Write results to shared memory:
curl -s -X POST $MEMCLAWZ_URL/api/v1/add \
  -H "Content-Type: application/json" \
  -d '{"content": "<what was done>", "agent_id": "$MEMCLAWZ_AGENT_ID", "memory_type": "<type>"}'

Remote Agents

For agents on a different server, just change MEMCLAWZ_URL from localhost:3500 to the master's IP/hostname:

export MEMCLAWZ_URL="http://76.13.154.71:3500"

Everything else stays the same.