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agentMemo

作者 Karl Yang · GitHub ↗ · v3.2.2 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install agentmemo-karl
功能描述
agentMemo is a Semantic Memory Mesh server for AI agents. Use this skill when you need to store, search, or retrieve agent memory across sessions with semant...
使用说明 (SKILL.md)

agentMemo — Semantic Memory Mesh

FastAPI-based memory server with HNSW embeddings, hybrid search, versioning, RBAC, and real-time event bus for AI agents.

Prerequisites

  • Python 3.12+ and pip
  • AGENTMEMO_ADMIN_KEY environment variable (required, secret) — the server refuses to start without it
  • Network access on first run: the embedding model (all-MiniLM-L6-v2, ~90MB) is downloaded from HuggingFace on first startup and cached locally at ~/.cache/torch/sentence_transformers/

Install

pip install -r requirements.txt

This installs FastAPI, uvicorn, sentence-transformers, hnswlib, aiosqlite, and other dependencies. Review requirements.txt before running. Prefer installing inside a virtualenv or container.

Required Environment Variables

Variable Required Secret Default Description
AGENTMEMO_ADMIN_KEY yes yes API key for RBAC auth. Server exits if unset.
AGENTMEMO_PORT no no 8790 HTTP port (localhost only)
AGENTMEMO_DB no no agentmemo.db SQLite DB path
AGENTMEMO_RATE_LIMIT no no 120 Requests/min per key
AGENTMEMO_POOL_SIZE no no 5 DB connection pool size

Start

export AGENTMEMO_ADMIN_KEY="your-secret-key"
python server.py

The server binds to 127.0.0.1:8790 (localhost only). For networked deployments, use a reverse proxy with TLS + auth. Never expose port 8790 to the internet directly.

Security

  • Auth is mandatory: server refuses to start without AGENTMEMO_ADMIN_KEY
  • All endpoints require X-API-Key header (except /health)
  • Localhost binding by default: only accessible from the local machine
  • First-run network activity: downloads embedding model (~90MB) from HuggingFace; subsequent starts use local cache

Quick Reference

Store

curl -X POST http://localhost:8790/v1/memories \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: your-secret-key' \
  -d '{"text": "User prefers dark mode", "namespace": "prefs", "tags": ["ui"], "importance": 0.9}'

Search

curl -H 'X-API-Key: your-secret-key' \
  'http://localhost:8790/v1/memories/search?q=dark+mode&mode=hybrid&tags=ui'

Python Client

from client import AgentMemoClient
memo = AgentMemoClient("http://localhost:8790", api_key="your-secret-key")
memo.store("Decision: use PostgreSQL", namespace="arch", tags=["db"], importance=0.8)
results = memo.search("database choice", mode="hybrid")

Batch API

curl -X POST http://localhost:8790/v1/memories/batch \
  -H 'Content-Type: application/json' \
  -H 'X-API-Key: your-secret-key' \
  -d '{"operations": [{"op": "create", "text": "fact A"}, {"op": "create", "text": "fact B"}]}'

Versioning & Rollback

curl -H 'X-API-Key: your-secret-key' http://localhost:8790/v1/memories/{id}/versions
curl -X POST -H 'X-API-Key: your-secret-key' \
  http://localhost:8790/v1/memories/{id}/rollback -d '{"version": 2}'

API Endpoints

Method Path Description
GET /health Health check (no auth)
GET /metrics Server metrics
GET /dashboard Web dashboard
POST /v1/memories Store memory
GET /v1/memories/search Search (semantic/keyword/hybrid)
PUT /v1/memories/{id} Update (creates new version)
DELETE /v1/memories/{id} Delete
GET /v1/memories/{id}/versions Version history
POST /v1/memories/{id}/rollback Rollback to version
POST /v1/memories/batch Batch operations
POST /v1/import Bulk import
GET /v1/export Bulk export
GET /v1/events/stream SSE event stream
WS /v1/ws WebSocket stream

Key Features

  • Hybrid Search: RRF fusion of semantic (HNSW cosine) + keyword (BM25-style)
  • Importance Decay: score = importance × 0.5^(age/half_life) — older memories fade naturally
  • Versioning: Every update creates a new version; full rollback support
  • RBAC: Namespace isolation + API key access control
  • Event Bus: SSE + WebSocket for real-time agent-to-agent notifications
  • Dashboard: Web UI at /dashboard for browsing and searching memories
安全使用建议
This package implements the memory server it claims, but there are incongruities you should verify before installing: - Metadata mismatch: the registry shows no required env vars, but SKILL.md requires AGENTMEMO_ADMIN_KEY (mandatory). Confirm which is authoritative and how your deployment will supply/store that secret. - Inspect files before running: open requirements.txt, install.sh and server.py to ensure no unexpected network calls or shell actions. The first run downloads a ~90MB embedding model from HuggingFace — be prepared for network activity and disk use (~model cache + SQLite DB). - Do not expose the service to the public internet without a reverse proxy and TLS; SKILL.md says it binds to 127.0.0.1 by default, which is good. If you need networked access, put it behind authenticated TLS and rotate the admin key. - Run inside a virtualenv or container; consider setting the DB path and cache locations to a controlled directory and ensure proper file permissions. - If you plan to allow agents to call this autonomously, be careful which agents receive scoped API keys — RBAC is implemented but verify the API-key creation/listing endpoints and ensure keys are not leaked to other subsystems. If you want a safer thumbs-up: ask the publisher for clarification on the required env vars and a short audit of install.sh and server.py (search for unexpected external endpoints, subprocess execution, or code that reads secrets from other paths). If you cannot inspect the code yourself, treat the package as untrusted and run it in an isolated container with limited network access.
功能分析
Type: OpenClaw Skill Name: agentmemo-karl Version: 3.2.2 The bundle implements "agentMemo," a comprehensive semantic memory server for AI agents using FastAPI, SQLite, and HNSW indexing. It is classified as suspicious due to high-risk capabilities including the creation of network listeners, local file system persistence, and the automated download of model weights from HuggingFace. A significant security discrepancy exists where `server.py` binds the service to `0.0.0.0` by default, potentially exposing the memory mesh to the local network despite documentation in `SKILL.md` and `README.md` claiming it is restricted to `127.0.0.1`. While the code includes legitimate security features like RBAC and API key enforcement, these architectural risks and documentation mismatches warrant a cautious classification.
能力评估
Purpose & Capability
Name/description (semantic memory mesh) align with included source files (server.py, client.py, database.py, embeddings.py, etc.). The code and documentation implement the FastAPI + SQLite + HNSW memory server described.
Instruction Scope
SKILL.md gives concrete startup/install instructions (pip install -r requirements.txt, set AGENTMEMO_ADMIN_KEY, python server.py), binds to localhost, and documents endpoints. The runtime instructions do not direct the agent to read unrelated system files or external endpoints beyond downloading the embedding model from HuggingFace. However SKILL.md requires a secret env var (AGENTMEMO_ADMIN_KEY) even though the registry metadata lists no required env — an inconsistency worth verifying.
Install Mechanism
Registry lists no install spec (instruction-only), which is lower risk, but the bundle actually contains full source code, requirements.txt and install.sh scripts. SKILL.md recommends pip install -r requirements.txt and warns about a ~90MB model download from HuggingFace. No remote arbitrary binary downloads are specified, but the presence of install scripts and full source means you will be running code on your machine — inspect requirements.txt and scripts before installing.
Credentials
The SKILL.md requires AGENTMEMO_ADMIN_KEY (secret) and documents other optional env vars (port, DB path, rate limits), but the registry metadata reports no required env vars or primary credential. This mismatch is an incoherence that could confuse permissioning and automated deployments. Requesting one service-specific secret (admin API key) is reasonable for a kiosk memory server, but confirm the key is stored/handled securely and not transmitted elsewhere.
Persistence & Privilege
always:false and normal autonomous invocation defaults are used. The skill does persist data (SQLite DB, cached embeddings, model cache under ~/.cache) — expected for a memory server. Nothing requests system-wide privileges or modifications to other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agentmemo-karl
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agentmemo-karl 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v3.2.2
Fix auth documentation contradictions: README, .env.example, benchmark all now consistently say AGENTMEMO_ADMIN_KEY is mandatory. No more open-access-if-unset language anywhere.
v3.2.1
Fix metadata mismatch: remove metadata.openclaw block from frontmatter (not parsed by ClawHub registry), instead declare all requirements in description field and SKILL.md body. Add model download disclosure (~90MB HuggingFace first run). Explicit Prerequisites section with env vars, install, and network requirements.
v3.2.0
Complete rename: all AgentVault references replaced with agentMemo. Env vars now AGENTMEMO_* (AGENTVAULT_* still works as fallback). Client class AgentMemoClient (AgentVaultClient alias preserved). Dashboard title updated.
v3.1.0
BREAKING: AGENTVAULT_ADMIN_KEY is now mandatory — server refuses to start without it. Eliminates open-access mode entirely. Registry metadata declares install spec and required env vars.
v3.0.3
Fix registry metadata: declare install spec (pip requirements.txt), declare AGENTVAULT_ADMIN_KEY as required env var with secret flag, declare AGENTVAULT_PORT and AGENTVAULT_DB as optional env vars. Resolves ClawHub suspicious flag for metadata/instruction mismatch.
v3.0.2
Security hardening: AGENTVAULT_ADMIN_KEY now marked as required in docs, all API examples include X-API-Key header, removed nohup background run instructions, added Security section with localhost-only binding guidance.
v3.0.1
Fix: remove hardcoded local paths that triggered suspicious pattern scan. Cleaner SKILL.md.
v3.0.0
Semantic Memory Mesh for AI Agents - hybrid search, version history, importance decay, RBAC, real-time event bus
元数据
Slug agentmemo-karl
版本 3.2.2
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 8
常见问题

agentMemo 是什么?

agentMemo is a Semantic Memory Mesh server for AI agents. Use this skill when you need to store, search, or retrieve agent memory across sessions with semant... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 103 次。

如何安装 agentMemo?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agentmemo-karl」即可一键安装,无需额外配置。

agentMemo 是免费的吗?

是的,agentMemo 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

agentMemo 支持哪些平台?

agentMemo 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 agentMemo?

由 Karl Yang(@yxjsxy)开发并维护,当前版本 v3.2.2。

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