/install agentmemo-karl
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-Keyheader (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
/dashboardfor browsing and searching memories
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install agentmemo-karl - 安装完成后,直接呼叫该 Skill 的名称或使用
/agentmemo-karl触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。