/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
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install agentmemo-karl - After installation, invoke the skill by name or use
/agentmemo-karl - Provide required inputs per the skill's parameter spec and get structured output
What is agentMemo?
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... It is an AI Agent Skill for Claude Code / OpenClaw, with 103 downloads so far.
How do I install agentMemo?
Run "/install agentmemo-karl" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is agentMemo free?
Yes, agentMemo is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does agentMemo support?
agentMemo is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created agentMemo?
It is built and maintained by Karl Yang (@yxjsxy); the current version is v3.2.2.