代码插件

Openclaw — 插件工具

v0.2.0

The plugin's behavior is largely consistent with a semantic-memory/memsearch integration, but the SKILL.md triggered a prompt-injection pattern and the instructions/code allow reading and indexing workspace files (including following anchor paths), so you should review anchors, file access, and any invoked CLI/tool installs before enabling it.

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by @zc277584121

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下载插件包项目主页

最后更新

2026/4/10

版本

latestv0.2.02026/3/30

安装命令

点击复制

官方npx clawhub@latest install memsearch

镜像加速npx clawhub@latest install memsearch --registry https://cn.clawhub-mirror.com

插件文档

memsearch — OpenClaw Plugin

Automatic persistent memory for OpenClaw. Every conversation turn is summarized and indexed — your next session picks up where you left off.

Prerequisites

OpenClaw >= 2026.3.22
Python 3.10+

Install

From ClawHub (recommended)

# 1. Install memsearch
uv tool install "memsearch[onnx]"

# 2. Install the plugin from ClawHub
openclaw plugins install clawhub:memsearch

# 3. Restart the gateway
openclaw gateway restart

From Source (development)

# 1. Install memsearch
uv tool install "memsearch[onnx]"

# 2. Clone the repo and install the plugin
git clone https://github.com/zilliztech/memsearch.git
cd memsearch
openclaw plugins install ./plugins/openclaw

# 3. Restart the gateway
openclaw gateway restart

Usage

Start a TUI session as normal:

openclaw tui

What happens automatically

When	What
Agent starts	Recent memories injected as context
Each turn ends	Conversation summarized (bullet-points) and saved to daily `.md`
LLM needs history	Calls `memory_search` / `memory_get` / `memory_transcript` tools

Recall memories

Two ways to trigger:

/memory-recall what was the caching strategy we chose?

Or just ask naturally — the LLM auto-invokes memory tools when it senses the question needs history:

We discussed caching strategies before, what did we decide?

Three-layer progressive recall

The plugin registers three tools the LLM uses progressively:

memory_search — Semantic search across past memories. Always starts here.
memory_get — Expand a chunk to see the full markdown section with context.
memory_transcript — Parse the original session transcript for exact dialogue.

The LLM decides how deep to go based on the question — simple recall uses only L1, detailed questions go to L2/L3.

Multi-agent isolation

Each OpenClaw agent stores memory independently under its own workspace:

~/.openclaw/workspace/.memsearch/memory/          ← main agent
~/.openclaw/workspace-work/.memsearch/memory/      ← work agent

Collection names are derived from the workspace path (same algorithm as Claude Code, Codex, and OpenCode), so agents with different workspaces have isolated memories. When an agent's workspace points to a project directory used by other platforms, memories are automatically shared across platforms.

Configuration

Works out of the box with zero configuration (ONNX embedding, no API key needed).

Optional settings via openclaw plugins config memsearch:

Setting	Default	Description
`provider`	`onnx`	Embedding provider (onnx, openai, google, voyage, ollama)
`autoCapture`	`true`	Auto-capture conversation summaries after each turn
`autoRecall`	`true`	Auto-inject recent memories at agent start

Memory files

Each agent's memory is stored as plain markdown:

# 2026-03-25

## Session 14:47

### 14:47
<!-- session:UUID transcript:~/.openclaw/agents/main/sessions/UUID.jsonl -->
- User asked about the memsearch architecture.
- OpenClaw explained core components: chunker, scanner, embedder, MilvusStore.

These files are human-readable, editable, and version-controllable. Milvus is a derived index that can be rebuilt anytime.

Uninstall

openclaw plugins install --remove memsearch
# Or manually:
rm -rf ~/.openclaw/extensions/memsearch

数据来源：ClawHub ↗ · 中文优化：龙虾技能库