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 (2026.4+ recommended for full CLI support)
• Python 3.10+

Install

From ClawHub (recommended)

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

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

# 3. Allow memsearch to read conversation turns and inject recall context
openclaw config set plugins.entries.memsearch.hooks.allowConversationAccess true
openclaw config set plugins.entries.memsearch.hooks.allowPromptInjection true

# 4. 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 --force ./plugins/openclaw

# 3. Allow memsearch to read conversation turns and inject recall context
openclaw config set plugins.entries.memsearch.hooks.allowConversationAccess true
openclaw config set plugins.entries.memsearch.hooks.allowPromptInjection true

# 4. Restart the gateway
openclaw gateway restart

Usage

Start a TUI session as normal:

openclaw tui

What happens automatically

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:

1. memory_search — Semantic search across past memories. Always starts here.
2. memory_get — Expand a chunk to see the full markdown section with context.
3. 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:

To override only the OpenClaw native capture summarization model:

memsearch config set plugins.openclaw.summarize.model qwen3-coder

To use a memsearch-managed API provider instead:

memsearch config set llm.providers.openai.type openai
memsearch config set llm.providers.openai.model gpt-5-mini
memsearch config set llm.providers.openai.api_key env:OPENAI_API_KEY
memsearch config set plugins.openclaw.summarize.provider openai

Leave plugins.openclaw.summarize.provider empty or set it to native to keep the default OpenClaw agent model. This setting does not fall back to llm.model.

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 uninstall memsearch
openclaw gateway restart

Uninstalling the plugin does not delete memory files in .memsearch/memory/.