memsearch

📰 What's New

• Skills from memory — MemSearch now distills the workflows you repeat into reusable, installable agent skills (a third "procedural memory" layer) and keeps them up to date in the background. See Skills from Memory.
• Advanced memory maintenance — optional background tasks keep durable PROJECT.md and USER.md notes current across sessions. See Advanced Memory Maintenance.

Why memsearch?

• 🌐 All Platforms, One Memory — memories flow across Claude Code, OpenClaw, OpenCode, and Codex CLI. A conversation in one agent becomes searchable context in all others — no extra setup
• 👥 For Agent Users, install a plugin and get persistent memory with zero effort; for Agent Developers, use the full CLI and Python API to build memory and harness engineering into your own agents
• 📄 Markdown is the source of truth — inspired by OpenClaw. Your memories are just .md files — human-readable, editable, version-controllable. Milvus is a "shadow index": a derived, rebuildable cache
• 🔍 Progressive retrieval, hybrid search, smart dedup, live sync — 3-layer recall (search → expand → transcript); dense vector + BM25 sparse + RRF reranking; SHA-256 content hashing skips unchanged content; file watcher auto-indexes in real time

🧑‍💻 For Agent Users

Pick your platform, install the plugin, and you're done. Each plugin captures conversations automatically and provides semantic recall with zero configuration.

# Install
/plugin marketplace add zilliztech/memsearch
/plugin install memsearch
# Restart Claude Code to activate the plugin

After restarting, just chat with Claude Code as usual. The plugin captures every conversation turn automatically.

Verify it's working — after a few conversations, check your memory files:

ls .memsearch/memory/          # you should see daily .md files
cat .memsearch/memory/$(date +%Y-%m-%d).md

Recall memories — two ways to trigger:

/memory-recall what did we discuss about Redis?

Or just ask naturally — Claude auto-invokes the skill when it senses the question needs history:

We discussed Redis caching before, what was the TTL we chose?

📖 Claude Code Plugin docs · Troubleshooting

# Install
git clone --depth 1 https://github.com/zilliztech/memsearch.git
bash memsearch/plugins/codex/scripts/install.sh
codex --yolo  # needed for ONNX model network access

After installing, chat as usual. Hooks capture and summarize each turn.

Verify it's working:

ls .memsearch/memory/

Recall memories — use the skill:

$memory-recall what did we discuss about deployment?

📖 Codex CLI Plugin docs

# Install from ClawHub
openclaw plugins install --force clawhub:memsearch
openclaw config set plugins.entries.memsearch.hooks.allowConversationAccess true
openclaw config set plugins.entries.memsearch.hooks.allowPromptInjection true
openclaw gateway restart

After installing, chat in TUI as usual. The plugin captures each turn automatically.

Verify it's working — memory files are stored in your agent's workspace:

# For the main agent:
ls ~/.openclaw/workspace/.memsearch/memory/
# For other agents (e.g. work):
ls ~/.openclaw/workspace-work/.memsearch/memory/

Recall memories — two ways to trigger:

/memory-recall what was the batch size limit we set?

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

We discussed batch size limits before, what did we decide?

📖 OpenClaw Plugin docs · Browse on ClawHub

// In ~/.config/opencode/opencode.json
{ "plugin": ["@zilliz/memsearch-opencode"] }

After installing, chat in TUI as usual. A background daemon captures conversations.

Verify it's working:

ls .memsearch/memory/    # daily .md files appear after a few conversations

Recall memories — two ways to trigger:

/memory-recall what did we discuss about authentication?

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

We discussed the authentication flow before, what was the approach?

📖 OpenCode Plugin docs

⚙️ Configuration (all platforms)

All plugins share the same memsearch backend. Configure once, works everywhere.

Embedding

Defaults to ONNX bge-m3 — runs locally on CPU, no API key, no cost. On first launch the model (~558 MB) is downloaded from HuggingFace Hub.

memsearch config set embedding.provider onnx     # default — local, free
memsearch config set embedding.provider openai   # needs OPENAI_API_KEY
memsearch config set embedding.provider ollama   # local, any model

All providers and models: Configuration — Embedding Provider

Milvus Backend

Just change milvus_uri (and optionally milvus_token) to switch between deployment modes:

Milvus Lite (default) — zero config, single file. Great for getting started:

# Works out of the box, no setup needed
memsearch config get milvus.uri   # → ~/.memsearch/milvus.db

⭐ Zilliz Cloud (recommended) — fully managed, free tier available — sign up 👇:

memsearch config set milvus.uri "https://in03-xxx.api.gcp-us-west1.zillizcloud.com"
memsearch config set milvus.token "your-api-key"

You can sign up on Zilliz Cloud to get a free cluster and API key.

For multi-user or team environments with a dedicated Milvus instance. Requires Docker. See the official installation guide.

memsearch config set milvus.uri http://localhost:19530

📖 Full configuration guide: Configuration · [Platform comparison](https://zilliztech.github.i