memsearch
zilliztech · zilliztech/memsearch
This plugin provides a persistent, unified semantic memory layer for AI coding agents like Claude Code, Codex, and DeepSeek Harness, also distilling workflows into reusable skills.
A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.
See it in action
From the author's READMEMemSearch distilling skills from memory within a Claude Code terminal session
From the author's README zilliztech ·Source
What you can do with it
- Give your Claude Code agent a long-term memory to recall past conversations and code snippets across sessions.
- Automatically capture project-specific knowledge and make it searchable for future AI agent tasks.
- Transform frequently used agent workflows into reusable, installable skills for consistent execution.
Safety check
No risky patterns found in the files we scanned.
Automated scan of the repo files. Always review code you run.
From the README
memsearch
Cross-platform semantic memory for AI coding agents.
📰 What's New
-
Optional Jev reranking — rerank memory search results with Jev through the TypeSafe API, with no local model download. See configuration and the Chinese/English evaluation.
-
DeepSeek Harness support — MemSearch now brings automatic capture, pre-step memory injection, native skill-based recall, background maintenance, and a read-only memory browser to DeepSeek Harness (DSH).
-
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.mdandUSER.mdnotes current across sessions. See Advanced Memory Maintenance.
Why memsearch?
- 🌐 All Platforms, One Memory — memories flow across Claude Code, Codex, DeepSeek Harness, OpenClaw, and OpenCode. 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
.mdfiles — 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.
For Claude Code Users
# 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?
For Codex Users
# 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?
For DeepSeek Harness Users
# Install the published plugin into your DSH profile
uv tool install "memsearch[onnx]"
dsh plugin --profile web add @zilliz/memsearch-dsh
# Restart that DSH profile, or start a new session
After installing, use DSH normally. Completed turns are captured automatically, and relevant memories are injected before the first model step only when they are useful.
Verify it's working:
ls .memsearch/memory/
Recall memories — ask naturally or tell DSH to use the registered memory-recall skill:
Use memory-recall to find what we decided about the deployment architecture.
The web profile also adds a compact MemSearch dock where you can review skill candidates and browse supported files under .memsearch/ without editing them.
For OpenClaw Users
# 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?
For OpenCode Users
// In ~/.config/opencode/opencode.json
{ "plugin": ["@zilliz/memsearch
