Claude Code Plugin¶

Automatic persistent memory for Claude Code. No commands to learn, no manual saving -- just install the plugin and Claude remembers what you worked on across sessions.

The plugin is built entirely on Claude Code's own primitives: Hooks for lifecycle events, Skills for intelligent retrieval, and CLI for tool access. No MCP servers, no sidecar services, no extra network round-trips. Everything runs locally as shell scripts, a skill definition, and a Python CLI.

How the pieces fit together¶

graph LR
    subgraph "memsearch (Python library)"
        LIB[Core: chunker, embeddings,<br/>vector store, scanner]
    end

    subgraph "memsearch CLI"
        CLI["CLI commands:<br/>search · index · watch<br/>expand · transcript · config"]
    end

    subgraph "ccplugin (Claude Code Plugin)"
        HOOKS["Shell hooks:<br/>SessionStart · UserPromptSubmit<br/>Stop · SessionEnd"]
        SKILL["Skill:<br/>memory-recall (context: fork)"]
    end

    LIB --> CLI
    CLI --> HOOKS
    CLI --> SKILL
    HOOKS -->|"runs inside"| CC[Claude Code]
    SKILL -->|"subagent"| CC

    style LIB fill:#1a2744,stroke:#6ba3d6,color:#a8b2c1
    style CLI fill:#1a2744,stroke:#e0976b,color:#a8b2c1
    style HOOKS fill:#1a2744,stroke:#7bc67e,color:#a8b2c1
    style CC fill:#2a1a44,stroke:#c97bdb,color:#a8b2c1

The memsearch Python library provides the core engine (chunking, embedding, vector storage, search). The memsearch CLI wraps the library into shell-friendly commands. The Claude Code Plugin ties those CLI commands to Claude Code's hook lifecycle and skill system — hooks handle session management and memory capture, while the memory-recall skill handles intelligent retrieval in a forked subagent context.

Without vs. With the Plugin¶

sequenceDiagram
    participant You
    participant Claude as Claude Code

    rect rgb(60, 30, 30)
    note right of You: Without plugin
    You->>Claude: Monday: "Add Redis caching with 5min TTL"
    Claude->>You: ✅ Done — implements caching
    note over Claude: Session ends. Context is gone.
    You->>Claude: Wednesday: "The /orders endpoint is slow"
    Claude->>You: ❌ Suggests solutions from scratch<br/>(forgot about the Redis cache from Monday)
    end

    rect rgb(20, 50, 30)
    note right of You: With plugin
    You->>Claude: Monday: "Add Redis caching with 5min TTL"
    Claude->>You: ✅ Done — implements caching
    note over Claude: Plugin auto-summarizes → memory/2026-02-10.md
    You->>Claude: Wednesday: "The /orders endpoint is slow"
    note over Claude: Plugin injects: "Added Redis caching<br/>middleware with 5min TTL..."
    Claude->>You: ✅ "We already have Redis caching —<br/>let me add the /orders endpoint to it"
    end

When Is This Useful?¶

Picking up where you left off. You debugged an auth issue yesterday but didn't finish. Today Claude remembers the root cause, which files you touched, and what you tried — no re-explaining needed.
Recalling past decisions. "Why did we switch from JWT to session cookies?" Claude can trace back to the original conversation where the trade-offs were discussed, thanks to the 3-layer progressive disclosure that drills from summary → full section → original transcript.
Long-running projects. Over days or weeks of development, architectural context accumulates automatically. Claude stays aware of your codebase conventions, past refactors, and resolved issues without you having to maintain a manual changelog.

Quick Start¶

Install from Marketplace (recommended)¶

# 1. In Claude Code, add the marketplace and install the plugin
/plugin marketplace add zilliztech/memsearch
/plugin install memsearch

# 2. Have a conversation, then exit. Check your memories:
cat .memsearch/memory/$(date +%Y-%m-%d).md

# 3. Start a new session -- Claude automatically remembers!

Note: The plugin defaults to the ONNX bge-m3 embedding model -- no API key required, runs locally on CPU. This model was selected through a comprehensive benchmark of 12+ models on bilingual memory retrieval. If memsearch is not already installed, the plugin will install memsearch[onnx] automatically via uvx on first run. To use a different embedding provider (e.g. OpenAI), set it with memsearch config set embedding.provider openai and export the required API key.

First-time download: On the first session, the ONNX model (~558 MB) is downloaded from HuggingFace Hub in the background. If your first session appears to hang or memory search is unavailable, the model is still downloading. You can pre-download it manually:
uvx --from 'memsearch[onnx]' memsearch search --provider onnx "warmup" 2>/dev/null || true
If the download is slow or stuck, set the HuggingFace mirror first:
export HF_ENDPOINT=https://hf-mirror.com

Memory Storage¶

All memories live in .memsearch/memory/ inside your project directory.

Directory Structure¶

your-project/
├── .memsearch/
│   ├── .watch.pid            <-- singleton watcher PID file
│   └── memory/
│       ├── 2026-02-07.md     <-- daily memory log
│       ├── 2026-02-08.md
│       └── 2026-02-09.md     <-- today's session summaries
└── ... (your project files)

Example Memory File¶

A typical daily memory file (2026-02-09.md) looks like this:

## Session 14:30

### 14:30
<!-- session:abc123def turn:ghi789jkl transcript:/home/user/.claude/projects/.../abc123def.jsonl -->
- Implemented caching system with Redis L1 and in-process LRU L2
- Fixed N+1 query issue in order-service using selectinload
- Decided to use Prometheus counters for cache hit/miss metrics

## Session 17:45

### 17:45
<!-- session:mno456pqr turn:stu012vwx transcript:/home/user/.claude/projects/.../mno456pqr.jsonl -->
- Debugged React hydration mismatch caused by Date.now() during SSR
- Added comprehensive test suite for the caching middleware
- Reviewed PR #42: approved with minor naming suggestions

Each file accumulates all sessions from that day. The format is plain markdown -- human-readable, grep-able, and git-friendly.

Markdown Is the Source of Truth¶

The Milvus vector index is a derived cache that can be rebuilt at any time:

memsearch index .memsearch/memory/

This means:

No data loss. Even if Milvus is corrupted or deleted, your memories are safe in .md files.
Portable. Copy .memsearch/memory/ to another machine and rebuild the index.
Auditable. You can read, edit, or delete any memory entry with a text editor.
Git-friendly. Commit your memory files to version control for a complete project history.

Comparison with claude-mem¶

claude-mem is another memory solution for Claude Code. Here is a detailed comparison:

Aspect	memsearch	claude-mem
Architecture	4 shell hooks + 1 skill + 1 watch process	5 JS hooks + 1 skill + MCP tools + Express worker service (port 37777) + React viewer
Integration	Native hooks + skill + CLI -- no MCP, no sidecar service	Hooks + skill + MCP tools + HTTP worker service
Memory recall	Skill in forked subagent -- `memory-recall` runs in `context: fork`, intermediate results stay isolated from main context	Skill + MCP hybrid -- `mem-search` skill for auto-recall, plus 5 MCP tools (`search`, `timeline`, `get_observations`, `save_memory`, ...) for explicit access
Progressive disclosure	3-layer in subagent: search → expand → transcript, all in forked context -- only curated summary reaches main conversation	3-layer: `mem-search` skill for auto-recall; MCP tools for explicit drill-down
Session capture	1 async `claude -p --model haiku` call at session end	AI observation compression on every tool use (`PostToolUse` hook) + session summary
Vector backend	Milvus -- hybrid search (dense + BM25 + RRF), scales from embedded to distributed cluster	ChromaDB -- dense only; SQLite FTS5 for keyword search (separate, not fused)
Embedding model	Pluggable: OpenAI, Google, Voyage, Ollama, local, ONNX (default: bge-m3 int8)	Fixed: all-MiniLM-L6-v2 (384-dim, WASM backend)
Storage format	Transparent `.md` files -- human-readable, git-friendly	SQLite database + ChromaDB binary
Data portability	Copy `.memsearch/memory/*.md` and rebuild index	Export from SQLite + ChromaDB
Runtime dependency	Python (`memsearch` CLI) + `claude` CLI	Node.js / Bun + Express worker service
Context window cost	No MCP tool definitions; skill runs in forked context -- only curated summary enters main context	MCP tool definitions permanently loaded + each MCP tool call/result consumes main context

The Key Difference: Forked Subagent vs. MCP Tools¶

Both projects use hooks for session lifecycle and skills for memory recall. The architectural divergence is in how retrieval interacts with the main context window.

memsearch runs memory recall in a forked subagent (context: fork). The memory-recall skill gets its own isolated context window -- all search, expand, and transcript operations happen there. Only the curated summary is returned to the main conversation. This means: (1) intermediate search results never pollute the main context, (2) multi-step retrieval is autonomous, and (3) no MCP tool definitions consume context tokens.

claude-mem combines a mem-search skill with MCP tools (search, timeline, get_observations, save_memory). The MCP tools give Claude explicit control over memory access in the main conversation, at the cost of tool definitions permanently consuming context tokens. The PostToolUse hook also records every tool call as an observation, providing richer per-action granularity but incurring more API calls.

The other key difference is storage philosophy: memsearch treats markdown files as the source of truth (human-readable, git-friendly, rebuildable), while claude-mem uses SQLite + ChromaDB (opaque but structured, with richer queryable metadata).

Comparison with Claude's Native Memory¶

Claude Code has built-in memory features: CLAUDE.md files and auto-memory (the /memory command). Here is why memsearch provides a stronger solution:

Aspect	Claude Native Memory	memsearch
Storage	Single `CLAUDE.md` file (or per-project)	Unlimited daily `.md` files with full history
Recall mechanism	File is loaded at session start (no search)	Skill-based semantic search -- Claude auto-invokes when context is needed
Granularity	One monolithic file, manually edited	Per-session bullet points, automatically generated
Search	None -- Claude reads the whole file or nothing	Hybrid semantic search (dense + BM25) returning top-k relevant chunks
History depth	Limited to what fits in one file	Unlimited -- every session is logged, every entry is searchable
Automatic capture	`/memory` command requires manual intervention	Fully automatic -- hooks capture every session
Progressive disclosure	None -- entire file is loaded into context	3-layer model (L1 auto-inject, L2 expand, L3 transcript) minimizes context usage
Deduplication	Manual -- user must avoid adding duplicates	SHA-256 content hashing prevents duplicate embeddings
Portability	Tied to Claude Code's internal format	Standard markdown files, usable with any tool

Why This Matters¶

CLAUDE.md is a blunt instrument: it loads the entire file into context at session start, regardless of relevance. As the file grows, it wastes context window on irrelevant information and eventually hits size limits. There is no search -- Claude cannot selectively recall a specific decision from three weeks ago.

memsearch solves this with skill-based semantic search and progressive disclosure. When Claude judges that historical context would help, it auto-invokes the memory-recall skill, which runs in a forked subagent and autonomously searches, expands, and curates relevant memories. History can grow indefinitely without degrading performance, because the vector index handles the filtering. And the three-layer model (search → expand → transcript) runs entirely in the subagent, keeping the main context window clean.

Plugin Files¶

The plugin lives in the ccplugin/ directory at the root of the memsearch repository:

ccplugin/
├── .claude-plugin/
│   └── plugin.json              # Plugin manifest (name, version, description)
├── hooks/
│   ├── hooks.json               # Hook definitions (4 lifecycle hooks)
│   ├── common.sh                # Shared setup: env, PATH, memsearch detection, watch management
│   ├── session-start.sh         # Start watch + write session heading + inject cold-start context
│   ├── user-prompt-submit.sh    # Lightweight systemMessage hint ("[memsearch] Memory available")
│   ├── stop.sh                  # Parse transcript -> haiku summary -> append to daily .md
│   ├── parse-transcript.sh      # Deterministic JSONL-to-text parser with truncation
│   └── session-end.sh           # Stop watch process (cleanup)
├── scripts/
│   └── derive-collection.sh     # Derive per-project collection name from project path
└── skills/
    └── memory-recall/
        └── SKILL.md             # Memory retrieval skill (context: fork subagent)

File Descriptions¶

File	Purpose
`plugin.json`	Claude Code plugin manifest. Declares the plugin name (`memsearch`), version, and description.
`hooks.json`	Defines the 4 lifecycle hooks (SessionStart, UserPromptSubmit, Stop, SessionEnd) with their types, timeouts, and async flags.
`common.sh`	Shared shell library sourced by all hooks. Handles stdin JSON parsing, PATH setup, memsearch binary detection (prefers PATH, falls back to `uv run`), memory directory management, and the watch singleton (start/stop with PID file and orphan cleanup).
`session-start.sh`	SessionStart hook implementation. Starts the watcher, writes the session heading, and reads recent memory files for cold-start context injection.
`user-prompt-submit.sh`	UserPromptSubmit hook implementation. Returns a lightweight `systemMessage` hint to keep Claude aware of the memory system. No search -- retrieval is handled by the memory-recall skill.
`stop.sh`	Stop hook implementation. Extracts the transcript path, validates it, delegates parsing to `parse-transcript.sh`, calls Haiku for summarization (with `CLAUDECODE=` to bypass nested session detection), and appends the result with session anchors to the daily memory file.
`parse-transcript.sh`	Standalone transcript parser. Extracts the last turn (last user question + all responses to EOF) from a JSONL transcript using Python 3. Outputs with role labels (`[Human]`, `[Claude Code]`, `[Claude Code calls tool]`, `[Tool output]`/`[Tool error]`) so the summarizer treats it as a third-party transcript. Skips progress, thinking, and file-history-snapshot entries. No `jq` dependency. Used by `stop.sh`.
`session-end.sh`	SessionEnd hook implementation. Calls `stop_watch` to terminate the background watcher and clean up.

The `memsearch` CLI¶

The plugin is built entirely on the memsearch CLI -- every hook is a shell script calling memsearch subcommands. Here are the commands most relevant to the plugin:

Command	Used By	What It Does
`search <query>`	memory-recall skill	Semantic search over indexed memories (`--top-k` for result count, `--json-output` for JSON)
`watch <paths>`	SessionStart hook	Background watcher that auto-indexes on file changes (1500ms debounce)
`index <paths>`	Manual / rebuild	One-shot index of markdown files (`--force` to re-index all)
`expand <chunk_hash>`	memory-recall skill (L2)	Show full markdown section around a chunk, with anchor metadata
`transcript <jsonl>`	memory-recall skill (L3)	Parse Claude Code JSONL transcript into readable conversation turns
`config init`	Quick Start	Interactive config wizard for first-time setup
`stats`	Manual	Show index statistics (collection size, chunk count)
`reset`	Manual	Drop all indexed data (requires `--yes` to confirm)

For the full CLI reference, see the CLI Reference page.

Development Mode¶

For contributors or if you want to modify the plugin locally:

git clone https://github.com/zilliztech/memsearch.git
cd memsearch && uv sync
claude --plugin-dir ./ccplugin