cursor-mem 0.2.0

Persistent memory system for Cursor IDE — remembers context across sessions

cursor-mem

Persistent memory for Cursor IDE — automatically records session context and keeps memory across sessions.

中文说明 (README_CN.md)

Tired of re-explaining "where we left off" in every new chat? cursor-mem gives Cursor persistent memory: it automatically records your edits, shell commands, and MCP calls, then injects recent session summaries and key context into Cursor Rules so the next conversation picks up right away. No API key required — just pip install cursor-mem and cursor-mem install --global. When the agent needs details, it queries history on demand via MCP, so you say less and use fewer tokens. Like claude-mem, but built for Cursor and lighter.

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Features

• Cross-session memory: Remembers last session’s actions, edited files, and shell commands
• Zero-config: Works out of the box with rule-based compression; no API key required
• Optional AI summarization: Use any OpenAI-compatible API (e.g. free Gemini) for smarter summaries
• Full-text search: FTS5 search over observations and sessions
• MCP tools: 3-layer search workflow (~10x token savings) — memory_search (compact index), memory_timeline (anchor context), memory_get (full details), plus memory_important (workflow guide)
• Web viewer: Browse memory stream in the browser with live updates
• Multi-project isolation: Separate memory per project

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Comparison with claude-mem

criterion	cursor-mem	claude-mem
Target	Cursor IDE only, native hooks	Claude Code first, Cursor via adapter
Stack	Python 3.10+, FastAPI, SQLite	TypeScript/Bun, Express, SQLite + ChromaDB
Setup	pip install cursor-mem → cursor-mem install	Clone, build, plugin/marketplace or Cursor standalone setup
Out-of-the-box	Works immediately with no API key (rule-based compression)	AI processing is central; free tier needs Gemini/OpenRouter config
Codebase size	~20 core modules, single package	600+ files, plugin + worker + skills
Context injection	.cursor/rules/cursor-mem.mdc (Cursor Rules)	Same for Cursor; Claude Code uses additionalContext
Search	SQLite FTS5 only (simple, no extra deps)	FTS5 + ChromaDB vector search (hybrid)
Dependencies	Python stdlib + FastAPI/Click/httpx	Node/Bun, Claude Agent SDK, ChromaDB, etc.

When to choose cursor-mem: You use Cursor only, want minimal setup and no required API key, and prefer a small Python codebase. When to choose claude-mem: You use Claude Code or want vector/semantic search, token economics, or the full plugin ecosystem.

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Quick start

# Install from PyPI
pip install cursor-mem

# One-shot setup (global; applies to all projects)
cursor-mem install --global

# Restart Cursor

From source (development):

pip install -e .
cursor-mem install --global

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How it works

User submits prompt → beforeSubmitPrompt hook
  → init session + inject history into .cursor/rules/cursor-mem.mdc

Agent runs → afterShellExecution / afterFileEdit / afterMCPExecution hooks
  → capture operations, compress, store in SQLite

Agent stops → stop hook
  → generate session summary + refresh context file for next session

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Commands

cursor-mem install [--global]   # Install hooks + start worker
cursor-mem start                # Start worker
cursor-mem stop                 # Stop worker
cursor-mem restart              # Restart worker
cursor-mem status               # Show status

cursor-mem config set <key> <val>   # Set config
cursor-mem config get [key]         # Show config

cursor-mem data stats             # Data stats
cursor-mem data projects          # List projects
cursor-mem data cleanup           # Clean old data
cursor-mem data export [file]     # Export data

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Optional AI summarization

# Gemini (free tier)
cursor-mem config set ai.enabled true
cursor-mem config set ai.base_url "https://generativelanguage.googleapis.com/v1beta/openai"
cursor-mem config set ai.api_key "your-gemini-api-key"
cursor-mem config set ai.model "gemini-2.0-flash"

# Or any OpenAI-compatible API
cursor-mem config set ai.base_url "https://api.openai.com/v1"
cursor-mem config set ai.api_key "sk-..."
cursor-mem config set ai.model "gpt-4o-mini"

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Web viewer

After install, open http://127.0.0.1:37800 for:

• Session list and details
• Observation timeline
• Full-text search
• Live SSE updates

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MCP tools (3-layer workflow)

Registered in ~/.cursor/mcp.json on install. Follow the 3-layer pattern for ~10x token savings:

1. memory_important — Workflow guide (always visible). Read first.
2. memory_search(query, project?, type?, limit?, offset?, dateStart?, dateEnd?, orderBy?) — Step 1: Compact index (ID, time, title, type). ~50–100 tokens/result.
3. memory_timeline(anchor?, depth_before?, depth_after?, query?, session_id?, project?, limit?) — Step 2: Context around an observation. Use anchor (observation ID) with depths. ~100–200 tokens/entry.
4. memory_get(ids, orderBy?, limit?) — Step 3: Full details only for filtered IDs. ~500–1000 tokens/observation.

Never fetch full details without filtering via search/timeline first.

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Project layout

cursor-mem/
├── cli.py           # CLI entry
├── installer.py     # Install logic
├── hook_handler.py  # Unified hook handler
├── config.py        # Config and paths
├── worker/          # FastAPI HTTP service
├── storage/         # SQLite layer
├── context/         # Context build and inject
├── summarizer/      # Rule-based + AI summarizer
├── mcp/             # MCP search tools
├── ui/              # Web viewer
├── pyproject.toml
└── README.md

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Documentation

Doc	English	中文
Design	DESIGN.md	DESIGN_CN.md
Roadmap	ROADMAP.md	ROADMAP_CN.md
User manual	USER_MANUAL.md	USER_MANUAL_CN.md

Testing

• Automated: pip install -e ".[dev]" then pytest tests/ -v
• In Cursor: See TESTING.md for manual test cases (hooks, MCP, worker, CLI).

License

This project is licensed under the Apache License 2.0. See LICENSE for the full text.

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Data locations

• Database: ~/.cursor-mem/cursor-mem.db
• Config: ~/.cursor-mem/config.json
• Logs: ~/.cursor-mem/logs/
• Injected context: <project>/.cursor/rules/cursor-mem.mdc