cursor-mem0 0.1.2

AI agent memory layer (mem0-compatible) powered by Cursor SDK

cursor-mem

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Long-term memory for Cursor agents, built on the mem0 pipeline. Use your existing CURSOR_API_KEY for memory extraction (via Cursor SDK); run embeddings locally with fastembed; store vectors in on-disk Qdrant. Optional MCP server exposes memory tools to agents in Cursor IDE.

Overview

Who it’s for	Developers using Cursor who want agents to remember preferences and facts across chats
What you need	Python 3.11+ (library); MCP via uvx uses Python 3.12 in mcp.json (see fastembed/onnxruntime). Cursor API key and (for MCP) uv
PyPI package	cursor-mem0 — pip install cursor-mem0 (import: from cursor_mem import Memory)

Features

• One LLM key — extraction uses CURSOR_API_KEY, not a separate OpenAI/Anthropic account for memory
• Structured memory — mem0-style fact extraction, hybrid retrieval (vectors + BM25 + entities), SQLite history
• Efficient context — search returns only top_k relevant memories instead of reloading a growing markdown file each chat
• Local embeddings — fastembed; no embedding API key required
• MCP tools — add_memory, search_memories, list_memories, get_memory for Cursor agents

How it compares to common alternatives

Approach	Typical trade-off
Default mem0 / multi-key stacks	Extra LLM and often embedding API keys and billing
File-based memory (MEMORY.md, logs in every prompt)	Token cost grows with file size; weaker retrieval
cursor-mem0	CURSOR_API_KEY + local vectors + retrieval bounded by top_k

Extraction uses Cursor SDK quota when infer=true. Embedding runs on your machine.

Demo

Before/after memory in Cursor, MCP tool calls, and context-size comparison (~49s).

Full MP4 with controls

Quick start

1. Install

pip install cursor-mem0

# To use memory tools from Cursor agents:
pip install "cursor-mem0[mcp]"

PyPI name cursor-mem0 is not cursor-mem (that package is unrelated IDE session tooling).

2. Get your CURSOR_API_KEY

cursor-mem0 uses the Cursor SDK for memory extraction. You need a user API key from your Cursor account (not a separate OpenAI key).

1. Sign in at cursor.com.
2. Open Dashboard → Integrations (API Keys).
3. Click Create API key, name it (e.g. cursor-mem), and copy the value. Keys usually start with cursor_.
4. Keep the key secret — do not commit it to git.

Team Admin API keys are not supported by the Cursor SDK yet. Use a personal user key.

3. Configure the key locally

Option A — .env in your project (recommended for MCP):

cp .env.example .env
# Edit .env:
# CURSOR_API_KEY=cursor_...

Option B — shell export:

export CURSOR_API_KEY="cursor_..."

4. Use from Python

from cursor_mem import Memory

memory = Memory()

memory.add("I prefer dark mode and vim keybindings", user_id="alice")

results = memory.search(
    "What are Alice's editor preferences?",
    filters={"user_id": "alice"},
    top_k=3,
)
for item in results["results"]:
    print(item["memory"], item.get("score"))

Interactive example:

python examples/chat_with_memory.py

Data is stored under ~/.cursor-mem/ unless you set CURSOR_MEM_DIR.

Find in Cursor

Install from the community directory (recommended), or submit this repo for official listing:

Where	How users find it
cursor.directory	Search “memory” / “mem0” — uses root .mcp.json / mcp.json. Submit repo after release.
Cursor Marketplace	Settings → Plugins → Browse Marketplace. Requires .cursor-plugin/plugin.json and publish review.

This repository ships a Cursor plugin manifest and MCP config so directories can offer Add to Cursor without cloning the repo. The server runs from PyPI via uvx --python 3.12 (requires uv installed; avoids Python 3.10 wheel gaps for onnxruntime).

Use with Cursor (MCP)

Option A — One-click / directory install (recommended)

1. Install uv if you do not have it (curl -LsSf https://astral.sh/uv/install.sh | sh).
2. Set CURSOR_API_KEY (see Get your key above).
3. Copy mcp.json into your project as .cursor/mcp.json, or use Add to Cursor from cursor.directory once listed.
4. Put CURSOR_API_KEY in your project .env (optional; envFile in config loads it).

No pip install needed — uvx pulls cursor-mem0[mcp] from PyPI on first run.

Option B — pip install

pip install "cursor-mem0[mcp]"

Copy docs/mcp.pip.json.example to .cursor/mcp.json in your project.

Enable in Cursor

1. Open your project in Cursor.
2. Go to Settings → MCP (or type /mcp in chat) and enable cursor-mem.
3. If tools are missing, reload the window and check MCP logs (uvx / uv on PATH, or pip + mcp package for Option B).

To use memory in all projects, add the same server block to ~/.cursor/mcp.json.

MCP tools

Tool	Description
add_memory	Save facts from the conversation (infer=true runs mem0 extraction via Cursor SDK)
search_memories	Semantic and hybrid search
list_memories	List memories for a user_id
get_memory	Get one memory by id

Configuration

Defaults (Memory()):

Setting	Value
LLM	cursor provider, model composer-2.5
Embedder	fastembed, thenlper/gte-large (1024 dims)
Vector store	Local Qdrant at ~/.cursor-mem/qdrant

Environment variables

Variable	Description
CURSOR_API_KEY	Required for LLM / extraction
CURSOR_MEM_DIR	Storage root (default ~/.cursor-mem)
CURSOR_MEM_USER_ID	Default user_id in examples

Custom config (mem0-style):

from cursor_mem import Memory

memory = Memory.from_config({
    "llm": {
        "provider": "cursor",
        "config": {
            "api_key": "cursor_...",
            "model": "composer-2.5",
            "cwd": "/path/to/your/project",
        },
    },
    "embedder": {"provider": "fastembed", "config": {"model": "thenlper/gte-large"}},
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "path": "/path/to/qdrant-data",
            "collection_name": "my_memories",
            "embedding_model_dims": 1024,
        },
    },
})

Optional NLP extra (BM25 + entity boosting, same idea as mem0[nlp]):

pip install "cursor-mem0[nlp]"
python -m spacy download en_core_web_sm

Install from source

git clone https://github.com/xwqiang/cursor-mem0.git
cd cursor-mem0
pip install -e ".[mcp]"

Architecture

Component	Technology	API key
Extraction & reasoning	Cursor SDK Agent.prompt	CURSOR_API_KEY
Embeddings	fastembed (local ONNX)	None
Vector store	Qdrant (on disk)	None
History	SQLite	None

flowchart LR
  subgraph input [Conversation]
    M[messages]
  end
  subgraph cursor_mem [cursor-mem]
    S[search: embed + BM25 + entities]
    L[Cursor SDK LLM extract facts]
    V[(Qdrant)]
    H[(SQLite history)]
  end
  M --> S
  S --> L
  L --> V
  L --> H

Relationship to mem0

cursor-mem0 keeps mem0’s public Memory API and retrieval pipeline. Changes:

• LLM provider: cursor_mem.llms.cursor.CursorLLM (provider: "cursor") instead of OpenAI by default
• Default embedder: fastembed instead of openai, so vectors work without OPENAI_API_KEY

Cursor SDK does not expose a dedicated embeddings API; local fastembed preserves semantic search without another cloud key.

License

Apache-2.0