• [01/20/2026] SimpleMem is now available on PyPI! 📦 You can now install SimpleMem directly via pip install simplemem for easier setup and integration. View Package Usage Guide →
• [01/19/2026] Added Local Memory Storage for SimpleMem Skill! 💾 SimpleMem Skill now supports local memory storage, enabling seamless memory retention and management directly within Claude Skills.
• [01/18/2026] SimpleMem now supports Claude Skills! 🚀 Use SimpleMem in claude.ai to remember long-term information and project history across conversations. Register at mcp.simplemem.cloud, add the domain to Claude's network whitelist, configure with your token in the skill file, and import the skill!
• [01/14/2026] SimpleMem MCP Server is now LIVE and Open Source! 🎉 Experience SimpleMem as a cloud-hosted memory service at mcp.simplemem.cloud. Easily integrate with your favorite chat platforms (LM Studio, Cherry Studio) and AI agents (Cursor, Claude Desktop) using the Streamable HTTP MCP protocol. The MCP implementation features production-ready optimizations including multi-tenant user isolation, faster response times, and enhanced security. View MCP Documentation →
• [01/08/2026] We've set up a Discord server and WeChat group to make it easier to collaborate and exchange ideas on this project. Welcome to join the Group to share your thoughts, ask questions, or contribute your ideas! 🔥 Join our Discord and WeChat Group Now!
• [01/05/2026] SimpleMem paper was released on arXiv!

• 🌟 Overview
• 🎯 Key Contributions
• 🚀 Performance Highlights
• 📦 Installation
• ⚡ Quick Start
• 🔌 MCP Server
• 📊 Evaluation
• 📁 File Structure
• 📝 Citation
• 📄 License
• 🙏 Acknowledgments

SimpleMem achieves superior F1 score (43.24%) with minimal token cost (~550), occupying the ideal top-left position.

SimpleMem is an efficient memory framework based on semantic lossless compression that addresses the fundamental challenge of efficient long-term memory for LLM agents. Unlike existing systems that either passively accumulate redundant context or rely on expensive iterative reasoning loops, SimpleMem maximizes information density and token utilization through a three-stage pipeline:

The SimpleMem Architecture: (1) Semantic Structured Compression filters low-utility dialogue and converts informative windows into compact, context-independent memory units. (2) Online Semantic Synthesis consolidates related fragments during writing, maintaining a compact and coherent memory topology. (3) Intent-Aware Retrieval Planning infers search intent to adapt retrieval scope and query forms, enabling parallel multi-view retrieval and token-efficient context construction.

💡 Key Advantages:

• 🏆 Highest F1 Score: 43.24% (+26.4% vs. Mem0, +75.6% vs. LightMem)
• ⚡ Fastest Retrieval: 388.3s (32.7% faster than LightMem, 51.3% faster than Mem0)
• 🚀 Fastest End-to-End: 480.9s total processing time (12.5× faster than A-Mem)

SimpleMem applies an implicit semantic density gating mechanism integrated into the LLM generation process to filter redundant interaction content. The system reformulates raw dialogue streams into compact memory units — self-contained facts with resolved coreferences and absolute timestamps. Each unit is indexed through three complementary representations for flexible retrieval:

✨ Example Transformation:

- Input:  "He'll meet Bob tomorrow at 2pm"  [❌ relative, ambiguous]
+ Output: "Alice will meet Bob at Starbucks on 2025-11-16T14:00:00"  [✅ absolute, atomic]

Unlike traditional systems that rely on asynchronous background maintenance, SimpleMem performs synthesis on-the-fly during the write phase. Related memory units are synthesized into higher-level abstract representations within the current session scope, allowing repetitive or structurally similar experiences to be denoised and compressed immediately.

✨ Example Synthesis:

- Fragment 1: "User wants coffee"
- Fragment 2: "User prefers oat milk"
- Fragment 3: "User likes it hot"
+ Consolidated: "User prefers hot coffee with oat milk"

This proactive synthesis ensures the memory topology remains compact and free of redundant fragmentation.

Instead of fixed-depth retrieval, SimpleMem leverages the reasoning capabilities of the LLM to generate a comprehensive retrieval plan. Given a query, the planning module infers latent search intent to dynamically determine retrieval scope and depth:

$${ q_{\text{sem}}, q_{\text{lex}}, q_{\text{sym}}, d } \sim \mathcal{P}(q, H)$$

The system then executes parallel multi-view retrieval across semantic, lexical, and symbolic indexes, and merges results through ID-based deduplication:

📈 Result: 43.24% F1 score with 30× fewer tokens than full-context methods.

• Ensure you are using Python 3.10 in your active environment, not just installed globally.
• An OpenAI-compatible API key must be configured before running any memory construction or retrieval, otherwise initialization may fail.
• When using non-OpenAI providers (e.g., Qwen or Azure OpenAI), verify both the model name and OPENAI_BASE_URL in config.py.
• For large dialogue datasets, enabling parallel processing can significantly reduce memory construction time.

• 🐍 Python 3.10
• 🔑 OpenAI-compatible API (OpenAI, Qwen, Azure OpenAI, etc.)

# 📥 Clone repository
git clone https://github.com/aiming-lab/SimpleMem.git
cd SimpleMem

# 📦 Install dependencies
pip install -r requirements.txt

# ⚙️ Configure API settings
cp config.py.example config.py
# Edit config.py with your API key and preferences

# config.py
OPENAI_API_KEY = "your-api-key"
OPENAI_BASE_URL = None  # or custom endpoint for Qwen/Azure

LLM_MODEL = "gpt-4.1-mini"
EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-0.6B"  # State-of-the-art retrieval

At a high level, SimpleMem works as a long-term memory system for LLM-based agents. The workflow consists of three simple steps:

1. Store information – Dialogues or facts are processed and converted into structured, atomic memories.
2. Index memory – Stored memories are organized using semantic embeddings and structured metadata.
3. Retrieve relevant memory – When a query is made, SimpleMem retrieves the most relevant stored information based on meaning rather than keywords.

This design allows LLM agents to maintain context, recall past information efficiently, and avoid repeatedly processing redundant history.

If you encounter issues while setting up or running SimpleMem for the first time, check the following common cases:

• Ensure your API key is correctly set in config.py
• For OpenAI-compatible providers (Qwen, Azure, etc.), verify that OPENAI_BASE_URL is configured correctly
• Restart your Python environment after updating the key

• SimpleMem requires Python 3.10
• Check your version using:

  python --version
  

from main import SimpleMemSystem

# 🚀 Initialize system
system = SimpleMemSystem(clear_db=True)

# 💬 Add dialogues (Stage 1: Semantic Structured Compression)
system.add_dialogue("Alice", "Bob, let's meet at Starbucks tomorrow at 2pm", "2025-11-15T14:30:00")
system.add_dialogue("Bob", "Sure, I'll bring the market analysis report", "2025-11-15T14:31:00")

# ✅ Finalize atomic encoding
system.finalize()

# 🔎 Query with intent-aware retrieval (Stage 3: Intent-Aware Retrieval Planning)
answer = system.ask("When and where will Alice and Bob meet?")
print(answer)
# Output: "16 November 2025 at 2:00 PM at Starbucks"

For large-scale dialogue processing, enable parallel mode:

system = SimpleMemSystem(
    clear_db=True,
    enable_parallel_processing=True,  # ⚡ Parallel memory building
    max_parallel_workers=8,
    enable_parallel_retrieval=True,   # 🔍 Parallel query execution
    max_retrieval_workers=4
)

💡 Pro Tip: Parallel processing significantly reduces latency for batch operations!

SimpleMem is available as a cloud-hosted memory service via the Model Context Protocol (MCP), enabling seamless integration with AI assistants like Claude Desktop, Cursor, and other MCP-compatible clients.

🌐 Cloud Service: mcp.simplemem.cloud

{
  "mcpServers": {
    "simplemem": {
      "url": "https://mcp.simplemem.cloud/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      }
    }
  }
}

📖 For detailed setup instructions and self-hosting guide, see MCP Documentation

# 🎯 Full LoCoMo benchmark
python test_locomo10.py

# 📉 Subset evaluation (5 samples)
python test_locomo10.py --num-samples 5

# 💾 Custom output file
python test_locomo10.py --result-file my_results.json

Use the exact configurations in config.py:

• 🚀 High-capability: GPT-4.1-mini, Qwen3-Plus
• ⚙️ Efficient: Qwen2.5-1.5B, Qwen2.5-3B
• 🔍 Embedding: Qwen3-Embedding-0.6B (1024-d)

If you use SimpleMem in your research, please cite:

@article{simplemem2025,
  title={SimpleMem: Efficient Lifelong Memory for LLM Agents},
  author={Liu, Jiaqi and Su, Yaofeng and Xia, Peng and Zhou, Yiyang and Han, Siwei and  Zheng, Zeyu and Xie, Cihang and Ding, Mingyu and Yao, Huaxiu},
  journal={arXiv preprint arXiv:2601.02553},
  year={2025},
  url={https://github.com/aiming-lab/SimpleMem}
}

This project is licensed under the MIT License - see the LICENSE file for details.

We would like to thank the following projects and teams:

• 🔍 Embedding Model: Qwen3-Embedding - State-of-the-art retrieval performance
• 🗄️ Vector Database: LanceDB - High-performance columnar storage
• 📊 Benchmark: LoCoMo - Long-context memory evaluation framework