SimpleMem智能记忆系统

🧬 Advanced: Optimize Retrieval Config

Tune retrieval hyperparameters offline on your own dev set, then deploy the resulting Config for inference. This is a thin wrapper around EvolveMem's self-evolution loop:

```python import simplemem from simplemem import SimpleMem, load_config

⚙️ 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 ```

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Custom configuration

1. Copy the environment template and edit it:

   cp .env.example .env
   # Edit .env: set JWT_SECRET_KEY, ENCRYPTION_KEY, LLM_PROVIDER, model URLs, etc.
   

   docker compose --env-file .env up -d
   

Quick Configuration

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

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

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🧠 Understanding the Basic Workflow

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.