Simplemem

SimpleMem

Efficient lifelong memory for LLM agents via semantic lossless compression.

SimpleMem converts unstructured dialogue into compact, atomic memory units with coreference resolution and temporal anchoring. It achieves 43.24% F1 on the LoCoMo benchmark with 30x fewer tokens than full-context methods.

Integration Paths

Choose the path that fits your use case:

MCP Server Setup (Recommended)

Cloud Service

The fastest path. Uses the hosted service at mcp.simplemem.cloud.

1. Visit https://mcp.simplemem.cloud
2. Enter your OpenRouter API key to get an auth token
3. Add to your MCP client config:

Claude Desktop / Claude Code (~/.claude.json):

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

Cursor (.cursor/mcp.json):

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

Self-Hosted (Docker)

For privacy-sensitive deployments or custom infrastructure:

git clone https://github.com/aiming-lab/SimpleMem.git
cd SimpleMem/MCP

# Configure environment
export JWT_SECRET_KEY="your-secure-random-secret"
export ENCRYPTION_KEY="your-32-byte-encryption-key!!"

# Run with Docker
docker compose up -d

The server exposes:

• Web UI: http://localhost:8000/
• REST API: http://localhost:8000/api/
• MCP endpoint: http://localhost:8000/mcp

See references/mcp-setup.md for full self-hosting guide.

MCP Tools Available

Python API

Install via pip or uv:

pip install simplemem
# or
uv add simplemem

Basic Usage

from simplemem import SimpleMemSystem

# Initialize (uses config.py for API settings)
system = SimpleMemSystem(clear_db=True)

# Stage 1: Add dialogues (semantic structured compression)
system.add_dialogue("Alice", "Let's meet at Starbucks tomorrow at 2pm", "2025-11-15T14:30:00")
system.add_dialogue("Bob", "I'll bring the market analysis report", "2025-11-15T14:31:00")

# Finalize atomic encoding
system.finalize()

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

Parallel Processing

For large dialogue datasets:

system = SimpleMemSystem(
    clear_db=True,
    enable_parallel_processing=True,
    max_parallel_workers=8,
    enable_parallel_retrieval=True,
    max_retrieval_workers=4
)

Memory Deletion

Delete entries by ID:

# Delete by entry_id
system.vector_store.delete_by_id(entry_id="uuid-here")

# Delete by ref_id (application-level reference)
system.vector_store.delete_by_ref_id(ref_id="my-ref")

Configuration

SimpleMem supports any OpenAI-compatible API provider:

# config.py
OPENAI_API_KEY = "your-api-key"         # OpenAI, OpenRouter, or compatible
OPENAI_BASE_URL = None                   # Set for non-OpenAI providers

# Models
LLM_MODEL = "gpt-4.1-mini"              # Or any OpenAI-compatible model
EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-0.6B"  # State-of-the-art retrieval

# Via OpenRouter (multi-provider gateway)
OPENROUTER_API_KEY = "sk-or-..."
LLM_MODEL = "openai/gpt-4.1-mini"       # OpenRouter model path
EMBEDDING_MODEL = "qwen/qwen3-embedding-8b"
EMBEDDING_DIMENSION = 4096

Provider options:

• OpenAI direct - Set OPENAI_API_KEY
• OpenRouter - Set OPENROUTER_API_KEY, prefix model names with provider
• LiteLLM - Multi-provider abstraction, configure via LITELLM_* settings
• Azure OpenAI - Set OPENAI_BASE_URL to your Azure endpoint
• Qwen / local models - Set OPENAI_BASE_URL to your endpoint

See references/api-reference.md for full configuration.

Cross-Session Memory (SimpleMem-Cross)

SimpleMem-Cross enables persistent memory across conversations. Agents recall context, decisions, and learnings from previous sessions automatically.

Performance: 64% improvement over Claude-Mem on LoCoMo benchmark (score 48 vs 29.3).

Quick Example

from cross.orchestrator import create_orchestrator

async def main():
    orch = create_orchestrator(project="my-project")

    # Start session - previous context injected automatically
    result = await orch.start_session(
        content_session_id="session-001",
        user_prompt="Continue building the REST API",
    )
    print(result["context"])  # Relevant context from past sessions

    # Record events during the session
    await orch.record_message(result["memory_session_id"], "User asked about JWT")
    await orch.record_tool_use(
        result["memory_session_id"],
        tool_name="read_file",
        tool_input="auth/jwt.py",
        tool_output="class JWTHandler: ...",
    )

    # Finalize - extracts observations, generates summary, stores memories
    report = await orch.stop_session(result["memory_session_id"])
    print(f"Stored {report.entries_stored} memory entries")

    await orch.end_session(result["memory_session_id"])
    orch.close()

Key Features

See references/cross-session.md for full API and architecture.

Three-Stage Pipeline

SimpleMem's architecture is based on semantic lossless compression:

Stage 1: Semantic Structured Compression

Raw dialogue is converted to atomic, self-contained memory units:

• Coreference resolution: "He" becomes "Bob"
• Temporal anchoring: "tomorrow" becomes "2025-11-16T14:00:00"
• Density gating: Filters low-information content

Input:  "He'll meet Bob tomorrow at 2pm"
Output: "Alice will meet Bob at Starbucks on 2025-11-16T14:00:00"

Stage 2: Online Semantic Synthesis

Related fragments are consolidated during writes:

• Eliminates redundant information
• Merges related facts into unified representations
• Maintains compact memory topology

Stage 3: Intent-Aware Retrieval Planning

Queries trigger parallel multi-view retrieval:

• Semantic layer: Dense vector similarity (1024-d embeddings)
• Lexical layer: BM25 keyword matching
• Symbolic layer: Metadata filtering (persons, locations, timestamps, entities)
• Dynamic retrieval depth based on query complexity

See references/architecture.md for detailed architecture.

Data Model

Each memory entry contains:

Troubleshooting

Embedding dimension mismatch:

RuntimeError: lance error: Invalid: ListType can only be casted to FixedSizeListType

The EMBEDDING_DIMENSION in config doesn't match the model's output. Update the value and clear the database: rm -rf data/lancedb/*

API key not detected:

• Verify key is set in config.py (not config.py.example)
• For OpenRouter keys, ensure they start with sk-or-
• Restart Python after updating the key

Model not found:

• Use full provider path for OpenRouter: openai/gpt-4.1-mini not gpt-4.1-mini
• Check available models at openrouter.ai/models