Welcome to ReMe

Next

Memory Management Framework for Agents

ReMe Logo

Python Version PyPI Version License GitHub Stars

ReMe (formerly MemoryScope): Memory Management Framework for Agents
Remember Me, Refine Me.

ReMe provides AI agents with a unified memory system—enabling the ability to extract, reuse, and share memories across users, tasks, and agents.

Personal Memory + Task Memory = Agent Memory

Personal memory helps "understand user preferences", while task memory helps agents "perform better".

Architecture Design

ReMe Logo

ReMe integrates two complementary memory capabilities:

Task Memory/Experience

Procedural knowledge reused across agents

  • Success Pattern Recognition: Identify effective strategies and understand their underlying principles
  • Failure Analysis Learning: Learn from mistakes and avoid repeating the same issues
  • Comparative Patterns: Different sampling trajectories provide more valuable memories through comparison
  • Validation Patterns: Confirm the effectiveness of extracted memories through validation modules

Learn more about how to use task memory from task memory

Personal Memory

Contextualized memory for specific users

  • Individual Preferences: User habits, preferences, and interaction styles
  • Contextual Adaptation: Intelligent memory management based on time and context
  • Progressive Learning: Gradually build deep understanding through long-term interaction
  • Time Awareness: Time sensitivity in both retrieval and integration

Learn more about how to use personal memory from personal memory

Installation

pip install reme-ai

Install from Source

git clone https://github.com/modelscope/ReMe.git
cd ReMe
pip install .

Environment Configuration

Copy example.env to .env and modify the corresponding parameters:

FLOW_APP_NAME=ReMe
FLOW_LLM_API_KEY=sk-xxxx
FLOW_LLM_BASE_URL=https://xxxx/v1
FLOW_EMBEDDING_API_KEY=sk-xxxx
FLOW_EMBEDDING_BASE_URL=https://xxxx/v1

Quick Start

HTTP Service Startup

reme \
  backend=http \
  http.port=8002 \
  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local

MCP Server Support

reme \
  backend=mcp \
  mcp.transport=stdio \
  llm.default.model_name=qwen3-30b-a3b-thinking-2507 \
  embedding_model.default.model_name=text-embedding-v4 \
  vector_store.default.backend=local

Core API Usage

Task Memory Management

import requests

# Experience Summarizer: Learn from execution trajectories
response = requests.post("http://localhost:8002/summary_task_memory", json={
    "workspace_id": "task_workspace",
    "trajectories": [
        {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
    ]
})

# Retriever: Get relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
    "workspace_id": "task_workspace",
    "query": "How to efficiently manage project progress?",
    "top_k": 1
})
curl version 
# Experience Summarizer: Learn from execution trajectories
curl -X POST http://localhost:8002/summary_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [{"role": "user", "content": "Help me create a project plan"}], "score": 1.0}
    ]
  }'

# Retriever: Get relevant memories
curl -X POST http://localhost:8002/retrieve_task_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "How to efficiently manage project progress?",
    "top_k": 1
  }'
Node.js version 
// Experience Summarizer: Learn from execution trajectories
fetch("http://localhost:8002/summary_task_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    trajectories: [
      {messages: [{role: "user", content: "Help me create a project plan"}], score: 1.0}
    ]
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Retriever: Get relevant memories
fetch("http://localhost:8002/retrieve_task_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    query: "How to efficiently manage project progress?",
    top_k: 1
  })
})
.then(response => response.json())
.then(data => console.log(data));

Personal Memory Management

# Memory Integration: Learn from user interactions
response = requests.post("http://localhost:8002/summary_personal_memory", json={
    "workspace_id": "task_workspace",
    "trajectories": [
        {"messages":
            [
                {"role": "user", "content": "I like to drink coffee while working in the morning"},
                {"role": "assistant",
                 "content": "I understand, you prefer to start your workday with coffee to stay energized"}
            ]
        }
    ]
})

# Memory Retrieval: Get personal memory fragments
response = requests.post("http://localhost:8002/retrieve_personal_memory", json={
    "workspace_id": "task_workspace",
    "query": "What are the user's work habits?",
    "top_k": 5
})
curl version 
# Memory Integration: Learn from user interactions
curl -X POST http://localhost:8002/summary_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "trajectories": [
      {"messages": [
        {"role": "user", "content": "I like to drink coffee while working in the morning"},
        {"role": "assistant", "content": "I understand, you prefer to start your workday with coffee to stay energized"}
      ]}
    ]
  }'

# Memory Retrieval: Get personal memory fragments
curl -X POST http://localhost:8002/retrieve_personal_memory \
  -H "Content-Type: application/json" \
  -d '{
    "workspace_id": "task_workspace",
    "query": "What are the user's work habits?",
    "top_k": 5
  }'
Node.js version 
// Memory Integration: Learn from user interactions
fetch("http://localhost:8002/summary_personal_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    trajectories: [
      {messages: [
        {role: "user", content: "I like to drink coffee while working in the morning"},
        {role: "assistant", content: "I understand, you prefer to start your workday with coffee to stay energized"}
      ]}
    ]
  })
})
.then(response => response.json())
.then(data => console.log(data));

// Memory Retrieval: Get personal memory fragments
fetch("http://localhost:8002/retrieve_personal_memory", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    workspace_id: "task_workspace",
    query: "What are the user's work habits?",
    top_k: 5
  })
})
.then(response => response.json())
.then(data => console.log(data));

Resources

  • Personal memory & Task memory : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.
  • Example Collection: Real use cases and best practices
  • Library: Directly use existing task memory/experience for your tasks, and you can also contribute more task memory/experience to us.
  • Contribution: welcome to your contributions!
  • Vector Storage Setup: Configure local/vector databases and usage
  • MCP Guide: Create MCP services

Citation

@software{ReMe2025,
  title = {ReMe: Memory Management Framework for Agents},
  author = {Li Yu, Jiaji Deng, Zouying Cao},
  url = {https://github.com/modelscope/ReMe},
  year = {2025}
}
Library Home