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

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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 needs", while task memory helps agents "perform better".

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📰 Latest Updates

• [2025-09] 🎉 ReMe v0.1 officially released, integrating task memory and personal memory. If you want to use the original memoryscope project, you can find it in MemoryScope.
• [2025-09] 🧪 We validated the effectiveness of task memory extraction and reuse in agents in appworld, bfcl(v3), and frozenlake environments. For more information, check appworld exp, bfcl exp, and frozenlake exp.
• [2025-08] 🚀 MCP protocol support is now available -> MCP Quick Start.
• [2025-06] 🚀 Multiple backend vector storage support (Elasticsearch & ChromaDB) -> Vector DB quick start.
• [2024-09] 🧠 MemoryScope v0.1.x released, personalized and time-aware memory storage and usage.

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✨ Architecture Design

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

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🛠️ Installation

Install from PyPI (Recommended)

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:

# Required: LLM API Configuration
FLOW_LLM_API_KEY=sk-xxxx
FLOW_LLM_BASE_URL=https://xxxx/v1

# Required: Embedding Model Configuration  
FLOW_EMBEDDING_API_KEY=sk-xxxx
FLOW_EMBEDDING_BASE_URL=https://xxxx/v1

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🚀 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));

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📦 Ready-to-Use Libraries

ReMe provides pre-built memory libraries that agents can immediately use with verified best practices:

Available Libraries

• appworld.jsonl: Memory library for Appworld agent interactions, covering complex task planning and execution patterns
• bfcl_v3.jsonl: Working memory library for BFCL tool calls

Quick Usage

# Load pre-built memories
response = requests.post("http://localhost:8002/vector_store", json={
    "workspace_id": "appworld",
    "action": "load",
    "path": "./library/"
})

# Query relevant memories
response = requests.post("http://localhost:8002/retrieve_task_memory", json={
    "workspace_id": "appworld",
    "query": "How to navigate to settings and update user profile?",
    "top_k": 1
})

🧪 Experiments

🌍 Appworld Experiment

We tested ReMe on Appworld using qwen3-8b:

Method	pass@1	pass@2	pass@4
without ReMe	0.083	0.140	0.228
with ReMe	0.109 (+2.6%)	0.175 (+3.5%)	0.281 (+5.3%)

Pass@K measures the probability that at least one of the K generated samples successfully completes the task ( score=1).
The current experiment uses an internal AppWorld environment, which may have slight differences.

You can find more details on reproducing the experiment in quickstart.md.

🧊 Frozenlake Experiment

without ReMe	with ReMe

We tested on 100 random frozenlake maps using qwen3-8b:

Method	pass rate
without ReMe	0.66
with ReMe	0.72 (+6.0%)

You can find more details on reproducing the experiment in quickstart.md.

🔧 BFCL-V3 Experiment

We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using qwen3-8b:

Method	pass@1	pass@2	pass@4
without ReMe	0.2472	0.2733	0.2922
with ReMe	0.3061 (+5.89%)	0.3500 (+7.67%)	0.3888 (+9.66%)

📚 Resources

• Quick Start: Get started quickly with practical examples
• Vector Storage Setup: Configure local/vector databases and usage
• MCP Guide: Create MCP services
• Link Description: Operators used in personal memory and task memory and their meanings can be found in personal memory and task memory respectively. You can modify the config to customize the links
• Example Collection: Real use cases and best practices

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🤝 Contribution

We believe the best memory systems come from collective wisdom. Contributions welcome 👉Guide:

Code Contributions

• New operation and tool development
• Backend implementation and optimization
• API enhancements and new endpoints

Documentation Improvements

• Usage examples and tutorials
• Best practice guides

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📄 Citation

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

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⚖️ License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

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