| Name | reme-ai JSON |
| Version |
0.1.8
JSON |
| download |
| home_page | None |
| Summary | Remember me |
| upload_time | 2025-09-08 08:33:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | Apache License
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|
| keywords |
llm
memory
experience
memoryscope
ai
mcp
http
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English | [**δΈζ**](./README_ZH.md)
<p align="center">
<img src="docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
</p>
<p align="center">
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-v0.1-blue?logo=pypi" alt="PyPI Version"></a>
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
<a href="https://github.com/modelscope/ReMe"><img src="https://img.shields.io/github/stars/modelscope/ReMe?style=social" alt="GitHub Stars"></a>
</p>
<p align="center">
<strong>ReMe (formerly MemoryScope): Memory Management Framework for Agents</strong><br>
<em>Remember Me, Refine Me.</em>
</p>
---
ReMe provides AI agents with a unified memory systemβenabling 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**".
---
## π° Latest Updates
- **[2025-09]** π 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](https://github.com/modelscope/Reme/tree/memoryscope_branch).
- **[2025-09]** π§ͺ 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](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md),
and [frozenlake exp](./cookbook/frozenlake/quickstart.md).
- **[2025-08]** π MCP protocol support is now available -> [MCP Quick Start](docs/mcp_quick_start.md).
- **[2025-06]** π Multiple backend vector storage support (Elasticsearch &
ChromaDB) -> [Vector DB quick start](docs/vector_store_api_guide.md).
- **[2024-09]** π§ [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 released,
personalized and time-aware memory storage and usage.
---
## β¨ Architecture Design
<p align="center">
<img src="docs/figure/reme_structure.jpg" alt="ReMe Logo" width="100%">
</p>
ReMe integrates two complementary memory capabilities:
#### π§ **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](docs/task_memory/task_memory.md)
#### π€ **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](docs/personal_memory/personal_memory.md)
---
## π οΈ Installation
### Install from PyPI (Recommended)
```bash
pip install reme-ai
```
### Install from Source
```bash
git clone https://github.com/modelscope/ReMe.git
cd ReMe
pip install .
```
### Environment Configuration
Copy `example.env` to .env and modify the corresponding parameters:
```bash
# 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
```
---
## π Quick Start
### HTTP Service Startup
```bash
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
```bash
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
```python
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
})
```
<details>
<summary>curl version</summary>
```bash
# 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
}'
```
</details>
<details>
<summary>Node.js version</summary>
```javascript
// 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));
```
</details>
#### Personal Memory Management
```python
# 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
})
```
<details>
<summary>curl version</summary>
```bash
# 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
}'
```
</details>
<details>
<summary>Node.js version</summary>
```javascript
// 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));
```
</details>
---
## π¦ 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
```python
# Load pre-built memories
response = requests.post("http://localhost:8002/vector_store", json={
"workspace_id": "appworld",
"action": "load",
"path": "./docs/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
})
```
## π§ͺ Experiments
### π [Appworld Experiment](./cookbook/appworld/quickstart.md)
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](cookbook/appworld/quickstart.md).
### π§ [Frozenlake Experiment](./cookbook/frozenlake/quickstart.md)
| without ReMe | with ReMe |
|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|
| <p align="center"><img src="docs/figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p> | <p align="center"><img src="docs/figure/frozenlake_success.gif" alt="GIF 2" width="30%"></p> |
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](cookbook/frozenlake/quickstart.md).
### π§ [BFCL-V3 Experiment](./cookbook/bfcl/quickstart.md)
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%)** |
## π Resources
- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples
- **[Vector Storage Setup](docs/vector_store_api_guide.md)**: Configure local/vector databases and usage
- **[MCP Guide](docs/mcp_quick_start.md)**: Create MCP services
- **[personal memory](docs/personal_memory)** & **[task memory](docs/task_memory)** : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.
- **[Example Collection](./cookbook)**: Real use cases and best practices
---
## π€ Contribution
We believe the best memory systems come from collective wisdom. Contributions welcome π[Guide](docs/contribution.md):
### 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
---
## π Citation
```bibtex
@software{ReMe2025,
title = {ReMe: Memory Management Framework for Agents},
author = {Li Yu, Jiaji Deng, Zouying Cao},
url = {https://github.com/modelscope/ReMe},
year = {2025}
}
```
---
## βοΈ License
This project is licensed under the Apache License 2.0 - see the [LICENSE](./LICENSE) file for details.
---
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"description": "English | [**\u4e2d\u6587**](./README_ZH.md)\n\n<p align=\"center\">\n <img src=\"docs/figure/reme_logo.png\" alt=\"ReMe Logo\" width=\"50%\">\n</p>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/reme-ai/\"><img src=\"https://img.shields.io/badge/python-3.12+-blue\" alt=\"Python Version\"></a>\n <a href=\"https://pypi.org/project/reme-ai/\"><img src=\"https://img.shields.io/badge/pypi-v0.1-blue?logo=pypi\" alt=\"PyPI Version\"></a>\n <a href=\"./LICENSE\"><img src=\"https://img.shields.io/badge/license-Apache--2.0-black\" alt=\"License\"></a>\n <a href=\"https://github.com/modelscope/ReMe\"><img src=\"https://img.shields.io/github/stars/modelscope/ReMe?style=social\" alt=\"GitHub Stars\"></a>\n</p>\n\n<p align=\"center\">\n <strong>ReMe (formerly MemoryScope): Memory Management Framework for Agents</strong><br>\n <em>Remember Me, Refine Me.</em>\n</p>\n\n---\nReMe provides AI agents with a unified memory system\u2014enabling the ability to extract, reuse, and share memories across\nusers, tasks, and agents.\n\n```\nPersonal Memory + Task Memory = Agent Memory\n```\n\nPersonal memory helps \"**understand user preferences**\", while task memory helps agents \"**perform better**\".\n\n---\n\n## \ud83d\udcf0 Latest Updates\n\n- **[2025-09]** \ud83c\udf89 ReMe v0.1\n officially released, integrating task memory and personal memory. If you want to use the original memoryscope project,\n you can find it in [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch).\n- **[2025-09]** \ud83e\uddea We validated the effectiveness of task memory extraction and reuse in agents in appworld, bfcl(v3),\n and frozenlake environments. For more information,\n check [appworld exp](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md),\n and [frozenlake exp](./cookbook/frozenlake/quickstart.md).\n- **[2025-08]** \ud83d\ude80 MCP protocol support is now available -> [MCP Quick Start](docs/mcp_quick_start.md).\n- **[2025-06]** \ud83d\ude80 Multiple backend vector storage support (Elasticsearch &\n ChromaDB) -> [Vector DB quick start](docs/vector_store_api_guide.md).\n- **[2024-09]** \ud83e\udde0 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 released,\n personalized and time-aware memory storage and usage.\n\n---\n\n## \u2728 Architecture Design\n\n<p align=\"center\">\n <img src=\"docs/figure/reme_structure.jpg\" alt=\"ReMe Logo\" width=\"100%\">\n</p>\n\nReMe integrates two complementary memory capabilities:\n\n#### \ud83e\udde0 **Task Memory/Experience**\n\nProcedural knowledge reused across agents\n\n- **Success Pattern Recognition**: Identify effective strategies and understand their underlying principles\n- **Failure Analysis Learning**: Learn from mistakes and avoid repeating the same issues\n- **Comparative Patterns**: Different sampling trajectories provide more valuable memories through comparison\n- **Validation Patterns**: Confirm the effectiveness of extracted memories through validation modules\n\nLearn more about how to use task memory from [task memory](docs/task_memory/task_memory.md)\n\n#### \ud83d\udc64 **Personal Memory**\n\nContextualized memory for specific users\n\n- **Individual Preferences**: User habits, preferences, and interaction styles\n- **Contextual Adaptation**: Intelligent memory management based on time and context\n- **Progressive Learning**: Gradually build deep understanding through long-term interaction\n- **Time Awareness**: Time sensitivity in both retrieval and integration\n\nLearn more about how to use personal memory from [personal memory](docs/personal_memory/personal_memory.md)\n\n---\n\n## \ud83d\udee0\ufe0f Installation\n\n### Install from PyPI (Recommended)\n\n```bash\npip install reme-ai\n```\n\n### Install from Source\n\n```bash\ngit clone https://github.com/modelscope/ReMe.git\ncd ReMe\npip install .\n```\n\n### Environment Configuration\n\nCopy `example.env` to .env and modify the corresponding parameters:\n\n```bash\n# Required: LLM API Configuration\nFLOW_LLM_API_KEY=sk-xxxx\nFLOW_LLM_BASE_URL=https://xxxx/v1\n\n# Required: Embedding Model Configuration \nFLOW_EMBEDDING_API_KEY=sk-xxxx\nFLOW_EMBEDDING_BASE_URL=https://xxxx/v1\n```\n\n---\n\n## \ud83d\ude80 Quick Start\n\n### HTTP Service Startup\n\n```bash\nreme \\\n backend=http \\\n http.port=8002 \\\n llm.default.model_name=qwen3-30b-a3b-thinking-2507 \\\n embedding_model.default.model_name=text-embedding-v4 \\\n vector_store.default.backend=local\n```\n\n### MCP Server Support\n\n```bash\nreme \\\n backend=mcp \\\n mcp.transport=stdio \\\n llm.default.model_name=qwen3-30b-a3b-thinking-2507 \\\n embedding_model.default.model_name=text-embedding-v4 \\\n vector_store.default.backend=local\n```\n\n### Core API Usage\n\n#### Task Memory Management\n\n```python\nimport requests\n\n# Experience Summarizer: Learn from execution trajectories\nresponse = requests.post(\"http://localhost:8002/summary_task_memory\", json={\n \"workspace_id\": \"task_workspace\",\n \"trajectories\": [\n {\"messages\": [{\"role\": \"user\", \"content\": \"Help me create a project plan\"}], \"score\": 1.0}\n ]\n})\n\n# Retriever: Get relevant memories\nresponse = requests.post(\"http://localhost:8002/retrieve_task_memory\", json={\n \"workspace_id\": \"task_workspace\",\n \"query\": \"How to efficiently manage project progress?\",\n \"top_k\": 1\n})\n```\n\n<details>\n<summary>curl version</summary>\n\n```bash\n# Experience Summarizer: Learn from execution trajectories\ncurl -X POST http://localhost:8002/summary_task_memory \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"workspace_id\": \"task_workspace\",\n \"trajectories\": [\n {\"messages\": [{\"role\": \"user\", \"content\": \"Help me create a project plan\"}], \"score\": 1.0}\n ]\n }'\n\n# Retriever: Get relevant memories\ncurl -X POST http://localhost:8002/retrieve_task_memory \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"workspace_id\": \"task_workspace\",\n \"query\": \"How to efficiently manage project progress?\",\n \"top_k\": 1\n }'\n```\n\n</details>\n\n<details>\n<summary>Node.js version</summary>\n\n```javascript\n// Experience Summarizer: Learn from execution trajectories\nfetch(\"http://localhost:8002/summary_task_memory\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n workspace_id: \"task_workspace\",\n trajectories: [\n {messages: [{role: \"user\", content: \"Help me create a project plan\"}], score: 1.0}\n ]\n })\n})\n.then(response => response.json())\n.then(data => console.log(data));\n\n// Retriever: Get relevant memories\nfetch(\"http://localhost:8002/retrieve_task_memory\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n workspace_id: \"task_workspace\",\n query: \"How to efficiently manage project progress?\",\n top_k: 1\n })\n})\n.then(response => response.json())\n.then(data => console.log(data));\n```\n\n</details>\n\n#### Personal Memory Management\n\n```python\n# Memory Integration: Learn from user interactions\nresponse = requests.post(\"http://localhost:8002/summary_personal_memory\", json={\n \"workspace_id\": \"task_workspace\",\n \"trajectories\": [\n {\"messages\":\n [\n {\"role\": \"user\", \"content\": \"I like to drink coffee while working in the morning\"},\n {\"role\": \"assistant\",\n \"content\": \"I understand, you prefer to start your workday with coffee to stay energized\"}\n ]\n }\n ]\n})\n\n# Memory Retrieval: Get personal memory fragments\nresponse = requests.post(\"http://localhost:8002/retrieve_personal_memory\", json={\n \"workspace_id\": \"task_workspace\",\n \"query\": \"What are the user's work habits?\",\n \"top_k\": 5\n})\n```\n\n<details>\n<summary>curl version</summary>\n\n```bash\n# Memory Integration: Learn from user interactions\ncurl -X POST http://localhost:8002/summary_personal_memory \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"workspace_id\": \"task_workspace\",\n \"trajectories\": [\n {\"messages\": [\n {\"role\": \"user\", \"content\": \"I like to drink coffee while working in the morning\"},\n {\"role\": \"assistant\", \"content\": \"I understand, you prefer to start your workday with coffee to stay energized\"}\n ]}\n ]\n }'\n\n# Memory Retrieval: Get personal memory fragments\ncurl -X POST http://localhost:8002/retrieve_personal_memory \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"workspace_id\": \"task_workspace\",\n \"query\": \"What are the user's work habits?\",\n \"top_k\": 5\n }'\n```\n\n</details>\n\n<details>\n<summary>Node.js version</summary>\n\n```javascript\n// Memory Integration: Learn from user interactions\nfetch(\"http://localhost:8002/summary_personal_memory\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n workspace_id: \"task_workspace\",\n trajectories: [\n {messages: [\n {role: \"user\", content: \"I like to drink coffee while working in the morning\"},\n {role: \"assistant\", content: \"I understand, you prefer to start your workday with coffee to stay energized\"}\n ]}\n ]\n })\n})\n.then(response => response.json())\n.then(data => console.log(data));\n\n// Memory Retrieval: Get personal memory fragments\nfetch(\"http://localhost:8002/retrieve_personal_memory\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n workspace_id: \"task_workspace\",\n query: \"What are the user's work habits?\",\n top_k: 5\n })\n})\n.then(response => response.json())\n.then(data => console.log(data));\n```\n\n</details>\n\n---\n\n## \ud83d\udce6 Ready-to-Use Libraries\n\nReMe provides pre-built memory libraries that agents can immediately use with verified best practices:\n\n### Available Libraries\n\n- **`appworld.jsonl`**: Memory library for Appworld agent interactions, covering complex task planning and execution\n patterns\n- **`bfcl_v3.jsonl`**: Working memory library for BFCL tool calls\n\n### Quick Usage\n\n```python\n# Load pre-built memories\nresponse = requests.post(\"http://localhost:8002/vector_store\", json={\n \"workspace_id\": \"appworld\",\n \"action\": \"load\",\n \"path\": \"./docs/library/\"\n})\n\n# Query relevant memories\nresponse = requests.post(\"http://localhost:8002/retrieve_task_memory\", json={\n \"workspace_id\": \"appworld\",\n \"query\": \"How to navigate to settings and update user profile?\",\n \"top_k\": 1\n})\n```\n\n## \ud83e\uddea Experiments\n\n### \ud83c\udf0d [Appworld Experiment](./cookbook/appworld/quickstart.md)\n\nWe tested ReMe on Appworld using qwen3-8b:\n\n| Method | pass@1 | pass@2 | pass@4 |\n|--------------|-------------------|-------------------|-------------------|\n| without ReMe | 0.083 | 0.140 | 0.228 |\n| with ReMe | 0.109 **(+2.6%)** | 0.175 **(+3.5%)** | 0.281 **(+5.3%)** |\n\nPass@K measures the probability that at least one of the K generated samples successfully completes the task (\nscore=1). \nThe current experiment uses an internal AppWorld environment, which may have slight differences.\n\nYou can find more details on reproducing the experiment in [quickstart.md](cookbook/appworld/quickstart.md).\n\n### \ud83e\uddca [Frozenlake Experiment](./cookbook/frozenlake/quickstart.md)\n\n| without ReMe | with ReMe |\n|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|\n| <p align=\"center\"><img src=\"docs/figure/frozenlake_failure.gif\" alt=\"GIF 1\" width=\"30%\"></p> | <p align=\"center\"><img src=\"docs/figure/frozenlake_success.gif\" alt=\"GIF 2\" width=\"30%\"></p> |\n\nWe tested on 100 random frozenlake maps using qwen3-8b:\n\n| Method | pass rate |\n|--------------|------------------|\n| without ReMe | 0.66 |\n| with ReMe | 0.72 **(+6.0%)** |\n\nYou can find more details on reproducing the experiment in [quickstart.md](cookbook/frozenlake/quickstart.md).\n\n### \ud83d\udd27 [BFCL-V3 Experiment](./cookbook/bfcl/quickstart.md)\n\nWe tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using qwen3-8b:\n\n| Method | pass@1 | pass@2 | pass@4 |\n|--------------|---------------------|---------------------|---------------------|\n| without ReMe | 0.2472 | 0.2733 | 0.2922 |\n| with ReMe | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |\n\n## \ud83d\udcda Resources\n\n- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples\n- **[Vector Storage Setup](docs/vector_store_api_guide.md)**: Configure local/vector databases and usage\n- **[MCP Guide](docs/mcp_quick_start.md)**: Create MCP services\n- **[personal memory](docs/personal_memory)** & **[task memory](docs/task_memory)** : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.\n- **[Example Collection](./cookbook)**: Real use cases and best practices\n\n---\n\n## \ud83e\udd1d Contribution\n\nWe believe the best memory systems come from collective wisdom. Contributions welcome \ud83d\udc49[Guide](docs/contribution.md):\n\n### Code Contributions\n\n- New operation and tool development\n- Backend implementation and optimization\n- API enhancements and new endpoints\n\n### Documentation Improvements\n\n- Usage examples and tutorials\n- Best practice guides\n\n\n---\n\n## \ud83d\udcc4 Citation\n\n```bibtex\n@software{ReMe2025,\n title = {ReMe: Memory Management Framework for Agents},\n author = {Li Yu, Jiaji Deng, Zouying Cao},\n url = {https://github.com/modelscope/ReMe},\n year = {2025}\n}\n```\n\n---\n\n## \u2696\ufe0f License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](./LICENSE) file for details.\n\n---\n",
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