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docs(readme): update README with improved structure and content
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README.md
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README.md
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@ -6,23 +6,19 @@
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.10+-blue" alt="Python Version"></a>
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<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/pypi-0.2.0.0-blue?logo=pypi" alt="PyPI Version"></a>
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
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<a href="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
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<a href="./README_ZH.md"><img src="https://img.shields.io/badge/简体中文-点击查看-orange" alt="简体中文"></a>
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<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/stars/agentscope-ai/ReMe?style=social" alt="GitHub Stars"></a>
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</p>
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<p align="center">
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<strong>Memory Management Kit for Agents, Remember Me, Refine Me.</strong><br>
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<em><sub>If you find it useful, please give us a ⭐ Star. Your support drives our continuous improvement.</sub></em>
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</p>
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<p align="center">
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English | <a href="./README_zh.md">简体中文</a>
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<em><sub>If you find it useful, please give us a ⭐ Star.</sub></em>
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</p>
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---
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ReMe provides AI agents with a unified memory system—enabling the ability to extract, reuse, and share memories across
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users, tasks, and agents.
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ReMe is a **modular memory management kit** that provides AI agents with unified memory capabilities—enabling the ability to extract, reuse, and share memories across users, tasks, and agents.
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Agent memory can be viewed as:
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```text
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@ -30,12 +26,16 @@ Agent Memory = Long-Term Memory + Short-Term Memory
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= (Personal + Task + Tool) Memory + (Working Memory)
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```
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Personal memory helps "**understand user preferences**", task memory helps agents "**perform better**", and tool memory enables "**smarter tool usage**". Working memory provides **short-term contextual memory** by keeping recent reasoning and tool results compact and accessible without overflowing the model's context window.
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- **Personal Memory**: Understand user preferences and adapt to context
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- **Task Memory**: Learn from experience and perform better on similar tasks
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- **Tool Memory**: Optimize tool selection and parameter usage based on historical performance
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- **Working Memory**: Manage short-term context for long-running agents without context overflow
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---
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## 📰 Latest Updates
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- **[2025-12]** 📄 Our procedural (task) memory paper has been released on arXiv "Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution" ([Paper](https://arxiv.org/abs/2512.10696))
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- **[2025-11]** 🧠 React-agent with working-memory demo ([Intro](docs/work_memory/message_offload.md)) with ([Quick Start](docs/cookbook/working/quick_start.md)) and ([Code](cookbook/working_memory/work_memory_demo.py))
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- **[2025-10]** 🚀 Direct Python import support: use `from reme_ai import ReMeApp` without HTTP/MCP service
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- **[2025-10]** 🔧 Tool Memory: data-driven tool selection and parameter optimization ([Guide](docs/tool_memory/tool_memory.md))
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## ✨ Architecture Design
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<p align="center">
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<img src="docs/_static/figure/reme_usage.jpg" alt="ReMe Logo" width="100%">
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<img src="docs/_static/figure/reme_structure.jpg" alt="ReMe Architecture" width="80%">
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</p>
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ReMe integrates three complementary memory capabilities:
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ReMe provides a **modular memory management kit** with pluggable components that can be integrated into any agent framework. The system consists of:
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#### 🧠 **Task Memory/Experience**
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@ -122,7 +122,7 @@ pip install .
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### Environment Configuration
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Copy `example.env` to .env and modify the corresponding parameters:
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ReMe requires LLM and embedding model configurations. Copy `example.env` to `.env` and configure:
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```bash
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FLOW_LLM_API_KEY=sk-xxxx
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---
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## 📦 Ready-to-Use Memories
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## 📦 Pre-built Memory Library
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ReMe provides pre-built memories that agents can immediately use with verified best practices:
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ReMe provides a **memory library** with pre-extracted, production-ready memories that agents can load and use immediately:
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### Available Memories
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### Available Memory Packs
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- **`appworld.jsonl`**: Memory for Appworld agent interactions, covering complex task planning and execution
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patterns
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- **`bfcl_v3.jsonl`**: Working memory for BFCL tool calls
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| Memory Pack | Domain | Size | Description |
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|----------------------|----------------|---------------|-------------------------------------------------------------------------------------|
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| **`appworld.jsonl`** | Task Execution | ~100 memories | Complex task planning patterns, multi-step workflows, and error recovery strategies |
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| **`bfcl_v3.jsonl`** | Tool Usage | ~150 memories | Function calling patterns, parameter optimization, and tool selection strategies |
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### Quick Usage
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### Loading Pre-built Memories
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```python
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# Load pre-built memories
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@ -773,13 +774,26 @@ You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and
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## 📚 Resources
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- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples
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### Getting Started
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- **[Quick Start](./cookbook/simple_demo)**: Practical examples for immediate use
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- [Tool Memory Demo](cookbook/simple_demo/use_tool_memory_demo.py): Complete lifecycle demonstration of tool memory
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- [Tool Memory Benchmark](cookbook/tool_memory/run_reme_tool_bench.py): Evaluate tool memory effectiveness
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- **[Vector Storage Setup](docs/vector_store_api_guide.md)**: Configure local/vector databases and usage
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- **[MCP Guide](docs/mcp_quick_start.md)**: Create MCP services
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- **[Personal Memory](docs/personal_memory)**, **[Task Memory](docs/task_memory)** & **[Tool Memory](docs/tool_memory)**: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines.
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- **[Example Collection](./cookbook)**: Real use cases and best practices
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### Integration Guides
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- **[Direct Python Import](docs/cookbook/working/quick_start.md)**: Embed ReMe directly into your agent code
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- **[HTTP Service API](docs/vector_store_api_guide.md)**: RESTful API for multi-agent systems
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- **[MCP Protocol](docs/mcp_quick_start.md)**: Integration with Claude Desktop and MCP-compatible clients
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### Memory System Configuration
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- **[Personal Memory](docs/personal_memory)**: User preference learning and contextual adaptation
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- **[Task Memory](docs/task_memory)**: Procedural knowledge extraction and reuse
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- **[Tool Memory](docs/tool_memory)**: Data-driven tool selection and optimization
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- **[Working Memory](docs/work_memory/message_offload.md)**: Short-term context management for long-running agents
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### Advanced Topics
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- **[Operator Pipelines](reme_ai/config/default.yaml)**: Customize memory processing workflows by modifying operator chains
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- **[Vector Store Backends](docs/vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, or ChromaDB storage
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- **[Example Collection](./cookbook)**: Real-world use cases and best practices
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---
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@ -797,14 +811,17 @@ We believe the best memory systems come from collective wisdom. Contributions we
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### Code Contributions
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- New operation and tool development
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- Backend implementation and optimization
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- API enhancements and new endpoints
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- **New Operators**: Develop custom memory processing operators (retrieval, summarization, etc.)
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- **Backend Implementations**: Add support for new vector stores or LLM providers
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- **Memory Services**: Extend with new memory types or capabilities
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- **API Enhancements**: Improve existing endpoints or add new ones
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### Documentation Improvements
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- Usage examples and tutorials
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- Best practice guides
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- **Integration Examples**: Show how to integrate ReMe with different agent frameworks
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- **Operator Tutorials**: Document custom operator development
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- **Best Practice Guides**: Share effective memory management patterns
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- **Use Case Studies**: Demonstrate ReMe in real-world applications
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---
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```bibtex
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@software{AgentscopeReMe2025,
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title = {AgentscopeReMe: Memory Management Kit for Agents},
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author = {Li Yu, Jiaji Deng, Zouying Cao, Weikang Zhou},
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author = {Li Yu and Jiaji Deng and Zouying Cao and Weikang Zhou and Tiancheng Qin and Qingxu Fu and Sen Huang and Xianzhe Xu and Zhaoyang Liu and Boyin Liu},
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url = {https://reme.agentscope.io},
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year = {2025}
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}
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