From c872b92db0a36092c2a5fa27b5664aeaba24f16e Mon Sep 17 00:00:00 2001 From: "jinli.yl" Date: Thu, 18 Dec 2025 14:29:49 +0800 Subject: [PATCH] docs(readme): update README with improved structure and content --- README.md | 77 +++++++++++++++++++++++++++++++++---------------------- 1 file changed, 47 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 810dcd2c..83efb9d1 100644 --- a/README.md +++ b/README.md @@ -6,23 +6,19 @@ Python Version PyPI Version License + English + 简体中文 GitHub Stars

Memory Management Kit for Agents, Remember Me, Refine Me.
- If you find it useful, please give us a ⭐ Star. Your support drives our continuous improvement. -

- -

- English | 简体中文 + If you find it useful, please give us a ⭐ Star.

--- -ReMe provides AI agents with a unified memory system—enabling the ability to extract, reuse, and share memories across -users, tasks, and agents. - +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. Agent memory can be viewed as: ```text @@ -30,12 +26,16 @@ Agent Memory = Long-Term Memory + Short-Term Memory = (Personal + Task + Tool) Memory + (Working Memory) ``` -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. +- **Personal Memory**: Understand user preferences and adapt to context +- **Task Memory**: Learn from experience and perform better on similar tasks +- **Tool Memory**: Optimize tool selection and parameter usage based on historical performance +- **Working Memory**: Manage short-term context for long-running agents without context overflow --- ## 📰 Latest Updates +- **[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)) - **[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)) - **[2025-10]** 🚀 Direct Python import support: use `from reme_ai import ReMeApp` without HTTP/MCP service - **[2025-10]** 🔧 Tool Memory: data-driven tool selection and parameter optimization ([Guide](docs/tool_memory/tool_memory.md)) @@ -51,10 +51,10 @@ Personal memory helps "**understand user preferences**", task memory helps agent ## ✨ Architecture Design

- ReMe Logo + ReMe Architecture

-ReMe integrates three complementary memory capabilities: +ReMe provides a **modular memory management kit** with pluggable components that can be integrated into any agent framework. The system consists of: #### 🧠 **Task Memory/Experience** @@ -122,7 +122,7 @@ pip install . ### Environment Configuration -Copy `example.env` to .env and modify the corresponding parameters: +ReMe requires LLM and embedding model configurations. Copy `example.env` to `.env` and configure: ```bash FLOW_LLM_API_KEY=sk-xxxx @@ -649,17 +649,18 @@ curl -X POST http://localhost:8002/summary_working_memory \ --- -## 📦 Ready-to-Use Memories +## 📦 Pre-built Memory Library -ReMe provides pre-built memories that agents can immediately use with verified best practices: +ReMe provides a **memory library** with pre-extracted, production-ready memories that agents can load and use immediately: -### Available Memories +### Available Memory Packs -- **`appworld.jsonl`**: Memory for Appworld agent interactions, covering complex task planning and execution - patterns -- **`bfcl_v3.jsonl`**: Working memory for BFCL tool calls +| Memory Pack | Domain | Size | Description | +|----------------------|----------------|---------------|-------------------------------------------------------------------------------------| +| **`appworld.jsonl`** | Task Execution | ~100 memories | Complex task planning patterns, multi-step workflows, and error recovery strategies | +| **`bfcl_v3.jsonl`** | Tool Usage | ~150 memories | Function calling patterns, parameter optimization, and tool selection strategies | -### Quick Usage +### Loading Pre-built Memories ```python # Load pre-built memories @@ -773,13 +774,26 @@ You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and ## 📚 Resources -- **[Quick Start](./cookbook/simple_demo)**: Get started quickly with practical examples +### Getting Started +- **[Quick Start](./cookbook/simple_demo)**: Practical examples for immediate use - [Tool Memory Demo](cookbook/simple_demo/use_tool_memory_demo.py): Complete lifecycle demonstration of tool memory - [Tool Memory Benchmark](cookbook/tool_memory/run_reme_tool_bench.py): Evaluate tool memory effectiveness -- **[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)** & **[Tool Memory](docs/tool_memory)**: Operators used in personal memory, task memory and tool memory. You can modify the config to customize the pipelines. -- **[Example Collection](./cookbook)**: Real use cases and best practices + +### Integration Guides +- **[Direct Python Import](docs/cookbook/working/quick_start.md)**: Embed ReMe directly into your agent code +- **[HTTP Service API](docs/vector_store_api_guide.md)**: RESTful API for multi-agent systems +- **[MCP Protocol](docs/mcp_quick_start.md)**: Integration with Claude Desktop and MCP-compatible clients + +### Memory System Configuration +- **[Personal Memory](docs/personal_memory)**: User preference learning and contextual adaptation +- **[Task Memory](docs/task_memory)**: Procedural knowledge extraction and reuse +- **[Tool Memory](docs/tool_memory)**: Data-driven tool selection and optimization +- **[Working Memory](docs/work_memory/message_offload.md)**: Short-term context management for long-running agents + +### Advanced Topics +- **[Operator Pipelines](reme_ai/config/default.yaml)**: Customize memory processing workflows by modifying operator chains +- **[Vector Store Backends](docs/vector_store_api_guide.md)**: Configure local, Elasticsearch, Qdrant, or ChromaDB storage +- **[Example Collection](./cookbook)**: Real-world use cases and best practices --- @@ -797,14 +811,17 @@ We believe the best memory systems come from collective wisdom. Contributions we ### Code Contributions -- New operation and tool development -- Backend implementation and optimization -- API enhancements and new endpoints +- **New Operators**: Develop custom memory processing operators (retrieval, summarization, etc.) +- **Backend Implementations**: Add support for new vector stores or LLM providers +- **Memory Services**: Extend with new memory types or capabilities +- **API Enhancements**: Improve existing endpoints or add new ones ### Documentation Improvements -- Usage examples and tutorials -- Best practice guides +- **Integration Examples**: Show how to integrate ReMe with different agent frameworks +- **Operator Tutorials**: Document custom operator development +- **Best Practice Guides**: Share effective memory management patterns +- **Use Case Studies**: Demonstrate ReMe in real-world applications --- @@ -814,7 +831,7 @@ We believe the best memory systems come from collective wisdom. Contributions we ```bibtex @software{AgentscopeReMe2025, title = {AgentscopeReMe: Memory Management Kit for Agents}, - author = {Li Yu, Jiaji Deng, Zouying Cao, Weikang Zhou}, + 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}, url = {https://reme.agentscope.io}, year = {2025} }