mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
docs(reme): update README with comprehensive documentation for file-b… (#122)
* docs(reme): update README
This commit is contained in:
jinliyl
GitHub
parent
206da3e937
commit
0bb6321285
5 changed files with 2560 additions and 1549 deletions
1162
README_ZH.md
1162
README_ZH.md
File diff suppressed because it is too large
Load diff
897
docs/README_0_2_x.md
Normal file
897
docs/README_0_2_x.md
Normal file
|
|
@ -0,0 +1,897 @@
|
|||
<p align="center">
|
||||
<img src="docs/_static/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-3.10+-blue" alt="Python Version"></a>
|
||||
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/pypi/v/reme-ai.svg?logo=pypi" alt="PyPI Version"></a>
|
||||
<a href="https://pepy.tech/project/reme-ai/"><img src="https://img.shields.io/pypi/dm/reme-ai" alt="PyPI Downloads"></a>
|
||||
<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/commit-activity/m/agentscope-ai/ReMe?style=flat-square" alt="GitHub commit activity"></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
|
||||
<a href="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
|
||||
<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>
|
||||
<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>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<strong>Memory Management Kit for Agents, Remember Me, Refine Me.</strong><br>
|
||||
<em><sub>If you find it useful, please give us a ⭐ Star.</sub></em>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
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
|
||||
Agent Memory = Long-Term Memory + Short-Term Memory
|
||||
= (Personal + Task + Tool) Memory + (Working Memory)
|
||||
```
|
||||
|
||||
- **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
|
||||
|
||||
- **[2026-02]** 💻 ReMeCli: A terminal-based AI chat assistant with built-in memory management. Automatically compacts long conversations into summaries to free up context space, and persists important information as Markdown files for retrieval in future sessions. Memory design inspired by [OpenClaw](https://github.com/openclaw/openclaw).
|
||||
- [Quick Start](docs/cli/quick_start_en.md)
|
||||
- Type `/horse` to trigger the Year of the Horse Easter egg -- fireworks, a galloping horse animation, and a random blessing.
|
||||
<table border="0" cellspacing="0" cellpadding="0" style="border: none;">
|
||||
<tr style="border: none;">
|
||||
<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
|
||||
<strong>马<br>上<br>有<br>钱</strong>
|
||||
</td>
|
||||
<td width="80%" style="border: none;">
|
||||
<video src="https://github.com/user-attachments/assets/d731ae5c-80eb-498b-a22c-8ab2b9169f87" autoplay muted loop controls></video>
|
||||
</td>
|
||||
<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
|
||||
<strong>马<br>到<br>成<br>功</strong>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
- **[2025-12]** 📄 Our procedural (task) memory paper has been released on [arXiv](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))
|
||||
- **[2025-09]** 🎉 Async operations support, integrated into agentscope-runtime
|
||||
- **[2025-09]** 🎉 Task memory and personal memory integration
|
||||
- **[2025-09]** 🧪 Validated effectiveness in appworld, bfcl(v3), and frozenlake ([Experiments](docs/cookbook))
|
||||
- **[2025-08]** 🚀 MCP protocol support ([Quick Start](docs/mcp_quick_start.md))
|
||||
- **[2025-06]** 🚀 Multiple backend vector storage (Elasticsearch & ChromaDB) ([Guide](docs/vector_store_api_guide.md))
|
||||
- **[2024-09]** 🧠 Personalized and time-aware memory storage
|
||||
|
||||
---
|
||||
|
||||
## ✨ Architecture Design
|
||||
|
||||
<p align="center">
|
||||
<img src="docs/_static/figure/reme_structure.jpg" alt="ReMe Architecture" width="80%">
|
||||
</p>
|
||||
|
||||
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**
|
||||
|
||||
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)
|
||||
|
||||
#### 👤 **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)
|
||||
|
||||
#### 🔧 **Tool Memory**
|
||||
|
||||
Data-driven tool selection and usage optimization
|
||||
|
||||
- **Historical Performance Tracking**: Success rates, execution times, and token costs from real usage
|
||||
- **LLM-as-Judge Evaluation**: Qualitative insights on why tools succeed or fail
|
||||
- **Parameter Optimization**: Learn optimal parameter configurations from successful calls
|
||||
- **Dynamic Guidelines**: Transform static tool descriptions into living, learned manuals
|
||||
|
||||
Learn more about how to use tool memory from [tool memory](docs/tool_memory/tool_memory.md)
|
||||
|
||||
#### 🧠 Working Memory
|
||||
|
||||
Short‑term contextual memory for long‑running agents via **message offload & reload**:
|
||||
- **Message Offload**: Compact large tool outputs to external files or LLM summaries
|
||||
- **Message Reload**: Search (`grep_working_memory`) and read (`read_working_memory`) offloaded content on demand
|
||||
📖 **Concept & API**:
|
||||
- Message offload overview: [Message Offload](docs/work_memory/message_offload.md)
|
||||
- Offload / reload operators: [Message Offload Ops](docs/work_memory/message_offload_ops.md), [Message Reload Ops](docs/work_memory/message_reload_ops.md)
|
||||
💻 **End‑to‑End Demo**:
|
||||
- Working memory quick start: [Working Memory Quick Start](docs/cookbook/working/quick_start.md)
|
||||
- ReAct agent with working memory: [react_agent_with_working_memory.py](cookbook/working_memory/react_agent_with_working_memory.py)
|
||||
- Runnable demo: [work_memory_demo.py](cookbook/working_memory/work_memory_demo.py)
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Installation
|
||||
|
||||
### Install from PyPI (Recommended)
|
||||
|
||||
```bash
|
||||
pip install reme-ai
|
||||
```
|
||||
|
||||
### Install from Source
|
||||
|
||||
```bash
|
||||
git clone https://github.com/agentscope-ai/ReMe.git
|
||||
cd ReMe
|
||||
pip install .
|
||||
```
|
||||
|
||||
### Environment Configuration
|
||||
|
||||
ReMe requires LLM and embedding model configurations. Copy `example.env` to `.env` and configure:
|
||||
|
||||
```bash
|
||||
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
|
||||
|
||||
```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>Python import version</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# Experience Summarizer: Learn from execution trajectories
|
||||
result = await app.async_execute(
|
||||
name="summary_task_memory",
|
||||
workspace_id="task_workspace",
|
||||
trajectories=[
|
||||
{
|
||||
"messages": [
|
||||
{"role": "user", "content": "Help me create a project plan"}
|
||||
],
|
||||
"score": 1.0
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# Retriever: Get relevant memories
|
||||
result = await app.async_execute(
|
||||
name="retrieve_task_memory",
|
||||
workspace_id="task_workspace",
|
||||
query="How to efficiently manage project progress?",
|
||||
top_k=1
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<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>
|
||||
|
||||
#### 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>Python import version</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# Memory Integration: Learn from user interactions
|
||||
result = await app.async_execute(
|
||||
name="summary_personal_memory",
|
||||
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"}
|
||||
]
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# Memory Retrieval: Get personal memory fragments
|
||||
result = await app.async_execute(
|
||||
name="retrieve_personal_memory",
|
||||
workspace_id="task_workspace",
|
||||
query="What are the user's work habits?",
|
||||
top_k=5
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<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>
|
||||
|
||||
#### Tool Memory Management
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# Record tool execution results
|
||||
response = requests.post("http://localhost:8002/add_tool_call_result", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_call_results": [
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": True,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
})
|
||||
|
||||
# Generate usage guidelines from history
|
||||
response = requests.post("http://localhost:8002/summary_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
|
||||
# Retrieve tool guidelines before use
|
||||
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python import version</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# Record tool execution results
|
||||
result = await app.async_execute(
|
||||
name="add_tool_call_result",
|
||||
workspace_id="tool_workspace",
|
||||
tool_call_results=[
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": True,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# Generate usage guidelines from history
|
||||
result = await app.async_execute(
|
||||
name="summary_tool_memory",
|
||||
workspace_id="tool_workspace",
|
||||
tool_names="web_search"
|
||||
)
|
||||
print(result)
|
||||
|
||||
# Retrieve tool guidelines before use
|
||||
result = await app.async_execute(
|
||||
name="retrieve_tool_memory",
|
||||
workspace_id="tool_workspace",
|
||||
tool_names="web_search"
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl version</summary>
|
||||
|
||||
```bash
|
||||
# Record tool execution results
|
||||
curl -X POST http://localhost:8002/add_tool_call_result \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_call_results": [
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": true,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
}'
|
||||
|
||||
# Generate usage guidelines from history
|
||||
curl -X POST http://localhost:8002/summary_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
|
||||
# Retrieve tool guidelines before use
|
||||
curl -X POST http://localhost:8002/retrieve_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### Working Memory Management
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# Summarize and compact working memory for a long-running conversation
|
||||
response = requests.post("http://localhost:8002/summary_working_memory", json={
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
"working_summary_mode": "auto",
|
||||
"compact_ratio_threshold": 0.75,
|
||||
"max_total_tokens": 20000,
|
||||
"max_tool_message_tokens": 2000,
|
||||
"group_token_threshold": 4000,
|
||||
"keep_recent_count": 2,
|
||||
"store_dir": "test_working_memory",
|
||||
"chat_id": "demo_chat_id"
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python import version</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# Summarize and compact working memory for a long-running conversation
|
||||
result = await app.async_execute(
|
||||
name="summary_working_memory",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
working_summary_mode="auto",
|
||||
compact_ratio_threshold=0.75,
|
||||
max_total_tokens=20000,
|
||||
max_tool_message_tokens=2000,
|
||||
group_token_threshold=4000,
|
||||
keep_recent_count=2,
|
||||
store_dir="test_working_memory",
|
||||
chat_id="demo_chat_id",
|
||||
)
|
||||
print(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl version</summary>
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8002/summary_working_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
"working_summary_mode": "auto",
|
||||
"compact_ratio_threshold": 0.75,
|
||||
"max_total_tokens": 20000,
|
||||
"max_tool_message_tokens": 2000,
|
||||
"group_token_threshold": 4000,
|
||||
"keep_recent_count": 2,
|
||||
"store_dir": "test_working_memory",
|
||||
"chat_id": "demo_chat_id"
|
||||
}'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 📦 Pre-built Memory Library
|
||||
|
||||
ReMe provides a **memory library** with pre-extracted, production-ready memories that agents can load and use immediately:
|
||||
|
||||
### Available Memory Packs
|
||||
|
||||
| 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 |
|
||||
|
||||
### Loading Pre-built Memories
|
||||
|
||||
```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
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python import version</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# Load pre-built memories
|
||||
result = await app.async_execute(
|
||||
name="vector_store",
|
||||
workspace_id="appworld",
|
||||
action="load",
|
||||
path="./docs/library/"
|
||||
)
|
||||
print(result)
|
||||
|
||||
# Query relevant memories
|
||||
result = await app.async_execute(
|
||||
name="retrieve_task_memory",
|
||||
workspace_id="appworld",
|
||||
query="How to navigate to settings and update user profile?",
|
||||
top_k=1
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 🧪 Experiments
|
||||
|
||||
### 🌍 [Appworld Experiment](docs/cookbook/appworld/quickstart.md)
|
||||
|
||||
We tested ReMe on Appworld using Qwen3-8B (non-thinking mode):
|
||||
|
||||
| Method | Avg@4 | Pass@4 |
|
||||
|--------------|---------------------|---------------------|
|
||||
| without ReMe | 0.1497 | 0.3285 |
|
||||
| with ReMe | 0.1706 **(+2.09%)** | 0.3631 **(+3.46%)** |
|
||||
|
||||
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](docs/cookbook/appworld/quickstart.md).
|
||||
|
||||
### 🔧 [BFCL-V3 Experiment](docs/cookbook/bfcl/quickstart.md)
|
||||
|
||||
We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using Qwen3-8B (thinking mode):
|
||||
|
||||
| Method | Avg@4 | Pass@4 |
|
||||
|--------------|---------------------|---------------------|
|
||||
| without ReMe | 0.4033 | 0.5955 |
|
||||
| with ReMe | 0.4450 **(+4.17%)** | 0.6577 **(+6.22%)** |
|
||||
|
||||
### 🧊 [Frozenlake Experiment](docs/cookbook/frozenlake/quickstart.md)
|
||||
|
||||
| without ReMe | with ReMe |
|
||||
|:----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------:|
|
||||
| <p align="center"><img src="docs/_static/figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p> | <p align="center"><img src="docs/_static/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](docs/cookbook/frozenlake/quickstart.md).
|
||||
|
||||
### 🛠️ [Tool Memory Benchmark](docs/tool_memory/tool_bench.md)
|
||||
|
||||
We evaluated Tool Memory effectiveness using a controlled benchmark with three mock search tools using Qwen3-30B-Instruct:
|
||||
|
||||
| Scenario | Avg Score | Improvement |
|
||||
|------------------------|-----------|-------------|
|
||||
| Train (No Memory) | 0.650 | - |
|
||||
| Test (No Memory) | 0.672 | Baseline |
|
||||
| **Test (With Memory)** | **0.772** | **+14.88%** |
|
||||
|
||||
**Key Findings:**
|
||||
- Tool Memory enables data-driven tool selection based on historical performance
|
||||
- Success rates improved by ~15% with learned parameter configurations
|
||||
|
||||
You can find more details in [tool_bench.md](docs/tool_memory/tool_bench.md) and the implementation at [run_reme_tool_bench.py](cookbook/tool_memory/run_reme_tool_bench.py).
|
||||
|
||||
## 📚 Resources
|
||||
|
||||
### 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
|
||||
|
||||
### 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
|
||||
|
||||
---
|
||||
|
||||
## ⭐ Support & Community
|
||||
|
||||
- **Star & Watch**: Stars surface ReMe to more agent builders; watching keeps you updated on new releases.
|
||||
- **Share your wins**: Open an issue or discussion with what ReMe unlocked for your agents—we love showcasing community builds.
|
||||
- **Need a feature?** File a request and we’ll help shape it together.
|
||||
|
||||
---
|
||||
|
||||
## 🤝 Contribution
|
||||
|
||||
We believe the best memory systems come from collective wisdom. Contributions welcome 👉[Guide](docs/contribution.md):
|
||||
|
||||
### Code Contributions
|
||||
|
||||
- **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
|
||||
|
||||
- **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
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 📄 Citation
|
||||
|
||||
```bibtex
|
||||
@software{AgentscopeReMe2025,
|
||||
title = {AgentscopeReMe: Memory Management Kit for Agents},
|
||||
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}
|
||||
}
|
||||
|
||||
@misc{AgentscopeReMe2025Paper,
|
||||
title={Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution},
|
||||
author={Zouying Cao and
|
||||
Jiaji Deng and
|
||||
Li Yu and
|
||||
Weikang Zhou and
|
||||
Zhaoyang Liu and
|
||||
Bolin Ding and
|
||||
Hai Zhao},
|
||||
year={2025},
|
||||
eprint={2512.10696},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI},
|
||||
url={https://arxiv.org/abs/2512.10696},
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚖️ License
|
||||
|
||||
This project is licensed under the Apache License 2.0 - see the [LICENSE](./LICENSE) file for details.
|
||||
|
||||
---
|
||||
|
||||
## Star History
|
||||
|
||||
[](https://www.star-history.com/#agentscope-ai/ReMe&Date)
|
||||
902
docs/README_0_2_x_ZH.md
Normal file
902
docs/README_0_2_x_ZH.md
Normal file
|
|
@ -0,0 +1,902 @@
|
|||
<p align="center">
|
||||
<img src="docs/_static/figure/reme_logo.png" alt="ReMe 标志" width="50%">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/badge/python-3.10+-blue" alt="Python Version"></a>
|
||||
<a href="https://pypi.org/project/reme-ai/"><img src="https://img.shields.io/pypi/v/reme-ai.svg?logo=pypi" alt="PyPI Version"></a>
|
||||
<a href="https://pepy.tech/project/reme-ai/"><img src="https://img.shields.io/pypi/dm/reme-ai" alt="PyPI Downloads"></a>
|
||||
<a href="https://github.com/agentscope-ai/ReMe"><img src="https://img.shields.io/github/commit-activity/m/agentscope-ai/ReMe?style=flat-square" alt="GitHub commit activity"></a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
|
||||
<a href="./README.md"><img src="https://img.shields.io/badge/English-Click-yellow" alt="English"></a>
|
||||
<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>
|
||||
<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>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<strong>面向智能体的记忆管理工具包, Remember Me, Refine Me.</strong><br>
|
||||
<em><sub>如果 ReMe 对你有帮助，欢迎点一个 ⭐ Star，你的支持是我们持续改进的动力。</sub></em>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
ReMe 是一个**模块化的记忆管理工具包**，为 AI 智能体提供统一的记忆能力——支持在用户、任务与智能体之间提取、复用与共享记忆。
|
||||
|
||||
智能体的记忆可以被视为：
|
||||
|
||||
```text
|
||||
Agent Memory = Long-Term Memory + Short-Term Memory
|
||||
= (Personal + Task + Tool) Memory + (Working Memory)
|
||||
```
|
||||
|
||||
- **个人记忆（Personal Memory）**：理解用户偏好并适应上下文
|
||||
- **任务记忆（Task Memory）**：从经验中学习并在类似任务中表现更好
|
||||
- **工具记忆（Tool Memory）**：基于历史表现优化工具选择和参数使用
|
||||
- **工作记忆（Working Memory）**：管理长运行智能体的短期上下文，避免上下文溢出
|
||||
|
||||
---
|
||||
|
||||
## 📰 最新进展
|
||||
|
||||
- **[2026-02]** 💻 ReMeCli：终端 AI 聊天助手，内置记忆管理能力。当对话过长时自动将旧内容压缩为摘要以释放上下文空间，同时将重要信息以 Markdown 文件持久化存储，供未来会话自动检索使用。记忆设计灵感来源于 [OpenClaw](https://github.com/openclaw/openclaw)。
|
||||
- [快速开始](docs/cli/quick_start_en.md)
|
||||
- 输入 `/horse` 触发马年彩蛋——烟花、奔马动画和随机马年祝福。
|
||||
<table border="0" cellspacing="0" cellpadding="0" style="border: none;">
|
||||
<tr style="border: none;">
|
||||
<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
|
||||
<strong>马<br>上<br>有<br>钱</strong>
|
||||
</td>
|
||||
<td width="80%" style="border: none;">
|
||||
<video src="https://github.com/user-attachments/assets/befa7e40-63ba-4db2-8251-516024616e00" autoplay muted loop controls></video>
|
||||
</td>
|
||||
<td width="10%" style="border: none; vertical-align: middle; text-align: center;">
|
||||
<strong>马<br>到<br>成<br>功</strong>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
- **[2025-12]** 📄 我们的程序性（任务）记忆论文已在 [arXiv](https://arxiv.org/abs/2512.10696) 发布
|
||||
- **[2025-11]** 🧠 基于工作记忆的 react-agent demo（[介绍](docs/work_memory/message_offload.md)、[Quick Start](docs/cookbook/working/quick_start.md)、[代码](cookbook/working_memory/work_memory_demo.py)）
|
||||
- **[2025-10]** 🚀 直接 Python 导入：支持 `from reme_ai import ReMeApp`，无需 HTTP/MCP 服务
|
||||
- **[2025-10]** 🔧 工具记忆：支持基于数据驱动的工具选择与参数优化（[指南](docs/tool_memory/tool_memory.md)）
|
||||
- **[2025-09]** 🎉 支持异步操作，并已集成至 agentscope-runtime
|
||||
- **[2025-09]** 🎉 集成任务记忆与个人记忆
|
||||
- **[2025-09]** 🧪 在 appworld、bfcl(v3)、frozenlake 等环境中验证有效性（[实验文档](docs/cookbook)）
|
||||
- **[2025-08]** 🚀 支持 MCP 协议（[快速开始](docs/mcp_quick_start.md)）
|
||||
- **[2025-06]** 🚀 支持多种向量存储后端（Elasticsearch & ChromaDB）（[向量库指南](docs/vector_store_api_guide.md)）
|
||||
- **[2024-09]** 🧠 支持个性化与时间敏感的记忆存储
|
||||
|
||||
---
|
||||
|
||||
## ✨ 架构设计
|
||||
|
||||
<p align="center">
|
||||
<img src="docs/_static/figure/reme_structure.jpg" alt="ReMe 架构" width="80%">
|
||||
</p>
|
||||
|
||||
ReMe 提供了一个**模块化的记忆管理工具包**，具有可插拔的组件，可以集成到任何智能体框架中。系统包括：
|
||||
|
||||
#### 🧠 **任务记忆 / 经验记忆（Task Memory/Experience）**
|
||||
|
||||
可在不同智能体之间复用的程序性知识：
|
||||
|
||||
- **成功模式识别**：识别有效策略并理解其背后的原理
|
||||
- **失败分析学习**：从错误中学习，避免重复踩坑
|
||||
- **对比式模式**：通过多条采样轨迹的对比获取更有价值的记忆
|
||||
- **验证模式**：通过验证模块确认提炼出的经验是否有效
|
||||
|
||||
了解如何使用任务记忆可参考：[任务记忆文档](docs/task_memory/task_memory.md)
|
||||
|
||||
#### 👤 **个人记忆（Personal Memory）**
|
||||
|
||||
面向特定用户的情境化长期记忆：
|
||||
|
||||
- **个体偏好**：记录用户的习惯、偏好与交互风格
|
||||
- **情境自适应**：基于时间与上下文动态管理记忆
|
||||
- **渐进式学习**：在长期多轮交互中不断加深对用户的理解
|
||||
- **时间敏感**：在记忆检索与整合中考虑时间因素
|
||||
|
||||
了解如何使用个人记忆可参考：[个人记忆文档](docs/personal_memory/personal_memory.md)
|
||||
|
||||
#### 🔧 **工具记忆（Tool Memory）**
|
||||
|
||||
基于真实调用数据的工具选择与使用优化：
|
||||
|
||||
- **历史表现追踪**：记录成功率、调用耗时与 Token 成本
|
||||
- **LLM-as-Judge 评估**：提供工具成功 / 失败原因的定性洞察
|
||||
- **参数优化**：从历史成功调用中学习最优参数配置
|
||||
- **动态指南**：将静态工具描述演化为可持续更新的「活文档」
|
||||
|
||||
了解如何使用工具记忆可参考：[工具记忆文档](docs/tool_memory/tool_memory.md)
|
||||
|
||||
#### 🧠 **工作记忆（Working Memory）**
|
||||
|
||||
面向长流程智能体的短期上下文记忆，通过**消息卸载与重载（message offload & reload）**实现：
|
||||
- **消息卸载（Message Offload）**：将体积巨大的工具输出压缩为外部文件或 LLM 摘要
|
||||
- **消息重载（Message Reload）**：按需搜索（`grep_working_memory`）并读取（`read_working_memory`）已卸载的内容
|
||||
|
||||
📖 **概念与 API：**
|
||||
- 消息卸载概览：[Message Offload](docs/work_memory/message_offload.md)
|
||||
- 卸载 / 重载算子：[Message Offload Ops](docs/work_memory/message_offload_ops.md)、[Message Reload Ops](docs/work_memory/message_reload_ops.md)
|
||||
|
||||
💻 **端到端 Demo：**
|
||||
- 工作记忆快速上手：[Working Memory Quick Start](docs/cookbook/working/quick_start.md)
|
||||
- 带工作记忆的 ReAct 智能体：[react_agent_with_working_memory.py](cookbook/working_memory/react_agent_with_working_memory.py)
|
||||
- 可运行 Demo：[work_memory_demo.py](cookbook/working_memory/work_memory_demo.py)
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ 安装
|
||||
|
||||
### 通过 PyPI 安装（推荐）
|
||||
|
||||
```bash
|
||||
pip install reme-ai
|
||||
```
|
||||
|
||||
### 从源码安装
|
||||
|
||||
```bash
|
||||
git clone https://github.com/agentscope-ai/ReMe.git
|
||||
cd ReMe
|
||||
pip install .
|
||||
```
|
||||
|
||||
### 环境变量配置
|
||||
|
||||
复制 `example.env` 为 `.env` 并按需修改：
|
||||
|
||||
```bash
|
||||
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
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🚀 快速开始
|
||||
|
||||
### 启动 HTTP 服务
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
### 启动 MCP Server
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
### 核心 API 用法
|
||||
|
||||
#### 任务记忆管理
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# 经验总结：从执行轨迹中学习
|
||||
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}
|
||||
]
|
||||
})
|
||||
|
||||
# 记忆检索：获取相关经验
|
||||
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>Python 导入版本</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# 经验总结：从执行轨迹中学习
|
||||
result = await app.async_execute(
|
||||
name="summary_task_memory",
|
||||
workspace_id="task_workspace",
|
||||
trajectories=[
|
||||
{
|
||||
"messages": [
|
||||
{"role": "user", "content": "Help me create a project plan"}
|
||||
],
|
||||
"score": 1.0
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# 记忆检索：获取相关经验
|
||||
result = await app.async_execute(
|
||||
name="retrieve_task_memory",
|
||||
workspace_id="task_workspace",
|
||||
query="How to efficiently manage project progress?",
|
||||
top_k=1
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl 版本</summary>
|
||||
|
||||
```bash
|
||||
# 经验总结：从执行轨迹中学习
|
||||
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}
|
||||
]
|
||||
}'
|
||||
|
||||
# 记忆检索：获取相关经验
|
||||
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>
|
||||
|
||||
#### 个人记忆管理
|
||||
|
||||
```python
|
||||
# 记忆整合：从用户交互中学习
|
||||
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"}
|
||||
]
|
||||
}
|
||||
]
|
||||
})
|
||||
|
||||
# 记忆检索：获取个人记忆片段
|
||||
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>Python 导入版本</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# 记忆整合：从用户交互中学习
|
||||
result = await app.async_execute(
|
||||
name="summary_personal_memory",
|
||||
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"}
|
||||
]
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# 记忆检索：获取个人记忆片段
|
||||
result = await app.async_execute(
|
||||
name="retrieve_personal_memory",
|
||||
workspace_id="task_workspace",
|
||||
query="What are the user's work habits?",
|
||||
top_k=5
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl 版本</summary>
|
||||
|
||||
```bash
|
||||
# 记忆整合：从用户交互中学习
|
||||
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"}
|
||||
]}
|
||||
]
|
||||
}'
|
||||
|
||||
# 记忆检索：获取个人记忆片段
|
||||
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>
|
||||
|
||||
#### 工具记忆管理
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# 记录工具调用结果
|
||||
response = requests.post("http://localhost:8002/add_tool_call_result", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_call_results": [
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": True,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
})
|
||||
|
||||
# 从历史生成使用指南
|
||||
response = requests.post("http://localhost:8002/summary_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
|
||||
# 在使用前检索工具指南
|
||||
response = requests.post("http://localhost:8002/retrieve_tool_memory", json={
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python 导入版本</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# 记录工具调用结果
|
||||
result = await app.async_execute(
|
||||
name="add_tool_call_result",
|
||||
workspace_id="tool_workspace",
|
||||
tool_call_results=[
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": True,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
)
|
||||
print(result)
|
||||
|
||||
# 从历史生成使用指南
|
||||
result = await app.async_execute(
|
||||
name="summary_tool_memory",
|
||||
workspace_id="tool_workspace",
|
||||
tool_names="web_search"
|
||||
)
|
||||
print(result)
|
||||
|
||||
# 在使用前检索工具指南
|
||||
result = await app.async_execute(
|
||||
name="retrieve_tool_memory",
|
||||
workspace_id="tool_workspace",
|
||||
tool_names="web_search"
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl 版本</summary>
|
||||
|
||||
```bash
|
||||
# 记录工具调用结果
|
||||
curl -X POST http://localhost:8002/add_tool_call_result \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_call_results": [
|
||||
{
|
||||
"create_time": "2025-10-21 10:30:00",
|
||||
"tool_name": "web_search",
|
||||
"input": {"query": "Python asyncio tutorial", "max_results": 10},
|
||||
"output": "Found 10 relevant results...",
|
||||
"token_cost": 150,
|
||||
"success": true,
|
||||
"time_cost": 2.3
|
||||
}
|
||||
]
|
||||
}'
|
||||
|
||||
# 从历史生成使用指南
|
||||
curl -X POST http://localhost:8002/summary_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
|
||||
# 在使用前检索工具指南
|
||||
curl -X POST http://localhost:8002/retrieve_tool_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"workspace_id": "tool_workspace",
|
||||
"tool_names": "web_search"
|
||||
}'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
#### 工作记忆管理
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# 对长对话 / 长流程的工作记忆进行压缩与总结
|
||||
response = requests.post("http://localhost:8002/summary_working_memory", json={
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
"working_summary_mode": "auto",
|
||||
"compact_ratio_threshold": 0.75,
|
||||
"max_total_tokens": 20000,
|
||||
"max_tool_message_tokens": 2000,
|
||||
"group_token_threshold": 4000,
|
||||
"keep_recent_count": 2,
|
||||
"store_dir": "test_working_memory",
|
||||
"chat_id": "demo_chat_id"
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python 导入版本</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# 对长对话 / 长流程的工作记忆进行压缩与总结
|
||||
result = await app.async_execute(
|
||||
name="summary_working_memory",
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
working_summary_mode="auto",
|
||||
compact_ratio_threshold=0.75,
|
||||
max_total_tokens=20000,
|
||||
max_tool_message_tokens=2000,
|
||||
group_token_threshold=4000,
|
||||
keep_recent_count=2,
|
||||
store_dir="test_working_memory",
|
||||
chat_id="demo_chat_id",
|
||||
)
|
||||
print(result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>curl 版本</summary>
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8002/summary_working_memory \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"messages": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant. First use `Grep` to find the line numbers that match the keywords or regular expressions, and then use `ReadFile` to read the code around those locations. If no matches are found, never give up; try different parameters, such as searching with only part of the keywords. After `Grep`, use the `ReadFile` command to view content starting from a specified `offset` and `limit`, and do not exceed 100 lines. If the current content is insufficient, you can continue trying different `offset` and `limit` values with the `ReadFile` command."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "搜索下reme项目的的README内容"
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_6596dafa2a6a46f7a217da",
|
||||
"function": {
|
||||
"arguments": "{\"query\": \"readme\"}",
|
||||
"name": "web_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "ultra large context , over 50000 tokens......"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "根据readme回答task memory在appworld的效果是多少，需要具体的数值"
|
||||
}
|
||||
],
|
||||
"working_summary_mode": "auto",
|
||||
"compact_ratio_threshold": 0.75,
|
||||
"max_total_tokens": 20000,
|
||||
"max_tool_message_tokens": 2000,
|
||||
"group_token_threshold": 4000,
|
||||
"keep_recent_count": 2,
|
||||
"store_dir": "test_working_memory",
|
||||
"chat_id": "demo_chat_id"
|
||||
}'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 📦 开箱即用的记忆库
|
||||
|
||||
ReMe 提供一个**记忆库**，包含预先提取的、生产就绪的记忆，智能体可以立即加载和使用：
|
||||
|
||||
### 可用记忆包
|
||||
|
||||
| 记忆包 | 领域 | 规模 | 描述 |
|
||||
|----------------------|------------|----------------|--------------------------------------------------------|
|
||||
| **`appworld.jsonl`** | 任务执行 | ~100 条记忆 | 复杂任务规划模式、多步骤工作流和错误恢复策略 |
|
||||
| **`bfcl_v3.jsonl`** | 工具使用 | ~150 条记忆 | 函数调用模式、参数优化和工具选择策略 |
|
||||
|
||||
### 加载预构建记忆
|
||||
|
||||
```python
|
||||
# 加载内置记忆
|
||||
response = requests.post("http://localhost:8002/vector_store", json={
|
||||
"workspace_id": "appworld",
|
||||
"action": "load",
|
||||
"path": "./docs/library/"
|
||||
})
|
||||
|
||||
# 查询相关记忆
|
||||
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
|
||||
})
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Python 导入版本</summary>
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from reme_ai import ReMeApp
|
||||
|
||||
async def main():
|
||||
async with ReMeApp(
|
||||
"llm.default.model_name=qwen3-30b-a3b-thinking-2507",
|
||||
"embedding_model.default.model_name=text-embedding-v4",
|
||||
"vector_store.default.backend=memory"
|
||||
) as app:
|
||||
# 加载内置记忆
|
||||
result = await app.async_execute(
|
||||
name="vector_store",
|
||||
workspace_id="appworld",
|
||||
action="load",
|
||||
path="./docs/library/"
|
||||
)
|
||||
print(result)
|
||||
|
||||
# 查询相关记忆
|
||||
result = await app.async_execute(
|
||||
name="retrieve_task_memory",
|
||||
workspace_id="appworld",
|
||||
query="How to navigate to settings and update user profile?",
|
||||
top_k=1
|
||||
)
|
||||
print(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 🧪 实验结果
|
||||
|
||||
### 🌍 [Appworld 实验](docs/cookbook/appworld/quickstart.md)
|
||||
|
||||
我们在 Appworld 环境上使用 Qwen3-8B（非思考模式）进行评测：
|
||||
|
||||
| 方法 | Avg@4 | Pass@4 |
|
||||
|-----------|-------------------|-------------------|
|
||||
| 无 ReMe | 0.1497 | 0.3285 |
|
||||
| 使用 ReMe | 0.1706 **(+2.09%)** | 0.3631 **(+3.46%)** |
|
||||
|
||||
Pass@K 衡量在生成 K 个候选中，至少一个成功完成任务（score=1）的概率。
|
||||
当前实验使用的是内部 AppWorld 环境，可能与对外版本存在轻微差异。
|
||||
|
||||
关于如何复现实验的更多细节，见 [quickstart.md](docs/cookbook/appworld/quickstart.md)。
|
||||
|
||||
### 🔧 [BFCL-V3 实验](docs/cookbook/bfcl/quickstart.md)
|
||||
|
||||
我们在 BFCL-V3 multi-turn-base 任务（随机划分 50 train / 150 val）上，使用 Qwen3-8B（思考模式）进行评测：
|
||||
|
||||
| 方法 | Avg@4 | Pass@4 |
|
||||
|------------|-----------------|---------------------|
|
||||
| 无 ReMe | 0.4033 | 0.5955 |
|
||||
| 使用 ReMe | 0.4450 **(+4.17%)** | 0.6577 **(+6.22%)** |
|
||||
|
||||
### 🧊 [Frozenlake 实验](docs/cookbook/frozenlake/quickstart.md)
|
||||
|
||||
| 无 ReMe | 使用 ReMe |
|
||||
|:------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------:|
|
||||
| <p align="center"><img src="docs/_static/figure/frozenlake_failure.gif" alt="失败示例" width="30%"></p> | <p align="center"><img src="docs/_static/figure/frozenlake_success.gif" alt="成功示例" width="30%"></p> |
|
||||
|
||||
我们在 100 张随机 frozenlake 地图上，使用 qwen3-8b 进行测试：
|
||||
|
||||
| 方法 | 通过率 |
|
||||
|------------|-----------------|
|
||||
| 无 ReMe | 0.66 |
|
||||
| 使用 ReMe | 0.72 **(+6.0%)** |
|
||||
|
||||
更多复现实验细节见 [quickstart.md](docs/cookbook/frozenlake/quickstart.md)。
|
||||
|
||||
### 🛠️ [工具记忆基准](docs/tool_memory/tool_bench.md)
|
||||
|
||||
我们在一个受控基准上，使用三个模拟搜索工具与 Qwen3-30B-Instruct 评估工具记忆的效果：
|
||||
|
||||
| 场景 | 平均分 | 提升 |
|
||||
|-----------------------|--------|------------|
|
||||
| 训练集（无记忆） | 0.650 | - |
|
||||
| 测试集（无记忆） | 0.672 | 基线 |
|
||||
| **测试集（使用记忆）** | **0.772** | **+14.88%** |
|
||||
|
||||
**关键结论：**
|
||||
- 工具记忆可以基于历史表现进行数据驱动的工具选择
|
||||
- 通过学习参数配置，成功率约提升 15%
|
||||
|
||||
更多细节见 [tool_bench.md](docs/tool_memory/tool_bench.md) 与实现代码 [run_reme_tool_bench.py](cookbook/tool_memory/run_reme_tool_bench.py)。
|
||||
|
||||
---
|
||||
|
||||
## 📚 资源
|
||||
|
||||
### 快速入门
|
||||
- **[Quick Start](./cookbook/simple_demo)**：实用示例，可立即使用
|
||||
- [工具记忆 Demo](cookbook/simple_demo/use_tool_memory_demo.py)：工具记忆的完整生命周期演示
|
||||
- [工具记忆基准](cookbook/tool_memory/run_reme_tool_bench.py)：评估工具记忆效果
|
||||
|
||||
### 集成指南
|
||||
- **[直接 Python 导入](docs/cookbook/working/quick_start.md)**：将 ReMe 直接嵌入到你的智能体代码中
|
||||
- **[HTTP 服务 API](docs/vector_store_api_guide.md)**：用于多智能体系统的 RESTful API
|
||||
- **[MCP 协议](docs/mcp_quick_start.md)**：与 Claude Desktop 和 MCP 兼容客户端集成
|
||||
|
||||
### 记忆系统配置
|
||||
- **[个人记忆](docs/personal_memory)**：用户偏好学习和上下文自适应
|
||||
- **[任务记忆](docs/task_memory)**：程序性知识提取和复用
|
||||
- **[工具记忆](docs/tool_memory)**：数据驱动的工具选择和优化
|
||||
- **[工作记忆](docs/work_memory/message_offload.md)**：长流程智能体的短期上下文管理
|
||||
|
||||
### 高级主题
|
||||
- **[算子管道](reme_ai/config/default.yaml)**：通过修改算子链来自定义记忆处理工作流
|
||||
- **[向量存储后端](docs/vector_store_api_guide.md)**：配置本地、Elasticsearch、Qdrant 或 ChromaDB 存储
|
||||
- **[案例集](./cookbook)**：真实场景的用例和最佳实践
|
||||
|
||||
---
|
||||
|
||||
## ⭐ 社区与支持
|
||||
|
||||
- **Star & Watch**：Star 可以让更多智能体开发者发现 ReMe；Watch 能帮助你第一时间获知新版本与特性。
|
||||
- **分享你的成果**：在 Issue 或 Discussion 中分享 ReMe 为你的智能体解锁了什么——我们非常乐意展示社区的优秀案例。
|
||||
- **需要新功能？** 提交 Feature Request，我们将一起完善它。
|
||||
|
||||
---
|
||||
|
||||
## 🤝 参与贡献
|
||||
|
||||
我们相信，最好的记忆系统来自社区的集体智慧。欢迎贡献 👉[贡献指南](docs/contribution.md)：
|
||||
|
||||
### 代码贡献
|
||||
|
||||
- **新算子**：开发自定义记忆处理算子（检索、总结等）
|
||||
- **后端实现**：添加对新向量存储或 LLM 提供商的支持
|
||||
- **记忆服务**：扩展新的记忆类型或能力
|
||||
- **API 增强**：改进现有端点或添加新端点
|
||||
|
||||
### 文档改进
|
||||
|
||||
- **集成示例**：展示如何将 ReMe 与不同智能体框架集成
|
||||
- **算子教程**：记录自定义算子开发
|
||||
- **最佳实践指南**：分享有效的记忆管理模式
|
||||
- **用例研究**：展示 ReMe 在实际应用中的使用
|
||||
|
||||
---
|
||||
|
||||
## 📄 引用
|
||||
|
||||
```bibtex
|
||||
@software{AgentscopeReMe2025,
|
||||
title = {AgentscopeReMe: Memory Management Kit for Agents},
|
||||
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}
|
||||
}
|
||||
|
||||
@misc{AgentscopeReMe2025Paper,
|
||||
title={Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution},
|
||||
author={Zouying Cao and
|
||||
Jiaji Deng and
|
||||
Li Yu and
|
||||
Weikang Zhou and
|
||||
Zhaoyang Liu and
|
||||
Bolin Ding and
|
||||
Hai Zhao},
|
||||
year={2025},
|
||||
eprint={2512.10696},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.AI},
|
||||
url={https://arxiv.org/abs/2512.10696},
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚖️ 许可证
|
||||
|
||||
本项目基于 Apache License 2.0 开源，详情参见 [LICENSE](./LICENSE) 文件。
|
||||
|
||||
---
|
||||
|
||||
## Star 历史
|
||||
|
||||
[](https://www.star-history.com/#agentscope-ai/ReMe&Date)
|
||||
0
reme/memory/skills/__init__.py
Normal file
0
reme/memory/skills/__init__.py
Normal file
Loading…
Add table
Reference in a new issue