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docs(restructure): reorganize documentation , add experiment overview & use library guide
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39
docs/experiment_overview.md
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39
docs/experiment_overview.md
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@ -0,0 +1,39 @@
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### 🌍 [Appworld Experiment](cookbook/appworld/quickstart.md)
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We tested ReMe on Appworld using qwen3-8b:
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| Method | pass@1 | pass@2 | pass@4 |
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|--------------|-------------------|-------------------|-------------------|
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| without ReMe | 0.083 | 0.140 | 0.228 |
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| with ReMe | 0.109 **(+2.6%)** | 0.175 **(+3.5%)** | 0.281 **(+5.3%)** |
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Pass@K measures the probability that at least one of the K generated samples successfully completes the task (
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score=1).
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The current experiment uses an internal AppWorld environment, which may have slight differences.
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You can find more details on reproducing the experiment in [quickstart.md](cookbook/appworld/quickstart.md).
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### 🧊 [Frozenlake Experiment](./cookbook/frozenlake/quickstart.md)
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| without ReMe | with ReMe |
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|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
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| <p align="center"><img src="figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p> | <p align="center"><img src="figure/frozenlake_success.gif" alt="GIF 2" width="30%"></p> |
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We tested on 100 random frozenlake maps using qwen3-8b:
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| Method | pass rate |
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|--------------|------------------|
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| without ReMe | 0.66 |
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| with ReMe | 0.72 **(+6.0%)** |
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You can find more details on reproducing the experiment in [quickstart.md](cookbook/frozenlake/quickstart.md).
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### 🔧 [BFCL-V3 Experiment](./cookbook/bfcl/quickstart.md)
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We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using qwen3-8b:
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| Method | pass@1 | pass@2 | pass@4 |
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|--------------|---------------------|---------------------|---------------------|
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| without ReMe | 0.2472 | 0.2733 | 0.2922 |
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| with ReMe | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |
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128
docs/index.md
128
docs/index.md
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@ -33,25 +33,6 @@ Personal memory helps "**understand user preferences**", while task memory helps
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---
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## 📰 Latest Updates
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- **[2025-09]** 🎉 ReMe v0.1.8 has been officially released, adding support for asynchronous operations. It has also been
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integrated into the memory service of agentscope-runtime.
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- **[2025-09]** 🎉 ReMe v0.1 officially released, integrating task memory and personal memory. If you want to use the
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original memoryscope project, you can find it
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in [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch).
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- **[2025-09]** 🧪 We validated the effectiveness of task memory extraction and reuse in agents in appworld, bfcl(v3),
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and frozenlake environments. For more information,
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check [appworld exp](./cookbook/appworld/quickstart.md), [bfcl exp](./cookbook/bfcl/quickstart.md),
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and [frozenlake exp](./cookbook/frozenlake/quickstart.md).
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- **[2025-08]** 🚀 MCP protocol support is now available -> [MCP Quick Start](mcp_quick_start.md).
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- **[2025-06]** 🚀 Multiple backend vector storage support (Elasticsearch &
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ChromaDB) -> [Vector DB quick start](vector_store_api_guide.md).
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- **[2024-09]** 🧠 [MemoryScope](https://github.com/modelscope/Reme/tree/memoryscope_branch) v0.1 released,
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personalized and time-aware memory storage and usage.
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---
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## ✨ Architecture Design
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<p align="center">
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@ -322,104 +303,18 @@ fetch("http://localhost:8002/retrieve_personal_memory", {
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---
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## 📦 Ready-to-Use Libraries
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ReMe provides pre-built memory libraries that agents can immediately use with verified best practices:
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### Available Libraries
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- **`appworld.jsonl`**: Memory library for Appworld agent interactions, covering complex task planning and execution
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patterns
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- **`bfcl_v3.jsonl`**: Working memory library for BFCL tool calls
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### Quick Usage
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```python
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# Load pre-built memories
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response = requests.post("http://localhost:8002/vector_store", json={
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"workspace_id": "appworld",
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"action": "load",
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"path": "./docs/library/"
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})
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# Query relevant memories
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response = requests.post("http://localhost:8002/retrieve_task_memory", json={
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"workspace_id": "appworld",
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"query": "How to navigate to settings and update user profile?",
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"top_k": 1
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})
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```
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## 🧪 Experiments
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### 🌍 [Appworld Experiment](cookbook/appworld/quickstart.md)
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We tested ReMe on Appworld using qwen3-8b:
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| Method | pass@1 | pass@2 | pass@4 |
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|--------------|-------------------|-------------------|-------------------|
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| without ReMe | 0.083 | 0.140 | 0.228 |
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| with ReMe | 0.109 **(+2.6%)** | 0.175 **(+3.5%)** | 0.281 **(+5.3%)** |
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Pass@K measures the probability that at least one of the K generated samples successfully completes the task (
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score=1).
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The current experiment uses an internal AppWorld environment, which may have slight differences.
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You can find more details on reproducing the experiment in [quickstart.md](cookbook/appworld/quickstart.md).
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### 🧊 [Frozenlake Experiment](./cookbook/frozenlake/quickstart.md)
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| without ReMe | with ReMe |
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|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
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| <p align="center"><img src="figure/frozenlake_failure.gif" alt="GIF 1" width="30%"></p> | <p align="center"><img src="figure/frozenlake_success.gif" alt="GIF 2" width="30%"></p> |
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We tested on 100 random frozenlake maps using qwen3-8b:
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| Method | pass rate |
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|--------------|------------------|
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| without ReMe | 0.66 |
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| with ReMe | 0.72 **(+6.0%)** |
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You can find more details on reproducing the experiment in [quickstart.md](cookbook/frozenlake/quickstart.md).
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### 🔧 [BFCL-V3 Experiment](./cookbook/bfcl/quickstart.md)
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We tested ReMe on BFCL-V3 multi-turn-base (randomly split 50train/150val) using qwen3-8b:
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| Method | pass@1 | pass@2 | pass@4 |
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|--------------|---------------------|---------------------|---------------------|
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| without ReMe | 0.2472 | 0.2733 | 0.2922 |
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| with ReMe | 0.3061 **(+5.89%)** | 0.3500 **(+7.67%)** | 0.3888 **(+9.66%)** |
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## 📚 Resources
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- **[Quick Start](https://github.com/modelscope/ReMe/tree/main/cookbook/simple_demo)**: Get started quickly with practical examples
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-
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- **[Vector Storage Setup](vector_store_api_guide.md)**: Configure local/vector databases and usage
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- **[MCP Guide](mcp_quick_start.md)**: Create MCP services
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- **[personal memory](personal_memory/personal_memory.md)** & **[task memory](task_memory/task_memory.md)** : Operators used in personal memory and task memory, You can modify the config to customize the pipelines.
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- **[Example Collection](./cookbook/appworld/quickstart.md)**: Real use cases and best practices
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- **[Example Collection](./experiment_overview.md)**: Real use cases and best practices
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- **[Library](./library/library.md)**: Directly use existing task memory/experience for your tasks, and you can also contribute more task memory/experience to us.
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- **[Contribution](contribution.md)**: welcome to your Contributions!
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---
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## 🤝 Contribution
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We believe the best memory systems come from collective wisdom. Contributions welcome 👉[Guide](contribution.md):
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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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### Documentation Improvements
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- Usage examples and tutorials
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- Best practice guides
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---
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## 📄 Citation
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## Citation
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```bibtex
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@software{ReMe2025,
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@ -429,16 +324,3 @@ We believe the best memory systems come from collective wisdom. Contributions we
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year = {2025}
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}
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```
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---
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## ⚖️ License
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This project is licensed under the Apache License 2.0 - see the [LICENSE](https://github.com/modelscope/ReMe/blob/main/LICENSE) file for details.
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---
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## Star History
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[](https://www.star-history.com/#modelscope/ReMe&Date)
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30
docs/library/use_library.md
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30
docs/library/use_library.md
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@ -0,0 +1,30 @@
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---
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title: Use Library
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summary: Ready-to-use libraries at your disposal.
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---
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ReMe provides pre-built memory libraries that agents can immediately use with verified best practices:
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### Available Libraries
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- **`appworld.jsonl`**: Memory library for Appworld agent interactions, covering complex task planning and execution
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patterns
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- **`bfcl_v3.jsonl`**: Working memory library for BFCL tool calls
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### Quick Usage
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```python
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# Load pre-built memories
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response = requests.post("http://localhost:8002/vector_store", json={
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"workspace_id": "appworld",
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"action": "load",
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"path": "./docs/library/"
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})
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# Query relevant memories
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response = requests.post("http://localhost:8002/retrieve_task_memory", json={
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"workspace_id": "appworld",
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"query": "How to navigate to settings and update user profile?",
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"top_k": 1
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})
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```
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@ -14,6 +14,7 @@ nav:
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- Library:
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- Library Home: library/library.md
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- Use Library: library/use_library.md
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- Personal Memory:
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- Overview: personal_memory/personal_memory.md
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@ -33,6 +34,7 @@ nav:
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- Vector Store: vector_store_api_guide.md
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- Experimental Tutorials:
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- Overview: experiment_overview.md
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- AppWorld: cookbook/appworld/quickstart.md
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- BFCL: cookbook/bfcl/quickstart.md
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- FrozenLake: cookbook/frozenlake/quickstart.md
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