chore: bump version number to v0.2.0

.github/CONTRIBUTING

@@ -1,3 +1,3 @@
-Please read https://memos.openmem.net/docs/contribution/overview to learn how to contribute to this repository. 🌟
+Please read https://memos-docs.openmem.net/contribution/overview to learn how to contribute to this repository. 🌟
 
-请阅读 https://memos.openmem.net/docs/contribution/overview 了解如何为此项目贡献代码。🌟
+请阅读 https://memos-docs.openmem.net/contribution/overview 了解如何为此项目贡献代码。🌟

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -2,40 +2,34 @@ name: "\U0001F680 Feature request"
 description: Submit a request for a new feature
 labels: ["enhancement", "pending"]
 body:
-  - type: markdown
-    attributes:
-      value: |
-        Please do not create issues that are not related to new features under this category.
-        请勿在此分类下创建和新特性无关的 issues。
-
   - type: checkboxes
-    id: reminder
+    id: checklist
     attributes:
-      label: Reminder
-      description: |
-        Please ensure you have read the above rules carefully and searched the existing issues.
-        请确保您已经认真阅读了上述规则并且搜索过现有的 issues。
-
+      label: Pre-submission checklist
       options:
-        - label: I have read the above rules and searched the existing issues.
+        - label: I have searched existing issues and this feature hasn't been requested before | 我已搜索现有问题，确认此功能尚未被请求
+          required: true
+        - label: I have read the project documentation and confirmed this feature doesn't already exist | 我已阅读项目文档并确认此功能尚未存在
+          required: true
+        - label: This feature request is specific to MemOS and not a general software issue | 该功能请求是针对 MemOS 的，而不是一般软件问题
           required: true
 
   - type: textarea
-    id: description
+    id: problem
     validations:
       required: true
     attributes:
-      label: Description
-      description: |
-        A clear and concise description of the feature proposal.
-        请详细描述您希望加入的新功能特性。
+      label: Problem Statement
+      placeholder: |
+        Describe the problem you're trying to solve...
+        Example: "As a developer using MemOS, I find it difficult to..."
 
-  - type: textarea
+  - type: checkboxes
     id: contribution
-    validations:
-      required: false
     attributes:
-      label: Pull Request
-      description: |
-        Have you already created the relevant PR and submitted the code?
-        您是否已经创建了相关 PR 并提交了代码？
+      label: Implementation Contribution
+      options:
+        - label: I'm willing to implement this feature myself | 我愿意自己实现此功能
+          required: false
+        - label: I would like someone else to implement this | 我希望其他人来实现此功能
+          required: false

.gitignore

@@ -11,6 +11,7 @@ evaluation/*tmp/
 evaluation/results
 evaluation/.env
 evaluation/configs/*
+**tree_textual_memory_locomo**
 .env
 
 # Byte-compiled / optimized / DLL files
@@ -165,6 +166,7 @@ venv.bak/
 *.xlsx
 *.json
 *.pkl
+*.html
 
 # but do not ignore docs/openapi.json
 !docs/openapi.json

README.md

@@ -1,12 +1,10 @@
 <div align="center">
   <a href="https://memos.openmem.net/">
-    <img src="docs/assets/banner_new.gif" alt="MemOS Banner">
+    <img src="https://statics.memtensor.com.cn/memos/memos-banner.gif" alt="MemOS Banner">
   </a>
 
-
-
 <h1 align="center">
-  <img src="docs/assets/memos_logo.png" alt="MemOS Logo" width="50"/> MemOS 1.0: 星河 (Stellar)  <img src="https://img.shields.io/badge/status-Preview-blue" alt="Preview Badge"/>
+  <img src="https://statics.memtensor.com.cn/logo/memos_color_m.png" alt="MemOS Logo" width="50"/> MemOS 1.0: 星河 (Stellar)  <img src="https://img.shields.io/badge/status-Preview-blue" alt="Preview Badge"/>
 </h1>
 
   <p>
@@ -19,7 +17,7 @@
     <a href="https://pypi.org/project/MemoryOS">
       <img src="https://img.shields.io/pypi/pyversions/MemoryOS.svg" alt="Supported Python versions">
     </a>
-    <a href="https://memos.openmem.net/docs/home">
+    <a href="https://memos-docs.openmem.net/home/overview/">
       <img src="https://img.shields.io/badge/Documentation-view-blue.svg" alt="Documentation">
     </a>
     <a href="https://arxiv.org/abs/2507.03724">
@@ -31,7 +29,7 @@
     <a href="https://discord.gg/Txbx3gebZR">
       <img src="https://img.shields.io/badge/Discord-join%20chat-7289DA.svg?logo=discord" alt="Discord">
     </a>
-    <a href="docs/assets/qr_code.png">
+    <a href="https://statics.memtensor.com.cn/memos/qr-code.png">
       <img src="https://img.shields.io/badge/WeChat-Group-07C160.svg?logo=wechat" alt="WeChat Group">
     </a>
     <a href="https://opensource.org/license/apache-2-0/">
@@ -42,17 +40,14 @@
 
 ---
 
-  <a href="https://memos.openmem.net/">
-    <img src="docs/assets/sota_score.jpg" alt="SOTA SCORE">
-  </a>
-
+<img src="https://statics.memtensor.com.cn/memos/sota_score.jpg" alt="SOTA SCORE">
 
 **MemOS** is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions.
 
-- **Website**: <a href="https://memos.openmem.net/" target="_blank">https://memos.openmem.net/</a>
-- **Documentation**: <a href="https://memos.openmem.net/docs/home" target="_blank">https://memos.openmem.net/docs/home</a>
-- **API Reference**: <a href="https://memos.openmem.net/docs/api/info" target="_blank">https://memos.openmem.net/docs/api/info</a>
-- **Source Code**: <a href="https://github.com/MemTensor/MemOS" target="_blank">https://github.com/MemTensor/MemOS</a>
+- **Website**: https://memos.openmem.net/
+- **Documentation**: https://memos-docs.openmem.net/home/overview/
+- **API Reference**: https://memos-docs.openmem.net/docs/api/info/
+- **Source Code**: https://github.com/MemTensor/MemOS
 
 ## 📈 Performance Benchmark
 
@@ -66,19 +61,12 @@ MemOS demonstrates significant improvements over baseline memory solutions in mu
 
 > 💡 **Temporal reasoning accuracy improved by 159% compared to the OpenAI baseline.**
 
-
-
 ### Details of End-to-End Evaluation on LOCOMO
 
 > [!NOTE]
 > Comparison of LLM Judge Scores across five major tasks in the LOCOMO benchmark. Each bar shows the mean evaluation score judged by LLMs for a given method-task pair, with standard deviation as error bars. MemOS-0630 consistently outperforms baseline methods (LangMem, Zep, OpenAI, Mem0) across all task types, especially in multi-hop and temporal reasoning scenarios.
 
-<a href="https://memos.openmem.net/">
-  <img src="docs/assets/score_all_end2end.jpg" alt="END2END SCORE">
-</a>
-
-
-
+<img src="https://statics.memtensor.com.cn/memos/score_all_end2end.jpg" alt="END2END SCORE">
 
 ## ✨ Key Features
 
@@ -196,7 +184,7 @@ Join our community to ask questions, share your projects, and connect with other
 - **Discord**: Join our <a href="https://discord.gg/Txbx3gebZR" target="_blank">Discord Server</a>.
 - **WeChat**: Scan the QR code to join our WeChat group.
 
-<img src="docs/assets/qr_code.png" alt="QR Code" width="600">
+<img src="https://statics.memtensor.com.cn/memos/qr-code.png" alt="QR Code" width="600">
 
 ## 📜 Citation
 
@@ -239,7 +227,7 @@ url = {https://global-sci.com/article/91443/memory3-language-modeling-with-expli
 
 ## 🙌 Contributing
 
-We welcome contributions from the community! Please read our [contribution guidelines](https://memos.openmem.net/docs/contribution/overview) to get started.
+We welcome contributions from the community! Please read our [contribution guidelines](https://memos-docs.openmem.net/contribution/overview) to get started.
 
 ## 📄 License
 

evaluation/README.md

@@ -25,10 +25,12 @@ This repository provides tools and scripts for evaluating the LoCoMo dataset usi
 ## Evaluation Scripts
 
 ### LoCoMo Evaluation
-To evaluate the **LoCoMo** dataset using one of the supported memory frameworks — `memos`, `mem0`, or `zep` — run the following command:
+⚙️ To evaluate the **LoCoMo** dataset using one of the supported memory frameworks — `memos`, `mem0`, or `zep` — run the following [script](./scripts/run_locomo_eval.sh):
 
 ```bash
 # Edit the configuration in ./scripts/run_locomo_eval.sh
 # Specify the model and memory backend you want to use (e.g., mem0, zep, etc.)
 ./scripts/run_locomo_eval.sh
 ```
+
+✍️ For evaluating OpenAI's native memory feature with the LoCoMo dataset, please refer to the detailed guide: [OpenAI Memory on LoCoMo - Evaluation Guide](./scripts/locomo/openai_memory_locomo_eval_guide.md).

evaluation/scripts/locomo/locomo_openai.py

@@ -0,0 +1,173 @@
+import argparse
+import json
+import os
+import time
+
+from collections import defaultdict
+from multiprocessing.dummy import Pool
+
+from dotenv import load_dotenv
+from openai import OpenAI
+from tenacity import retry, stop_after_attempt, wait_random_exponential
+from tqdm import tqdm
+
+
+load_dotenv()
+
+# Retry policy constants
+WAIT_MIN = 5  # minimum backoff delay in seconds
+WAIT_MAX = 30  # maximum backoff delay in seconds
+MAX_TRIES = 10  # maximum number of retry attempts
+
+WORKERS = 5  # number of parallel worker processes
+
+ANSWER_PROMPT = """
+    You are an intelligent memory assistant tasked with retrieving accurate information from conversation memories.
+
+    # CONTEXT:
+    You have access to memories from a conversation. These memories contain
+    timestamped information that may be relevant to answering the question.
+
+    # INSTRUCTIONS:
+    1. Carefully analyze all provided memories
+    2. Pay special attention to the timestamps to determine the answer
+    3. If the question asks about a specific event or fact, look for direct evidence in the memories
+    4. If the memories contain contradictory information, prioritize the most recent memory
+    5. If there is a question about time references (like "last year", "two months ago", etc.),
+       calculate the actual date based on the memory timestamp. For example, if a memory from
+       4 May 2022 mentions "went to India last year," then the trip occurred in 2021.
+    6. Always convert relative time references to specific dates, months, or years. For example,
+       convert "last year" to "2022" or "two months ago" to "March 2023" based on the memory
+       timestamp. Ignore the reference while answering the question.
+    7. Focus only on the content of the memories. Do not confuse character
+       names mentioned in memories with the actual users who created those memories.
+    8. The answer should be less than 5-6 words.
+
+    # APPROACH (Think step by step):
+    1. First, examine all memories that contain information related to the question
+    2. Examine the timestamps and content of these memories carefully
+    3. Look for explicit mentions of dates, times, locations, or events that answer the question
+    4. If the answer requires calculation (e.g., converting relative time references), show your work
+    5. Formulate a precise, concise answer based solely on the evidence in the memories
+    6. Double-check that your answer directly addresses the question asked
+    7. Ensure your final answer is specific and avoids vague time references
+
+    Memories:
+
+    {context}
+
+    Question: {question}
+    Answer:
+    """
+
+
+class OpenAIPredict:
+    def __init__(self, model="gpt-4o-mini"):
+        self.model = model
+        self.openai_client = OpenAI(
+            api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("OPENAI_BASE_URL")
+        )
+        self.results = defaultdict(list)
+
+    def search_memory(self, idx):
+        with open(f"openai_memory/{idx}.txt", encoding="utf-8") as file:
+            memories = file.read().strip().replace("\n\n", "\n")
+
+        return memories, 0
+
+    def process_question(self, val, idx):
+        question = val.get("question", "")
+        answer = val.get("answer", "")
+        category = val.get("category", -1)
+
+        response, search_memory_time, response_time, context = self.answer_question(idx, question)
+
+        result = {
+            "question": question,
+            "answer": response,
+            "category": category,
+            "golden_answer": answer,
+            "search_context": context,
+            "response_duration_ms": response_time,
+            "search_duration_ms": search_memory_time,
+        }
+
+        return result
+
+    @retry(
+        wait=wait_random_exponential(min=WAIT_MIN, max=WAIT_MAX),
+        stop=stop_after_attempt(MAX_TRIES),
+        reraise=True,
+    )
+    def answer_question(self, idx, question):
+        memories, search_memory_time = self.search_memory(idx)
+
+        answer_prompt = ANSWER_PROMPT.format(context=memories, question=question)
+
+        t1 = time.time()
+        response = self.openai_client.chat.completions.create(
+            model=self.model,
+            messages=[{"role": "system", "content": answer_prompt}],
+            temperature=0.0,
+        )
+        t2 = time.time()
+        response_time = (t2 - t1) * 1000
+        return response.choices[0].message.content, search_memory_time, response_time, memories
+
+    def process_data_file(self, file_path, output_file_path):
+        with open(file_path, encoding="utf-8") as f:
+            data = json.load(f)
+
+        # Function to process each conversation
+        def process_conversation(item):
+            idx, conversation = item
+            results_for_conversation = []
+
+            # Process each question in the conversation
+            for question_item in tqdm(
+                conversation["qa"], desc=f"Processing questions for conversation {idx}", leave=False
+            ):
+                if int(question_item.get("category", "")) == 5:
+                    continue
+                result = self.process_question(question_item, idx)
+                results_for_conversation.append(result)
+
+            return idx, results_for_conversation
+
+        # Use multiprocessing to process the conversations in parallel
+        with Pool(processes=WORKERS) as pool:
+            results = list(
+                tqdm(
+                    pool.imap(process_conversation, list(enumerate(data))),
+                    total=len(data),
+                    desc="Processing conversations",
+                )
+            )
+
+        # Reorganize results and store them in self.results
+        for idx, results_for_conversation in results:
+            self.results[f"locomo_exp_user_{idx}"] = results_for_conversation
+
+        # Save results to output file
+        with open(output_file_path, "w") as f:
+            json.dump(self.results, f, indent=4)
+
+
+def main(version):
+    os.makedirs(f"results/locomo/openai-{version}/", exist_ok=True)
+    output_file_path = f"results/locomo/openai-{version}/openai_locomo_responses.json"
+    openai_predict = OpenAIPredict()
+    openai_predict.process_data_file("data/locomo/locomo10.json", output_file_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--version",
+        type=str,
+        default="default",
+        help="Version identifier for loading results (e.g., 1010)",
+    )
+    args = parser.parse_args()
+    version = args.version
+    main(version)