Reme v2

.github/workflows/pre-commit.yml

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+name: Pre-commit
+
+on: [ push, pull_request ]
+
+jobs:
+  run:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: True
+      matrix:
+        os: [ ubuntu-latest ]
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: '3.10'
+    steps:
+      - uses: actions/checkout@master
+      - name: Setup Python
+        uses: actions/setup-python@master
+        with:
+          python-version: '3.10'
+      - name: Update setuptools
+        run: |
+          pip install -U setuptools wheel
+      - name: Install
+        run: |
+          pip install -q -e .[dev]
+      - name: Install pre-commit
+        run: |
+          pre-commit install
+      - name: Pre-commit starts
+        run: |
+          pre-commit run --all-files > pre-commit.log 2>&1 || true
+          cat pre-commit.log
+          if grep -q Failed pre-commit.log; then
+            echo -e "\e[41m  [**FAIL**] Please install pre-commit and format your code first. \e[0m"
+            exit 1
+          fi
+          echo -e "\e[46m  ********************************Passed******************************** \e[0m"

docs/mem0_learn.md

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+# ReMeV2的设计文档
+
+## 竞品调研（开源框架+商业化Memory+论文）
+### mem0
+
+开源方案 vs 闭源方案
+1. from mem0.client.main import AsyncMemoryClient, MemoryClient
+2. from mem0.memory.main import AsyncMemory, Memory
+https://docs.mem0.ai/platform/platform-vs-oss
+
+问题：之前对比的是闭源方案？
+
+memory分类【短期记忆与长期记忆】：
+https://docs.mem0.ai/core-concepts/memory-types
+短期记忆使当前的对话保持连贯性。它包括：
+- Conversation history – recent turns in order so the agent remembers what was just said.
+- Working memory – temporary state such as tool outputs or intermediate calculations.
+- Attention context – the immediate focus of the assistant, similar to what a person holds in mind mid-sentence.
+长期记忆能够将知识在不同阶段间保存下来。它记录了：
+- Factual memory – user preferences, account details, and domain facts.
+- Episodic memory – summaries of past interactions or completed tasks.
+- Semantic memory – relationships between concepts so agents can reason about them later.
+``` ?
+class MemoryType(Enum):
+    SEMANTIC = "semantic_memory"  -> personal
+    EPISODIC = "episodic_memory"   -> summary 
+    PROCEDURAL = "procedural_memory"  -> procedural
+```
+完全没有SEMANTIC和EPISODIC的引用，只有PROCEDURAL
+
+提供了哪些接口：
+https://docs.mem0.ai/core-concepts/memory-operations/add
+1. add 对应 summary workflow
+2. search 对应 retrieve workflow
+3. update 原子操作
+4. delete 原子操作
+接口层面：开源版本和闭源版本是对齐的
+
+MCP接口，
+1. add_memory	Save text or conversation history for a user/agent
+2. search_memories	Semantic search across existing memories with filters 
+3. get_memories	List memories with structured filters and pagination
+4. get_memory	Retrieve one memory by its memory_id
+5. update_memory	Overwrite a memory’s text after confirming the ID
+6. delete_memory	Delete a single memory by memory_id
+7. delete_all_memories	Bulk delete all memories in scope
+8. delete_entities	Delete a user/agent/app/run entity and its memories
+9. list_entities	Enumerate users/agents/apps/runs stored in Mem0
+前4个对齐的是闭源版本的python接口，add增加了text
+
+
+user_id概念
+user_id="alice" <==> memory_target
+agent_id/session_id可选
+多个user_id # 带讨论，库隔离
+
+### letta(MemGPT)  @weikang
+TODO
+
+
+
+## 我们相比竞品的优势
+1. 算法层面：workflow -> agentic & 渐进式方案
+2. 模型层面：mem-agent-rl
+3. 支持用户友好的接口，只用用户二开
+
+``` 同步方案
+from reme_ai import xxx_summarizer, xxx_retriever # v2的agent
+from reme_ai import xxx_tool # v2的agent
+from reme_ai import xxx_op # v1的op
+from reme_ai import OpenAILLM, OpenAIEMBEDDING
+from reme_ai import VectorStore
+
+def main():
+    reme = ReMe(
+        llm={},
+        embedding={},
+        vector_store={},
+        retriever=Retriever(ops=Opa(top_k=5) >> OpB()]
+        retriever=Retriever(tools=[ToolA(), ToolB()],
+        summarizer=Summarizer(ops=Opa(top_k=5) >> OpB()],
+        summarizer=Summarizer(tools=[ToolA(), ToolB()],
+    )
+
+    # 服务模式
+    reme.serve()
+
+    # 直接调用
+    result = reme.retrieve(
+        "food preferences",
+        filters={"user_id": "alice"}, top_k=10)
+
+    result = reme.summary(
+        user_id="alice",
+        messages=[{"role": "user", "content": "Help me create a project plan"}])
+
+    reme.close()
+
+if __name__ == "__main__":
+    main()
+```
+Reme提供接口
+1. 顶层：提供 summary 和 retrieve 的python使用 & http接口
+2. 底层db：http接口 / mcp接口（和http保持一致）
+
+
+### 引用的库希望不是外部一个小库
+合并flowllm部分逻辑回来
+
+### vector_store
+1. 复用之前的，有不断开发的需求
+2. 复用mem0，没有异步接口
+3. 使用langchain和llama-index
+   1. 需要额外封装
+   2. 之前碰到es同步接口底层是es异步封装，外部要异步很复杂
+
+增加下载量