2026-08-28 05:25:04 +00:00
538 changed files with 9055 additions and 85664 deletions
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@ -1,97 +0,0 @@
-name: Bug report
-description: Report reproducible incorrect or unexpected ReMe behavior
-title: "[Bug]: "
-labels: [bug]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for helping improve ReMe. Please remove secrets, API keys, and private memory content before submitting.
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Description
-      description: What happened, and what did you expect instead?
-      placeholder: Describe the observed and expected behavior.
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduce
-    attributes:
-      label: Steps to reproduce
-      description: Provide the smallest configuration and command sequence that reproduces the problem.
-      placeholder: |
-        1. Configure ...
-        2. Run ...
-        3. Observe ...
-    validations:
-      required: true
-
-  - type: textarea
-    id: config
-    attributes:
-      label: Relevant configuration
-      description: Include only relevant values and redact credentials, tokens, endpoints, and private paths.
-      render: yaml
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Logs or traceback
-      description: Paste relevant output after removing secrets and private workspace content.
-      render: shell
-
-  - type: input
-    id: reme-version
-    attributes:
-      label: ReMe version
-      placeholder: e.g. 0.4.1.8 or a commit SHA
-    validations:
-      required: true
-
-  - type: input
-    id: python-version
-    attributes:
-      label: Python version
-      placeholder: e.g. 3.11.9
-    validations:
-      required: true
-
-  - type: dropdown
-    id: os
-    attributes:
-      label: Operating system
-      options:
-        - Linux
-        - macOS
-        - Windows
-        - Other
-    validations:
-      required: true
-
-  - type: dropdown
-    id: area
-    attributes:
-      label: Affected area
-      options:
-        - CLI or configuration
-        - HTTP, MCP, or local service
-        - Memory or workspace files
-        - Search, catalog, graph, or index
-        - Model or agent integration
-        - ReMe Studio
-        - Plugin or external integration
-        - Packaging or installation
-        - Other
-    validations:
-      required: true
-
-  - type: checkboxes
-    id: safety
-    attributes:
-      label: Data safety
-      options:
-        - label: I removed credentials and private memory content from this report.
-          required: true
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@ -1,8 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: ReMe documentation
-    url: https://reme.agentscope.io
-    about: Read the installation, configuration, and usage guides.
-  - name: Existing issues
-    url: https://github.com/agentscope-ai/ReMe/issues
-    about: Search for existing reports and discussions before opening a new issue.
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@ -1,64 +0,0 @@
-name: Feature request
-description: Propose a focused enhancement to ReMe
-title: "[Feature]: "
-labels: [enhancement]
-body:
-  - type: textarea
-    id: problem
-    attributes:
-      label: Problem
-      description: What user problem or limitation should this change address?
-    validations:
-      required: true
-
-  - type: textarea
-    id: proposal
-    attributes:
-      label: Proposed behavior
-      description: Describe the desired behavior and its user-visible contract.
-    validations:
-      required: true
-
-  - type: dropdown
-    id: area
-    attributes:
-      label: Area
-      options:
-        - CLI or configuration
-        - Jobs or steps
-        - Memory or workspace files
-        - Search, catalog, graph, or index
-        - Service or client
-        - Model or agent integration
-        - ReMe Studio
-        - Plugin or external integration
-        - Documentation
-        - Other
-    validations:
-      required: true
-
-  - type: textarea
-    id: ownership
-    attributes:
-      label: Local-first and compatibility considerations
-      description: Explain any effect on user-owned files, rebuildable state, configuration, schemas, or service interfaces.
-
-  - type: textarea
-    id: alternatives
-    attributes:
-      label: Alternatives considered
-      description: Describe workarounds or alternative designs you considered.
-
-  - type: textarea
-    id: examples
-    attributes:
-      label: Example usage
-      description: Show the proposed CLI, configuration, API, or UI behavior when useful.
-      render: shell
-
-  - type: checkboxes
-    id: contribution
-    attributes:
-      label: Contribution
-      options:
-        - label: I am willing to help implement or test this feature.
--- a/.github/ISSUE_TEMPLATE/question.yml
+++ b/.github/ISSUE_TEMPLATE/question.yml
@ -1,53 +0,0 @@
-name: Usage question
-description: Ask for help using or configuring ReMe
-title: "[Question]: "
-labels: [question]
-body:
-  - type: markdown
-    attributes:
-      value: Please check the documentation and existing issues before asking a new question.
-
-  - type: textarea
-    id: goal
-    attributes:
-      label: What are you trying to achieve?
-    validations:
-      required: true
-
-  - type: textarea
-    id: attempted
-    attributes:
-      label: What have you tried?
-      description: Include relevant commands or configuration, with secrets and private memory content removed.
-    validations:
-      required: true
-
-  - type: input
-    id: reme-version
-    attributes:
-      label: ReMe version
-      placeholder: e.g. 0.4.1.8 or a commit SHA
-
-  - type: dropdown
-    id: area
-    attributes:
-      label: Area
-      options:
-        - Installation
-        - Configuration
-        - CLI or service usage
-        - Memory and workspace management
-        - Search and retrieval
-        - ReMe Studio
-        - Plugin or integration
-        - Other
-
-  - type: checkboxes
-    id: checked
-    attributes:
-      label: Before submitting
-      options:
-        - label: I checked the [ReMe documentation](https://reme.agentscope.io) and searched existing issues.
-          required: true
-        - label: I removed credentials and private memory content.
-          required: true
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@ -1,35 +0,0 @@
-## Summary
-
-<!-- Explain the problem and the smallest coherent change that addresses it. -->
-
-## Related issue
-
-<!-- Use "Fixes #123" when applicable. -->
-
-## Contract and data impact
-
- [ ] No public configuration, schema, CLI, endpoint, streaming, or workspace-layout contract changes
- [ ] No user-owned memory files are deleted or rewritten
- [ ] Derived indexes, catalogs, graphs, caches, and metadata remain rebuildable
-
-<!-- If any item is unchecked, describe the impact and migration or recovery path. -->
-
-## Validation
-
-<!-- List the exact checks run and their results. Explain relevant checks that were not run. -->
-
- [ ] Focused tests pass
- [ ] Unit tests pass, or omitted tests are explained below
- [ ] `pre-commit run --all-files` passes, or omitted checks are explained below
- [ ] Frontend checks were run when `reme_studio/` changed
-
-## Checklist
-
- [ ] I reviewed the diff for unrelated changes and sensitive data
- [ ] Tests cover intentional behavior changes
- [ ] Defaults, schemas, and concise documentation were updated together when required
- [ ] Long-lived clients, tasks, services, and executors follow the application lifecycle
-
-## Screenshots or additional notes
-
-<!-- Include UI screenshots, compatibility notes, or follow-up work when relevant. -->
--- a/.github/workflows/_build-docs.yml
+++ b/.github/workflows/_build-docs.yml
@ -1,58 +0,0 @@
-name: _Build documentation
-
-on:
-  workflow_call:
-    inputs:
-      run_tests:
-        description: Run the documentation test suite before building
-        required: false
-        default: true
-        type: boolean
-      upload_pages_artifact:
-        description: Upload the build for a later GitHub Pages deployment job
-        required: false
-        default: false
-        type: boolean
-
-permissions:
-  contents: read
-
-jobs:
-  build:
-    name: Build documentation
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: github-pages
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Node
-        uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: '22.22.3'
-          cache: npm
-          cache-dependency-path: github-pages/package-lock.json
-
-      - name: Install dependencies
-        run: npm ci
-
-      - name: Run tests
-        if: inputs.run_tests
-        run: npm test
-
-      - name: Build documentation
-        run: npm run build
-
-      - name: Configure Pages
-        if: inputs.upload_pages_artifact
-        uses: actions/configure-pages@45bfe0192ca1faeb007ade9deae92b16b8254a0d # v6
-
-      - name: Upload Pages artifact
-        if: inputs.upload_pages_artifact
-        uses: actions/upload-pages-artifact@7b1f4a764d45c48632c6b24a0339c27f5614fb0b # v4
-        with:
-          path: github-pages/dist
--- a/.github/workflows/_build-python-packages.yml
+++ b/.github/workflows/_build-python-packages.yml
@ -1,88 +0,0 @@
-name: _Build Python packages
-
-on:
-  workflow_call:
-    inputs:
-      expected_version:
-        description: Expected release version; omit for a consistency-only check
-        required: false
-        default: ''
-        type: string
-      upload_artifacts:
-        description: Upload distributions for later publish jobs
-        required: false
-        default: false
-        type: boolean
-
-permissions:
-  contents: read
-
-jobs:
-  distributions:
-    name: Build Python distributions
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Python
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: '3.11'
-
-      - name: Install build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install build packaging pytest twine
-
-      - name: Validate package versions
-        if: inputs.expected_version == ''
-        run: python scripts/bump_version.py --check
-
-      - name: Validate release version
-        if: inputs.expected_version != ''
-        env:
-          EXPECTED_VERSION: ${{ inputs.expected_version }}
-        run: python scripts/bump_version.py --check --expected-version "${EXPECTED_VERSION}"
-
-      - name: Run package tests
-        run: PYTHONPATH=. python -m pytest tests/unit/test_package_versions.py -q
-
-      - name: Build and check distributions
-        run: |
-          mkdir -p dist/reme
-          python -m build --outdir dist/reme
-          python -m twine check dist/reme/*
-
-      - name: Verify distributions and isolated installation
-        run: |
-          REME_WHEEL="$(pwd)/$(ls dist/reme/reme_ai-[0-9]*.whl)"
-          python -m zipfile -l "${REME_WHEEL}" | (! grep 'reme/web/')
-          python -m zipfile -l "${REME_WHEEL}" | (! grep 'reme_studio/')
-          python -m venv "${RUNNER_TEMP}/reme-package-smoke"
-          "${RUNNER_TEMP}/reme-package-smoke/bin/python" -m pip install "${REME_WHEEL}[as]"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-package-smoke/bin/python" -c "import reme"
-
-      - name: Verify released core dependencies
-        if: inputs.expected_version != ''
-        run: |
-          REME_WHEEL="$(pwd)/$(ls dist/reme/reme_ai-[0-9]*.whl)"
-          python -m venv "${RUNNER_TEMP}/reme-core-package-smoke"
-          "${RUNNER_TEMP}/reme-core-package-smoke/bin/python" -m pip install "${REME_WHEEL}[core]"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-core-package-smoke/bin/python" - <<'PY'
-          from reme_studio import static_dir
-
-          assert (static_dir() / "index.html").is_file()
-          PY
-
-      - name: Upload ReMe distributions
-        if: inputs.upload_artifacts
-        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6
-        with:
-          name: reme-distributions
-          path: dist/reme/
-          if-no-files-found: error
--- a/.github/workflows/ci-docs.yml
+++ b/.github/workflows/ci-docs.yml
@ -1,48 +0,0 @@
-name: CI / Documentation
-
-on:
-  push:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-docs.yml'
-      - '.github/workflows/_build-docs.yml'
-      - 'AGENTS.md'
-      - 'README.md'
-      - 'README_ZH.md'
-      - 'docs/**'
-      - 'github-pages/**'
-      - 'reme_studio/README*.md'
-      - 'reme_studio/public/og.jpg'
-      - 'typescript/README*.md'
-      - 'plugins/*/README*.md'
-      - 'benchmark/*/README*.md'
-  pull_request:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-docs.yml'
-      - '.github/workflows/_build-docs.yml'
-      - 'AGENTS.md'
-      - 'README.md'
-      - 'README_ZH.md'
-      - 'docs/**'
-      - 'github-pages/**'
-      - 'reme_studio/README*.md'
-      - 'reme_studio/public/og.jpg'
-      - 'typescript/README*.md'
-      - 'plugins/*/README*.md'
-      - 'benchmark/*/README*.md'
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  documentation:
-    name: Test and build documentation
-    uses: ./.github/workflows/_build-docs.yml
-    with:
-      run_tests: true
--- a/.github/workflows/ci-packages.yml
+++ b/.github/workflows/ci-packages.yml
@ -1,40 +0,0 @@
-name: CI / Python packages
-
-on:
-  push:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-packages.yml'
-      - '.github/workflows/_build-python-packages.yml'
-      - '.github/workflows/release-python.yml'
-      - 'pyproject.toml'
-      - 'README.md'
-      - 'reme/**'
-      - 'scripts/bump_version.py'
-      - 'tests/unit/test_package_versions.py'
-      - 'LICENSE'
-  pull_request:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-packages.yml'
-      - '.github/workflows/_build-python-packages.yml'
-      - '.github/workflows/release-python.yml'
-      - 'pyproject.toml'
-      - 'README.md'
-      - 'reme/**'
-      - 'scripts/bump_version.py'
-      - 'tests/unit/test_package_versions.py'
-      - 'LICENSE'
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  distributions:
-    name: Build and verify distributions
-    uses: ./.github/workflows/_build-python-packages.yml
--- a/.github/workflows/ci-python-quality.yml
+++ b/.github/workflows/ci-python-quality.yml
@ -1,40 +0,0 @@
-name: CI / Python quality
-
-on:
-  push:
-  pull_request:
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  pre-commit:
-    name: Pre-commit
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Setup Python
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: '3.11'
-          cache: pip
-
-      - name: Update setuptools
-        run: |
-          pip install -U setuptools wheel
-
-      - name: Install
-        run: |
-          pip install -q -e reme_studio -e ".[dev,core]"
-          pip install -q --no-deps -e plugins/auto-fin -e plugins/daily_paper
-
-      - name: Pre-commit starts
-        run: pre-commit run --all-files
--- a/.github/workflows/ci-reme-studio.yml
+++ b/.github/workflows/ci-reme-studio.yml
@ -1,90 +0,0 @@
-name: CI / ReMe Studio
-
-on:
-  push:
-    paths:
-      - "reme_studio/**"
-      - ".github/workflows/ci-reme-studio.yml"
-      - ".github/workflows/release-reme-studio.yml"
-      - "scripts/package_studio.py"
-      - "tests/unit/test_package_versions.py"
-      - "pyproject.toml"
-      - "LICENSE"
-  pull_request:
-    paths:
-      - "reme_studio/**"
-      - ".github/workflows/ci-reme-studio.yml"
-      - ".github/workflows/release-reme-studio.yml"
-      - "scripts/package_studio.py"
-      - "tests/unit/test_package_versions.py"
-      - "pyproject.toml"
-      - "LICENSE"
-
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  studio:
-    name: Studio checks
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: reme_studio
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Setup Node
-        uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: "22.22.3"
-          cache: npm
-          cache-dependency-path: reme_studio/package-lock.json
-
-      - name: Install dependencies
-        run: npm ci
-
-      - name: Run format check
-        run: npm run format:check
-
-      - name: Run lint
-        run: npm run lint
-
-      - name: Run tests
-        run: npm test
-
-      - name: Verify npm package
-        run: |
-          npm pack --pack-destination "${RUNNER_TEMP}"
-          tar -tzf "${RUNNER_TEMP}"/agentscope-ai-reme_studio-*.tgz | grep '^package/dist-static/index.html$'
-
-      - name: Set up Python
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: "3.11"
-
-      - name: Build and verify Python package
-        working-directory: .
-        run: |
-          python -m pip install build packaging pytest twine
-          PYTHONPATH=. python -m pytest tests/unit/test_package_versions.py -q
-          python scripts/package_studio.py
-          python -m build reme_studio --outdir dist/studio
-          python -m twine check dist/studio/*
-          STUDIO_WHEEL="$(pwd)/$(ls dist/studio/reme_studio-*.whl)"
-          python -m venv "${RUNNER_TEMP}/reme-studio-package-smoke"
-          "${RUNNER_TEMP}/reme-studio-package-smoke/bin/python" -m pip install "${STUDIO_WHEEL}"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-studio-package-smoke/bin/python" - <<'PY'
-          from reme_studio import static_dir
-
-          assert (static_dir() / "index.html").is_file()
-          PY
--- a/.github/workflows/ci-typescript.yml
+++ b/.github/workflows/ci-typescript.yml
@ -1,51 +0,0 @@
-name: CI / TypeScript integrations
-
-on:
-  push:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-typescript.yml'
-      - '.github/workflows/release-typescript.yml'
-      - 'typescript/**'
-  pull_request:
-    branches: [main, master, dev, develop]
-    paths:
-      - '.github/workflows/ci-typescript.yml'
-      - '.github/workflows/release-typescript.yml'
-      - 'typescript/**'
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  package:
-    name: Type-check, test, and pack
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: typescript
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: '22.22.3'
-          cache: npm
-          cache-dependency-path: typescript/package-lock.json
-
-      - run: npm ci
-      - run: npm run format:check
-      - run: npm run lint
-      - run: npm run typecheck
-      - run: npm test
-      - run: npm run test:package
-      - name: Validate OpenClaw package contract
-        run: npx --yes clawhub@0.23.3 package validate . --json
--- a/.github/workflows/ci-windows.yml
+++ b/.github/workflows/ci-windows.yml
@ -1,51 +0,0 @@
-name: CI / Windows
-
-on:
-  push:
-    branches: [main, master, dev, develop]
-  pull_request:
-    branches: [main, master, dev, develop]
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-permissions:
-  contents: read
-
-jobs:
-  cli-smoke:
-    name: CLI smoke - py${{ matrix.python-version }}
-    runs-on: windows-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.11"]
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-
-      - name: Install package
-        run: |
-          python -m pip install --upgrade pip setuptools wheel
-          pip install -e ".[dev,as]"
-
-      - name: Run version job
-        run: reme start config=tests/fixtures/config/version-smoke.yaml job=version
-
-      - name: Run Windows path tests
-        run: |
-          python -m pytest `
-            tests/unit/test_auto_dream.py::test_scan_day_files_includes_nested_md_and_excludes_interests `
-            tests/unit/test_auto_dream.py::test_dream_extract_matches_posix_catalog_paths `
-            tests/unit/test_read_with_neighbors.py::test_read_with_neighbors_uses_posix_nested_path `
-            -v
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@ -1,52 +0,0 @@
-name: Deploy / Documentation
-
-on:
-  push:
-    branches: [main]
-    paths:
-      - "github-pages/**"
-      - "docs/**"
-      - "README.md"
-      - "README_ZH.md"
-      - "reme_studio/README*.md"
-      - "reme_studio/public/og.jpg"
-      - "typescript/README*.md"
-      - "plugins/*/README*.md"
-      - "benchmark/*/README*.md"
-      - "AGENTS.md"
-      - ".github/workflows/deploy-docs.yml"
-      - ".github/workflows/_build-docs.yml"
-  workflow_dispatch:
-
-permissions:
-  contents: read
-
-concurrency:
-  group: pages
-  cancel-in-progress: true
-
-jobs:
-  build:
-    name: Build documentation
-    uses: ./.github/workflows/_build-docs.yml
-    with:
-      run_tests: true
-      upload_pages_artifact: true
-    permissions:
-      contents: read
-      pages: write
-      id-token: write
-
-  deploy:
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-latest
-    needs: build
-    permissions:
-      pages: write
-      id-token: write
-    steps:
-      - name: Deploy
-        id: deployment
-        uses: actions/deploy-pages@cd2ce8fcbc39b97be8ca5fce6e763baed58fa128 # v5
--- a/.github/workflows/policy-pr-title.yml
+++ b/.github/workflows/policy-pr-title.yml
@ -1,20 +1,16 @@
-name: Policy / PR title
+name: PR Title Check

 on:
  pull_request:
    branches: [main, master, dev, develop]
    types: [opened, edited, synchronize, reopened]

-permissions:
-  contents: read
-  pull-requests: read
-
 jobs:
  check-pr-title:
    runs-on: ubuntu-latest
    steps:
      - name: Check PR title format
-        uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6.1.1
+        uses: amannn/action-semantic-pull-request@v6.1.1
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@ -0,0 +1,38 @@
+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: '3.11'
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - name: Update setuptools
+        run: |
+          pip install -U setuptools wheel
+      - name: Install
+        run: |
+          pip install -q -e ".[dev,core]"
+      - 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"
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@ -0,0 +1,45 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Publish Python Package to Pypi
+
+on:
+  workflow_dispatch:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v6
+    - name: Set up Python
+      uses: actions/setup-python@v6
+      with:
+        python-version: '3.11'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel build
+    - name: Build package
+      run: python -m build
+    - name: Test installation
+      run: |
+        WHEEL="$(ls dist/*.whl)"
+        pip install "${WHEEL}[core]"
+        python -c "import reme; print(reme.__version__)"
+    - name: Publish package to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}
--- a/.github/workflows/release-auto-fin.yml
+++ b/.github/workflows/release-auto-fin.yml
@ -1,157 +0,0 @@
-# 发布操作手册：
-# 1. 先将 plugins/auto-fin/pyproject.toml 中的 project.version 更新为待发布版本并合入目标分支。
-# 2. 确认插件依赖的 reme-ai 版本已经发布到 PyPI；本工作流会在构建阶段验证该依赖可下载。
-# 3. 确认 PyPI Trusted Publisher 已绑定本仓库、此工作流和 pypi environment，且 PyPI 上不存在相同版本。
-# 4. 在 GitHub 仓库的 Actions 页面选择“Release / Auto Fin plugin”，点击“Run workflow”。
-# 5. 输入与 project.version 完全一致的版本号（例如 0.1.0）后运行；版本也可以带 v 前缀。
-#
-# 推荐发布顺序：reme-ai -> reme-auto-fin -> QwenPaw 更新依赖并通过 plugins: [auto-fin] 启用。
-# 当前仅支持 workflow_dispatch 手动触发，不会因 push、tag 或 release 自动发布。
-
-name: Release / Auto Fin plugin
-
-run-name: Publish reme-auto-fin ${{ inputs.version }}
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: Version from plugins/auto-fin/pyproject.toml (for example, 0.1.0)
-        required: true
-        type: string
-
-permissions:
-  contents: read
-
-concurrency:
-  group: publish-reme-auto-fin
-  cancel-in-progress: false
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    env:
-      RELEASE_VERSION: ${{ inputs.version }}
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Python
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: '3.11'
-
-      - name: Install test and build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install build packaging pytest pytest-asyncio twine
-          python -m pip install -e ".[core]"
-          python -m pip install --no-deps -e plugins/auto-fin
-
-      - name: Validate package name and release version
-        id: package
-        run: |
-          python - "${RELEASE_VERSION}" <<'PY'
-          import os
-          import sys
-          import tomllib
-          from pathlib import Path
-
-          from packaging.requirements import Requirement
-          from packaging.version import Version
-
-          project = tomllib.loads(Path("plugins/auto-fin/pyproject.toml").read_text(encoding="utf-8"))["project"]
-          expected = Version(sys.argv[1].removeprefix("v"))
-          actual = Version(project["version"])
-          if project["name"] != "reme-auto-fin":
-              raise SystemExit(f"Expected project name 'reme-auto-fin', found {project['name']!r}")
-          if actual != expected:
-              raise SystemExit(f"Package version is {actual}, but workflow input is {expected}")
-          requirements = [requirement for requirement in project["dependencies"] if requirement.startswith("reme-ai")]
-          if len(requirements) != 1:
-              raise SystemExit(f"Expected one reme-ai dependency, found {requirements!r}")
-          reme_requirement = Requirement(requirements[0])
-          if reme_requirement.name != "reme-ai" or reme_requirement.extras:
-              raise SystemExit(f"Expected a base reme-ai dependency, found {requirements[0]!r}")
-          if Version("0.4.1.8") in reme_requirement.specifier or Version("0.4.1.9") not in reme_requirement.specifier:
-              raise SystemExit(f"Expected reme-ai>=0.4.1.9, found {requirements[0]!r}")
-          with Path(os.environ["GITHUB_OUTPUT"]).open("a", encoding="utf-8") as output:
-              print(f"reme_requirement={reme_requirement}", file=output)
-          print(f"Publishing {project['name']} {actual}")
-          PY
-
-      - name: Run Auto Fin tests
-        run: python -m pytest plugins/auto-fin -q
-
-      - name: Require the plugin-enabled ReMe release on PyPI
-        env:
-          REME_REQUIREMENT: ${{ steps.package.outputs.reme_requirement }}
-        run: |
-          python -m pip download --no-deps \
-            --dest "${RUNNER_TEMP}/reme-auto-fin-base" \
-            "${REME_REQUIREMENT}"
-
-      - name: Build and check distributions
-        run: |
-          mkdir -p dist/auto-fin
-          python -m build plugins/auto-fin --outdir dist/auto-fin
-          python -m twine check dist/auto-fin/*
-
-      - name: Verify distributions and isolated installation
-        run: |
-          AUTO_FIN_WHEEL="$(pwd)/$(ls dist/auto-fin/reme_auto_fin-*.whl)"
-          AUTO_FIN_SDIST="$(pwd)/$(ls dist/auto-fin/reme_auto_fin-*.tar.gz)"
-          python -m zipfile -l "${AUTO_FIN_WHEEL}" | grep 'dist-info/licenses/LICENSE'
-          python -m tarfile -l "${AUTO_FIN_SDIST}" | grep '/LICENSE'
-          python -m venv "${RUNNER_TEMP}/reme-auto-fin-smoke"
-          "${RUNNER_TEMP}/reme-auto-fin-smoke/bin/python" -m pip install \
-            "agentscope[model-ollama]==2.0.7" "${AUTO_FIN_WHEEL}"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-auto-fin-smoke/bin/python" - <<'PY'
-          from importlib.metadata import distribution
-
-          from reme.plugin_manifest import load_package_manifest
-
-          package = distribution("reme-auto-fin")
-          plugins = {entry.name: entry for entry in package.entry_points if entry.group == "reme.plugins"}
-          assert plugins["auto-fin"].value == "reme_auto_fin"
-          manifest = load_package_manifest("reme_auto_fin", plugin_name="auto-fin")
-          assert set(manifest.backends) == {
-              "auto_fin_data_step",
-              "auto_fin_topic_step",
-              "auto_fin_merge_step",
-          }
-          assert set(manifest.application_defaults["jobs"]) == {
-              "auto_fin",
-              "auto_fin_cron",
-          }
-          PY
-
-      - name: Upload distributions
-        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6
-        with:
-          name: reme-auto-fin-${{ inputs.version }}
-          path: dist/auto-fin/
-          if-no-files-found: error
-
-  publish:
-    needs: build
-    runs-on: ubuntu-latest
-    environment: pypi
-    permissions:
-      contents: read
-      id-token: write
-
-    steps:
-      - name: Download distributions
-        uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: reme-auto-fin-${{ inputs.version }}
-          path: dist/auto-fin
-
-      - name: Publish reme-auto-fin
-        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # release/v1
-        with:
-          packages-dir: dist/auto-fin
--- a/.github/workflows/release-daily-paper.yml
+++ b/.github/workflows/release-daily-paper.yml
@ -1,157 +0,0 @@
-# Release checklist:
-# 1. Update project.version in plugins/daily_paper/pyproject.toml and merge it into the target branch.
-# 2. Publish the required reme-ai version before this plugin; the build verifies that dependency on PyPI.
-# 3. Configure PyPI Trusted Publishing for this repository/workflow and its pypi environment.
-# 4. Run "Release / Daily Paper plugin" from GitHub Actions with the exact project version (a v prefix is accepted).
-#
-# Recommended order: reme-ai -> reme-daily-paper -> downstream applications enabling plugins: [daily-paper].
-# This workflow is intentionally manual and never publishes from a push, tag, or GitHub release event.
-
-name: Release / Daily Paper plugin
-
-run-name: Publish reme-daily-paper ${{ inputs.version }}
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: Version from plugins/daily_paper/pyproject.toml (for example, 0.1.0)
-        required: true
-        type: string
-
-permissions:
-  contents: read
-
-concurrency:
-  group: publish-reme-daily-paper
-  cancel-in-progress: false
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    env:
-      RELEASE_VERSION: ${{ inputs.version }}
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Python
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: '3.11'
-
-      - name: Install test and build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install build packaging pytest pytest-asyncio twine
-          python -m pip install -e ".[core]"
-          python -m pip install -e plugins/daily_paper
-
-      - name: Validate package name, dependencies, and release version
-        id: package
-        run: |
-          python - "${RELEASE_VERSION}" <<'PY'
-          import os
-          import sys
-          import tomllib
-          from pathlib import Path
-
-          from packaging.requirements import Requirement
-          from packaging.version import Version
-
-          project = tomllib.loads(Path("plugins/daily_paper/pyproject.toml").read_text(encoding="utf-8"))["project"]
-          expected = Version(sys.argv[1].removeprefix("v"))
-          actual = Version(project["version"])
-          if project["name"] != "reme-daily-paper":
-              raise SystemExit(f"Expected project name 'reme-daily-paper', found {project['name']!r}")
-          if actual != expected:
-              raise SystemExit(f"Package version is {actual}, but workflow input is {expected}")
-          requirements = [Requirement(value) for value in project["dependencies"]]
-          reme_requirements = [requirement for requirement in requirements if requirement.name == "reme-ai"]
-          if len(reme_requirements) != 1 or reme_requirements[0].extras:
-              raise SystemExit(f"Expected one base reme-ai dependency, found {reme_requirements!r}")
-          if Version("0.4.1.8") in reme_requirements[0].specifier or Version("0.4.1.9") not in reme_requirements[0].specifier:
-              raise SystemExit(f"Expected reme-ai>=0.4.1.9, found {reme_requirements!r}")
-          if sum(requirement.name == "pypdf" for requirement in requirements) != 1:
-              raise SystemExit("Expected exactly one pypdf dependency")
-          with Path(os.environ["GITHUB_OUTPUT"]).open("a", encoding="utf-8") as output:
-              print(f"reme_requirement={reme_requirements[0]}", file=output)
-          print(f"Publishing {project['name']} {actual}")
-          PY
-
-      - name: Run Daily Paper tests
-        run: python -m pytest plugins/daily_paper -q
-
-      - name: Require the plugin-enabled ReMe release on PyPI
-        env:
-          REME_REQUIREMENT: ${{ steps.package.outputs.reme_requirement }}
-        run: |
-          python -m pip download --no-deps \
-            --dest "${RUNNER_TEMP}/reme-daily-paper-base" \
-            "${REME_REQUIREMENT}"
-
-      - name: Build and check distributions
-        run: |
-          mkdir -p dist/daily-paper
-          python -m build plugins/daily_paper --outdir dist/daily-paper
-          python -m twine check dist/daily-paper/*
-
-      - name: Verify distributions and isolated installation
-        run: |
-          DAILY_PAPER_WHEEL="$(pwd)/$(ls dist/daily-paper/reme_daily_paper-*.whl)"
-          DAILY_PAPER_SDIST="$(pwd)/$(ls dist/daily-paper/reme_daily_paper-*.tar.gz)"
-          python -m zipfile -l "${DAILY_PAPER_WHEEL}" | grep 'reme_daily_paper/plugin.yaml'
-          python -m zipfile -l "${DAILY_PAPER_WHEEL}" | grep 'reme_daily_paper/analyze.yaml'
-          python -m zipfile -l "${DAILY_PAPER_WHEEL}" | grep 'dist-info/licenses/LICENSE'
-          python -m tarfile -l "${DAILY_PAPER_SDIST}" | grep '/LICENSE'
-          python -m venv "${RUNNER_TEMP}/reme-daily-paper-smoke"
-          "${RUNNER_TEMP}/reme-daily-paper-smoke/bin/python" -m pip install \
-            "agentscope[model-ollama]==2.0.7" "${DAILY_PAPER_WHEEL}"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-daily-paper-smoke/bin/python" - <<'PY'
-          from importlib.metadata import distribution
-
-          from reme.plugin_manifest import load_package_manifest
-
-          package = distribution("reme-daily-paper")
-          plugins = {entry.name: entry for entry in package.entry_points if entry.group == "reme.plugins"}
-          assert plugins["daily-paper"].value == "reme_daily_paper"
-          manifest = load_package_manifest("reme_daily_paper", plugin_name="daily-paper")
-          assert set(manifest.backends) == {
-              "daily_paper_collect_step",
-              "daily_paper_rank_step",
-              "daily_paper_select_step",
-              "daily_paper_analyze_step",
-              "daily_paper_digest_step",
-          }
-          assert set(manifest.application_defaults["jobs"]) == {"daily_paper", "daily_paper_cron"}
-          PY
-
-      - name: Upload distributions
-        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6
-        with:
-          name: reme-daily-paper-${{ inputs.version }}
-          path: dist/daily-paper/
-          if-no-files-found: error
-
-  publish:
-    needs: build
-    runs-on: ubuntu-latest
-    environment: pypi
-    permissions:
-      contents: read
-      id-token: write
-
-    steps:
-      - name: Download distributions
-        uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: reme-daily-paper-${{ inputs.version }}
-          path: dist/daily-paper
-
-      - name: Publish reme-daily-paper
-        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # release/v1
-        with:
-          packages-dir: dist/daily-paper
--- a/.github/workflows/release-python.yml
+++ b/.github/workflows/release-python.yml
@ -1,47 +0,0 @@
-name: Release / Python packages
-
-# Configure a PyPI Trusted Publisher for this repository, workflow, and its
-# pypi environment before running the manual release.
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: Release version
-        required: true
-        type: string
-
-permissions:
-  contents: read
-
-concurrency:
-  group: publish-reme-ai
-  cancel-in-progress: false
-
-jobs:
-  build:
-    name: Build and verify distributions
-    uses: ./.github/workflows/_build-python-packages.yml
-    with:
-      expected_version: ${{ inputs.version }}
-      upload_artifacts: true
-
-  publish-reme:
-    needs: build
-    runs-on: ubuntu-latest
-    environment: pypi
-    permissions:
-      contents: read
-      id-token: write
-    steps:
-      - name: Download ReMe distributions
-        uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: reme-distributions
-          path: dist/reme
-
-      - name: Publish ReMe
-        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # release/v1
-        with:
-          packages-dir: dist/reme
-          skip-existing: true
--- a/.github/workflows/release-reme-studio.yml
+++ b/.github/workflows/release-reme-studio.yml
@ -1,158 +0,0 @@
-# Release checklist:
-# 1. Update reme_studio/pyproject.toml, package.json, and package-lock.json to the same Studio version.
-# 2. Configure npm Trusted Publishing and PyPI Trusted Publishing with the pypi environment.
-# 3. Run this workflow manually with the exact Studio version.
-
-name: Release / ReMe Studio
-
-run-name: Publish ReMe Studio ${{ inputs.version }} (${{ inputs.npm_tag }})
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: Version from the Studio Python and npm manifests
-        required: true
-        type: string
-      npm_tag:
-        description: npm distribution tag
-        required: true
-        default: latest
-        type: choice
-        options:
-          - next
-          - latest
-
-permissions:
-  contents: read
-
-concurrency:
-  group: publish-reme-studio
-  cancel-in-progress: false
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    env:
-      RELEASE_VERSION: ${{ inputs.version }}
-      NPM_TAG: ${{ inputs.npm_tag }}
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: "22.22.3"
-          cache: npm
-          cache-dependency-path: reme_studio/package-lock.json
-
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
-        with:
-          python-version: "3.11"
-
-      - name: Validate Studio package names and version
-        run: |
-          python - <<'PY'
-          import json
-          import os
-          import tomllib
-          from pathlib import Path
-
-          studio = Path("reme_studio")
-          python_manifest = tomllib.loads((studio / "pyproject.toml").read_text(encoding="utf-8"))["project"]
-          npm_manifest = json.loads((studio / "package.json").read_text(encoding="utf-8"))
-          expected = os.environ["RELEASE_VERSION"].removeprefix("v")
-          if python_manifest["name"] != "reme_studio":
-              raise SystemExit(f"Unexpected Python package name: {python_manifest['name']}")
-          if npm_manifest["name"] != "@agentscope-ai/reme_studio":
-              raise SystemExit(f"Unexpected npm package name: {npm_manifest['name']}")
-          if python_manifest["version"] != expected or npm_manifest["version"] != expected:
-              raise SystemExit(
-                  f"Studio manifests are {python_manifest['version']} and {npm_manifest['version']}; "
-                  f"workflow input is {expected}",
-              )
-          prerelease = "-" in expected
-          if prerelease != (os.environ["NPM_TAG"] == "next"):
-              raise SystemExit("Prereleases must use next; stable releases must use latest")
-          PY
-
-      - name: Install dependencies and run checks
-        working-directory: reme_studio
-        run: |
-          npm ci
-          npm run format:check
-          npm run lint
-          npm test
-
-      - name: Build Studio distributions
-        run: |
-          python -m pip install build twine
-          mkdir -p dist/studio-python dist/studio-npm
-          npm pack ./reme_studio --pack-destination dist/studio-npm
-          python scripts/package_studio.py
-          python -m build reme_studio --outdir dist/studio-python
-          python -m twine check dist/studio-python/*
-
-      - name: Verify Studio distributions and isolated installation
-        run: |
-          STUDIO_WHEEL="$(pwd)/$(ls dist/studio-python/reme_studio-*.whl)"
-          tar -tzf dist/studio-npm/*.tgz | grep '^package/dist-static/index.html$'
-          python -m venv "${RUNNER_TEMP}/reme-studio-package-smoke"
-          "${RUNNER_TEMP}/reme-studio-package-smoke/bin/python" -m pip install "${STUDIO_WHEEL}"
-          cd "${RUNNER_TEMP}"
-          "${RUNNER_TEMP}/reme-studio-package-smoke/bin/python" - <<'PY'
-          from reme_studio import static_dir
-
-          assert (static_dir() / "index.html").is_file()
-          PY
-
-      - uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6
-        with:
-          name: reme-studio-${{ inputs.version }}
-          path: |
-            dist/studio-python/*
-            dist/studio-npm/*
-          if-no-files-found: error
-
-  publish-python:
-    needs: build
-    runs-on: ubuntu-latest
-    environment: pypi
-    permissions:
-      contents: read
-      id-token: write
-    steps:
-      - uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: reme-studio-${{ inputs.version }}
-          path: dist
-
-      - name: Publish ReMe Studio to PyPI
-        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # release/v1
-        with:
-          packages-dir: dist/studio-python
-          skip-existing: true
-
-  publish-npm:
-    needs: build
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      id-token: write
-    steps:
-      - uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: "24"
-          registry-url: https://registry.npmjs.org
-
-      - uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: reme-studio-${{ inputs.version }}
-          path: dist
-
-      - name: Publish ReMe Studio to npm
-        env:
-          NPM_TAG: ${{ inputs.npm_tag }}
-        run: npm publish dist/studio-npm/*.tgz --access public --tag "${NPM_TAG}" --provenance
--- a/.github/workflows/release-typescript.yml
+++ b/.github/workflows/release-typescript.yml
@ -1,167 +0,0 @@
-# Release checklist:
-# 1. Update typescript/package.json and package-lock.json to the release version and merge them.
-# 2. Configure npm Trusted Publishing for agentscope-ai/ReMe and this workflow file.
-# 3. Run this workflow manually with the exact package version (an optional v prefix is accepted).
-# 4. Configure ClawHub Trusted Publishing or CLAWHUB_TOKEN before enabling ClawHub publication.
-# 5. Use the `next` tag for prereleases and `latest` only for stable releases.
-
-name: Release / TypeScript integrations
-
-run-name: Publish @agentscope-ai/reme ${{ inputs.version }} (${{ inputs.npm_tag }})
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: Version from typescript/package.json (for example, 0.1.0)
-        required: true
-        type: string
-      npm_tag:
-        description: npm distribution tag
-        required: true
-        default: latest
-        type: choice
-        options:
-          - next
-          - latest
-      publish_clawhub:
-        description: Also publish the verified tarball to ClawHub
-        required: true
-        default: false
-        type: boolean
-
-permissions:
-  contents: read
-
-concurrency:
-  group: publish-agentscope-ai-reme
-  cancel-in-progress: false
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    outputs:
-      version: ${{ steps.validate.outputs.version }}
-    env:
-      RELEASE_VERSION: ${{ inputs.version }}
-      NPM_TAG: ${{ inputs.npm_tag }}
-
-    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Set up Node
-        uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: '22.22.3'
-
-      - name: Validate package name and release version
-        id: validate
-        working-directory: typescript
-        run: |
-          node --input-type=module <<'JS'
-          import { appendFileSync, readFileSync } from 'node:fs';
-
-          const manifest = JSON.parse(readFileSync('package.json', 'utf8'));
-          const expected = process.env.RELEASE_VERSION.replace(/^v/, '');
-          if (manifest.name !== '@agentscope-ai/reme') {
-            throw new Error(`Unexpected package name: ${manifest.name}`);
-          }
-          if (manifest.version !== expected) {
-            throw new Error(`package.json is ${manifest.version}, workflow input is ${expected}`);
-          }
-          const prerelease = manifest.version.includes('-');
-          const npmTag = process.env.NPM_TAG;
-          if (prerelease !== (npmTag === 'next')) {
-            throw new Error(prerelease
-              ? 'Prerelease versions must use the next npm tag'
-              : 'Stable versions must use the latest npm tag');
-          }
-          console.log(`Preparing ${manifest.name}@${manifest.version}`);
-          appendFileSync(process.env.GITHUB_OUTPUT, `version=${manifest.version}\n`);
-          JS
-
-      - name: Install dependencies
-        working-directory: typescript
-        run: npm ci
-
-      - name: Type-check and test
-        working-directory: typescript
-        run: |
-          npm run format:check
-          npm run lint
-          npm run typecheck
-          npm test
-          npm run test:package
-          npx --yes clawhub@0.23.3 package validate . --json
-
-      - name: Pack npm tarball
-        working-directory: typescript
-        run: |
-          mkdir -p "${RUNNER_TEMP}/reme-typescript-package"
-          npm pack --pack-destination "${RUNNER_TEMP}/reme-typescript-package"
-
-      - name: Upload npm tarball
-        uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6
-        with:
-          name: agentscope-ai-reme-${{ inputs.version }}
-          path: ${{ runner.temp }}/reme-typescript-package/*.tgz
-          if-no-files-found: error
-
-  publish:
-    needs: build
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      id-token: write
-
-    steps:
-      - name: Set up Node for npm
-        uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6
-        with:
-          node-version: '24'
-          registry-url: https://registry.npmjs.org
-
-      - name: Download npm tarball
-        uses: actions/download-artifact@37930b1c2abaa49bbe596cd826c3c89aef350131 # v7
-        with:
-          name: agentscope-ai-reme-${{ inputs.version }}
-          path: dist/typescript
-
-      - name: Reject an existing package version
-        env:
-          PACKAGE_VERSION: ${{ inputs.version }}
-        run: |
-          PACKAGE_VERSION="${PACKAGE_VERSION#v}"
-          if npm view "@agentscope-ai/reme@${PACKAGE_VERSION}" version >/dev/null 2>&1; then
-            echo "@agentscope-ai/reme@${PACKAGE_VERSION} already exists" >&2
-            exit 1
-          fi
-
-      - name: Publish to npm
-        env:
-          NPM_TAG: ${{ inputs.npm_tag }}
-        run: npm publish dist/typescript/*.tgz --access public --tag "${NPM_TAG}" --provenance
-
-  publish-clawhub:
-    if: ${{ inputs.publish_clawhub }}
-    needs: build
-    permissions:
-      actions: read
-      contents: read
-      id-token: write
-    uses: openclaw/clawhub/.github/workflows/package-publish.yml@87ca030c30f3cfb78ab15c8e66b5ff1469c8f9c8 # v0.23.3
-    with:
-      owner: agentscope-ai
-      family: code-plugin
-      version: ${{ needs.build.outputs.version }}
-      tags: ${{ inputs.npm_tag }}
-      source_repo: ${{ github.repository }}
-      source_commit: ${{ github.sha }}
-      source_ref: ${{ github.ref }}
-      source_path: typescript
-      package_artifact_name: agentscope-ai-reme-${{ inputs.version }}
-      wait_for_publication: true
-    secrets:
-      clawhub_token: ${{ secrets.CLAWHUB_TOKEN }}
--- a/.github/workflows/security-codeql.yml
+++ b/.github/workflows/security-codeql.yml
@ -1,46 +0,0 @@
-name: Security / CodeQL
-
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-  schedule:
-    - cron: '0 1 * * 1'
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-  packages: read
-  security-events: write
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  analyze:
-    name: Analyze ${{ matrix.language }}
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        language: [python, javascript-typescript]
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
-
-      - name: Initialize CodeQL
-        uses: github/codeql-action/init@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
-        with:
-          languages: ${{ matrix.language }}
-          build-mode: none
-
-      - name: Perform CodeQL analysis
-        uses: github/codeql-action/analyze@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
-        with:
-          category: /language:${{ matrix.language }}
--- a/.github/workflows/ci-python-tests.yml
+++ b/.github/workflows/ci-python-tests.yml
@ -1,4 +1,4 @@
-name: CI / Python tests
+name: Tests ReMe

 on:
  push:
@ -11,9 +11,6 @@ concurrency:
  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
  cancel-in-progress: true

-permissions:
-  contents: read
-
 jobs:
  unit-tests:
    name: Unit Tests - py${{ matrix.python-version }}
@ -24,12 +21,10 @@ jobs:
        python-version: ["3.11", "3.12", "3.13"]

    steps:
-      - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6
-        with:
-          persist-credentials: false
+      - uses: actions/checkout@v4

      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
          cache: 'pip'
@ -37,14 +32,12 @@ jobs:
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip setuptools wheel
-          pip install -e reme_studio -e ".[dev,core]"
-          pip install --no-deps -e plugins/auto-fin
-          pip install -e plugins/daily_paper
+          pip install -e ".[dev,core]"
          pip install coverage

      - name: Run unit tests
        run: |
-          coverage run -m pytest tests/unit plugins/auto-fin plugins/daily_paper \
+          coverage run -m pytest tests/unit \
            -v \
            --tb=long \
            -s \
--- a/.github/workflows/windows-smoke.yml
+++ b/.github/workflows/windows-smoke.yml
@ -0,0 +1,38 @@
+name: Windows Smoke
+
+on:
+  push:
+    branches: [main, master, dev, develop]
+  pull_request:
+    branches: [main, master, dev, develop]
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  cli-smoke:
+    name: CLI smoke - py${{ matrix.python-version }}
+    runs-on: windows-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+
+      - name: Install package
+        run: |
+          python -m pip install --upgrade pip setuptools wheel
+          pip install -e ".[core,benchmark]"
+
+      - name: Run version job
+        run: reme start service.backend=cli job=version
--- a/.gitignore
+++ b/.gitignore
@ -2,8 +2,8 @@
 .DS_Store
 .idea/
 .vscode/
-.qoder/
 *.code-workspace
+.qoder/

 # Local environment
 .env
@ -30,9 +30,7 @@ htmlcov/
 # Packaging / build outputs
 build/
 dist/
-node_modules/
 *.egg-info/
-typescript/reports/

 # Logs / temporary files
 *.log
@ -47,8 +45,6 @@ temp*/

 # ReMe runtime data
 .reme/
-reme_workspace/
-reme_workspace_auto_fin_real_test*/
 vault/
 *.db
 *.sqlite
@ -59,24 +55,4 @@ docs/_build/
 site/

 evaluation/
-# The pi-Bench suite ships its own trace-history render config, which must
-# stay in git even though it lives under an evaluation/ directory.
-!benchmark/pibench/config/bench/evaluation/
-!benchmark/pibench/config/bench/evaluation/**
 datasets/
-
-# Claude Code skills (local only)
-.claude/skills/
-
-# Benchmark memory workspaces (created on demand by run.py via mkdir)
-benchmark/*/workspaces/
-
-# Benchmark datasets (LongMemEval via download.py, BEAM via git clone)
-benchmark/*/dataset/
-
-# Benchmark outputs (created on demand by run.py via mkdir)
-benchmark/*/results/
-
-# integration tests outputs
-tests/integration/logs/
-daily/
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -1,5 +1,3 @@
-exclude: ^skills/
-
 repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v6.0.0
--- a/AGENTS.md
+++ b/AGENTS.md
@ -1,19 +1,20 @@
 # AGENTS.md

-This file guides coding agents working in the ReMe repository. Keep changes small, testable, and consistent with the
-contracts expressed by the current code.
+This file guides coding agents working in the ReMe repository. Keep changes small,
+testable, and consistent with the contracts already expressed by the code.

 ## Project Principles

 ReMe is a local-first, file-native memory system for agents.

- User-owned workspace files are the durable source of truth.
- Indexes, catalogs, graphs, caches, and generated metadata must remain rebuildable.
- Prefer transparent formats and predictable behavior over hidden state.
- Preserve user control over workspace paths, configuration, and service boundaries.
+- User-owned memory files are the source of truth.
+- Indexes, caches, metadata, and generated state must be rebuildable.
+- Prefer transparent formats and behavior over hidden state.
+- Preserve user control over storage, configuration, and service boundaries.
 - Keep concepts focused on project intent; let code and schemas describe implementation.

-When convenience conflicts with these principles, favor data ownership, recoverability, and explicit behavior.
+When a proposed convenience conflicts with these principles, favor data ownership,
+recoverability, and predictable behavior.

 ## Sources of Truth

@ -21,194 +22,166 @@ Use this order when documentation and implementation disagree:

 1. Current code and public Pydantic schemas.
 2. Tests that describe supported behavior.
-3. CLI behavior and the built-in configuration.
-4. README files and other development documentation.
+3. CLI help and the built-in configuration.
+4. Development documentation and historical notes.

-Do not duplicate large implementation descriptions in documentation. Express the stable contract and link to the
-relevant module where useful. When behavior changes intentionally, update the implementation, schemas, tests, defaults,
-and concise documentation together.
+Do not copy large implementation descriptions into documentation. Link to the relevant
+module or express the stable contract instead. If behavior changes intentionally, update
+the code, schema, tests, configuration, and concise documentation together as needed.

 ## Repository Map

- `reme/reme.py`: CLI entry point; dispatches `start`, `find_reme`, and client calls.
- `reme/application.py`: application assembly, dependency ordering, job execution, and lifecycle.
- `reme/config/config_parser.py`: YAML/JSON loading, environment expansion, dot-notation parsing, and deep config
-  merging.
- `reme/config/default.yaml`: default service, jobs, steps, and components. Other files in
-  `reme/config/` are named configuration variants.
- `reme/schema/application_config.py`: typed application, component, and job configuration.
- `reme/schema/`: request, response, streaming, memory, graph, and file contracts.
- `reme/components/application_context.py`: application-wide wiring and in-memory shared state.
- `reme/components/runtime_context.py`: request-scoped data, response, streaming queue, and stop event.
- `reme/components/base_component.py`: component lifecycle, dependency binding, and workspace helpers.
- `reme/components/component_registry.py`: the frozen built-in registry template and application-local registry factory.
- `reme/components/job/`: base, stream, background, and cron job implementations.
- `reme/components/service/`: local CLI, HTTP, and MCP service backends.
- `reme/components/`: agent wrappers, model adapters, stores, catalogs, graphs, indexes, clients, tokenizers, and
-  outbound proxies.
- `reme/steps/`: registered job steps grouped by common, file I/O, index, evolve, cookbook, benchmark, and transfer
-  concerns.
- `reme/utils/`: shared utilities, including service discovery, logging, web-static resolution, session I/O, token
-  accounting, and wikilink handling.
- `tests/unit/`: primary fast, isolated validation suite.
- `tests/integration/`: service/model tests that may need credentials or external processes.
- `reme_studio/`: ReMe Studio frontend source plus the independently published `reme_studio` Python package and
-  `@agentscope-ai/reme_studio` npm static distribution.
- `typescript/`: the independently published `@agentscope-ai/reme` package, including the shared TypeScript client and
-  DeepSeek Harness and OpenClaw adapters.
- `plugins/`: installable ReMe extensions, such as Auto Fin.
- `integrations/`: adapters that connect ReMe to external agent hosts, such as Claude Code, DSH, and Hermes Agent.
- `skills/`: standalone skills; `reme_memory` calls ReMe, while other skills may use separate tools or direct-file
-  conventions.
- `benchmark/` and `cookbook/`: runnable evaluations and example workflows.
- `docs/`: README-linked supporting pages and figures.
+- `reme/reme.py`: CLI entry point and client/server dispatch.
+- `reme/application.py`: application assembly, dependency ordering, and lifecycle.
+- `reme/components/application_context.py`: application-wide wiring and shared in-memory metadata.
+- `reme/components/runtime_context.py`: scratch state shared by steps within one execution.
+- `reme/config/default.yaml`: built-in jobs, components, and defaults.
+- `reme/schema/`: public and runtime Pydantic contracts.
+- `reme/components/`: services, stores, clients, jobs, and component registration.
+- `reme/steps/`: executable job steps.
+- `tests/unit/`: primary fast validation suite.
+- `tests/integration/`: tests that may require real credentials or services.
+- `tests/vector/` and `tests/light/`: specialized suites.
+- `plugins/reme/`: Claude Code integration.
+- `skills/reme_memory/`: skill that communicates with the ReMe service.
+- `skills/qwenpaw_memory/`: separate direct-file memory convention; it does not call ReMe.
+- `docs/`: pages and assets that support the repository README; not the deployed docs site.

 ## Development Setup

-ReMe requires Python 3.11 or newer. Install the editable development environment with:
+ReMe requires Python 3.11 or newer.

 ```bash
-pip install -e reme_studio -e ".[dev,core]"
+pip install -e ".[dev,core]"
 ```

-Before changing behavior, inspect the adjacent implementation, schema, built-in config, and focused tests. Follow
-existing async and typing patterns unless the task explicitly requires a new contract.
+Before changing behavior, inspect the adjacent implementation, schemas, configuration,
+and focused tests. Follow existing patterns unless the task explicitly calls for a new
+contract or architecture.

-## Configuration and CLI Contracts
+## Change Workflow

- CLI syntax is `reme ACTION key=value ...`; leading `-` or `--` on arguments is accepted.
- Nested overrides use dot notation. Values support null, booleans, numbers, JSON collections, and quoted JSON strings;
-  leading-zero numeric-looking values remain strings.
- `config=<name-or-path>` loads a discovered config name or a `.yaml`, `.yml`, or `.json` file. With no explicit config
-  path, `default` is loaded when available.
- Config files expand `${VAR}` and `${VAR:-default}` recursively. An undefined variable without a default is an error.
- CLI/config overrides are deep-merged over the loaded file. Do not silently change this merge behavior or stable
-  configuration keys.
- `ApplicationConfig` normalizes `workspace_dir` to an expanded absolute path. `session_dir`
-  must remain workspace-relative; standard transcripts live under `{session_dir}/dialog`.
- `reme start` runs the configured service. `reme start job=<name> ...` switches to the one-shot CLI service and runs
-  the job through the normal application lifecycle.
- Other actions use a client selected from the running service configuration when discoverable, otherwise from local
-  config. Client-selection arguments must not leak into the job payload.
+1. Identify the narrowest supported contract affected by the request.
+2. Read the relevant implementation and tests before editing.
+3. Make the smallest coherent change; avoid unrelated cleanup.
+4. Update related schemas, defaults, registrations, and imports when required.
+5. Add or adjust focused tests for observable behavior.
+6. Run proportionate validation and report anything not run.

-## Registration and Application Lifecycle
+Component and step discovery depends on registration imports:

-Component and Step discovery is import-driven:
+- Components use `R.register(...)` in `reme/components/component_registry.py`.
+- Component packages must be reachable through `reme/components/__init__.py`.
+- Step modules must be reachable through `reme/steps/__init__.py`.

- Implementations declare a non-`BASE` `component_type` and register with `@R.register("backend")`
-  or `R.register(Class, "backend")`.
- Component packages must be imported through `reme/components/__init__.py`.
- Step packages/modules must be reachable through their package `__init__.py` chain and ultimately
-  `reme/steps/__init__.py`.
- Adding an implementation without its registration import leaves it undiscoverable at runtime. Treat implementation,
-  registration, import side effect, defaults, and tests as one change.
+Adding an implementation without its registration import can leave it undiscoverable at
+runtime. Treat the implementation, registry entry, and import side effect as one change.

-`Application` validates config through `ApplicationContext`, creates workspace directories, instantiates the service,
-configured components, and jobs, and then manages lifecycle as follows:
+Do not silently change stable CLI flags, configuration keys, workspace layouts, serialized
+schemas, or service interfaces. When such a change is required, preserve compatibility
+where practical and make the migration explicit.

- Components start in topological dependency order. Missing required dependencies and cycles fail explicitly; optional
-  dependencies may resolve to `None`.
- Jobs start after components in this order: base jobs, stream jobs, background jobs, then cron jobs.
- Shutdown closes everything in reverse start order and then shuts down the optional thread pool.
- If startup fails, already-started resources are closed.
- `BaseComponent.start()` and `close()` are lock-protected and idempotent. Dependencies created by a standalone
-  `default_factory` are owned and closed by the parent component.
+## Step State Model

-Keep async clients, tasks, executors, and services under this lifecycle. Do not introduce an untracked long-lived
-resource.
+Treat every Step as stateless. `BaseJob` stores Step specifications and builds fresh Step
+instances for each Job invocation. A Step instance must not use `self` or class variables to
+retain mutable runtime state between calls.

-## Jobs, Steps, and State
+Place state according to its lifetime:

-`BaseJob` resolves configured Step classes during job startup and constructs fresh Step instances for every invocation.
-Job-level kwargs are merged into each `RuntimeContext`, with call-time kwargs taking precedence. Sequential Steps in one
-invocation share the same `RuntimeContext` and `Response`.
+- Constructor fields on `self`: immutable Step configuration and resolved dependencies only.
+- `self.context` (`RuntimeContext`): request data and intermediate results for one Job
+  execution; sequential Steps share this context.
+- `self.app_context.metadata`: in-memory state that must be shared across Step or Job
+  invocations for the lifetime of the Application.
+- Workspace files or a dedicated Component/store: durable state that must survive an
+  Application restart.

-Treat Step instances as invocation-scoped:
+Use narrow, namespaced keys in `app_context.metadata`, following existing patterns such as
+`tool_contexts` and `channel_sink`. The ApplicationContext is shared, so account for
+concurrent access when values are mutable. New Step code must not fall back to `self.kwargs`
+or another Step field to emulate shared state when `app_context` is absent; tests of shared
+state should construct an `ApplicationContext`. If shared state grows into a stable
+service-level contract or needs its own lifecycle, locking, or persistence, promote it to a
+typed ApplicationContext field or a dedicated Component instead of expanding an ad hoc
+metadata bucket.

- Constructor fields and `self.kwargs` hold Step configuration and resolved dependencies. They may be cached or adjusted
-  during that one invocation, but must not be relied on across Job calls.
- `self.context.data` holds request inputs and intermediate values shared by sequential Steps.
- `self.context.response.answer`, `success`, and `metadata` are request-scoped output. Because the same response travels
-  through the Step chain, later Steps may consume metadata produced earlier, but it is not application-lifetime or
-  durable storage.
- `self.app_context.metadata` holds in-memory state shared across Job/Step invocations for the life of one
-  `Application`, such as counters, tool-context state, session maps, or locks.
- Workspace files or a dedicated Component/store hold durable state that must survive restart.
-
-Use narrow, namespaced keys in `app_context.metadata` and protect shared mutable values against concurrent access. The
-search/draft helpers intentionally mirror tool-context state into
-`self.kwargs` only when no `ApplicationContext` exists for standalone use and unit tests; do not generalize that
-compatibility fallback into persistent runtime state. If shared state becomes a stable service contract or needs
-dedicated lifecycle, locking, or persistence, promote it to a typed context field or Component.
-
-Additional Step contracts:
-
- `Ref` dependencies resolve in this order: Step kwargs, current `RuntimeContext`, then the named application component.
-  The value is cached only on the current Step instance and cleared before each call.
- `input_mapping` and `output_mapping` copy keys within `RuntimeContext.data`; missing sources are ignored.
- Dispatched Steps receive the current `RuntimeContext`, so their data and response are shared.
- Base jobs convert uncaught Step errors into `Response(success=False)`; stream jobs emit an error chunk and always a
-  terminal `DONE`; background jobs let errors reach their supervisor.
- Background jobs are never service-exposed. MCP also skips stream jobs. Respect `enable_serve`
-  and any configured service job allowlist.
-
-## Workspace and File Safety
-
- Application startup creates the workspace plus configured metadata, session, memory-session, resource, daily, and
-  digest directories.
- File-operation paths are resolved against the workspace and must stay inside it. Home-relative paths are unsupported,
-  traversal escapes are rejected, and `_allowed_paths` restrictions fail closed when invalid.
- Preserve per-path locking, encoding detection, byte limits, truncation behavior, and optimistic
-  `expected_mtime` checks when modifying file operations.
- Do not bypass the existing file steps or stores in a way that weakens workspace containment.
- Never write test state into the repository's `.reme/`; use `tmp_path` or another isolated workspace.
- Do not delete or rewrite user memory to repair an index or make a test pass. Rebuild derived state from source files
-  instead.
+Do not use `Response.metadata` as a state store. It is request-scoped output for callers and
+diagnostics, distinct from `ApplicationContext.metadata`.

 ## Validation

 Use the narrowest useful check while iterating, then broaden it according to risk.

-Focused test:
+Run a focused test:

 ```bash
 pytest tests/unit/path/to/test_file.py -v
 ```

-Main unit suite:
+Run the main unit suite:

 ```bash
 pytest tests/unit -v --tb=long -s --log-cli-level=WARNING
 ```

-Repository formatting and lint checks:
+Run repository formatting and lint checks when the change warrants it:

 ```bash
 pre-commit run --all-files
 ```

-Black and Flake8 use a 120-character line limit and Python 3.11 formatting; Pylint is also run by pre-commit. If
-`reme_studio/` changes, use its Node 22.13+ scripts and run the proportionate checks from that directory, such as
-`npm run format:check`, `npm run lint`, or `npm test`.
+Formatting and lint configuration is authoritative. Python code currently uses a maximum
+line length of 120 for Black and Flake8, with Pylint also run by pre-commit.

-Integration tests may contact real model providers, services, or agent subprocesses and can require credentials. Do not
-run credentialed or externally mutating tests automatically; run them only when the task requires them and the necessary
-environment has been supplied or authorized. Mock network, model, and subprocess boundaries in unit tests.
+Integration tests may contact real services and require credentials such as
+`LLM_API_KEY` or `EMBEDDING_API_KEY`. Do not run credentialed or externally mutating tests
+automatically. Run them only when the task requires them and the user has supplied or
+authorized the necessary environment.

-## Change Guardrails
+## Coding and Test Conventions
+
+- Target Python 3.11+ and follow the surrounding typing and async style.
+- Steps are stateless. If a step needs to persist state, store it in
+  `self.app_context.metadata` rather than on the step instance.
+- Keep public schemas explicit and backward-compatible where practical.
+- Close async clients, services, tasks, and other lifecycle resources deterministically.
+- Prefer clear failures over silently falling back to corrupt or ambiguous state.
+- Keep indexes and caches derivable from user-owned source files.
+- Use `tmp_path` or another isolated temporary workspace in tests.
+- Never write test state into the repository's `.reme/` directory.
+- Mock network or model boundaries in unit tests.
+- Do not commit `.env` files, credentials, runtime memory, logs, indexes, or caches.
+
+## Documentation Boundaries
+
+ReMe's local docs and the deployed documentation site have separate responsibilities.
+
+- Keep `docs/` focused on content and assets used by `README.md` and `README_ZH.md`.
+- Preserve README-linked pages under `docs/en/` and `docs/zh/`, including their relative
+  paths, unless the README is updated in the same change.
+- Keep README-required images under `docs/figure/`.
+- Keep the README's main documentation index pointed at `docs.agentscope.io` or the
+  `agentscope-ai/docs` repository, following the existing link style.
+- Do not treat local README-supporting pages as the source for the deployed website.
+
+The separate `agentscope-ai/docs` repository owns website content, navigation, versioning,
+and deployment. Public ReMe pages live there under `reme/<version>/`. Make website changes
+in that repository and follow its existing version-management conventions.
+
+Do not add website build configuration or deployment workflows to ReMe unless the task
+explicitly changes this repository boundary.
+
+## Agent Guardrails

 - Preserve unrelated user changes in a dirty working tree.
- Make the smallest coherent change and avoid unrelated cleanup or broad refactors.
- Do not edit generated output when the source can be changed instead. The publish workflow builds
-  `reme_studio/dist-static` and stages it under `reme_studio/src/reme_studio/static`; change `reme_studio/` source for
-  frontend work.
- Do not silently change CLI flags, configuration keys, workspace layouts, serialized schemas, endpoint shapes,
-  streaming termination, or service interfaces. Preserve compatibility where practical and document intentional
-  migrations.
- Do not introduce dependencies without a concrete repository-level need.
- Do not commit `.env` files, credentials, runtime memory, logs, indexes, caches, benchmark outputs, or generated
-  Studio distributions.
- State which validations passed and which relevant checks were not run in the final handoff.
+- Do not edit generated output when the source can be changed instead.
+- Do not delete or rewrite user data to make a test pass.
+- Avoid broad refactors unless they are necessary for the requested outcome.
+- Do not introduce dependencies without a concrete need and repository-level justification.
+- Treat network access, real credentials, and external service mutations as opt-in.
+- State which validations passed and which were not run in the final handoff.

-If a requirement is ambiguous, infer intent from nearby code, schemas, defaults, and tests. Ask the user only when the
-remaining choice would materially alter a public contract, user data, or an external system.
+If a requirement is ambiguous, first infer intent from nearby code, tests, and schemas. Ask
+the user only when the remaining choice would materially alter a public contract, user data,
+or external system.
--- a/README.md
+++ b/README.md
@ -1,5 +1,5 @@
 <p align="center">
- <img src="https://raw.githubusercontent.com/agentscope-ai/ReMe/main/docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
+ <img src="docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
 </p>

 <p align="center">
@ -8,7 +8,6 @@
  <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>
  <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
-  <a href="https://reme.agentscope.io"><img src="https://img.shields.io/badge/docs-ReMe-blue" alt="Documentation"></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>
@ -20,44 +19,45 @@
 </p>

 <p align="center">
-  <strong>A local-first, self-evolving personal knowledge base for AI agents.</strong><br>
+  <strong>An agent memory layer that turns conversations and resources into readable, editable, searchable Markdown memory.</strong><br>
 </p>

 > Previous versions: [0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) ·
 > [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) ·
 > [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch)

-## ✨ Why ReMe?
+🧠 ReMe is a local-first memory layer for **AI agents**. It turns conversations and resources into file-based long-term
+memory, then continuously indexes, links, and consolidates that memory for future recall.

-🧠 ReMe turns conversations and resources into readable, editable, searchable, and interconnected Markdown memory. Agents
-such as QwenPaw and DeepSeek Harness can share the same workspace to retrieve, maintain, and evolve knowledge, while
-users retain control of the durable files.
+## ✨ Core Ideas

- **Memory as File, File as Memory**: ReMe stores durable memory as ordinary Markdown with frontmatter and wikilinks.
-  Users and agents can inspect, edit, move, sync, and back it up with familiar tools, while indexes and generated
-  metadata remain rebuildable.
- **Self-evolving knowledge base**: ReMe progressively turns conversations and resources into daily notes and long-term
-  knowledge, preserving sources while refining facts, preferences, procedures, and relationships over time.
- **Recall is precise and context-aware.** BM25, optional embeddings, and wikilink expansion retrieve relevant
-  line-level passages and their relationships without loading the entire knowledge base into the agent context.
- **One memory workspace works across agents.** Personal assistants, coding agents, and other agent runtimes can share
-  the same local workspace through native integrations, SKILL.md, CLI, HTTP, MCP, or Python APIs.
+- **Memory as File**: Markdown files with frontmatter and wikilinks serve as memory nodes that both users and agents can
+  read and write directly.
+- **Self-evolving knowledge base**: Auto Memory, Auto Resource, and Auto Dream progressively transform conversations and
+  resources into long-term memories, while automatically building wikilink relationships.
+- **Progressive hybrid search**: ReMe combines wikilinks, BM25, and embeddings for hybrid retrieval across keyword
+  matching, semantic recall, and relationship expansion.
+- **Agent-friendly integration**: SKILL.md + CLI integration makes it easy for different agents to read, write,
+  maintain, and reuse memory.

 <p align="center">
  <img src="docs/figure/design-philosophy.svg" alt="ReMe Design Philosophy" width="92%">
 </p>

-## 📰 Latest Updates
+## 🔭 Use Cases
+
+- **Personal assistants**: Give personal assistants such as
+  [QwenPaw](https://github.com/agentscope-ai/QwenPaw), [OpenClaw](https://github.com/openclaw/openclaw), and
+  [Hermes](https://github.com/nousresearch/hermes-agent) a user-editable long-term memory layer.
+- **Coding agents**: Preserve coding style, project background, repository decisions, and workflow
+  experience across sessions when integrating with coding agents such as [Claude Code](plugins/reme).
+- **LLM Wiki**: Turn conversations, notes, and resources into a searchable, traceable, and linked Markdown
+  knowledge base that both users and agents can maintain.
+- **Self-evolving agents**: Support agents that learn from experience by saving successful paths, failed attempts,
+  reusable procedures, and periodic reflections as memory.
+
+## 📰 News

- [2026.08] - Published [`@agentscope-ai/reme`](https://www.npmjs.com/package/@agentscope-ai/reme), providing native
-  ReMe memory integrations for DeepSeek Harness and OpenClaw plus a shared TypeScript HTTP client.
- [2026.08] - Published the [ReMe blog](https://agentscope-ai.github.io/ReMe/?doc=en-reme-blog), an end-to-end introduction to its local-first memory
-  architecture, self-evolving workflows, hybrid search, proactive discovery, and benchmark results.
- [2026.08] - [Experience-driven enhancement method](https://reme.agentscope.io/?doc=toolmemory-en) of agent tool-use execution built
-  on ReMe is available on [arXiv:2608.03403](https://arxiv.org/abs/2608.03403).
- [2026.07] - Introduced optional plugins: [Daily Paper](https://reme.agentscope.io/?doc=daily-paper-en) for paper discovery and
-  analysis, and [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-en) for researching the latest 24 hours of topic-related CLS news
-  with local-memory search and validated historical wikilinks.
 - [2026.07] - Our
  paper [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
  has been accepted to Findings of ACL 2026.
@ -79,14 +79,33 @@ Install from source:
 ```bash
 git clone https://github.com/agentscope-ai/ReMe.git
 cd ReMe
-pip install -e reme_studio -e ".[core]"
-cd reme_studio
-npm ci
-npm run build:static
-cd ..
+pip install -e ".[core]"
 ```

-The static build requires Node.js 22.13 or newer and makes Studio available from the source tree.
+### Environment Variables
+
+Configure environment variables when you want LLM-powered memory evolution or embedding retrieval. Embeddings are
+disabled by default, so the default setup does not start an embedding model or require an embedding API key.
+
+```bash
+cat > .env <<'EOF'
+# Optional: used only after embedding components are explicitly enabled in the config.
+# EMBEDDING_API_KEY=sk-xxx
+# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+
+# Required for auto_memory, auto_resource, and auto_dream.
+LLM_API_KEY=sk-xxx
+LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+EOF
+```
+
+Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials.
+
+> [!NOTE]
+> To enable embedding-based semantic retrieval, uncomment `components.as_embedding` and
+> `components.embedding_store` in [`reme/config/default.yaml`](reme/config/default.yaml), then change
+> `components.file_store.default.embedding_store` from `""` to `default`. See the
+> [memory search guide](docs/en/memory_search.md) for details.

 ### Start the Service

@ -101,10 +120,10 @@ reme start service.port=8181
 # reme start workspace_dir=/tmp/reme-demo service.port=8181
 ```

+After startup, check the service status. If you use a custom port, replace `2333` in the URL below with that port.
+
 ```bash
 reme version
-reme health_check
-reme help
 curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}'
 ```

@ -142,51 +161,94 @@ ReMe stores agent memory as readable Markdown.
 Related: [[digest/wiki/memory-as-file.md]]
 ```

-### ReMe Studio (Optional)
+## 📁 Memory System

-The `core` installation includes Studio. After starting ReMe, open <http://127.0.0.1:2333/> to browse, edit, and search
-the workspace. To add Studio to a base installation, use `pip install "reme-ai[web]"`. See the
-[ReMe Studio guide](https://reme.agentscope.io/?doc=studio-en) for source builds, configuration, and development.
+> Memory as File, File as Memory.

-### Optional Model Configuration
+ReMe treats **memory as files**, progressively processing raw conversations and external resources from `session/` and
+`resource/` into `daily/`, then consolidating them into reusable long-term memory nodes under `digest/`.

-Configure environment variables when you want LLM-powered memory evolution or embedding retrieval. Embeddings are
-disabled by default, so the default setup does not start an embedding model or require an embedding API key.
+### Directory Structure

-```bash
-cat > .env <<'EOF'
-# Optional: used only after embedding components are explicitly enabled in the config.
-# EMBEDDING_API_KEY=sk-xxx
-# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-
-# Required for auto_memory, auto_resource, and auto_dream.
-LLM_API_KEY=sk-xxx
-LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-EOF
+```text
+<workspace_dir>/
+├── metadata/       # Persistent system state such as indexes, graphs, and catalogs
+├── session/        # Raw conversations and agent sessions
+│   ├── dialog/
+│   │   └── <session_id>.jsonl
+│   ├── agentscope/
+│   └── claude_code/
+├── resource/            # External raw materials
+│   └── YYYY-MM-DD/
+│       └── <resource>.<ext>
+├── daily/               # Lightly processed memory: daily facts, conversation summaries, resource readings
+│   ├── YYYY-MM-DD.md
+│   └── YYYY-MM-DD/
+│       ├── <session_event>.md
+│       ├── <resource_stem>.md
+│       └── interests.yaml
+└── digest/              # Long-term memory: personal facts, procedural experience, knowledge nodes
+    ├── personal/
+    │   └── {topic/event}.md
+    ├── procedure/
+    │   └── {topic/event}.md
+    └── wiki/
+        └── {topic/event}.md
 ```

-Basic file operations, BM25 search, wikilink traversal, and reading proactive topics can run without LLM credentials.
+<p align="center">
+  <img src="docs/figure/reme-overview.svg" alt="ReMe file-based memory system overview" width="92%">
+</p>

-> [!NOTE]
-> To enable embedding-based semantic retrieval, uncomment `components.as_embedding` and
-> `components.embedding_store` in [`reme/config/default.yaml`](reme/config/default.yaml), then change
-> `components.file_store.default.embedding_store` from `""` to `default`. See the
-> [memory search guide](docs/en/memory_search.md) for details.
+## 🧭 Memory Design Philosophy

-## 🤝 Use ReMe with Your Agent
+> Capture raw dialogs and resources, refine them into long-term preferences, reusable experience, and valuable
+> knowledge,
+> while keeping the result editable by humans and agents.

-ReMe can run as a local memory service accessed through the CLI, HTTP API, or MCP server, or it can be embedded in the
-host process through its Python API. Host integrations can add memory guidance, recall, and capture to the agent
-lifecycle according to the capabilities of each runtime.
+### Automatic Memory Flow

-| Agent                          | Recommended path                                                                                                                         | Available after integration                                                                             |
-| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- |
-| **DeepSeek Harness**           | Install [`@agentscope-ai/reme`](typescript/README.md#deepseek-harness) with `dsh plugin --profile web add @agentscope-ai/reme`. | Long-term memory guidance, the `reme_search` tool, and automatic capture of completed main-agent turns. |
-| **OpenClaw**                   | Install [`@agentscope-ai/reme`](typescript/README.md#openclaw) with `openclaw plugins install @agentscope-ai/reme`.             | Native memory tools, recall before user-triggered runs, and automatic turn capture.                     |
-| **QwenPaw**                    | Embed ReMe in-process through its Python API.                                                                                            | Reuse the host lifecycle and model config while keeping memory local and file-based.                    |
-| **Claude Code**                | Start the streamable HTTP MCP service and install [the ReMe plugin](integrations/claude_code/reme).                                      | MCP recall tools, the `reme-memory` skill, and a Stop hook that records sessions automatically.         |
-| **Hermes**                     | Start the HTTP service and install [the ReMe provider](integrations/hermes_agent).                                                       | Recall before model calls and asynchronous `auto_memory` after each completed turn.                     |
-| **Codex and other CLI agents** | Install or copy the [ReMe Memory skill](skills/reme_memory/SKILL.md).                                                                    | Search, read, and write memory through the CLI; automatic capture requires host lifecycle integration.  |
+ReMe follows a capture → index → consolidate → recall loop. Conversations and resources first become daily memory cards;
+background jobs keep files searchable; `auto_dream` distills stable knowledge into `digest/`; agents recall memory
+through search, wikilinks, or proactive topics.
+
+| Capability                                  | Entry point                                     | What it does                                                                                    | Output                                                  |
+|---------------------------------------------|-------------------------------------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------|
+| [`auto_memory`](docs/en/auto_memory.md)     | Agent hook or `reme auto_memory`                | Distills useful conversation facts while preserving the raw session.                            | `session/dialog/*.jsonl`, `daily/<date>/<session>.md`   |
+| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource`        | Turns files under `resource/<date>/` into source-linked daily cards.                            | `daily/<date>/<resource-card>.md`                       |
+| [`auto_index`](docs/en/memory_search.md)    | Background watcher or `reme reindex`            | Maintains chunks, the BM25 index, the wikilink graph, and the optional embedding index.         | Searchable `daily/`, `digest/`, and `resource/` content |
+| [`auto_dream`](docs/en/auto_dream.md)       | `dream_cron` or `reme auto_dream`               | Consolidates changed daily cards into long-term personal, procedure, and wiki memory.           | `digest/**`, `daily/<date>/interests.yaml`              |
+| [`proactive`](docs/en/proactive.md)         | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them. | Structured topics from `daily/<date>/interests.yaml`    |
+
+<table>
+  <tr>
+    <td align="center" width="50%">
+      <img src="docs/figure/memory-as-file.svg" alt="Memory as File" width="92%">
+    </td>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Resource" width="92%">
+    </td>
+  </tr>
+  <tr>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-dream-and-proactive.svg" alt="Auto Dream and Proactive" width="92%">
+    </td>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-index-and-memory-search.svg" alt="Auto Index and Memory Search" width="92%">
+    </td>
+  </tr>
+</table>
+
+## 🤝 Agent-friendly Integration
+
+ReMe runs as a local memory service and offers multiple integration paths: CLI, HTTP API, MCP server, and SDK. Different
+agents can choose the path that fits their runtime while sharing the same local memory workspace.
+
+| Agents                                               | Recommended path                                                            | What works out of the box                                                                     |
+|------------------------------------------------------|-----------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
+| **QwenPaw**                                          | Embed ReMe via the Python SDK.                                              | Reuse the app's own lifecycle and model config while keeping memory local and file-based.     |
+| **Claude Code**                                      | Start ReMe as an MCP service and install [plugins/reme](plugins/reme).      | MCP recall tools, a `reme-memory` skill, and a Stop hook that records sessions automatically. |
+| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | Copy or install [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md). | Search/read/write memory and call `auto_memory`, `auto_dream`, and `proactive` via the CLI.   |

 <p align="center"><b>Integration demos</b></p>

@ -216,166 +278,39 @@ lifecycle according to the capabilities of each runtime.
  </tr>
 </table>

-## 🧠 How ReMe Works
+## 🛠️ ReMe Operations

-> Memory as File, File as Memory.
+ReMe operates the workspace through a unified job interface exposed by the CLI. Agents usually only need retrieval,
+reading, writing, editing, and automatic memory commands. Lower-level indexing, frontmatter, and file operation commands
+are mainly for maintenance, debugging, or advanced integration. Run `reme help` for the full job list.

-ReMe treats **memory as files**, progressively processing filtered conversation source records and external resources
-from `session/` and `resource/` into `daily/`, then `digest/`. The default workspace is `.reme/` under the current
-directory; `workspace_dir=...` selects a different user-owned location.
+| Command                                   | Purpose                                                                                |
+|-------------------------------------------|----------------------------------------------------------------------------------------|
+| `reme start`                              | Start the local ReMe service.                                                          |
+| `reme version` / `reme health_check`      | Check package and component status.                                                    |
+| `reme status`                             | Show stateful data-component memory estimates and process RSS.                         |
+| [`reme search`](docs/en/memory_search.md) | Retrieve memory with BM25 and wikilinks by default, plus vectors when enabled.         |
+| `reme read` / `reme write` / `reme edit`  | Inspect and maintain Markdown memory files.                                            |
+| `reme auto_memory`                        | Turn conversation messages into daily memory cards. Requires LLM credentials.          |
+| `reme auto_resource`                      | Interpret files under `resource/` into daily resource cards. Requires LLM credentials. |
+| `reme auto_dream` / `reme proactive`      | Consolidate daily memory into long-term digest and surface topics worth attention.     |
+| `reme reindex`                            | Rebuild search and wikilink indexes from existing files.                               |

-### Workspace Layout
+## 🤝 Community and Support

-```text
-<workspace_dir>/
-├── metadata/       # Rebuildable indexes, graphs, catalogs, and caches
-├── session/        # Conversation source records and agent sessions
-│   ├── dialog/
-│   │   └── <session_id>.jsonl  # Source messages saved by auto_memory
-│   └── claude_code/
-│       └── <session_id>.jsonl  # ReMe copy used by auto_memory_cc
-├── mem_session/    # Generated agent-wrapper sessions/config, not user memory
-│   ├── agentscope/
-│   ├── claude_config/
-│   └── codex/
-├── resource/            # External raw materials
-│   ├── <resource>.<ext>  # Root-level files enter today's daily layer
-│   └── YYYY-MM-DD/
-│       └── <resource>.<ext>
-├── daily/               # Lightly processed memory: daily facts, conversation summaries, resource readings
-│   ├── YYYY-MM-DD.md
-│   └── YYYY-MM-DD/
-│       ├── <generated_name>.md  # Topic-named conversation or resource card
-│       └── interests.yaml
-└── digest/              # Long-term memory: personal facts, procedural experience, knowledge nodes
-    ├── personal/
-    │   └── {topic/event}.md
-    ├── procedure/
-    │   └── {topic/event}.md
-    └── wiki/
-        └── {topic/event}.md
-```
-
-<p align="center">
-  <img src="docs/figure/reme-overview.svg" alt="ReMe file-based memory system overview" width="92%">
-</p>
-
-### Memory Lifecycle
-
-ReMe follows a capture → index → consolidate → recall loop. Workspace files remain the durable source of truth;
-everything under `metadata/` is rebuildable.
-
-| Capability                                  | Entry point                                     | What it does                                                                                                                                                   | Output                                                        |
-| ------------------------------------------- | ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |
-| [`auto_memory`](docs/en/auto_memory.md)     | Agent hook or `reme auto_memory`                | Distills useful conversation facts while preserving a filtered conversation source record.                                                                     | `session/dialog/*.jsonl`, `daily/<date>/<generated-name>.md`  |
-| [`auto_resource`](docs/en/auto_resource.md) | Resource watcher or `reme auto_resource`        | Turns files under `resource/` into source-linked, content-named daily cards.                                                                                   | `daily/<date>/<resource-card>.md`                             |
-| [`auto_index`](docs/en/memory_search.md)    | Background watcher or `reme reindex`            | Live-indexes Markdown in `daily/` and `digest/`; a full rebuild also scans `resource/` and JSONL.                                                              | Searchable chunks, BM25, wikilink graph, and optional vectors |
-| [`auto_dream`](docs/en/auto_dream.md)       | `dream_cron` or `reme auto_dream`               | By default, extracts up to five reusable units from changed files in the latest two-day window, then creates, corroborates, refines, or corrects digest nodes. | `digest/**`, `daily/<date>/interests.yaml`                    |
-| [`proactive`](docs/en/proactive.md)         | `reme proactive` before an agent decides to act | Reads topics generated by `auto_dream`; the host agent decides whether and how to mention them.                                                                | Structured topics from `daily/<date>/interests.yaml`          |
-
-<table>
-  <tr>
-    <td align="center" width="50%">
-      <img src="docs/figure/memory-as-file.svg" alt="Memory as File" width="92%">
-    </td>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Resource" width="92%">
-    </td>
-  </tr>
-  <tr>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-dream-and-proactive.svg" alt="Auto Dream and Proactive" width="92%">
-    </td>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-index-and-memory-search.svg" alt="Auto Index and Memory Search" width="92%">
-    </td>
-  </tr>
-</table>
-
-Search returns matching chunks with line ranges and bounded wikilink neighbors. Optional vector results are fused with
-BM25 through reciprocal rank fusion (RRF).
-
-> [!IMPORTANT]
->
-> `proactive` only reads and exposes interest topics produced by Auto Dream. It does not independently browse the web,
-> send notifications, or rewrite the knowledge base; the host agent decides whether and how to act on a topic.
-
-## 📊 Benchmarks
-
-ReMe evaluates multi-session and long-context memory with agentic search-and-read workflows. The figures below are the
-published reference runs in this repository; model, prompt, dataset, and judging details are documented with each
-benchmark.
-
-| Benchmark                                                                   | Setting      |              Sample size | Agentic score | Focus                                                              |
-| --------------------------------------------------------------------------- | ------------ | -----------------------: | ------------: | ------------------------------------------------------------------ |
-| **[LongMemEval cleaned-s](https://reme.agentscope.io/?doc=longmemeval-en)** | **Overall**  |        **500 questions** |     **89.4%** | Cross-session retrieval, knowledge updates, and temporal reasoning |
-| [BEAM](https://reme.agentscope.io/?doc=beam-en)                             | 100K context | 20 cases / 400 questions |         66.1% | Ten types of long-context memory tasks                             |
-| [BEAM](https://reme.agentscope.io/?doc=beam-en)                             | 1M context   | 35 cases / 700 questions |         65.0% | Ultra-long conversation settings                                   |
-
-ReMe also achieved a **0.580 PROC score across five user personas** in the repository's
-[π-Bench evaluation](https://reme.agentscope.io/?doc=pibench-en), 2.4% above NanoBot under the same test-model configuration. PROC
-measures proactive handling of hidden intent, clarification, cross-session preferences and conventions, task
-dependencies, and underspecified requests.
-
-## 🧩 Extensions and Plugins
-
-Plugins are optional Python distributions that contribute Component, Step, or Job backends and configuration. They are
-installed separately and enabled explicitly by configuration. Daily Paper and Auto Fin are independently packaged
-plugins; see the source distributions and their documentation for [Daily Paper](plugins/daily_paper/README.md) and
-[Auto Fin](plugins/auto-fin/README.md).
-
-| Plugin                                                        | Capability                                                                                                    |
-| ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
-| [Daily Paper](https://reme.agentscope.io/?doc=daily-paper-en) | Discover and rank papers, analyze PDFs with an agent, and generate file-native notes and a five-minute brief. |
-| [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-en)       | Fetch topic-related CLS news, search ReMe history, and generate wikilink-backed Markdown reports.             |
-
-See [Plugin Management](docs/en/plugin_management.md) to install, inspect, validate, enable, and uninstall ReMe plugins.
-
-## 📚 Documentation
-
-These guides cover the main user workflows and the runtime contracts implemented by the current code.
-
-| Guide                                                                     | What you will learn                                                                                 |
-| ------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
-| [Quick Start](docs/en/quick_start.md)                                     | Install ReMe, start the service, and run the first file and memory operations.                      |
-| [Memory as File](docs/en/memory_as_file.md)                               | Understand workspace layers, frontmatter, wikilinks, chunks, and the file-as-source-of-truth model. |
-| [Auto Memory](docs/en/auto_memory.md)                                     | Preserve source conversations and distill reusable daily memory cards.                              |
-| [Auto Resource](docs/en/auto_resource.md)                                 | Import supported text resources and turn them into source-linked daily cards.                       |
-| [Auto Dream](docs/en/auto_dream.md) and [Auto Link](docs/en/auto_link.md) | Consolidate daily notes into evolving digest nodes and readable wikilink relationships.             |
-| [Memory Search](docs/en/memory_search.md)                                 | Use BM25, optional vectors, RRF fusion, line-range recall, and progressive link expansion.          |
-| [Proactive](docs/en/proactive.md)                                         | Read interest topics safely and integrate them into a host agent's decision flow.                   |
-| [Application Scenarios](docs/en/reme_scene.md)                            | Follow concrete financial research, coding-memory, and personal knowledge-base examples.           |
-| [Framework](docs/en/framework.md)                                         | Understand Application, Job, Step, Component, service, configuration, and lifecycle boundaries.     |
-| [TypeScript integrations](typescript/README.md)                            | Configure the shared client and native DeepSeek Harness and OpenClaw adapters.                       |
-| [ReMe Blog](https://agentscope-ai.github.io/ReMe/?doc=en-reme-blog)       | Read the product story, design rationale, examples, and benchmark summary.                          |
-
-## 🛠️ Common Commands
-
-Run `reme help` for the full job list. Common workspace and maintenance commands are:
-
-| Command                                   | Purpose                                                                           |
-| ----------------------------------------- | --------------------------------------------------------------------------------- |
-| `reme status`                             | Show stateful data-component memory estimates and process RSS.                    |
-| [`reme search`](docs/en/memory_search.md) | Retrieve memory with BM25 and wikilinks by default, plus vectors when enabled.    |
-| `reme read` / `reme write` / `reme edit`  | Inspect and maintain Markdown memory files.                                       |
-| `reme traverse` / `reme graph_snapshot`   | Explore wikilink neighborhoods or the category-rooted digest graph.               |
-| `reme chat`                               | Stream a read-only, workspace-aware agent conversation. Requires LLM credentials. |
-| `reme reindex`                            | Rebuild search and wikilink indexes from existing files.                          |
-
-## 🤝 Community and Contributing
-
- **Issues, requests, and help**: Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) first. If there is no
-  related discussion, open one with the background, expected behavior, and impact scope.
- **Code contributions**: Before making changes, read the repository's
-  [contribution guide](docs/en/contributing.md). Source, schemas, and tests are the authoritative architecture and
-  extension guide.
- **Documentation contributions**: Update the canonical files under `docs/en/`, `docs/zh/`, or the relevant package
-  directory in this repository. The documentation site is generated from these files.
+- **Issues and requests**: Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) first. If there is no
+  related discussion, open a new issue with background, expected behavior, and impact scope.
+- **Code contributions**: Before making changes, read
+  the [contribution guide](https://docs.agentscope.io/reme/stable/en/contributing). Source,
+  schemas, and tests are the authoritative architecture and extension guide.
+- **Documentation contributions**: Submit user-facing documentation changes to the
+  [unified documentation repository](https://github.com/agentscope-ai/docs) under `reme/<version>/{en,zh}/`.
 - **Commit convention**: Conventional Commits are recommended, for example `feat(search): add link expansion option` or
  `docs(zh): update quick start`.
 - **Pre-submit checks**: Before submitting a PR, try to run `pre-commit run --all-files` and `pytest`. If tests that
  depend on LLMs, embeddings, or external services cannot run, explain that in the PR.
- **Documentation**: Visit [reme.agentscope.io](https://reme.agentscope.io).
+- **Get help**: Use [GitHub Issues](https://github.com/agentscope-ai/ReMe/issues) for bugs and feature requests. Project
+  documentation is available at [https://docs.agentscope.io/](https://docs.agentscope.io/reme/stable/en/).

 ### Contributors

--- a/README_ZH.md
+++ b/README_ZH.md
@ -1,5 +1,5 @@
 <p align="center">
- <img src="https://raw.githubusercontent.com/agentscope-ai/ReMe/main/docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
+ <img src="docs/figure/reme_logo.png" alt="ReMe Logo" width="50%">
 </p>

 <p align="center">
@ -8,7 +8,6 @@
  <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>
  <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
-  <a href="https://reme.agentscope.io"><img src="https://img.shields.io/badge/docs-ReMe-blue" alt="文档"></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>
@ -20,43 +19,38 @@
 </p>

 <p align="center">
-  <strong>面向 AI Agent 的 local-first 自进化个人知识库。</strong><br>
+  <strong>一个将对话和资料转化为可读、可编辑、可检索 Markdown 记忆的 Agent 记忆层。</strong><br>
 </p>

 > 历史版本：[0.3.x](https://github.com/agentscope-ai/ReMe/tree/reme_v3) ·
 > [0.2.x](https://github.com/agentscope-ai/ReMe/tree/v0.2.0.6) ·
 > [MemoryScope](https://github.com/agentscope-ai/ReMe/tree/memoryscope_branch)

-## ✨ 为什么选择 ReMe？
+🧠 ReMe 是一个面向 **AI 智能体** 的 local-first 记忆层。它把对话和资料沉淀为文件化长期记忆，并持续完成索引、链接和整理，让后续
+Agent 能够可靠召回。

-🧠 ReMe 将对话和资料持续沉淀为可读、可编辑、可检索、相互链接的 Markdown 记忆。QwenPaw、DeepSeek Harness 等 Agent
-可以共享同一个 workspace，共同检索、维护和演化知识，而持久文件始终由用户掌控。
+## ✨ 核心创新

- **Memory as File, File as Memory**：ReMe 使用带 frontmatter 和 wikilink 的普通 Markdown 保存持久记忆。用户和 Agent
-  都可以使用熟悉的工具查看、编辑、移动、同步和备份；索引及生成的元数据均可重建。
- **自进化知识库**：ReMe 将对话和资料逐步加工为 daily note 与长期知识，在保留来源的同时，持续提炼事实、偏好、
-  流程经验及其关系。
- **精准召回所需上下文。** ReMe 结合 BM25、可选 embedding 和 wikilink 展开，召回带行号的相关片段及其关系，无需把整个知识库塞入
-  Agent 上下文。
- **一个 workspace，可供不同 Agent 共同使用。** 个人助理、coding agent 和其他 Agent runtime 可以通过原生集成、SKILL.md、CLI、
-  HTTP、MCP 或 Python API 共享同一个本地记忆空间。
+- **Memory as File**：以带 frontmatter 和 wikilink 的 Markdown 作为记忆节点，让用户和 Agent 都能直接读写。
+- **自进化知识库**：通过 Auto Memory、Auto Resource 和 Auto Dream，把对话与资料逐步加工为长期记忆，并自动建立 wikilink 关系。
+- **渐进式混合搜索**：融合 wikilink、BM25 和 embedding，支持从关键词匹配到语义召回、关系扩展的混合检索。
+- **Agent 友好集成**：通过 SKILL.md + CLI 接入，方便不同 Agent 读写、维护与复用记忆。

 <p align="center">
  <img src="docs/figure/design-philosophy.svg" alt="ReMe 设计理念" width="92%">
 </p>

-## 📰 最新动态
+## 🔭 适用场景
+
+- **Personal assistants**：为 [QwenPaw](https://github.com/agentscope-ai/QwenPaw)、
+  [OpenClaw](https://github.com/openclaw/openclaw)、[Hermes](https://github.com/nousresearch/hermes-agent)
+  等个人助理提供用户可编辑的长期记忆层。
+- **Coding agents**：在接入 [Claude Code](plugins/reme) 等 coding agent 时，跨会话保留代码风格、项目背景、仓库决策和流程经验。
+- **LLM Wiki**：把对话、笔记和资料转化为可检索、可追溯、可链接的 Markdown 知识库，由用户和 Agent 共同维护。
+- **Self-evolving agents**：帮助 Agent 从经验中学习，把成功路径、失败尝试、可复用流程和阶段性反思沉淀为记忆。
+
+## 📰 新闻

- [2026.08] - 发布 [`@agentscope-ai/reme`](https://www.npmjs.com/package/@agentscope-ai/reme)，提供统一 TypeScript HTTP
-  client，以及 DeepSeek Harness 和 OpenClaw 的原生 ReMe 记忆集成。
- [2026.08] - 发布 [ReMe 博客](https://agentscope-ai.github.io/ReMe/?doc=zh-reme-blog)，系统介绍本地优先的记忆架构、自进化工作流、混合检索、
-  主动发现与评测结果。
- [2026.08] - 基于 ReMe 的智能体工具使用
-  [经验驱动增强方法](https://reme.agentscope.io/?doc=toolmemory-zh)已发布，见
-  [arXiv:2608.03403](https://arxiv.org/abs/2608.03403)。
- [2026.07] - 新增可选插件：[每日论文](https://reme.agentscope.io/?doc=daily-paper-zh)用于论文发现与解析，
-  [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-zh)用于研究最近 24 小时的主题相关财联社新闻，通过本地记忆搜索回顾历史材料并构建
-  wikilink。
 - [2026.07] -
  我们的论文 [Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution](https://aclanthology.org/2026.findings-acl.829/)
  已被 Findings of ACL 2026 接收。
@ -78,14 +72,33 @@ pip install "reme-ai[core]"
 ```bash
 git clone https://github.com/agentscope-ai/ReMe.git
 cd ReMe
-pip install -e reme_studio -e ".[core]"
-cd reme_studio
-npm ci
-npm run build:static
-cd ..
+pip install -e ".[core]"
 ```

-静态构建要求 Node.js 22.13 或更高版本，并让源码安装可以直接使用 Studio。
+### 环境变量
+
+如果需要 LLM 驱动的记忆演化或 embedding 检索，可以配置环境变量。embedding 默认关闭，因此默认配置不会启动
+embedding 模型，也不需要 embedding API key。
+
+```bash
+cat > .env <<'EOF'
+# 可选：仅在配置中显式启用 embedding 组件后使用。
+# EMBEDDING_API_KEY=sk-xxx
+# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+
+# 必须：auto_memory、auto_resource 和 auto_dream 需要 LLM。
+LLM_API_KEY=sk-xxx
+LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+EOF
+```
+
+基础文件读写、BM25 检索、wikilink 遍历和 proactive topics 读取可以先不配置 LLM 凭证。
+
+> [!NOTE]
+> 如需启用基于 embedding 的语义检索，请取消 [`reme/config/default.yaml`](reme/config/default.yaml) 中
+> `components.as_embedding` 和 `components.embedding_store` 的注释，并将
+> `components.file_store.default.embedding_store` 从 `""` 改为 `default`。完整说明见
+> [记忆检索文档](docs/zh/memory_search.md)。

 ### 启动服务

@ -100,10 +113,10 @@ reme start service.port=8181
 # reme start workspace_dir=/tmp/reme-demo service.port=8181
 ```

+启动后可以检查服务状态；如果使用了自定义端口，请将下面 URL 中的 `2333` 替换为对应端口。
+
 ```bash
 reme version
-reme health_check
-reme help
 curl -s http://127.0.0.1:2333/version -H 'Content-Type: application/json' -d '{}'
 ```

@ -141,50 +154,91 @@ ReMe 会把 Agent 记忆保存为可读的 Markdown。
 相关链接：[[digest/wiki/memory-as-file.md]]
 ```

-### ReMe Studio（可选）
+## 📁 记忆系统

-上面的 `core` 安装已包含 Studio。启动 ReMe 后，打开 <http://127.0.0.1:2333/> 即可浏览、编辑和搜索 workspace。
-如需为基础安装单独添加 Studio，可使用 `pip install "reme-ai[web]"`。源码构建、配置和开发说明见
-[ReMe Studio 指南](https://reme.agentscope.io/?doc=studio-zh)。
+> Memory as File, File as Memory.

-### 可选模型配置
+ReMe 将**记忆视为文件**，让原始对话和外部资料从 `session/`、`resource/` 渐进加工到 `daily/`，再沉淀为 `digest/`
+中可长期复用的知识节点。

-如果需要 LLM 驱动的记忆演化或 embedding 检索，可以配置环境变量。embedding 默认关闭，因此默认配置不会启动 embedding 模型，也不需要
-embedding API key。
+### 目录结构

-```bash
-cat > .env <<'EOF'
-# 可选：仅在配置中显式启用 embedding 组件后使用。
-# EMBEDDING_API_KEY=sk-xxx
-# EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-
-# 必须：auto_memory、auto_resource 和 auto_dream 需要 LLM。
-LLM_API_KEY=sk-xxx
-LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
-EOF
+```text
+<workspace_dir>/
+├── metadata/       # 系统索引、图谱、catalog 等持久状态
+├── session/        # 原始对话和 Agent session
+│   ├── dialog/
+│   │   └── <session_id>.jsonl
+│   ├── agentscope/
+│   └── claude_code/
+├── resource/            # 外部原始材料
+│   └── YYYY-MM-DD/
+│       └── <resource>.<ext>
+├── daily/               # 浅加工记忆：当天事实、对话摘要、资源解读
+│   ├── YYYY-MM-DD.md
+│   └── YYYY-MM-DD/
+│       ├── <session_event>.md
+│       ├── <resource_stem>.md
+│       └── interests.yaml
+└── digest/              # 长期记忆：个人事实、流程经验、知识节点
+    ├── personal/
+    │   └── {topic/event}.md
+    ├── procedure/
+    │   └── {topic/event}.md
+    └── wiki/
+        └── {topic/event}.md
 ```

-基础文件读写、BM25 检索、wikilink 遍历和 proactive topics 读取可以先不配置 LLM 凭证。
+<p align="center">
+  <img src="docs/figure/reme-overview.svg" alt="ReMe 文件化记忆系统总览" width="92%">
+</p>

-> [!NOTE]
-> 如需启用基于 embedding 的语义检索，请取消 [`reme/config/default.yaml`](reme/config/default.yaml) 中
-> `components.as_embedding` 和 `components.embedding_store` 的注释，并将
-> `components.file_store.default.embedding_store` 从 `""` 改为 `default`。完整说明见
-> [记忆检索文档](docs/zh/memory_search.md)。
+## 🧭 记忆设计理念

-## 🤝 将 ReMe 接入你的 Agent
+> 捕获原始对话和资料，将其整理为长期偏好、可复用经验和有价值的知识，并让结果始终能被用户和 Agent 直接编辑。

-ReMe 既可以作为本地记忆服务，通过 CLI、HTTP API 或 MCP server 接入，也可以通过 Python API 嵌入宿主进程。宿主集成可根据不同
-runtime 的能力，将记忆指引、召回和捕获接入 Agent 生命周期。
+### 自动记忆流程

-| Agent                      | 推荐接入方式                                                                                                                              | 接入后能力                                                            |
-| -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
-| **DeepSeek Harness**       | 使用 `dsh plugin --profile web add @agentscope-ai/reme` 安装 [`@agentscope-ai/reme`](typescript/README_ZH.md#deepseek-harness)。 | 长期记忆指引、`reme_search` 工具，以及自动捕获已完成的主 Agent 对话。 |
-| **OpenClaw**               | 使用 `openclaw plugins install @agentscope-ai/reme` 安装 [`@agentscope-ai/reme`](typescript/README_ZH.md#openclaw)。             | 原生记忆工具、用户触发运行前召回和自动对话捕获。                      |
-| **QwenPaw**                | 通过 Python API 在进程内嵌入 ReMe。                                                                                                       | 复用宿主生命周期和模型配置，同时保持记忆本地、文件化。                |
-| **Claude Code**            | 启动 streamable HTTP MCP service，并安装 [ReMe 插件](integrations/claude_code/reme)。                                                     | MCP 召回工具、`reme-memory` skill，以及自动记录会话的 Stop hook。     |
-| **Hermes**                 | 启动 HTTP service，并安装 [ReMe provider](integrations/hermes_agent)。                                                                    | 模型调用前召回，每轮对话完成后异步执行 `auto_memory`。                |
-| **Codex 及其他 CLI Agent** | 安装或复制 [ReMe Memory skill](skills/reme_memory/SKILL.md)。                                                                             | 通过 CLI 搜索、读取和写入记忆；自动捕获需要显式接入宿主生命周期。     |
+ReMe 遵循 capture → index → consolidate → recall 的循环。对话和资料先变成 daily 记忆卡片；后台任务保持文件可检索；
+`auto_dream` 将稳定知识沉淀到 `digest/`；Agent 再通过搜索、wikilink 或 proactive topics 召回记忆。
+
+| 能力                                          | 入口                               | 作用                                                 | 输出                                                   |
+|---------------------------------------------|----------------------------------|----------------------------------------------------|------------------------------------------------------|
+| [`auto_memory`](docs/zh/auto_memory.md)     | Agent hook 或 `reme auto_memory`  | 提炼有长期价值的对话事实，同时保留原始 session。                       | `session/dialog/*.jsonl`、`daily/<date>/<session>.md` |
+| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听或 `reme auto_resource`       | 将 `resource/<date>/` 下的文件转为带来源链接的 daily 卡片。        | `daily/<date>/<resource-card>.md`                    |
+| [`auto_index`](docs/zh/memory_search.md)    | 后台监听或 `reme reindex`             | 维护 chunks、BM25 索引、wikilink 图谱及可选的 embedding 索引。    | 可检索的 `daily/`、`digest/`、`resource/` 内容               |
+| [`auto_dream`](docs/zh/auto_dream.md)       | `dream_cron` 或 `reme auto_dream` | 将变化的 daily 卡片整理为长期 personal、procedure 和 wiki 记忆。   | `digest/**`、`daily/<date>/interests.yaml`            |
+| [`proactive`](docs/zh/proactive.md)         | Agent 决定主动行动前调用 `reme proactive` | 读取 `auto_dream` 生成的 topics；是否以及如何提醒用户由宿主 Agent 决定。 | 来自 `daily/<date>/interests.yaml` 的结构化 topics         |
+
+<table>
+  <tr>
+    <td align="center" width="50%">
+      <img src="docs/figure/memory-as-file.svg" alt="Memory as File" width="92%">
+    </td>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Resource" width="92%">
+    </td>
+  </tr>
+  <tr>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-dream-and-proactive.svg" alt="Auto Dream and Proactive" width="92%">
+    </td>
+    <td align="center" width="50%">
+      <img src="docs/figure/auto-index-and-memory-search.svg" alt="Auto Index and Memory Search" width="92%">
+    </td>
+  </tr>
+</table>
+
+## 🤝 Agent-friendly Integration
+
+ReMe 作为本地记忆服务运行，并提供 CLI、HTTP API、MCP server 和 SDK 等多种接入方式。不同 Agent 可以选择适合自身 runtime
+的路径，同时共享同一个本地 memory workspace。
+
+| Agent                                                | 推荐接入方式                                                            | 开箱可用能力                                                          |
+|------------------------------------------------------|-------------------------------------------------------------------|-----------------------------------------------------------------|
+| **QwenPaw**                                          | 通过 Python SDK 嵌入 ReMe。                                            | 复用应用自身生命周期和模型配置，同时保持 memory 本地、文件化。                             |
+| **Claude Code**                                      | 以 MCP service 启动 ReMe，并安装 [plugins/reme](plugins/reme)。           | MCP recall tools、`reme-memory` skill，以及自动记录会话的 Stop hook。       |
+| **Other CLI-capable agents (OpenClaw/Hermes/Codex)** | 复制或安装 [skills/reme_memory/SKILL.md](skills/reme_memory/SKILL.md)。 | 通过 CLI 搜索/读取/写入记忆，并调用 `auto_memory`、`auto_dream` 和 `proactive`。 |

 <p align="center"><b>集成演示</b></p>

@ -214,155 +268,36 @@ runtime 的能力，将记忆指引、召回和捕获接入 Agent 生命周期
  </tr>
 </table>

-## 🧠 ReMe 如何工作
+## 🛠️ ReMe Operations

-> Memory as File, File as Memory.
+ReMe 通过 CLI 暴露的统一 job interface 操作 workspace。Agent 通常只需要使用检索、读取、写入、编辑和自动记忆相关命令；更底层的索引、
+frontmatter 和文件操作接口主要用于维护、调试或高级集成。完整 job 列表可以运行 `reme help` 查看。

-ReMe 将 **记忆视为文件**，让过滤后的对话来源记录和外部资料从 `session/`、`resource/` 渐进加工到 `daily/`，再沉淀为
-`digest/`。默认 workspace 是当前目录下的 `.reme/`；可通过 `workspace_dir=...` 选择其他由用户控制的位置。
-
-### Workspace 结构
-
-```text
-<workspace_dir>/
-├── metadata/       # 可重建的索引、图谱、catalog 和缓存
-├── session/        # 对话来源记录和 Agent session
-│   ├── dialog/
-│   │   └── <session_id>.jsonl  # auto_memory 保存的来源消息
-│   └── claude_code/
-│       └── <session_id>.jsonl  # auto_memory_cc 使用的 ReMe 副本
-├── mem_session/    # Agent wrapper 生成的 session/配置，不是用户记忆
-│   ├── agentscope/
-│   ├── claude_config/
-│   └── codex/
-├── resource/            # 外部原始材料
-│   ├── <resource>.<ext>  # 根目录文件进入当天 daily 层
-│   └── YYYY-MM-DD/
-│       └── <resource>.<ext>
-├── daily/               # 浅加工记忆：当天事实、对话摘要、资源解读
-│   ├── YYYY-MM-DD.md
-│   └── YYYY-MM-DD/
-│       ├── <generated_name>.md  # 按主题命名的对话或资源卡片
-│       └── interests.yaml
-└── digest/              # 长期记忆：个人事实、流程经验、知识节点
-    ├── personal/
-    │   └── {topic/event}.md
-    ├── procedure/
-    │   └── {topic/event}.md
-    └── wiki/
-        └── {topic/event}.md
-```
-
-<p align="center">
-  <img src="docs/figure/reme-overview.svg" alt="ReMe 文件化记忆系统总览" width="92%">
-</p>
-
-### 记忆生命周期
-
-ReMe 遵循 capture → index → consolidate → recall 的循环。workspace 文件是持久化的事实来源，`metadata/` 中的内容均可重建。
-
-| 能力                                        | 入口                                      | 作用                                                                                         | 输出                                                         |
-| ------------------------------------------- | ----------------------------------------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
-| [`auto_memory`](docs/zh/auto_memory.md)     | Agent hook 或 `reme auto_memory`          | 提炼有长期价值的对话事实，同时保留过滤后的对话来源记录。                                     | `session/dialog/*.jsonl`、`daily/<date>/<generated-name>.md` |
-| [`auto_resource`](docs/zh/auto_resource.md) | 资源监听或 `reme auto_resource`           | 将 `resource/` 下的文件转为带来源链接、按内容命名的 daily 卡片。                             | `daily/<date>/<resource-card>.md`                            |
-| [`auto_index`](docs/zh/memory_search.md)    | 后台监听或 `reme reindex`                 | 实时索引 `daily/` 和 `digest/` 中的 Markdown；全量重建还会扫描 `resource/` 和 JSONL。        | 可检索的 chunks、BM25、wikilink 图谱和可选向量               |
-| [`auto_dream`](docs/zh/auto_dream.md)       | `dream_cron` 或 `reme auto_dream`         | 默认从最近两天内变化的文件中最多提取 5 个可复用 unit，再创建、印证、补充或修正 digest 节点。 | `digest/**`、`daily/<date>/interests.yaml`                   |
-| [`proactive`](docs/zh/proactive.md)         | Agent 决定主动行动前调用 `reme proactive` | 读取 `auto_dream` 生成的 topics；是否以及如何提醒用户由宿主 Agent 决定。                     | 来自 `daily/<date>/interests.yaml` 的结构化 topics           |
-
-<table>
-  <tr>
-    <td align="center" width="50%">
-      <img src="docs/figure/memory-as-file.svg" alt="Memory as File" width="92%">
-    </td>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-memory-resource.svg" alt="Auto Memory and Resource" width="92%">
-    </td>
-  </tr>
-  <tr>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-dream-and-proactive.svg" alt="Auto Dream and Proactive" width="92%">
-    </td>
-    <td align="center" width="50%">
-      <img src="docs/figure/auto-index-and-memory-search.svg" alt="Auto Index and Memory Search" width="92%">
-    </td>
-  </tr>
-</table>
-
-搜索返回带行号范围的相关 chunks 和数量受限的 wikilink 邻居；可选向量结果通过 RRF 与 BM25 融合。
-
-> [!IMPORTANT]
->
-> `proactive` 只读取并暴露 Auto Dream 生成的兴趣主题，不会自行联网、发送通知或改写知识库；是否以及如何使用主题，由宿主 Agent
-> 决定。
-
-## 📊 评测结果
-
-ReMe 通过 Agent 多轮搜索与读取的方式，评测多会话和超长上下文中的记忆能力。下表为仓库中已公开的参考实验结果；模型、prompt、数据集和评判细节见各评测文档。
-
-| 基准                                                                        | 设置        |            样本量 | Agentic 得分 | 主要检验内容                   |
-| --------------------------------------------------------------------------- | ----------- | ----------------: | -----------: | ------------------------------ |
-| **[LongMemEval cleaned-s](https://reme.agentscope.io/?doc=longmemeval-zh)** | **整体**    |        **500 题** |    **89.4%** | 跨会话检索、知识更新与时间推理 |
-| [BEAM](https://reme.agentscope.io/?doc=beam-zh)                             | 100K 上下文 | 20 cases / 400 题 |        66.1% | 十类长上下文记忆任务           |
-| [BEAM](https://reme.agentscope.io/?doc=beam-zh)                             | 1M 上下文   | 35 cases / 700 题 |        65.0% | 超长对话设置                   |
-
-在仓库的 [π-Bench 评测](https://reme.agentscope.io/?doc=pibench-zh)中，ReMe Agent 在 5 种用户角色上的平均 **PROC 得分为 0.580**
-，比相同测试模型配置的 NanoBot 高 2.4%。PROC 用于评估隐藏意图完成、针对性澄清、跨会话偏好和规范复用、跨任务依赖推断以及欠规格请求推进等主动性能力。
-
-## 🧩 扩展与插件
-
-插件是可选的独立 Python distribution，可以贡献 Component、Step、Job backend 和配置，并通过配置显式启用。每日论文与 Auto Fin
-均已独立打包，源码 distribution 及说明分别见[每日论文](plugins/daily_paper/README_ZH.md)和
-[Auto Fin](plugins/auto-fin/README_ZH.md)。
-
-| 插件                                                       | 能力                                                                           |
-| ---------------------------------------------------------- | ------------------------------------------------------------------------------ |
-| [每日论文](https://reme.agentscope.io/?doc=daily-paper-zh) | 发现并排序论文，使用 Agent 解读 PDF，生成文件化论文笔记和五分钟简报。          |
-| [Auto Fin](https://reme.agentscope.io/?doc=auto-fin-zh)    | 拉取主题相关财联社新闻，搜索 ReMe 历史材料并生成带 wikilink 的 Markdown 报告。 |
-
-安装、查看、校验、启用和卸载 ReMe 插件的方法见[插件管理](docs/zh/plugin_management.md)。
-
-## 📚 文档
-
-下列文档覆盖主要使用流程，并以当前代码的运行时契约为准。
-
-| 文档                                                                     | 主要内容                                                               |
-| ------------------------------------------------------------------------ | ---------------------------------------------------------------------- |
-| [快速开始](docs/zh/quick_start.md)                                       | 安装 ReMe、启动服务，并执行首次文件和记忆操作。                        |
-| [Memory as File](docs/zh/memory_as_file.md)                              | 理解 workspace 分层、frontmatter、wikilink、chunk 和文件事实来源模型。 |
-| [Auto Memory](docs/zh/auto_memory.md)                                    | 保留过滤后的对话来源记录，并提炼可复用的 daily 记忆卡片。              |
-| [Auto Resource](docs/zh/auto_resource.md)                                | 导入支持的文本资料，转换为可追溯来源的 daily 卡片。                    |
-| [Auto Dream](docs/zh/auto_dream.md) 与 [Auto Link](docs/zh/auto_link.md) | 将 daily 记忆整理为持续演化的 digest 节点和可读 wikilink 关系。        |
-| [记忆检索](docs/zh/memory_search.md)                                     | 使用 BM25、可选向量、RRF 融合、行号范围召回和渐进式链接扩展。          |
-| [Proactive](docs/zh/proactive.md)                                        | 安全读取兴趣主题，并将其接入宿主 Agent 的决策流程。                    |
-| [应用场景](docs/zh/reme_scene.md)                                        | 查看金融研究、研发记忆和个人知识库的完整使用示例。                     |
-| [框架说明](docs/zh/framework.md)                                         | 理解 Application、Job、Step、Component、service、配置和生命周期边界。  |
-| [TypeScript 集成](typescript/README_ZH.md)                               | 配置统一 client，以及 DeepSeek Harness 和 OpenClaw 原生适配器。        |
-| [ReMe 博客](https://agentscope-ai.github.io/ReMe/?doc=zh-reme-blog)      | 了解完整产品故事、设计动机、使用示例和评测摘要。                       |
-
-## 🛠️ 常用命令
-
-运行 `reme help` 可查看完整 job 列表。常用 workspace 与维护命令如下：
-
-| 命令                                      | 作用                                                          |
-| ----------------------------------------- | ------------------------------------------------------------- |
-| `reme status`                             | 查看有状态数据组件的内存估算及进程 RSS。                      |
+| 命令                                        | 作用                                          |
+|-------------------------------------------|---------------------------------------------|
+| `reme start`                              | 启动本地 ReMe 服务。                               |
+| `reme version` / `reme health_check`      | 检查包版本和组件状态。                                 |
+| `reme status`                             | 查看有状态数据组件的内存估算及进程 RSS。               |
 | [`reme search`](docs/zh/memory_search.md) | 默认使用 BM25 和 wikilink 检索，启用后增加向量检索。          |
-| `reme read` / `reme write` / `reme edit`  | 检查和维护 Markdown 记忆文件。                                |
-| `reme traverse` / `reme graph_snapshot`   | 浏览 wikilink 邻域或按类别组织的 digest 图。                  |
-| `reme chat`                               | 与可感知 workspace 的只读 Agent 进行流式对话；需要 LLM 凭证。 |
-| `reme reindex`                            | 基于已有文件重建检索和 wikilink 索引。                        |
+| `reme read` / `reme write` / `reme edit`  | 检查和维护 Markdown 记忆文件。                        |
+| `reme auto_memory`                        | 将对话 messages 转为 daily 记忆卡片；需要 LLM 凭证。       |
+| `reme auto_resource`                      | 将 `resource/` 下的文件解读为 daily 资料卡片；需要 LLM 凭证。 |
+| `reme auto_dream` / `reme proactive`      | 将 daily 记忆整理为长期 digest，并暴露值得关注的主题。          |
+| `reme reindex`                            | 基于已有文件重建检索和 wikilink 索引。                    |

-## 🤝 社区与贡献
+## 🤝 社区与支持

- **问题反馈、需求与帮助**：请先查看 [Open Issues](https://github.com/agentscope-ai/ReMe/issues)；如无相关讨论，可新建 Issue
+- **问题反馈与需求**：请先查看 [Open Issues](https://github.com/agentscope-ai/ReMe/issues)；如无相关讨论，可新建 Issue
  说明背景、目标行为和影响范围。
- **代码贡献**：改动前建议阅读仓库内的[贡献指南](docs/zh/contributing.md)。架构与扩展方式以源码、schema 和测试为准。
- **文档贡献**：请直接更新本仓库 `docs/en/`、`docs/zh/` 或对应 package 目录中的规范源文件；文档站点会从这些文件生成。
+- **代码贡献**：改动前建议阅读 [贡献指南](https://docs.agentscope.io/reme/stable/zh/contributing)。架构与扩展方式以源码、schema
+  和测试为准。
+- **文档贡献**：用户可见文档请提交到[统一文档仓库](https://github.com/agentscope-ai/docs)的 `reme/<version>/{en,zh}/` 目录。
 - **提交规范**：建议使用 Conventional Commits，例如 `feat(search): add link expansion option`、
  `docs(zh): update quick start`。
 - **提交前检查**：提交 PR 前请尽量运行 `pre-commit run --all-files` 和 `pytest`；如有依赖 LLM、embedding 或外部服务的测试无法运行，请在
  PR 中说明。
- **项目文档**：访问 [reme.agentscope.io](https://reme.agentscope.io)。
+- **获取帮助**：如需反馈 Bug 或功能请求，请使用 [GitHub Issues](https://github.com/agentscope-ai/ReMe/issues)；项目文档见
+  [https://docs.agentscope.io/](https://docs.agentscope.io/reme/stable/zh/)。

 ### 贡献者

--- a/benchmark/beam/README.md
+++ b/benchmark/beam/README.md
@ -1,124 +0,0 @@
-[中文版 / Chinese version](./README_ZH.md)
-
-# BEAM Benchmark
-
-BEAM is a benchmark for **memory capability over long-context chat cases**. Each
-case contains a very long chat history split into batches; ReMe converts each
-batch into a session, ingests them in chronological order, then answers probing
-questions via an agentic (ReAct) mode. Answers are scored with BEAM's
-rubric-based `answer_judge` job, which produces both a graded score and a binary
-verdict, and per-type averages are reported.
-
-BEAM ships dataset variants by chat size — `100K` / `500K` / `1M` / `10M` — so
-memory systems can be stressed at different context lengths. Question types
-include abstention, contradiction resolution, event ordering, information
-extraction, instruction following, knowledge update, multi-session reasoning,
-preference following, summarization, and temporal reasoning.
-
-> For the shared setup (dependencies, credentials, log conventions) see the
-> [top-level benchmark README](../README.md).
-
-## 1. Get the Dataset
-
-BEAM is a public repository, cloned into `benchmark/beam/dataset/`:
-
-```bash
-mkdir -p benchmark/beam/dataset
-cd benchmark/beam/dataset
-git clone https://github.com/mohammadtavakoli78/BEAM.git
-```
-
-After cloning, `benchmark/beam/dataset/BEAM/` should contain `chats/`, `src/`,
-`topics/` and other subdirectories.
-
-## 2. Run
-
-From the repository root:
-
-```bash
-python benchmark/beam/run.py
-python benchmark/beam/run.py --config benchmark/beam/config.yaml
-python benchmark/beam/run.py -q                        # quiet
-python benchmark/beam/run.py --eval_only               # reuse existing workspaces, query + judge only
-```
-
-## 3. Pipeline
-
-1. For each case, load `chat.json` and convert each batch into a ReMe session.
-2. Ingest sessions in chronological order into an isolated workspace, then `digest_update`.
-3. Answer each probing question via agentic (ReAct) mode.
-4. Score answers with BEAM's rubric-based `answer_judge` job and print per-type averages.
-
-## 4. Key config — `benchmark/beam/config.yaml`
-
-| Key | Meaning |
-| --- | --- |
-| `dataset.beam_root` | BEAM dataset root (`benchmark/beam/dataset/BEAM`). |
-| `dataset.chat_size` | Variant to run: `100K` / `500K` / `1M` / `10M`. |
-| `dataset.case_ids` | Specific cases (e.g. `["1","2"]`); empty = all cases. |
-| `dataset.start_index` / `num_items` | Case pagination (`num_items` `0` = all). |
-| `dataset.workspace_root` | Per-case workspace root (`benchmark/beam/workspaces/beam`). |
-| `evaluation.num_workers` | `0` = auto, `1` = sequential, `>1` = parallel. |
-| `reme.config` | ReMe config used (`beam.yaml`). |
-| `output.dir` | Results directory (`benchmark/beam/results`). |
-
-## 5. Outputs
-
-Results are JSON files written to `output.dir` as
-`results_<chat_size>_<timestamp>.json`, with a per-type score summary also
-printed to the console. Logging conventions are shared across benchmarks — see
-the [top-level README](../README.md#outputs--logs).
-
-## 6. Reference Results
-
-> The results below use the longmemeval-version prompt.
-
-### 100K
-
-agentscope==2.0.4.post1, conda reme env, 20 workers, eval-only (reusing prebuilt memory)
-(2026-08-05, 20 cases / 400 Qs, total 46.0 min)
-
-| Type | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|---|
-| abstention | 0.550 | 0.550 | 96,031 | 1,070 | 97,101 | 4.58 |
-| contradiction_resolution | 0.438 | 0.412 | 32,263 | 872 | 33,135 | 2.48 |
-| event_ordering | 0.501 | 0.423 | 140,195 | 5,163 | 145,358 | 4.70 |
-| information_extraction | 0.873 | 0.832 | 50,245 | 883 | 51,128 | 3.15 |
-| instruction_following | 0.750 | 0.725 | 37,986 | 848 | 38,834 | 2.67 |
-| knowledge_update | 0.688 | 0.675 | 31,198 | 651 | 31,849 | 2.27 |
-| multi_session_reasoning | 0.626 | 0.584 | 85,038 | 4,563 | 89,601 | 4.28 |
-| preference_following | 0.925 | 0.912 | 34,281 | 989 | 35,270 | 2.50 |
-| summarization | 0.623 | 0.461 | 89,657 | 2,056 | 91,713 | 4.12 |
-| temporal_reasoning | 0.637 | 0.625 | 34,563 | 1,049 | 35,612 | 2.52 |
-| **OVERALL** | **0.661** | **0.620** | **63,146** | **1,814** | **64,960** | **3.33** |
-
-Memory Construction average token consumption (default agent, full build over 20 cases):
-
-| Agent | input tok/case | output tok/case | total tok/case |
-|---|---|---|---|
-| default | 2,172,316 | 136,697 | 2,309,013 |
-
-### 1M
-
-agentscope==2.0.4.post1, conda reme env, 20 workers, full memory build
-(2026-08-05, 35 cases / 700 Qs, total 459.2 min)
-
-| Type | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|---|
-| abstention | 0.429 | 0.429 | 118,707 | 1,178 | 119,886 | 4.20 |
-| contradiction_resolution | 0.391 | 0.364 | 49,787 | 810 | 50,597 | 2.50 |
-| event_ordering | 0.558 | 0.456 | 201,514 | 3,889 | 205,403 | 4.79 |
-| information_extraction | 0.809 | 0.772 | 78,950 | 894 | 79,844 | 3.00 |
-| instruction_following | 0.852 | 0.832 | 55,757 | 924 | 56,681 | 2.81 |
-| knowledge_update | 0.779 | 0.771 | 45,981 | 665 | 46,646 | 2.37 |
-| multi_session_reasoning | 0.658 | 0.612 | 138,133 | 2,873 | 141,006 | 4.40 |
-| preference_following | 0.798 | 0.777 | 51,796 | 920 | 52,716 | 2.53 |
-| summarization | 0.693 | 0.537 | 158,794 | 2,905 | 161,700 | 4.44 |
-| temporal_reasoning | 0.536 | 0.536 | 100,176 | 3,148 | 103,324 | 3.90 |
-| **OVERALL** | **0.650** | **0.609** | **99,959** | **1,821** | **101,780** | **3.49** |
-
-Memory Construction average token consumption (default agent, full build over 35 cases):
-
-| Agent | input tok/case | output tok/case | total tok/case |
-|---|---|---|---|
-| default | 31,943,817 | 1,417,061 | 33,360,878 |
--- a/benchmark/beam/README_ZH.md
+++ b/benchmark/beam/README_ZH.md
@ -1,119 +0,0 @@
-# BEAM 评测
-
-[English version](./README.md)
-
-BEAM 是一个面向**长上下文对话场景**的记忆能力评测基准。每个 case 包含一段被切分为多个
-batch 的超长对话；ReMe 将每个 batch 转换为一个会话，按时间顺序摄入后，以 agentic（ReAct）
-模式回答探测问题。答案由 BEAM 基于 rubric 的 `answer_judge` 任务打分，同时给出分级分数与二元
-判定，并输出各类型平均分。
-
-BEAM 按对话规模提供多种数据变体 —— `100K` / `500K` / `1M` / `10M`，可在不同上下文长度下
-压测记忆系统。题型包括 abstention（拒答）、contradiction resolution（矛盾消解）、event
-ordering（事件排序）、information extraction（信息抽取）、instruction following（指令遵循）、
-knowledge update（知识更新）、multi-session reasoning（多会话推理）、preference following
-（偏好遵循）、summarization（摘要）与 temporal reasoning（时间推理）。
-
-> 公共设置（依赖、凭据、日志约定）见[总评测说明](../README_ZH.md)。
-
-## 1. 获取数据集
-
-BEAM 是公开仓库，clone 到 `benchmark/beam/dataset/` 下：
-
-```bash
-mkdir -p benchmark/beam/dataset
-cd benchmark/beam/dataset
-git clone https://github.com/mohammadtavakoli78/BEAM.git
-```
-
-clone 完成后，`benchmark/beam/dataset/BEAM/` 目录下应包含 `chats/`、`src/`、`topics/` 等子目录。
-
-## 2. 运行
-
-在仓库根目录执行：
-
-```bash
-python benchmark/beam/run.py
-python benchmark/beam/run.py --config benchmark/beam/config.yaml
-python benchmark/beam/run.py -q                        # 安静模式
-python benchmark/beam/run.py --eval_only               # 复用已有工作区，仅执行查询 + 评判
-```
-
-## 3. 流程
-
-1. 为每个 case 加载 `chat.json`，将每个 batch 转换为一个 ReMe 会话。
-2. 按时间顺序将会话摄入独立工作区，随后执行 `digest_update`。
-3. 以 agentic（ReAct）模式回答每个探测问题。
-4. 通过 BEAM 基于 rubric 的 `answer_judge` 任务打分，并输出各类型平均分。
-
-## 4. 关键配置 —— `benchmark/beam/config.yaml`
-
-| 配置项 | 含义 |
-| --- | --- |
-| `dataset.beam_root` | BEAM 数据集根目录（`benchmark/beam/dataset/BEAM`）。 |
-| `dataset.chat_size` | 运行的变体：`100K` / `500K` / `1M` / `10M`。 |
-| `dataset.case_ids` | 指定 case（如 `["1","2"]`），空表示全部。 |
-| `dataset.start_index` / `num_items` | case 分页（`num_items` 为 `0` 表示全部）。 |
-| `dataset.workspace_root` | case 工作区根目录（`benchmark/beam/workspaces/beam`）。 |
-| `evaluation.num_workers` | `0` = 自动，`1` = 串行，`>1` = 并行。 |
-| `reme.config` | 使用的 ReMe 配置（`beam.yaml`）。 |
-| `output.dir` | 结果目录（`benchmark/beam/results`）。 |
-
-## 5. 输出
-
-结果以 JSON 文件写入 `output.dir`，文件名为 `results_<chat_size>_<timestamp>.json`，
-同时控制台会打印含各类型分数的汇总。日志约定在各基准间通用，见
-[总说明](../README_ZH.md#输出与日志)。
-
-## 6. 参考结果
-
-> 以下结果使用 longmemeval 版本的 prompt。
-
-### 100K
-
-agentscope==2.0.4.post1，conda reme 环境，20 并发，eval-only（复用已构建 memory）
-（2026-08-05，20 cases / 400 Qs，总耗时 46.0 min）
-
-| 题型 | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|---|
-| abstention | 0.550 | 0.550 | 96,031 | 1,070 | 97,101 | 4.58 |
-| contradiction_resolution | 0.438 | 0.412 | 32,263 | 872 | 33,135 | 2.48 |
-| event_ordering | 0.501 | 0.423 | 140,195 | 5,163 | 145,358 | 4.70 |
-| information_extraction | 0.873 | 0.832 | 50,245 | 883 | 51,128 | 3.15 |
-| instruction_following | 0.750 | 0.725 | 37,986 | 848 | 38,834 | 2.67 |
-| knowledge_update | 0.688 | 0.675 | 31,198 | 651 | 31,849 | 2.27 |
-| multi_session_reasoning | 0.626 | 0.584 | 85,038 | 4,563 | 89,601 | 4.28 |
-| preference_following | 0.925 | 0.912 | 34,281 | 989 | 35,270 | 2.50 |
-| summarization | 0.623 | 0.461 | 89,657 | 2,056 | 91,713 | 4.12 |
-| temporal_reasoning | 0.637 | 0.625 | 34,563 | 1,049 | 35,612 | 2.52 |
-| **OVERALL** | **0.661** | **0.620** | **63,146** | **1,814** | **64,960** | **3.33** |
-
-Memory Construction 平均 token 消耗（default agent，20 cases 全量构建）：
-
-| Agent | input tok/case | output tok/case | total tok/case |
-|---|---|---|---|
-| default | 2,172,316 | 136,697 | 2,309,013 |
-
-### 1M
-
-agentscope==2.0.4.post1，conda reme 环境，20 并发，全量构建 memory
-（2026-08-05，35 cases / 700 Qs，总耗时 459.2 min）
-
-| 题型 | Agentic | Binary | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|---|
-| abstention | 0.429 | 0.429 | 118,707 | 1,178 | 119,886 | 4.20 |
-| contradiction_resolution | 0.391 | 0.364 | 49,787 | 810 | 50,597 | 2.50 |
-| event_ordering | 0.558 | 0.456 | 201,514 | 3,889 | 205,403 | 4.79 |
-| information_extraction | 0.809 | 0.772 | 78,950 | 894 | 79,844 | 3.00 |
-| instruction_following | 0.852 | 0.832 | 55,757 | 924 | 56,681 | 2.81 |
-| knowledge_update | 0.779 | 0.771 | 45,981 | 665 | 46,646 | 2.37 |
-| multi_session_reasoning | 0.658 | 0.612 | 138,133 | 2,873 | 141,006 | 4.40 |
-| preference_following | 0.798 | 0.777 | 51,796 | 920 | 52,716 | 2.53 |
-| summarization | 0.693 | 0.537 | 158,794 | 2,905 | 161,700 | 4.44 |
-| temporal_reasoning | 0.536 | 0.536 | 100,176 | 3,148 | 103,324 | 3.90 |
-| **OVERALL** | **0.650** | **0.609** | **99,959** | **1,821** | **101,780** | **3.49** |
-
-Memory Construction 平均 token 消耗（default agent，35 cases 全量构建）：
-
-| Agent | input tok/case | output tok/case | total tok/case |
-|---|---|---|---|
-| default | 31,943,817 | 1,417,061 | 33,360,878 |
--- a/benchmark/beam/config.yaml
+++ b/benchmark/beam/config.yaml
@ -1,24 +0,0 @@
-# BEAM evaluation configuration
-# This file controls what/how to evaluate.
-
-dataset:
-  beam_root: "benchmark/beam/dataset/BEAM"   # BEAM dataset root
-  chat_size: "1M"                        # 100K | 500K | 1M | 10M (dataset variant)
-  case_ids: []                          # empty = all cases; or ["1", "2", "3"]
-  start_index: 0                        # first case index (for pagination)
-  num_items: 0                          # 0 = all cases; >0 = limit
-  workspace_root: "benchmark/beam/workspaces/beam"  # workspace root for case workspaces
-
-evaluation:
-  num_workers: 20                       # 0 = auto; 1 = sequential; >1 = parallel (per-case)
-  compress_session: false               # true = compress session chunks in search_v2 (query-aware); false = no compression
-
-reme:
-  config: "beam.yaml"                   # reme config (in reme/config/)
-
-output:
-  dir: "benchmark/beam/results"
-  log_dir: "logs"                       # log directory (relative to project root)
-  log_prefix: "beam"                    # benchmark name used in log filenames
-  log_to_console: true
-  log_to_file: true
--- a/benchmark/beam/kill.sh
+++ b/benchmark/beam/kill.sh
@ -1,76 +0,0 @@
-#!/bin/bash
-# 杀死指定进程及其所有子进程
-# Usage: bash kill.sh <PID>
-
-if [ -z "$1" ]; then
-    echo "Usage: bash kill.sh <PID>"
-    echo "  杀死指定进程及其所有子进程"
-    exit 1
-fi
-
-PID=$1
-
-# 检查进程是否存在
-if ! kill -0 "$PID" 2>/dev/null; then
-    echo "进程 $PID 不存在"
-    exit 1
-fi
-
-# 递归收集所有子进程（包括子进程的子进程）
-collect_children() {
-    local parent=$1
-    local children
-    children=$(ps -o pid= --ppid "$parent" 2>/dev/null | tr -d ' ')
-    for child in $children; do
-        collect_children "$child"
-    done
-    echo "$parent"
-}
-
-# 收集进程树（子进程在前，父进程在后，保证先杀子再杀父）
-PROCESS_TREE=$(collect_children "$PID")
-TOTAL=$(echo "$PROCESS_TREE" | wc -l | tr -d ' ')
-
-echo "进程树（共 $TOTAL 个进程）:"
-while read -r p; do
-    cmd=$(ps -o args= -p "$p" 2>/dev/null | head -c 80)
-    printf "  PID=%-8s %s\n" "$p" "$cmd"
-done <<< "$PROCESS_TREE"
-
-# 先 SIGTERM 优雅终止
-echo ""
-echo "发送 SIGTERM..."
-while read -r p; do
-    kill "$p" 2>/dev/null
-done <<< "$PROCESS_TREE"
-
-# 等待最多 5 秒
-for i in $(seq 1 5); do
-    alive=false
-    while read -r p; do
-        if kill -0 "$p" 2>/dev/null; then
-            alive=true
-        fi
-    done <<< "$PROCESS_TREE"
-    if [ "$alive" = false ]; then
-        break
-    fi
-    sleep 1
-done
-
-# 检查是否还有残留，强制 SIGKILL
-remaining=false
-while read -r p; do
-    if kill -0 "$p" 2>/dev/null; then
-        remaining=true
-    fi
-done <<< "$PROCESS_TREE"
-
-if [ "$remaining" = true ]; then
-    echo "部分进程未响应，发送 SIGKILL..."
-    while read -r p; do
-        kill -9 "$p" 2>/dev/null
-    done <<< "$PROCESS_TREE"
-fi
-
-echo "已终止进程树（根 PID=$PID，共 $TOTAL 个进程）"
--- a/benchmark/beam/run.py
+++ b/benchmark/beam/run.py
@ -1,891 +0,0 @@
-"""BEAM evaluation runner for ReMe.
-
-Evaluates ReMe's memory capability using the BEAM dataset.
-Each case gets an isolated workspace; chat.json batches are ingested as
-sessions in chronological order; finally probing questions are answered
-via an agentic (ReAct) approach, then
-judged by BEAM's rubric-based LLM-as-judge.
-
-Usage:
-    python benchmark/beam/run.py
-    python benchmark/beam/run.py --config benchmark/beam/config.yaml
-    python benchmark/beam/run.py -q                          # quiet: only eval-level logs
-    python benchmark/beam/run.py --log-level WARNING         # reduce eval runner logs
-    python benchmark/beam/run.py --reme-log-level WARNING    # reduce reme internal logs
-    python benchmark/beam/run.py --eval_only                 # query+judge only, reuse existing workspace
-"""
-
-import json
-import logging
-import os
-import re
-import shutil
-import time
-import threading
-from datetime import datetime
-from pathlib import Path
-
-import yaml
-from dotenv import load_dotenv
-
-# Load .env from project root
-_PROJECT_ROOT = Path(__file__).parent.parent.parent
-load_dotenv(_PROJECT_ROOT / ".env")
-
-# Workspace root — read from config.yaml (dataset.workspace_root)
-_WORKSPACE_ROOT_DEFAULT = "benchmark/beam/workspaces/beam"
-
-# ---------------------------------------------------------------------------
-# Logging
-# ---------------------------------------------------------------------------
-_DEFAULT_LOG_FORMAT = "%(asctime)s | %(levelname)s | %(message)s"
-
-logging.basicConfig(level=logging.INFO, format=_DEFAULT_LOG_FORMAT)
-logger = logging.getLogger("beam")
-
-# Noisy library loggers silenced by default
-_NOISY_LOGGERS = [
-    "httpx",
-    "httpcore",
-    "openai",
-    "uvicorn",
-    "multipart",
-    "asyncio",
-    "watchfiles",
-    "filelock",
-]
-
-
-def setup_logging(
-    log_level: str,
-    reme_log_level: str,
-    log_dir: str | None = None,
-):
-    """Configure logging for the eval runner and reme internals.
-
-    Args:
-        log_level: Level for the eval runner logger (DEBUG/INFO/WARNING/ERROR).
-        reme_log_level: Level for reme's internal loguru logger.
-        log_dir: Per-run log directory (absolute path). None = no file logging.
-    """
-    numeric = getattr(logging, log_level.upper(), logging.INFO)
-    # Eval runner logger
-    logging.getLogger().setLevel(numeric)
-    logger.setLevel(numeric)
-
-    # Suppress noisy library loggers when above DEBUG
-    if numeric > logging.DEBUG:
-        for name in _NOISY_LOGGERS:
-            lib_logger = logging.getLogger(name)
-            lib_logger.setLevel(max(numeric, logging.WARNING))
-
-    # Add file handler for eval runner if log_dir is specified
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-        log_filepath = os.path.join(log_dir, "runner.log")
-        file_handler = logging.FileHandler(log_filepath, encoding="utf-8")
-        file_handler.setLevel(numeric)
-        file_handler.setFormatter(logging.Formatter(_DEFAULT_LOG_FORMAT))
-        logging.getLogger().addHandler(file_handler)
-        logger.info(f"Eval runner log file: {log_filepath}")
-
-    # Reme internal logger (loguru) — will be applied per-worker via _configure_worker
-    os.environ["REME_LOG_LEVEL"] = reme_log_level.upper()
-    if log_dir:
-        os.environ["REME_LOG_DIR"] = log_dir
-
-
-def _configure_worker(
-    log_level: str,
-    reme_log_level: str,
-    log_dir: str | None = None,
-):
-    """Set up logging inside a multiprocessing worker process.
-
-    Must be called at the top of each worker because child processes inherit
-    parent state but loguru sinks are NOT shared across fork/spawn.
-    """
-    numeric = getattr(logging, log_level.upper(), logging.INFO)
-    logging.basicConfig(level=numeric, format=_DEFAULT_LOG_FORMAT, force=True)
-    logging.getLogger("beam").setLevel(numeric)
-    if numeric > logging.DEBUG:
-        for name in _NOISY_LOGGERS:
-            logging.getLogger(name).setLevel(max(numeric, logging.WARNING))
-
-    # Add file handler for eval runner in worker process
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-        pid = os.getpid()
-        log_filepath = os.path.join(log_dir, f"worker-{pid}.log")
-        file_handler = logging.FileHandler(log_filepath, encoding="utf-8")
-        file_handler.setLevel(numeric)
-        file_handler.setFormatter(logging.Formatter(_DEFAULT_LOG_FORMAT))
-        logging.getLogger().addHandler(file_handler)
-
-    # Re-initialize loguru for reme internals at the desired level
-    from reme.utils import get_logger
-
-    reme_log_dir = log_dir or "logs"
-    get_logger(log_dir=reme_log_dir, level=reme_log_level.upper(), force_init=True)
-
-
-# ---------------------------------------------------------------------------
-# Config loading
-# ---------------------------------------------------------------------------
-def load_eval_config(config_path: str | None = None) -> dict:
-    """Load evaluation config yaml with env-var expansion."""
-    if config_path is None:
-        config_path = str(Path(__file__).parent / "config.yaml")
-    with open(config_path, encoding="utf-8") as f:
-        raw = f.read()
-
-    # Expand ${VAR} and ${VAR:-default}
-    def _expand(m):
-        expr = m.group(1)
-        if ":-" in expr:
-            key, default = expr.split(":-", 1)
-            return os.environ.get(key, default)
-        return os.environ.get(expr, "")
-
-    raw = re.sub(r"\$\{([^}]+)\}", _expand, raw)
-    return yaml.safe_load(raw)
-
-
-# ---------------------------------------------------------------------------
-# BEAM data loading
-# ---------------------------------------------------------------------------
-def parse_beam_time_anchor(time_str: str) -> datetime:
-    """Parse BEAM time_anchor format: 'March-15-2024' -> datetime."""
-    for fmt in ("%B-%d-%Y", "%b-%d-%Y"):
-        try:
-            return datetime.strptime(time_str, fmt)
-        except ValueError:
-            continue
-    raise ValueError(f"Cannot parse time_anchor: {time_str!r}")
-
-
-def load_beam_chat(chat_path: Path, chat_size: str, case_id: str) -> list[dict]:
-    """Load BEAM chat.json and convert to ReMe session format.
-
-    Each batch becomes one session with all its turns flattened.
-    Each turn resolves its own time_anchor independently; turns without
-    an explicit time_anchor inherit from the most recent preceding turn.
-    Returns list of sessions, each with:
-      - session_id: str
-      - date: str (YYYY-MM-DD)  — derived from the *first* turn's time
-      - messages: list[dict] with name, role, content, created_at
-    """
-    with open(chat_path, encoding="utf-8") as f:
-        batches = json.load(f)
-
-    sessions = []
-    for batch in batches:
-        batch_num = batch["batch_number"]
-
-        # Resolve batch-level fallback (used when no turn has a time_anchor)
-        batch_anchor = batch.get("time_anchor")
-        if not batch_anchor:
-            batch_anchor = "January-1-2024"
-
-        # Flatten all turns, resolving time_anchor per turn
-        messages = []
-        prev_dt = None  # carries forward from previous turn
-        first_dt = None  # for session-level date
-
-        for turn in batch["turns"]:
-            # Find this turn's own time_anchor from its messages
-            turn_anchor = None
-            for msg in turn:
-                if msg.get("time_anchor"):
-                    turn_anchor = msg["time_anchor"]
-                    break
-
-            if turn_anchor:
-                dt = parse_beam_time_anchor(turn_anchor)
-            elif prev_dt is not None:
-                dt = prev_dt  # inherit from previous turn
-            else:
-                dt = parse_beam_time_anchor(batch_anchor)
-
-            if first_dt is None:
-                first_dt = dt
-            prev_dt = dt
-
-            for msg in turn:
-                role = msg["role"]
-                messages.append(
-                    {
-                        "name": role,
-                        "role": role,
-                        "content": msg["content"],
-                        "created_at": dt.strftime("%Y-%m-%dT%H:%M:%S"),
-                    },
-                )
-
-        sessions.append(
-            {
-                "session_id": f"beam_{chat_size}_{case_id}_batch{batch_num}",
-                "date": first_dt.strftime("%Y-%m-%d"),
-                "messages": messages,
-            },
-        )
-
-    return sessions
-
-
-def get_available_cases(beam_root: Path, chat_size: str) -> list[str]:
-    """Return sorted list of case IDs for a given chat size."""
-    chats_dir = beam_root / "chats" / chat_size
-    if not chats_dir.exists():
-        return []
-    return sorted(
-        [d.name for d in chats_dir.iterdir() if d.is_dir()],
-        key=int,
-    )
-
-
-# ---------------------------------------------------------------------------
-# Answer generation
-# ---------------------------------------------------------------------------
-async def answer_question_agentic(app, question: str, compress_session: bool = False) -> tuple[str, dict]:
-    """Answer a probing question using ReMe's agentic_answer job.
-
-    Returns (answer, metadata)
-    """
-    from reme.utils.evaluation_interface import track_agent_token_usage, track_job_counts
-
-    with (
-        track_job_counts(["search"], app.context) as tool_counts,
-        track_agent_token_usage(
-            ["bench"],
-            app.context,
-        ) as token_usages,
-    ):
-        query_resp = await app.run_job(
-            "agentic_answer",
-            query=question,
-            compress_session=compress_session,
-        )
-    answer = (query_resp.answer or "").strip()
-
-    return answer, {
-        "mode": "agentic",
-        "tool_counts": tool_counts,
-        "token_usage": token_usages["bench"],
-    }
-
-
-# ---------------------------------------------------------------------------
-# BEAM rubric-based LLM-as-Judge
-# ---------------------------------------------------------------------------
-async def judge_answer(
-    app,
-    question: str,
-    llm_response: str,
-    rubric: list[str],
-    question_type: str = "",
-) -> dict:
-    """Judge an answer via the answer_judge job (beam_rubric_judge_step)."""
-    judge_resp = await app.run_job(
-        "answer_judge",
-        llm_response=llm_response,
-        rubric=rubric,
-        probing_question=question,
-        question_type=question_type,
-    )
-    result = {
-        "llm_judge_score": (judge_resp.metadata or {}).get("llm_judge_score", 0.0),
-        "llm_judge_responses": (judge_resp.metadata or {}).get("llm_judge_responses", []),
-    }
-    # Include event_ordering extra metrics if present
-    eo = (judge_resp.metadata or {}).get("event_ordering")
-    if eo:
-        result["event_ordering"] = eo
-    return result
-
-
-# ---------------------------------------------------------------------------
-# Main evaluation pipeline
-# ---------------------------------------------------------------------------
-async def evaluate_case(eval_config: dict, case_id: str, eval_only: bool = False) -> dict:
-    """Evaluate a single BEAM case end-to-end.
-
-    Args:
-        eval_config: The evaluation configuration dict.
-        case_id: The case directory name (e.g. "1").
-        eval_only: If True, skip ingestion and only run query+judge
-            using the existing workspace.
-
-    Returns:
-        A results dict with all questions, answers, and judgments.
-    """
-    from reme import Application
-    from reme.config import resolve_app_config
-
-    dataset_cfg = eval_config["dataset"]
-    chat_size = dataset_cfg["chat_size"]
-    compress_session = bool(eval_config["evaluation"].get("compress_session", False))
-    beam_root = _PROJECT_ROOT / dataset_cfg.get("beam_root", "benchmark/beam/dataset/BEAM")
-    chat_path = beam_root / "chats" / chat_size / case_id / "chat.json"
-    probing_questions_path = beam_root / "chats" / chat_size / case_id / "probing_questions" / "probing_questions.json"
-
-    if not chat_path.exists():
-        raise FileNotFoundError(f"Chat file not found: {chat_path}")
-    if not probing_questions_path.exists():
-        raise FileNotFoundError(f"Probing questions not found: {probing_questions_path}")
-
-    logger.info(
-        "[Case %s] size=%s%s",
-        case_id,
-        chat_size,
-        " [eval_only]" if eval_only else "",
-    )
-
-    # Workspace setup
-    workspace_root = _PROJECT_ROOT / dataset_cfg.get("workspace_root", _WORKSPACE_ROOT_DEFAULT)
-    case_dir = workspace_root / f"{chat_size}_{case_id}"
-    workspace_dir = str(case_dir / ".reme")
-
-    if eval_only:
-        if not case_dir.exists() or not Path(workspace_dir).exists():
-            raise FileNotFoundError(
-                f"[Case {case_id}] eval_only: workspace not found at {case_dir}. "
-                f"Run without --eval_only first to build the workspace.",
-            )
-    else:
-        if case_dir.exists():
-            shutil.rmtree(case_dir)
-            logger.info(f"[Case {case_id}] Cleaned existing workspace: {case_dir}")
-        else:
-            logger.info(f"[Case {case_id}] Workspace not found, creating: {case_dir}")
-        case_dir.mkdir(parents=True, exist_ok=True)
-
-    # Pre-initialize ReMe's loguru logger with the correct log_dir
-    output_cfg = eval_config.get("output", {})
-    if output_cfg.get("log_to_file", False):
-        reme_log_dir = os.environ.get("REME_LOG_DIR")
-        if reme_log_dir:
-            from reme.utils import get_logger
-
-            get_logger(
-                log_dir=reme_log_dir,
-                level=os.environ.get("REME_LOG_LEVEL", "INFO"),
-                log_to_console=output_cfg.get("log_to_console", True),
-                log_to_file=True,
-                force_init=True,
-            )
-
-    cfg = resolve_app_config(
-        config=eval_config["reme"]["config"],
-        workspace_dir=workspace_dir,
-        log_to_console=output_cfg.get("log_to_console", True),
-        log_to_file=output_cfg.get("log_to_file", False),
-        enable_logo=False,
-    )
-
-    app = Application(**cfg)
-    await app.start()
-
-    from reme.utils.evaluation_interface import check_agent_token_usage  # noqa: E402
-
-    _MEM_AGENT_NAMES = ("default", "bench")
-    sessions_ingested = 0
-    memory_token_usage: dict[str, dict[str, int | None]] = {}
-    try:
-        if not eval_only:
-            # ── Phase 1: Ingest sessions (with token tracking) ─────────
-            sessions = load_beam_chat(chat_path, chat_size, case_id)
-            logger.info(f"[Case {case_id}] Loaded {len(sessions)} sessions from chat.json")
-
-            # Snapshot token counters before memory construction
-            mem_token_start = {name: check_agent_token_usage(name, app.context) for name in _MEM_AGENT_NAMES}
-
-            for i, session in enumerate(sessions):
-                logger.info(
-                    f"[Case {case_id}] Ingesting session {i+1}/{len(sessions)}: "
-                    f"id={session['session_id']} date={session['date']} "
-                    f"msgs={len(session['messages'])}",
-                )
-                resp = await app.run_job(
-                    "auto_memory",
-                    messages=session["messages"],
-                    session_id=session["session_id"],
-                    date=session["date"],
-                )
-                if not resp.success:
-                    logger.warning(f"[Case {case_id}] auto_memory failed: {resp.answer}")
-                else:
-                    logger.info(
-                        f"[Case {case_id}] auto_memory success: " f"{resp.answer[:100] if resp.answer else ''}",
-                    )
-                await app.run_job("index_update")
-                sessions_ingested += 1
-
-            # Final digest update
-            logger.info(f"[Case {case_id}] Running digest_update...")
-            await app.run_job("digest_update")
-            logger.info(f"[Case {case_id}] Ingestion complete.")
-
-            # Compute memory construction token deltas
-            for name in _MEM_AGENT_NAMES:
-                end_usage = check_agent_token_usage(name, app.context)
-                delta: dict[str, int | None] = {}
-                for metric in _TOKEN_USAGE_METRICS:
-                    current = end_usage[metric]
-                    start = mem_token_start[name][metric]
-                    delta[metric] = None if current is None else current - (start or 0)
-                memory_token_usage[name] = delta
-            logger.info(f"[Case {case_id}] Memory construction token usage: {memory_token_usage}")
-
-        # ── Phase 2: Answer + Judge probing questions ───────────────
-        with open(probing_questions_path, encoding="utf-8") as f:
-            probing_questions = json.load(f)
-
-        total_questions = sum(len(v) for v in probing_questions.values())
-        logger.info(f"[Case {case_id}] Total probing questions: {total_questions}")
-
-        all_question_results = []
-        q_idx = 0
-
-        for q_type in probing_questions:
-            logger.info(
-                f"[Case {case_id}] Question type: {q_type} " f"({len(probing_questions[q_type])} questions)",
-            )
-
-            for i, q in enumerate(probing_questions[q_type]):
-                q_idx += 1
-                question = q["question"]
-                rubric = q.get("rubric", [])
-                logger.info(
-                    f"[Case {case_id}] [{q_idx}/{total_questions}] " f"{q_type} Q{i+1}: {question[:100]}...",
-                )
-
-                q_result = {
-                    "question_type": q_type,
-                    "question_index": i,
-                    "question": question,
-                    "rubric": rubric,
-                }
-
-                # Agentic answer
-                try:
-                    agentic_answer, agentic_meta = await answer_question_agentic(
-                        app,
-                        question,
-                        compress_session=compress_session,
-                    )
-                except Exception as e:
-                    logger.error(f"[Case {case_id}] Agentic answer failed: {e}")
-                    agentic_answer = f"(error: {e})"
-                    agentic_meta = {"error": str(e)}
-
-                if not agentic_answer:
-                    agentic_answer = "(no answer generated)"
-                logger.info(f"[Case {case_id}] Agentic answer: {agentic_answer[:200]}...")
-                logger.info(
-                    f"[Case {case_id}] Agentic tool calls: {agentic_meta.get('tool_counts', {})}",
-                )
-                logger.info(f"[Case {case_id}] Bench token usage: {agentic_meta.get('token_usage', {})}")
-
-                # Judge agentic answer
-                logger.info(f"[Case {case_id}] Judging agentic ({q_type})...")
-                agentic_judgment = await judge_answer(
-                    app,
-                    question,
-                    agentic_answer,
-                    rubric,
-                    question_type=q_type,
-                )
-                logger.info(
-                    f"[Case {case_id}] Agentic score: " f"{agentic_judgment['llm_judge_score']:.3f}",
-                )
-
-                q_result["agentic_response"] = agentic_answer
-                q_result["agentic_judgment"] = agentic_judgment
-                q_result["agentic_metadata"] = agentic_meta
-
-                all_question_results.append(q_result)
-
-    finally:
-        await app.close()
-
-    return {
-        "case_id": case_id,
-        "chat_size": chat_size,
-        "sessions_ingested": sessions_ingested,
-        "total_questions": len(all_question_results),
-        "questions": all_question_results,
-        "memory_token_usage": memory_token_usage,
-    }
-
-
-# ---------------------------------------------------------------------------
-# Worker: runs a single case in its own process with its own event loop
-# ---------------------------------------------------------------------------
-def _evaluate_case_worker(task_input: tuple) -> dict:
-    """Worker function for multiprocessing. Each process gets its own event loop."""
-    eval_config, case_id, log_level, reme_log_level, eval_only, log_dir = task_input
-    import asyncio  # pylint: disable=import-outside-toplevel
-
-    _configure_worker(log_level, reme_log_level, log_dir=log_dir)
-
-    # Suppress httpx GC noise
-    logging.getLogger("asyncio").setLevel(logging.CRITICAL)
-
-    return asyncio.run(evaluate_case(eval_config, case_id, eval_only=eval_only))
-
-
-def _indexed_worker(indexed_input: tuple) -> tuple:
-    """Module-level wrapper for imap_unordered with index tracking."""
-    idx, task_input = indexed_input
-    return idx, _evaluate_case_worker(task_input)
-
-
-def _resolve_num_workers(configured: int) -> int:
-    """Resolve num_workers: 0=auto (cpu_count-2, min 1), 1=sequential, >1=parallel."""
-    if configured == 0:
-        return max(1, (os.cpu_count() or 4) - 2)
-    return max(1, configured)
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-def main(  # pylint: disable=too-many-statements
-    config_path: str | None = None,
-    log_level: str = "INFO",
-    reme_log_level: str = "INFO",
-    eval_only: bool = False,
-):
-    """Run the BEAM evaluation pipeline.
-
-    Args:
-        config_path: Path to the YAML config file.
-        log_level: Log level for the eval runner.
-        reme_log_level: Log level for reme internal logs.
-        eval_only: If True, skip ingestion and only run query+judge using
-            existing workspaces.
-    """
-    from multiprocessing import Pool  # pylint: disable=import-outside-toplevel
-
-    # Load config BEFORE logging setup so log_dir is available
-    eval_config = load_eval_config(config_path)
-
-    # Resolve per-run log directory from config
-    output_cfg = eval_config.get("output", {})
-    log_dir_abs = None
-    if output_cfg.get("log_to_file", False):
-        log_dir_raw = output_cfg.get("log_dir", "logs")
-        log_prefix = output_cfg.get("log_prefix", "beam")
-        run_ts = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-        log_dir_abs = str(_PROJECT_ROOT / log_dir_raw / f"{log_prefix}_{run_ts}")
-
-    setup_logging(log_level, reme_log_level, log_dir=log_dir_abs)
-    dataset_cfg = eval_config["dataset"]
-    chat_size = dataset_cfg["chat_size"]
-    beam_root = _PROJECT_ROOT / dataset_cfg.get("beam_root", "benchmark/beam/dataset/BEAM")
-
-    # Determine which cases to run
-    case_ids = dataset_cfg.get("case_ids") or []
-    if not case_ids:
-        case_ids = get_available_cases(beam_root, chat_size)
-
-    # Pagination
-    start = dataset_cfg.get("start_index", 0)
-    num_items = dataset_cfg.get("num_items", 0)
-    if num_items > 0:
-        case_ids = case_ids[start : start + num_items]
-    elif start > 0:
-        case_ids = case_ids[start:]
-
-    if not case_ids:
-        logger.error(f"No cases found for chat_size={chat_size}")
-        return
-
-    logger.info(
-        "Evaluating %d case(s) for chat_size=%s: %s%s",
-        len(case_ids),
-        chat_size,
-        case_ids,
-        " [eval_only: query+judge only]" if eval_only else "",
-    )
-
-    # Resolve parallelism
-    num_workers = _resolve_num_workers(eval_config["evaluation"].get("num_workers", 1))
-    logger.info(f"Using {num_workers} worker(s)")
-
-    # Create output directory
-    output_dir = _PROJECT_ROOT / output_cfg.get("dir", "benchmark/beam/results")
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Create workspace root directory
-    workspace_root = _PROJECT_ROOT / dataset_cfg.get("workspace_root", _WORKSPACE_ROOT_DEFAULT)
-    workspace_root.mkdir(parents=True, exist_ok=True)
-
-    # Pre-check: verify all workspaces exist in eval_only mode
-    if eval_only:
-        missing_cases = []
-        for case_id in case_ids:
-            case_dir = workspace_root / f"{chat_size}_{case_id}"
-            if not case_dir.exists() or not (case_dir / ".reme").exists():
-                missing_cases.append(case_id)
-        if missing_cases:
-            preview = missing_cases[:10]
-            suffix = "..." if len(missing_cases) > 10 else ""
-            raise FileNotFoundError(
-                f"eval_only: {len(missing_cases)} workspace(s) not found under {workspace_root}. "
-                f"Missing cases: {preview}{suffix}. "
-                f"Run without --eval_only first to build the workspaces.",
-            )
-
-    # Build task args
-    task_args = [(eval_config, case_id, log_level, reme_log_level, eval_only, log_dir_abs) for case_id in case_ids]
-
-    # Progress tracking
-    total_items = len(task_args)
-    completed_count = [0]
-    start_time = time.time()
-    progress_lock = threading.Lock()
-
-    def _print_progress(prefix: str = "PROGRESS"):
-        elapsed = time.time() - start_time
-        elapsed_min = elapsed / 60
-        done = completed_count[0]
-        pct = 100.0 * done / total_items if total_items else 0
-        eta_str = "N/A"
-        if done > 0:
-            eta_sec = elapsed / done * (total_items - done)
-            eta_str = f"{eta_sec/60:.1f}min"
-        print(
-            f"[{prefix}] {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | "
-            f"{done}/{total_items} ({pct:.1f}%) completed | "
-            f"elapsed={elapsed_min:.1f}min | ETA={eta_str}",
-            flush=True,
-        )
-
-    def _progress_timer():
-        """Background thread: print progress every 10 minutes."""
-        while not _timer_stop.is_set():
-            _timer_stop.wait(600)
-            if not _timer_stop.is_set():
-                with progress_lock:
-                    _print_progress()
-
-    _timer_stop = threading.Event()
-    timer_thread = threading.Thread(target=_progress_timer, daemon=True)
-    timer_thread.start()
-
-    # Run evaluation
-    if num_workers == 1:
-        results = []
-        for task_input in task_args:
-            result = _evaluate_case_worker(task_input)
-            results.append(result)
-            with progress_lock:
-                completed_count[0] += 1
-    else:
-        results = [None] * total_items
-        indexed_args = list(enumerate(task_args))
-
-        with Pool(processes=num_workers) as pool:
-            for idx, result in pool.imap_unordered(_indexed_worker, indexed_args):
-                results[idx] = result
-                with progress_lock:
-                    completed_count[0] += 1
-
-    # Stop progress timer
-    _timer_stop.set()
-    timer_thread.join(timeout=2)
-
-    # Save results
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    output_file = output_dir / f"results_{chat_size}_{timestamp}.json"
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(results, f, ensure_ascii=False, indent=2)
-    logger.info(f"Results saved to {output_file}")
-
-    # Final progress
-    _print_progress("FINAL")
-
-    # Print concise summary
-    print("\n" + "=" * 70)
-    print(f"  BEAM EVALUATION RESULTS  |  size={chat_size}  cases={len(results)}")
-    print("=" * 70)
-
-    # Per-type stats (agentic only)
-    type_scores: dict[str, list[float]] = {}
-    type_binary_scores: dict[str, list[float]] = {}
-    all_scores: list[float] = []
-    all_binary_scores: list[float] = []
-    all_tool_call_totals: list[int] = []
-    all_token_usages: list[dict[str, int | None]] = []
-    all_memory_token_usages: list[dict[str, dict[str, int | None]]] = []
-
-    for case_result in results:
-        if "error" in case_result:
-            continue
-        mem_usage = case_result.get("memory_token_usage", {})
-        if mem_usage:
-            all_memory_token_usages.append(mem_usage)
-        for q in case_result.get("questions", []):
-            judgment = q.get("agentic_judgment", {})
-            score = judgment.get("llm_judge_score", 0.0)
-            # Binary: convert each rubric item score to 0/1, then average
-            judge_responses = judgment.get("llm_judge_responses", [])
-            if judge_responses:
-                binary_scores_per_item = [1.0 if r.get("score", 0) >= 1.0 else 0.0 for r in judge_responses]
-                binary_score = sum(binary_scores_per_item) / len(binary_scores_per_item)
-            else:
-                binary_score = 1.0 if score > 0.99 else 0.0
-            qtype = q["question_type"]
-            if qtype not in type_scores:
-                type_scores[qtype] = []
-                type_binary_scores[qtype] = []
-            type_scores[qtype].append(score)
-            type_binary_scores[qtype].append(binary_score)
-            all_scores.append(score)
-            all_binary_scores.append(binary_score)
-            metadata = q.get("agentic_metadata", {})
-            all_tool_call_totals.append(sum(metadata.get("tool_counts", {}).values()))
-            all_token_usages.append(metadata.get("token_usage", {}))
-
-    # Memory construction token usage summary
-    if all_memory_token_usages:
-        print("\n  ── Memory Construction Token Usage ──")
-        for agent_name in ("default", "bench"):
-            for metric in _TOKEN_USAGE_METRICS:
-                values = [
-                    usage[agent_name][metric]
-                    for usage in all_memory_token_usages
-                    if usage.get(agent_name, {}).get(metric) is not None
-                ]
-                if values:
-                    total = sum(values)
-                    mean, std = _mean_and_std(values)
-                    print(
-                        f"    {agent_name}/{metric}: total={total} mean={mean:.2f} std={std:.2f} ({len(values)} cases)",
-                    )
-                else:
-                    print(f"    {agent_name}/{metric}: unavailable")
-        print()
-
-    print("\n  ── AGENTIC ──")
-    if all_scores:
-        for qtype in sorted(type_scores.keys()):
-            scores = type_scores[qtype]
-            avg = sum(scores) / len(scores) if scores else 0
-            bin_scores = type_binary_scores[qtype]
-            bin_avg = sum(bin_scores) / len(bin_scores) if bin_scores else 0
-            print(f"    {qtype:<40s}: {avg:.3f}  binary={bin_avg:.3f}  ({len(scores)} Qs)")
-        overall = sum(all_scores) / len(all_scores) if all_scores else 0
-        binary_overall = sum(all_binary_scores) / len(all_binary_scores) if all_binary_scores else 0
-        print(f"    {'-'*38}")
-        print(f"    {'OVERALL':<40s}: {overall:.3f}  binary={binary_overall:.3f}  ({len(all_scores)} Qs)")
-        tool_call_mean, tool_call_std = _mean_and_std(all_tool_call_totals)
-        print(f"    Tool calls/query: mean={tool_call_mean:.2f} std={tool_call_std:.2f}")
-        print("    Bench reported tokens/query:")
-        for metric in _TOKEN_USAGE_METRICS:
-            values = [usage[metric] for usage in all_token_usages if usage.get(metric) is not None]
-            if values:
-                mean, std = _mean_and_std(values)
-                print(f"      {metric}: mean={mean:.2f} std={std:.2f}")
-            else:
-                print(f"      {metric}: unavailable")
-    else:
-        print("    (no results)")
-
-    # Per-case summary
-    print("\n  ── Per-Case Summary ──")
-    for case_result in results:
-        case_id = case_result["case_id"]
-        if "error" in case_result:
-            print(f"    Case {case_id}: ERROR — {case_result['error']}")
-            continue
-        n_qs = case_result.get("total_questions", 0)
-        n_sessions = case_result.get("sessions_ingested", 0)
-        mem_usage = case_result.get("memory_token_usage", {})
-        parts = [f"Case {case_id}: {n_sessions} sessions, {n_qs} questions"]
-        # Append memory construction total tokens if available
-        for agent_name in ("default", "bench"):
-            agent_usage = mem_usage.get(agent_name, {})
-            total = agent_usage.get("total_tokens")
-            if total is not None:
-                parts.append(f"mem_{agent_name}_tokens={total}")
-        questions = case_result.get("questions", [])
-        scores = [q.get("agentic_judgment", {}).get("llm_judge_score", 0.0) for q in questions]
-        if scores:
-            avg = sum(scores) / len(scores)
-            # Binary: 0/1 per rubric item, average per question, then across questions
-            bin_scores = []
-            for q in questions:
-                judge_responses = q.get("agentic_judgment", {}).get("llm_judge_responses", [])
-                if judge_responses:
-                    item_bins = [1.0 if r.get("score", 0) >= 1.0 else 0.0 for r in judge_responses]
-                    bin_scores.append(sum(item_bins) / len(item_bins))
-                else:
-                    s = q.get("agentic_judgment", {}).get("llm_judge_score", 0.0)
-                    bin_scores.append(1.0 if s > 0.99 else 0.0)
-            bin_avg = sum(bin_scores) / len(bin_scores)
-            parts.append(f"agentic={avg:.3f} binary={bin_avg:.3f}")
-        print(f"    {' | '.join(parts)}")
-
-    print("=" * 70)
-    total_elapsed = time.time() - start_time
-    print(f"\n  Total time: {total_elapsed/60:.1f} min")
-    print("\n" + "=" * 70)
-    print("  [DONE] BEAM EVALUATION COMPLETED SUCCESSFULLY")
-    print("=" * 70 + "\n")
-
-
-_TOKEN_USAGE_METRICS = (
-    "input_tokens",
-    "output_tokens",
-    "total_tokens",
-)
-
-
-def _mean_and_std(values: list[int]) -> tuple[float, float]:
-    """Return population mean and standard deviation for one per-question metric."""
-    if not values:
-        return 0.0, 0.0
-    mean = sum(values) / len(values)
-    return mean, (sum((value - mean) ** 2 for value in values) / len(values)) ** 0.5
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(description="BEAM evaluation runner")
-    parser.add_argument("--config", type=str, default=None, help="Path to config.yaml")
-    parser.add_argument(
-        "--log-level",
-        type=str,
-        default="INFO",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        help="Log level for the eval runner (default: INFO)",
-    )
-    parser.add_argument(
-        "--reme-log-level",
-        type=str,
-        default="INFO",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        help="Log level for reme internal logs — loguru (default: INFO)",
-    )
-    parser.add_argument(
-        "-q",
-        "--quiet",
-        action="store_true",
-        help="Shortcut for --log-level WARNING --reme-log-level WARNING",
-    )
-    parser.add_argument(
-        "--eval_only",
-        action="store_true",
-        help="Skip ingestion. Reuse existing workspaces and only run query+judge.",
-    )
-    args = parser.parse_args()
-
-    if args.quiet:
-        args.log_level = "WARNING"
-        args.reme_log_level = "WARNING"
-
-    main(args.config, args.log_level, args.reme_log_level, eval_only=args.eval_only)
--- a/benchmark/longmemeval/README.md
+++ b/benchmark/longmemeval/README.md
@ -1,96 +0,0 @@
-[中文版 / Chinese version](./README_ZH.md)
-
-# LongMemEval Benchmark
-
-LongMemEval is a benchmark for **long-term memory over multi-session chat
-histories**. Each item provides a chronologically ordered set of chat sessions
-between a user and an assistant, followed by a probing question whose answer is
-only recoverable by reasoning over the user-owned memory. ReMe ingests the
-sessions into an isolated per-item workspace, answers the question via an
-agentic (ReAct) mode, and scores the answer with an LLM-as-judge.
-
-Question types include single-session (user / assistant / preference),
-multi-session reasoning, knowledge update, and temporal reasoning.
-
-> For the shared setup (dependencies, credentials, log conventions) see the
-> [top-level benchmark README](../README.md).
-
-## 1. Get the Dataset
-
-ReMe uses only the **cleaned-S** split, hosted on HuggingFace:
-[agentscope-ai/ReMe_longmemeval_clean_s_v2](https://huggingface.co/datasets/agentscope-ai/ReMe_longmemeval_clean_s_v2).
-The download script fetches it via the hf-mirror.com mirror; to use a different
-mirror, modify `BASE_URL` in [`download.py`](./download.py).
-
-```bash
-cd benchmark/longmemeval
-python download.py            # saves dataset/longmemeval_s_reme_cleaned.json; skips if already present
-```
-
-Ground truth is embedded in the data file.
-
-## 2. Run
-
-From the repository root:
-
-```bash
-python benchmark/longmemeval/run.py
-python benchmark/longmemeval/run.py --config benchmark/longmemeval/config.yaml
-python benchmark/longmemeval/run.py -q                        # quiet: only eval-level logs
-python benchmark/longmemeval/run.py --log-level WARNING       # reduce eval runner logs
-python benchmark/longmemeval/run.py --reme-log-level WARNING  # reduce reme internal logs
-python benchmark/longmemeval/run.py --eval_only               # reuse existing workspaces, query + judge only
-```
-
-## 3. Pipeline
-
-1. Load the dataset (ground truth is embedded in the data file).
-2. For each item, create an isolated workspace and ingest sessions in chronological order.
-3. Trigger `auto_dream` when consecutive sessions cross the configured hour (default 23:00).
-4. Answer each question via agentic (ReAct) mode.
-5. Judge the answer (binary yes/no) with the `answer_judge` job and print per-type accuracy.
-
-## 4. Key config — `benchmark/longmemeval/config.yaml`
-
-| Key | Meaning |
-| --- | --- |
-| `dataset.path` | Dataset file to evaluate (e.g. `longmemeval_s_reme_cleaned.json`); ground truth is included. |
-| `dataset.start_index` / `num_items` | Slice of items to evaluate. |
-| `dataset.question_types` | Filter by question type; empty = all. |
-| `dataset.workspace_root` | Per-item workspace root (`benchmark/longmemeval/workspaces/longmemeval-s`). |
-| `evaluation.num_workers` | `0` = auto (cpu-2), `1` = sequential, `>1` = parallel. |
-| `evaluation.filter_future_sessions` | Only ingest sessions with timestamp ≤ `question_date`. |
-| `reme.config` | ReMe config used (`lme.yaml`). |
-| `reme.dream_trigger_hour` / `dream_scan_days` / `dream_max_units` | Dream triggering behavior. |
-| `output.dir` | Results directory (`benchmark/longmemeval/results`). |
-
-## 5. Outputs
-
-Results are JSON files written to `output.dir` as `results_<timestamp>.json`,
-with a per-type accuracy summary also printed to the console. Logging
-conventions are shared across benchmarks — see the
-[top-level README](../README.md#outputs--logs).
-
-## 6. Reference Results
-
-### cleaned-s
-
-**Basic settings**
-
-1. Modified auto-memory prompt, auto-dream disabled.
-2. All sessions in reme-memory are strictly earlier than the question time.
-
-**Results**
-
-agentscope==2.0.4.post1, conda reme env, 32 workers, eval-only (reusing prebuilt memory)
-(2026-08-06, 500 items, total 10.0 min)
-
-| Type | Agentic | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|
-| knowledge-update | 0.910 | 31,581 | 589 | 32,169 | 2.90 |
-| multi-session | 0.842 | 52,837 | 1,474 | 54,311 | 4.21 |
-| single-session-assistant | 1.000 | 15,596 | 279 | 15,875 | 1.89 |
-| single-session-preference | 0.633 | 36,802 | 818 | 37,620 | 3.60 |
-| single-session-user | 0.986 | 27,433 | 359 | 27,792 | 2.60 |
-| temporal-reasoning | 0.902 | 62,674 | 985 | 63,659 | 4.97 |
-| **OVERALL** | **0.894** | **43,448** | **876** | **44,324** | **3.69** |
--- a/benchmark/longmemeval/README_ZH.md
+++ b/benchmark/longmemeval/README_ZH.md
@ -1,90 +0,0 @@
-# LongMemEval 评测
-
-[English version](./README.md)
-
-LongMemEval 是一个面向**多轮多会话历史的长期记忆能力**的评测基准。每个条目提供一组按时间
-顺序排列的用户与助手之间的会话，以及一个只能通过推理用户自有记忆才能回答的探测问题。ReMe
-将会话摄入按条目隔离的工作区，以 agentic（ReAct）模式回答问题，最后由 LLM-as-judge 打分。
-
-题型包括单会话（user / assistant / preference）、多会话推理、知识更新与时间推理等。
-
-> 公共设置（依赖、凭据、日志约定）见[总评测说明](../README_ZH.md)。
-
-## 1. 获取数据集
-
-ReMe 仅使用 **cleaned-S** 版本，数据托管在 HuggingFace：
-[agentscope-ai/ReMe_longmemeval_clean_s_v2](https://huggingface.co/datasets/agentscope-ai/ReMe_longmemeval_clean_s_v2)。
-下载脚本经 hf-mirror.com 镜像源获取，如需更换源请修改 [`download.py`](./download.py) 中的
-`BASE_URL`。
-
-```bash
-cd benchmark/longmemeval
-python download.py            # 保存为 dataset/longmemeval_s_reme_cleaned.json，已存在则自动跳过
-```
-
-ground truth 已内嵌在数据文件中。
-
-## 2. 运行
-
-在仓库根目录执行：
-
-```bash
-python benchmark/longmemeval/run.py
-python benchmark/longmemeval/run.py --config benchmark/longmemeval/config.yaml
-python benchmark/longmemeval/run.py -q                        # 安静模式：仅评测级日志
-python benchmark/longmemeval/run.py --log-level WARNING       # 降低评测 runner 日志
-python benchmark/longmemeval/run.py --reme-log-level WARNING  # 降低 reme 内部日志
-python benchmark/longmemeval/run.py --eval_only               # 复用已有工作区，仅执行查询 + 评判
-```
-
-## 3. 流程
-
-1. 加载数据集（ground truth 已内嵌在数据文件中）。
-2. 为每个条目创建独立工作区，按时间顺序摄入会话。
-3. 当相邻会话跨越配置的时刻（默认 23:00）时触发 `auto_dream`。
-4. 以 agentic（ReAct）模式回答每个问题。
-5. 通过 `answer_judge` 任务对答案做二元（yes/no）评判，并输出各类型准确率。
-
-## 4. 关键配置 —— `benchmark/longmemeval/config.yaml`
-
-| 配置项 | 含义 |
-| --- | --- |
-| `dataset.path` | 待评测的数据集文件（如 `longmemeval_s_reme_cleaned.json`），已包含 ground truth。 |
-| `dataset.start_index` / `num_items` | 评测条目的切片范围。 |
-| `dataset.question_types` | 按问题类型过滤，空表示全部。 |
-| `dataset.workspace_root` | 条目工作区根目录（`benchmark/longmemeval/workspaces/longmemeval-s`）。 |
-| `evaluation.num_workers` | `0` = 自动（cpu-2），`1` = 串行，`>1` = 并行。 |
-| `evaluation.filter_future_sessions` | 仅摄入时间戳 ≤ `question_date` 的会话。 |
-| `reme.config` | 使用的 ReMe 配置（`lme.yaml`）。 |
-| `reme.dream_trigger_hour` / `dream_scan_days` / `dream_max_units` | dream 触发行为。 |
-| `output.dir` | 结果目录（`benchmark/longmemeval/results`）。 |
-
-## 5. 输出
-
-结果以 JSON 文件写入 `output.dir`，文件名为 `results_<timestamp>.json`，
-同时控制台会打印含各类型准确率的汇总。日志约定在各基准间通用，见
-[总说明](../README_ZH.md#输出与日志)。
-
-## 6. 参考结果
-
-### cleaned-s
-
-**基础设置**
-
-1. 使用修改后的 auto-memory prompt，关闭 auto-dream 机制
-2. reme-memory 中的全部 session 的时间一定早于 question 的时间
-
-**结果**
-
-agentscope==2.0.4.post1, conda reme env, 32 workers, eval-only（复用预构建记忆）
-（2026-08-06，500 题，总计 10.0 min）
-
-| 类型 | Agentic | input tok/q | output tok/q | total tok/q | tool calls/q |
-|---|---|---|---|---|---|
-| knowledge-update | 0.910 | 31,581 | 589 | 32,169 | 2.90 |
-| multi-session | 0.842 | 52,837 | 1,474 | 54,311 | 4.21 |
-| single-session-assistant | 1.000 | 15,596 | 279 | 15,875 | 1.89 |
-| single-session-preference | 0.633 | 36,802 | 818 | 37,620 | 3.60 |
-| single-session-user | 0.986 | 27,433 | 359 | 27,792 | 2.60 |
-| temporal-reasoning | 0.902 | 62,674 | 985 | 63,659 | 4.97 |
-| **OVERALL** | **0.894** | **43,448** | **876** | **44,324** | **3.69** |
--- a/benchmark/longmemeval/clean_sample_outputs.py
+++ b/benchmark/longmemeval/clean_sample_outputs.py
@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""Remove generated LongMemEval files while keeping source inputs.
+
+For each ``datasets/longmemeval/<idx>`` workspace, this keeps only:
+  - query.json
+  - answer.json
+  - session/
+
+All other files or directories in the sample root are considered generated
+artifacts and can be removed. AppleDouble files whose names start with ``._``
+are also removed recursively, including under ``session/``. The script is
+dry-run by default; pass ``--apply`` to actually delete. To delete only specific
+root-level generated files, pass one or more ``--filename`` values.
+
+Examples:
+    python benchmark/longmemeval/clean_sample_outputs.py
+    python benchmark/longmemeval/clean_sample_outputs.py --apply
+    python benchmark/longmemeval/clean_sample_outputs.py --start 36 --end 79 --apply
+    python benchmark/longmemeval/clean_sample_outputs.py --filename check_golden.json --apply
+    python benchmark/longmemeval/clean_sample_outputs.py --filename session_review.json --apply
+"""
+
+import argparse
+import shutil
+import time
+from collections.abc import Iterator
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+KEEP = {"query.json", "answer.json", "session"}
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--start", type=int, default=0, help="first numeric sample id to clean, inclusive (default 0)")
+    p.add_argument("--end", type=int, default=499, help="last numeric sample id to clean, inclusive (default 499)")
+    p.add_argument("--limit", type=int, default=0, help="only clean the first N selected samples (0 = all)")
+    p.add_argument("--progress-every", type=int, default=25, help="print progress every N samples when applying")
+    p.add_argument(
+        "--filename",
+        action="append",
+        default=[],
+        help="delete only this root-level file or directory name; can be passed multiple times",
+    )
+    p.add_argument("--apply", action="store_true", help="actually delete files; default is dry-run")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all numeric sample IDs."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def delete_path(path: Path) -> None:
+    """Delete a file, symlink, or directory."""
+    if path.is_dir() and not path.is_symlink():
+        shutil.rmtree(path)
+    else:
+        path.unlink()
+
+
+def iter_sample_targets(sample_dir: Path, filenames: set[str] | None = None) -> Iterator[Path]:
+    """Yield generated artifacts for one sample.
+
+    Root-level generated directories are yielded as a whole, so there is no
+    need to recurse into them. AppleDouble files are only searched inside the
+    kept ``session/`` directory.
+    """
+    if filenames:
+        for name in sorted(filenames):
+            path = sample_dir / name
+            if path.exists():
+                yield path
+        return
+
+    for path in sorted(sample_dir.iterdir(), key=lambda p: p.name):
+        if path.name not in KEEP:
+            yield path
+
+    session_dir = sample_dir / "session"
+    if session_dir.is_dir():
+        yield from session_dir.rglob("._*")
+
+
+def main() -> int:
+    """Main entry point."""
+    args = parse_args()
+    if args.end < args.start:
+        raise ValueError(f"--end ({args.end}) must be >= --start ({args.start})")
+    filenames = {name.strip() for name in args.filename if name.strip()}
+    invalid_filenames = [name for name in filenames if Path(name).name != name]
+    if invalid_filenames:
+        raise ValueError(f"--filename only accepts root-level names, got: {invalid_filenames}")
+
+    ids = [idx for idx in sample_ids() if args.start <= int(idx) <= args.end]
+    if args.limit:
+        ids = ids[: args.limit]
+
+    total_targets = 0
+    deleted = 0
+    started_at = time.time()
+    for ordinal, idx in enumerate(ids, start=1):
+        sample_dir = DATA / idx
+        sample_started_at = time.time()
+        targets = list(iter_sample_targets(sample_dir, filenames=filenames))
+        total_targets += len(targets)
+        print(f"[sample {ordinal}/{len(ids)}] {idx} targets={len(targets)}", flush=True)
+        for path in targets:
+            if args.apply:
+                target_started_at = time.time()
+                print(f"[delete] {path}", flush=True)
+                delete_path(path)
+                deleted += 1
+                print(f"[deleted] {path} elapsed={time.time() - target_started_at:.1f}s", flush=True)
+            else:
+                print(f"[would-delete] {path}")
+        if args.apply and args.progress_every > 0 and (int(idx) + 1) % args.progress_every == 0:
+            elapsed = time.time() - started_at
+            print(
+                f"[progress] processed={ordinal}/{len(ids)} through={idx} " f"deleted={deleted} elapsed={elapsed:.1f}s",
+                flush=True,
+            )
+        print(f"[sample-done] {idx} elapsed={time.time() - sample_started_at:.1f}s", flush=True)
+
+    mode = "DELETE" if args.apply else "DRY-RUN"
+    print(
+        f"{mode} LongMemEval generated artifacts: samples={len(ids)} "
+        f"targets={total_targets} deleted={deleted if args.apply else 0} range={args.start}..{args.end}",
+        flush=True,
+    )
+
+    if not args.apply:
+        print("No files deleted. Re-run with --apply to delete these paths.", flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
--- a/benchmark/longmemeval/config.yaml
+++ b/benchmark/longmemeval/config.yaml
@ -1,33 +0,0 @@
-# LongMemEval evaluation configuration
-# This file controls what/how to evaluate.
-
-dataset:
-  path: "benchmark/longmemeval/dataset/longmemeval_s_reme_cleaned.json"
-  start_index: 0             # first item index
-  num_items: 500            # how many items to evaluate (starting from start_index)
-  max_sessions: 0            # 0 = all sessions; >0 = limit sessions per item for testing
-  question_types: []         # filter by question_type; empty list = no filtering (all types)
-  workspace_root: "benchmark/longmemeval/workspaces/longmemeval-s"  # workspace root for item workspaces
-
-evaluation:
-  # LLM-as-judge uses the 'judge' as_llm component defined in lme.yaml
-  # Model and credentials are configured there (reading from .env)
-  # Judgment is always binary (yes/no) — defined in lme/llm_judge.yaml
-  num_workers: 32            # 0 = auto (cpu_count - 2, min 1); 1 = sequential; >1 = parallel
-  filter_future_sessions: true  # true = only ingest sessions with timestamp <= question_date
-  compress_session: false   # true = compress session chunks in search_v2 (query-aware); false = no compression
-
-reme:
-  config: "lme.yaml" # reme config to use (in reme/config/)
-  # Dream trigger: when gap between consecutive sessions crosses this hour (23:00)
-  dream_trigger_hour: 23
-  # Dream scan_days for each trigger
-  dream_scan_days: 2
-  dream_max_units: 5
-
-output:
-  dir: "benchmark/longmemeval/results"
-  log_dir: "logs"                # log directory (relative to project root)
-  log_prefix: "longmemeval"      # benchmark name used in log filenames
-  log_to_console: true
-  log_to_file: true
--- a/benchmark/longmemeval/download.py
+++ b/benchmark/longmemeval/download.py
@ -1,67 +0,0 @@
-"""Download the LongMemEval cleaned-S dataset used by ReMe.
-
-Source: https://huggingface.co/datasets/agentscope-ai/ReMe_longmemeval_clean_s_v2
-(downloaded via the hf-mirror.com mirror for reliability).
-
-The file ``longmemeval_s_reme_cleaned.json`` is saved under ``dataset/`` next to this
-script using the same name as on the remote (``benchmark/longmemeval/config.yaml``
-points to it).
-
-Usage:
-    python download.py           # download cleaned-S (skip if it already exists)
-"""
-
-import os
-import sys
-import urllib.request
-
-BASE_URL = "https://hf-mirror.com/datasets/agentscope-ai/ReMe_longmemeval_clean_s_v2/resolve/main"
-TARGET_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "dataset")
-
-# Files to download (saved with the same name as on the remote).
-FILES = [
-    "longmemeval_s_reme_cleaned.json",
-]
-
-
-def download_file(filename: str):
-    """Download a single file from the mirror to the target directory."""
-    url = f"{BASE_URL}/{filename}"
-    dest = os.path.join(TARGET_DIR, filename)
-
-    if os.path.exists(dest):
-        size = os.path.getsize(dest)
-        print(f"  [skip] {filename} already exists ({size / 1024 / 1024:.1f} MB)")
-        return
-
-    print(f"  [downloading] {filename} ...")
-    try:
-        urllib.request.urlretrieve(url, dest, reporthook=_progress)
-        size = os.path.getsize(dest)
-        print(f"\n  [done] {filename} ({size / 1024 / 1024:.1f} MB)")
-    except Exception as e:
-        print(f"\n  [error] {filename}: {e}")
-        if os.path.exists(dest):
-            os.remove(dest)
-        sys.exit(1)
-
-
-def _progress(block_num, block_size, total_size):
-    downloaded = block_num * block_size
-    if total_size > 0:
-        pct = min(100, downloaded * 100 / total_size)
-        mb = downloaded / 1024 / 1024
-        total_mb = total_size / 1024 / 1024
-        sys.stdout.write(f"\r    {mb:.1f}/{total_mb:.1f} MB ({pct:.1f}%)")
-    else:
-        mb = downloaded / 1024 / 1024
-        sys.stdout.write(f"\r    {mb:.1f} MB downloaded")
-    sys.stdout.flush()
-
-
-if __name__ == "__main__":
-    os.makedirs(TARGET_DIR, exist_ok=True)
-    print(f"Downloading LongMemEval cleaned-S dataset to: {TARGET_DIR}\n")
-    for fname in FILES:
-        download_file(fname)
-    print("\nAll files downloaded successfully!")
--- a/benchmark/longmemeval/kill.sh
+++ b/benchmark/longmemeval/kill.sh
@ -1,76 +0,0 @@
-#!/bin/bash
-# 杀死指定进程及其所有子进程
-# Usage: bash kill.sh <PID>
-
-if [ -z "$1" ]; then
-    echo "Usage: bash kill.sh <PID>"
-    echo "  杀死指定进程及其所有子进程"
-    exit 1
-fi
-
-PID=$1
-
-# 检查进程是否存在
-if ! kill -0 "$PID" 2>/dev/null; then
-    echo "进程 $PID 不存在"
-    exit 1
-fi
-
-# 递归收集所有子进程（包括子进程的子进程）
-collect_children() {
-    local parent=$1
-    local children
-    children=$(ps -o pid= --ppid "$parent" 2>/dev/null | tr -d ' ')
-    for child in $children; do
-        collect_children "$child"
-    done
-    echo "$parent"
-}
-
-# 收集进程树（子进程在前，父进程在后，保证先杀子再杀父）
-PROCESS_TREE=$(collect_children "$PID")
-TOTAL=$(echo "$PROCESS_TREE" | wc -l | tr -d ' ')
-
-echo "进程树（共 $TOTAL 个进程）:"
-while read -r p; do
-    cmd=$(ps -o args= -p "$p" 2>/dev/null | head -c 80)
-    printf "  PID=%-8s %s\n" "$p" "$cmd"
-done <<< "$PROCESS_TREE"
-
-# 先 SIGTERM 优雅终止
-echo ""
-echo "发送 SIGTERM..."
-while read -r p; do
-    kill "$p" 2>/dev/null
-done <<< "$PROCESS_TREE"
-
-# 等待最多 5 秒
-for i in $(seq 1 5); do
-    alive=false
-    while read -r p; do
-        if kill -0 "$p" 2>/dev/null; then
-            alive=true
-        fi
-    done <<< "$PROCESS_TREE"
-    if [ "$alive" = false ]; then
-        break
-    fi
-    sleep 1
-done
-
-# 检查是否还有残留，强制 SIGKILL
-remaining=false
-while read -r p; do
-    if kill -0 "$p" 2>/dev/null; then
-        remaining=true
-    fi
-done <<< "$PROCESS_TREE"
-
-if [ "$remaining" = true ]; then
-    echo "部分进程未响应，发送 SIGKILL..."
-    while read -r p; do
-        kill -9 "$p" 2>/dev/null
-    done <<< "$PROCESS_TREE"
-fi
-
-echo "已终止进程树（根 PID=$PID，共 $TOTAL 个进程）"
--- a/benchmark/longmemeval/run.py
+++ b/benchmark/longmemeval/run.py
@ -1,816 +0,0 @@
-"""LongMemEval evaluation runner for ReMe.
-
-Evaluates ReMe's long-term memory capability using the LongMemEval dataset.
-Each item gets an isolated workspace; sessions are ingested in chronological order;
-dream is triggered when sessions cross midnight (23:00); finally questions are
-answered via an agentic (ReAct) approach and judged by an LLM.
-
-Usage:
-    python benchmark/longmemeval/run.py
-    python benchmark/longmemeval/run.py --config benchmark/longmemeval/config.yaml
-    python benchmark/longmemeval/run.py -q                          # quiet: only eval-level logs
-    python benchmark/longmemeval/run.py --log-level WARNING         # reduce eval runner logs
-    python benchmark/longmemeval/run.py --reme-log-level WARNING    # reduce reme internal logs
-    python benchmark/longmemeval/run.py --eval_only                 # query+judge only, reuse existing workspace
-"""
-
-import json
-import logging
-import os
-import re
-import shutil
-import time
-import threading
-from datetime import datetime
-from pathlib import Path
-
-import yaml
-from dotenv import load_dotenv
-
-# Load .env from project root
-_PROJECT_ROOT = Path(__file__).parent.parent.parent
-load_dotenv(_PROJECT_ROOT / ".env")
-
-# Workspace root for evaluation items — read from config.yaml (dataset.workspace_root)
-_WORKSPACE_ROOT_DEFAULT = "benchmark/longmemeval/workspaces/longmemeval-s"
-
-# ---------------------------------------------------------------------------
-# Logging
-# ---------------------------------------------------------------------------
-_DEFAULT_LOG_FORMAT = "%(asctime)s | %(levelname)s | %(message)s"
-
-logging.basicConfig(level=logging.INFO, format=_DEFAULT_LOG_FORMAT)
-logger = logging.getLogger("longmemeval")
-
-# Noisy library loggers silenced by default
-_NOISY_LOGGERS = [
-    "httpx",
-    "httpcore",
-    "openai",
-    "uvicorn",
-    "multipart",
-    "asyncio",
-    "watchfiles",
-    "filelock",
-]
-
-
-def setup_logging(
-    log_level: str,
-    reme_log_level: str,
-    log_dir: str | None = None,
-):
-    """Configure logging for the eval runner and reme internals.
-
-    Args:
-        log_level: Level for the eval runner logger (DEBUG/INFO/WARNING/ERROR).
-        reme_log_level: Level for reme's internal loguru logger.
-        log_dir: Per-run log directory (absolute path). None = no file logging.
-    """
-    numeric = getattr(logging, log_level.upper(), logging.INFO)
-    # Eval runner logger
-    logging.getLogger().setLevel(numeric)
-    logger.setLevel(numeric)
-
-    # Suppress noisy library loggers when above DEBUG
-    if numeric > logging.DEBUG:
-        for name in _NOISY_LOGGERS:
-            lib_logger = logging.getLogger(name)
-            lib_logger.setLevel(max(numeric, logging.WARNING))
-
-    # Add file handler for eval runner if log_dir is specified
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-        log_filepath = os.path.join(log_dir, "runner.log")
-        file_handler = logging.FileHandler(log_filepath, encoding="utf-8")
-        file_handler.setLevel(numeric)
-        file_handler.setFormatter(logging.Formatter(_DEFAULT_LOG_FORMAT))
-        logging.getLogger().addHandler(file_handler)
-        logger.info(f"Eval runner log file: {log_filepath}")
-
-    # Reme internal logger (loguru) — will be applied per-worker via _configure_worker
-    os.environ["REME_LOG_LEVEL"] = reme_log_level.upper()
-    if log_dir:
-        os.environ["REME_LOG_DIR"] = log_dir
-
-
-def _configure_worker(
-    log_level: str,
-    reme_log_level: str,
-    log_dir: str | None = None,
-):
-    """Set up logging inside a multiprocessing worker process.
-
-    Must be called at the top of each worker because child processes inherit
-    parent state but loguru sinks are NOT shared across fork/spawn.
-    """
-    numeric = getattr(logging, log_level.upper(), logging.INFO)
-    logging.basicConfig(level=numeric, format=_DEFAULT_LOG_FORMAT, force=True)
-    logging.getLogger("longmemeval").setLevel(numeric)
-    if numeric > logging.DEBUG:
-        for name in _NOISY_LOGGERS:
-            logging.getLogger(name).setLevel(max(numeric, logging.WARNING))
-
-    # Add file handler for eval runner in worker process
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-        pid = os.getpid()
-        log_filepath = os.path.join(log_dir, f"worker-{pid}.log")
-        file_handler = logging.FileHandler(log_filepath, encoding="utf-8")
-        file_handler.setLevel(numeric)
-        file_handler.setFormatter(logging.Formatter(_DEFAULT_LOG_FORMAT))
-        logging.getLogger().addHandler(file_handler)
-
-    # Re-initialize loguru for reme internals at the desired level
-    from reme.utils import get_logger
-
-    reme_log_dir = log_dir or "logs"
-    get_logger(log_dir=reme_log_dir, level=reme_log_level.upper(), force_init=True)
-
-
-# ---------------------------------------------------------------------------
-# Config loading
-# ---------------------------------------------------------------------------
-def load_eval_config(config_path: str | None = None) -> dict:
-    """Load evaluation config yaml with env-var expansion."""
-    if config_path is None:
-        config_path = str(Path(__file__).parent / "config.yaml")
-    with open(config_path, encoding="utf-8") as f:
-        raw = f.read()
-
-    # Expand ${VAR} and ${VAR:-default}
-    def _expand(m):
-        expr = m.group(1)
-        if ":-" in expr:
-            key, default = expr.split(":-", 1)
-            return os.environ.get(key, default)
-        return os.environ.get(expr, "")
-
-    raw = re.sub(r"\$\{([^}]+)\}", _expand, raw)
-    return yaml.safe_load(raw)
-
-
-# ---------------------------------------------------------------------------
-# Date utilities
-# ---------------------------------------------------------------------------
-def parse_haystack_date(date_str: str) -> datetime:
-    """Parse LongMemEval date format: '2023/05/20 (Sat) 02:21' -> datetime."""
-    m = re.match(r"(\d{4}/\d{2}/\d{2})\s+\(\w+\)\s+(\d{2}:\d{2})", date_str)
-    if not m:
-        raise ValueError(f"Cannot parse haystack date: {date_str!r}")
-    return datetime.strptime(f"{m.group(1)} {m.group(2)}", "%Y/%m/%d %H:%M")
-
-
-def to_iso(dt: datetime) -> str:
-    """Convert datetime to ISO-8601 string precise to seconds."""
-    return dt.strftime("%Y-%m-%dT%H:%M:%S")
-
-
-def should_trigger_dream(prev_dt: datetime, curr_dt: datetime, _trigger_hour: int = 23) -> bool:
-    """Check if the time gap between two sessions crosses trigger_hour (e.g. 23:00)."""
-    if prev_dt.date() == curr_dt.date():
-        return False
-    # There's at least one midnight crossing; check if trigger_hour is between them
-    # Simple heuristic: if dates differ, dream should run for the previous day
-    return True
-
-
-def sessions_sorted_by_time(item: dict) -> list[tuple[int, datetime, str, list[dict]]]:
-    """Return (original_index, parsed_datetime, session_id, messages) sorted by time."""
-    entries = []
-    for i, (date_str, sid, msgs) in enumerate(
-        zip(item["haystack_dates"], item["haystack_session_ids"], item["haystack_sessions"]),
-    ):
-        dt = parse_haystack_date(date_str)
-        entries.append((i, dt, sid, msgs))
-    # Sort by time (ascending)
-    entries.sort(key=lambda x: x[1])
-    return entries
-
-
-# ---------------------------------------------------------------------------
-# Message formatting
-# ---------------------------------------------------------------------------
-def format_messages_for_reme(messages: list[dict], session_dt: datetime) -> list[dict]:
-    """Convert LongMemEval messages to ReMe auto_memory format.
-
-    Adds: name, created_at (ISO seconds). All messages in a session share the
-    same created_at (the session timestamp).
-    """
-    formatted = []
-    for msg in messages:
-        role = msg["role"]
-        formatted.append(
-            {
-                "name": role,
-                "role": role,
-                "content": msg["content"],
-                "created_at": to_iso(session_dt),
-            },
-        )
-    return formatted
-
-
-# ---------------------------------------------------------------------------
-# LLM-as-Judge (delegated to answer_judge_step via app.run_job)
-# ---------------------------------------------------------------------------
-
-
-async def judge_response_via_job(
-    app,
-    question: str,
-    ground_truth: str,
-    response: str,
-    question_type: str,
-) -> dict:
-    """Use the answer_judge_step to evaluate a response against the golden answer."""
-    judge_resp = await app.run_job(
-        "answer_judge",
-        query=question,
-        agent_answer=response,
-        golden_answer=ground_truth,
-        question_type=question_type,
-    )
-
-    verdict = (judge_resp.answer or "").strip().lower()
-    raw_answer = (judge_resp.metadata or {}).get("raw_answer_judgement", "")
-
-    return {
-        "verdict": verdict,
-        "reason": raw_answer if verdict not in ("yes", "no") else "",
-        "metric": "binary",
-        "question_type": question_type,
-    }
-
-
-# ---------------------------------------------------------------------------
-# Main evaluation pipeline
-# ---------------------------------------------------------------------------
-async def evaluate_item(item: dict, eval_config: dict, item_index: int, eval_only: bool = False) -> dict:
-    """Evaluate a single LongMemEval item end-to-end.
-
-    Args:
-        item: The dataset item containing question, answer, sessions, etc.
-        eval_config: The evaluation configuration dict.
-        item_index: The index of this item in the dataset.
-        eval_only: If True, skip ingestion (phases 1-3) and only run query+judge
-            using the existing workspace. Useful for re-evaluating different query
-            configurations without re-ingesting sessions.
-    """
-    from reme import Application
-    from reme.config import resolve_app_config
-    from reme.utils.evaluation_interface import track_agent_token_usage, track_job_counts
-
-    reme_cfg = eval_config["reme"]
-    dream_trigger_hour = reme_cfg.get("dream_trigger_hour", 23)
-    dream_scan_days = reme_cfg.get("dream_scan_days", 2)
-    dream_max_units = reme_cfg.get("dream_max_units", 5)
-
-    # Sort sessions by time
-    sorted_sessions = sessions_sorted_by_time(item)
-
-    # Filter out sessions that occur after question_date (if enabled)
-    filter_future = eval_config["evaluation"].get("filter_future_sessions", True)
-    if filter_future and item.get("question_date"):
-        question_dt = parse_haystack_date(item["question_date"])
-        total_before_filter = len(sorted_sessions)
-        sorted_sessions = [(i, dt, sid, msgs) for i, dt, sid, msgs in sorted_sessions if dt <= question_dt]
-        if len(sorted_sessions) < total_before_filter:
-            logger.info(
-                f"[Item {item_index}] Filtered sessions: {total_before_filter} -> {len(sorted_sessions)} "
-                f"(removed {total_before_filter - len(sorted_sessions)} future sessions "
-                f"after question_date={item['question_date']})",
-            )
-
-    logger.info(
-        "[Item %s] question_id=%s type=%s sessions=%d%s",
-        item_index,
-        item["question_id"],
-        item["question_type"],
-        len(sorted_sessions),
-        " [eval_only]" if eval_only else "",
-    )
-
-    # Use fixed workspace directory (clean it for fresh evaluation)
-    workspace_root = _PROJECT_ROOT / eval_config["dataset"].get("workspace_root", _WORKSPACE_ROOT_DEFAULT)
-    item_dir = workspace_root / f"item_{item_index}"
-    workspace_dir = str(item_dir / ".reme")
-    if eval_only:
-        if not item_dir.exists() or not Path(workspace_dir).exists():
-            raise FileNotFoundError(
-                f"[Item {item_index}] eval_only: workspace not found at {item_dir}. "
-                f"Run without --eval_only first to build the workspace.",
-            )
-    else:
-        if item_dir.exists():
-            shutil.rmtree(item_dir)
-            logger.info(f"[Item {item_index}] Cleaned existing workspace: {item_dir}")
-        else:
-            logger.info(f"[Item {item_index}] Workspace not found, creating: {item_dir}")
-        item_dir.mkdir(parents=True, exist_ok=True)
-
-    # Pre-initialize ReMe's loguru logger with the correct log_dir
-    # (singleton — Application.__init__ will reuse this instance)
-    output_cfg = eval_config.get("output", {})
-    if output_cfg.get("log_to_file", False):
-        reme_log_dir = os.environ.get("REME_LOG_DIR")
-        if reme_log_dir:
-            from reme.utils import get_logger
-
-            get_logger(
-                log_dir=reme_log_dir,
-                level=os.environ.get("REME_LOG_LEVEL", "INFO"),
-                log_to_console=output_cfg.get("log_to_console", True),
-                log_to_file=True,
-                force_init=True,
-            )
-
-    cfg = resolve_app_config(
-        config=reme_cfg["config"],
-        workspace_dir=workspace_dir,
-        log_to_console=output_cfg.get("log_to_console", True),
-        log_to_file=output_cfg.get("log_to_file", False),
-        enable_logo=False,
-    )
-
-    app = Application(**cfg)
-    await app.start()
-
-    try:
-        dream_dates_triggered = set()
-        dream_available = True  # Set to False if auto_dream job is not found
-
-        if not eval_only:
-            # ── Phase 1: Ingest sessions ──────────────────────────────
-            prev_dt = None
-
-            for idx, (_, session_dt, session_id, messages) in enumerate(sorted_sessions):
-                # Check if dream should be triggered before this session
-                if (
-                    dream_available
-                    and prev_dt is not None
-                    and should_trigger_dream(prev_dt, session_dt, dream_trigger_hour)
-                ):
-                    dream_date = prev_dt.strftime("%Y-%m-%d")
-                    if dream_date not in dream_dates_triggered:
-                        logger.info(f"[Item {item_index}] Triggering dream for date={dream_date}")
-                        try:
-                            dream_resp = await app.run_job(
-                                "auto_dream",
-                                date=dream_date,
-                                scan_days=dream_scan_days,
-                                max_units=dream_max_units,
-                            )
-                            logger.info(
-                                f"[Item {item_index}] Dream done: success={dream_resp.success} "
-                                f"answer={dream_resp.answer[:100] if dream_resp.answer else ''}",
-                            )
-                        except Exception as e:
-                            if "not found" in str(e).lower():
-                                dream_available = False
-                                logger.warning(f"[Item {item_index}] auto_dream job not found, skipping all dreams")
-                            else:
-                                logger.warning(f"[Item {item_index}] Dream failed for {dream_date}: {e}")
-                        dream_dates_triggered.add(dream_date)
-                        # Index update after dream to pick up new digest nodes
-                        await app.run_job("index_update")
-
-                # Format and ingest the session
-                formatted_msgs = format_messages_for_reme(messages, session_dt)
-                date_str = session_dt.strftime("%Y-%m-%d")
-
-                logger.info(
-                    f"[Item {item_index}] Ingesting session {idx+1}/{len(sorted_sessions)} "
-                    f"id={session_id} date={date_str} msgs={len(formatted_msgs)}",
-                )
-                resp = await app.run_job(
-                    "auto_memory",
-                    messages=formatted_msgs,
-                    session_id=session_id,
-                    date=date_str,
-                )
-                if not resp.success:
-                    logger.warning(
-                        f"[Item {item_index}] auto_memory failed for session {session_id}: {resp.answer}",
-                    )
-
-                # Manual index update after each session
-                await app.run_job("index_update")
-
-                prev_dt = session_dt
-
-            # ── Phase 2: Final dream for the last day ─────────────────
-            if dream_available and prev_dt is not None:
-                last_dream_date = prev_dt.strftime("%Y-%m-%d")
-                if last_dream_date not in dream_dates_triggered:
-                    logger.info(f"[Item {item_index}] Final dream for date={last_dream_date}")
-                    try:
-                        await app.run_job(
-                            "auto_dream",
-                            date=last_dream_date,
-                            scan_days=dream_scan_days,
-                            max_units=dream_max_units,
-                        )
-                    except Exception as e:
-                        if "not found" in str(e).lower():
-                            dream_available = False
-                            logger.warning(f"[Item {item_index}] auto_dream job not found, skipping all dreams")
-                        else:
-                            logger.warning(f"[Item {item_index}] Final dream failed: {e}")
-                    dream_dates_triggered.add(last_dream_date)
-                    # Index update after final dream
-                    await app.run_job("index_update")
-
-            # ── Phase 3: Digest update ────────────────────────────────
-            await app.run_job("digest_update")
-
-        # ── Phase 4: Ask question via agentic_answer job (ReAct agent) ──
-        question = item["question"]
-        compress_session = bool(eval_config["evaluation"].get("compress_session", False))
-        question_date_raw = item.get("question_date", "")
-        question_dt = parse_haystack_date(question_date_raw) if question_date_raw else None
-        query_time = to_iso(question_dt) if question_dt else ""
-        logger.info(
-            f"[Item {item_index}] Asking (agentic): {question[:80]}... query_time={query_time}",
-        )
-
-        with (
-            track_job_counts(["search"], app.context) as tool_counts,
-            track_agent_token_usage(
-                ["bench"],
-                app.context,
-            ) as token_usages,
-        ):
-            query_resp = await app.run_job(
-                "agentic_answer",
-                query=question,
-                query_time=query_time,
-                compress_session=compress_session,
-            )
-        agentic_tool_counts = tool_counts
-        agentic_token_usage = token_usages["bench"]
-        agentic_response = (query_resp.answer or "").strip()
-        if not agentic_response:
-            agentic_response = "(no answer generated)"
-
-        logger.info(f"[Item {item_index}] Agentic response: {agentic_response[:200]}...")
-        logger.info(f"[Item {item_index}] Agentic tool calls: {agentic_tool_counts}")
-        logger.info(f"[Item {item_index}] Bench token usage: {agentic_token_usage}")
-
-        # ── Phase 5: Judge agentic response (via answer_judge_step) ──────────
-        logger.info(f"[Item {item_index}] Judging agentic (binary, type={item['question_type']})...")
-        agentic_judgment = await judge_response_via_job(
-            app=app,
-            question=question,
-            ground_truth=item["answer"],
-            response=agentic_response,
-            question_type=item["question_type"],
-        )
-        logger.info(f"[Item {item_index}] agentic binary result: {agentic_judgment}")
-
-    finally:
-        await app.close()
-
-    return {
-        "question_id": item["question_id"],
-        "question_type": item["question_type"],
-        "question": question,
-        "ground_truth": item["answer"],
-        "agentic_response": agentic_response,
-        "agentic_judgment": agentic_judgment,
-        "agentic_tool_counts": agentic_tool_counts,
-        "agentic_token_usage": agentic_token_usage,
-        "sessions_ingested": len(sorted_sessions),
-        "dreams_triggered": len(dream_dates_triggered),
-    }
-
-
-# ---------------------------------------------------------------------------
-# Worker: runs a single item in its own process with its own event loop
-# ---------------------------------------------------------------------------
-def _evaluate_item_worker(task_input: tuple) -> dict:
-    """Worker function for multiprocessing. Each process gets its own event loop."""
-    item, eval_config, item_index, log_level, reme_log_level, eval_only, log_dir = task_input
-    import asyncio  # pylint: disable=import-outside-toplevel
-
-    _configure_worker(log_level, reme_log_level, log_dir=log_dir)
-
-    # Permanently suppress "Task exception was never retrieved" /
-    # "Event loop is closed" noise from httpx AsyncClient GC cleanup.
-    # These fire AFTER asyncio.run() closes the loop, during Python's
-    # garbage collection of httpx connection-pool tasks — harmless.
-    logging.getLogger("asyncio").setLevel(logging.CRITICAL)
-
-    return asyncio.run(evaluate_item(item, eval_config, item_index, eval_only=eval_only))
-
-
-def _indexed_worker(indexed_input: tuple) -> tuple:
-    """Module-level wrapper for imap_unordered with index tracking."""
-    idx, task_input = indexed_input
-    return idx, _evaluate_item_worker(task_input)
-
-
-def _resolve_num_workers(configured: int) -> int:
-    """Resolve num_workers: 0=auto (cpu_count-2, min 1), 1=sequential, >1=parallel."""
-    if configured == 0:
-        return max(1, (os.cpu_count() or 4) - 2)
-    return max(1, configured)
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-def main(
-    config_path: str | None = None,
-    log_level: str = "INFO",
-    reme_log_level: str = "INFO",
-    eval_only: bool = False,
-):
-    """Run the LongMemEval evaluation pipeline.
-
-    Args:
-        config_path: Path to the YAML config file.
-        log_level: Log level for the eval runner.
-        reme_log_level: Log level for reme internal logs.
-        eval_only: If True, skip ingestion and only run query+judge using
-            existing workspaces.
-    """
-    from multiprocessing import Pool  # pylint: disable=import-outside-toplevel
-
-    # Load config BEFORE logging setup so log_dir is available
-    eval_config = load_eval_config(config_path)
-
-    # Resolve per-run log directory from config
-    output_cfg = eval_config.get("output", {})
-    log_dir_abs = None
-    if output_cfg.get("log_to_file", False):
-        log_dir_raw = output_cfg.get("log_dir", "logs")
-        log_prefix = output_cfg.get("log_prefix", "longmemeval")
-        run_ts = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-        log_dir_abs = str(_PROJECT_ROOT / log_dir_raw / f"{log_prefix}_{run_ts}")
-
-    setup_logging(log_level, reme_log_level, log_dir=log_dir_abs)
-    dataset_cfg = eval_config["dataset"]
-
-    # Load dataset
-    dataset_path = _PROJECT_ROOT / dataset_cfg["path"]
-    logger.info(f"Loading dataset from {dataset_path}")
-    with open(dataset_path, encoding="utf-8") as f:
-        data = json.load(f)
-
-    start = dataset_cfg.get("start_index", 0)
-    num_items = dataset_cfg.get("num_items", 0)
-    if num_items > 0:
-        raw_items = data[start : start + num_items]
-    else:
-        raw_items = data[start:]
-
-    # Build item list
-    items_with_idx = [(start + i, item) for i, item in enumerate(raw_items)]
-
-    # Filter by question_type if specified
-    question_types = dataset_cfg.get("question_types") or []
-    if question_types:
-        before_filter = len(items_with_idx)
-        items_with_idx = [(idx, item) for idx, item in items_with_idx if item.get("question_type") in question_types]
-        logger.info(
-            f"Filtered by question_types={question_types}: {before_filter} -> {len(items_with_idx)} items",
-        )
-
-    # Filter by question_id if specified
-    question_ids = dataset_cfg.get("question_ids") or []
-    if question_ids:
-        qid_set = set(question_ids)
-        before_filter = len(items_with_idx)
-        items_with_idx = [(idx, item) for idx, item in items_with_idx if item.get("question_id") in qid_set]
-        logger.info(
-            f"Filtered by question_ids ({len(qid_set)} ids): {before_filter} -> {len(items_with_idx)} items",
-        )
-
-    logger.info(
-        "Evaluating %d item(s) starting from index %d%s",
-        len(items_with_idx),
-        start,
-        " [eval_only: query+judge only]" if eval_only else "",
-    )
-
-    # Resolve parallelism
-    num_workers = _resolve_num_workers(eval_config["evaluation"].get("num_workers", 1))
-    logger.info(f"Using {num_workers} worker(s)")
-
-    # Create output directory
-    output_dir = _PROJECT_ROOT / output_cfg.get("dir", "benchmark/longmemeval/results")
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Create workspace root directory
-    workspace_root = _PROJECT_ROOT / dataset_cfg.get("workspace_root", _WORKSPACE_ROOT_DEFAULT)
-    workspace_root.mkdir(parents=True, exist_ok=True)
-
-    # Pre-check: verify all workspaces exist in eval_only mode
-    if eval_only:
-        missing_items = []
-        for orig_idx, _ in items_with_idx:
-            item_dir = workspace_root / f"item_{orig_idx}"
-            if not item_dir.exists() or not (item_dir / ".reme").exists():
-                missing_items.append(orig_idx)
-        if missing_items:
-            preview = missing_items[:10]
-            suffix = "..." if len(missing_items) > 10 else ""
-            raise FileNotFoundError(
-                f"eval_only: {len(missing_items)} workspace(s) not found under {workspace_root}. "
-                f"Missing item indices: {preview}{suffix}. "
-                f"Run without --eval_only first to build the workspaces.",
-            )
-
-    # Build task args — include log levels, eval_only flag, and log paths (use original index for workspace lookup)
-    task_args = [
-        (item, eval_config, orig_idx, log_level, reme_log_level, eval_only, log_dir_abs)
-        for orig_idx, item in items_with_idx
-    ]
-
-    # Progress tracking (force print regardless of log level, every 10 minutes)
-    total_items = len(task_args)
-    completed_count = [0]  # use list for mutability in closure
-    start_time = time.time()
-    progress_lock = threading.Lock()
-
-    def _print_progress(prefix: str = "PROGRESS"):
-        elapsed = time.time() - start_time
-        elapsed_min = elapsed / 60
-        done = completed_count[0]
-        pct = 100.0 * done / total_items if total_items else 0
-        eta_str = "N/A"
-        if done > 0:
-            eta_sec = elapsed / done * (total_items - done)
-            eta_str = f"{eta_sec/60:.1f}min"
-        print(
-            f"[{prefix}] {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | "
-            f"{done}/{total_items} ({pct:.1f}%) completed | "
-            f"elapsed={elapsed_min:.1f}min | ETA={eta_str}",
-            flush=True,
-        )
-
-    def _progress_timer():
-        """Background thread: print progress every 10 minutes."""
-        while not _timer_stop.is_set():
-            _timer_stop.wait(600)  # 10 minutes
-            if not _timer_stop.is_set():
-                with progress_lock:
-                    _print_progress()
-
-    _timer_stop = threading.Event()
-    timer_thread = threading.Thread(target=_progress_timer, daemon=True)
-    timer_thread.start()
-
-    # Run evaluation
-    if num_workers == 1:
-        # Sequential mode
-        results = []
-        for task_input in task_args:
-            result = _evaluate_item_worker(task_input)
-            results.append(result)
-            with progress_lock:
-                completed_count[0] += 1
-    else:
-        # Parallel mode — use imap_unordered for progress tracking
-        results = [None] * total_items
-        indexed_args = list(enumerate(task_args))
-
-        with Pool(processes=num_workers) as pool:
-            for idx, result in pool.imap_unordered(_indexed_worker, indexed_args):
-                results[idx] = result
-                with progress_lock:
-                    completed_count[0] += 1
-
-    # Stop progress timer
-    _timer_stop.set()
-    timer_thread.join(timeout=2)
-
-    # Save results
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    output_file = output_dir / f"results_{timestamp}.json"
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(results, f, ensure_ascii=False, indent=2)
-    logger.info(f"Results saved to {output_file}")
-
-    # Final progress
-    _print_progress("FINAL")
-
-    _print_summary(results, start_time)
-
-
-# ---------------------------------------------------------------------------
-# Summary printing
-# ---------------------------------------------------------------------------
-def _print_summary(results: list[dict], start_time: float) -> None:
-    """Print per-item verdicts and per-type accuracy."""
-    print("\n" + "=" * 60)
-    print("EVALUATION RESULTS")
-    print("=" * 60)
-
-    def _accumulate(judgment_key):
-        correct = 0
-        stats: dict = {}  # {question_type: {correct: int, total: int}}
-        for r in results:
-            qtype = r["question_type"]
-            verdict = r.get(judgment_key, {}).get("verdict", "N/A")
-            if qtype not in stats:
-                stats[qtype] = {"correct": 0, "total": 0}
-            stats[qtype]["total"] += 1
-            if verdict == "yes":
-                correct += 1
-                stats[qtype]["correct"] += 1
-        return correct, stats
-
-    agentic_correct, agentic_type_stats = _accumulate("agentic_judgment")
-
-    total = len(results)
-
-    # Per-item verdict rows
-    for r in results:
-        a_verdict = r.get("agentic_judgment", {}).get("verdict", "N/A")
-        print(f"  [{r['question_id']}] type={r['question_type']}  agentic={a_verdict}")
-
-    print("\n" + "-" * 60)
-    print(f"  Items: {total}")
-
-    # Agentic stats
-    print("\n  ── Agentic (ReAct) ──")
-    print(f"  Overall accuracy: {agentic_correct}/{total} ({100*agentic_correct/total:.1f}%)")
-    tool_call_totals = [sum(r.get("agentic_tool_counts", {}).values()) for r in results]
-    tool_call_mean, tool_call_std = _mean_and_std(tool_call_totals)
-    print(f"  Tool calls/query: mean={tool_call_mean:.2f} std={tool_call_std:.2f}")
-    token_usages = [r.get("agentic_token_usage", {}) for r in results]
-    print("  Bench reported tokens/query:")
-    for metric in _TOKEN_USAGE_METRICS:
-        values = [usage[metric] for usage in token_usages if usage.get(metric) is not None]
-        if values:
-            mean, std = _mean_and_std(values)
-            print(f"    {metric}: mean={mean:.2f} std={std:.2f}")
-        else:
-            print(f"    {metric}: unavailable")
-    print("  Per-type accuracy:")
-    for qtype, stats in sorted(agentic_type_stats.items()):
-        acc = 100 * stats["correct"] / stats["total"] if stats["total"] else 0
-        print(f"    {qtype}: {stats['correct']}/{stats['total']} ({acc:.1f}%)")
-
-    print("=" * 60)
-    total_elapsed = time.time() - start_time
-    print(f"\n  Total time: {total_elapsed/60:.1f} min")
-    print("\n" + "=" * 60)
-    print("  [DONE] EVALUATION COMPLETED SUCCESSFULLY")
-    print("=" * 60 + "\n")
-
-
-_TOKEN_USAGE_METRICS = (
-    "input_tokens",
-    "output_tokens",
-    "total_tokens",
-)
-
-
-def _mean_and_std(values: list[int]) -> tuple[float, float]:
-    """Return population mean and standard deviation for one per-query metric."""
-    if not values:
-        return 0.0, 0.0
-    mean = sum(values) / len(values)
-    return mean, (sum((value - mean) ** 2 for value in values) / len(values)) ** 0.5
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(description="LongMemEval evaluation runner")
-    parser.add_argument("--config", type=str, default=None, help="Path to config.yaml")
-    parser.add_argument(
-        "--log-level",
-        type=str,
-        default="INFO",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        help="Log level for the eval runner (default: INFO)",
-    )
-    parser.add_argument(
-        "--reme-log-level",
-        type=str,
-        default="INFO",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        help="Log level for reme internal logs — loguru (default: INFO)",
-    )
-    parser.add_argument(
-        "-q",
-        "--quiet",
-        action="store_true",
-        help="Shortcut for --log-level WARNING --reme-log-level WARNING",
-    )
-    parser.add_argument(
-        "--eval_only",
-        action="store_true",
-        help="Skip ingestion (phases 1-3). Reuse existing workspaces and only run query+judge.",
-    )
-    args = parser.parse_args()
-
-    if args.quiet:
-        args.log_level = "WARNING"
-        args.reme_log_level = "WARNING"
-
-    main(args.config, args.log_level, args.reme_log_level, eval_only=args.eval_only)
--- a/benchmark/longmemeval/run_agentic_answer.py
+++ b/benchmark/longmemeval/run_agentic_answer.py
@ -0,0 +1,343 @@
+#!/usr/bin/env python3
+"""Drive the LongMemEval memory pipeline across all samples.
+
+For every workspace under ``datasets/longmemeval/<idx>`` this launches one or more
+``reme start config=jinli_lme job=<job>`` runs with ``LME_WORKSPACE_DIR`` pointed
+at that sample. The pipeline jobs, in order, are:
+
+  1. auto_memory   — distil every raw session into a daily note (``daily/*.md``)
+  2. update_index  — clear the store and rebuild the index over ``daily/*.md``
+  3. agentic_answer — read ``query.json`` and answer it, writing ``mem_answer.json``
+  4. llm_judge      — judge ``mem_answer.json`` against ``answer.json``
+
+Pick one with ``--job``, or ``--job all`` to run the full pipeline *serially per sample*.
+Runs are capped at ``--concurrency`` (default 1 for ``--job auto_memory``, otherwise
+3) samples at once and each launch is staggered by ``--stagger`` seconds so they
+do not all hit the LLM API at once.
+
+By default every selected job is rerun for every sample — each job's own clear
+step (configured in jinli_lme.yaml) wipes stale output first, so a run is always
+a clean rebuild. Pass ``--resume`` to instead skip samples whose output already
+exists (``daily/`` for auto_memory, ``metadata/embedding_store/`` for
+update_index, ``mem_answer.json`` for agentic_answer, ``mem_answer.json`` with
+``llm_judge.judgement`` for llm_judge) and continue an interrupted batch. Each
+sample's stdout/stderr goes to ``logs/agentic_answer/<job>/<idx>.log``.
+
+After an agentic_answer run finishes, the driver aggregates every sample's query,
+golden answer, predicted answer, LLM judgement and a best-effort tool-call trail
+into one big JSON at ``logs/agentic_answer/aggregate.json``.
+
+Examples:
+    python benchmark/longmemeval/run_agentic_answer.py                       # agentic_answer, all 500, conc 3
+    python benchmark/longmemeval/run_agentic_answer.py --job all             # full pipeline serially per sample
+    python benchmark/longmemeval/run_agentic_answer.py --job auto_memory     # just step 1
+    python benchmark/longmemeval/run_agentic_answer.py --job llm_judge       # just judge existing answers
+    python benchmark/longmemeval/run_agentic_answer.py --limit 5 --dry-run   # list what would run
+    python benchmark/longmemeval/run_agentic_answer.py --start 187           # samples 187..499
+    python benchmark/longmemeval/run_agentic_answer.py --start 187 --end 499 # samples 187..499
+    python benchmark/longmemeval/run_agentic_answer.py --job all --resume    # continue an interrupted batch
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import re
+import time
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGDIR = REPO / "logs" / "agentic_answer"
+AGGREGATE = LOGDIR / "aggregate.json"
+
+# Pipeline jobs in execution order.
+JOB_ORDER = ["auto_memory", "update_index", "agentic_answer", "llm_judge"]
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument(
+        "--job",
+        choices=[*JOB_ORDER, "all"],
+        default="agentic_answer",
+        help="which job to run per sample; 'all' runs the full pipeline serially (default: agentic_answer)",
+    )
+    p.add_argument("--concurrency", type=int, default=1, help="max samples running at once (default 3)")
+    p.add_argument("--stagger", type=float, default=1.0, help="seconds between consecutive launches (default 1)")
+    p.add_argument("--start", type=int, default=0, help="first numeric sample id to process, inclusive (default 0)")
+    p.add_argument(
+        "--end",
+        type=int,
+        default=0,
+        help="last numeric sample id to process, inclusive (0 = no upper bound)",
+    )
+    p.add_argument("--limit", type=int, default=0, help="only process the first N samples (0 = all)")
+    p.add_argument(
+        "--resume",
+        action="store_true",
+        help="skip a sample when the job's output already exists (resume an interrupted run); "
+        "by default every selected job is rerun so the config's clear step rebuilds cleanly",
+    )
+    p.add_argument("--dry-run", action="store_true", help="list what would run, launch nothing")
+    p.add_argument("--no-aggregate", action="store_true", help="skip writing aggregate.json after answer/judge jobs")
+    return p.parse_args()
+
+
+def selected_jobs(job: str) -> list[str]:
+    """Expand the --job choice into an ordered list of jobs."""
+    return list(JOB_ORDER) if job == "all" else [job]
+
+
+def sample_ids() -> list[str]:
+    """List all sample IDs (numeric workspace dirs), numerically sorted."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def job_done(idx: str, job: str) -> bool:
+    """Return True when ``job``'s expected output already exists for sample ``idx``."""
+    ws = DATA / idx
+    if job == "auto_memory":
+        daily = ws / "daily"
+        return daily.is_dir() and any(daily.rglob("*.md"))
+    if job == "update_index":
+        store = ws / "metadata" / "embedding_store"
+        return store.is_dir() and any(store.iterdir())
+    if job == "agentic_answer":
+        return (ws / "mem_answer.json").exists()
+    if job == "llm_judge":
+        judge = _load_json(ws / "mem_answer.json").get("llm_judge")
+        return isinstance(judge, dict) and bool(str(judge.get("judgement") or "").strip())
+    raise ValueError(f"unknown job: {job}")
+
+
+async def run_job(idx: str, job: str, counters: dict) -> bool:
+    """Run a single job for a single sample. Returns True on success."""
+    log = LOGDIR / job / f"{idx}.log"
+    log.parent.mkdir(parents=True, exist_ok=True)
+    env = dict(os.environ, LME_WORKSPACE_DIR=f"datasets/longmemeval/{idx}")
+    started = time.strftime("%H:%M:%S")
+    print(f"[start {started}] {idx}/{job}", flush=True)
+    with log.open("w", encoding="utf-8") as f:
+        proc = await asyncio.create_subprocess_exec(
+            "reme",
+            "start",
+            "config=jinli_lme",
+            f"job={job}",
+            cwd=str(REPO),
+            env=env,
+            stdout=f,
+            stderr=asyncio.subprocess.STDOUT,
+        )
+        rc = await proc.wait()
+    ok = rc == 0 and job_done(idx, job)
+    counters["done" if ok else "fail"] += 1
+    tag = "done" if ok else "fail"
+    print(f"[{tag}] {idx}/{job} rc={rc} ({counters['done']} done / {counters['fail']} fail)", flush=True)
+    return ok
+
+
+async def run_one(idx: str, jobs: list[str], sem: asyncio.Semaphore, resume: bool, counters: dict) -> None:
+    """Run the selected jobs for one sample, serially.
+
+    By default every selected job is rerun (the job's own clear step wipes stale
+    output first). With ``resume`` a job is skipped when its output already
+    exists, so an interrupted batch can continue without redoing finished work.
+    """
+    async with sem:
+        for job in jobs:
+            if resume and job_done(idx, job):
+                counters["skip"] += 1
+                print(f"[skip] {idx}/{job} (output exists)", flush=True)
+                continue
+            ok = await run_job(idx, job, counters)
+            if not ok:
+                # Later jobs depend on earlier ones; don't waste a run on a broken workspace.
+                print(f"[abort] {idx}: {job} failed, skipping remaining jobs", flush=True)
+                break
+
+
+# --------------------------------------------------------------------------- #
+# Aggregation of agentic_answer results into one big JSON.
+# --------------------------------------------------------------------------- #
+
+# Match ``session_id=abc123`` headers and ``"...session_id": "abc123"`` fields in
+# tool-result text, so we can list which sessions each search actually surfaced.
+_SID_RE = re.compile(r'session_id["\s:=]+"?([A-Za-z0-9_\-]+)')
+
+
+def _load_json(path: Path) -> dict:
+    """Load a JSON object, returning {} on any error."""
+    try:
+        with path.open(encoding="utf-8") as f:
+            data = json.load(f)
+        return data if isinstance(data, dict) else {}
+    except (OSError, json.JSONDecodeError):
+        return {}
+
+
+def parse_tool_calls(idx: str, session_id: str) -> list[dict]:
+    """Best-effort: parse the agent trajectory into an ordered tool-call summary.
+
+    Reads ``mem_session/agentscope/<session_id>.jsonl`` — the trajectory the
+    agentic_answer run dumped — and pairs every ``tool_call`` (name + parsed
+    args) with the ``session_id`` hits found in its ``tool_result``. Returns an
+    empty list if the file is missing or unreadable (never raises).
+    """
+    if not session_id:
+        return []
+    path = DATA / idx / "mem_session" / "agentscope" / f"{session_id}.jsonl"
+    if not path.exists():
+        return []
+
+    calls: dict[str, dict] = {}
+    order: list[str] = []
+    try:
+        for line in path.read_text(encoding="utf-8").splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                msg = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            for c in msg.get("content") or []:
+                if not isinstance(c, dict):
+                    continue
+                cid = c.get("id")
+                if c.get("type") == "tool_call" and cid:
+                    try:
+                        args = json.loads(c.get("input") or "{}")
+                    except (json.JSONDecodeError, TypeError):
+                        args = c.get("input")
+                    calls[cid] = {"name": c.get("name"), "args": args, "hit_session_ids": []}
+                    order.append(cid)
+                elif c.get("type") == "tool_result" and cid in calls:
+                    text = ""
+                    for o in c.get("output") or []:
+                        if isinstance(o, dict) and isinstance(o.get("text"), str):
+                            text += o["text"]
+                    hits = list(dict.fromkeys(_SID_RE.findall(text)))
+                    calls[cid]["hit_session_ids"] = hits
+    except OSError:
+        return []
+
+    return [{"iter": i + 1, **calls[cid]} for i, cid in enumerate(order)]
+
+
+def build_record(idx: str) -> dict:
+    """Assemble one sample's aggregate record from its on-disk artifacts."""
+    ws = DATA / idx
+    query = _load_json(ws / "query.json")
+    golden = _load_json(ws / "answer.json")
+    mem = _load_json(ws / "mem_answer.json")
+
+    pred = str(mem.get("answer") or "").strip()
+    session_id = str(mem.get("session_id") or "")
+    llm_judge = mem.get("llm_judge") if isinstance(mem.get("llm_judge"), dict) else {}
+    tool_calls = parse_tool_calls(idx, session_id) if mem else []
+
+    if not mem:
+        status = "missing"
+    elif not pred:
+        status = "empty"
+    elif "not provided" in pred.lower():
+        status = "not_provided"
+    else:
+        status = "answered"
+
+    return {
+        "idx": idx,
+        "question_id": query.get("question_id"),
+        "question_type": query.get("question_type"),
+        "question": query.get("question"),
+        "question_date": query.get("question_date"),
+        "golden_answer": golden.get("answer"),
+        "golden_answer_session_ids": golden.get("answer_session_ids"),
+        "pred_answer": pred,
+        "session_id": session_id,
+        "status": status,
+        "llm_judge": llm_judge.get("judgement"),
+        "llm_judge_raw": llm_judge.get("raw_judgement"),
+        "num_tool_calls": len(tool_calls),
+        "tool_calls": tool_calls,
+    }
+
+
+def write_aggregate(ids: list[str]) -> None:
+    """Aggregate every sample's agentic_answer artifacts into one big JSON."""
+    records = [build_record(idx) for idx in ids]
+    finished = [r for r in records if r["status"] != "missing"]
+    by_status: dict[str, int] = {}
+    by_llm_judge: dict[str, int] = {}
+    for r in records:
+        by_status[r["status"]] = by_status.get(r["status"], 0) + 1
+        judgement = r.get("llm_judge") or "missing"
+        by_llm_judge[judgement] = by_llm_judge.get(judgement, 0) + 1
+
+    payload = {
+        "generated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
+        "total": len(records),
+        "finished": len(finished),
+        "by_status": by_status,
+        "by_llm_judge": by_llm_judge,
+        "samples": records,
+    }
+    AGGREGATE.parent.mkdir(parents=True, exist_ok=True)
+    AGGREGATE.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(f"[aggregate] wrote {len(records)} samples ({len(finished)} finished) -> {AGGREGATE}", flush=True)
+
+
+async def main() -> int:
+    """Run the driver."""
+    args = parse_args()
+    LOGDIR.mkdir(parents=True, exist_ok=True)
+    jobs = selected_jobs(args.job)
+
+    ids = sample_ids()
+    if args.end and args.end < args.start:
+        raise ValueError(f"--end ({args.end}) must be >= --start ({args.start})")
+    ids = [i for i in ids if int(i) >= args.start and (not args.end or int(i) <= args.end)]
+    if args.limit:
+        ids = ids[: args.limit]
+
+    # Without --resume every job reruns; with --resume, jobs whose output exists are skipped.
+    def todo_jobs(i: str) -> list[str]:
+        return [j for j in jobs if not (args.resume and job_done(i, j))]
+
+    pending = [i for i in ids if todo_jobs(i)]
+    print(
+        f"jobs={jobs} resume={args.resume} samples total={len(ids)} pending={len(pending)} "
+        f"concurrency={args.concurrency} stagger={args.stagger}s",
+        flush=True,
+    )
+
+    if args.dry_run:
+        for i in pending:
+            print(f"[would-run] {i}: {todo_jobs(i)}")
+        return 0
+
+    sem = asyncio.Semaphore(args.concurrency)
+    counters = {"done": 0, "fail": 0, "skip": 0}
+    tasks: list[asyncio.Task] = []
+    for n, idx in enumerate(ids):
+        if n and args.stagger > 0:
+            await asyncio.sleep(args.stagger)  # stagger each launch relative to the previous
+        tasks.append(asyncio.create_task(run_one(idx, jobs, sem, args.resume, counters)))
+
+    await asyncio.gather(*tasks, return_exceptions=True)
+    print(
+        f"ALL FINISHED done={counters['done']} fail={counters['fail']} skip={counters['skip']}",
+        flush=True,
+    )
+
+    if any(j in jobs for j in ("agentic_answer", "llm_judge")) and not args.no_aggregate:
+        write_aggregate(ids)
+
+    return 0 if counters["fail"] == 0 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(asyncio.run(main()))
--- a/benchmark/longmemeval/run_golden_check.py
+++ b/benchmark/longmemeval/run_golden_check.py
@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+"""Run LongMemEval ``golden_check`` concurrently across samples.
+
+For every workspace under ``datasets/longmemeval/<idx>`` in the selected numeric
+range, this launches:
+
+    reme start config=jinli_lme job=golden_check
+
+with ``LME_WORKSPACE_DIR`` pointed at that sample. Multiple samples can run at
+once, capped by ``--concurrency``. The ``golden_check`` job itself waits for
+``session_review.json`` when configured with ``wait_for_paths_step`` in
+``jinli_lme.yaml``. Each sample's stdout/stderr goes to
+``logs/golden_check/<idx>.log``.
+
+By default the script processes samples 0..499 inclusive and reruns every sample
+in that range. Pass ``--resume`` to skip samples whose ``check_golden.json``
+already exists.
+
+Examples:
+    python benchmark/longmemeval/run_golden_check.py
+    python benchmark/longmemeval/run_golden_check.py --start 187 --end 499
+    python benchmark/longmemeval/run_golden_check.py --concurrency 8 --stagger 1
+    python benchmark/longmemeval/run_golden_check.py --progress-interval 10
+    python benchmark/longmemeval/run_golden_check.py --resume
+    python benchmark/longmemeval/run_golden_check.py --limit 5 --dry-run
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import time
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGDIR = REPO / "logs" / "golden_check"
+OUTPUT_FILENAME = "check_golden.json"
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--start", type=int, default=0, help="first numeric sample id to process, inclusive (default 0)")
+    p.add_argument("--end", type=int, default=499, help="last numeric sample id to process, inclusive (default 499)")
+    p.add_argument("--limit", type=int, default=0, help="only process the first N selected samples (0 = all)")
+    p.add_argument("--concurrency", type=int, default=3, help="max samples running at once (default 3)")
+    p.add_argument("--stagger", type=float, default=1.0, help="seconds between consecutive launches (default 1)")
+    p.add_argument(
+        "--progress-interval",
+        type=float,
+        default=30.0,
+        help="seconds between progress reports while running (0 = disabled, default 30)",
+    )
+    p.add_argument(
+        "--resume",
+        action="store_true",
+        help=f"skip samples whose {OUTPUT_FILENAME} already exists",
+    )
+    p.add_argument("--dry-run", action="store_true", help="list what would run, launch nothing")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all sample IDs (numeric workspace dirs), numerically sorted."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def output_is_current(idx: str) -> bool:
+    """Return True when the sample already has a current-schema golden-check artifact."""
+    path = DATA / idx / OUTPUT_FILENAME
+    try:
+        with path.open(encoding="utf-8") as f:
+            data = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return False
+    verdict = data.get("verdict") if isinstance(data, dict) else None
+    if not isinstance(verdict, dict):
+        return False
+    return isinstance(verdict.get("golden_answer_correct"), bool) and isinstance(
+        verdict.get("answer_session_ids_correct"),
+        bool,
+    )
+
+
+def print_progress(counters: dict, active: set[str], selected_total: int, started_at: float) -> None:
+    """Print a one-line progress snapshot."""
+    finished = counters["done"] + counters["fail"] + counters["skip"]
+    running = len(active)
+    outstanding = max(selected_total - finished - running, 0)
+    elapsed = time.monotonic() - started_at
+    print(
+        f"[progress] selected={selected_total} done={counters['done']} fail={counters['fail']} "
+        f"skip={counters['skip']} running={running} outstanding={outstanding} "
+        f"elapsed={elapsed:.0f}s",
+        flush=True,
+    )
+
+
+async def progress_reporter(
+    counters: dict,
+    active: set[str],
+    selected_total: int,
+    started_at: float,
+    interval: float,
+    stop: asyncio.Event,
+) -> None:
+    """Periodically report progress until ``stop`` is set."""
+    if interval <= 0:
+        return
+    while not stop.is_set():
+        try:
+            await asyncio.wait_for(stop.wait(), timeout=interval)
+        except asyncio.TimeoutError:
+            print_progress(counters, active, selected_total, started_at)
+
+
+async def run_one(idx: str, sem: asyncio.Semaphore, resume: bool, counters: dict, active: set[str]) -> None:
+    """Run ``golden_check`` for one sample."""
+    if resume and output_is_current(idx):
+        counters["skip"] += 1
+        print(f"[skip] {idx} ({OUTPUT_FILENAME} exists)", flush=True)
+        return
+
+    async with sem:
+        active.add(idx)
+        log = LOGDIR / f"{idx}.log"
+        log.parent.mkdir(parents=True, exist_ok=True)
+        env = dict(os.environ, LME_WORKSPACE_DIR=f"datasets/longmemeval/{idx}")
+
+        started = time.strftime("%H:%M:%S")
+        print(f"[start {started}] {idx}", flush=True)
+        try:
+            with log.open("w", encoding="utf-8") as f:
+                proc = await asyncio.create_subprocess_exec(
+                    "reme",
+                    "start",
+                    "config=jinli_lme",
+                    "job=golden_check",
+                    cwd=str(REPO),
+                    env=env,
+                    stdout=f,
+                    stderr=asyncio.subprocess.STDOUT,
+                )
+                rc = await proc.wait()
+
+            ok = rc == 0 and output_is_current(idx)
+            counters["done" if ok else "fail"] += 1
+            tag = "done" if ok else "fail"
+            print(
+                f"[{tag}] {idx} rc={rc} log={log} ({counters['done']} done / {counters['fail']} fail)",
+                flush=True,
+            )
+        finally:
+            active.discard(idx)
+
+
+async def main() -> int:
+    """Run the concurrent driver."""
+    args = parse_args()
+    if args.end < args.start:
+        raise ValueError(f"--end ({args.end}) must be >= --start ({args.start})")
+    if args.concurrency < 1:
+        raise ValueError("--concurrency must be >= 1")
+    if args.progress_interval < 0:
+        raise ValueError("--progress-interval must be >= 0")
+
+    LOGDIR.mkdir(parents=True, exist_ok=True)
+
+    ids = [i for i in sample_ids() if args.start <= int(i) <= args.end]
+    if args.limit:
+        ids = ids[: args.limit]
+
+    pending = [i for i in ids if not (args.resume and output_is_current(i))]
+    print(
+        f"job=golden_check samples total={len(ids)} pending={len(pending)} "
+        f"range={args.start}..{args.end} resume={args.resume} "
+        f"concurrency={args.concurrency} stagger={args.stagger}s",
+        flush=True,
+    )
+
+    if args.dry_run:
+        for idx in pending:
+            print(f"[would-run] {idx}")
+        return 0
+
+    sem = asyncio.Semaphore(args.concurrency)
+    counters = {"done": 0, "fail": 0, "skip": 0}
+    active: set[str] = set()
+    started_at = time.monotonic()
+    stop_progress = asyncio.Event()
+    progress_task = asyncio.create_task(
+        progress_reporter(counters, active, len(ids), started_at, args.progress_interval, stop_progress),
+    )
+    tasks: list[asyncio.Task] = []
+    try:
+        for n, idx in enumerate(ids):
+            if n and args.stagger > 0:
+                await asyncio.sleep(args.stagger)
+            tasks.append(asyncio.create_task(run_one(idx, sem, args.resume, counters, active)))
+
+        await asyncio.gather(*tasks)
+    finally:
+        stop_progress.set()
+        await progress_task
+        print_progress(counters, active, len(ids), started_at)
+    print(
+        f"ALL FINISHED done={counters['done']} fail={counters['fail']} skip={counters['skip']}",
+        flush=True,
+    )
+    return 0 if counters["fail"] == 0 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(asyncio.run(main()))
--- a/benchmark/longmemeval/run_session_review.py
+++ b/benchmark/longmemeval/run_session_review.py
@ -0,0 +1,203 @@
+#!/usr/bin/env python3
+"""Run LongMemEval ``session_review`` concurrently across samples.
+
+For every workspace under ``datasets/longmemeval/<idx>`` in the selected numeric
+range, this launches:
+
+    reme start config=jinli_lme job=session_review
+
+with ``LME_WORKSPACE_DIR`` pointed at that sample. Multiple samples can run at
+once, capped by ``--concurrency``. By default this runner launches one sample at
+a time; request submission is throttled inside each ``session_review`` process.
+Each sample's stdout/stderr goes to ``logs/session_review/<idx>.log``.
+
+By default the script processes samples 0..499 inclusive and reruns every sample
+in that range. Pass ``--resume`` to skip samples whose ``session_review.json``
+already exists.
+
+Examples:
+    python benchmark/longmemeval/run_session_review.py
+    python benchmark/longmemeval/run_session_review.py --start 187 --end 499
+    python benchmark/longmemeval/run_session_review.py --concurrency 2
+    python benchmark/longmemeval/run_session_review.py --resume
+    python benchmark/longmemeval/run_session_review.py --limit 5 --dry-run
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import time
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGDIR = REPO / "logs" / "session_review"
+OUTPUT_FILENAME = "session_review.json"
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--start", type=int, default=0, help="first numeric sample id to process, inclusive (default 0)")
+    p.add_argument("--end", type=int, default=499, help="last numeric sample id to process, inclusive (default 499)")
+    p.add_argument("--limit", type=int, default=0, help="only process the first N selected samples (0 = all)")
+    p.add_argument("--concurrency", type=int, default=1, help="max samples running at once (default 1)")
+    p.add_argument("--stagger", type=float, default=1.0, help="seconds between worker launches (default 1)")
+    p.add_argument(
+        "--resume",
+        action="store_true",
+        help=f"skip samples whose {OUTPUT_FILENAME} already exists",
+    )
+    p.add_argument("--dry-run", action="store_true", help="list what would run, launch nothing")
+    p.add_argument("--stop-on-fail", action="store_true", help="stop immediately after the first failed sample")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all sample IDs (numeric workspace dirs), numerically sorted."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def output_exists(idx: str) -> bool:
+    """Return True when the sample already has a session review artifact."""
+    return (DATA / idx / OUTPUT_FILENAME).exists()
+
+
+def output_is_healthy(idx: str) -> bool:
+    """Return True when ``session_review.json`` exists and has no failed reviews."""
+    path = DATA / idx / OUTPUT_FILENAME
+    if not path.exists():
+        return False
+    try:
+        with path.open(encoding="utf-8") as f:
+            data = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return False
+    review = data.get("review") if isinstance(data, dict) else None
+    if not isinstance(review, dict):
+        return False
+    raw = review.get("num_failed_reviews")
+    if isinstance(raw, int):
+        return raw == 0
+    failed_reviews = review.get("failed_reviews")
+    return not failed_reviews
+
+
+async def run_one(idx: str, active: set[str]) -> bool:
+    """Run ``session_review`` for one sample. Returns True on success."""
+    log = LOGDIR / f"{idx}.log"
+    log.parent.mkdir(parents=True, exist_ok=True)
+    env = dict(os.environ, LME_WORKSPACE_DIR=f"datasets/longmemeval/{idx}")
+
+    started = time.strftime("%H:%M:%S")
+    print(f"[start {started}] {idx}", flush=True)
+    active.add(idx)
+    try:
+        with log.open("w", encoding="utf-8") as f:
+            proc = await asyncio.create_subprocess_exec(
+                "reme",
+                "start",
+                "config=jinli_lme",
+                "job=session_review",
+                cwd=str(REPO),
+                env=env,
+                stdout=f,
+                stderr=asyncio.subprocess.STDOUT,
+            )
+            rc = await proc.wait()
+    finally:
+        active.discard(idx)
+
+    ok = rc == 0 and output_exists(idx)
+    tag = "done" if ok else "fail"
+    print(f"[{tag}] {idx} rc={rc} log={log}", flush=True)
+    return ok
+
+
+async def worker(
+    name: int,
+    queue: asyncio.Queue[str],
+    args: argparse.Namespace,
+    counters: dict[str, int],
+    active: set[str],
+    stop: asyncio.Event,
+) -> None:
+    """Run samples from ``queue`` until exhausted or fail-fast is triggered."""
+    if name and args.stagger > 0:
+        await asyncio.sleep(args.stagger * name)
+
+    while not stop.is_set():
+        try:
+            idx = queue.get_nowait()
+        except asyncio.QueueEmpty:
+            return
+
+        try:
+            if args.resume and output_is_healthy(idx):
+                counters["skip"] += 1
+                print(f"[skip] {idx} (healthy {OUTPUT_FILENAME} exists)", flush=True)
+                continue
+
+            if await run_one(idx, active):
+                counters["done"] += 1
+            else:
+                counters["fail"] += 1
+                if args.stop_on_fail:
+                    stop.set()
+        finally:
+            queue.task_done()
+
+
+async def main() -> int:
+    """Run the concurrent driver."""
+    args = parse_args()
+    if args.end < args.start:
+        raise ValueError(f"--end ({args.end}) must be >= --start ({args.start})")
+    if args.concurrency < 1:
+        raise ValueError("--concurrency must be >= 1")
+    if args.stagger < 0:
+        raise ValueError("--stagger must be >= 0")
+
+    LOGDIR.mkdir(parents=True, exist_ok=True)
+
+    ids = [i for i in sample_ids() if args.start <= int(i) <= args.end]
+    if args.limit:
+        ids = ids[: args.limit]
+
+    pending = [i for i in ids if not (args.resume and output_exists(i))]
+    print(
+        f"job=session_review samples total={len(ids)} pending={len(pending)} "
+        f"range={args.start}..{args.end} resume={args.resume} "
+        f"concurrency={args.concurrency} stagger={args.stagger}s",
+        flush=True,
+    )
+
+    if args.dry_run:
+        for idx in pending:
+            print(f"[would-run] {idx}")
+        return 0
+
+    counters: dict[str, int] = {"done": 0, "fail": 0, "skip": 0}
+    active: set[str] = set()
+    stop = asyncio.Event()
+    queue: asyncio.Queue[str] = asyncio.Queue()
+    for idx in ids:
+        queue.put_nowait(idx)
+
+    workers = [
+        asyncio.create_task(worker(n, queue, args, counters, active, stop))
+        for n in range(min(args.concurrency, len(ids)))
+    ]
+    await asyncio.gather(*workers)
+
+    print(
+        f"ALL FINISHED done={counters['done']} fail={counters['fail']} skip={counters['skip']}",
+        flush=True,
+    )
+    return 0 if counters["fail"] == 0 else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(asyncio.run(main()))
--- a/benchmark/longmemeval/stats_agentic_answer.py
+++ b/benchmark/longmemeval/stats_agentic_answer.py
@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""Summarise the ``agentic_answer`` results across all LongMemEval samples.
+
+Reports progress (how many of the 500 samples produced ``mem_answer.json``) and a
+breakdown of answer *status*:
+  - answered      — a non-empty answer that is not "not provided";
+  - not_provided  — the agent gave up ("not provided");
+  - empty         — ``mem_answer.json`` exists but the answer is blank;
+  - missing       — no ``mem_answer.json`` yet.
+
+Everything is broken down by ``question_type``. This script does NOT judge answer
+correctness (there is no grader for ``mem_answer`` yet) — it only tracks progress
+and collects predicted-vs-golden pairs. Tool-call statistics are read from the
+aggregate written by ``run_agentic_answer.py`` when it is present.
+
+Examples:
+    python benchmark/longmemeval/stats_agentic_answer.py
+    python benchmark/longmemeval/stats_agentic_answer.py --list-run-failed
+    python benchmark/longmemeval/stats_agentic_answer.py --list-unanswered
+    python benchmark/longmemeval/stats_agentic_answer.py --json
+"""
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGBASE = REPO / "logs" / "agentic_answer"
+AGGREGATE = LOGBASE / "aggregate.json"
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--list-unanswered", action="store_true", help="list samples answered 'not provided' or empty")
+    p.add_argument("--list-run-failed", action="store_true", help="list launched samples with no readable output")
+    p.add_argument("--json", action="store_true", help="emit the summary as JSON")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all sample IDs (numeric workspace dirs), numerically sorted."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def pct(num: int, den: int) -> str:
+    """Format a percentage."""
+    return f"{(100.0 * num / den):.1f}%" if den else "n/a"
+
+
+def logged_sample_ids() -> list[str]:
+    """List sample IDs that have an agentic_answer launch log."""
+    logdir = LOGBASE / "agentic_answer"
+    if not logdir.exists():
+        return []
+    ids = [p.stem for p in logdir.glob("*.log") if p.stem.isdigit()]
+    return sorted(ids, key=int)
+
+
+def answer_status(pred: str, has_file: bool) -> str:
+    """Classify an answer into answered / not_provided / empty / missing."""
+    if not has_file:
+        return "missing"
+    if not pred:
+        return "empty"
+    if "not provided" in pred.lower():
+        return "not_provided"
+    return "answered"
+
+
+def load_tool_calls() -> dict[str, int]:
+    """Map idx -> num_tool_calls from the aggregate, if it exists."""
+    if not AGGREGATE.exists():
+        return {}
+    try:
+        with AGGREGATE.open(encoding="utf-8") as f:
+            data = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return {}
+    return {s["idx"]: s.get("num_tool_calls", 0) for s in data.get("samples", []) if "idx" in s}
+
+
+def main() -> int:
+    """Main entry point."""
+    args = parse_args()
+    ids = sample_ids()
+    total = len(ids)
+    tool_calls = load_tool_calls()
+
+    rows, unreadable = [], []
+    finished_ids = set()
+    for idx in ids:
+        query_path = DATA / idx / "query.json"
+        mem_path = DATA / idx / "mem_answer.json"
+        qtype = "(unknown)"
+        try:
+            with query_path.open(encoding="utf-8") as f:
+                qtype = json.load(f).get("question_type") or "(unknown)"
+        except (OSError, json.JSONDecodeError):
+            pass
+
+        has_file = mem_path.exists()
+        pred = ""
+        if has_file:
+            try:
+                with mem_path.open(encoding="utf-8") as f:
+                    pred = str(json.load(f).get("answer") or "").strip()
+                finished_ids.add(idx)
+            except (OSError, json.JSONDecodeError):
+                unreadable.append(idx)
+                has_file = False
+
+        rows.append({"idx": idx, "type": qtype, "status": answer_status(pred, has_file)})
+
+    finished = [r for r in rows if r["status"] != "missing"]
+    n = len(finished)
+    launched = logged_sample_ids()
+    run_failed = [idx for idx in launched if idx not in finished_ids]
+
+    # Overall status tallies.
+    status_counts: dict[str, int] = defaultdict(int)
+    for r in rows:
+        status_counts[r["status"]] += 1
+    answered = status_counts["answered"]
+    unanswered = [r["idx"] for r in rows if r["status"] in ("not_provided", "empty")]
+
+    calls_vals = [tool_calls[i] for i in finished_ids if i in tool_calls]
+    avg_calls = sum(calls_vals) / len(calls_vals) if calls_vals else 0.0
+
+    # Per question_type breakdown.
+    by_type: dict[str, dict[str, int]] = defaultdict(lambda: {"n": 0, "answered": 0})
+    for r in finished:
+        by_type[r["type"]]["n"] += 1
+        by_type[r["type"]]["answered"] += 1 if r["status"] == "answered" else 0
+
+    if args.json:
+        print(
+            json.dumps(
+                {
+                    "total": total,
+                    "finished": n,
+                    "pending": total - n - len(unreadable),
+                    "unreadable": unreadable,
+                    "launched": len(launched),
+                    "run_failed": run_failed,
+                    "status_counts": dict(status_counts),
+                    "answered_rate": round(answered / n, 4) if n else None,
+                    "avg_tool_calls": round(avg_calls, 2) if calls_vals else None,
+                    "by_type": {
+                        t: {**c, "answered_rate": round(c["answered"] / c["n"], 4)} for t, c in by_type.items()
+                    },
+                    "unanswered": unanswered,
+                    "aggregate": str(AGGREGATE) if AGGREGATE.exists() else None,
+                },
+                ensure_ascii=False,
+                indent=2,
+            ),
+        )
+        return 0
+
+    print("=" * 60)
+    print("LongMemEval agentic_answer 统计")
+    print("=" * 60)
+    print(f"样例总数            : {total}")
+    print(f"已完成 (有产出)     : {n}  ({pct(n, total)})")
+    print(f"未完成              : {total - n - len(unreadable)}")
+    if unreadable:
+        print(f"损坏/无法解析       : {len(unreadable)}  {unreadable}")
+    print(f"已启动过 (有 log)   : {len(launched)}")
+    print(f"运行失败/无可读产出 : {len(run_failed)}")
+    print("-" * 60)
+    print(f"已作答 (非 not provided): {answered}  ({pct(answered, n)} of finished)")
+    print(f"  其中 not provided : {status_counts['not_provided']}")
+    print(f"  其中 空答案       : {status_counts['empty']}")
+    if calls_vals:
+        print(f"平均工具调用次数    : {avg_calls:.1f}   (来自 {AGGREGATE.name})")
+    else:
+        print("平均工具调用次数    : n/a   (先跑 run_agentic_answer.py 生成 aggregate.json)")
+    print("-" * 60)
+    print("按 question_type:")
+    print(f"  {'type':<24} {'n':>4} {'已作答率':>12}")
+    for t in sorted(by_type):
+        c = by_type[t]
+        print(f"  {t:<24} {c['n']:>4} {pct(c['answered'], c['n']):>12}")
+
+    if args.list_unanswered:
+        print("-" * 60)
+        print(f"not provided / 空答案的样例 ({len(unanswered)}): {unanswered}")
+    if args.list_run_failed:
+        print("-" * 60)
+        print(f"运行失败/无可读 mem_answer.json 的样例 ({len(run_failed)}): {run_failed}")
+        for idx in run_failed:
+            print(f"  {idx}: {LOGBASE / 'agentic_answer' / f'{idx}.log'}")
+    print("=" * 60)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
--- a/benchmark/longmemeval/stats_golden_check.py
+++ b/benchmark/longmemeval/stats_golden_check.py
@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""Summarise the ``check_golden.json`` verdicts across all LongMemEval samples.
+
+Reports progress (how many of the 500 samples have finished) and accuracy:
+  - golden answer accuracy = share of finished samples whose golden answer the
+    auditor judged correct (``verdict.golden_answer_correct``);
+  - answer_session_ids accuracy = share whose claimed answer sessions the auditor
+    judged exactly correct (``verdict.answer_session_ids_correct``).
+
+Everything is also broken down by ``question_type``. Use ``--list-bad`` to print
+the samples whose golden answer was judged NOT correct.
+
+Examples:
+    python benchmark/longmemeval/stats_golden_check.py
+    python benchmark/longmemeval/stats_golden_check.py --list-bad
+    python benchmark/longmemeval/stats_golden_check.py --list-run-failed
+    python benchmark/longmemeval/stats_golden_check.py --json
+"""
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGDIR = REPO / "logs" / "golden_check"
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--list-bad", action="store_true", help="list samples whose golden answer is NOT correct")
+    p.add_argument(
+        "--list-bad-sessions",
+        action="store_true",
+        help="list samples whose answer_session_ids is NOT correct",
+    )
+    p.add_argument(
+        "--list-run-failed",
+        action="store_true",
+        help="list launched samples that did not produce readable output",
+    )
+    p.add_argument("--json", action="store_true", help="emit the summary as JSON")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all sample IDs."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def pct(num: int, den: int) -> str:
+    """Format a percentage."""
+    return f"{(100.0 * num / den):.1f}%" if den else "n/a"
+
+
+def logged_sample_ids() -> list[str]:
+    """List all sample IDs that have been launched but not finished."""
+    if not LOGDIR.exists():
+        return []
+    ids = [p.stem for p in LOGDIR.glob("*.log") if p.stem.isdigit()]
+    return sorted(ids, key=int)
+
+
+def load_json(path: Path) -> dict:
+    """Load a JSON object, returning {} on any error."""
+    try:
+        with path.open(encoding="utf-8") as f:
+            data = json.load(f)
+        return data if isinstance(data, dict) else {}
+    except (OSError, json.JSONDecodeError):
+        return {}
+
+
+def question_type_for(idx: str, data: dict) -> str:
+    """Return question_type from the output, session review, or query.json."""
+    question_type = str(data.get("question_type") or "").strip()
+    if question_type:
+        return question_type
+
+    review_path_raw = str(data.get("session_review_path") or "").strip()
+    review_path = Path(review_path_raw) if review_path_raw else DATA / idx / "session_review.json"
+    if not review_path.is_absolute():
+        review_path = REPO / review_path
+    review = load_json(review_path)
+    review_question_type = str((review.get("query") or {}).get("question_type") or "").strip()
+    if review_question_type:
+        return review_question_type
+
+    query = load_json(DATA / idx / "query.json")
+    return str(query.get("question_type") or "(unknown)").strip() or "(unknown)"
+
+
+def question_id_for(idx: str, data: dict) -> str:
+    """Return question_id from the output, session review, or query.json."""
+    question_id = str(data.get("question_id") or "").strip()
+    if question_id:
+        return question_id
+
+    review_path_raw = str(data.get("session_review_path") or "").strip()
+    review_path = Path(review_path_raw) if review_path_raw else DATA / idx / "session_review.json"
+    if not review_path.is_absolute():
+        review_path = REPO / review_path
+    review = load_json(review_path)
+    review_question_id = str((review.get("query") or {}).get("question_id") or "").strip()
+    if review_question_id:
+        return review_question_id
+
+    query = load_json(DATA / idx / "query.json")
+    return str(query.get("question_id") or "").strip()
+
+
+def sample_label(data: dict) -> str:
+    """Format sample id as idx(question_id) when question_id is available."""
+    idx = str(data.get("_idx") or "")
+    qid = str(data.get("_question_id") or "").strip()
+    return f"{idx}({qid})" if qid else idx
+
+
+def related_session_ids(data: dict) -> list[str]:
+    """Return the best available session ids for a bad verdict record."""
+    verdict = data.get("verdict") if isinstance(data, dict) else None
+    if isinstance(verdict, dict):
+        true_ids = verdict.get("true_answer_session_ids")
+        if isinstance(true_ids, list):
+            ids = [str(session_id) for session_id in true_ids if str(session_id).strip()]
+            if ids:
+                return ids
+
+    summaries = data.get("session_summaries")
+    if isinstance(summaries, list):
+        return [
+            str(summary.get("session_id"))
+            for summary in summaries
+            if isinstance(summary, dict) and str(summary.get("session_id") or "").strip()
+        ]
+    return []
+
+
+def grouped_records(records: list[dict]) -> dict[str, list[dict]]:
+    """Group records by question_type for human-readable list output."""
+    grouped: dict[str, list[dict]] = defaultdict(list)
+    for data in records:
+        question_type = str(data.get("_question_type") or "(unknown)")
+        grouped[question_type].append(
+            {
+                "index": str(data.get("_idx") or ""),
+                "question_id": str(data.get("_question_id") or ""),
+                "session_id": related_session_ids(data),
+            },
+        )
+    return dict(sorted(grouped.items()))
+
+
+def verdict_bool(verdict: dict, new_key: str, old_key: str) -> bool:
+    """Read a verdict boolean, accepting the old field name for compatibility."""
+    if verdict.get(new_key) is True:
+        return True
+    if verdict.get(new_key) is False:
+        return False
+    return verdict.get(old_key) is True
+
+
+def has_current_verdict(data: dict) -> bool:
+    """Return True when ``check_golden.json`` uses the current golden_check schema."""
+    verdict = data.get("verdict") if isinstance(data, dict) else None
+    if not isinstance(verdict, dict):
+        return False
+    return isinstance(verdict.get("golden_answer_correct"), bool) and isinstance(
+        verdict.get("answer_session_ids_correct"),
+        bool,
+    )
+
+
+def write_golden_check_list(done: list[dict], output_path: Path) -> None:
+    """Write all readable check_golden records as JSONL."""
+    with output_path.open("w", encoding="utf-8") as f:
+        for data in done:
+            f.write(json.dumps(data, ensure_ascii=False))
+            f.write("\n")
+
+
+def main() -> int:
+    """Main entry point."""
+    args = parse_args()
+    ids = sample_ids()
+    total = len(ids)
+
+    done, unreadable, stale = [], [], []
+    finished_ids = set()
+    for idx in ids:
+        path = DATA / idx / "check_golden.json"
+        if not path.exists():
+            continue
+        try:
+            with path.open(encoding="utf-8") as f:
+                data = json.load(f)
+            if not has_current_verdict(data):
+                stale.append(idx)
+                continue
+            data["_idx"] = idx
+            data["_question_type"] = question_type_for(idx, data)
+            data["_question_id"] = question_id_for(idx, data)
+            done.append(data)
+            finished_ids.add(idx)
+        except (OSError, json.JSONDecodeError):
+            unreadable.append(idx)
+
+    n = len(done)
+    output_path = Path.cwd() / "golden_check_list.jsonl"
+    write_golden_check_list(done, output_path)
+    launched = logged_sample_ids()
+    run_failed = [idx for idx in launched if idx not in finished_ids]
+
+    # Overall tallies.
+    golden_ok = sum(
+        1 for d in done if verdict_bool(d.get("verdict", {}), "golden_answer_correct", "golden_answer_reasonable")
+    )
+    sess_ok = sum(
+        1
+        for d in done
+        if verdict_bool(d.get("verdict", {}), "answer_session_ids_correct", "answer_session_ids_reasonable")
+    )
+    both_ok = sum(
+        1
+        for d in done
+        if verdict_bool(d.get("verdict", {}), "golden_answer_correct", "golden_answer_reasonable")
+        and verdict_bool(d.get("verdict", {}), "answer_session_ids_correct", "answer_session_ids_reasonable")
+    )
+
+    # Per question_type breakdown.
+    by_type: dict[str, dict[str, int]] = defaultdict(lambda: {"n": 0, "golden_ok": 0, "sess_ok": 0, "both_ok": 0})
+    for d in done:
+        v = d.get("verdict", {})
+        golden_is_ok = verdict_bool(v, "golden_answer_correct", "golden_answer_reasonable")
+        sess_is_ok = verdict_bool(v, "answer_session_ids_correct", "answer_session_ids_reasonable")
+        t = d.get("_question_type") or "(unknown)"
+        by_type[t]["n"] += 1
+        by_type[t]["golden_ok"] += 1 if golden_is_ok else 0
+        by_type[t]["sess_ok"] += 1 if sess_is_ok else 0
+        by_type[t]["both_ok"] += 1 if golden_is_ok and sess_is_ok else 0
+
+    bad_golden_records = [
+        d for d in done if not verdict_bool(d.get("verdict", {}), "golden_answer_correct", "golden_answer_reasonable")
+    ]
+    bad_session_records = [
+        d
+        for d in done
+        if not verdict_bool(d.get("verdict", {}), "answer_session_ids_correct", "answer_session_ids_reasonable")
+    ]
+    bad_golden = [d["_idx"] for d in bad_golden_records]
+    bad_sessions = [d["_idx"] for d in bad_session_records]
+
+    if args.json:
+        print(
+            json.dumps(
+                {
+                    "total": total,
+                    "finished": n,
+                    "pending": total - n - len(unreadable),
+                    "unreadable": unreadable,
+                    "stale": stale,
+                    "launched": len(launched),
+                    "run_failed": run_failed,
+                    "golden_answer_accuracy": round(golden_ok / n, 4) if n else None,
+                    "answer_session_ids_accuracy": round(sess_ok / n, 4) if n else None,
+                    "both_correct_rate": round(both_ok / n, 4) if n else None,
+                    "golden_ok": golden_ok,
+                    "sess_ok": sess_ok,
+                    "both_ok": both_ok,
+                    "by_type": {
+                        t: {
+                            **c,
+                            "golden_bad": c["n"] - c["golden_ok"],
+                            "session_bad": c["n"] - c["sess_ok"],
+                            "both_bad": c["n"] - c["both_ok"],
+                            "golden_acc": round(c["golden_ok"] / c["n"], 4),
+                            "session_acc": round(c["sess_ok"] / c["n"], 4),
+                            "both_acc": round(c["both_ok"] / c["n"], 4),
+                        }
+                        for t, c in by_type.items()
+                    },
+                    "bad_golden": bad_golden,
+                    "bad_sessions": bad_sessions,
+                    "golden_check_list": str(output_path),
+                },
+                ensure_ascii=False,
+                indent=2,
+            ),
+        )
+        return 0
+
+    print("=" * 60)
+    print("LongMemEval golden_check 统计")
+    print("=" * 60)
+    print(f"样例总数            : {total}")
+    print(f"已完成 (有产出)     : {n}  ({pct(n, total)})")
+    print(f"未完成              : {total - n - len(unreadable)}")
+    if unreadable:
+        print(f"损坏/无法解析       : {len(unreadable)}  {unreadable}")
+    if stale:
+        print(f"旧格式待重跑        : {len(stale)}  {stale}")
+    print(f"已合并 JSONL        : {output_path}")
+    print(f"已启动过 (有 log)   : {len(launched)}")
+    print(f"运行失败/无可读产出 : {len(run_failed)}")
+    print("-" * 60)
+    print(f"golden answer 正确率 : {pct(golden_ok, n)}   ({golden_ok}/{n})")
+    print(f"answer_session 正确率: {pct(sess_ok, n)}   ({sess_ok}/{n})")
+    print(f"两者都正确          : {pct(both_ok, n)}   ({both_ok}/{n})")
+    print("-" * 60)
+    print("按 question_type:")
+    print(
+        f"  {'type':<24} {'n':>4} {'golden正确率':>14} {'golden错误':>10} "
+        f"{'session正确率':>14} {'session错误':>11} {'都正确':>10} {'都正确错误':>12}",
+    )
+    for t in sorted(by_type):
+        c = by_type[t]
+        print(
+            f"  {t:<24} {c['n']:>4} {pct(c['golden_ok'], c['n']):>14} {c['n'] - c['golden_ok']:>10} "
+            f"{pct(c['sess_ok'], c['n']):>14} {c['n'] - c['sess_ok']:>11} "
+            f"{pct(c['both_ok'], c['n']):>10} {c['n'] - c['both_ok']:>12}",
+        )
+
+    if args.list_bad:
+        print("-" * 60)
+        print(f"golden answer 判为不正确的样例 ({len(bad_golden_records)}):")
+        print(json.dumps(grouped_records(bad_golden_records), ensure_ascii=False))
+    if args.list_bad_sessions:
+        print("-" * 60)
+        print(f"answer_session_ids 判为不正确的样例 ({len(bad_session_records)}):")
+        print(json.dumps(grouped_records(bad_session_records), ensure_ascii=False))
+    if args.list_run_failed:
+        print("-" * 60)
+        print(f"运行失败/无可读 check_golden.json 的样例 ({len(run_failed)}): {run_failed}")
+        for idx in run_failed:
+            print(f"  {idx}: {LOGDIR / f'{idx}.log'}")
+    print("=" * 60)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
--- a/benchmark/longmemeval/stats_session_review.py
+++ b/benchmark/longmemeval/stats_session_review.py
@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+"""Summarise LongMemEval ``session_review.json`` artifacts.
+
+This script is for upstream health checks before running ``golden_check``.
+Samples with retryable per-session failures should be rerun as a whole; samples
+with non-retryable fallback reviews are reported separately.
+
+Examples:
+    python benchmark/longmemeval/stats_session_review.py
+    python benchmark/longmemeval/stats_session_review.py --list-failed
+    python benchmark/longmemeval/stats_session_review.py --list-fallback
+    python benchmark/longmemeval/stats_session_review.py --json
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[2]
+DATA = REPO / "datasets" / "longmemeval"
+LOGDIR = REPO / "logs" / "session_review"
+OUTPUT_FILENAME = "session_review.json"
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse command-line arguments."""
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--list-failed", action="store_true", help="list samples with retryable failed per-session reviews")
+    p.add_argument("--list-fallback", action="store_true", help="list non-retryable fallback reviews")
+    p.add_argument("--list-missing", action="store_true", help="list samples missing session_review.json")
+    p.add_argument("--list-run-failed", action="store_true", help="list launched samples without a healthy output")
+    p.add_argument("--json", action="store_true", help="emit the summary as JSON")
+    return p.parse_args()
+
+
+def sample_ids() -> list[str]:
+    """List all numeric sample IDs."""
+    ids = [p.name for p in DATA.iterdir() if p.is_dir() and p.name.isdigit()]
+    return sorted(ids, key=int)
+
+
+def pct(num: int, den: int) -> str:
+    """Format a percentage."""
+    return f"{(100.0 * num / den):.1f}%" if den else "n/a"
+
+
+def load_json(path: Path) -> dict:
+    """Load a JSON object, returning {} on any error."""
+    try:
+        with path.open(encoding="utf-8") as f:
+            data = json.load(f)
+        return data if isinstance(data, dict) else {}
+    except (OSError, json.JSONDecodeError):
+        return {}
+
+
+def logged_sample_ids() -> list[str]:
+    """List sample IDs that have a session_review runner log."""
+    if not LOGDIR.exists():
+        return []
+    ids = [p.stem for p in LOGDIR.glob("*.log") if p.stem.isdigit()]
+    return sorted(ids, key=int)
+
+
+def review_block(data: dict) -> dict:
+    """Return the review block when present."""
+    review = data.get("review") if isinstance(data, dict) else None
+    return review if isinstance(review, dict) else {}
+
+
+def failure_details(data: dict) -> list[dict]:
+    """Return retryable failed_reviews when present."""
+    failed_reviews = review_block(data).get("failed_reviews")
+    if not isinstance(failed_reviews, list):
+        return []
+    return [item for item in failed_reviews if isinstance(item, dict) and not item.get("fallback")]
+
+
+def fallback_details(data: dict) -> list[dict]:
+    """Return non-retryable fallback review details when present."""
+    review = review_block(data)
+    fallback_reviews = review.get("fallback_reviews")
+    if isinstance(fallback_reviews, list):
+        return [item for item in fallback_reviews if isinstance(item, dict)]
+
+    failed_reviews = review.get("failed_reviews")
+    if isinstance(failed_reviews, list):
+        return [item for item in failed_reviews if isinstance(item, dict) and item.get("fallback")]
+    return []
+
+
+def failure_count(data: dict) -> int:
+    """Return retryable failed review count."""
+    review = review_block(data)
+    raw = review.get("num_failed_reviews")
+    raw_fallback = review.get("num_fallback_reviews")
+    if isinstance(raw, int) and isinstance(raw_fallback, int):
+        return max(0, raw - raw_fallback)
+    return len(failure_details(data))
+
+
+def fallback_count(data: dict) -> int:
+    """Return non-retryable fallback review count."""
+    review = review_block(data)
+    raw = review.get("num_fallback_reviews")
+    if isinstance(raw, int):
+        return raw
+    return len(fallback_details(data))
+
+
+def question_id(data: dict) -> str:
+    """Return query.question_id when present."""
+    query = data.get("query") if isinstance(data, dict) else None
+    if not isinstance(query, dict):
+        return ""
+    return str(query.get("question_id") or "").strip()
+
+
+def main() -> int:
+    """Main entry point."""
+    args = parse_args()
+    ids = sample_ids()
+    total = len(ids)
+
+    healthy, failed, fallback, missing, unreadable = [], [], [], [], []
+    total_failed_sessions = 0
+    total_fallback_sessions = 0
+    failed_details_by_id: dict[str, list[dict]] = {}
+    fallback_details_by_id: dict[str, list[dict]] = {}
+    question_id_by_id: dict[str, str] = {}
+
+    for idx in ids:
+        path = DATA / idx / OUTPUT_FILENAME
+        if not path.exists():
+            missing.append(idx)
+            continue
+        data = load_json(path)
+        if not data:
+            unreadable.append(idx)
+            continue
+        question_id_by_id[idx] = question_id(data)
+        n_failed = failure_count(data)
+        n_fallback = fallback_count(data)
+        if n_failed:
+            failed.append(idx)
+            total_failed_sessions += n_failed
+            failed_details_by_id[idx] = failure_details(data)
+        if n_fallback:
+            fallback.append(idx)
+            total_fallback_sessions += n_fallback
+            fallback_details_by_id[idx] = fallback_details(data)
+        if not n_failed:
+            healthy.append(idx)
+
+    launched = logged_sample_ids()
+    healthy_set = set(healthy)
+    run_failed = [idx for idx in launched if idx not in healthy_set]
+
+    if args.json:
+        print(
+            json.dumps(
+                {
+                    "total": total,
+                    "healthy": len(healthy),
+                    "failed_samples": failed,
+                    "failed_sample_count": len(failed),
+                    "failed_session_count": total_failed_sessions,
+                    "fallback_samples": fallback,
+                    "fallback_sample_count": len(fallback),
+                    "fallback_session_count": total_fallback_sessions,
+                    "missing": missing,
+                    "unreadable": unreadable,
+                    "launched": len(launched),
+                    "run_failed_or_unhealthy": run_failed,
+                    "failed_details": failed_details_by_id,
+                    "fallback_details": fallback_details_by_id,
+                },
+                ensure_ascii=False,
+                indent=2,
+            ),
+        )
+        return 0
+
+    print("=" * 60)
+    print("LongMemEval session_review 统计")
+    print("=" * 60)
+    print(f"样例总数              : {total}")
+    print(f"可继续产出            : {len(healthy)}  ({pct(len(healthy), total)})")
+    print(f"有可重试失败          : {len(failed)}")
+    print(f"可重试失败 session    : {total_failed_sessions}")
+    print(f"有不可重试 fallback   : {len(fallback)}")
+    print(f"fallback session      : {total_fallback_sessions}")
+    print(f"缺少 session_review   : {len(missing)}")
+    print(f"损坏/无法解析         : {len(unreadable)}")
+    print(f"已启动过 (有 log)     : {len(launched)}")
+    print(f"运行失败/非健康产出   : {len(run_failed)}")
+    print("-" * 60)
+    print("有可重试 failed_reviews 的样例需要整体重跑：")
+    if failed:
+        print(" ".join(failed))
+        print("重跑命令示例：")
+        print(f"python benchmark/longmemeval/run_session_review.py --start {failed[0]} --end {failed[0]}")
+    else:
+        print("(none)")
+    if fallback:
+        print("-" * 60)
+        print("不可重试 fallback 的样例不用重跑：")
+        for idx in fallback:
+            details = fallback_details_by_id.get(idx) or []
+            session_ids = [str(item.get("session_id") or "(unknown)") for item in details]
+            qid = question_id_by_id.get(idx)
+            sample_label = f"{idx}({qid})" if qid else idx
+            print(f"{sample_label}: {' '.join(session_ids) if session_ids else '(unknown)'}")
+
+    if args.list_failed and failed:
+        print("-" * 60)
+        for idx in failed:
+            details = failed_details_by_id.get(idx) or []
+            print(f"{idx}: {DATA / idx / OUTPUT_FILENAME} failed_sessions={len(details)}")
+            for item in details:
+                session_id = item.get("session_id", "(unknown)")
+                error = str(item.get("error") or "").replace("\n", " ")
+                print(f"  - {session_id}: {error}")
+    if args.list_fallback and fallback:
+        print("-" * 60)
+        for idx in fallback:
+            details = fallback_details_by_id.get(idx) or []
+            print(f"{idx}: {DATA / idx / OUTPUT_FILENAME} fallback_sessions={len(details)}")
+            for item in details:
+                session_id = item.get("session_id", "(unknown)")
+                reason = str(item.get("fallback_reason") or "fallback")
+                error = str(item.get("error") or "").replace("\n", " ")
+                raw_saved = "yes" if item.get("raw_session") else "no"
+                print(f"  - {session_id}: reason={reason} raw_session_saved={raw_saved} error={error}")
+    if args.list_missing and missing:
+        print("-" * 60)
+        print(f"缺少 session_review.json 的样例 ({len(missing)}): {missing}")
+    if args.list_run_failed and run_failed:
+        print("-" * 60)
+        print(f"运行失败/非健康产出的样例 ({len(run_failed)}): {run_failed}")
+        for idx in run_failed:
+            print(f"  {idx}: {LOGDIR / f'{idx}.log'}")
+    print("=" * 60)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
--- a/benchmark/pibench/.gitignore
+++ b/benchmark/pibench/.gitignore
@ -1,14 +0,0 @@
-# 含真实 API key，绝不入库
-env.sh
-
-# 运行时产物（含对话内容，勿入库）
-logs/
-outputs/
-reme_workspace/
-nanobot_workspace/
-
-# 数据符号链接（指向外部 π-Bench 仓库）
-data
-
-__pycache__/
-*.pyc
--- a/benchmark/pibench/README.md
+++ b/benchmark/pibench/README.md
@ -1,327 +0,0 @@
-[中文版 / Chinese version](./README_ZH.md)
-
-# π-Bench Evaluation Suite
-
-A glue layer that connects the **ReMe agent (with persistent memory)** to
-**π-Bench** (Proactive Personal Assistant Benchmark). This directory contains
-only the minimal code and configuration needed for the integration: the
-π-Bench framework (`src/`), evaluation data (`data/`), the AppWorld tool
-environment, and ReMe itself are all **external third-party dependencies**,
-referenced in place via symlink and environment variables and never bundled
-with this suite.
-
- π-Bench: https://github.com/Simplified-Reasoning/Pi-Bench (arXiv: 2605.14678)
- ReMe: the root of the ReMe repository this suite lives in (recommended
-  location: `ReMe/benchmark/pibench/`)
-
-## 1. Architecture
-
-```
-π-Bench runner (src.main --mode run)
-  │  user_agent (simulated-user LLM) walks data/{persona}/episode.yaml
-  │  task by task, chatting with the agent over multiple turns and judging
-  │  hidden intents (PROC) during the run phase
-  ▼
-test server (π-Bench scripts/test_server.py, HTTP long-polling)
-  ▲ /send            │ /poll
-  │                  ▼
-bridge_reme.py ──────────────► ReMe Application (embedded as a library)
-  │                             ├─ agent_wrapper: agent under test (AgentScope)
-  │                             ├─ jobs: search / auto_memory / daily_write
-  │                             └─ workspace: reme_workspace/{persona}/
-  │                                (isolated persistent memory per persona)
-  └──── MCP ────► AppWorld MCP ────► AppWorld APIs (tool/app environment)
-
-π-Bench runner (src.main --mode eval)
-  judger (judge LLM) reads the traces and scores each checklist item (COMP)
-```
-
-Key points:
- The bridge runs on **ReMe's own venv python** and uses ReMe as a library
-  (`resolve_app_config` + `Application`); **no ReMe source modification** is
-  required.
- Every incoming user message automatically triggers a ReMe memory `search`
-  and injects the matched memories (tuning knobs in §8); on task end (reset)
-  the session is distilled into daily notes by `auto_memory`.
- Tool calls executed by the agent (AppWorld MCP + ReMe job tools) are
-  captured per turn into the trace as `tool_steps`, so π-Bench
-  `tools_evaluation_path` scripts can score tool behavior (§7).
- π-Bench's `data/`, `src/` and AppWorld are not part of this suite; install
-  π-Bench first (§3.1).
-
-## 2. Directory layout
-
-```
-pibench/
-├── README.md / README_ZH.md  # this document (English / Chinese)
-├── env.sh.example            # environment template (copy to env.sh, fill TODOs)
-├── bridge_reme.py            # ReMe ↔ test server bridge (memory inject/save,
-│                             #   profile injection, tool-trace capture)
-├── run_persona.sh            # full pipeline for ONE persona (5 services + run + eval)
-├── run_all.sh                # batch over 5 personas (fresh/resume, default parallel=2)
-├── resume.py                 # checkpoint resume: completion detection + surgical
-│                             #   cleanup of interrupted tasks' residual memory
-├── fix_trace_logs.py         # run outputs → ~/.nanobot/trace_logs conversion,
-│                             #   merging tool sidecars into turn files (pre-eval)
-├── .gitignore                # excludes env.sh and all runtime artifacts
-└── config/
-    ├── models/reme.yaml      # runner model config (model_id=reme)
-    └── bench/evaluation/trace_history.yaml   # trace render policy (shipped with
-                                              #   the suite; passed via --history-config-path)
-```
-
-Generated at runtime (all git-ignored): `data` (symlink), `logs/`, `outputs/`,
-`reme_workspace/`, `nanobot_workspace/`.
-
-## 3. Prerequisites (third-party, install first)
-
-### 3.1 π-Bench repository (with AppWorld)
-
-```bash
-git clone https://github.com/Simplified-Reasoning/Pi-Bench.git <pi-bench-dir>
-cd <pi-bench-dir>
-python3.11 -m venv .venv            # scripts expect exactly this venv name
-source .venv/bin/activate
-pip install -e .                    # pibench runner (src.main)
-bash scripts/setup_appworld.sh      # install AppWorld and download its data (large)
-```
-
-Post-install sanity checks:
-```bash
-ls data/                    # should contain researcher marketer pharmacist law_trainee Financier
-.venv/bin/python -c "import src" && echo OK
-.venv/bin/appworld --help >/dev/null && echo OK
-```
-
-### 3.2 ReMe repository
-
-```bash
-cd <reme-dir>               # ReMe repository root (contains the reme/ package)
-python3.11 -m venv .venv    # scripts expect exactly this venv name
-source .venv/bin/activate
-pip install -e .            # or ReMe's own install flow; `import reme` must work
-```
-
-Sanity check: `.venv/bin/python -c "import reme; print('ok')"`
-
-## 4. Install this suite (step by step)
-
-1. **Place the suite** (recommended inside the ReMe repo so `REME_DIR` is
-   inferred automatically):
-   ```bash
-   cp -r pibench <reme-dir>/benchmark/pibench
-   cd <reme-dir>/benchmark/pibench
-   ```
-   If placed elsewhere, set `REME_DIR` explicitly in env.sh later.
-
-2. **Create the environment file and fill in the custom parameters**:
-   ```bash
-   cp env.sh.example env.sh
-   ```
-   Open `env.sh`; required items (marked TODO):
-   | Variable | Description |
-   |---|---|
-   | `PI_BENCH_ROOT` | π-Bench repo root (contains `src/` `data/` `.venv` `third_party/appworld`) |
-   | `USER_API_KEY` | API key of the simulated-user LLM (run phase, hidden-intent judging) |
-   | `JUDGER_API_KEY` | API key of the judger LLM (eval phase, checklist scoring) |
-   | `BRAVE_SEARCH_API_KEY` | optional; for the agent's web_search tool, `dummy` when unused |
-
-   Optional tuning: `REME_MODEL_NAME` (base model of the agent under test),
-   `REME_DIR`, `REME_LLM_BASE_URL` (default: DashScope OpenAI-compatible
-   endpoint).
-
-3. **Link the evaluation data** (referenced in place, never copied):
-   ```bash
-   ln -s "$PI_BENCH_ROOT/data" data
-   ```
-
-4. **(Optional) adjust model config** `config/models/reme.yaml`:
-   - `user_agent.model` / `judger.model`: model names for the simulated user
-     and the judger (literal values; π-Bench only expands `${ENV}` in
-     base_url/api_key).
-   - `run.turn_timeout`, `max_tool_iterations`, etc. as needed.
-
-5. **Smoke check** (does not start the evaluation):
-   ```bash
-   bash -n run_all.sh && bash -n run_persona.sh
-   source env.sh && "$REME_DIR/.venv/bin/python" -c "import reme; print('reme ok')"
-   ```
-
-## 5. Run the evaluation
-
-> ⚠️ For long runs use `screen`, **not nohup** (nohup loses the permission
-> context in sandboxed/restricted environments and breaks child processes).
-
-```bash
-# Full official run: wipe ALL personas' memory/outputs/traces first (default
-# fresh mode, parallel=2)
-mkdir -p logs   # on a fresh deployment logs/ does not exist yet
-screen -dmS pibench_suite bash -c "cd $(pwd) && bash run_all.sh > logs/run_all_master.log 2>&1"
-
-# Checkpoint continuation (after an interruption; no wipe, completed tasks skipped)
-bash run_all.sh --resume
-
-# Other usages
-bash run_all.sh --parallel 1          # sequential
-bash run_all.sh --resume --skip-eval  # run phase only
-bash run_persona.sh researcher        # single persona (default --resume semantics)
-bash run_persona.sh researcher --fresh
-```
-
-Time reference: 5 personas × 20 tasks, parallel=2, fresh full run ≈ 12–14 hours.
-
-`run_all.sh` exits non-zero when any persona fails, so upstream automation
-cannot mistake a partially failed suite run for a success.
-
-## 6. Port allocation (parallel personas never collide)
-
-| persona     | AppWorld API | AppWorld MCP | Test Server | ReMe internal service |
-|-------------|------|-------|------|-------|
-| marketer    | 9001 | 10001 | 9998 | 18766 |
-| law_trainee | 9002 | 10002 | 9997 | 18767 |
-| pharmacist  | 9003 | 10003 | 9996 | 18768 |
-| researcher  | 9004 | 10004 | 9995 | 18765 |
-| Financier   | 9005 | 10005 | 9994 | 18769 |
-
-## 7. Outputs and scores
-
- **Results**: `outputs/reme/{persona}/{task}/eval/results/*_result.json`
-  - `overall_average_score`: checklist completeness (COMP; the judger scores
-    each criterion YES/NO, weighted across dependency groups)
-  - `overall_proactiveness_average_score`: proactiveness (PROC; the
-    user_agent judges hidden-intent coverage during the run phase; each task
-    file also carries the global average)
- **Traces**: `~/.nanobot/trace_logs/reme/{persona}/{task}/...` (the scoring
-  input of the eval phase)
- **Logs**: `logs/` (`suite_<persona>.log` per persona; `bridge_*`,
-  `runner_run/eval_*`, `appworld_*`, `test_server_*` per service)
- **Memory store**: `reme_workspace/{persona}/` (daily/digest notes, raw
-  session dialogs, BM25 index, etc.; persistent across runs, wiped only in
-  fresh mode)
-
-Score summary:
-```bash
-grep -h "overall_average_score\|overall_proactiveness" \
-  outputs/reme/*/*/eval/results/*_result.json | head
-```
-
-### Tool-trace capture (tools_evaluation support)
-
-Some tasks define `objectives.tools_evaluation_path`: Python scripts that
-score tool behavior (e.g. "the temporary Todoist board was created and
-removed"). They need the executed tool calls in the trace. The pipeline:
-
-1. During `reply()`, the bridge reads the persisted AgentScope session state
-   after each turn and extracts the new `tool_call` / `tool_result` blocks
-   (tool name, arguments, result).
-2. Records are appended to
-   `outputs/reme/{persona}/{task}/history/{ts}-tools.jsonl`, tagged with the
-   turn number; AgentScope MCP names (`mcp__AppWorld__<tool>`) are normalized
-   to the π-Bench convention (`mcp_appworld_<tool>`).
-3. `fix_trace_logs.py` pairs each `{ts}-messages.jsonl` run with the
-   temporally closest tools sidecar and merges the records into the generated
-   `turn_N.json` files under the `tool_steps` key — one of the two
-   tool-history formats understood by π-Bench's `collect_tool_history()`.
-4. The eval phase then feeds `tool_steps` to both the tools_evaluation
-   scripts and the rendered `<tool_trace_extracts>` seen by the judger.
-
-## 8. Memory mechanism (core design of this suite)
-
- **Persona isolation**: each persona has its own workspace
-  (`reme_workspace/{persona}/`); the bridge takes an exclusive
-  `.bridge.lock` on it at startup, so two bridges can never share one memory
-  store, and one persona's memory search can never reach another's memories.
- **Writes**: on task end (runner sends reset), the session is distilled by
-  the `auto_memory` job into daily notes and indexed by the background
-  watcher (BM25). Saves are non-blocking background tasks; the first message
-  of a new session waits for in-flight writes before searching.
- **Reads**: on every incoming user message the bridge runs one `search` and
-  injects matched memories (`[Relevant memories from previous sessions]`
-  prefix); without matches the message passes through unchanged. Retrieval
-  tuning (bridge CLI flags, adjustable in run_persona.sh):
-  - `--search-limit 3`: at most 3 memory chunks injected per message;
-  - `--search-min-score 2.0`: weak BM25 hits are filtered out;
-  - `tool_context_id` rotates per task: chunks already injected within the
-    same task are not re-injected (ReMe's seen-chunk dedup, 24h TTL); normal
-    recall resumes after task boundaries.
- **No self-leakage**: the in-progress session is not in the store yet
-  (saves happen on reset), so a task can never retrieve its own unfinished
-  content.
- The agent also holds `search`/`daily_write` tools and can retrieve/record
-  proactively.
- **System prompt**: `bridge_reme.py:build_system_prompt()` embeds the
-  HIDDEN-NEEDS protocol (proactiveness-oriented) and injects the persona
-  profile from `data/{persona}/profile.yaml` into every turn's system prompt.
-
-## 9. Checkpoint resume and memory-cleanup semantics
-
- **Completion detection** (resume.py): scans
-  `outputs/reme/{persona}/**/history/*-log.jsonl` and
-  `outputs/reme/{persona}/run/*-log.jsonl` for
-  `Task finished task_id=X status=Y`. The status with the **newest event
-  timestamp** wins per task (record `timestamp`, falling back to
-  `timestamp_iso`, then to the timestamp embedded in the log file name) —
-  file category and read order alone can never override a newer record, so an
-  old run-level SUCCESS cannot mask a newer per-task ERROR. `SUCCESS /
-  MAX_TURNS / TIMEOUT` count as completed; `ERROR` and never-started tasks
-  are re-run (passed to the runner as repeated `--task-id` flags in episode
-  order).
- **Answer-leak prevention**: an interrupted task may already have been
-  distilled into daily notes during graceful shutdown; re-running it with
-  that memory injected would inflate scores. Before resuming,
-  `resume.py cleanup` therefore removes residual memory **only for tasks
-  about to be re-run** (daily/digest notes, session/dialog, mem_session;
-  matched via `session_id = pibench_{task}_*`). Completed tasks' memories are
-  never touched. Daily index files are refreshed **only for the dates that
-  lost notes**, by full workspace-relative wikilink path — and when the ReMe
-  package is importable, the refresh reuses ReMe's own daily-index rebuild
-  logic (`refresh_day_index`), so same-named notes on other dates are never
-  modified.
- **fresh vs resume are mutually exclusive**: a full memory wipe belongs to
-  fresh mode only (`run_all.sh` default, executed before any service starts);
-  resume never wipes.
-
-## 10. Customization entry points
-
-| Goal | Location |
-|---|---|
-| Base model of the agent under test | `REME_MODEL_NAME` in `env.sh` |
-| user_agent / judger models | `config/models/reme.yaml` |
-| Agent system prompt | `bridge_reme.py` `build_system_prompt()` |
-| Memory retrieval limit/threshold | `--search-limit/--search-min-score` on the bridge command in `run_persona.sh` |
-| ReMe internal parameters | **Do not modify ReMe source**; write a dedicated config modeled on `reme/config/beam.yaml` and override via `resolve_app_config(config=...)` (see bridge `_init_reme_app`) |
-| Turn timeout / tool iteration cap | `config/models/reme.yaml` `run.turn_timeout`, `model.max_tool_iterations` |
-
-## 11. Troubleshooting
-
- **Port already in use**: the scripts auto-kill residual processes on the
-  four port groups above; if another suite (e.g. a different π-Bench
-  experiment) holds them, stop it first or change the port table in
-  run_persona.sh.
- **Bridge exits immediately with workspace locked**: another bridge already
-  holds the same workspace; make sure each persona uses its own
-  `--workspace-dir` (the scripts allocate one per persona).
- **Runner reports `${USER_API_KEY} ... empty`**: env.sh is unfilled or not
-  sourced; run_persona.sh sources env.sh automatically — when running the
-  runner manually, `source env.sh` first.
- **`Cannot import 'reme'`**: the bridge must run with
-  `${REME_DIR}/.venv/bin/python` (run_persona.sh already does); otherwise
-  check that `REME_DIR` points at the ReMe repository root.
- **AppWorld fails to start**: run `bash scripts/setup_appworld.sh` in the
-  π-Bench repo first (downloads data); inspect
-  `logs/appworld_*_<persona>.log`.
- **trace_history.yaml not found**: the runner needs
-  `config/bench/evaluation/trace_history.yaml`; this suite ships the file and
-  passes it explicitly via `--history-config-path`, and run_persona.sh fails
-  fast with a clear error if it is missing. Always launch run_persona.sh /
-  run_all.sh from the suite directory.
-
-## 12. Privacy and security
-
- The suite code and config templates contain **no real API keys, user names
-  or absolute paths**; real keys live only in your local `env.sh`
-  (git-ignored).
- `logs/`, `outputs/`, `reme_workspace/` and `nanobot_workspace/` contain
-  full conversations and model outputs; never commit or share them.
- The `data` symlink points at the official π-Bench evaluation data; respect
-  its data license terms.
--- a/benchmark/pibench/README_ZH.md
+++ b/benchmark/pibench/README_ZH.md
@ -1,284 +0,0 @@
-# π-Bench 评测说明
-
-[English version](./README.md)
-
-将 **ReMe agent（带持久记忆）** 接入 **π-Bench**（Proactive Personal Assistant
-Benchmark）的胶水层评测套件。只含对接所需的最小代码与配置；π-Bench 框架
-（`src/`）、评测数据（`data/`）、AppWorld 工具环境、ReMe 本体均为**外部第三方
-依赖**，通过符号链接与环境变量原位引用，不随本套件分发。
-
- π-Bench: https://github.com/Simplified-Reasoning/Pi-Bench （arXiv: 2605.14678）
- ReMe: 你所在 ReMe 仓库的根目录（本套件推荐放在 `ReMe/benchmark/pibench/`）
-
-## 1. 架构总览
-
-```
-π-Bench runner (src.main --mode run)
-  │  user_agent（模拟用户 LLM）按 data/{persona}/episode.yaml 顺序
-  │  逐任务、多轮地与 agent 对话，并在 run 阶段判定隐藏意图(PROC)
-  ▼
-test server (π-Bench scripts/test_server.py, HTTP 长轮询)
-  ▲ /send            │ /poll
-  │                  ▼
-bridge_reme.py ──────────────► ReMe Application（以库方式内嵌启动）
-  │                             ├─ agent_wrapper: 被测 agent（AgentScope）
-  │                             ├─ jobs: search / auto_memory / daily_write
-  │                             └─ workspace: reme_workspace/{persona}/
-  │                                （每 persona 独立持久记忆库，互不可见）
-  └──── MCP ────► AppWorld MCP ────► AppWorld API（工具/应用环境）
-
-π-Bench runner (src.main --mode eval)
-  judger（裁判 LLM）读取 trace，按 checklist 逐条 YES/NO 打分(COMP)
-```
-
-要点：
- bridge 用 **ReMe 自己的 venv python** 运行，把 ReMe 当库用（`resolve_app_config`
-  + `Application`），**ReMe 源码零改动**。
- 每条用户消息都会自动触发一次 ReMe memory `search` 并把命中记忆注入当前消息
-  （参数见 §8）；任务结束（reset）时会话被 `auto_memory` 提炼为 daily 笔记落盘。
- agent 执行的每一轮工具调用（AppWorld MCP + ReMe job 工具）都会被采集并以
-  `tool_steps` 形式写入 trace，供 π-Bench 的 `tools_evaluation_path` 脚本
-  对工具行为评分（§7）。
- π-Bench 的 `data/`、`src/`、AppWorld 均不属于本套件，需先装好 π-Bench（§3.1）。
-
-## 2. 目录结构
-
-```
-pibench/
-├── README.md / README_ZH.md  # 本文档（英文 / 中文）
-├── env.sh.example            # 环境配置模板（复制为 env.sh 后填写 TODO 项）
-├── bridge_reme.py            # ReMe ↔ test server 桥接（记忆注入/保存、
-│                             #   profile 注入、工具调用轨迹采集）
-├── run_persona.sh            # 单 persona 全流程（5 个服务 + run + eval）
-├── run_all.sh                # 5 个 persona 批跑（fresh/resume，默认 2 并行）
-├── resume.py                 # 断点续跑：完成判定 + 中断任务残留记忆的外科清理
-├── fix_trace_logs.py         # run 输出 → ~/.nanobot/trace_logs 转换，
-│                             #   并把工具轨迹合并进 turn 文件（eval 前置）
-├── .gitignore                # 排除 env.sh 与全部运行产物
-└── config/
-    ├── models/reme.yaml      # runner 模型配置（model_id=reme）
-    └── bench/evaluation/trace_history.yaml   # trace 渲染策略（随套件提供，
-                                              #   经 --history-config-path 显式传入）
-```
-
-运行时自动生成（均被 .gitignore 排除）：`data`（符号链接）、`logs/`、
-`outputs/`、`reme_workspace/`、`nanobot_workspace/`。
-
-## 3. 前置依赖（第三方，先装好）
-
-### 3.1 π-Bench 仓库（含 AppWorld）
-
-```bash
-git clone https://github.com/Simplified-Reasoning/Pi-Bench.git <pi-bench-dir>
-cd <pi-bench-dir>
-python3.11 -m venv .venv            # 脚本约定使用 .venv 这个目录名
-source .venv/bin/activate
-pip install -e .                    # pibench runner（src.main）
-bash scripts/setup_appworld.sh      # 安装 AppWorld 并下载其数据（体积较大，需网络）
-```
-
-装完自检：
-```bash
-ls data/                    # 应含 researcher marketer pharmacist law_trainee Financier
-.venv/bin/python -c "import src" && echo OK
-.venv/bin/appworld --help >/dev/null && echo OK
-```
-
-### 3.2 ReMe 仓库
-
-```bash
-cd <reme-dir>               # ReMe 仓库根目录（含 reme/ 包）
-python3.11 -m venv .venv    # 脚本约定使用 .venv 这个目录名
-source .venv/bin/activate
-pip install -e .            # 或按 ReMe 自身安装方式，保证 `import reme` 可用
-```
-
-自检：`.venv/bin/python -c "import reme; print('ok')"`
-
-## 4. 安装本套件（逐步）
-
-1. **放置套件**（推荐放进 ReMe 仓库，`REME_DIR` 可自动推断）：
-   ```bash
-   cp -r pibench <reme-dir>/benchmark/pibench
-   cd <reme-dir>/benchmark/pibench
-   ```
-   若放在其他位置，稍后在 env.sh 中显式设置 `REME_DIR`。
-
-2. **创建环境文件并填写自定义参数**：
-   ```bash
-   cp env.sh.example env.sh
-   ```
-   打开 `env.sh`，必填项（标 TODO 的）：
-   | 变量 | 说明 |
-   |---|---|
-   | `PI_BENCH_ROOT` | π-Bench 仓库根目录（含 `src/` `data/` `.venv` `third_party/appworld`） |
-   | `USER_API_KEY` | 模拟用户 LLM 的 API key（run 阶段判定隐藏意图） |
-   | `JUDGER_API_KEY` | 裁判 LLM 的 API key（eval 阶段 checklist 打分） |
-   | `BRAVE_SEARCH_API_KEY` | 可选；agent 的 web_search 工具用，不用填 `dummy` |
-
-   可选调整：`REME_MODEL_NAME`（被测 agent 基模）、`REME_DIR`、
-   `REME_LLM_BASE_URL`（默认 DashScope OpenAI 兼容端点）。
-
-3. **链接评测数据**（π-Bench 数据原位引用，不复制）：
-   ```bash
-   ln -s "$PI_BENCH_ROOT/data" data
-   ```
-
-4. **（可选）调整模型配置** `config/models/reme.yaml`：
-   - `user_agent.model` / `judger.model`：模拟用户与裁判的模型名（字面量，
-     π-Bench 仅对 base_url/api_key 做 `${ENV}` 展开）。
-   - `run.turn_timeout`、`max_tool_iterations` 等按需。
-
-5. **冒烟自检**（不启动评测）：
-   ```bash
-   bash -n run_all.sh && bash -n run_persona.sh
-   source env.sh && "$REME_DIR/.venv/bin/python" -c "import reme; print('reme ok')"
-   ```
-
-## 5. 运行评测
-
-> ⚠️ 长时间运行请放进 `screen`，**不要用 nohup**（nohup 在沙箱/受限环境下
-> 会丢失权限上下文导致子进程异常）。
-
-```bash
-# 完整正式评测：先清空全部 persona 的记忆/输出/trace，再从头跑（默认 fresh，2 并行）
-mkdir -p logs   # 全新部署时 logs/ 尚不存在，先建再重定向
-screen -dmS pibench_suite bash -c "cd $(pwd) && bash run_all.sh > logs/run_all_master.log 2>&1"
-
-# 断点续跑（中断后继续；不清记忆，跳过已完成任务）
-bash run_all.sh --resume
-
-# 其他用法
-bash run_all.sh --parallel 1        # 串行
-bash run_all.sh --resume --skip-eval  # 只跑 run 阶段
-bash run_persona.sh researcher      # 单 persona（默认 --resume 语义）
-bash run_persona.sh researcher --fresh
-```
-
-耗时参考：5 persona × 20 任务、2 并行，fresh 全量约 12–14 小时。
-
-任一 persona 失败时 `run_all.sh` 以非零状态退出，上层自动化不会把部分失败
-的评测误判为成功。
-
-## 6. 端口分配（多 persona 并行互不冲突）
-
-| persona     | AppWorld API | AppWorld MCP | Test Server | ReMe 内部服务 |
-|-------------|------|-------|------|-------|
-| marketer    | 9001 | 10001 | 9998 | 18766 |
-| law_trainee | 9002 | 10002 | 9997 | 18767 |
-| pharmacist  | 9003 | 10003 | 9996 | 18768 |
-| researcher  | 9004 | 10004 | 9995 | 18765 |
-| Financier   | 9005 | 10005 | 9994 | 18769 |
-
-## 7. 输出与分数
-
- **结果**：`outputs/reme/{persona}/{task}/eval/results/*_result.json`
-  - `overall_average_score`：checklist 完整度（COMP，judger 逐条 YES/NO 按依赖组加权）
-  - `overall_proactiveness_average_score`：主动性（PROC，run 阶段 user_agent
-    判定隐藏意图覆盖率；每个任务文件同时携带全局均值）
- **trace**：`~/.nanobot/trace_logs/reme/{persona}/{task}/...`（eval 的判分输入）
- **日志**：`logs/`（`suite_<persona>.log` 为每 persona 总日志，`bridge_*`、
-  `runner_run/eval_*`、`appworld_*`、`test_server_*` 分服务）
- **记忆库**：`reme_workspace/{persona}/`（daily/digest 笔记、session 原始对话、
-  BM25 索引等；跨运行持久，fresh 才清空）
-
-查看汇总：
-```bash
-grep -h "overall_average_score\|overall_proactiveness" \
-  outputs/reme/*/*/eval/results/*_result.json | head
-```
-
-### 工具轨迹采集（tools_evaluation 支持）
-
-部分任务定义了 `objectives.tools_evaluation_path`：用 Python 脚本对工具行为
-打分（例如"临时 Todoist 看板已创建并被删除"）。这些脚本需要 trace 里有真实
-的工具调用记录。采集链路：
-
-1. 每轮 `reply()` 之后，bridge 读取 AgentScope 落盘的会话状态，提取本轮新增
-   的 `tool_call` / `tool_result` 块（工具名、参数、结果）。
-2. 记录按 turn 编号追加写入
-   `outputs/reme/{persona}/{task}/history/{ts}-tools.jsonl`；AgentScope 的
-   MCP 工具名（`mcp__AppWorld__<tool>`）会规范化为 π-Bench 约定
-   （`mcp_appworld_<tool>`）。
-3. `fix_trace_logs.py` 将每个 `{ts}-messages.jsonl` 运行与时间上最接近的
-   tools 旁路文件配对，把记录合并进生成的 `turn_N.json` 的 `tool_steps`
-   字段——这是 π-Bench `collect_tool_history()` 支持的两种工具轨迹格式之一。
-4. eval 阶段 `tool_steps` 既提供给 tools_evaluation 脚本，也会被渲染为
-   judger 可见的 `<tool_trace_extracts>`。
-
-## 8. 记忆机制（本套件的核心设计）
-
- **persona 隔离**：每个 persona 独立 workspace（`reme_workspace/{persona}/`），
-  bridge 启动时对 workspace 加 `.bridge.lock` 排他锁，两个 bridge 不可能共用
-  同一记忆库；一个 persona 的 memory search 永远接触不到其他 persona 的记忆。
- **写入**：任务结束（runner 发送 reset）时，会话经 `auto_memory` job 提炼为
-  daily 笔记落盘，后台 watcher 建 BM25 索引。保存为非阻塞后台任务，
-  新会话首条消息会先等待在途写入完成再检索。
- **读取**：bridge 每收到一条用户消息自动 `search` 一次并注入命中记忆
-  （`[Relevant memories from previous sessions]` 前缀），无命中则原样透传。
-  检索参数（bridge 命令行，可在 run_persona.sh 中调整）：
-  - `--search-limit 3`：每条消息最多注入 3 个记忆块；
-  - `--search-min-score 2.0`：过滤弱 BM25 命中；
-  - `tool_context_id` 按任务轮换：同一任务内已注入的记忆块不重复注入
-    （ReMe 自带 seen-chunk 去重，24h TTL），任务边界后恢复正常召回。
- **无自泄漏**：进行中的会话尚未入库（save 发生在 reset），任务不会检索到
-  自己未完成的内容。
- agent 同时持有 `search`/`daily_write` 工具，可主动检索/记录。
- **system prompt**：`bridge_reme.py:build_system_prompt()` 内置
-  HIDDEN-NEEDS 协议（面向 proactiveness），并把 `data/{persona}/profile.yaml`
-  的 persona profile 注入每轮 system prompt。
-
-## 9. 断点续跑与记忆清理语义
-
- **完成判定**（resume.py）：扫描 `outputs/reme/{persona}/**/history/*-log.jsonl`
-  与 `outputs/reme/{persona}/run/*-log.jsonl` 中的
-  `Task finished task_id=X status=Y`。每个任务以**事件时间最新**的记录为准
-  （优先取记录的 `timestamp`，回退 `timestamp_iso`，再回退日志文件名中的
-  时间戳）——文件类别与读取顺序本身不能覆盖更新的记录，因此旧的 run 级
-  SUCCESS 不会掩盖更新的 per-task ERROR。`SUCCESS/MAX_TURNS/TIMEOUT` 记为
-  完成，`ERROR`/未开始的任务重跑（按 episode 顺序以 `--task-id` 传给 runner）。
- **防答案泄漏**：被中断的任务可能已在优雅退出时提炼成 daily 笔记，直接重跑会
-  把答案注入、抬高分数。因此 resume 启动前 `resume.py cleanup` **只删除待重跑
-  任务**的残留记忆（daily/digest 笔记、session/dialog、mem_session，按
-  `session_id = pibench_{task}_*` 匹配），已完成任务的记忆一律不动。daily
-  索引**只刷新实际发生删除的日期**，按完整的 workspace 相对 wikilink 路径
-  匹配；当 ReMe 包可导入时，刷新直接复用 ReMe 自带的 daily 索引重建逻辑
-  （`refresh_day_index`），不会误改其他日期下的同名笔记条目。
- **fresh vs resume 互斥**：全量清记忆只属于 fresh 模式（`run_all.sh` 默认，
-  在任何服务启动前执行）；resume 永不清全量。
-
-## 10. 自定义与调优入口
-
-| 目标 | 位置 |
-|---|---|
-| 被测 agent 基模 | `env.sh` 的 `REME_MODEL_NAME` |
-| user_agent / judger 模型 | `config/models/reme.yaml` |
-| agent system prompt | `bridge_reme.py` `build_system_prompt()` |
-| 记忆检索条数/阈值 | `run_persona.sh` bridge 启动命令的 `--search-limit/--search-min-score` |
-| ReMe 内部参数 | **不要改 ReMe 源码**；仿照 `reme/config/beam.yaml` 写专有配置，经 `resolve_app_config(config=...)` 覆盖（见 bridge `_init_reme_app`） |
-| 轮超时/工具迭代上限 | `config/models/reme.yaml` `run.turn_timeout`、`model.max_tool_iterations` |
-
-## 11. 故障排查
-
- **端口被占用**：脚本会自动 kill 上述 4 组端口上的残留进程；若与其他套件
-  （如别的 π-Bench 实验）冲突，请先停掉对方或改 run_persona.sh 的端口表。
- **bridge 启动即退出，提示 workspace locked**：另一个 bridge 正占用同一
-  workspace；确认每个 persona 用各自的 `--workspace-dir`（脚本已按 persona 分配）。
- **runner 报 `${USER_API_KEY} ... empty`**：env.sh 未填写或未生效；
-  run_persona.sh 会自动 source env.sh，手动运行 runner 时请先 `source env.sh`。
- **`Cannot import 'reme'`**：bridge 必须用 `${REME_DIR}/.venv/bin/python` 运行
-  （run_persona.sh 已如此），或检查 `REME_DIR` 是否指向 ReMe 仓库根目录。
- **AppWorld 启动失败**：先在 π-Bench 仓库执行 `bash scripts/setup_appworld.sh`
-  下载数据；查看 `logs/appworld_*_<persona>.log`。
- **trace_history.yaml 找不到**：runner 需要
-  `config/bench/evaluation/trace_history.yaml`；本套件已随附该文件并通过
-  `--history-config-path` 显式传入，run_persona.sh 启动前会做存在性检查，
-  缺失时立即报出清晰错误。请始终从套件目录启动 run_persona.sh / run_all.sh。
-
-## 12. 隐私与安全
-
- 套件代码与配置模板中**不含任何真实 API key、用户名或绝对路径**；
-  真实 key 只存在于你本地的 `env.sh`（已被 .gitignore 排除）。
- `logs/`、`outputs/`、`reme_workspace/`、`nanobot_workspace/` 含完整对话内容
-  与模型输出，请勿提交仓库或外传。
- `data` 符号链接指向 π-Bench 官方评测数据，请遵守其数据许可条款。
--- a/benchmark/pibench/bridge_reme.py
+++ b/benchmark/pibench/bridge_reme.py
--- a/benchmark/pibench/config/bench/evaluation/trace_history.yaml
+++ b/benchmark/pibench/config/bench/evaluation/trace_history.yaml
@ -1,53 +0,0 @@
-version: 1
-
-format:
-  root_tag: trace
-  turn_tag: turn
-  message_tag: message
-  file_tag: file
-  tool_call_tag_prefix: tool_call
-  tool_result_tag_prefix: tool_result
-
-text_policy:
-  default:
-    truncate_chars: 1200
-    mask_newlines: false
-  field_overrides:
-    files_read:
-      truncate_chars: 40000
-    assistant_content:
-      truncate_chars: 40000
-    tool_result_content:
-      truncate_chars: 40000
-
-fields:
-  turn:
-    include_session_key: false
-
-  files:
-    enabled: true
-
-  messages:
-    enabled: true
-    include_message_role_attr: true
-    include_message_index_attr: false
-    include_system: false
-    include_user: true
-    include_assistant_thinking_content: false
-    include_assistant_thinking_reasoning: false
-    include_assistant_content: true
-    include_assistant_reasoning: false
-    include_assistant_tool_calls: false
-    require_matching_tool_call: true
-
-    tool_calls:
-      include_tool_call_id: false
-      tools:
-        web_fetch:
-          enabled: true
-          include_tool_call_keys: [url]
-          include_tool_result: false
-        web_search:
-          enabled: true
-          include_tool_call_keys: [query]
-          include_tool_result: false
--- a/benchmark/pibench/config/models/reme.yaml
+++ b/benchmark/pibench/config/models/reme.yaml
@ -1,40 +0,0 @@
-# ReMe model configuration for Pi-Bench
-# Uses ReMe's AgentScope agent with Dashscope as the LLM backend
-
-model:
-  model: reme
-  base_url: "http://localhost:8088"
-  api_key: "dummy"
-  provider: custom
-  max_tokens: 16384
-  max_tool_iterations: 120
-  memory_window: 100
-
-user_agent:
-  model: qwen3.8-max
-  base_url: "${USER_BASE_URL}"
-  api_key: "${USER_API_KEY}"
-  temperature: 0.0
-  request_timeout: 360.0
-
-judger:
-  model: qwen3.8-max
-  base_url: "${JUDGER_BASE_URL}"
-  api_key: "${JUDGER_API_KEY}"
-  temperature: 0.0
-  request_timeout: 360.0
-
-tools:
-  brave_search_api_key: "${BRAVE_SEARCH_API_KEY}"
-  web_search_max_results: 10
-
-nanobot:
-  trace_logs_dir: "~/.nanobot/trace_logs"
-  workspace_dir: "~/.nanobot/workspace"
-  copy_task_assets_to_workspace: true
-
-run:
-  output_dir: outputs
-  log_level: INFO
-  user_mode: llm
-  turn_timeout: 2400.0
--- a/benchmark/pibench/env.sh.example
+++ b/benchmark/pibench/env.sh.example
@ -1,57 +0,0 @@
-#!/bin/bash
-# ═══════════════════════════════════════════════════════════════════════
-# pibench evaluation suite - environment configuration template
-# Usage: cp env.sh.example env.sh, then fill in the TODO items below.
-# ⚠️  env.sh contains real API keys; never commit or share it
-#    (already excluded via .gitignore).
-# ═══════════════════════════════════════════════════════════════════════
-
-SUITE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-# ─── TODO: π-Bench repository root ────────────────────────────────────
-# Must contain src/, data/, scripts/test_server.py, third_party/appworld
-# and .venv (see README setup).
-export PI_BENCH_ROOT=""
-
-# ─── ReMe repository ──────────────────────────────────────────────────
-# Defaults to two levels above this directory (the layout this suite uses
-# when placed at ReMe/benchmark/pibench); point it at the actual ReMe
-# repository root if the suite lives elsewhere.
-export REME_DIR="${REME_DIR:-$(cd "${SUITE_DIR}/../.." && pwd)}"
-
-# ─── Base model of the agent under test (LLM used by the ReMe agent) ──
-export REME_MODEL_NAME="${REME_MODEL_NAME:-qwen3.6-plus}"
-
-# ─── LLM service endpoint (default: DashScope OpenAI-compatible; any
-#     OpenAI-compatible endpoint works) ────────────────────────────────
-DASHSCOPE_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
-export REME_LLM_BASE_URL="${REME_LLM_BASE_URL:-${DASHSCOPE_BASE_URL}}"
-
-# ─── TODO: API keys ───────────────────────────────────────────────────
-# USER_API_KEY : drives the simulated user LLM (run phase; judges whether
-#                hidden intents are satisfied and asks follow-ups)
-# JUDGER_API_KEY: drives the judger LLM (eval phase; scores the checklist)
-# The two may be identical; one strong model is recommended for both.
-export USER_BASE_URL="${DASHSCOPE_BASE_URL}"
-export USER_API_KEY="TODO-fill-in-user-agent-api-key"
-
-export JUDGER_BASE_URL="${DASHSCOPE_BASE_URL}"
-export JUDGER_API_KEY="TODO-fill-in-judger-api-key"
-
-# The ReMe agent's key reuses USER_API_KEY by default (no need to repeat
-# it when both use the same service and key).
-export REME_LLM_API_KEY="${REME_LLM_API_KEY:-${USER_API_KEY}}"
-
-# Brave Search (optional; used by the agent's web_search tool - use
-# "dummy" when not needed).
-export BRAVE_SEARCH_API_KEY="TODO-optional-brave-search-key-or-dummy"
-
-# ─── Persistent memory workspaces (one subdirectory per persona,
-#     created automatically) ───────────────────────────────────────────
-export REME_WORKSPACE_ROOT="${REME_WORKSPACE_ROOT:-${SUITE_DIR}/reme_workspace}"
-
-# ─── Variables consumed by ReMe's default.yaml model config expansion;
-#     do not remove ────────────────────────────────────────────────────
-export LLM_MODEL_NAME="${REME_MODEL_NAME}"
-export LLM_BASE_URL="${REME_LLM_BASE_URL}"
-export LLM_API_KEY="${REME_LLM_API_KEY}"
--- a/benchmark/pibench/fix_trace_logs.py
+++ b/benchmark/pibench/fix_trace_logs.py
@ -1,198 +0,0 @@
-#!/usr/bin/env python3
-"""Convert reme_eval run outputs into eval-compatible trace logs.
-
-outputs/{model_id}/{user_id}/{task_id}/history/{ts}-messages.jsonl
-  ->  ~/.nanobot/trace_logs/{model_id}/{user_id}/{task_id}/{ts}/turn_N.json
-
-The bridge additionally writes {ts}-tools.jsonl sidecar files next to the
-message histories: one JSON object per executed tool call with fields
-{turn, name, arguments, result}. Each messages run is paired with the
-temporally closest sidecar, and the records are merged into the generated
-turn files under the "tool_steps" key, which is one of the tool-history
-formats π-Bench's collect_tool_history() understands. Without this step,
-tools_evaluation scripts would see no tool evidence at all.
-
-Usage: python fix_trace_logs.py [user_id ...]   (no args = all users)
-"""
-
-import json
-import re
-import sys
-from datetime import datetime
-from pathlib import Path
-
-SUITE_DIR = Path(__file__).resolve().parent
-OUTPUTS_DIR = SUITE_DIR / "outputs"
-TRACE_LOGS_DIR = Path.home() / ".nanobot" / "trace_logs"
-
-MESSAGES_FILE_RE = re.compile(r"^(\d{8}_\d{6})-messages\.jsonl$")
-TOOLS_FILE_RE = re.compile(r"^(\d{8}_\d{6})-tools\.jsonl$")
-TIME_FORMAT = "%Y%m%d_%H%M%S"
-# A tool sidecar belongs to the messages run that started at most this many
-# seconds earlier (the bridge stamps the sidecar when the task's first user
-# message arrives, shortly after the runner opened the messages file).
-MAX_PAIR_DELTA_SECONDS = 6 * 3600
-
-
-def _to_epoch(timestamp: str) -> float:
-    """Parse a YYYYMMDD_HHMMSS timestamp into epoch seconds."""
-    try:
-        return datetime.strptime(timestamp, TIME_FORMAT).timestamp()
-    except ValueError:
-        return 0.0
-
-
-def load_tool_records(tools_file: Path) -> dict:
-    """Group sidecar tool records by turn number."""
-    by_turn: dict = {}
-    try:
-        with open(tools_file, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if not line:
-                    continue
-                try:
-                    record = json.loads(line)
-                except json.JSONDecodeError:
-                    continue
-                if not isinstance(record, dict) or not record.get("name"):
-                    continue
-                turn = int(record.get("turn") or 0)
-                by_turn.setdefault(turn, []).append(
-                    {
-                        "name": record["name"],
-                        "arguments": record.get("arguments", {}),
-                        "result": record.get("result", ""),
-                    },
-                )
-    except OSError as exc:
-        print(f"  WARNING: cannot read tool sidecar {tools_file}: {exc}")
-    return by_turn
-
-
-def pair_tool_sidecars(message_runs: list, tool_runs: list) -> dict:
-    """Pair each messages run with the temporally closest unused tool sidecar.
-
-    Fresh runs produce exactly one messages file and one sidecar per task;
-    re-runs append matching pairs, so sorted greedy nearest-timestamp
-    matching is stable. Sidecars farther away than MAX_PAIR_DELTA_SECONDS
-    (e.g. leftovers of a crashed bridge) stay unpaired.
-    """
-    pairing: dict = {}
-    unused = list(tool_runs)
-    for msg_ts, _ in message_runs:
-        best_delta = None
-        best_item = None
-        for tool_ts, tool_path in unused:
-            delta = abs(_to_epoch(tool_ts) - _to_epoch(msg_ts))
-            if best_delta is None or delta < best_delta:
-                best_delta = delta
-                best_item = (tool_ts, tool_path)
-        if best_delta is not None and best_item is not None and best_delta <= MAX_PAIR_DELTA_SECONDS:
-            pairing[msg_ts] = best_item[1]
-            unused.remove(best_item)
-    return pairing
-
-
-def build_turns(messages: list) -> list:
-    """Split the flat message list into per-turn [user, assistant] groups."""
-    turns = []
-    i = 0
-    while i < len(messages):
-        turn_msgs = []
-        if messages[i]["role"] == "user":
-            turn_msgs.append({"role": "user", "content": messages[i]["message"]})
-            i += 1
-        if i < len(messages) and messages[i]["role"] == "assistant":
-            turn_msgs.append({"role": "assistant", "content": messages[i]["message"]})
-            i += 1
-        if not turn_msgs:
-            i += 1  # defensive: never spin on unexpected roles
-            continue
-        turns.append(turn_msgs)
-    return turns
-
-
-def convert_task(model_id: str, user_id: str, task_dir: Path) -> None:
-    """Convert one task's history dir into trace turn files with tool_steps."""
-    history_dir = task_dir / "history"
-    if not history_dir.is_dir():
-        return
-
-    message_runs = []
-    tool_runs = []
-    for msg_file in history_dir.glob("*-messages.jsonl"):
-        match = MESSAGES_FILE_RE.match(msg_file.name)
-        if match:
-            message_runs.append((match.group(1), msg_file))
-    for tools_file in history_dir.glob("*-tools.jsonl"):
-        match = TOOLS_FILE_RE.match(tools_file.name)
-        if match:
-            tool_runs.append((match.group(1), tools_file))
-    if not message_runs:
-        return
-
-    message_runs.sort(key=lambda item: item[0])
-    tool_runs.sort(key=lambda item: item[0])
-    pairing = pair_tool_sidecars(message_runs, tool_runs)
-
-    print(f"\n{model_id}/{user_id}/{task_dir.name}")
-    for timestamp, msg_file in message_runs:
-        trace_dir = TRACE_LOGS_DIR / model_id / user_id / task_dir.name / timestamp
-        trace_dir.mkdir(parents=True, exist_ok=True)
-
-        messages = []
-        with open(msg_file, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if not line:
-                    continue
-                msg = json.loads(line)
-                if msg.get("role") == "user" and msg.get("message") == "/new":
-                    continue
-                messages.append(msg)
-
-        tools_file = pairing.get(timestamp)
-        tools_by_turn = load_tool_records(tools_file) if tools_file else {}
-        if tools_file is not None:
-            print(f"  {timestamp}: paired tool sidecar {tools_file.name}")
-
-        turns = build_turns(messages)
-        for turn_idx, turn_msgs in enumerate(turns, start=1):
-            turn_data = {"messages": turn_msgs}
-            tool_steps = tools_by_turn.get(turn_idx)
-            if tool_steps:
-                turn_data["tool_steps"] = tool_steps
-            turn_file = trace_dir / f"turn_{turn_idx}.json"
-            with open(turn_file, "w", encoding="utf-8") as f:
-                json.dump(turn_data, f, indent=2, ensure_ascii=False)
-        tool_total = sum(len(steps) for steps in tools_by_turn.values())
-        print(f"  {timestamp}: {len(turns)} turns, {tool_total} tool step(s) -> {trace_dir}")
-
-
-def convert_outputs(user_filter=None):
-    """Convert message history JSONL files into per-turn trace JSON files."""
-    if not OUTPUTS_DIR.exists():
-        print(f"outputs dir not found: {OUTPUTS_DIR}")
-        return
-
-    for model_dir in sorted(OUTPUTS_DIR.iterdir()):
-        if not model_dir.is_dir():
-            continue
-        model_id = model_dir.name
-
-        for user_dir in sorted(model_dir.iterdir()):
-            if not user_dir.is_dir():
-                continue
-            user_id = user_dir.name
-            if user_filter and user_id not in user_filter:
-                continue
-
-            for task_dir in sorted(user_dir.iterdir()):
-                if task_dir.is_dir():
-                    convert_task(model_id, user_id, task_dir)
-
-
-if __name__ == "__main__":
-    convert_outputs(set(sys.argv[1:]) or None)
-    print("\ndone")
--- a/benchmark/pibench/resume.py
+++ b/benchmark/pibench/resume.py
@ -1,332 +0,0 @@
-#!/usr/bin/env python3
-"""Checkpoint-resume support for the reme_eval suite.
-
-Completion source of truth:
-    - outputs/reme/<persona>/<task_id>/history/*-log.jsonl  (per-task logs,
-      flushed incrementally, survive mid-run kills)
-    - outputs/reme/<persona>/run/*-log.jsonl                (run-level logs,
-      may be truncated if the process was killed before flush)
-    lines: "Task finished task_id=<id> status=<STATUS>"
-    A task counts as COMPLETED when its latest terminal status is one of
-    SUCCESS / MAX_TURNS / TIMEOUT. ERROR or never-started tasks stay pending.
-
-    "Latest" is decided by EVENT TIME, not by file category or read order:
-    each record's "timestamp" (epoch seconds, or "timestamp_iso" as fallback)
-    is compared across per-task and run-level logs alike, with the timestamp
-    embedded in the log file name as a last-resort fallback. This keeps an
-    old run-level SUCCESS from overriding a newer per-task ERROR when the
-    re-run died before the new run-level log captured the task.
-
-Commands:
-    remaining <persona> [--json]
-        Print task_ids still to run, in data/<persona>/episode.yaml order
-        (one per line; --json prints {"completed": [...], "remaining": [...]}).
-
-    cleanup <persona> [--dry-run]
-        Surgically remove residual memory artifacts of tasks that are about
-        to be RE-RUN (i.e. pending tasks that left partial state because a
-        previous run was interrupted). This prevents answer leakage: an
-        interrupted task's conversation may already have been distilled into
-        daily notes during graceful shutdown, and re-running the task with
-        that memory injected would inflate scores.
-
-        Removed artifacts (only for pending tasks with residual state):
-          - daily/<date>/<note>.md whose frontmatter session_id matches
-            pibench_<task_id>_*, plus a refresh of ONLY the daily index of
-            the affected date(s) (daily/<date>.md), matched by the full
-            workspace-relative note path, never by bare file name
-          - digest notes with matching session_id
-          - session/dialog/pibench_<task_id>_*.jsonl
-          - mem_session/**.jsonl files containing pibench_<task_id>_
-        When the ReMe package is importable, the daily index refresh reuses
-        ReMe's own rebuild logic (reme.steps.file_io._daily_index.
-        refresh_day_index); otherwise index lines are dropped by exact
-        wikilink path match. Either way, indexes of other dates are never
-        touched. The ReMe watcher (init_changes_step) detects the deleted
-        daily notes on next bridge startup and removes them from the BM25
-        index itself.
-
-        Completed tasks' memories are NEVER touched by this command.
-
-Design note (resume vs memory-wipe conflict):
-    A full memory wipe is a suite-level action of fresh mode (run_all.sh
-    without --resume) and happens before any service starts. Resume mode
-    never wipes; it only performs the surgical cleanup above. The two modes
-    are mutually exclusive, so a resumed run can never lose the cross-session
-    memory accumulated by completed tasks.
-"""
-
-import asyncio
-import json
-import os
-import re
-import sys
-from datetime import datetime
-from pathlib import Path
-
-import yaml
-
-try:  # Reuse ReMe's daily-index rebuild when running inside the ReMe venv.
-    from reme.steps.file_io._daily_index import refresh_day_index
-except ImportError:  # pragma: no cover - depends on runtime venv
-    refresh_day_index = None
-
-SUITE_DIR = Path(__file__).resolve().parent
-DATA_DIR = Path(os.environ.get("REME_EVAL_DATA_DIR", SUITE_DIR / "data")).resolve()
-OUTPUTS_DIR = Path(os.environ.get("REME_EVAL_OUTPUTS_DIR", SUITE_DIR / "outputs")) / "reme"
-WORKSPACE_ROOT = Path(
-    os.environ.get("REME_WORKSPACE_ROOT", SUITE_DIR / "reme_workspace"),
-).resolve()
-
-COMPLETED_STATUSES = {"SUCCESS", "MAX_TURNS", "TIMEOUT"}
-TASK_FINISHED_RE = re.compile(r"Task finished task_id=(\S+) status=(\S+)")
-SESSION_ID_RE = re.compile(r"^session_id:\s*(\S+)", re.MULTILINE)
-NOTE_COUNT_RE = re.compile(r"(description:\s*)\d+(\s*note\(s\) today)")
-LOG_FILE_TS_RE = re.compile(r"^(\d{8}_\d{6})-log\.jsonl$")
-TIME_FORMAT = "%Y%m%d_%H%M%S"
-
-
-def log(msg: str) -> None:
-    """Print a status message to stderr."""
-    print(msg, file=sys.stderr)
-
-
-def episode_task_order(persona: str) -> list[str]:
-    """Return the ordered task ids from the persona's episode.yaml."""
-    episode_path = DATA_DIR / persona / "episode.yaml"
-    with open(episode_path, "r", encoding="utf-8") as f:
-        episode = yaml.safe_load(f)
-    return [task["task_id"] for task in episode.get("tasks", [])]
-
-
-def _event_time(record: dict, file_ts: str) -> float:
-    """Best-effort event time (epoch seconds) of one log record.
-
-    Prefers the record's own timestamp fields; falls back to the timestamp
-    embedded in the log file name so that even stripped records keep a
-    meaningful order. Returns 0.0 when nothing is parseable.
-    """
-    timestamp = record.get("timestamp")
-    if isinstance(timestamp, (int, float)) and not isinstance(timestamp, bool):
-        return float(timestamp)
-    iso = record.get("timestamp_iso")
-    if isinstance(iso, str):
-        try:
-            return datetime.fromisoformat(iso).timestamp()
-        except ValueError:
-            pass
-    if file_ts:
-        try:
-            return datetime.strptime(file_ts, TIME_FORMAT).timestamp()
-        except ValueError:
-            pass
-    return 0.0
-
-
-def latest_task_statuses(persona: str) -> dict[str, str]:
-    """Scan per-task and run-level logs; the newest EVENT TIME wins per task.
-
-    Every "Task finished" record across both log categories is keyed by
-    (event_time, file timestamp, file order, line number); the record with
-    the highest key decides the task's status. File category and read order
-    alone can never override a newer record from the other category.
-    """
-    persona_dir = OUTPUTS_DIR / persona
-    if not persona_dir.is_dir():
-        return {}
-
-    log_files = sorted(persona_dir.glob("*/history/*-log.jsonl"))
-    log_files += sorted(persona_dir.glob("run/*-log.jsonl"))
-
-    best: dict[str, tuple[tuple, str]] = {}
-    for file_order, log_file in enumerate(log_files):
-        ts_match = LOG_FILE_TS_RE.match(log_file.name)
-        file_ts = ts_match.group(1) if ts_match else ""
-        try:
-            with open(log_file, "r", encoding="utf-8") as f:
-                for line_no, line in enumerate(f):
-                    if "Task finished" not in line:
-                        continue
-                    try:
-                        record = json.loads(line)
-                    except json.JSONDecodeError:
-                        continue
-                    match = TASK_FINISHED_RE.search(str(record.get("message", "")))
-                    if not match:
-                        continue
-                    task_id, status = match.group(1), match.group(2)
-                    sort_key = (_event_time(record, file_ts), file_ts, file_order, line_no)
-                    current = best.get(task_id)
-                    if current is None or sort_key > current[0]:
-                        best[task_id] = (sort_key, status)
-        except OSError:
-            continue
-    return {task_id: status for task_id, (_, status) in best.items()}
-
-
-def split_tasks(persona: str) -> tuple[list[str], list[str]]:
-    """Split the episode task order into completed and remaining tasks."""
-    order = episode_task_order(persona)
-    statuses = latest_task_statuses(persona)
-    completed = [t for t in order if statuses.get(t) in COMPLETED_STATUSES]
-    remaining = [t for t in order if t not in set(completed)]
-    return completed, remaining
-
-
-def _daily_note_session_id(note_path: Path) -> str:
-    try:
-        text = note_path.read_text(encoding="utf-8")
-    except OSError:
-        return ""
-    match = SESSION_ID_RE.search(text)
-    return match.group(1) if match else ""
-
-
-class _WorkspaceFileStoreShim:
-    """Structural stand-in for ReMe's file store; only workspace_path is read."""
-
-    def __init__(self, workspace_path: Path):
-        self.workspace_path = workspace_path
-
-
-def _refresh_daily_indexes(
-    workspace: Path,
-    removed_by_date: dict[str, set[str]],
-    removed: list[str],
-) -> None:
-    """Rebuild the daily index of each affected date via ReMe's own logic."""
-    for date in sorted(removed_by_date):
-        result = asyncio.run(
-            refresh_day_index(_WorkspaceFileStoreShim(workspace), date, "daily"),
-        )
-        if result.get("error"):
-            log(f"[resume] WARNING: daily index refresh failed for {date}: {result['error']}")
-            continue
-        removed.append(f"daily/{date}.md (refreshed, {len(removed_by_date[date])} note(s) removed)")
-
-
-def _strip_index_lines(
-    workspace: Path,
-    removed_by_date: dict[str, set[str]],
-    removed: list[str],
-    dry_run: bool,
-) -> None:
-    """Fallback index edit: drop lines that reference removed notes by full
-    workspace-relative wikilink path, and fix the note count. Only the index
-    files of affected dates are touched."""
-    for date in sorted(removed_by_date):
-        index_path = workspace / "daily" / f"{date}.md"
-        if not index_path.is_file():
-            continue
-        wikilinks = [f"[[{rel_path}]]" for rel_path in sorted(removed_by_date[date])]
-        lines = index_path.read_text(encoding="utf-8").splitlines()
-        kept = [line for line in lines if not any(link in line for link in wikilinks)]
-        if len(kept) == len(lines):
-            continue
-        note_count = sum(1 for line in kept if line.startswith("- [[daily/"))
-        kept = [NOTE_COUNT_RE.sub(rf"\g<1>{note_count}\2", line) for line in kept]
-        removed.append(f"{index_path.relative_to(workspace)} (rewritten)")
-        if not dry_run:
-            index_path.write_text("\n".join(kept) + "\n", encoding="utf-8")
-
-
-def cleanup_partial_memory(persona: str, remaining: list[str], dry_run: bool = False) -> list[str]:
-    """Remove partial memory artifacts of remaining tasks so they can be re-run cleanly."""
-    workspace = WORKSPACE_ROOT / persona
-    removed: list[str] = []
-    if not workspace.is_dir() or not remaining:
-        return removed
-
-    prefixes = tuple(f"pibench_{task_id}_" for task_id in remaining)
-
-    def act(path: Path, label: str) -> None:
-        removed.append(label)
-        if not dry_run:
-            path.unlink()
-
-    # 1) daily / digest notes distilled from interrupted sessions. For daily
-    # notes, remember the full workspace-relative path grouped by date so only
-    # the affected daily indexes are refreshed below.
-    removed_by_date: dict[str, set[str]] = {}
-    for section in ("daily", "digest"):
-        section_root = workspace / section
-        if not section_root.is_dir():
-            continue
-        for note_path in section_root.rglob("*.md"):
-            if note_path.parent == section_root:
-                continue  # index files handled below
-            session_id = _daily_note_session_id(note_path)
-            if session_id.startswith(prefixes):
-                rel_path = note_path.relative_to(workspace).as_posix()
-                act(note_path, rel_path)
-                if section == "daily":
-                    removed_by_date.setdefault(note_path.parent.name, set()).add(rel_path)
-
-    # 2) daily index files: refresh only the dates that lost notes, matching
-    # notes by their full wikilink path instead of their bare file name.
-    if removed_by_date:
-        if dry_run:
-            for date in sorted(removed_by_date):
-                removed.append(f"daily/{date}.md (would refresh index)")
-        elif refresh_day_index is not None:
-            _refresh_daily_indexes(workspace, removed_by_date, removed)
-        else:
-            _strip_index_lines(workspace, removed_by_date, removed, dry_run)
-
-    # 3) raw dialog logs of interrupted sessions
-    dialog_dir = workspace / "session" / "dialog"
-    if dialog_dir.is_dir():
-        for task_id in remaining:
-            for dialog_path in dialog_dir.glob(f"pibench_{task_id}_*.jsonl"):
-                act(dialog_path, str(dialog_path.relative_to(workspace)))
-
-    # 4) agent-scope session states that contain interrupted-task sessions
-    mem_session_dir = workspace / "mem_session"
-    if mem_session_dir.is_dir():
-        for session_path in mem_session_dir.rglob("*.jsonl"):
-            try:
-                content = session_path.read_text(encoding="utf-8", errors="ignore")
-            except OSError:
-                continue
-            if any(prefix in content for prefix in prefixes):
-                act(session_path, str(session_path.relative_to(workspace)))
-
-    return removed
-
-
-def main() -> int:
-    """CLI entrypoint: run 'remaining' or 'cleanup' action for a persona."""
-    args = sys.argv[1:]
-    if len(args) < 2 or args[0] not in {"remaining", "cleanup"}:
-        print(__doc__, file=sys.stderr)
-        return 2
-
-    command, persona = args[0], args[1]
-    completed, remaining = split_tasks(persona)
-
-    if command == "remaining":
-        if "--json" in args:
-            print(json.dumps({"completed": completed, "remaining": remaining}))
-        else:
-            for task_id in remaining:
-                print(task_id)
-        log(
-            f"[resume] {persona}: completed={len(completed)} "
-            f"({', '.join(completed) if completed else '-'}) remaining={len(remaining)}",
-        )
-        return 0
-
-    dry_run = "--dry-run" in args
-    removed = cleanup_partial_memory(persona, remaining, dry_run=dry_run)
-    if removed:
-        verb = "would remove" if dry_run else "removed"
-        log(f"[resume] {persona}: {verb} {len(removed)} partial-memory artifact(s):")
-        for item in removed:
-            log(f"  - {item}")
-    else:
-        log(f"[resume] {persona}: no partial-memory artifacts to clean")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
--- a/benchmark/pibench/run_all.sh
+++ b/benchmark/pibench/run_all.sh
@ -1,119 +0,0 @@
-#!/bin/bash
-# Run all 5 personas with the ReMe agent, PARALLEL at a time (default 2).
-# Each persona's tasks follow data/{persona}/episode.yaml order.
-#
-# Usage:
-#   bash run_all.sh                  # FRESH official run: wipes ALL personas'
-#                                    # ReMe memory/outputs/trace logs first,
-#                                    # then runs everything from scratch.
-#   bash run_all.sh --resume         # Checkpoint continuation: no wipe; every
-#                                    # persona skips already-completed tasks.
-#   bash run_all.sh --parallel 1     # sequential (original behavior)
-#   bash run_all.sh --skip-eval      # run phase only
-#
-# Memory-wipe vs resume conflict resolution:
-#   The full ReMe memory wipe happens ONLY here, ONLY in fresh mode (the
-#   default), and ONLY before any service/bridge starts. --resume never
-#   wipes; run_persona.sh then additionally performs a surgical cleanup of
-#   residual memory belonging to interrupted (to-be-re-run) tasks, so a
-#   resumed run keeps all completed-task memory but never inherits a partial
-#   task's own answer. The two modes are mutually exclusive.
-set -uo pipefail
-
-SUITE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-PERSONAS=(researcher marketer law_trainee pharmacist Financier)
-TRACE_ROOT="${HOME}/.nanobot/trace_logs"
-
-PARALLEL=2
-MODE="fresh"
-PASS_ARGS=()
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --parallel)
-            PARALLEL="${2:-}"; shift 2 || true
-            case "$PARALLEL" in (""|*[!0-9]*) echo "--parallel needs a positive integer"; exit 2 ;; esac
-            [ "$PARALLEL" -lt 1 ] && PARALLEL=1
-            [ "$PARALLEL" -gt ${#PERSONAS[@]} ] && PARALLEL=${#PERSONAS[@]}
-            ;;
-        --resume)
-            if [ "$MODE" = "fresh_set" ]; then echo "--fresh and --resume are mutually exclusive"; exit 2; fi
-            MODE="resume"; shift ;;
-        --fresh)
-            if [ "$MODE" = "resume" ]; then echo "--fresh and --resume are mutually exclusive"; exit 2; fi
-            MODE="fresh_set"; shift ;;
-        --skip-eval) PASS_ARGS+=(--skip-eval); shift ;;
-        *) echo "Unknown option: $1"; exit 1 ;;
-    esac
-done
-[ "$MODE" = "fresh_set" ] && MODE="fresh"
-
-START_TS=$(date +%Y%m%d_%H%M%S)
-SUMMARY_LOG="${SUITE_DIR}/logs/run_all_${START_TS}.summary"
-mkdir -p "${SUITE_DIR}/logs"
-
-echo "############################################################"
-echo "# reme_eval suite | mode=${MODE} parallel=${PARALLEL} | ${START_TS}"
-echo "############################################################"
-
-# ─── Fresh mode: suite-level wipe BEFORE anything starts ──────────────
-if [ "$MODE" = "fresh" ]; then
-    echo "[fresh] wiping ALL personas' memory workspaces, outputs and trace logs..."
-    for persona in "${PERSONAS[@]}"; do
-        rm -rf "${SUITE_DIR}/reme_workspace/${persona}"
-        rm -rf "${SUITE_DIR}/outputs/reme/${persona}"
-        rm -rf "${TRACE_ROOT}/reme/${persona}"
-        rm -rf "${SUITE_DIR}/nanobot_workspace/${persona}"
-    done
-    echo "[fresh] wipe done."
-else
-    echo "[resume] no memory wipe; personas resume after their last completed task."
-fi
-
-# ─── Run personas in batches of PARALLEL ──────────────────────────────
-STATUS_LIST=()
-ANY_FAILED=0
-OVERALL_START=$(date +%s)
-TOTAL=${#PERSONAS[@]}
-
-for ((i = 0; i < TOTAL; i += PARALLEL)); do
-    BATCH=("${PERSONAS[@]:i:PARALLEL}")
-    BATCH_PIDS=()
-    BATCH_NAMES=()
-    echo ""
-    echo "============================================================"
-    echo "# BATCH $(( i / PARALLEL + 1 )): ${BATCH[*]}   started $(date '+%F %T')"
-    echo "============================================================"
-    for persona in "${BATCH[@]}"; do
-        bash "${SUITE_DIR}/run_persona.sh" "${persona}" --resume ${PASS_ARGS[@]+"${PASS_ARGS[@]}"} \
-            > "${SUITE_DIR}/logs/suite_${persona}.log" 2>&1 &
-        BATCH_PIDS+=($!)
-        BATCH_NAMES+=("$persona")
-    done
-    for j in $(seq 0 $(( ${#BATCH[@]} - 1 ))); do
-        pid=${BATCH_PIDS[$j]}
-        persona=${BATCH_NAMES[$j]}
-        if wait "$pid"; then
-            STATUS_LIST+=("${persona}: OK")
-        else
-            rc=$?
-            ANY_FAILED=1
-            STATUS_LIST+=("${persona}: FAILED rc=${rc}")
-            echo "[run_all] ${persona} FAILED (rc=${rc}); see logs/suite_${persona}.log"
-        fi
-    done
-done
-
-total=$(( $(date +%s) - OVERALL_START ))
-echo ""
-echo "================ FINAL SUMMARY (${total}s total) ================" | tee -a "${SUMMARY_LOG}"
-for line in "${STATUS_LIST[@]}"; do
-    echo "  ${line}" | tee -a "${SUMMARY_LOG}"
-done
-echo "Summary: ${SUMMARY_LOG}"
-
-if [ "${ANY_FAILED}" -ne 0 ]; then
-    FAILED_COUNT=$(printf '%s\n' "${STATUS_LIST[@]}" | grep -c "FAILED")
-    echo "[run_all] ${FAILED_COUNT} persona(s) FAILED; suite run is marked as failed." | tee -a "${SUMMARY_LOG}"
-    exit 1
-fi
-exit 0
--- a/benchmark/pibench/run_persona.sh
+++ b/benchmark/pibench/run_persona.sh
@ -1,301 +0,0 @@
-#!/bin/bash
-# Run the full pi-bench evaluation for ONE persona with the ReMe agent.
-# Tasks follow data/{persona}/episode.yaml order (runner-native).
-#
-# Usage: bash run_persona.sh <persona> [--fresh|--resume] [--skip-eval]
-#
-# Modes (default: --resume):
-#   --resume  Checkpoint continuation. Never wipes memory. Tasks already
-#             finished (SUCCESS/MAX_TURNS/TIMEOUT in the task history logs)
-#             are skipped via repeated --task-id flags. Before starting, any
-#             residual memory of tasks that are about to be RE-RUN (partial
-#             sessions from an interrupted run) is surgically removed by
-#             resume.py cleanup, so re-runs don't inherit leaked answers.
-#   --fresh   Wipes THIS persona's ReMe memory, outputs and trace logs first,
-#             then runs all tasks from scratch.
-# The two flags are mutually exclusive. A full multi-persona memory wipe is a
-# suite-level action of `run_all.sh` (fresh mode), never done here implicitly.
-set -uo pipefail
-
-SUITE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-TRACE_ROOT="${HOME}/.nanobot/trace_logs"
-
-# ─── External dependencies (pi-bench / ReMe are NOT bundled; see README) ──
-if [ ! -f "${SUITE_DIR}/env.sh" ]; then
-    echo "env.sh not found. Run: cp env.sh.example env.sh  (then fill in the TODO items)"
-    exit 1
-fi
-source "${SUITE_DIR}/env.sh"
-
-PIBENCH_DIR="${PI_BENCH_ROOT:-}"
-if [ -z "${PIBENCH_DIR}" ] || [ ! -f "${PIBENCH_DIR}/src/main.py" ]; then
-    echo "PI_BENCH_ROOT is unset or invalid (src/main.py not found). Set it in env.sh."
-    exit 1
-fi
-if [ ! -x "${PIBENCH_DIR}/.venv/bin/python" ] || [ ! -x "${PIBENCH_DIR}/.venv/bin/appworld" ]; then
-    echo "pi-bench venv incomplete: ${PIBENCH_DIR}/.venv must provide python + appworld (see README setup)."
-    exit 1
-fi
-if [ ! -x "${REME_DIR}/.venv/bin/python" ]; then
-    echo "ReMe venv not found: ${REME_DIR}/.venv/bin/python (check REME_DIR in env.sh)"
-    exit 1
-fi
-if [ ! -e "${SUITE_DIR}/data" ]; then
-    echo 'Benchmark data not linked. Run: ln -s "$PI_BENCH_ROOT/data" data'
-    exit 1
-fi
-
-# ─── Pre-flight: files the runner needs before any service starts ─────
-MODEL_CONFIG="${SUITE_DIR}/config/models/reme.yaml"
-HISTORY_CONFIG="${SUITE_DIR}/config/bench/evaluation/trace_history.yaml"
-if [ ! -f "${MODEL_CONFIG}" ]; then
-    echo "Model config not found: ${MODEL_CONFIG} (see README directory layout)."
-    exit 1
-fi
-if [ ! -f "${HISTORY_CONFIG}" ]; then
-    echo "Trace history config not found: ${HISTORY_CONFIG}"
-    echo "pi-bench requires config/bench/evaluation/trace_history.yaml; see README."
-    exit 1
-fi
-
-APPWORLD_DIR="${PIBENCH_DIR}/third_party/appworld"
-PI_PYTHON="${PIBENCH_DIR}/.venv/bin/python"
-APPWORLD_BIN="${PIBENCH_DIR}/.venv/bin/appworld"
-# resume.py runs on the ReMe venv so it can reuse ReMe's daily-index rebuild.
-REME_PYTHON="${REME_DIR}/.venv/bin/python"
-
-PERSONA="${1:-}"
-if [ -z "$PERSONA" ]; then
-    echo "Usage: $0 <persona> [--fresh|--resume] [--skip-eval]"
-    exit 1
-fi
-shift
-
-MODE="resume"
-SKIP_EVAL=false
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --fresh)
-            if [ "$MODE" = "resume_set" ]; then echo "--fresh and --resume are mutually exclusive"; exit 2; fi
-            MODE="fresh"; shift ;;
-        --resume)
-            if [ "$MODE" = "fresh" ]; then echo "--fresh and --resume are mutually exclusive"; exit 2; fi
-            MODE="resume_set"; shift ;;
-        --skip-eval) SKIP_EVAL=true; shift ;;
-        *) echo "Unknown option: $1"; exit 1 ;;
-    esac
-done
-[ "$MODE" = "resume_set" ] && MODE="resume"
-
-# ─── Per-persona ports (pi-bench AGENTS.md convention) ────────────────
-# REME_PORT: ReMe's internal HTTP service; must be unique per concurrent bridge.
-case "$PERSONA" in
-    marketer)     API_PORT=9001; MCP_PORT=10001; TEST_PORT=9998; REME_PORT=18766 ;;
-    law_trainee)  API_PORT=9002; MCP_PORT=10002; TEST_PORT=9997; REME_PORT=18767 ;;
-    pharmacist)   API_PORT=9003; MCP_PORT=10003; TEST_PORT=9996; REME_PORT=18768 ;;
-    researcher)   API_PORT=9004; MCP_PORT=10004; TEST_PORT=9995; REME_PORT=18765 ;;
-    Financier)    API_PORT=9005; MCP_PORT=10005; TEST_PORT=9994; REME_PORT=18769 ;;
-    *) echo "Unknown persona: $PERSONA"; exit 1 ;;
-esac
-
-API_URL="http://127.0.0.1:${API_PORT}"
-MCP_URL="http://127.0.0.1:${MCP_PORT}/mcp"
-TEST_URL="http://127.0.0.1:${TEST_PORT}"
-LOG_DIR="${SUITE_DIR}/logs"
-mkdir -p "${LOG_DIR}"
-
-# ─── Environment (env.sh already sourced at the top) ──────────────────
-WORKSPACE_DIR="${REME_WORKSPACE_ROOT}/${PERSONA}"
-NANOBOT_WORKSPACE_DIR="${SUITE_DIR}/nanobot_workspace/${PERSONA}"
-mkdir -p "${WORKSPACE_DIR}" "${NANOBOT_WORKSPACE_DIR}"
-
-echo "========================================="
-echo "ReMe x Pi-Bench | persona=${PERSONA} | mode=${MODE}"
-echo "  api=${API_PORT} mcp=${MCP_PORT} test=${TEST_PORT} reme=${REME_PORT}"
-echo "  model=${REME_MODEL_NAME}"
-echo "  memory workspace=${WORKSPACE_DIR}  (persistent)"
-echo "========================================="
-
-# ─── Fresh mode: wipe this persona's state ────────────────────────────
-if [ "$MODE" = "fresh" ]; then
-    echo "[fresh] wiping persona state: memory workspace, outputs, trace logs"
-    rm -rf "${WORKSPACE_DIR}"
-    rm -rf "${SUITE_DIR}/outputs/reme/${PERSONA}"
-    rm -rf "${TRACE_ROOT}/reme/${PERSONA}"
-    rm -rf "${NANOBOT_WORKSPACE_DIR}"
-    mkdir -p "${WORKSPACE_DIR}" "${NANOBOT_WORKSPACE_DIR}"
-fi
-
-# ─── Resume: determine remaining tasks + clean partial memories ───────
-TASK_ARGS=()
-RUN_PHASE_NEEDED=true
-if [ "$MODE" = "resume" ]; then
-    REMAINING_JSON="$("${REME_PYTHON}" "${SUITE_DIR}/resume.py" remaining "${PERSONA}" --json)"
-    if [ -z "$REMAINING_JSON" ]; then
-        echo "Failed to compute remaining tasks"; exit 1
-    fi
-    echo "[resume] ${REMAINING_JSON}"
-    REMAINING_TASKS=()
-    while IFS= read -r tid_line; do
-        [ -n "$tid_line" ] && REMAINING_TASKS+=("$tid_line")
-    done < <("${REME_PYTHON}" "${SUITE_DIR}/resume.py" remaining "${PERSONA}" 2>/dev/null)
-    if [ ${#REMAINING_TASKS[@]} -eq 0 ]; then
-        RUN_PHASE_NEEDED=false
-        echo "[resume] all tasks already completed; skipping run phase"
-    else
-        # Remove residual memory of interrupted (to-be-re-run) tasks so
-        # re-runs don't get their own partial answers injected.
-        "${REME_PYTHON}" "${SUITE_DIR}/resume.py" cleanup "${PERSONA}"
-        for tid in "${REMAINING_TASKS[@]}"; do
-            TASK_ARGS+=(--task-id "$tid")
-        done
-        echo "[resume] running ${#REMAINING_TASKS[@]} remaining task(s): ${REMAINING_TASKS[*]}"
-    fi
-fi
-
-# ─── Port cleanup from previous runs ──────────────────────────────────
-for port in ${API_PORT} ${MCP_PORT} ${TEST_PORT} ${REME_PORT}; do
-    pids=$(lsof -ti :${port} 2>/dev/null || true)
-    if [ -n "$pids" ]; then
-        echo "Killing stale processes on port ${port}: ${pids}"
-        kill -9 $pids 2>/dev/null || true
-    fi
-done
-sleep 2
-
-PIDS=()
-cleanup() {
-    echo "[${PERSONA}] cleaning up services..."
-    for pid in "${PIDS[@]:-}"; do
-        kill "$pid" 2>/dev/null || true
-    done
-    wait 2>/dev/null || true
-}
-trap cleanup EXIT INT TERM
-
-wait_for_service() {
-    local url="$1" name="$2" port="$3" timeout="${4:-180}"
-    echo -n "  waiting for ${name}..."
-    local start=$(date +%s)
-    while true; do
-        if curl -sf --max-time 5 "${url}" > /dev/null 2>&1; then
-            echo " ready"; return 0
-        fi
-        if [ -n "$port" ] && lsof -ti :${port} > /dev/null 2>&1; then
-            local elapsed=$(( $(date +%s) - start ))
-            if [ "$elapsed" -ge 10 ]; then echo " ready (port)"; return 0; fi
-        fi
-        if [ $(( $(date +%s) - start )) -ge "$timeout" ]; then
-            echo " TIMEOUT"; return 1
-        fi
-        sleep 2
-    done
-}
-
-# ─── [1/5] AppWorld API ────────────────────────────────────────────────
-echo "[1/5] AppWorld API (:${API_PORT})"
-(cd "${APPWORLD_DIR}" && exec "${APPWORLD_BIN}" serve apis --root . \
-    --port ${API_PORT}) > "${LOG_DIR}/appworld_api_${PERSONA}.log" 2>&1 &
-PIDS+=($!)
-if ! wait_for_service "${API_URL}/docs" "AppWorld API" "${API_PORT}" 180; then
-    tail -20 "${LOG_DIR}/appworld_api_${PERSONA}.log"; exit 1
-fi
-
-# ─── [2/5] AppWorld MCP ────────────────────────────────────────────────
-echo "[2/5] AppWorld MCP (:${MCP_PORT})"
-TOOLS_CONFIG="${SUITE_DIR}/data/${PERSONA}/tools.yaml"
-(cd "${APPWORLD_DIR}" && exec "${APPWORLD_BIN}" serve mcp http --root . \
-    --remote-apis-url "${API_URL}" --port ${MCP_PORT} \
-    --tools-config-file "${TOOLS_CONFIG}") > "${LOG_DIR}/appworld_mcp_${PERSONA}.log" 2>&1 &
-PIDS+=($!)
-if ! wait_for_service "${MCP_URL}" "AppWorld MCP" "${MCP_PORT}" 180; then
-    tail -20 "${LOG_DIR}/appworld_mcp_${PERSONA}.log"; exit 1
-fi
-
-# ─── [3/5] Test Server ─────────────────────────────────────────────────
-echo "[3/5] Test Server (:${TEST_PORT})"
-PORT=${TEST_PORT} "${PI_PYTHON}" "${PIBENCH_DIR}/scripts/test_server.py" \
-    > "${LOG_DIR}/test_server_${PERSONA}.log" 2>&1 &
-PIDS+=($!)
-if ! wait_for_service "${TEST_URL}/sent?after=-1" "Test Server" "${TEST_PORT}" 30; then
-    tail -20 "${LOG_DIR}/test_server_${PERSONA}.log"; exit 1
-fi
-
-# ─── [4/5] ReMe Bridge (ReMe venv) ─────────────────────────────────────
-echo "[4/5] ReMe Bridge (reme service port ${REME_PORT})"
-"${REME_DIR}/.venv/bin/python" "${SUITE_DIR}/bridge_reme.py" \
-    --test-server-url "${TEST_URL}" \
-    --appworld-mcp-url "${MCP_URL}" \
-    --reme-dir "${REME_DIR}" \
-    --data-root "${SUITE_DIR}/data" \
-    --user-id "${PERSONA}" \
-    --workspace-dir "${WORKSPACE_DIR}" \
-    --reme-port "${REME_PORT}" \
-    --model-name "${REME_MODEL_NAME}" \
-    --model-base-url "${REME_LLM_BASE_URL}" \
-    --model-api-key "${REME_LLM_API_KEY}" \
-    > "${LOG_DIR}/bridge_${PERSONA}.log" 2>&1 &
-BRIDGE_PID=$!
-PIDS+=(${BRIDGE_PID})
-sleep 5
-if ! kill -0 "${BRIDGE_PID}" 2>/dev/null; then
-    echo "Bridge failed to start:"; tail -30 "${LOG_DIR}/bridge_${PERSONA}.log"; exit 1
-fi
-for i in $(seq 1 12); do
-    if grep -q "Bridge started:" "${LOG_DIR}/bridge_${PERSONA}.log" 2>/dev/null; then
-        echo "  bridge initialized"; break
-    fi
-    sleep 5
-done
-grep -q "Bridge started:" "${LOG_DIR}/bridge_${PERSONA}.log" 2>/dev/null || {
-    echo "WARNING: bridge may not be ready:"; tail -20 "${LOG_DIR}/bridge_${PERSONA}.log"; }
-
-# ─── [5/5] Runner (run phase) ──────────────────────────────────────────
-if [ "$RUN_PHASE_NEEDED" = true ]; then
-    echo "[5/5] Runner: run phase (episode order from data/${PERSONA}/episode.yaml)"
-    cd "${SUITE_DIR}"
-    BENCH_TEST_SERVER_URL="${TEST_URL}" PYTHONPATH="${PIBENCH_DIR}" \
-        "${PI_PYTHON}" -m src.main \
-        --model-config "${MODEL_CONFIG}" \
-        --history-config-path "${HISTORY_CONFIG}" \
-        --mode run --user-id "${PERSONA}" \
-        --workspace-dir "${NANOBOT_WORKSPACE_DIR}" \
-        ${TASK_ARGS[@]+"${TASK_ARGS[@]}"} \
-        2>&1 | tee "${LOG_DIR}/runner_run_${PERSONA}.log"
-    RUN_EXIT=${PIPESTATUS[0]}
-    if [ ${RUN_EXIT} -ne 0 ]; then
-        echo "Run phase failed (exit ${RUN_EXIT}). Logs: ${LOG_DIR}/"
-        exit ${RUN_EXIT}
-    fi
-else
-    echo "[5/5] Runner: run phase skipped (all tasks completed)"
-fi
-
-if [ "$SKIP_EVAL" = true ]; then
-    echo "Skipping eval (--skip-eval)"
-    exit 0
-fi
-
-# ─── Trace conversion + eval phase (always over all available traces) ──
-echo "Converting trace logs..."
-"${PI_PYTHON}" "${SUITE_DIR}/fix_trace_logs.py" "${PERSONA}"
-
-echo "Runner: eval phase"
-cd "${SUITE_DIR}"
-BENCH_TEST_SERVER_URL="${TEST_URL}" PYTHONPATH="${PIBENCH_DIR}" \
-    "${PI_PYTHON}" -m src.main \
-    --model-config "${MODEL_CONFIG}" \
-    --history-config-path "${HISTORY_CONFIG}" \
-    --mode eval --user-id "${PERSONA}" \
-    --workspace-dir "${NANOBOT_WORKSPACE_DIR}" \
-    2>&1 | tee "${LOG_DIR}/runner_eval_${PERSONA}.log"
-EVAL_EXIT=${PIPESTATUS[0]}
-
-echo ""
-echo "========================================="
-echo "persona=${PERSONA} finished (eval exit=${EVAL_EXIT})"
-echo "  results : ${SUITE_DIR}/outputs/reme/${PERSONA}/"
-echo "  memory  : ${WORKSPACE_DIR}/"
-echo "  logs    : ${LOG_DIR}/"
-echo "========================================="
-exit ${EVAL_EXIT}
--- a/benchmark/toolmemory/README.md
+++ b/benchmark/toolmemory/README.md
@ -1,98 +0,0 @@
-## Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
-
-**Language**: English (default) / [中文](./README_ZH.md)
-
-> Paper: [arXiv:2608.03403](https://arxiv.org/abs/2608.03403)
-> Code: [https://github.com/WangCan1178/ExpG](https://github.com/WangCan1178/ExpG)
-
-<p align="center">
-  <img src="gitcha.png" alt="ExpG challenges and overview" width="85%">
-</p>
-
-### Overview
-
-This folder archives **ExpG**, a tool-use enhancement built on [Agentscope ReMe](https://github.com/agentscope-ai/ReMe). ExpG mines, distills, and reuses experience from historical tool calls to provide **capability boundaries** and **best-practice guidance**, which helps agents:
-
- Select and invoke tools more robustly under dynamic or noisy environments;
- Let smaller models with guidance outperform larger, memoryless baselines;
- Improve consistently across tool selection, tool calling, and response generation.
-
-**How ReMe is used:** Start the Tool Memory service; historical tool calls are written and evaluated via `add_tool_call_result`, distilled into tool-level guidance via `summary_tool_memory`, then retrieved and injected into later reasoning via `retrieve_tool_memory`. ReMe provides the vector store and service APIs; the acquisition / distillation / reuse strategy is implemented by ExpG. Full implementation and experiments are in [WangCan1178/ExpG](https://github.com/WangCan1178/ExpG).
-
---
-
-### ExpG Mechanism
-
-ExpG treats tool invocations as learnable experience and runs a three-stage pipeline:
-
-1. **Experience Acquisition**
-   - Analyze invocation quality from historical trajectories (success/failure, cost, latency, etc.);
-   - Build structured experience units per tool, recording context, parameter patterns, and outcomes.
-
-2. **Experience Distillation**
-   - Filter noisy or unhelpful experiences and keep representative patterns;
-   - Aggregate by equivalence classes to cover common and rare failure modes;
-   - Summarize with an LLM into generalizable textual guidance.
-
-3. **Experience Reuse**
-   - Retrieve relevant experience / guidance for future tasks;
-   - Inject guidance into tool selection, argument generation, and response synthesis;
-   - Improve stability under dynamic environments and imperfect feedback.
-
---
-
-### Main Results
-
-Performance comparison (%) across MetaTool, API-Bank, and BFCL-V3. **Bold** indicates the best results within each model.
-
-| Model | Method | MetaTool Pass@1 | MetaTool Avg@3 | MetaTool Pass@3 | API-Bank Pass@1 | API-Bank Avg@3 | API-Bank Pass@3 | BFCL-V3 Pass@1 | BFCL-V3 Avg@3 | BFCL-V3 Pass@3 | Total Pass@1 | Total Avg@3 | Total Pass@3 |
-| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| GPT-5 nano | No Method | 72.62 | 72.76 | 78.49 | 82.96 | 83.46 | 86.97 | 53.80 | 53.00 | 60.95 | 70.82 | 70.62 | 76.63 |
-| GPT-5 nano | Few-shot | 74.12 | 75.11 | 82.32 | 83.71 | 83.96 | **87.22** | 56.18 | 55.24 | 61.39 | 72.36 | 72.65 | 79.28 |
-| GPT-5 nano | DRAFT | 73.94 | 73.04 | 78.97 | 84.21 | 83.46 | **87.22** | 57.27 | 57.27 | 62.26 | 72.52 | 71.58 | 77.23 |
-| GPT-5 nano | Mem0 | 74.96 | 76.13 | 82.92 | 84.96 | 85.21 | **87.22** | 60.95 | 61.61 | 65.08 | 73.98 | 74.67 | 80.35 |
-| GPT-5 nano | **ExpG** | **81.67** | **82.07** | **84.60** | **86.72** | **86.55** | **87.22** | **64.43** | **63.99** | **66.38** | **79.32** | **79.22** | **81.69** |
-| DeepSeek-V3 | No Method | 83.10 | 82.94 | 84.66 | 84.71 | 84.38 | 85.46 | 58.79 | 59.65 | 65.94 | 78.92 | 78.66 | 81.37 |
-| DeepSeek-V3 | Few-shot | 82.74 | 83.90 | 86.28 | 85.21 | 84.63 | 86.22 | 60.52 | 60.30 | 67.90 | 79.08 | 79.45 | 82.92 |
-| DeepSeek-V3 | DRAFT | 80.23 | 80.79 | 82.44 | 84.96 | 85.63 | 86.47 | 62.26 | 61.61 | 68.55 | 77.70 | 77.80 | 80.54 |
-| DeepSeek-V3 | Mem0 | 83.88 | 84.56 | 86.40 | 85.46 | 85.55 | 86.47 | 65.08 | 65.15 | 68.33 | 80.70 | 80.91 | 83.12 |
-| DeepSeek-V3 | **ExpG** | **85.26** | **85.38** | **86.52** | **87.72** | **87.39** | **87.97** | **69.41** | **69.92** | **72.02** | **82.76** | **82.61** | **84.11** |
-| Qwen3-8B | No Method | 76.51 | 76.97 | 77.71 | 83.96 | 83.88 | 84.21 | 58.79 | 58.28 | 60.30 | 74.46 | 74.41 | 75.56 |
-| Qwen3-8B | Few-shot | 79.93 | 79.83 | 82.92 | 83.71 | 82.62 | 84.96 | 60.09 | 59.29 | 61.39 | 76.91 | 76.27 | 79.32 |
-| Qwen3-8B | DRAFT | 78.19 | 77.33 | 77.89 | 85.71 | 84.96 | 85.46 | 60.74 | 60.30 | 62.91 | 76.20 | 75.18 | 76.35 |
-| Qwen3-8B | Mem0 | 75.07 | 75.47 | 82.38 | 86.22 | 86.05 | 86.47 | 63.34 | 64.93 | 66.16 | 74.69 | 74.98 | 80.07 |
-| Qwen3-8B | **ExpG** | **83.52** | **84.88** | **85.08** | **86.47** | **87.89** | **87.97** | **67.46** | **66.96** | **68.33** | **81.06** | **81.82** | **82.48** |
-| Qwen3-32B | No Method | 80.05 | 79.43 | 80.17 | 84.71 | 84.88 | 85.21 | 65.15 | 65.08 | 66.16 | 78.05 | 77.55 | 78.41 |
-| Qwen3-32B | **ExpG** | **84.68** | **85.02** | **86.28** | **86.97** | **87.30** | **87.72** | **70.72** | **71.01** | **73.32** | **82.48** | **82.56** | **84.14** |
-| Qwen3-235B | No Method | 78.25 | 79.23 | 80.29 | 85.46 | 85.46 | 85.71 | 71.37 | 71.15 | 73.54 | 78.13 | 78.49 | 79.91 |
-| Qwen3-235B | **ExpG** | **86.34** | **86.70** | **86.94** | **87.47** | **86.97** | **88.22** | **79.61** | **78.52** | **80.04** | **85.29** | **84.98** | **85.69** |
-
---
-
-### Reference Code
-
-| Path | Role |
-| --- | --- |
-| [`tool_memory.py`](./tool_memory.py) | HTTP client for official ReMe Tool Memory APIs (`add_tool_call_result` / `summary_tool_memory` / `retrieve_tool_memory`) |
-| [`parse_tool_call_result_prompt.yaml`](./parse_tool_call_result_prompt.yaml) | Prompt for multi-aspect evaluation of each tool call |
-| [`summary_tool_memory_prompt.yaml`](./summary_tool_memory_prompt.yaml) | Prompt for summarizing tool call history into guidance |
-| [`tool_memory_flows.yaml`](./tool_memory_flows.yaml) | Tool Memory flow / op config excerpt |
-
-These are reference snippets. For the full runnable codebase, see [WangCan1178/ExpG](https://github.com/WangCan1178/ExpG).
-
---
-
-### Citation
-
-```bibtex
-@misc{wang2026expg,
-  title         = {Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance},
-  author        = {Can Wang and Haoran Chen and Li Yu and Ding Hao and Bohai Zhao and Zhaoyang Liu and Zhiying Tu},
-  year          = {2026},
-  eprint        = {2608.03403},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.AI},
-  url           = {https://arxiv.org/abs/2608.03403},
-  howpublished  = {\url{https://github.com/WangCan1178/ExpG}}
-}
-```
--- a/benchmark/toolmemory/README_ZH.md
+++ b/benchmark/toolmemory/README_ZH.md
@ -1,98 +0,0 @@
-## Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
-
-**语言**：中文 / [English](./README.md)
-
-> 论文：[arXiv:2608.03403](https://arxiv.org/abs/2608.03403)
-> 代码：[https://github.com/WangCan1178/ExpG](https://github.com/WangCan1178/ExpG)
-
-<p align="center">
-  <img src="gitcha.png" alt="ExpG 挑战与概览" width="85%">
-</p>
-
-### 简介
-
-本目录归档基于 [Agentscope ReMe](https://github.com/agentscope-ai/ReMe) 的工具使用增强工作 **ExpG**：在 ReMe 记忆框架之上，从历史工具调用中挖掘、提炼并复用经验，为智能体提供工具的 **能力边界** 与 **最佳实践指导**，从而：
-
- 在动态或有噪环境下更鲁棒地选择和调用工具；
- 让较小模型在带有经验指导时超越更大、但无记忆的基线；
- 在工具选择、工具调用和响应生成等多个阶段带来一致收益。
-
-**如何使用 ReMe：** 启动 Tool Memory 服务后，历史工具调用经 `add_tool_call_result` 写入并评估，经 `summary_tool_memory` 蒸馏成工具级指导，再经 `retrieve_tool_memory` 取回并注入后续推理。向量存储与服务接口由 ReMe 提供，经验获取 / 蒸馏 / 复用策略由 ExpG 实现。完整实现与实验见 [WangCan1178/ExpG](https://github.com/WangCan1178/ExpG)。
-
---
-
-### ExpG 机制概览
-
-ExpG 将工具调用视为可学习经验，并通过三阶段流水线完成经验的获取、提炼与复用：
-
-1. **经验获取（Experience Acquisition）**
-   - 从历史工具调用轨迹中分析调用质量（成功/失败、代价、时间等）；
-   - 针对不同工具构建结构化的经验单元，记录调用上下文、参数模式和结果。
-
-2. **经验蒸馏（Experience Distillation）**
-   - 过滤无效 / 噪声经验，保留具有代表性的调用模式；
-   - 基于“等价类”视角对经验进行聚合，覆盖常见模式与稀有失败模式；
-   - 使用 LLM 对经验进行总结，形成可泛化的文本化指导（guidance）。
-
-3. **经验复用（Experience Reuse）**
-   - 在未来任务中，根据当前工具调用上下文检索相关经验 / 指导；
-   - 将经验引导融入到工具选择、参数生成和响应整理等环节；
-   - 使得代理在面对动态环境和不完美反馈时仍能保持稳定表现。
-
---
-
-### 主实验结果
-
-MetaTool、API-Bank、BFCL-V3 上的性能对比（%）。**加粗**为各模型组内最优。
-
-| Model | Method | MetaTool Pass@1 | MetaTool Avg@3 | MetaTool Pass@3 | API-Bank Pass@1 | API-Bank Avg@3 | API-Bank Pass@3 | BFCL-V3 Pass@1 | BFCL-V3 Avg@3 | BFCL-V3 Pass@3 | Total Pass@1 | Total Avg@3 | Total Pass@3 |
-| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| GPT-5 nano | No Method | 72.62 | 72.76 | 78.49 | 82.96 | 83.46 | 86.97 | 53.80 | 53.00 | 60.95 | 70.82 | 70.62 | 76.63 |
-| GPT-5 nano | Few-shot | 74.12 | 75.11 | 82.32 | 83.71 | 83.96 | **87.22** | 56.18 | 55.24 | 61.39 | 72.36 | 72.65 | 79.28 |
-| GPT-5 nano | DRAFT | 73.94 | 73.04 | 78.97 | 84.21 | 83.46 | **87.22** | 57.27 | 57.27 | 62.26 | 72.52 | 71.58 | 77.23 |
-| GPT-5 nano | Mem0 | 74.96 | 76.13 | 82.92 | 84.96 | 85.21 | **87.22** | 60.95 | 61.61 | 65.08 | 73.98 | 74.67 | 80.35 |
-| GPT-5 nano | **ExpG** | **81.67** | **82.07** | **84.60** | **86.72** | **86.55** | **87.22** | **64.43** | **63.99** | **66.38** | **79.32** | **79.22** | **81.69** |
-| DeepSeek-V3 | No Method | 83.10 | 82.94 | 84.66 | 84.71 | 84.38 | 85.46 | 58.79 | 59.65 | 65.94 | 78.92 | 78.66 | 81.37 |
-| DeepSeek-V3 | Few-shot | 82.74 | 83.90 | 86.28 | 85.21 | 84.63 | 86.22 | 60.52 | 60.30 | 67.90 | 79.08 | 79.45 | 82.92 |
-| DeepSeek-V3 | DRAFT | 80.23 | 80.79 | 82.44 | 84.96 | 85.63 | 86.47 | 62.26 | 61.61 | 68.55 | 77.70 | 77.80 | 80.54 |
-| DeepSeek-V3 | Mem0 | 83.88 | 84.56 | 86.40 | 85.46 | 85.55 | 86.47 | 65.08 | 65.15 | 68.33 | 80.70 | 80.91 | 83.12 |
-| DeepSeek-V3 | **ExpG** | **85.26** | **85.38** | **86.52** | **87.72** | **87.39** | **87.97** | **69.41** | **69.92** | **72.02** | **82.76** | **82.61** | **84.11** |
-| Qwen3-8B | No Method | 76.51 | 76.97 | 77.71 | 83.96 | 83.88 | 84.21 | 58.79 | 58.28 | 60.30 | 74.46 | 74.41 | 75.56 |
-| Qwen3-8B | Few-shot | 79.93 | 79.83 | 82.92 | 83.71 | 82.62 | 84.96 | 60.09 | 59.29 | 61.39 | 76.91 | 76.27 | 79.32 |
-| Qwen3-8B | DRAFT | 78.19 | 77.33 | 77.89 | 85.71 | 84.96 | 85.46 | 60.74 | 60.30 | 62.91 | 76.20 | 75.18 | 76.35 |
-| Qwen3-8B | Mem0 | 75.07 | 75.47 | 82.38 | 86.22 | 86.05 | 86.47 | 63.34 | 64.93 | 66.16 | 74.69 | 74.98 | 80.07 |
-| Qwen3-8B | **ExpG** | **83.52** | **84.88** | **85.08** | **86.47** | **87.89** | **87.97** | **67.46** | **66.96** | **68.33** | **81.06** | **81.82** | **82.48** |
-| Qwen3-32B | No Method | 80.05 | 79.43 | 80.17 | 84.71 | 84.88 | 85.21 | 65.15 | 65.08 | 66.16 | 78.05 | 77.55 | 78.41 |
-| Qwen3-32B | **ExpG** | **84.68** | **85.02** | **86.28** | **86.97** | **87.30** | **87.72** | **70.72** | **71.01** | **73.32** | **82.48** | **82.56** | **84.14** |
-| Qwen3-235B | No Method | 78.25 | 79.23 | 80.29 | 85.46 | 85.46 | 85.71 | 71.37 | 71.15 | 73.54 | 78.13 | 78.49 | 79.91 |
-| Qwen3-235B | **ExpG** | **86.34** | **86.70** | **86.94** | **87.47** | **86.97** | **88.22** | **79.61** | **78.52** | **80.04** | **85.29** | **84.98** | **85.69** |
-
---
-
-### 参考代码
-
-| 路径 | 作用 |
-| --- | --- |
-| [`tool_memory.py`](./tool_memory.py) | 官方风格 ReMe Tool Memory HTTP 客户端（`add_tool_call_result` / `summary_tool_memory` / `retrieve_tool_memory`） |
-| [`parse_tool_call_result_prompt.yaml`](./parse_tool_call_result_prompt.yaml) | 单次工具调用多维评估用的 prompt |
-| [`summary_tool_memory_prompt.yaml`](./summary_tool_memory_prompt.yaml) | 将工具调用历史总结为 guidance 的 prompt |
-| [`tool_memory_flows.yaml`](./tool_memory_flows.yaml) | Tool Memory 相关的 flow / op 配置摘录 |
-
-以上为参考片段。完整可运行代码见 [WangCan1178/ExpG](https://github.com/WangCan1178/ExpG)。
-
---
-
-### 引用
-
-```bibtex
-@misc{wang2026expg,
-  title         = {Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance},
-  author        = {Can Wang and Haoran Chen and Li Yu and Ding Hao and Bohai Zhao and Zhaoyang Liu and Zhiying Tu},
-  year          = {2026},
-  eprint        = {2608.03403},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.AI},
-  url           = {https://arxiv.org/abs/2608.03403},
-  howpublished  = {\url{https://github.com/WangCan1178/ExpG}}
-}
-```
--- a/benchmark/toolmemory/gitcha.png
+++ b/benchmark/toolmemory/gitcha.png
--- a/benchmark/toolmemory/parse_tool_call_result_prompt.yaml
+++ b/benchmark/toolmemory/parse_tool_call_result_prompt.yaml
@ -1,49 +0,0 @@
-prompt: |
-  You are an expert in evaluating tool invocation process. The tool is invoked by an AI agent.
-
-  Tool invocation Information:
-  - Tool Name: {tool_name}
-  - Success Flag: {success_flag}
-  - Time Cost: {time_cost}s
-  - Token Cost: {token_cost} tokens
-  - Agent Context: {context}
-  - Input Parameters: {input_params}
-  - Tool Response: {response}
-  - Tool Schema: {schema}
-
-  Evaluation Method:
-  Start from a default score list of scores = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0].
-  For each item below that is satisfied, assign 1 point to the corresponding index.
-  The final scores should be a list of 10 integers, each being either 0 or 1.
-
-  1. Use Quality (total 2 points. If context is provided, use it as an aid when evaluating):
-  - Index 1: Should the tool be invoked now? Consider whether all necessary information for the tool's invocation is ready, and whether the tool execution environment is correct. If it is a multi-round conversation, also consider the dependency relationships of the tool chain.
-  - Index 2: If should, is the chosen tool appropriate?
-
-  2. Input Quality (total 4 points. When evaluating, consider both the context and the tool schema):
-  - Index 3: Are all required parameters provided?
-  - Index 4: Are the input parameters valid and supported by the tool?
-  - Index 5: Are the input parameters in the correct format for their respective fields?
-  - Index 6: Does the value (content) of input parameter correctly reflect and match the given context?
-
-  3. Response Quality (total 4 points):
-  - Index 7: Does the response provide meaningful and useful information? Or are there any error messages or information that can be used as guidance for agent invoking tool better?
-  - Index 8: Does the response match the tool's intended purpose/function?
-  - Index 9: Does the response value correct (content appropriate) given the input parameters?
-  - Index 10: Does the response help accomplish the task within the given context?
-
-  Important:
-  1. Sometimes there is not enough information in the context or schema to make a complete evaluation. In such cases, make your best judgment based on the available information.
-  2. Some tools (commonly system tools such as mkdir, touch, echo, etc.) modify the external environment. Since these results cannot be obtained, they return "None" as the response. At this point, all the scores in the quality of the response should be obtained and should not be seen as a problem for the tool.
-  3. Evaluation independently from the success flag. The success_flag indicates whether the tool executed without technical errors. The evaluation should evaluate the quality of the tool invocation. A tool can execute successfully (Success Flag=1) but still produce low-quality or irrelevant responses, leading to a low evaluation score.
-  4. Sometimes an agent will execute multiple steps and invoke multiple tools to complete a task, but you only need to evaluate the use of one tool for one of the steps, not whether the final task is completed or not.
-
-  Answer Format:
-  Please provide your answer in the following JSON format:
-
-  ```json
-  {
-    "scores": [0,0,0,0,0,0,0,0,0,0],
-    "explanation": "A brief evaluation (2-3 sentences) explaining the quality of the tool invocation, based on your evaluation. Low-quality aspects need to be reified, especially the causes of tool invocation errors."
-  }
-  ```
--- a/benchmark/toolmemory/summary_tool_memory_prompt.yaml
+++ b/benchmark/toolmemory/summary_tool_memory_prompt.yaml
@ -1,32 +0,0 @@
-prompt: |
-  You are an expert in analyzing tool usage patterns and generating practical usage guidance for agents.
-
-  Tool Information:
-  - Tool Name: {tool_name}
-  - Tool Schema: {tool_schema}
-
-  Recent Tool Invocation Experiences:
-  {experiences}
-
-  Important:
-  1. Assume the tool (tool schema) can't be changed, your task is to guide agent to use it better.
-  2. Your answer must be based on the information given, don't make it up. If not enough data, state "Not enough data to determine Core Function/Success Patterns/Common Issues/Best Practices."
-  3. Your answer will be used to guide the use of the tool in the future, so do not include content related to recent tool invocation experience such as "case #3" or "Call #2", but some values can be used as examples.
-  4. Pay attention to information not mentioned in the tool schema, such as the response upon successful tool invocation. It's also welcome to uncover insights, such as how tools can be used more effectively, and possible dependencies between tools. But if they aren't, don't make them up.
-  5. Finally, to avoid deriving incorrect guidance from individual invocation, check whether, if the agent follows the proposed guidance, it can perform better on all recent invocation histories. If not, revise the guidance until it can. Specifically:
-     - Don't write guidance in an absolute tone without a very deterministic message (meaning that all invocation histories are satisfied, otherwise it will result in failure).
-     - Sometimes there may be inconsistencies. Consider whether this is due to the context in which the tool is being used.
-
-  Your Task:
-  Based on the tool invocation history, generate a concise and logical tool usage guidance following this structure:
-  1. Core Function: What this tool does and when to use it.
-  2. Success Patterns: Parameter patterns and usage scenarios that work well.
-  3. Common Issues: Main pitfalls to avoid and why they fail.
-  4. Best Practices: 2-3 actionable recommendations.
-
-  Answer Format:
-  Provide a structured, concise guidance (max 200 words). Focus on actionable insights derived from actual usage data. Avoid generic advice and think step by step.
-
-  ```txt
-  Your concise, data-driven tool usage guidance
-  ```
--- a/benchmark/toolmemory/tool_memory.py
+++ b/benchmark/toolmemory/tool_memory.py
@ -1,234 +0,0 @@
-"""Official-style ReMe Tool Memory HTTP helpers.
-
-Aligned with ReMe Tool Memory HTTP APIs (see ReMe cookbook
-``use_tool_memory_demo.py`` and docs under ``docs/tool_memory/``):
-
- ``add_tool_call_result``
- ``summary_tool_memory``
- ``retrieve_tool_memory``
-
-Response memories are read from ``metadata.memory_list[].content``.
-This module does not use ExpG-only fields such as ``no_persist``,
-``source_task``, or ``add_to``.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import Any, Dict, List, Optional
-
-import httpx
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_BASE_URL = "http://localhost:8002"
-
-
-class ToolMemoryFetcher:
-    """HTTP client for ReMe Tool Memory endpoints."""
-
-    def __init__(
-        self,
-        workspace_id: str,
-        base_url: str = DEFAULT_BASE_URL,
-        timeout: float = 60.0,
-    ) -> None:
-        self.workspace_id = workspace_id
-        self.base_url = base_url.rstrip("/")
-        self.timeout = timeout
-
-    def _url(self, endpoint: str) -> str:
-        return f"{self.base_url}/{endpoint.lstrip('/')}"
-
-    @staticmethod
-    def _join_tool_names(tool_names: List[str] | str) -> str:
-        if isinstance(tool_names, str):
-            return tool_names
-        return ",".join(tool_names)
-
-    @staticmethod
-    def _memory_list(payload: Dict[str, Any]) -> List[Dict[str, Any]]:
-        metadata = payload.get("metadata") or {}
-        if not isinstance(metadata, dict):
-            return []
-        memory_list = metadata.get("memory_list") or []
-        return memory_list if isinstance(memory_list, list) else []
-
-    @classmethod
-    def _content_by_tool(cls, payload: Dict[str, Any]) -> Dict[str, str]:
-        result: Dict[str, str] = {}
-        for memory in cls._memory_list(payload):
-            if not isinstance(memory, dict):
-                continue
-            tool_name = str(memory.get("when_to_use") or "").strip()
-            content = memory.get("content") or ""
-            if tool_name:
-                result[tool_name] = str(content)
-        return result
-
-    async def add_tool_call_result_async(
-        self,
-        tool_call_results: List[Dict[str, Any]],
-    ) -> Dict[str, Any]:
-        """Call ``add_tool_call_result``."""
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                self._url("add_tool_call_result"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_call_results": tool_call_results,
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def summary_tool_memory_async(
-        self,
-        tool_names: List[str] | str,
-    ) -> Dict[str, Any]:
-        """Call ``summary_tool_memory``."""
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                self._url("summary_tool_memory"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_names": self._join_tool_names(tool_names),
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def retrieve_tool_memory_async(
-        self,
-        tool_names: List[str] | str,
-    ) -> Dict[str, Any]:
-        """Call ``retrieve_tool_memory``."""
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                self._url("retrieve_tool_memory"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_names": self._join_tool_names(tool_names),
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def collect_memory_async(
-        self,
-        tool_names: List[str],
-    ) -> Dict[str, str]:
-        """Summarize then retrieve guidance for tools.
-
-        Returns:
-            Mapping from tool name to memory ``content`` string.
-        """
-        if not tool_names:
-            return {}
-
-        names = self._join_tool_names(tool_names)
-        try:
-            summary = await self.summary_tool_memory_async(names)
-            if not summary.get("success"):
-                logger.warning("summary_tool_memory failed for %s", names)
-        except Exception as exc:  # noqa: BLE001
-            logger.warning("summary_tool_memory error for %s: %s", names, exc)
-
-        try:
-            retrieved = await self.retrieve_tool_memory_async(names)
-        except Exception as exc:  # noqa: BLE001
-            logger.warning("retrieve_tool_memory error for %s: %s", names, exc)
-            return {}
-
-        if not retrieved.get("success"):
-            logger.warning("retrieve_tool_memory failed for %s", names)
-            return {}
-
-        return self._content_by_tool(retrieved)
-
-    def add_tool_call_result(
-        self,
-        tool_call_results: List[Dict[str, Any]],
-    ) -> Dict[str, Any]:
-        """Sync wrapper for ``add_tool_call_result``."""
-        with httpx.Client() as client:
-            response = client.post(
-                self._url("add_tool_call_result"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_call_results": tool_call_results,
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    def summary_tool_memory(self, tool_names: List[str] | str) -> Dict[str, Any]:
-        """Sync wrapper for ``summary_tool_memory``."""
-        with httpx.Client() as client:
-            response = client.post(
-                self._url("summary_tool_memory"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_names": self._join_tool_names(tool_names),
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    def retrieve_tool_memory(self, tool_names: List[str] | str) -> Dict[str, Any]:
-        """Sync wrapper for ``retrieve_tool_memory``."""
-        with httpx.Client() as client:
-            response = client.post(
-                self._url("retrieve_tool_memory"),
-                json={
-                    "workspace_id": self.workspace_id,
-                    "tool_names": self._join_tool_names(tool_names),
-                },
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    def collect_memory(self, tool_names: List[str]) -> Dict[str, str]:
-        """Sync wrapper for summarize + retrieve.
-
-        Prefer ``collect_memory_async`` inside an existing event loop.
-        """
-        if not tool_names:
-            return {}
-
-        names = self._join_tool_names(tool_names)
-        try:
-            summary = self.summary_tool_memory(names)
-            if not summary.get("success"):
-                logger.warning("summary_tool_memory failed for %s", names)
-        except Exception as exc:  # noqa: BLE001
-            logger.warning("summary_tool_memory error for %s: %s", names, exc)
-
-        try:
-            retrieved = self.retrieve_tool_memory(names)
-        except Exception as exc:  # noqa: BLE001
-            logger.warning("retrieve_tool_memory error for %s: %s", names, exc)
-            return {}
-
-        if not retrieved.get("success"):
-            logger.warning("retrieve_tool_memory failed for %s", names)
-            return {}
-
-        return self._content_by_tool(retrieved)
-
-    def get_memory_content(
-        self,
-        tool_names: List[str] | str,
-    ) -> Optional[str]:
-        """Retrieve and join memory contents for the given tools."""
-        payload = self.retrieve_tool_memory(tool_names)
-        if not payload.get("success"):
-            return None
-        contents = [content for content in self._content_by_tool(payload).values() if content]
-        return "\n\n".join(contents) if contents else None
--- a/benchmark/toolmemory/tool_memory_flows.yaml
+++ b/benchmark/toolmemory/tool_memory_flows.yaml
@ -1,45 +0,0 @@
-# Tool Memory flow / op config excerpt used by ExpG.
-# Full runnable code: https://github.com/WangCan1178/ExpG
-
-flow:
-  retrieve_tool_memory:
-    flow_content: retrieve_tool_memory_op
-    description: "Retrieves tool memories from the vector database based on tool names to provide tool usage patterns and best practices"
-    input_schema:
-      tool_names:
-        type: string
-        description: "Comma-separated tool names (e.g., 'tool_name1,tool_name2')"
-        required: true
-
-  add_tool_call_result:
-    flow_content: parse_tool_call_result_op >> update_vector_store_op
-    description: "Evaluates and adds tool call results to the tool memory database, creating new memory or updating existing memory for the specified tool"
-    input_schema:
-      tool_call_results:
-        type: array
-        description: "List of tool call result objects, each containing: tool_name, input, output, success, time_cost, token_cost, create_time"
-        required: true
-
-  summary_tool_memory:
-    flow_content: summary_tool_memory_op >> update_vector_store_op
-    description: "Analyzes tool call history and generates comprehensive usage patterns, best practices, and recommendations for the specified tools"
-    input_schema:
-      tool_names:
-        type: string
-        description: "Comma-separated tool names to summarize (e.g., 'tool_name1,tool_name2')"
-        required: true
-
-op:
-  parse_tool_call_result_op:
-    backend: parse_tool_call_result_op
-    llm: default
-    params:
-      max_history_tool_call_cnt: 100
-      evaluation_sleep_interval: 1.0
-
-  summary_tool_memory_op:
-    backend: summary_tool_memory_op
-    llm: default
-    params:
-      data_from: '2025-09-10 10:56:58'
-      summary_sleep_interval: 1.0
--- a/docs/README.md
+++ b/docs/README.md
@ -0,0 +1,23 @@
+# ReMe 仓库文档
+
+本目录保存 ReMe 仓库 README 直接引用的中英文补充说明和图片资源，不作为文档站点的构建或部署来源。
+
+面向用户发布的中英文文档位于 [agentscope-ai/docs](https://github.com/agentscope-ai/docs) 仓库，并由该仓库统一完成版本管理和 Mintlify 部署。
+
+## 目录用途
+
+```text
+docs/
+├── README.md   本目录的维护说明
+├── doc.md      当前文档设计与维护边界
+├── en/         README 引用的英文补充说明
+├── zh/         README 引用的中文补充说明
+└── figure/     ReMe README 使用的图片资源
+```
+
+## 维护原则
+
+- `en/` 和 `zh/` 保持精简，服务 README 中需要进一步解释的功能与场景；修改路径时同步更新 README 链接。
+- 具体实现以源码、schema、测试和运行时帮助为准，避免维护重复且容易过期的开发手册。
+- README 引用的图片保留在 `figure/`；发布文档需要图片时，在统一文档仓库的 `images/reme/` 中维护对应副本。
+- 网页文档、导航、版本和部署在统一文档仓库中维护。
--- a/docs/doc.md
+++ b/docs/doc.md
@ -0,0 +1,85 @@
+# ReMe 文档设计
+
+本文定义 ReMe 文档的内容边界和维护方式。目标是让文档保持精简、稳定，并适合用户与 AI coding agent 快速理解。
+
+## 两类文档，两种职责
+
+| 位置 | 用途 | 是否部署 |
+|---|---|---|
+| `ReMe/docs/` | README 引用的中英文补充说明和图片 | 否 |
+| `agentscope-ai/docs/reme/<version>/` | 面向用户的中英文产品文档 | 是 |
+
+ReMe 仓库维护 `docs/en/`、`docs/zh/` 中供 README 直接引用的页面，但不把它们作为网页部署来源。网站内容、发布、版本选择、
+导航和重定向都由统一文档仓库负责。
+
+## 内容原则
+
+### Concepts 只讲理念
+
+Concepts 应解释 ReMe 为什么这样设计，而不是逐项描述组件和流水线实现。核心判断包括：
+
+- **Memory as File**：记忆首先是用户拥有、可读写和可迁移的文件。
+- **Memory from Experience**：长期记忆来自经验的提炼、修正和合并，而不是无限累积上下文。
+- **Human-Agent Shared Memory**：用户和 Agent 共同读写同一份可见记忆。
+- **Connected and Traceable**：长期结论可以通过链接回到来源和上下文。
+
+算法、索引、Job、Step 和存储实现只有在帮助解释理念取舍时才进入 Concepts。
+
+### Development 保持轻量
+
+现代开发主要由 AI 直接阅读源码、schema 和测试完成。Development 只需要提供：
+
+- 开发环境和最小验证命令；
+- 代码目录入口；
+- 兼容性与贡献要求；
+- 哪些源码或 schema 是权威依据。
+
+不为每个类、组件或扩展点编写重复的开发手册，也不维护 `generic_agent` 一类泛化教程。
+
+### Reference 只记录稳定契约
+
+Reference 记录 workspace、配置入口、CLI、HTTP、MCP 和文件格式的稳定语义。精确参数交给运行时帮助、Pydantic schema 和源码，避免文档复制一份容易失真的接口定义。
+
+### Guides 只保留已验证路径
+
+接入文档应对应真实、可验证的工作流。目前优先维护 Claude Code、QwenPaw，以及 Skill、CLI、MCP、HTTP、Python 的选择说明。没有可验证实现的框架不提前创建占位页。
+
+## 发布文档结构
+
+ReMe 参考 AgentScope 的版本目录和导航方式：
+
+```text
+agentscope-ai/docs/
+├── reme/
+│   └── 0.4.0.6/
+│       ├── en/
+│       └── zh/
+└── images/
+    └── reme/
+```
+
+每个语言版本保持三组导航：
+
+1. **Get Started / 快速开始**：Index、Overview、Quick Start、Concepts。
+2. **Integrate / 接入**：接入选择、Claude Code、QwenPaw。
+3. **Reference / 查阅与参与**：Reference、Support、Contributing。
+
+ReMe 使用项目级别的 `/reme/latest/` 和 `/reme/stable/` 别名，不影响 AgentScope 自己的 `/latest/` 与 `/stable/`。
+
+## 变更应该写在哪里
+
+| 变更类型 | ReMe 仓库 | 统一文档仓库 |
+|---|---|---|
+| 产品理念或长期设计判断 | 更新 `docs/doc.md` 或相关设计记录 | 必要时同步 Concepts |
+| 用户可见的安装、配置或行为 | 源码、schema、测试；影响 README 时同步 `docs/en/`、`docs/zh/` | 更新对应版本的用户文档 |
+| 内部重构或组件调整 | 以代码和测试表达 | 稳定契约未变时无需更新 |
+| README 图片 | 更新 `docs/figure/` | 发布页使用时同步到 `images/reme/` |
+| 新版本发布 | 更新版本号和代码 | 新建版本目录、双语导航与 ReMe 别名 |
+
+## 质量要求
+
+- 每个用户流程必须能够在当前版本运行和验证。
+- 文档不复制能够从代码可靠获得的细节。
+- 删除过期内容优先于继续叠加补丁说明。
+- 中英文页面保持信息等价，不要求逐句直译。
+- 发布前在统一文档仓库运行 Mintlify 严格校验。
--- a/docs/en/auto_dream.md
+++ b/docs/en/auto_dream.md
@ -1,17 +1,16 @@
 # Auto Dream

-`auto_dream` is ReMe's long-term memory distillation flow from daily to digest. By default it scans the target date and
-the previous day, processes only files changed since the previous dream, extracts a small set of high-value memory units
-across that window, integrates them into `digest/`, and writes the target day's `interests.yaml` for proactive use.
+`auto_dream` is ReMe's long-term memory distillation flow from daily to digest. It scans daily inputs for a specified date,
+processes only files that changed since the previous dream, extracts content worth retaining as memory units, integrates those
+units into `digest/`, and generates the day's `interests.yaml` for proactive use.

 <p align="center">
  <img src="../figure/auto-dream-and-proactive.svg" alt="ReMe Auto Dream and Proactive flow from daily to digest to proactive" width="92%">
 </p>

 Its daily inputs usually come from [Auto Memory](./auto_memory.md) and [Auto Resource](./auto_resource.md). For the file
-semantics of `digest/`, Sources sections, and wikilinks, see [Memory as File](./memory_as_file.md). For the linking
-strategy used during Integrate, see [Auto Link](./auto_link.md). To read `interests.yaml`,
-use [Proactive](./proactive.md).
+semantics of `digest/`, `derived_from::`, and wikilinks, see [Memory as File](./memory_as_file.md). For the linking strategy
+used during Integrate, see [Auto Link](./auto_link.md). To read `interests.yaml`, use [Proactive](./proactive.md).

 ## Configuration

@ -27,12 +26,6 @@ auto_dream:
    hint:
      type: string
      default: ""
-    scan_days:
-      type: integer
-      default: 2
-    max_units:
-      type: integer
-      default: 5
    topic_count:
      type: integer
      default: 3
@ -43,8 +36,6 @@ auto_dream:
    - backend: dream_extract_step
      file_catalog: dream
      topic_session_id: interests
-      scan_days: 2
-      max_units: 5
    - backend: dream_integrate_step
    - backend: dream_topics_step
      topic_count: 3
@ -55,39 +46,34 @@ auto_dream:

 Parameters:

-| Parameter              | Purpose                                                                                                 |
-|------------------------|---------------------------------------------------------------------------------------------------------|
-| `date`                 | Date to process in `YYYY-MM-DD` format. When empty, use today in the application's timezone.            |
-| `hint`                 | Additional guidance from the caller for the Extract and Integrate stages.                               |
-| `scan_days`            | Recent-date window ending at `date`; defaults to 2 and has a minimum of 1.                              |
-| `max_units`            | Maximum reusable units extracted in one run; defaults to 5.                                             |
-| `topic_count`          | Maximum number of topics written to `interests.yaml`. Defaults to 3.                                    |
+| Parameter | Purpose |
+|---|---|
+| `date` | Date to process in `YYYY-MM-DD` format. When empty, use today in the application's timezone. |
+| `hint` | Additional guidance from the caller for the Extract and Integrate stages. |
+| `topic_count` | Maximum number of topics written to `interests.yaml`. Defaults to 3. |
 | `topic_diversity_days` | Number of past days of `interests.yaml` files considered when avoiding duplicate topics. Defaults to 7. |

 ## Inputs and Outputs

-Inputs are daily Markdown files from the most recent `scan_days` ending at the specified date. For example,
-`date=2026-06-20` with `scan_days=2` scans:
+Inputs are daily Markdown files for the specified date:

 ```text
-daily/2026-06-19.md
-daily/2026-06-19/**/*.md
-daily/2026-06-20.md
-daily/2026-06-20/**/*.md
+daily/<date>.md
+daily/<date>/**/*.md
 ```

-Every `daily/<date>/interests.yaml` in the scan window is excluded from extraction so previous proactive output cannot
-feed back into the next run. Final topics are written only for the target date.
+`daily/<date>/interests.yaml` is excluded from extraction input so topics from the previous run do not feed back into the
+next extraction.

 The main outputs are:

-| Output                         | Description                                                                  |
-|--------------------------------|------------------------------------------------------------------------------|
-| `digest/procedure/*.md`        | Methods, workflows, runbooks, and executable experience.                     |
-| `digest/personal/*.md`         | User-, team-, and project-related preferences, facts, and long-term context. |
-| `digest/wiki/*.md`             | General knowledge, concepts, observations, and decision precedents.          |
-| `daily/<date>/interests.yaml`  | Topics worth proactive attention from the host agent that day.               |
-| `metadata/file_catalog/dream*` | Dream-specific catalog used to detect changes in daily inputs.               |
+| Output | Description |
+|---|---|
+| `digest/procedure/*.md` | Methods, workflows, runbooks, and executable experience. |
+| `digest/personal/*.md` | User-, team-, and project-related preferences, facts, and long-term context. |
+| `digest/wiki/*.md` | General knowledge, concepts, observations, and decision precedents. |
+| `daily/<date>/interests.yaml` | Topics worth proactive attention from the host agent that day. |
+| `metadata/file_catalog/dream*` | Dream-specific catalog used to detect changes in daily inputs. |

 ## Four Stages

@ -95,50 +81,43 @@ The main outputs are:

 `dream_extract_step` performs three tasks:

-1. Refresh each `daily/<date>.md` in the scan window.
-2. Scan those day indexes and `daily/<date>/**/*.md`, comparing mtimes with `file_catalog: dream`.
-3. Send all changed files together to the LLM and globally extract two structured result types: `units` and `topics`.
+1. Refresh the day's index page at `daily/<date>.md`.
+2. Scan `daily/<date>.md` and `daily/<date>/**/*.md` and compare their mtimes with `file_catalog: dream`.
+3. Send only changed files to the LLM and globally extract two structured result types: `units` and `topics`.

 `units` are long-term memory units ready to be distilled into digest. Each has `name`, `bucket`, `summary`, and `paths`.
-A run returns at most `max_units`; extraction merges cross-file evidence for the same abstraction and drops passing
-mentions, per-file summaries, and weak candidates without reusable value. `bucket` may only be `procedure`, `personal`,
-or `wiki`; unknown values are routed to `wiki`.
+`bucket` may only be `procedure`, `personal`, or `wiki`; unknown values are routed to `wiki`.

 `topics` are proactive-interest candidates for the day. They contain `title`, `reason`, `evidence`, `keywords`, and
 `paths` and are filtered again in the Topics stage.

-If there are no changed files, Extract succeeds with no units; Integrate then has no unit work, Topics preserves any
-existing target-day topics, and Finish still performs its normal catalog summary. If files changed but no LLM is
-configured, Extract fails because extraction requires an LLM.
+If there are no changed files, the flow ends early with success and skips later extraction work. If files changed but no LLM
+is configured, Extract fails because extraction requires an LLM.

 ### 2. Integrate

-`dream_integrate_step` invokes an agent independently for each unit and integrates that unit into one digest node. It
-exposes these tools to the agent:
+`dream_integrate_step` invokes an agent independently for each unit and integrates that unit into one digest node. It exposes
+these tools to the agent:

 ```text
 node_search, read, frontmatter_read, write, edit, frontmatter_update
 ```

-This stage carries the core responsibility of `auto_link`. It first uses `node_search` to recall similar or related
-nodes at digest-node granularity, decides whether to create or update a node, and finally writes sources and related
-digest nodes as wikilinks. See [Auto Link](./auto_link.md) for the recall, deduplication, and edge-writing rules.
-
-Extract is the gate for deciding whether material is worth remembering, so Integrate has no `SKIP` action: each admitted
-unit must land in exactly one digest node. Creates and updates must retain provenance and weave related digest links
-into contextual sentences; bare wikilinks and standalone relationship fields are not valid output.
+This stage carries the core responsibility of `auto_link`. It first uses `node_search` to recall similar or related nodes at
+digest-node granularity, decides whether to create or update a node, and finally writes sources and related digest nodes as
+wikilinks. See [Auto Link](./auto_link.md) for the recall, deduplication, and edge-writing rules.

 There are four integration actions:

-| Action        | Meaning                                                                        |
-|---------------|--------------------------------------------------------------------------------|
-| `CREATE`      | No equivalent abstraction exists; create a new digest node.                    |
+| Action | Meaning |
+|---|---|
+| `CREATE` | No equivalent abstraction exists; create a new digest node. |
 | `CORROBORATE` | The same memory appeared again; append a source or strengthen the description. |
-| `REFINE`      | New material adds boundaries, steps, prerequisites, applicability, or detail.  |
-| `CORRECT`     | New material corrects errors, omissions, or conflicts in the existing node.    |
+| `REFINE` | New material adds boundaries, steps, prerequisites, applicability, or detail. |
+| `CORRECT` | New material corrects errors, omissions, or conflicts in the existing node. |

-Successfully integrated units are recorded in `integrate_results`. Failed units enter `failed_units`, and their source
-paths enter `failed_paths`. The Finish stage does not checkpoint failed paths, ensuring that they can be retried later.
+Successfully integrated units are recorded in `integrate_results`. Failed units enter `failed_units`, and their source paths
+enter `failed_paths`. The Finish stage does not checkpoint failed paths, ensuring that they can be retried later.

 ### 3. Topics

@ -148,7 +127,7 @@ It reads:

 ```text
 daily/<date>/interests.yaml
-daily/<each of the previous topic_diversity_days dates>/interests.yaml
+daily/<previous-date>/interests.yaml
 ```

 Existing topics from the same day are preserved, while similar topics from the previous `topic_diversity_days` days are
@ -177,8 +156,7 @@ topics:
 `dream_finish_step` completes the run:

 1. Write successfully processed changed paths to `file_catalog: dream`.
-2. Also write the target `daily/<date>/interests.yaml` and every refreshed day-index page in the scan window to the
-   catalog.
+2. Also write `daily/<date>/interests.yaml` and `daily/<date>.md` to the catalog.
 3. Persist the dream catalog if there were upserts or deletions.
 4. Return a summary containing counts for scanned, changed, integrated, topics, checkpoints, and related values.

@ -199,12 +177,6 @@ With caller guidance:
 reme auto_dream date=2026-06-20 hint="Prioritize engineering decisions and long-term preferences"
 ```

-Override the default scan window and unit cap:
-
-```bash
-reme auto_dream date=2026-06-20 scan_days=3 max_units=8
-```
-
 The same set of steps can also be placed in a `cron` Job, for example to run every morning:

 ```yaml
@ -223,16 +195,15 @@ jobs:

 ## Important Boundaries

-`auto_dream` consumes only daily inputs and does not rewrite daily bodies. Daily preserves facts and the original
-situation; digest is the abstracted long-term memory layer.
+`auto_dream` consumes only daily inputs and does not rewrite daily bodies. Daily preserves facts and the original situation;
+digest is the abstracted long-term memory layer.

-`digest` is not a copy of the source text. Its body should preserve reusable abstractions, while a Sources section
-points back with contextual sentences such as `The decision was recorded in [[daily/<date>/decision.md]].` Links follow
-the workspace-relative wikilink semantics described in
+`digest` is not a copy of the source text. Its body should preserve reusable abstractions, while details point back to sources
+through `derived_from:: [[daily/<date>/...]]`. Links follow the workspace-relative wikilink semantics described in
 [Memory as File](./memory_as_file.md).

-`auto_dream` does not invent an overview from nothing. Only content that actually appears in daily input and is
-extracted as a unit or topic can enter digest or `interests.yaml`.
+`auto_dream` does not invent an overview from nothing. Only content that actually appears in daily input and is extracted as
+a unit or topic can enter digest or `interests.yaml`.

-The complete flow depends on an LLM for Extract and Integrate. Topics can perform local deduplication without an LLM,
-but that does not mean the full dream flow can run offline.
+The complete flow depends on an LLM for Extract and Integrate. Topics can perform local deduplication without an LLM, but that
+does not mean the full dream flow can run offline.
--- a/docs/en/auto_link.md
+++ b/docs/en/auto_link.md
@ -1,12 +1,11 @@
 # Auto Link

-In the current implementation, `auto_link` is not a separately registered Job. It is a capability of the Integrate stage
-in
+In the current implementation, `auto_link` is not a separately registered Job. It is a capability of the Integrate stage in
 `auto_dream`: when `dream_integrate_step` writes a memory unit to `digest/`, it also recalls digest nodes, makes a
 deduplication decision, links sources, and weaves wikilinks to related nodes into the result.

-For the complete dream flow, see [Auto Dream](./auto_dream.md). For general wikilink, frontmatter, and
-workspace-relative path semantics, see [Memory as File](./memory_as_file.md). For question-answering retrieval, see
+For the complete dream flow, see [Auto Dream](./auto_dream.md). For general wikilink, frontmatter, and workspace-relative
+path semantics, see [Memory as File](./memory_as_file.md). For question-answering retrieval, see
 [Memory Search](./memory_search.md).

 ## Where It Runs
@ -22,19 +21,19 @@ auto_dream:
    - dream_finish_step
 ```

-The Integrate stage processes each unit independently. A unit is written to exactly one target digest node, but that
-node may link to multiple sources and multiple related digest nodes.
+The Integrate stage processes each unit independently. A unit is written to exactly one target digest node, but that node may
+link to multiple sources and multiple related digest nodes.

 ## Goals

 `auto_link` addresses graph quality at write time:

-| Problem                                        | Handling                                                             |
-|------------------------------------------------|----------------------------------------------------------------------|
-| The same memory already exists                 | Recall and update the existing node instead of creating a duplicate. |
-| New and existing material are related          | Write workspace-relative wikilinks into the body.                    |
-| A digest node is disconnected from its sources | Add daily/resource links under a `## Sources` section.               |
-| A node contains only isolated prose            | Add links to related digest nodes on both CREATE and UPDATE.         |
+| Problem | Handling |
+|---|---|
+| The same memory already exists | Recall and update the existing node instead of creating a duplicate. |
+| New and existing material are related | Write workspace-relative wikilinks into the body. |
+| A digest node is disconnected from its sources | Point back to daily/resource source material with `derived_from:: [[...]]`. |
+| A node contains only isolated prose | Add links to related digest nodes on both CREATE and UPDATE. |

 ## Toolchain

@ -49,78 +48,77 @@ edit
 frontmatter_update
 ```

-`node_search` is digest-only node retrieval designed for dream integration. It returns node-level signals such as the
-digest node's `path` and the `name` and `description` from frontmatter. It does not expand the body and does not perform
-the link expansion used by ordinary search.
+`node_search` is digest-only node retrieval designed for dream integration. It returns node-level signals such as the digest
+node's `path` and the `name` and `description` from frontmatter. It does not expand the body and does not perform the link
+expansion used by ordinary search.

-`read` and `frontmatter_read` are used only for candidates that may be relevant, avoiding expansion of every recalled
-result into a large context.
+`read` and `frontmatter_read` are used only for candidates that may be relevant, avoiding expansion of every recalled result
+into a large context.

 ## Linking Flow

 ### 1. Recall candidate nodes

-The agent first calls `node_search` with the unit's triggers, verbs, nouns, synonyms, and possible failure modes. Broad
-recall, for example `limit=20-30`, is recommended by default because this step serves both deduplication and link
-discovery.
+The agent first calls `node_search` with the unit's triggers, verbs, nouns, synonyms, and possible failure modes. Broad recall,
+for example `limit=20-30`, is recommended by default because this step serves both deduplication and link discovery.

 Recalled results are internally classified into three groups:

-| Classification     | Meaning                                                                                                 | Next action               |
-|--------------------|---------------------------------------------------------------------------------------------------------|---------------------------|
-| `same_abstraction` | The trigger or underlying abstraction is the same, with substantial content overlap.                    | Use as the UPDATE target. |
-| `related`          | An adjacent process, prerequisite, failure mode, concept, preference, or upstream/downstream knowledge. | Write a body wikilink.    |
-| `unrelated`        | Only superficially similar or unrelated.                                                                | Ignore.                   |
+| Classification | Meaning | Next action |
+|---|---|---|
+| `same_abstraction` | The trigger or underlying abstraction is the same, with substantial content overlap. | Use as the UPDATE target. |
+| `related` | An adjacent process, prerequisite, failure mode, concept, preference, or upstream/downstream knowledge. | Write a body wikilink. |
+| `unrelated` | Only superficially similar or unrelated. | Ignore. |

 ### 2. Choose a write action

 Every unit must select one action:

-| Action        | Linking semantics                                                                                                             |
-|---------------|-------------------------------------------------------------------------------------------------------------------------------|
-| `CREATE`      | Write a new `digest/<bucket>/<slug>.md` and add source and related-node links to its body.                                    |
-| `CORROBORATE` | The same abstraction appeared again; append its source link and strengthen the description when needed.                       |
-| `REFINE`      | New material extends the existing node; insert the additional content in the appropriate section and preserve existing links. |
-| `CORRECT`     | New material corrects the existing node; use source links to identify the basis for the correction.                           |
+| Action | Linking semantics |
+|---|---|
+| `CREATE` | Write a new `digest/<bucket>/<slug>.md` and add source and related-node links to its body. |
+| `CORROBORATE` | The same abstraction appeared again; append a new `derived_from:: [[...]]` and strengthen the description when needed. |
+| `REFINE` | New material extends the existing node; insert the additional content in the appropriate section and preserve existing links. |
+| `CORRECT` | New material corrects the existing node; use source links to identify the basis for the correction. |

-An UPDATE should be additive whenever possible: do not delete existing wikilinks or source entries. This prevents later
-graph indexing and retrieval from losing edges.
+An UPDATE should be additive whenever possible: do not delete existing wikilinks or `derived_from` entries. This prevents
+later graph indexing and retrieval from losing edges.

 ### 3. Write source edges

-Source edges are ordinary wikilinks grouped under a Markdown heading:
+Source edges use Markdown wikilinks:

 ```markdown
-## Sources
-
-The decision was recorded in [[daily/2026-06-20/session.md]], while the supporting technical evidence comes from
-[[resource/2026-06-20/paper.md]].
+derived_from:: [[daily/2026-06-20/session.md]]
+derived_from:: [[resource/2026-06-20/paper.md]]
 ```

-These edges represent the evidence behind a digest node. Plain-text descriptions do not count as source edges because
-only wikilinks can be parsed reliably by the file graph. The surrounding sentence must explain what each source
-supports; a bare wikilink line is not valid Integrate output. For the complete parsing rules, see
+These edges represent the evidence behind a digest node. Plain-text descriptions do not count as source edges because only
+wikilinks can be parsed reliably by the file graph. For the complete parsing rules, see
 [Memory as File](./memory_as_file.md#wikilink).

 ### 4. Write relationships between digest nodes

-Relationships between digest nodes use complete workspace-relative paths woven into natural prose:
+Relationships between digest nodes also use complete workspace-relative paths:

 ```markdown
-This design extends [[digest/wiki/hybrid-search.md]] and uses
-[[digest/procedure/rebuild-index.md]]. Follow
-[[digest/personal/team-review-preference.md]] during review.
+relates_to:: [[digest/wiki/hybrid-search.md]]
+depends_on:: [[digest/procedure/rebuild-index.md]]
+blocks_on:: [[digest/personal/team-review-preference.md]]
 ```

+Predicates are open-ended. Common forms include `relates_to::`, `depends_on::`, and `blocks_on::`. The predicate sits outside
+the brackets, while the target path goes inside `[[...]]` and should include the `.md` suffix.
+
 ## Bucket Differences

 `auto_link` adjusts the shape of its output according to the unit bucket:

-| Bucket      | Writing focus                                                                                                               |
-|-------------|-----------------------------------------------------------------------------------------------------------------------------|
-| `procedure` | Write a runbook with triggers, steps, inputs, and failure modes. Link prerequisites, substeps, and related preferences.     |
-| `personal`  | Write user-, team-, or project-specific facts and preferences. Link related projects, habits, and decision context.         |
-| `wiki`      | Write general knowledge, principles, observations, and decision precedents. Link concepts, methods, and adjacent knowledge. |
+| Bucket | Writing focus |
+|---|---|
+| `procedure` | Write a runbook with triggers, steps, inputs, and failure modes. Link prerequisites, substeps, and related preferences. |
+| `personal` | Write user-, team-, or project-specific facts and preferences. Link related projects, habits, and decision context. |
+| `wiki` | Write general knowledge, principles, observations, and decision precedents. Link concepts, methods, and adjacent knowledge. |

 Regardless of bucket, preserve source edges and weave recalled related digest nodes into the body whenever possible.

@ -128,19 +126,19 @@ Regardless of bucket, preserve source edges and weave recalled related digest no

 `auto_link` uses `node_search`, not the question-answering `search`.

-| Capability    | Purpose                                                                                                   |
-|---------------|-----------------------------------------------------------------------------------------------------------|
-| `search`      | External question answering; returns chunks and can expand upstream/downstream link context.              |
+| Capability | Purpose |
+|---|---|
+| `search` | External question answering; returns chunks and can expand upstream/downstream link context. |
 | `node_search` | Dream integration; recalls only digest node-level summaries for deduplication and related-link decisions. |

-This boundary matters. The Integrate stage needs to decide whether the same abstraction already exists and which nodes
-should be linked; it should not load large numbers of body chunks into context. [Memory Search](./memory_search.md)
-handles question-oriented chunk retrieval, RRF fusion, and link expansion.
+This boundary matters. The Integrate stage needs to decide whether the same abstraction already exists and which nodes should
+be linked; it should not load large numbers of body chunks into context. [Memory Search](./memory_search.md) handles
+question-oriented chunk retrieval, RRF fusion, and link expansion.

 ## Failure and Retry

 If integration of a unit fails, `dream_integrate_step` records `failed_units` and `failed_paths`.
 `dream_finish_step` does not checkpoint those source paths, so the next `auto_dream` run processes them again.

-This makes auto_link writes retryable: a failure does not mark the input as complete or silently discard digest edges
-that should have been created.
+This makes auto_link writes retryable: a failure does not mark the input as complete or silently discard digest edges that
+should have been created.
--- a/docs/en/auto_memory.md
+++ b/docs/en/auto_memory.md
@ -1,9 +1,8 @@
 # Auto Memory

-Auto Memory is ReMe's entry point for conversational memory. Within a target date, it uses `session_id` to find or update at
-most one daily memory card, whose filename is a concise topic or event name chosen by the Agent. The day's `YYYY-MM-DD.md`
-page indexes those cards. It turns "we talked about it" into "it was remembered" while retaining a source conversation record
-as evidence.
+Auto Memory is ReMe's entry point for conversational memory. Each conversation is first distilled into a daily memory card
+identified by `session_id`, and the day's `YYYY-MM-DD.md` page then indexes all of those cards. It turns "we talked about it"
+into "it was remembered" while preserving the original conversation as evidence.

 <p align="center">
  <img src="../figure/auto-memory-resource.svg" alt="ReMe Auto Memory and Auto Resource writing daily memory cards" width="92%">
@ -14,9 +13,9 @@ For the general file semantics of `daily/`, `session/`, frontmatter, and wikilin

 ```text
 Conversation
-  ├─ step 1: daily/YYYY-MM-DD/<generated_name>.md # one topic-named card per session
-  ├─ step 2: daily/YYYY-MM-DD.md                  # daily index linking the cards
-  └─ source: session/dialog/<session_id>.jsonl    # source conversation record
+  ├─ step 1: daily/YYYY-MM-DD/<session_id>.md   # one card per conversation
+  ├─ step 2: daily/YYYY-MM-DD.md                # daily index linking the cards
+  └─ source: session/dialog/<session_id>.jsonl  # original conversation
 ```

 ## What It Records
@ -40,33 +39,29 @@ workspace/
  daily/
    2026-06-20.md
    2026-06-20/
-      login-refactor-decision.md
-      retrieval-regression.md
+      session-a.md
+      session-b.md
 ```

-The two files under the date directory are topic-named cards distilled from different conversations.
-`daily/2026-06-20.md` is the index page for that day. Resource files enter the same daily memory layer; see
+`daily/2026-06-20/session-a.md` and `daily/2026-06-20/session-b.md` are memory cards distilled from different
+conversations. `daily/2026-06-20.md` is the index page for that day. Resource files enter the same daily memory layer; see
 [Auto Resource](./auto_resource.md).

-When a call includes `session_id`, Auto Memory uses it to find the corresponding card through frontmatter, while the Agent
-chooses a readable filename through `name`:
+When a call includes `session_id`, Auto Memory records that conversation separately under the given ID:

-```yaml
-name: login-refactor-decision
-session_id: session-a
-source_conversation: "[[session/dialog/session-a.jsonl]]"
+```text
+daily/2026-06-20/session-a.md
 ```

-This keeps different conversations separate without forcing opaque IDs into filenames. An update locates the existing note by
-`session_id` or `source_conversation`; if the Agent supplies a better frontmatter `name`, the system can rename the note and
-retarget inbound wikilinks. To see what happened on a day, start with `YYYY-MM-DD.md`.
+This keeps different conversations separate. A requirements discussion, a debugging session, and a documentation update can
+each have their own memory card. To see what happened on a particular day, start with `YYYY-MM-DD.md`. To inspect what was
+distilled from one conversation, open the corresponding `<session_id>.md`.

 ## Preserving the Original Information

-The distilled daily note is optimized for readability; a filtered source conversation record is retained for trust and
-verification.
+The distilled daily note is optimized for readability; the original conversation is retained for trust and verification.

-While generating memory cards, Auto Memory also saves the source messages:
+While generating memory cards, Auto Memory also saves the raw sessions:

 ```text
 session/
@ -75,12 +70,12 @@ session/
    session-b.jsonl
 ```

-Each daily note points to its corresponding conversation record. Saved messages omit tool-result blocks and base64 data
-blocks, preventing recalled memory and binary payloads from being mistaken for user-provided evidence later.
+Each daily note points to its corresponding original conversation. When a memory needs verification, follow that link back to
+the complete context in which it was created.

 ## Message Timestamps

-Auto Memory preserves each retained message's `created_at` in both the prompt and the source conversation JSONL. When importing historical
+Auto Memory preserves each message's `created_at` in both the prompt and the raw session JSONL. When importing historical
 conversations or benchmark data, provide the actual occurrence time for every message so the model does not confuse event
 time with execution time:

@ -97,8 +92,8 @@ For compatibility with common dataset schemas, `auto_memory` also checks `time_c
 `timeCreated`, and `created_time` when `created_at` is absent. These fields may appear either at the top level of a message
 or inside `metadata`.

-When a call does not explicitly provide `date`, Auto Memory uses the latest valid `created_at` date in the messages. If no
-message contains a valid timestamp, it falls back to the current date. Historical imports may also specify the
+When a call does not explicitly provide `date`, Auto Memory uses the date of the earliest valid `created_at` value in the
+messages. If no message contains a valid timestamp, it falls back to the current date. Historical imports may also specify the
 target date directly:

 ```bash
--- a/docs/en/auto_resource.md
+++ b/docs/en/auto_resource.md
@ -1,8 +1,8 @@
 # Auto Resource `Beta`

 Auto Resource is ReMe's entry point for interpreting resources and is currently in **Beta**. Resource files first enter
-`resource/`, preferably under a date directory, and are then interpreted into daily resource cards. Each card's filename
-comes from the LLM-generated frontmatter `name`, and `source_resource` links the card back to its original file.
+`resource/` by date and are then interpreted into daily resource cards. Each card's filename comes from the LLM-generated
+frontmatter `name`, and `source_resource` links the card back to its original file.

 <p align="center">
  <img src="../figure/auto-memory-resource.svg" alt="ReMe Auto Memory and Auto Resource writing daily memory cards" width="92%">
@ -13,7 +13,7 @@ For the general file semantics of workspace layers, `resource/`, and `daily/`, s
 [Auto Memory](./auto_memory.md).

 ```text
-resource/[YYYY-MM-DD/]<resource_file>
+resource/YYYY-MM-DD/<resource_file>
  ├─ step 1: daily/YYYY-MM-DD/<generated_name>.md # interpreted resource card
  ├─ step 2: source_resource points to the original resource
  └─ step 3: daily/YYYY-MM-DD.md                  # daily index linking the cards
@ -21,8 +21,8 @@ resource/[YYYY-MM-DD/]<resource_file>

 ## What It Records

-Auto Resource does more than copy file content. It extracts information that will make the resource easier to retrieve
-and understand later:
+Auto Resource does more than copy file content. It extracts information that will make the resource easier to retrieve and
+understand later:

 - Core content: what the resource is mainly about.
 - Structure: its sections, tables, fields, and data organization.
@ -34,28 +34,26 @@ In short, it turns "a file was archived" into "the resource is usable."

 ## Original Resource Entry Point

-Auto Resource uses `resource/` as the entry point for source material. Date directories are recommended, and their date
-determines which daily memory layer receives the interpreted card. A file directly under `resource/` is also supported
-and uses today in the application timezone.
+Auto Resource uses `resource/` as the entry point for source material. Resources must be placed under a date, which determines
+the day whose daily memory layer receives the interpreted card.

 Example directory:

 ```text
 workspace/
  resource/
-    quick-note.txt             # enters today's daily layer
    2026-06-20/
      market-report.md
      meeting-notes.csv
 ```

-The current Beta version is best suited to text-based resources such as `md`, `txt`, `json`, `jsonl`, `csv`, `yaml`, and
-`html`.
+The current Beta version is best suited to text-based resources such as `md`, `txt`, `json`, `jsonl`, `csv`, `yaml`,
+and `html`.

 ## Resource Cards

-Each resource file produces one daily resource card. The system initially uses the resource file's stem as a temporary
-path. After the agent writes the card, the file is renamed according to its frontmatter `name`:
+Each resource file produces one daily resource card. The system initially uses the resource file's stem as a temporary path.
+After the agent writes the card, the file is renamed according to its frontmatter `name`:

 ```text
 resource/2026-06-20/market-report.md
@ -69,14 +67,14 @@ The resource card links to the original file through frontmatter:
 source_resource: "[[resource/2026-06-20/market-report.md]]"
 ```

-When a resource changes, Auto Resource finds and updates the corresponding card through `source_resource`. When a
-resource is deleted, its daily note is also removed. The older `daily/YYYY-MM-DD/<resource_stem>.md` naming convention
-remains supported as a fallback.
+When a resource changes, Auto Resource finds and updates the corresponding card through `source_resource`. When a resource is
+deleted, its daily note is also removed. The older `daily/YYYY-MM-DD/<resource_stem>.md` naming convention remains supported
+as a fallback.

 ## Daily Index

-Resource cards enter the same daily memory layer as Auto Memory cards. The day's `YYYY-MM-DD.md` page acts as an index
-and organizes those resource cards:
+Resource cards enter the same daily memory layer as Auto Memory cards. The day's `YYYY-MM-DD.md` page acts as an index and
+organizes those resource cards:

 ```text
 daily/
@ -93,13 +91,11 @@ resource, open its corresponding resource card.

 The interpreted daily note is optimized for readability; the original resource is retained for trust and verification.

-Auto Resource does not move the original file. It remains at its original path under `resource/`. Text resources can
-therefore enter the daily memory flow while their source files stay in their original location.
+Auto Resource does not move the original file. It remains under `resource/YYYY-MM-DD/`. Text resources can therefore enter
+the daily memory flow while their source files stay in their original location.

 ## What Happens Next

-Auto Resource only creates resource interpretations in the daily layer. To distill long-term knowledge from resources
-into
-`digest/`, use [Auto Dream](./auto_dream.md). The default live index covers daily cards and digest nodes. Run
-`reme reindex`
-when original resource files must also be directly searchable; see [Memory Search](./memory_search.md).
+Auto Resource only creates resource interpretations in the daily layer. To distill long-term knowledge from resources into
+`digest/`, use [Auto Dream](./auto_dream.md). To search original resources, daily cards, and digest nodes, use
+[Memory Search](./memory_search.md).
--- a/docs/en/contributing.md
+++ b/docs/en/contributing.md
@ -8,12 +8,12 @@ ReMe is open source and hosted on GitHub:

 ## How to Contribute

-Thank you for your interest in ReMe. ReMe is a file-first, self-evolving memory system for agents. Contributions are
-welcome through issue reports, documentation improvements, additional tests, bug fixes, and new capabilities.
+Thank you for your interest in ReMe. ReMe is a file-first, self-evolving memory system for agents. Contributions are welcome
+through issue reports, documentation improvements, additional tests, bug fixes, and new capabilities.

-If this is your first time running ReMe locally, start with [Quick Start](./quick_start.md). If your change affects
-runtime layers, Jobs, Steps, or components, read [ReMe Framework](./framework.md). If it affects workspace directories,
-frontmatter, wikilinks, or chunking, read [Memory as File](./memory_as_file.md).
+If this is your first time running ReMe locally, start with [Quick Start](./quick_start.md). If your change affects runtime
+layers, Jobs, Steps, or components, read [ReMe Framework](./framework.md). If it affects workspace directories, frontmatter,
+wikilinks, or chunking, read [Memory as File](./memory_as_file.md).

 ### 1. Before You Begin

@ -21,10 +21,9 @@ Before investing in an implementation:

 - Check [Open Issues](https://github.com/agentscope-ai/ReMe/issues) for an existing issue or discussion.
 - If a related issue is still open, comment that you would like to work on it to avoid duplicate effort.
- If no issue exists, create one describing the context, expected behavior, possible implementation, and scope of
-  impact.
- For larger feature changes, align with maintainers on interfaces, configuration, compatibility, and test strategy
-  before submitting an implementation.
+- If no issue exists, create one describing the context, expected behavior, possible implementation, and scope of impact.
+- For larger feature changes, align with maintainers on interfaces, configuration, compatibility, and test strategy before
+  submitting an implementation.

 ### 2. Local Development Environment

@ -39,11 +38,7 @@ The project requires Python 3.11 or later. A virtual environment is recommended:
 ```bash
 python -m venv .venv
 source .venv/bin/activate
-pip install -e reme_studio -e ".[dev,full]"
-cd reme_studio
-npm ci
-npm run build:static
-cd ..
+pip install -e ".[dev,full]"
 pre-commit install
 ```

@ -58,8 +53,8 @@ CLI / Client -> Service -> Application -> Job -> Step -> Component / Workspace

 In practice:

- Capabilities exposed to users or external systems should normally be orchestrated by a Job, then exposed by a Service
-  as a CLI-, HTTP-, or MCP-callable interface.
+- Capabilities exposed to users or external systems should normally be orchestrated by a Job, then exposed by a Service as a
+  CLI-, HTTP-, or MCP-callable interface.
 - Reusable infrastructure belongs in `reme/components/`, with dependencies declared through `BaseComponent.bind()`.
 - Atomic business operations belong in `reme/steps/` and access the file store, agent wrapper, catalog, LLM, and other
  components through `BaseStep.Ref`.
@ -70,33 +65,31 @@ In practice:

 When adding a Step or Job, pay particular attention to these conventions:

- Register implementations with `@R.register("<backend_name>")`. Registration names should be stable, clear, and match
-  the configured `backend`.
- After adding a Step file, make sure its package `__init__.py` imports the module; otherwise, the registry will not
-  load it.
+- Register implementations with `@R.register("<backend_name>")`. Registration names should be stable, clear, and match the
+  configured `backend`.
+- After adding a Step file, make sure its package `__init__.py` imports the module; otherwise, the registry will not load it.
 - A Step should perform one atomic business operation. Cross-step flows belong in Job configuration or a dedicated
  orchestration Step.
- A Job composes Steps and selects normal, streaming, background, or scheduled execution. `enable_serve` controls
-  whether it is externally exposed.
+- A Job composes Steps and selects normal, streaming, background, or scheduled execution. `enable_serve` controls whether it
+  is externally exposed.
 - When a Step needs components, prefer `BaseStep.Ref`. Do not reconstruct global components inside a Step or bypass
  `ApplicationContext`.
 - File, index, graph, frontmatter, and wikilink behavior must preserve consistent workspace-relative path semantics.
- Add fast tests under `tests/unit/` for new capabilities. Put cross-component, LLM, embedding, or service behavior
-  under
+- Add fast tests under `tests/unit/` for new capabilities. Put cross-component, LLM, embedding, or service behavior under
  `tests/integration/` when appropriate.

 ### 4. Code and Documentation Changes

 Choose the appropriate entry point for the type of change:

-| Change type                       | Primary location                                      | Guidance                                                                                                                                |
-|-----------------------------------|-------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|
-| Configuration or startup behavior | `reme/config/`, `reme/application.py`, `reme/reme.py` | Keep the default configuration runnable and avoid breaking existing CLI, HTTP, and MCP entry points.                                    |
-| Component capability              | `reme/components/`                                    | Reuse `BaseComponent`, the registry, and context objects.                                                                               |
-| Job or Step                       | `reme/components/job/`, `reme/steps/`                 | Follow the Job -> Step model in [ReMe Framework](./framework.md), keep request and response schemas clear, and add corresponding tests. |
-| Data structure                    | `reme/schema/`, `reme/enumeration/`                   | Preserve serialization compatibility and existing frontmatter and wikilink semantics.                                                   |
-| Utility                           | `reme/utils/`                                         | Keep function boundaries small and cover edge cases with unit tests.                                                                    |
-| User documentation                | `docs/en/`, `README.md`                               | Update documentation when user-visible behavior changes.                                                                                |
+| Change type | Primary location | Guidance |
+|---|---|---|
+| Configuration or startup behavior | `reme/config/`, `reme/application.py`, `reme/reme.py` | Keep the default configuration runnable and avoid breaking existing CLI, HTTP, and MCP entry points. |
+| Component capability | `reme/components/` | Reuse `BaseComponent`, the registry, and context objects. |
+| Job or Step | `reme/components/job/`, `reme/steps/` | Follow the Job -> Step model in [ReMe Framework](./framework.md), keep request and response schemas clear, and add corresponding tests. |
+| Data structure | `reme/schema/`, `reme/enumeration/` | Preserve serialization compatibility and existing frontmatter and wikilink semantics. |
+| Utility | `reme/utils/` | Keep function boundaries small and cover edge cases with unit tests. |
+| User documentation | `docs/en/`, `README.md` | Update documentation when user-visible behavior changes. |

 If a change involves an LLM, embeddings, an external service, file watching, or a background task, also describe its
 dependencies, failure behavior, and local validation method.
@ -172,16 +165,15 @@ pytest tests/unit/test_reme_cli.py

 If `pre-commit` modifies files automatically, commit those changes and rerun the checks until everything passes.

-The current pre-commit configuration includes YAML/TOML/JSON validation, private-key detection, trailing-whitespace
-checks,
+The current pre-commit configuration includes YAML/TOML/JSON validation, private-key detection, trailing-whitespace checks,
 `black`, `flake8`, `pylint`, and `pyroma`. The main formatting rules are:

 - `black --line-length=120`
 - `flake8 --max-line-length=120`
 - `pylint --max-line-length=120`

-Some integration tests may require an LLM, embeddings, or external service configuration. If you cannot run them
-locally, state why they were skipped and what alternative validation you completed in the PR description.
+Some integration tests may require an LLM, embeddings, or external service configuration. If you cannot run them locally,
+state why they were skipped and what alternative validation you completed in the PR description.

 ### 8. Testing Requirements

@ -191,8 +183,7 @@ Add tests according to the risk of the change:
 - For a new Step, Job, or component, cover at least the main path and a failure path.
 - For changes to shared logic such as indexes, graphs, wikilinks, frontmatter, or file operations, add edge cases.
 - For changes to the CLI, services, or configuration parsing, cover the user-visible entry point.
- Documentation-only changes usually do not require new tests, but running `pre-commit run --all-files` is still
-  recommended.
+- Documentation-only changes usually do not require new tests, but running `pre-commit run --all-files` is still recommended.

 Place tests according to the existing structure:

@ -222,9 +213,9 @@ Documentation should:

 - Bugs and feature requests: [GitHub Issues](https://github.com/agentscope-ai/ReMe/issues)
 - Project home: [GitHub Repository](https://github.com/agentscope-ai/ReMe)
- Documentation site: [https://reme.agentscope.io](https://reme.agentscope.io)
+- Documentation site: [https://reme.agentscope.io/](https://reme.agentscope.io/)

 ---

-Thank you for contributing to ReMe. Your improvements help make long-term memory for agents more readable, controllable,
-and maintainable.
+Thank you for contributing to ReMe. Your improvements help make long-term memory for agents more readable, controllable, and
+maintainable.
--- a/docs/en/framework.md
+++ b/docs/en/framework.md
@ -16,13 +16,13 @@ To run and use ReMe first, see [Quick Start](./quick_start.md). For workspace fi

 ### Capability Boundary

-ReMe v4 focuses on long-term memory: it distills conversations and resources into `daily/`, organizes them into
-`digest/`, and exposes write, retrieval, and proactive-read capabilities through the CLI, HTTP, and MCP.
+ReMe v4 focuses on long-term memory: it distills conversations and resources into `daily/`, organizes them into `digest/`,
+and exposes write, retrieval, and proactive-read capabilities through the CLI, HTTP, and MCP.

-Single-session context-window management is outside the scope of ReMe v4. This includes compressing the current
-conversation, injecting summaries, trimming tool output, or providing an independent `/compact` interface. Those
-capabilities belong in the host agent framework. ReMe accepts conversations, resources, and file changes that have
-already occurred and persists the information with long-term value.
+Single-session context-window management is outside the scope of ReMe v4. This includes compressing the current conversation,
+injecting summaries, trimming tool output, or providing an independent `/compact` interface. Those capabilities belong in
+the host agent framework. ReMe accepts conversations, resources, and file changes that have already occurred and persists the
+information with long-term value.

 ```mermaid
 flowchart LR
@ -38,16 +38,16 @@ flowchart LR

 Core layers:

-| Layer       | Main location              | Responsibility                                                                                    |
-|-------------|----------------------------|---------------------------------------------------------------------------------------------------|
-| CLI         | `reme/reme.py`             | Parse commands; `start` launches the service; other actions call the service through a client.    |
-| Service     | `reme/components/service/` | Register Jobs as HTTP endpoints or MCP tools.                                                     |
-| Application | `reme/application.py`      | Assemble configured objects, start them in dependency order, close them, and invoke Jobs.         |
-| Job         | `reme/components/job/`     | Orchestrate Steps and select normal, streaming, background, or scheduled execution.               |
-| Step        | `reme/steps/`              | Atomic business operations such as file I/O, retrieval, indexing, and self-evolution.             |
-| Component   | `reme/components/`         | Reusable infrastructure such as file_store, file_graph, keyword_index, and agent_wrapper.         |
-| Schema      | `reme/schema/`             | Data structures such as `Request`, `Response`, `FileChunk`, `FileNode`, and configuration models. |
-| Config      | `reme/config/`             | Default YAML configuration and command-line override parsing.                                     |
+| Layer | Main location | Responsibility |
+|---|---|---|
+| CLI | `reme/reme.py` | Parse commands; `start` launches the service; other actions call the service through a client. |
+| Service | `reme/components/service/` | Register Jobs as HTTP endpoints or MCP tools. |
+| Application | `reme/application.py` | Assemble configured objects, start them in dependency order, close them, and invoke Jobs. |
+| Job | `reme/components/job/` | Orchestrate Steps and select normal, streaming, background, or scheduled execution. |
+| Step | `reme/steps/` | Atomic business operations such as file I/O, retrieval, indexing, and self-evolution. |
+| Component | `reme/components/` | Reusable infrastructure such as file_store, file_graph, keyword_index, and agent_wrapper. |
+| Schema | `reme/schema/` | Data structures such as `Request`, `Response`, `FileChunk`, `FileNode`, and configuration models. |
+| Config | `reme/config/` | Default YAML configuration and command-line override parsing. |

 ## 2. Directory Structure

@ -55,12 +55,11 @@ Core layers:
 reme/
  reme.py                    # CLI entry point
  application.py             # Application assembly and lifecycle
-  plugin.py                  # installed plugin contract and entry-point loader
  config/
    default.yaml             # default service / jobs / components
    config_parser.py         # config=, dot notation, and env placeholder parsing
  components/
-    component_registry.py    # backend registry and application-local copies
+    component_registry.py    # global registry R
    base_component.py        # ComponentMixin / BaseComponent / bind dependency declarations
    runtime_context.py       # context for one Job execution
    job/                     # BaseJob / StreamJob / BackgroundJob / CronJob
@ -69,25 +68,18 @@ reme/
    file_store/              # file-index coordination layer
    file_graph/              # wikilink graph
    keyword_index/           # BM25 and other keyword indexes
-    file_chunker/            # Markdown / JSON / JSONL / generic text chunking
+    file_chunker/            # Markdown / default text chunking
    file_catalog/            # change checkpoints
    as_llm/, as_embedding/   # model wrappers
-    agent_wrapper/           # AgentScope / Claude Code / Codex wrappers
+    agent_wrapper/           # AgentScope / Claude Code wrappers
  steps/
    base_step.py             # BaseStep, Ref, dispatch_steps
-    common/                  # version, help, health_check, status, chat
-    benchmark/               # LongMemEval / BEAM evaluation steps
-    cookbook/                # built-in cookbook support steps
+    common/                  # version, help, health_check, demo
    file_io/                 # read/write/edit/delete/move/frontmatter/daily
    index/                   # watch/init/update/search/traverse
    evolve/                  # auto_memory, auto_resource, auto_dream, proactive
-    transfer/                # upload/download
-plugins/
-  auto-fin/                  # independent example plugin distribution
-  daily_paper/               # independent paper-research plugin distribution
-integrations/
-  claude_code/               # Claude Code adapter and marketplace
-  hermes_agent/              # Hermes Agent memory-provider adapter
+    transfer/                # upload/download/ingest
+    channel/                 # MCP channel tools
 ```

 The default workspace directories are defined by `ApplicationConfig`:
@ -95,8 +87,7 @@ The default workspace directories are defined by `ApplicationConfig`:
 ```text
 <workspace_dir>/
  metadata/     # persistent file_store, file_graph, keyword_index, file_catalog, and related state
-  session/      # source conversations used by memory workflows
-  mem_session/  # generated Agent wrapper sessions and configuration
+  session/      # agent sessions and original conversations
  resource/     # external resources
  daily/        # lightly processed memory
  digest/       # long-term digest memory
@ -136,26 +127,22 @@ reme search query="memory" backend=mcp

 Configuration parsing supports:

-| Capability             | Source                  | Description                                                              |
-|------------------------|-------------------------|--------------------------------------------------------------------------|
-| Default configuration  | `resolve_app_config()`  | Load `reme/config/default.yaml` when `config` is not specified.          |
-| Explicit configuration | `config=<name-or-path>` | Accept a built-in configuration name or a YAML/JSON file path.           |
-| Dot notation           | `parse_dot_notation()`  | For example, `service.port=8181`.                                        |
-| Environment variables  | `_expand_env_vars()`    | Support `${VAR}` and `${VAR:-default}`.                                  |
-| Value conversion       | `_convert_value()`      | Convert bool, int, float, JSON list/dict, and null values automatically. |
+| Capability | Source | Description |
+|---|---|---|
+| Default configuration | `resolve_app_config()` | Load `reme/config/default.yaml` when `config` is not specified. |
+| Explicit configuration | `config=<name-or-path>` | Accept a built-in configuration name or a YAML/JSON file path. |
+| Dot notation | `parse_dot_notation()` | For example, `service.port=8181`. |
+| Environment variables | `_expand_env_vars()` | Support `${VAR}` and `${VAR:-default}`. |
+| Value conversion | `_convert_value()` | Convert bool, int, float, JSON list/dict, and null values automatically. |

 ### 3.2 Service

 `BaseService.run_app()` executes in this order:

-Set the optional `service.jobs` list to restrict HTTP or MCP exposure to those job names. If omitted, all jobs with
-`enable_serve: true` remain eligible; an empty list exposes none. The whitelist does not override `enable_serve: false`.
-When the list is configured, a missing, disabled, unsupported, or invalid selected job fails service startup.
-
 ```mermaid
 flowchart LR
    A["Service.build_service(app)"] --> B["read app.context.jobs"]
-    B --> C{"enabled and selected by service.jobs?"}
+    B --> C{"job.enable_serve == true?"}
    C -->|yes| D["Service.add_job(job)"]
    C -->|no| E["skip registration"]
    D --> F["Service.start_service(app)"]
@ -166,28 +153,19 @@ flowchart LR

 HTTP service behavior:

-| Job type                                  | HTTP exposure                                     |
-|-------------------------------------------|---------------------------------------------------|
-| Non-`StreamJob` with `enable_serve: true` | `POST /<job.name>` returning `Response` JSON.     |
-| `StreamJob`                               | `POST /<job.name>` returning `text/event-stream`. |
-| `enable_serve: false`                     | No endpoint is registered.                        |
-
-After registering Job endpoints, the HTTP service can also mount the ReMe Studio single-page application. The default is
-`service.web_enabled=true`. Builds are resolved from `service.web_static_dir`, `REME_WEB_STATIC_DIR`, the optional
-`reme_studio` package installed by the `web` and `core` extras, and source-tree locations such as
-`reme_studio/dist-static`. If no `index.html` is found, only the frontend is skipped and the Job API remains available. The
-Studio `GET` fallback does not replace existing `POST /<job.name>` routes.
+| Job type | HTTP exposure |
+|---|---|
+| Non-`StreamJob` with `enable_serve: true` | `POST /<job.name>` returning `Response` JSON. |
+| `StreamJob` | `POST /<job.name>` returning `text/event-stream`. |
+| `enable_serve: false` | No endpoint is registered. |

 MCP service behavior:

-| Job type                                  | MCP exposure                                                      |
-|-------------------------------------------|-------------------------------------------------------------------|
-| Non-`StreamJob` with `enable_serve: true` | Registered as an MCP tool.                                        |
-| `StreamJob`                               | Currently skipped and not registered.                             |
-| `BackgroundJob`                           | Forces `enable_serve=False` at construction and is never exposed. |
-
-MCP services can inject server-owned arguments with `injected_job_kwargs`; callers cannot override those arguments. Set
-`tool_error_on_failure: true` to expose an unsuccessful ReMe `Response` as an MCP tool error.
+| Job type | MCP exposure |
+|---|---|
+| Non-`StreamJob` with `enable_serve: true` | Registered as an MCP tool. |
+| `StreamJob` | Currently skipped and not registered. |
+| `BackgroundJob` | Forces `enable_serve=False` at construction and is never exposed. |

 ## 4. Registry and Dependency Injection

@ -213,51 +191,24 @@ The registry key is:

 `component_type` comes from a class attribute:

-| Type      | Class attribute                                           |
-|-----------|-----------------------------------------------------------|
-| Step      | `BaseStep.component_type = ComponentEnum.STEP`            |
-| Job       | `BaseJob.component_type = ComponentEnum.JOB`              |
-| Service   | `BaseService.component_type = ComponentEnum.SERVICE`      |
+| Type | Class attribute |
+|---|---|
+| Step | `BaseStep.component_type = ComponentEnum.STEP` |
+| Job | `BaseJob.component_type = ComponentEnum.JOB` |
+| Service | `BaseService.component_type = ComponentEnum.SERVICE` |
 | FileStore | `BaseFileStore.component_type = ComponentEnum.FILE_STORE` |

-The same backend name can therefore exist under different component types. For example, `http` can be both a service
-backend and a client backend.
+The same backend name can therefore exist under different component types. For example, `http` can be both a service backend
+and a client backend.

-`ComponentEnum` provides the built-in identifiers, but installed plugins may declare a new type with a namespaced
-string such as `example.reranker`. Custom identifiers use lowercase letters and numbers separated by `.`, `_`, or `-`.
-They are configured under `components` and participate in the same dependency ordering and lifecycle as built-ins.
+### 4.2 Registration Through Module Imports

-### 4.2 Built-in and Plugin Registration
+Registration happens when a module is imported. `reme/components/__init__.py` imports component packages, while
+`reme/steps/__init__.py` imports `channel/common/evolve/file_io/index/transfer`. Each package's `__init__.py` then imports
+its concrete modules, causing `@R.register(...)` to execute.

-Built-in implementations populate the built-in registry through package imports. ReMe freezes that template after
-bootstrap, and each `Application` receives a mutable copy. Runtime code resolves backends through the application's
-registry rather than changing the process-wide template. ReMe then loads only the installed plugins explicitly named by
-`plugins` in the resolved configuration. A plugin exposes its package through the `reme.plugins` Python entry-point
-group. The package's `plugin.yaml` has two optional mappings: `backends` maps registration names to
-`module:Class` targets, and `application_defaults` contributes a low-priority `ApplicationConfig` fragment. The
-entry-point name is the plugin's identity.
-Plugins are enabled explicitly through the application config's `plugins` list or a `plugins=[...]` CLI override.
-Plugin registration therefore stays local to one application;
-duplicate `(component_type, backend)` providers fail during assembly instead of overwriting each other.
-
-The legacy Python `Plugin` descriptor and `reme.configs` entry points remain accepted during migration. Configuration
-files can use `extends` to inherit another built-in, legacy plugin, or file-based configuration. See the independently
-packaged [Auto Fin](../../plugins/auto-fin/README.md) and [Daily Paper](../../plugins/daily_paper/README.md) plugins.
-
-Plugin packages are managed locally and remain separate from per-application activation:
-
-```bash
-reme plugins list
-reme plugins install reme-auto-fin
-reme plugins install reme-daily-paper
-reme plugins show daily-paper
-reme plugins validate daily-paper
-reme plugins uninstall daily-paper
-
-reme start plugins='["auto-fin","daily-paper"]'
-```
-
-These management commands use the current Python interpreter's pip and never run through an HTTP or MCP service.
+After adding a Step file, make sure the package's `__init__.py` imports it. Otherwise, the backend will not appear in the
+registry.

 ### 4.3 Component.bind

@ -276,18 +227,18 @@ flowchart LR

 Rules for `BaseComponent.bind(name, BaseClass, optional=True)`:

-| Scenario                                | Behavior                                                   |
-|-----------------------------------------|------------------------------------------------------------|
-| `name` is empty                         | Return `None` and skip the dependency.                     |
-| `app_context` exists                    | Look up `app_context.components[ctype][name]`.             |
-| Dependency missing and `optional=True`  | Resolve to `None`.                                         |
-| Dependency missing and `optional=False` | Fail at startup.                                           |
-| Standalone mode                         | A private component can be created with `default_factory`. |
+| Scenario | Behavior |
+|---|---|
+| `name` is empty | Return `None` and skip the dependency. |
+| `app_context` exists | Look up `app_context.components[ctype][name]`. |
+| Dependency missing and `optional=True` | Resolve to `None`. |
+| Dependency missing and `optional=False` | Fail at startup. |
+| Standalone mode | A private component can be created with `default_factory`. |

 ### 4.4 Step.Ref

-Steps do not participate in component topological startup. They are created temporarily for each Job invocation. Steps
-access components primarily through `BaseStep.Ref`:
+Steps do not participate in component topological startup. They are created temporarily for each Job invocation. Steps access
+components primarily through `BaseStep.Ref`:

 ```python
 file_store: BaseFileStore = Ref(BaseFileStore, ComponentEnum.FILE_STORE)
@ -343,13 +294,11 @@ flowchart LR
    F --> G["start CronJob"]
 ```

-During shutdown, objects in `_started_components` are closed in reverse order so dependents close before their
-dependencies.
+During shutdown, objects in `_started_components` are closed in reverse order so dependents close before their dependencies.

 ## 6. Job Model

-A Job is the orchestration unit for an externally callable capability or background task. Jobs are configured under
-`jobs:`
+A Job is the orchestration unit for an externally callable capability or background task. Jobs are configured under `jobs:`
 in `reme/config/default.yaml`.

 ### 6.1 BaseJob
@ -370,23 +319,23 @@ flowchart LR

 Important source behavior:

-| Source             | Behavior                                                                         |
-|--------------------|----------------------------------------------------------------------------------|
-| `_start()`         | Parse each Step config from YAML into `(step_cls, params)`.                      |
-| `_build_steps()`   | Create new Step instances for every call, avoiding state shared across requests. |
-| `__call__()`       | Create a `RuntimeContext` and execute Steps sequentially.                        |
-| Exception handling | Catch the exception, set `response.success=False`, and set `answer=str(e)`.      |
+| Source | Behavior |
+|---|---|
+| `_start()` | Parse each Step config from YAML into `(step_cls, params)`. |
+| `_build_steps()` | Create new Step instances for every call, avoiding state shared across requests. |
+| `__call__()` | Create a `RuntimeContext` and execute Steps sequentially. |
+| Exception handling | Catch the exception, set `response.success=False`, and set `answer=str(e)`. |

 ### 6.2 StreamJob

 `StreamJob` extends `BaseJob` but returns streaming chunks:

-| Behavior    | Description                                                |
-|-------------|------------------------------------------------------------|
-| Context     | Includes `stream_queue`.                                   |
+| Behavior | Description |
+|---|---|
+| Context | Includes `stream_queue`. |
 | Step output | Call `context.add_stream_string(text, ChunkEnum.CONTENT)`. |
-| Exception   | Write `ChunkEnum.ERROR`.                                   |
-| Completion  | Always send a `DONE` chunk.                                |
+| Exception | Write `ChunkEnum.ERROR`. |
+| Completion | Always send a `DONE` chunk. |

 ### 6.3 BackgroundJob

@ -406,8 +355,8 @@ flowchart LR
    J --> K["wait close_timeout; cancel on timeout"]
 ```

-The default `BackgroundJob.__call__()` also executes configured Steps in sequence, but it does not swallow exceptions,
-which allows the supervisor to restart the task.
+The default `BackgroundJob.__call__()` also executes configured Steps in sequence, but it does not swallow exceptions, which
+allows the supervisor to restart the task.

 ### 6.4 CronJob

@ -433,9 +382,7 @@ The current implementation uses `croniter` to calculate the next trigger time. T
 ```mermaid
 flowchart LR
    Jobs["default.yaml jobs"] --> BG["background<br/>index_update_loop<br/>resource_watch_loop<br/>digest_watch_loop"]
-    Jobs --> Cron["cron<br/>dream_cron<br/>optimize_index_cron"]
-    Jobs --> Stream["stream<br/>chat"]
-    Jobs --> Base["base<br/>version / help / health_check / status / app_config<br/>search / node_search / traverse / graph_snapshot / reindex<br/>read / load / read_image / write / save / edit / delete / move / list / stat / frontmatter_*<br/>daily_list / daily_reindex / daily_write<br/>auto_memory / auto_memory_cc / auto_resource / auto_dream / proactive"]
+    Jobs --> Base["base<br/>version / help / health_check<br/>search / node_search / traverse / reindex<br/>read / write / edit / delete / move / list / stat<br/>daily_list / daily_reindex / daily_write<br/>auto_memory / auto_resource / auto_dream / proactive"]
 ```

 ## 7. Step Model
@ -459,12 +406,12 @@ flowchart LR

 `RuntimeContext` is shared by all Steps within one Job invocation:

-| Field          | Description                                                                |
-|----------------|----------------------------------------------------------------------------|
-| `response`     | Final `Response(answer, success, metadata)`.                               |
-| `data`         | Free-form dictionary containing input parameters and intermediate results. |
-| `stream_queue` | Output queue for streaming Jobs.                                           |
-| `stop_event`   | Stop signal for background Jobs.                                           |
+| Field | Description |
+|---|---|
+| `response` | Final `Response(answer, success, metadata)`. |
+| `data` | Free-form dictionary containing input parameters and intermediate results. |
+| `stream_queue` | Output queue for streaming Jobs. |
+| `stop_event` | Stop signal for background Jobs. |

 Common Step code:

@ -528,22 +475,21 @@ flowchart LR

 Current default components in `reme/config/default.yaml`:

-| ComponentEnum     | Name                            | Backend                                          | Description                                                                    |
-|-------------------|---------------------------------|--------------------------------------------------|--------------------------------------------------------------------------------|
-| `service`         | singleton                       | `http`                                           | Default HTTP service.                                                          |
-| `tokenizer`       | `default`                       | `regex`                                          | BM25 tokenizer.                                                                |
-| `as_embedding`    | `default`                       | Not configured by default; example uses `openai` | Provides the embedding model wrapper after uncommenting the example config.    |
-| `embedding_store` | `default`                       | Not configured by default; example uses `local`  | Depends on `as_embedding: default` after uncommenting the example config.      |
-| `as_llm`          | `default`                       | `${LLM_BACKEND:-openai}`                         | LLM model wrapper.                                                             |
-| `agent_wrapper`   | `default`                       | `agentscope`                                     | AgentScope wrapper.                                                            |
-| `agent_wrapper`   | `claude_code`                   | `claude_code`                                    | Claude Code wrapper.                                                           |
-| `agent_wrapper`   | `codex/codex_oauth`             | `codex`                                          | Codex wrappers for API-key and OAuth authentication.                           |
-| `file_graph`      | `default`                       | `local`                                          | Wikilink graph.                                                                |
-| `file_catalog`    | `default/resource/digest/dream` | `local`                                          | File-change checkpoints.                                                       |
-| `file_chunker`    | `markdown`                      | `markdown`                                       | Markdown AST chunking.                                                         |
-| `file_chunker`    | `json/jsonl/default`            | `json/jsonl/default`                             | JSON, JSONL, and generic text chunkers; generic text supports `txt` and `log`. |
-| `keyword_index`   | `default`                       | `bm25`                                           | BM25 keyword index.                                                            |
-| `file_store`      | `default`                       | `local`                                          | Combines file_graph and keyword_index; defaults to `embedding_store: ""`.      |
+| ComponentEnum | Name | Backend | Description |
+|---|---|---|---|
+| `service` | singleton | `http` | Default HTTP service. |
+| `tokenizer` | `default` | `regex` | BM25 tokenizer. |
+| `as_embedding` | `default` | `${EMBEDDING_BACKEND:-openai}` | Embedding model wrapper. |
+| `embedding_store` | `default` | `local` | Embedding store depending on `as_embedding: default`. |
+| `as_llm` | `default` | `${LLM_BACKEND:-openai}` | LLM model wrapper. |
+| `agent_wrapper` | `default` | `agentscope` | AgentScope wrapper. |
+| `agent_wrapper` | `claude_code` | `claude_code` | Claude Code wrapper. |
+| `file_graph` | `default` | `local` | Wikilink graph. |
+| `file_catalog` | `default/resource/digest/dream` | `local` | File-change checkpoints. |
+| `file_chunker` | `markdown` | `markdown` | Markdown AST chunking. |
+| `file_chunker` | `default` | `default` | Default text chunking, currently supporting `jsonl`. |
+| `keyword_index` | `default` | `bm25` | BM25 keyword index. |
+| `file_store` | `default` | `local` | Combines file_graph and keyword_index; defaults to `embedding_store: ""`. |

 Note that the `search` Step configuration contains `vector_weight`, but `file_store.default.embedding_store` is empty by
 default. Vector retrieval is available only when the runtime configuration enables an embedding store.
@ -601,12 +547,12 @@ class MySearchStep(BaseStep):

 Common attributes available directly:

-| Attribute            | Component resolved by default       |
-|----------------------|-------------------------------------|
-| `self.as_llm`        | `.model` from `as_llm: default`.    |
+| Attribute | Component resolved by default |
+|---|---|
+| `self.as_llm` | `.model` from `as_llm: default`. |
 | `self.agent_wrapper` | `agent_wrapper: default`; optional. |
-| `self.file_catalog`  | `file_catalog: default`; optional.  |
-| `self.file_store`    | `file_store: default`.              |
+| `self.file_catalog` | `file_catalog: default`; optional. |
+| `self.file_store` | `file_store: default`. |

 To select a non-default component from Job configuration:

@ -618,13 +564,13 @@ steps:

 ### 9.4 Step Design Guidance

-| Guidance                                                                        | Reason                                                                        |
-|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------|
-| Read input from `context` and write intermediate results to `context`.          | A multi-Step Job passes data through the same context.                        |
-| Write the final result to `context.response`.                                   | Services and clients consume the standard `Response`.                         |
-| Do not store request-scoped state on a Step instance.                           | A Step is rebuilt for every Job call, and stateless Steps are easier to test. |
-| A background loop that supports interruption should check `context.stop_event`. | `BackgroundJob.close()` relies on the stop event for graceful shutdown.       |
-| Call `add_stream_string()` only from a StreamJob.                               | A normal Job has no stream queue.                                             |
+| Guidance | Reason |
+|---|---|
+| Read input from `context` and write intermediate results to `context`. | A multi-Step Job passes data through the same context. |
+| Write the final result to `context.response`. | Services and clients consume the standard `Response`. |
+| Do not store request-scoped state on a Step instance. | A Step is rebuilt for every Job call, and stateless Steps are easier to test. |
+| A background loop that supports interruption should check `context.stop_event`. | `BackgroundJob.close()` relies on the stop event for graceful shutdown. |
+| Call `add_stream_string()` only from a StreamJob. | A normal Job has no stream queue. |

 ### 9.5 Unit Test Example

@ -647,8 +593,8 @@ async def test_uppercase_step():

 ## 10. Adding a Job

-A Job usually requires no new Python class; configure existing Steps instead. Add a new Job backend only when a new
-execution model is required.
+A Job usually requires no new Python class; configure existing Steps instead. Add a new Job backend only when a new execution
+model is required.

 ### 10.1 Adding a Normal Request Job

@ -783,11 +729,11 @@ jobs:

 Characteristics of a background Job:

-| Characteristic                  | Description                                                        |
-|---------------------------------|--------------------------------------------------------------------|
-| Not externally exposed          | `BackgroundJob.__init__()` forces `enable_serve=False`.            |
-| Has a supervisor                | Restarts with exponential backoff after an exception by default.   |
-| Has a stop event                | Notifies the loop to exit during close.                            |
+| Characteristic | Description |
+|---|---|
+| Not externally exposed | `BackgroundJob.__init__()` forces `enable_serve=False`. |
+| Has a supervisor | Restarts with exponential backoff after an exception by default. |
+| Has a stop event | Notifies the loop to exit during close. |
 | Suitable for watching/consuming | File watching, queue consumption, and periodic long-running loops. |

 ### 10.5 Adding a Cron Job
@ -814,14 +760,14 @@ An invalid `cron` expression fails at startup.

 Most use cases require only a new Step plus a YAML Job. Consider adding `reme/components/job/*.py` only in these cases:

-| Requirement                                                         | New Job class?                 |
-|---------------------------------------------------------------------|--------------------------------|
-| Add a business command                                              | No; use `backend: base`.       |
-| Chain existing steps                                                | No; use `steps:`.              |
-| Need SSE/streaming output                                           | No; use `backend: stream`.     |
-| Need a background loop                                              | No; use `backend: background`. |
-| Need cron scheduling                                                | No; use `backend: cron`.       |
-| Need entirely new scheduling, concurrency, or transaction semantics | Yes; add a Job backend.        |
+| Requirement | New Job class? |
+|---|---|
+| Add a business command | No; use `backend: base`. |
+| Chain existing steps | No; use `steps:`. |
+| Need SSE/streaming output | No; use `backend: stream`. |
+| Need a background loop | No; use `backend: background`. |
+| Need cron scheduling | No; use `backend: cron`. |
+| Need entirely new scheduling, concurrency, or transaction semantics | Yes; add a Job backend. |

 Minimal shape of a new Job backend:

--- a/docs/en/memory_as_file.md
+++ b/docs/en/memory_as_file.md
@ -6,39 +6,37 @@ ReMe's core idea is **Memory as File, File as Memory**.
  <img src="../figure/memory-as-file.svg" alt="ReMe Memory as File model" width="92%">
 </p>

-**Memory as File**: long-term memory is not hidden in a black-box database. Its source material and readable memories
-live in user-owned files under the workspace. Users and agents can directly read, write, move, and delete those files;
-indexes and snapshots under `metadata/` are derived state that can be rebuilt.
+**Memory as File**: long-term memory is not hidden in a black-box database. It lives in Markdown files, resource files, and
+index snapshots under the workspace. Users and agents can directly read, write, move, and delete those files.

-**File as Memory**: each file is more than ordinary text. It is an indexable, linkable, and evolvable memory node. ReMe
-parses frontmatter, body chunks, and wikilink edges from files and organizes them into retrieval indexes and a graph.
+**File as Memory**: each file is more than ordinary text. It is an indexable, linkable, and evolvable memory node. ReMe parses
+frontmatter, body chunks, and wikilink edges from files and organizes them into retrieval indexes and a graph.

 In other words, files are both a human-readable interface and an operational interface for agents. Directory structure
 carries the memory layers, while Markdown syntax expresses content, metadata, and relationships.

 ## Design Goals

-ReMe represents memory as files not merely for convenient storage, but to give long-term memory several essential
-properties:
+ReMe represents memory as files not merely for convenient storage, but to give long-term memory several essential properties:

-| Goal          | Meaning                                                                                                                                               |
-|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Readable      | Users can open the workspace directly and read daily notes, digest nodes, and source material like ordinary notes.                                    |
-| Editable      | Users and agents can correct, extend, move, or delete memory with file operations, without a specialized database client.                             |
-| Traceable     | Long-term conclusions in digest can point back to daily, resource, or session files from a Sources section.                                           |
-| Portable      | The workspace is an ordinary directory. Markdown, JSONL, YAML, and resource files can be backed up, synchronized, versioned, or moved to other tools. |
-| Indexable     | Although the files are plain text, ReMe parses frontmatter, chunks, and wikilinks to build a retrieval index and file graph.                          |
-| Collaborative | Humans judge and correct; agents organize, link, and retrieve. Both operate on the same files.                                                        |
+| Goal | Meaning |
+|---|---|
+| Readable | Users can open the workspace directly and read daily notes, digest nodes, and source material like ordinary notes. |
+| Editable | Users and agents can correct, extend, move, or delete memory with file operations, without a specialized database client. |
+| Traceable | Long-term conclusions in digest can point back to daily, resource, or session sources through `derived_from:: [[...]]`. |
+| Portable | The workspace is an ordinary directory. Markdown, JSONL, YAML, and resource files can be backed up, synchronized, versioned, or moved to other tools. |
+| Indexable | Although the files are plain text, ReMe parses frontmatter, chunks, and wikilinks to build a retrieval index and file graph. |
+| Collaborative | Humans judge and correct; agents organize, link, and retrieve. Both operate on the same files. |

-ReMe memory is therefore neither a hidden database record nor a prompt fragment visible only to an LLM. It is first a
-file owned by the user and only then indexed by the system for retrieval.
+ReMe memory is therefore neither a hidden database record nor a prompt fragment visible only to an LLM. It is first a file
+owned by the user and only then indexed by the system for retrieval.

 ## Memory Layers

 A ReMe workspace divides memory into four layers:

 ```text
-source records -> session/ + resource/
+raw input      -> session/ + resource/
 working memory -> daily/
 long memory    -> digest/
 system state   -> metadata/
@ -46,23 +44,19 @@ system state   -> metadata/

 Each layer solves a different problem.

-`session/` and `resource/` preserve source records. Files under `resource/` remain unchanged at their original path.
-Standard Auto Memory records retain conversation messages while intentionally omitting tool-result and base64 data
-blocks; this keeps recalled output and binary payloads from masquerading as user-provided evidence. Generated Agent
-runtime state instead lives under `mem_session/`.
+`session/` and `resource/` preserve raw input. Their purpose is to retain the original situation: conversations, agent
+sessions, uploaded material, web pages, and reports remain intact as evidence for later verification.

-`daily/` is the lightly processed layer. It organizes the day's conversations and resources into more readable daily
-notes:
-what happened, which conclusions were reached, which follow-up tasks remain, and where the source material lives. Daily
-does not aim for final abstraction; it is closer to a workbench for the day.
+`daily/` is the lightly processed layer. It organizes the day's conversations and resources into more readable daily notes:
+what happened, which conclusions were reached, which follow-up tasks remain, and where the source material lives. Daily does
+not aim for final abstraction; it is closer to a workbench for the day.

 `digest/` is the deeply processed layer. It stores memory nodes that can be reused over time, such as user preferences,
 project background, procedural experience, conceptual knowledge, and decision precedents. Digest should not merely copy
 daily. It should merge recurring facts, methods, and relationships into more stable descriptions.

-`metadata/` is the system index layer. It stores runtime state such as the file catalog, chunk index, and graph
-snapshots. Users normally do not edit this content manually. The actual editing surface is `daily/`, `digest/`, and,
-when necessary,
+`metadata/` is the system index layer. It stores runtime state such as the file catalog, chunk index, and graph snapshots.
+Users normally do not edit this content manually. The actual editing surface is `daily/`, `digest/`, and, when necessary,
 `resource/`.

 These layers let ReMe preserve both the original situation and its abstraction: daily reconstructs what happened, while
@ -79,23 +73,21 @@ The corresponding automatic flows are [Auto Memory](./auto_memory.md), [Auto Res
 ```text
 <workspace_dir>/
 ├── metadata/                    # system index layer; persistent indexes, graph, catalogs; not a manual editing surface
-├── session/                     # source-record layer; source conversations
+├── session/                     # raw input layer; original conversations and agent sessions
 │   ├── dialog/
-│   │   └── <session_id>.jsonl   # source messages saved by auto_memory
-│   └── claude_code/
-│       └── <session_id>.jsonl   # ReMe copy used by auto_memory_cc
-├── mem_session/                 # generated Agent wrapper sessions/config, not user memory
+│   │   └── <session_id>.jsonl   # conversation messages saved by auto_memory
 │   ├── agentscope/
-│   ├── claude_config/
-│   └── codex/
-├── resource/                    # source-record layer; original external material
-│   ├── <resource>.<ext>          # root-level input uses today's date
+│   │   └── <session_id>.jsonl
+│   └── claude_code/
+│       └── <session_id>.jsonl
+├── resource/                    # raw input layer; original external material
 │   └── YYYY-MM-DD/
-│       └── <resource>.<ext>       # dated input uses the directory date
+│       └── <resource>.<ext>
 ├── daily/                       # lightly processed layer; facts, conversation summaries, and resource interpretations by date
 │   ├── YYYY-MM-DD.md            # index page for the day
 │   └── YYYY-MM-DD/
-│       ├── <generated_name>.md  # topic-named conversation or resource card
+│       ├── <session_id>.md      # daily note distilled from a conversation
+│       ├── <resource_stem>.md   # daily note distilled from a resource
 │       └── interests.yaml       # proactive interest topics generated by auto_dream
 └── digest/                      # deeply processed layer; reusable personal facts, procedures, and knowledge nodes
    ├── personal/
@ -111,21 +103,17 @@ Typical flows:
 ```text
 conversation
  -> session/dialog/<session_id>.jsonl
-  -> daily/YYYY-MM-DD/<generated_name>.md
+  -> daily/YYYY-MM-DD/<session_id>.md
  -> digest/personal | digest/procedure | digest/wiki

 external resource
-  -> resource/[YYYY-MM-DD/]<resource>.<ext>
-  -> daily/YYYY-MM-DD/<generated_name>.md
+  -> resource/YYYY-MM-DD/<resource>.<ext>
+  -> daily/YYYY-MM-DD/<resource_stem>.md
  -> digest/wiki | digest/procedure
 ```

-The first two steps focus on recording and organizing; the final step focuses on long-term distillation. `auto_memory`
-and
-`auto_resource` generate daily notes from source input, and `auto_dream` extracts and integrates digest nodes from
-daily. The generated daily filename comes from validated frontmatter `name`; `session_id`, `source_conversation`, and
-`source_resource`
-provide stable provenance and lookup identity instead of determining the filename.
+The first two steps focus on recording and organizing; the final step focuses on long-term distillation. `auto_memory` and
+`auto_resource` generate daily notes from raw input, and `auto_dream` extracts and integrates digest nodes from daily.

 ## Markdown Format

@ -143,7 +131,9 @@ tags: [new energy, solar]
 # Conclusions

 The solar supply chain consists of [[digest/wiki/polysilicon.md]], wafers, cells, and modules.
-One major producer is [[digest/wiki/longi.md|LONGi]].
+
+upstream:: [[digest/wiki/polysilicon.md]]
+[company:: [[digest/wiki/longi.md|LONGi]]]
 ```

 ### Frontmatter
@ -158,8 +148,8 @@ source_conversation: [[session/dialog/abc.jsonl]]
 ---
 ```

-The current code recognizes `name` and `description` explicitly. Other fields are preserved as additional metadata. The
-write interface merges `name`, `description`, and `metadata` into frontmatter.
+The current code recognizes `name` and `description` explicitly. Other fields are preserved as additional metadata. The write
+interface merges `name`, `description`, and `metadata` into frontmatter.

 Treat frontmatter as a node-level summary and the body as evidence, explanation, and relationships. For example:

@ -173,31 +163,28 @@ confidence: observed

 The user repeatedly asks documentation to explain motivation, boundaries, and examples while avoiding marketing language.

-Apply this preference when following [[digest/procedure/technical-documentation.md]].
-
-## Sources
-
-This preference was recorded in [[daily/2026-06-20/documentation-style.md]], which captures the user's repeated guidance.
+derived_from:: [[daily/2026-06-20/session-a.md]]
+related:: [[digest/procedure/technical-documentation.md]]
 ```

 This has three benefits:

 1. `name` and `description` serve as lightweight summaries in lists, recall results, and agent decisions.
 2. The body can carry fuller facts, conditions, counterexamples, and sources.
-3. Ordinary wikilinks can be parsed by the graph and maintained when files move.
+3. Typed wikilinks such as `derived_from::` and `related::` can be parsed by the graph and maintained when files move.

-Frontmatter is best for stable, short, structured fields; the body is best for explanations meant for people. Do not put
-long body text into YAML fields.
+Frontmatter is best for stable, short, structured fields; the body is best for explanations meant for people. Do not put long
+body text into YAML fields.

 ### Wikilink

 Wikilinks express relationships between files with `[[...]]`:

 ```text
-[[daily/2026-06-20/session.md]]
-[[notes/example.md#L9]]
-[[notes/example.md#L9-L10]]
-[[notes/example.md#L9-L10,L15-L20]]
+[[digest/wiki/solar.md]]
+[[digest/wiki/solar.md#supply-chain]]
+[[digest/wiki/solar.md|solar]]
+![[resource/2026-06-01/report.md]]
 ```

 ReMe wikilinks use **literal path semantics**:
@ -209,73 +196,69 @@ ReMe wikilinks use **literal path semantics**:
 ReMe does not append `.md` automatically, search by filename, or automatically resolve folder notes. Use complete
 workspace-relative paths with their extensions.

-Ordinary Markdown links such as `[label](../wiki/example.md)` do not create `FileLink` edges and are not rewritten by
-move or retarget operations.
-
-Anchors such as `#L9`, `#L9-L10`, and `#L9-L10,L15-L20` remain ordinary `target_anchor` strings in the graph. The graph
-parser does not validate line-anchor syntax, so values such as `#L0`, `#L10-L9`, and `#L9,` are also stored. The `read`
-job does not interpret an anchor appended to `path`; use the separate 1-based, inclusive `start_line` and `end_line`
-arguments to read a range, for example `read(path="digest/wiki/solar.md", start_line=9, end_line=10)`.
-
 Wikilinks support these behaviors:

 ```text
 body link          -> create a FileLink
+predicate:: link   -> create a FileLink with a relationship name
 move a file        -> rewrite [[old path]] in inbound edges by default
 delete a file      -> return remaining inbound edges so references can be cleaned up
 search match       -> expand inbound and outbound links to provide context
 ```

+Supported relationship forms:
+
+```markdown
+industry:: [[digest/wiki/new-energy.md]]
+[competitor:: [[digest/wiki/byd.md]]]
+```
+
 Parsed result:

 ```text
 FileLink
  source_path = current file
-  target_path = notes/example.md
-  target_anchor = L9-L10,L15-L20
+  target_path = digest/wiki/new-energy.md
+  predicate   = industry
 ```

-Older documents containing wrappers such as `related:: [[path]]`,
-`- related:: [[path]]`, or `[related:: [[path]]]` remain readable. ReMe ignores the surrounding text and indexes the
-inner `[[path]]` as an ordinary link. After upgrading from a version that stored typed links, run `reme reindex`
-once to rebuild the derived graph without the removed relationship field.
-
 ### Sources and Relationships

 The two most important link types in ReMe are source links and conceptual relationship links.

-A Sources section records where a long-term memory came from:
+A source link explains where a long-term memory came from:

 ```markdown
-## Sources
-
-The preference was observed in [[daily/2026-06-20/documentation-style.md]], and the supporting report evidence is retained in
-[[resource/2026-06-20/report.pdf]].
+derived_from:: [[daily/2026-06-20/session-a.md]]
+derived_from:: [[resource/2026-06-20/report.pdf]]
 ```

-A conceptual relationship link explains which other long-term memories relate to the node. Weave it into natural prose:
+A conceptual relationship link explains which other long-term memories relate to the node:

 ```markdown
-This analysis extends [[digest/wiki/solar-supply-chain.md]], follows
-[[digest/procedure/research-report-analysis.md]], and contrasts with
-[[digest/wiki/central-inverter.md]].
+related:: [[digest/wiki/solar-supply-chain.md]]
+depends_on:: [[digest/procedure/research-report-analysis.md]]
+contrasts_with:: [[digest/wiki/central-inverter.md]]
 ```

+Ordinary body wikilinks also create graph edges, but when the relationship itself has semantic value, prefer
+`predicate:: [[path]]`. This makes the meaning of links clearer to search, graph traversal, and later agent integration.
+
 ## Human and Agent Editing

 Because memory is stored as files, users can edit the workspace directly, while agents can read and write the same files
 through ReMe's file tools. Both follow the same conventions:

-| Operation     | Guidance                                                                                                                                          |
-|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
-| Add memory    | Write to the appropriate directory, use frontmatter for Markdown, and prefer complete workspace-relative wikilinks.                               |
-| Edit a body   | Preserve existing sources and important wikilinks. When correcting an old conclusion, explain how the new material changes the previous judgment. |
-| Move a file   | ReMe's move tool rewrites old paths in inbound edges by default. After a manual move, inspect inbound links again.                                |
+| Operation | Guidance |
+|---|---|
+| Add memory | Write to the appropriate directory, use frontmatter for Markdown, and prefer complete workspace-relative wikilinks. |
+| Edit a body | Preserve existing sources and important wikilinks. When correcting an old conclusion, explain how the new material changes the previous judgment. |
+| Move a file | ReMe's move tool rewrites old paths in inbound edges by default. After a manual move, inspect inbound links again. |
 | Delete a file | Check inbound links first. ReMe's delete tool returns source files that still point to the target, making dangling references easier to clean up. |
-| Edit metadata | Use frontmatter for short fields. When the body changes substantially, update `description` as well.                                              |
+| Edit metadata | Use frontmatter for short fields. When the body changes substantially, update `description` as well. |

-A practical rule is: **an agent may rewrite the wording, but it must not lose evidence edges**. In particular, Sources
-entries and existing digest-to-digest wikilinks are the basis for traceable and extensible long-term memory.
+A practical rule is: **an agent may rewrite the wording, but it must not lose evidence edges**. In particular,
+`derived_from:: [[...]]` and existing digest-to-digest wikilinks are the basis for traceable and extensible long-term memory.

 ## Path Semantics

@ -283,7 +266,7 @@ All file tools and wikilinks use workspace-relative paths as their basic unit:

 ```text
 digest/wiki/solar.md
-daily/2026-06-20/documentation-style.md
+daily/2026-06-20/session-a.md
 resource/2026-06-20/report.pdf
 ```

@ -295,16 +278,16 @@ Recommended practices:
 1. Include `.md` when linking a Markdown file.
 2. Use the complete source path when linking from digest to daily or resource.
 3. Rename or move files through ReMe's move tool whenever possible to avoid stale paths.
-4. Put external source material under `resource/YYYY-MM-DD/...` and long-term abstractions under `digest/...`. Do not
-   put raw source material directly into digest.
+4. Put external source material under `resource/YYYY-MM-DD/...` and long-term abstractions under `digest/...`. Do not put
+   raw source material directly into digest.

-Explicit path semantics sacrifice a little convenience when writing by hand, but provide predictability, portability,
-and automatic maintainability.
+Explicit path semantics sacrifice a little convenience when writing by hand, but provide predictability, portability, and
+automatic maintainability.

 ## Memory Chunking

-Memory chunking divides a file into retrievable fragments. ReMe does not split Markdown at fixed lengths by default; it
-tries to preserve semantic structure.
+Memory chunking divides a file into retrievable fragments. ReMe does not split Markdown at fixed lengths by default; it tries
+to preserve semantic structure.

 This section explains how files become retrieval chunks. For index updates, BM25, vector recall, and link expansion, see
 [Memory Search](./memory_search.md).
@ -319,8 +302,8 @@ Document
 chunk 1 | chunk 2 | chunk 3 | ...
 ```

-This is simple, but it can cut headings, tables, code blocks, lists, and `[[wikilinks]]` in the middle. After a match,
-the agent often sees only an isolated fragment without knowing its section or relationship to other memory nodes.
+This is simple, but it can cut headings, tables, code blocks, lists, and `[[wikilinks]]` in the middle. After a match, the
+agent often sees only an isolated fragment without knowing its section or relationship to other memory nodes.

 ReMe chunking is closer to splitting memory by file structure:

@ -386,10 +369,5 @@ Matched body fragment

 This lets the agent see not only an isolated paragraph but also its structural position in the source file.

-Non-Markdown files use `DefaultFileChunker` by default. It splits by byte size and preserves a small overlap. For
-Markdown, the chunker also avoids cutting `[[wikilinks]]` in the middle.
-
-`DefaultFileChunker` and `MarkdownFileChunker` decode files with their configured `encoding` and normalize platform
-newlines to LF before indexing. Their default `invalid_encoding_policy: replace` keeps decodable content searchable
-when a source contains invalid bytes, without modifying the source file. Set `invalid_encoding_policy: strict` on a
-chunker component to reject such files instead.
+Non-Markdown files use `DefaultFileChunker` by default. It splits by byte size and preserves a small overlap. For Markdown,
+the chunker also avoids cutting `[[wikilinks]]` in the middle.
--- a/docs/en/memory_search.md
+++ b/docs/en/memory_search.md
@ -1,10 +1,8 @@
 # Memory Search

-Memory Search is ReMe's memory retrieval entry point. The default background loop continuously builds Markdown under
-`daily/` and `digest/` into a searchable chunk index and wikilink graph. At query time, it first recalls the most
-relevant fragments and then expands context along the bidirectional links of the files containing those fragments.
-`reme reindex` has a broader rebuild scope that also scans `resource/` and JSONL; it is intentionally different from the
-live watcher.
+Memory Search is ReMe's memory retrieval entry point. It continuously builds files under `daily/`, `digest/`, and `resource/`
+into a searchable chunk index and wikilink graph. At query time, it first recalls the most relevant fragments and then expands
+context along the bidirectional links of the files containing those fragments.

 <p align="center">
  <img src="../figure/auto-index-and-memory-search.svg" alt="ReMe Auto Index and Memory Search indexing, recall, fusion, and link expansion" width="92%">
@ -23,29 +21,24 @@ workspace files

 ## What It Searches

-The default `index_update_loop` watches two memory directories:
+The default `index_update_loop` watches three memory directories:

 - `daily_dir`: daily working memory and session memory cards generated by Auto Memory.
 - `digest_dir`: long-term distilled digest nodes.
+- `resource_dir`: external resources or imported material.

-The live watcher handles only the `md` suffix. A separate `resource_watch_loop` watches `resource_dir`, and Auto
-Resource turns those inputs into daily cards that enter the live index. When `reme reindex` is run manually, its
-configuration scans
-`daily_dir`, `digest_dir`, and `resource_dir` for `md` and `jsonl`; Markdown uses the `markdown` chunker and JSONL uses
-the
-`jsonl` chunker.
+The default suffixes are `md` and `jsonl`. Markdown uses the `markdown` chunker, which parses frontmatter, heading structure,
+and `[[wikilinks]]`. JSONL uses the `default` chunker and creates overlapping chunks by byte size.

 ## How the Index Is Built

-### Index Update
-
 The background Job `index_update_loop` maintains the index using configuration from `reme/config/default.yaml`:

 ```yaml
 index_update_loop:
  backend: background
-  watch_dirs: [daily_dir, digest_dir]
-  watch_suffixes: [md]
+  watch_dirs: [ daily_dir, digest_dir, resource_dir ]
+  watch_suffixes: [ md, jsonl ]
  steps:
    - backend: init_changes_step
      monitor_type: file_store
@ -59,9 +52,9 @@ index_update_loop:
 `FileNode.st_mtime` values already stored in `file_store`, calculates added, modified, and deleted changes, and passes
 `context["changes"]` to `update_index_step`.

-While the service is running, `watch_changes_step` takes over. It uses `watchfiles.awatch()` to watch the same
-directories, groups file events within a quiet window, and uses `coalesce_changes()` to collapse repeated events for the
-same path into one stable batch of changes.
+While the service is running, `watch_changes_step` takes over. It uses `watchfiles.awatch()` to watch the same directories,
+groups file events within a quiet window, and uses `coalesce_changes()` to collapse repeated events for the same path into one
+stable batch of changes.

 `update_index_step` performs the actual index writes:

@ -71,26 +64,9 @@ same path into one stable batch of changes.
 4. For a deleted file, remove its records from `file_store`, `keyword_index`, and `file_graph`.
 5. When changes exist, dump state to `metadata/` so it can be restored on the next startup.

-The Markdown chunker parses YAML frontmatter, heading structure, and wikilinks into `FileNode`, `FileChunk`, and
-`FileLink`
+The Markdown chunker parses YAML frontmatter, heading structure, and `[[...]]` into `FileNode`, `FileChunk`, and `FileLink`
 objects. For detailed chunking rules, see [Memory as File](./memory_as_file.md#memory-chunking).

-### Index Optimization
-
-Both BM25 and the FAISS HNSW vector index use tombstone markers instead of physical removal when deleting nodes; too
-many tombstones degrade search performance. An idle-time optimization mechanism is built in—the `optimize_index_cron`
-scheduled job compacts tombstones and rebuilds indexes during off-peak hours:
-
-```yaml
-optimize_index_cron:
-  backend: cron
-  cron: "0 2 * * *"
-  steps:
-    - backend: optimize_index_step
-```
-
-By default it runs at 2:00 AM daily; adjust the cron expression to customize the schedule.
-
 ## What file_store Contains

 The default `file_store.default` backend is `local`:
@ -106,24 +82,15 @@ file_store:

 It combines three kinds of capability:

-| Part                    | Default state | Purpose                                                                 |
-|-------------------------|---------------|-------------------------------------------------------------------------|
-| `file_chunks`           | Enabled       | Store `FileChunk` text, line numbers, scores, and optional embeddings.  |
-| `keyword_index.default` | Enabled       | BM25 inverted index where chunk ID is the document ID.                  |
-| `file_graph.default`    | Enabled       | Store `FileNode` objects and wikilink edges.                            |
-| `embedding_store`       | Disabled      | When enabled, generate embeddings for chunks and support vector recall. |
+| Part | Default state | Purpose |
+|---|---|---|
+| `file_chunks` | Enabled | Store `FileChunk` text, line numbers, scores, and optional embeddings. |
+| `keyword_index.default` | Enabled | BM25 inverted index where chunk ID is the document ID. |
+| `file_graph.default` | Enabled | Store `FileNode` objects and wikilink edges. |
+| `embedding_store` | Disabled | When enabled, generate embeddings for chunks and support vector recall. |

 Out of the box, search therefore uses primarily BM25 plus link expansion. After setting `embedding_store: default`,
-`SearchStep` runs vector and keyword recall together. Additionally, switching the `file_store` `backend` from `local` to
-`faiss` upgrades vector retrieval from a linear scan to a FAISS HNSW index, offering faster recall at scale.
-
-The embedding store accepts `health_check_timeout` for its startup probe. A temporary failure skips the current vector
-backfill while keeping BM25 available; a later successful provider request resumes the missing-vector backfill
-automatically.
-
-Embedded integrations that have already verified a provider can call `resume_embedding(verified=True)`. When changing
-the embedding vector space, pass `rebuild=True`; persisted vectors are invalidated before a serial background rebuild,
-and vector search remains unavailable until the rebuilt vectors are safely persisted.
+`SearchStep` runs vector and keyword recall together.

 ## How to Search

@ -137,12 +104,10 @@ search:
    query: string
    limit: integer
    min_score: number
-    start_date: string
-    end_date: string
  steps:
    - backend: search_step
      vector_weight: 0.7
-      candidate_multiplier: 5.0
+      candidate_multiplier: 3.0
      expand_links: true
      max_links_per_direction: 10
 ```
@ -153,17 +118,11 @@ Call it with:
 reme search query="recent discussions about indexing" limit=5
 ```

-Use `start_date` and `end_date` for inclusive `YYYY-MM-DD` filtering:
-
-```bash
-reme search query="index regression" start_date=2026-06-01 end_date=2026-06-20 limit=10
-```
-
 `search_step` executes in this order:

 ```mermaid
 flowchart LR
-    A["query + limit"] --> B["candidates = min(200, limit * candidate_multiplier)"]
+    A["query + limit"] --> B["candidates = limit * candidate_multiplier"]
    B --> C["file_store.vector_search(...)"]
    B --> D["file_store.keyword_search(...)"]
    C --> E["RRF fusion"]
@ -174,17 +133,17 @@ flowchart LR
    H --> I["Response.answer + metadata"]
 ```

-If only BM25 has results, the BM25 ranking is returned directly. If only vector search has results, the vector ranking
-is returned directly. When both have results, they are fused with RRF. RRF does not compare BM25 and cosine scores
-directly; it compares ranks in the two result lists:
+If only BM25 has results, the BM25 ranking is returned directly. If only vector search has results, the vector ranking is
+returned directly. When both have results, they are fused with RRF. RRF does not compare BM25 and cosine scores directly; it
+compares ranks in the two result lists:

 ```text
 fused_score = vector_weight / (60 + vector_rank)
            + keyword_weight / (60 + keyword_rank)
 ```

-The default `vector_weight=0.7` gives semantic recall more weight when embeddings are enabled, while keyword search can
-still promote chunks with exact term matches.
+The default `vector_weight=0.7` gives semantic recall more weight when embeddings are enabled, while keyword search can still
+promote chunks with exact term matches.

 ## How BM25 Works

@ -196,18 +155,15 @@ still promote chunks with exact term matches.
 - The inverted index records which chunks contain each token and its term frequency within each chunk.
 - A query scores only the posting lists matching its tokens and returns the highest-scoring chunk IDs.

-When a file changes, `LocalFileStore.upsert()` first removes the BM25 documents corresponding to the file's old
-`chunk_ids`
+When a file changes, `LocalFileStore.upsert()` first removes the BM25 documents corresponding to the file's old `chunk_ids`
 and then adds the new chunk text. Deletion is lazy; the index can later be compacted with optimize.

 ## Progressive Expansion

-"Progressive" in Memory Search does not mean putting the entire repository into one result. Retrieval expands in three
-layers:
+"Progressive" in Memory Search does not mean putting the entire repository into one result. Retrieval expands in three layers:

 1. Chunk recall: return only the `limit` most relevant text fragments.
-2. File location: each result includes `path:start_line-end_line`. Pass the path and line bounds separately as `path`,
-   `start_line`, and `end_line` when calling `read`; the range is not part of the `path` value.
+2. File location: each result includes `path:start_line-end_line`, allowing the caller to read the source precisely with `read`.
 3. Link neighbors: call `expand_links()` for each matched file and expand at most `max_links_per_direction` outlinks and
   inlinks.

@ -219,11 +175,11 @@ matched chunk
  -> file_store.get_outlinks(path)
  -> file_store.get_inlinks(path)
  -> file_store.get_nodes(neighbor_paths)
-  -> render neighbor path, name, description, and anchor
+  -> render neighbor path, name, description, predicate, and anchor
 ```

-This keeps search results short while still showing which long-term nodes, resources, or other daily notes a memory
-connects to. If a result is worth pursuing, use `read path=...` to open the source or
+This keeps search results short while still showing which long-term nodes, resources, or other daily notes a memory connects
+to. If a result is worth pursuing, use `read path=...` to open the source or
 `traverse path=... depth=2` to continue along the wikilink graph.

 ## Return Format
@ -237,13 +193,15 @@ connects to. If a result is worth pursuing, use `read path=...` to open the sour
 Typical text structure:

 ```text
-========== daily/2026-06-20/retrieval-regression.md:12-28 [score=0.0317 keyword=4.8120] ==========
+========== daily/2026-06-20/session-a.md:12-28 [score=0.0317 keyword=4.8120] ==========
 ...matched memory fragment...
  outlinks (2):
    -> digest/indexing.md  name="Indexing"  description="..."
+      via predicate=related
  inlinks (1):
    <- daily/2026-06-19.md  name="..."
+      via plain
 ```

-`counts` reports how many vector and keyword candidates were recalled and how many results were ultimately returned.
-With embeddings disabled by default, `vector` is usually `0` and `hybrid` is `false`.
+`counts` reports how many vector and keyword candidates were recalled and how many results were ultimately returned. With
+embeddings disabled by default, `vector` is usually `0` and `hybrid` is `false`.
--- a/docs/en/plugin_management.md
+++ b/docs/en/plugin_management.md
@ -1,225 +0,0 @@
-# Plugin Management
-
-ReMe plugins are ordinary Python distributions discovered through the `reme.plugins` entry-point group. Installing a
-plugin makes it available to the current Python environment; it does not enable the plugin in every ReMe application.
-
-Keep these two operations separate:
-
-```text
-reme plugins install ...        install a package into the current Python environment
-plugins: [auto-fin]             enable an installed plugin for one Application
-```
-
-Plugin package management is local-only. It does not run through a ReMe HTTP or MCP service and never edits application
-configuration files automatically.
-
-A typical plugin workflow has three stages:
-
-1. Install ReMe and the plugin distribution.
-2. Configure the plugin's runtime environment as described in the
-   [ReMe model-configuration guide](../../README.md#optional-model-configuration).
-3. Start an Application with the plugin explicitly enabled, for example
-   `reme start plugins='["auto-fin"]'`.
-
-## List installed plugins
-
-```bash
-reme plugins list
-```
-
-The table shows the plugin entry-point name, Python distribution, version, and plugin contract:
-
-```text
-PLUGIN    DISTRIBUTION   VERSION  FORMAT
--------  -------------  -------  --------
-auto-fin  reme-auto-fin  X.Y.Z    manifest
-```
-
-`manifest` plugins use the current package-level `plugin.yaml` contract. `legacy` plugins use the compatible Python
-descriptor contract.
-
-A manifest separates backend registration from application configuration:
-
-```yaml
-backends:
-  example_step: example_plugin.steps:ExampleStep
-
-application_defaults:
-  jobs:
-    example:
-      backend: base
-      steps:
-        - backend: example_step
-```
-
-`application_defaults` is a partial `ApplicationConfig`. It is kept below the manifest's `backends` namespace because
-backend import declarations are part of plugin discovery and are not application configuration.
-
-Use JSON when another local tool needs structured output:
-
-```bash
-reme plugins list --json
-```
-
-To compare installed plugins with one application config:
-
-```bash
-reme plugins list --config default
-```
-
-The optional `ENABLED` column reflects only the `plugins` list resolved from that config. A command-line override used
-by another running process is not a global enable state.
-
-## Install a plugin package
-
-Install a published distribution:
-
-```bash
-reme plugins install reme-auto-fin
-```
-
-Install or upgrade a pinned version:
-
-```bash
-reme plugins install 'reme-auto-fin==X.Y.Z'
-reme plugins install reme-auto-fin --upgrade
-```
-
-Install a local plugin project:
-
-```bash
-reme plugins install ./plugins/auto-fin
-```
-
-Use editable mode while developing it:
-
-```bash
-reme plugins install ./plugins/auto-fin --editable
-```
-
-ReMe invokes pip through the same Python interpreter that runs the `reme` command. Pip remains responsible for package
-resolution, downloads, dependency changes, and build execution. Install only packages and local projects you trust.
-
-After installation, confirm the discovered plugin name:
-
-```bash
-reme plugins list
-reme plugins validate auto-fin
-```
-
-## Inspect a plugin
-
-```bash
-reme plugins show auto-fin
-```
-
-For a manifest plugin, the result includes its registered backend names and default Job names. JSON output is also
-available:
-
-```bash
-reme plugins show auto-fin --json
-```
-
-`show` identifies the package contract without constructing a ReMe Application.
-
-## Validate a plugin
-
-Validate an installed plugin:
-
-```bash
-reme plugins validate auto-fin
-```
-
-Validate a local project before installation:
-
-```bash
-reme plugins validate ./plugins/auto-fin
-```
-
-Validation checks the entry point, `plugin.yaml`, backend imports and component types, registry collisions, merged
-`application_defaults`, and the resulting `ApplicationConfig`. Validation imports plugin backend modules, so run it
-only for trusted code.
-
-## Enable a plugin in a service
-
-Installation alone does not load plugin code into an Application. Enable plugins explicitly in configuration:
-
-```yaml
-plugins:
-  - auto-fin
-```
-
-Or add them for one service launch:
-
-```bash
-reme start plugins='["auto-fin"]'
-```
-
-When `config` is omitted, ReMe loads `default.yaml`. The plugin's `application_defaults` are merged below that config,
-so explicit config values and CLI overrides win. This mapping is an `ApplicationConfig` fragment, not a separate
-configuration schema. The plugin backends are registered only in that Application's local registry.
-
-After the default HTTP service starts, access plugin Jobs through ReMe's CLI client or HTTP:
-
-```bash
-reme auto_fin topics="黄金,AI,存储芯片"
-```
-
-```bash
-curl -s http://127.0.0.1:2333/auto_fin \
-  -H 'Content-Type: application/json' \
-  -d '{"topics":"黄金,AI,存储芯片"}'
-```
-
-When the application uses an MCP service, service-enabled plugin Jobs appear as MCP tools instead.
-
-Custom application configs must provide the plugin's runtime dependencies, including an `agent_wrapper.default` and
-the `search` and `read` Jobs used by Auto Fin.
-
-## Uninstall a plugin
-
-Use the plugin entry-point name, not necessarily the distribution name:
-
-```bash
-reme plugins uninstall auto-fin
-```
-
-Skip pip's confirmation prompt when needed:
-
-```bash
-reme plugins uninstall auto-fin --yes
-```
-
-ReMe resolves `auto-fin` to the distribution that provides it, such as `reme-auto-fin`. If one distribution provides
-multiple plugin entry points, the command lists the other plugins that will also be removed.
-
-Uninstallation does not rewrite user configuration. Remove the plugin from relevant `plugins` lists yourself;
-otherwise the next Application startup fails explicitly because the configured plugin is no longer installed. Restart
-already-running ReMe processes after installing, upgrading, or uninstalling packages.
-
-## Troubleshooting
-
-### Plugin is installed but unavailable
-
-Check that the `reme` command and pip package share one Python interpreter:
-
-```bash
-reme plugins list
-python -c 'import sys; print(sys.executable)'
-```
-
-Using `reme plugins install` avoids the most common interpreter mismatch because it runs `python -m pip` with ReMe's
-own interpreter.
-
-### Plugin is installed but not loaded
-
-Add its entry-point name to the Application's `plugins` list. ReMe intentionally has no global enable/disable state.
-
-### Startup reports that the plugin is not installed
-
-The active config still enables a missing plugin. Reinstall it or remove the corresponding name from `plugins`.
-
-### Changes are not visible in a running service
-
-Plugin discovery and backend registration happen during Application construction. Restart the service after changing
-installed packages.
--- a/docs/en/proactive.md
+++ b/docs/en/proactive.md
@ -1,17 +1,17 @@
 # Proactive

-`proactive` is ReMe's interface for reading proactive memory. It does not reanalyze daily notes or call an LLM. It only
-reads the current day's interest topics written by `auto_dream`:
+`proactive` is ReMe's interface for reading proactive memory. It does not reanalyze daily notes or call an LLM. It only reads
+the current day's interest topics written by `auto_dream`:

 ```text
 daily/<date>/interests.yaml
 ```

-A host agent can use it to learn "what is worth proactive attention today," then decide whether to remind the user, ask
-a follow-up question, recommend a next step, or produce a proactive insight.
+A host agent can use it to learn "what is worth proactive attention today," then decide whether to remind the user, ask a
+follow-up question, recommend a next step, or produce a proactive insight.

-`interests.yaml` is generated by the Topics stage of [Auto Dream](./auto_dream.md). `proactive` only reads and exposes
-the result.
+`interests.yaml` is generated by the Topics stage of [Auto Dream](./auto_dream.md). `proactive` only reads and exposes the
+result.

 ## Configuration

@ -34,10 +34,10 @@ proactive:

 Parameters:

-| Parameter         | Purpose                                                                                   |
-|-------------------|-------------------------------------------------------------------------------------------|
-| `date`            | Date to read in `YYYY-MM-DD` format. When empty, use today in the application's timezone. |
-| `include_content` | Whether to return the raw YAML in the answer and metadata. Defaults to `true`.            |
+| Parameter | Purpose |
+|---|---|
+| `date` | Date to read in `YYYY-MM-DD` format. When empty, use today in the application's timezone. |
+| `include_content` | Whether to return the raw YAML in metadata. Defaults to `true`. |

 ## Input Contract

@ -63,41 +63,24 @@ Only the `topics` list is parsed into structured results. Every topic requires a

 ## Return Value

-When the file is read successfully, `proactive_step` returns `summary` and `topics` in the primary answer. When
-`include_content=true`, the answer also contains `content`. The same result fields remain available in standard response
-metadata:
+When the file is read successfully, `proactive_step` writes these values to standard response metadata:

-| Field     | Description                                          |
-|-----------|------------------------------------------------------|
-| `date`    | The date actually read.                              |
-| `path`    | `daily/<date>/interests.yaml`.                       |
-| `topics`  | Parsed topic list.                                   |
+| Field | Description |
+|---|---|
+| `date` | The date actually read. |
+| `path` | `daily/<date>/interests.yaml`. |
+| `topics` | Parsed topic list. |
 | `content` | Raw YAML; returned only when `include_content=true`. |
-| `skipped` | `true` when the file does not exist.                 |
-| `error`   | Read or parse error.                                 |
-| `summary` | Short summary.                                       |
+| `skipped` | `true` when the file does not exist. |
+| `error` | Read or parse error. |
+| `summary` | Short summary. |

-When the file exists and parses successfully, the answer is structured data. For example:
+When the file exists and parses successfully, the answer looks like:

-```json
-{
-  "summary": "Read 1 proactive topic(s) from daily/2026-06-20/interests.yaml",
-  "topics": [
-    {
-      "title": "Quality regression in the memory retrieval pipeline",
-      "reason": "The user has recently made repeated changes to search, node_search, and dream integration.",
-      "evidence": "daily/2026-06-20/session.md",
-      "keywords": ["memory search", "auto dream"],
-      "paths": ["daily/2026-06-20/session.md"]
-    }
-  ],
-  "content": "date: 2026-06-20\n..."
-}
+```text
+Read 3 proactive topic(s) from daily/2026-06-20/interests.yaml
 ```

-With `include_content=false`, the `content` field is omitted from the answer. Missing-file and read-error answers remain
-explicit `Skipped: ...` and `Error: ...` messages, respectively.
-
 A missing file is not an error. The call succeeds with a skipped result:

 ```text
@ -135,21 +118,21 @@ daily notes
 The responsibilities are divided as follows. For the complete Extract, Integrate, Topics, and Finish flow, see
 [Auto Dream](./auto_dream.md):

-| Module               | Responsibility                                         |
-|----------------------|--------------------------------------------------------|
-| `dream_extract_step` | Extract topic candidates from changed daily inputs.    |
-| `dream_topics_step`  | Deduplicate, select, and write `interests.yaml`.       |
-| `proactive_step`     | Read `interests.yaml` and expose it to the host agent. |
+| Module | Responsibility |
+|---|---|
+| `dream_extract_step` | Extract topic candidates from changed daily inputs. |
+| `dream_topics_step` | Deduplicate, select, and write `interests.yaml`. |
+| `proactive_step` | Read `interests.yaml` and expose it to the host agent. |

-`proactive` does not modify files, update a catalog, or decide whether the user should be interrupted. It only provides
-the day's topic material. The caller's product policy determines whether, when, and in what tone to push it to the user.
+`proactive` does not modify files, update a catalog, or decide whether the user should be interrupted. It only provides the
+day's topic material. The caller's product policy determines whether, when, and in what tone to push it to the user.

 ## Failure Modes

-| Scenario                            | Behavior                                               |
-|-------------------------------------|--------------------------------------------------------|
-| `interests.yaml` does not exist     | `success=true`, `skipped=true`, `topics=[]`.           |
-| YAML cannot be read or parsed       | `success=false`; the answer contains an error summary. |
-| YAML exists but has no valid topics | `success=true`, `topics=[]`.                           |
+| Scenario | Behavior |
+|---|---|
+| `interests.yaml` does not exist | `success=true`, `skipped=true`, `topics=[]`. |
+| YAML cannot be read or parsed | `success=false`; the answer contains an error summary. |
+| YAML exists but has no valid topics | `success=true`, `topics=[]`. |

 Callers should therefore check `success` first, then `skipped`, and finally whether `topics` is empty.
--- a/docs/en/quick_start.md
+++ b/docs/en/quick_start.md
@ -15,17 +15,11 @@ Install from source:
 ```bash
 git clone https://github.com/agentscope-ai/ReMe.git
 cd ReMe
-pip install -e reme_studio -e ".[core]"
-cd reme_studio
-npm ci
-npm run build:static
-cd ..
+pip install -e ".[core]"
 ```

-The static build step requires Node.js 22.13 or newer and makes Studio available when running ReMe from the source tree.
-
-Installing the `core` extra is recommended. The current code imports the AgentScope wrapper, and self-evolving memory
-also depends on it.
+Installing the `core` extra is recommended. The current code imports the AgentScope wrapper, and self-evolving memory also
+depends on it.

 To use agent workflows such as `auto_memory`, `auto_resource`, and `auto_dream`, configure an LLM:

@ -57,16 +51,10 @@ reme start service.port=8181
 ```bash
 reme version
 reme health_check
-reme help
+reme list
 ```

-`reme help` lists server actions. Ordinary commands invoke server Jobs over HTTP.
-
-The base `reme-ai` package does not include frontend assets. Install `reme-ai[web]` or `reme-ai[core]`, then open
-<http://127.0.0.1:2333/> for ReMe Studio. It uses the same service to
-browse, edit, and search the workspace and inspect the digest wikilink graph. Disable it with
-`service.web_enabled=false`, or provide a custom build with `service.web_static_dir` / `REME_WEB_STATIC_DIR`. The Job
-API still starts if no web build is found.
+`reme list` lists server actions. Ordinary commands invoke server Jobs over HTTP.

 ---

@ -77,8 +65,7 @@ The default workspace is `.reme/` under the current directory. It is created aut
 ```text
 .reme/
 ├── metadata/   # persistent indexes, graph, catalogs, and related state
-├── session/    # source conversation records
-├── mem_session/ # generated Agent wrapper sessions/config
+├── session/    # agent sessions and original conversations
 ├── resource/   # external resources
 ├── daily/      # daily notes
 └── digest/     # long-term memory
@ -104,13 +91,12 @@ reme write \
  description="Example memory for the quick start" \
  content="# Quick Start Demo

-The default live watcher indexes Markdown under the daily and digest directories.
+ReMe indexes Markdown under the daily, digest, and resource directories.

 Related link: [[digest/wiki/search-demo.md]]"
 ```

-`path` is relative to the workspace. A missing suffix is automatically completed with `.md`. For Markdown files, `name`
-and
+`path` is relative to the workspace. A missing suffix is automatically completed with `.md`. For Markdown files, `name` and
 `description` are written to frontmatter.

 The background watcher builds the index automatically. You can also rebuild it manually:
@ -131,8 +117,8 @@ Read:
 reme read path=digest/wiki/quick-start-demo start_line=1 end_line=20
 ```

-With the default configuration, retrieval is primarily BM25 plus wikilink graph expansion. Vector retrieval is supported
-by the code, but the embedding store is disabled by default. For the full retrieval flow, see
+With the default configuration, retrieval is primarily BM25 plus wikilink graph expansion. Vector retrieval is supported by
+the code, but the embedding store is disabled by default. For the full retrieval flow, see
 [Memory Search](./memory_search.md).

 ---
@ -146,13 +132,7 @@ reme frontmatter_read path=digest/wiki/quick-start-demo
 reme frontmatter_update path=digest/wiki/quick-start-demo metadata='{"tags":["demo"]}'
 ```

-The file-listing Job can be called directly from the CLI:
-
-```bash
-reme list path=digest recursive=true limit=50
-```
-
-The equivalent HTTP call is:
+The name `list` is used by the CLI to list actions, so the file-listing Job must be called over HTTP:

 ```bash
 curl -s http://127.0.0.1:2333/list \
@ -181,8 +161,7 @@ reme auto_memory \
  memory_hint="Record the user's preference"
 ```

-After placing external material under `resource/YYYY-MM-DD/` or directly under `resource/`, the default background task
-watches
+After placing external material under `resource/YYYY-MM-DD/`, the default background task watches
 `md/txt/json/jsonl/csv/yaml/html`. You can also trigger processing manually:

 ```bash
@ -196,8 +175,7 @@ reme auto_dream date=2026-06-20
 reme proactive date=2026-06-20
 ```

-These flows require a working LLM. Without an LLM configuration, start with basic capabilities such as `write`, `read`,
-and
+These flows require a working LLM. Without an LLM configuration, start with basic capabilities such as `write`, `read`, and
 `search`.

 For more detail, see [Auto Memory](./auto_memory.md), [Auto Resource](./auto_resource.md),
--- a/docs/en/reme-blog.md
+++ b/docs/en/reme-blog.md
@ -1,339 +0,0 @@
-# ReMe: A Personal Knowledge Base That Keeps Growing After Every Conversation
-
-Every day, we talk with AI.
-
-It helps us analyze projects, read papers, and troubleshoot problems. We also tell it about our preferences, plans, and ideas we have not fully worked out yet.
-
-But most of the time, when a conversation ends, its value is locked away in the chat history. The next time we open a new window, the AI may remember a conclusion but not where it came from. It may find an old conversation but fail to connect it with materials we read or decisions we made later.
-
-Useful long-term memory should do more than preserve what once happened. It should keep organizing information, building connections, and bringing past knowledge back into future reasoning when needed.
-
-That is exactly what ReMe sets out to do.
-
-> **ReMe is a local-first, self-evolving personal knowledge base for AI agents. It continuously turns conversations and resources into readable, editable, searchable, and interconnected Markdown memories, while surfacing threads worth following.**
-
-GitHub: [https://github.com/agentscope-ai/ReMe](https://github.com/agentscope-ai/ReMe)
-
-Documentation: [https://reme.agentscope.io](https://reme.agentscope.io)
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-cover-benchmark.png" alt="ReMe self-evolving personal knowledge base and public benchmark results" width="100%">
-</p>
-
-## A Memory Loop That Keeps Growing
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-architecture.svg" alt="ReMe self-evolving memory loop" width="100%">
-</p>
-
-ReMe is not another chatbot, nor does it try to replace the agents you already use. It is a local memory layer that agents such as QwenPaw, OpenClaw, Hermes, and Claude Code can share.
-
-Built around a set of ordinary files, it does four things:
-
- Auto Memory extracts information worth keeping from conversations;
- Auto Resource turns external materials into traceable memories;
- Auto Dream consolidates daily memories into long-term knowledge;
- Index, Search, and Proactive bring old memories back into new tasks.
-
-Together, they form a `capture → index → consolidate → recall` loop:
-
- Conversations and external resources are preserved first;
- Valuable information is organized into daily memories;
- Scattered events are consolidated into long-term knowledge nodes;
- Search, knowledge links, and interest discovery bring old memories back into future reasoning.
-
-Most importantly, this loop is centered not on an opaque database, but on files owned by the user. Indexes, graphs, and caches are merely derived state that can always be rebuilt.
-
-## Memory as File: Your Memories Are Your Files
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-memory-as-file.svg" alt="ReMe Memory as File" width="100%">
-</p>
-
-ReMe's core design is called **Memory as File, File as Memory.**
-
-“Memory as File” means long-term memories are not hidden inside a product. They live in Markdown, JSONL, YAML, and original resource files within your workspace. You can open them directly in VS Code, Typora, or Obsidian, and back them up or move them with Git, cloud storage, or your own synchronization setup.
-
-“File as Memory” means each file is more than plain text. With YAML frontmatter, section structure, line ranges, and Wikilinks, it becomes a memory node that can be indexed, connected, and continuously evolved.
-
-For example, a long-term memory about writing preferences might look like this:
-
-```markdown
---
-name: "User preference: technical writing style"
-description: Prefers stating the problem and outcome first, followed by technical details and examples.
-kind: preference
---
-
-The user wants technical articles to have a clear narrative and avoid unnecessary jargon.
-
-When writing an article, refer to [[digest/procedure/Technical content writing process.md]].
-
-## Sources
-
-This preference was observed in [[daily/2026-08-07/content-discussion.md]], which records the user's writing guidance.
-```
-
-Months later, even if you have forgotten the conversation, the agent can still read the preference, find the related process, and follow `Sources` back to the original context.
-
-This is also the key difference between ReMe and “black-box memory”: agents can organize memories, but users always retain the right to inspect, correct, move, and delete them.
-
-## Auto Memory: Turning Conversations into a Daily Journal
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-auto-memory.svg" alt="ReMe Auto Memory turns conversations into daily memories" width="100%">
-</p>
-
-A great deal of valuable information does not begin with “please remember this.”
-
-For example, you might say in a conversation:
-
-> “Let's not refactor the login module this week. We can do it after the customer demo. Upgrading dependencies directly caused compatibility issues last time, so let's add regression tests first.”
-
-This short passage contains project status, a time constraint, a lesson from a previous failure, and a next action. Auto Memory extracts these details from the conversation stream and writes them into a daily memory card, while retaining a source conversation record in `session/dialog/`.
-
-```text
-session/dialog/project-a.jsonl                 Source conversation record
-daily/2026-08-07/login-refactor-decision.md    Content-named memory card
-daily/2026-08-07.md                            Daily index, providing an overview
-```
-
-`session_id` remains in the card's frontmatter for stable lookup and provenance; the filename comes from the Agent-generated
-topic/event `name`, so it does not have to match the session ID.
-
-The next time the login module comes up, the agent does not need to search through the entire chat history. It can immediately see why the refactor was postponed, what went wrong before, and what should happen next.
-
-It is like having a recorder who is always present—not one that mechanically transcribes every word, but one that organizes what will still matter later.
-
-## Auto Resource: Bringing External Materials into the Same Memory System
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-auto-resource.svg" alt="ReMe Auto Resource turns external materials into traceable personal memories" width="100%">
-</p>
-
-Not all valuable information comes from conversations. Research materials, project documents, meeting notes, archived web pages, and structured data may all become part of a personal knowledge base.
-
-Auto Resource provides a general entry point for external materials. After a resource enters `resource/`, ReMe preserves the original and organizes its topics, key facts, and actionable information into daily cards with `source_resource` links. It currently supports text-based resources including Markdown, plain text, JSON, JSONL, CSV, YAML, and HTML.
-
-In other words, Auto Memory builds personal knowledge from conversations, while Auto Resource builds it from non-conversational materials. Both streams flow into the same daily memory layer, where ReMe indexes, consolidates, and retrieves them together.
-
-### Daily Paper: An Example External-Resource Workflow
-
-Daily Paper is an optional plugin built on this file-based memory system. It collects papers from the weekly and monthly Hugging Face Papers rankings, removes items recommended recently, ranks the remaining papers, selects three, saves their PDFs, and generates Chinese paper notes and a briefing that takes about five minutes to read.
-
-Imagine that you regularly follow research on agent memory. Each morning, instead of receiving only three links, you get three detailed notes already saved locally. The briefing points to the original notes through Wikilinks, and each note links back to its PDF. A month later, when you ask, “What recent methods compress long-term memory?”, those materials are already in the same retrieval system. There is no need to search through browser history again.
-
-Daily Paper demonstrates how Auto Resource can be composed into a concrete workflow, but the external-resource pipeline is not limited to papers.
-
-## Auto Dream: Growing Daily Notes into Connected Long-Term Knowledge
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-auto-dream.svg" alt="ReMe Auto Dream extracts, classifies, and consolidates long-term knowledge from daily memories while adding Wikilinks" width="100%">
-</p>
-
-As daily notes accumulate, a new problem emerges: the information is all there, but it remains scattered across different dates.
-
-Suppose conversations and external materials give you three pieces of information about the same problem:
-
- The first time a build hung, clearing the cache did not help;
- A project document later confirmed that insufficient Node.js memory was the root cause;
- A third note added that the issue occurs more often in large TypeScript projects.
-
-By default, Auto Dream looks at the two most recent days ending at the target date and sends only daily files changed since
-the previous run to extraction. It merges cross-file evidence for the same abstraction and keeps only the strongest reusable
-memories within a default cap of five units, then writes them into three categories of long-term memory:
-
- `Personal`: preferences, conventions, and constraints specific to a user, team, or project;
- `Procedure`: repeatable processes, methods, and troubleshooting guides;
- `Wiki`: general definitions, principles, observations, and knowledge.
-
-For example, the information above would become `digest/procedure/Troubleshooting frozen frontend builds.md`, which records the triggering conditions, diagnostic sequence, failed attempts, solution, and scope of applicability—instead of simply concatenating several daily notes.
-
-When consolidating each memory unit, Auto Dream first searches existing nodes across `personal`, `procedure`, and `wiki`, distinguishing between the “same abstraction” and “related knowledge.” The same abstraction determines how the target node evolves:
-
- `CREATE`: no equivalent memory exists, so create a new node;
- `CORROBORATE`: the same conclusion appears again, so add its source and strengthen confidence;
- `REFINE`: new material adds conditions, steps, or details;
- `CORRECT`: new information corrects an earlier conclusion.
-
-Related knowledge is written into the body as Wikilinks during the same consolidation process. This is Auto Link. For example, “Troubleshooting frozen frontend builds” can connect general knowledge, team preferences, and original evidence at once:
-
-```markdown
-This issue often occurs in [[digest/wiki/Large TypeScript projects.md]]. When resolving it,
-follow the “add regression tests first” convention in [[digest/personal/Team change preferences.md]].
-
-## Sources
-
-The root cause and applicable scenarios were documented in
-[[daily/2026-08-07/build-debug.md|Build troubleshooting record]].
-```
-
-Knowledge evolves and links are created in the same workflow. Relationships are not invisible edges hidden in a graph database; they are readable, editable content in the files themselves. The files can rebuild the graph—the graph never takes control of the files.
-
-## Memory Index: Turning Ordinary Files into a Searchable Memory Network
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-memory-index.svg" alt="ReMe Memory Index build process" width="100%">
-</p>
-
-Markdown is easy for people to read, but if files are merely piled into directories, agents still struggle to find them
-quickly. The default live index watches Markdown under `daily/` and `digest/`. A separate resource workflow watches
-`resource/` and turns those files into daily cards that enter the same index. For a full rebuild from existing files,
-`reme reindex` also scans `resource/` and JSONL.
-
-A Markdown file is parsed into:
-
- One file node containing file-level information such as its path and frontmatter;
- Multiple semantic chunks split, wherever possible, along the boundaries of headings, paragraphs, lists, and code blocks, while retaining section structure and line numbers;
- Multiple Wikilink edges recording what the file points to and what points back to it.
-
-For retrieval, ReMe can combine three types of signals:
-
-| Retrieval signal | Problem it solves | Example |
-|------------------|-------------------|---------|
-| BM25 keywords | Exact names, terms, and identifiers must not be missed | “CATL”, “issue #184” |
-| Embedding vectors | Semantically similar wording should still match | “build frozen” and “packaging stage not responding” |
-| Wikilink graph | Reveal upstream and downstream relationships after finding a node | From “cobalt” to “ternary cathodes” and related research notes |
-
-The default configuration enables BM25 and Wikilink expansion out of the box. Embeddings are optional and participate in vector retrieval only when enabled. Indexes, graphs, and caches are stored in `metadata/`; even if deleted, they can be rebuilt from the user's source files.
-
-## Memory Search: Find the Answer First, Then Expand Relationships Progressively
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-memory-search.svg" alt="ReMe hybrid search and progressive expansion" width="100%">
-</p>
-
-Many RAG systems put all Top-K passages into the context at once. This is simple, but it creates two problems: isolated chunks lack context, while expanding every neighbor's full text quickly consumes tokens.
-
-ReMe's hybrid search lets BM25 and optional vector retrieval produce their own candidates, then fuses the rankings with RRF. Instead of directly comparing BM25 scores with cosine similarities—two different scales—RRF combines where each result appears in the two ranked lists.
-
-After retrieval, information expands progressively in three layers:
-
-1. **Start with the matching passage**: return the most relevant chunk, file path, and line numbers;
-2. **Then inspect the relationship directory**: show the file's outgoing and incoming links, including only each neighbor's path, name, description, and anchor rather than loading all of its content immediately;
-3. **Finally, go deeper as needed**: the agent decides which relationship is genuinely relevant, then reads the original file or continues traversing the graph.
-
-For example, you ask: “What was the name of the book about attention that Alice recommended last time?”
-
-The first step may find a dinner note that says only, “The title contains the word ‘deep.’” The result also shows that the note links to Alice's personal node and is backlinked by reading notes for *Deep Work*.
-
-The agent does not need to load Alice's entire profile, every reading note, and a whole month of journal entries into its context. It only needs to follow the most relevant link and read once more before answering:
-
-> It was *Deep Work*. Alice recommended it at that dinner, and you later read Chapter 3 and left notes.
-
-This resembles human association: first recall a fragment, then follow the trail to recover the full context.
-
-## Proactive: Discovering Needs You Have Not Yet Put into Words
-
-<p align="center">
-  <img src="../figure/reme-blog/reme-blog-proactive.svg" alt="ReMe Proactive's two-way memory loop" width="100%">
-</p>
-
-At this point, ReMe has two input streams that continuously enrich the knowledge base:
-
- Auto Memory distills personal context from ongoing conversations;
- Auto Resource adds new knowledge from external materials.
-
-Proactive reverses the direction. From accumulated conversations and materials, it discovers topics you have not yet resolved or may want to pursue, along with information you have not noticed but that closely relates to your recent work. These discoveries can then guide what external knowledge enters the system next.
-
-For example, over the past week you separately mentioned that:
-
- Search results lack sources;
- Long documents lose section context after chunking;
- You want to compare several agent-memory evaluation methods.
-
-Even though you never explicitly said, “Help me systematically study the explainability of memory retrieval,” Auto Dream can distill an interest topic from these daily memories:
-
-```yaml
-title: Evaluating the explainability of memory retrieval
-reason: The user has recently focused on source tracing, structure-aware chunking, and memory evaluation.
-evidence: daily/2026-08-07/search-discussion.md
-keywords:
-  - memory search
-  - source attribution
-  - benchmark
-```
-
-In a future beta release, after reading this topic through Proactive, a host agent could ask at an appropriate moment, “Would you like me to turn the retrieval issues we discussed recently into an evaluation plan?” It could also use the topic to initiate a user-authorized research workflow. Users would not need to identify and explicitly specify their interests and scope in advance; external resources related to needs implicit in their conversations could continue flowing into the knowledge base.
-
-There is an important boundary: **ReMe's Proactive feature only reads and exposes interest topics. It does not independently access the internet, send notifications, or rewrite the knowledge base.**
-It does not guess your interests from nowhere. It surfaces clues that already appeared in your behavior and conversations but have not yet been explicitly stated.
-
-## Performance: Can It Retrieve Information from Very Long Histories?
-
-ReMe uses LongMemEval and BEAM to evaluate memory across multiple sessions and extremely long conversations. During evaluation, the agent can use ReAct to search and read over multiple rounds, generate an answer, and then receive an LLM-as-judge score.
-
-| Benchmark | Setting | Sample size | Agentic score | Primary capabilities tested |
-|-----------|---------|------------:|---------------:|-----------------------------|
-| **LongMemEval cleaned-s** | **Overall** | **500 questions** | **89.4%** | Cross-session retrieval, knowledge updates, and temporal reasoning |
-| BEAM | 100K context | 20 cases / 400 questions | 66.1% | Ten types of long-context memory tasks |
-| BEAM | 1M context | 35 cases / 700 questions | 65.0% | Larger-scale, ultra-long conversation settings |
-
-LongMemEval cleaned-s includes single-session facts, preferences, multi-session reasoning, knowledge updates, temporal reasoning, and other question types. ReMe achieved an overall Agentic score of 89.4% across 500 questions. See the [LongMemEval evaluation guide](../../benchmark/longmemeval/README.md) for the complete workflow and breakdown.
-
-BEAM covers ten categories of tasks, including contradiction resolution, event ordering, information extraction, knowledge updates, multi-session reasoning, preference following, summarization, and temporal reasoning. ReMe scored 66.1% on 20 cases / 400 questions with a 100K context and 65.0% on 35 cases / 700 questions with a 1M context. See the [BEAM evaluation guide](../../benchmark/beam/README.md) for the complete setup.
-
-ReMe also uses $\pi$-Bench to evaluate the potential of multi-session reasoning to improve agent proactivity. The PROC score in $\pi$-Bench evaluates capabilities including directly fulfilling hidden intent, guiding targeted clarification, recovering cross-session preferences, reusing cross-session conventions, inferring cross-task dependencies, and advancing underspecified requests. Across five user personas, ReMe Agent achieved an average PROC score of 0.580, outperforming NanoBot by 2.4% under the same test-model configuration. See the [$\pi$-Bench paper](https://arxiv.org/abs/2605.14678) for details about the benchmark.
-
-## Who Is ReMe For?
-
-### People Who Use Agents Directly
-
-If you want AI to understand you continuously throughout a long-term collaboration, ReMe lets your personal assistant stop starting from scratch. Your preferences, project context, important materials, and past decisions accumulate through ongoing conversations and can be found again when they are genuinely relevant.
-
-Researchers, engineers, analysts, and other knowledge workers all fall into this category. Researchers can connect papers, discussions, and reading notes; engineers can preserve project decisions and cross-session troubleshooting experience; analysts can build an evolving record of events, perspectives, and sources. Their professions differ, but they share the same need: AI that can understand the past, accumulate experience, and recover supporting context for the next task.
-
-### Developers Who Build Agents
-
-If you are building an agent, harness, or AI product, ReMe provides an independent long-term memory layer. Through its CLI, HTTP API, MCP Server, or Python API, you can let multiple agents share the same file-based workspace without reimplementing memory extraction, knowledge organization, hybrid retrieval, and relationship expansion for every application.
-
-Files remain the source of truth, while indexes and caches can be rebuilt at any time. This also makes it easier to determine whether an incorrect retrieval originated in the source material, memory consolidation, or the retrieval pipeline.
-
-Ultimately, ReMe is for users and developers who want AI to do more than “answer this one request”: they want it to understand the past, accumulate experience, and know them better over the course of a long-term collaboration. We want agents to understand you better the more you use them—but that understanding should not live in a black box that you cannot inspect, correct, or take with you.
-
-ReMe's answer is straightforward:
-
- Memories are files owned by the user;
- Original information preserves what happened, while long-term knowledge preserves the abstraction;
- New conversations and resources keep flowing in, while existing knowledge is continuously supplemented and corrected;
- Every conclusion can be traced to relationships and sources through Wikilinks;
- Indexes and caches serve the files rather than replace them;
- Agents can remember, organize, search, and discover, but users always retain ultimate control.
-
-When these mechanisms come together, a personal knowledge base is no longer a repository you must maintain by hand.
-
-It remembers a little more after every conversation and understands a little more after every new resource. At night, it reorganizes scattered experiences. When a future question arises, it follows the connections between pieces of knowledge and brings back the memory you actually need.
-
-That is what ReMe sets out to do: **make memory not only persistent, but continuously evolving.**
-
-## Integrate ReMe with the Agents You Already Use
-
-ReMe can run as a local memory service accessed through its CLI, HTTP API, or MCP Server, or it can be embedded in a host
-process through its Python API. The default HTTP service can also serve ReMe Studio at the same address for browsing,
-editing, and searching the workspace and inspecting the digest wikilink graph. Different agents can choose the integration
-that best fits their runtime environment and share the same local memory workspace when needed.
-
-| Agent | Recommended integration | Capabilities after integration |
-|-------|-------------------------|--------------------------------|
-| **DeepSeek Harness** | Install [`@agentscope-ai/reme`](../../typescript/README.md#deepseek-harness) as a DSH profile bundle. | Long-term memory guidance, `reme_search`, automatic capture of completed main-agent turns, and scheduled Auto Dream. |
-| **OpenClaw** | Install [`@agentscope-ai/reme`](../../typescript/README.md#openclaw) as the native memory plugin. | Recall before conversational root-agent runs, explicit search, automatic turn capture, and scheduled Auto Dream. |
-| **QwenPaw** | Embed ReMe in-process through the Python API. | Reuse the host application's lifecycle and model configuration while keeping memories local and file-based. |
-| **Claude Code** | Start the streamable HTTP MCP Service and install [`integrations/claude_code/reme`](../../integrations/claude_code/reme). | MCP memory-recall tools, the `reme-memory` skill, and a Stop hook that automatically records sessions. |
-| **Hermes** | Start the HTTP Service and install [`integrations/hermes_agent`](../../integrations/hermes_agent). | Automatically recall relevant memories before model calls and invoke `auto_memory` asynchronously after each conversation turn. |
-| **Codex and other CLI-capable agents** | Copy or install [`skills/reme_memory/SKILL.md`](../../skills/reme_memory/SKILL.md). | Search, read, and write memories through the CLI; automatic recording requires the host agent to integrate explicitly with the conversation lifecycle. |
-
-For installation, configuration, and integration demos, see the [README](../../README.md).
-
-## Contributions Welcome
-
-ReMe is open source, and we welcome the community's help in making this self-evolving memory system more complete:
-
- Integrate more agents and harnesses so different runtime environments can use the same user-owned long-term memory;
- Contribute new Auto Resource sources and workflows so papers, news, and other public materials can continuously enter the knowledge base;
- Improve Auto Memory, Auto Dream, Auto Link, hybrid search, and Proactive so memories are organized more accurately, relationships are clearer, and retrieval is more reliable;
- Add application examples, evaluation tasks, and diagnostic reports to help us understand successes and failures in real long-term use;
- Improve documentation and tests, or share your needs and ideas for personal AI memory through an Issue.
-
-Whether it is a code contribution, a use case, a bug report, or a new memory workflow, every contribution can bring ReMe closer to a truly readable, controllable, and continuously evolving personal knowledge base.
-
-Contribution guide: [https://docs.agentscope.io/reme/latest/en/contribution](https://docs.agentscope.io/reme/latest/en/contribution)
--- a/docs/en/reme_scene.md
+++ b/docs/en/reme_scene.md
@ -54,22 +54,20 @@ session/
 daily/
 ├── 2026-05-18.md
 └── 2026-05-18/
-    ├── cobalt-supply-risk.md
-    ├── glencore-output-update.md
-    ├── drc-cobalt-policy.md
-    ├── high-nickel-cathode-trend.md
+    ├── 2026-05-18-close.md
+    ├── glencore-q3.md
+    ├── cobalt-policy.md
+    ├── cathode-trend.md
    └── interests.yaml          # generated after auto_dream
 ```

 The corresponding flow is:

- `auto_memory` saves a filtered source conversation record to `session/dialog/<session_id>.jsonl`, then asks the agent to write
-  important facts to a topic-named `daily/<date>/<generated_name>.md`. The note keeps `session_id` and
-  `source_conversation` in frontmatter for stable lookup and provenance.
- `resource_watch_loop` watches text-file changes under `resource/` and triggers `auto_resource_step` to write a daily note
-  with `source_resource`. The agent suggests a content-based filename, which the system sanitizes and de-duplicates; it is
-  not guaranteed to match the resource filename.
- Auto Memory, Auto Resource, and Auto Dream refresh `daily/<date>.md` after writing.
+- `auto_memory` saves the original conversation to `session/dialog/<session_id>.jsonl`, then asks the agent to write
+  important facts to `daily/<date>/<session_id>.md`.
+- `resource_watch_loop` watches text-file changes under `resource/` and triggers `auto_resource_step` to write a
+  same-named daily note.
+- `daily_create` maintains `daily/<date>.md` as the index page for that day.

 ### Day 1 evening: Auto Dream writes to Digest

@ -83,8 +81,8 @@ reme auto_dream date=2026-05-18

 ```text
 dream_extract_step
-  scan the daily window from 2026-05-17 through 2026-05-18 by default
-  output at most 5 units plus topics from changed files
+  scan daily/2026-05-18.md and changed files under daily/2026-05-18/
+  output units and topics
 dream_integrate_step
  recall existing digest nodes with node_search for each unit
  decide CREATE / CORROBORATE / REFINE / CORRECT
@ -112,19 +110,17 @@ name: Cobalt
 description: A key raw material for lithium-battery cathodes, with production concentrated in the DRC
 ---

-# Cobalt
+downstream_product:: [[digest/wiki/ternary-cathodes.md]]
+producer:: [[digest/wiki/glencore.md]]
+source_event:: [[daily/2026-05-18/2026-05-18-close.md]]

-Used by [[digest/wiki/ternary-cathodes.md]]; a major producer is [[digest/wiki/glencore.md]].
+# Cobalt

 ## Supply
 Glencore's third-quarter cobalt output fell 18% year over year. Continue monitoring how tighter supply affects prices.

 ## Policy risk
 Changes to mining-rights policy in the DRC may affect KFM mine operations and should be tracked together with CMOC.
-
-## Sources
-
-The production decline and policy risk were recorded in [[daily/2026-05-18/cobalt-supply-risk.md]].
 ```

 Note that wikilinks use literal path semantics. Prefer complete workspace-relative paths with the `.md` extension. ReMe
@ -189,10 +185,10 @@ The result shape is:
 Glencore's third-quarter cobalt output fell 18% year over year...

  outlinks:
-    -> digest/wiki/ternary-cathodes.md name="Ternary Cathodes"
-    -> digest/wiki/glencore.md name="Glencore"
+    -> digest/wiki/ternary-cathodes.md name="Ternary Cathodes" via predicate=downstream_product
+    -> digest/wiki/glencore.md name="Glencore" via predicate=producer
  inlinks:
-    <- digest/wiki/ternary-cathodes.md name="Ternary Cathodes"
+    <- digest/wiki/ternary-cathodes.md name="Ternary Cathodes" via predicate=upstream_material

 ========== digest/wiki/ternary-cathodes.md:5-18 [score=0.0139 keyword=3.2017] ==========
 ...
@ -237,7 +233,7 @@ topics:
    reason: The user repeatedly mentioned KFM and cobalt-price risk today
    keywords: [cobalt, DRC, CMOC, KFM]
    paths:
-      - daily/2026-05-18/cobalt-supply-risk.md
+      - daily/2026-05-18/2026-05-18-close.md
 ```

 Call:
@ -305,9 +301,10 @@ name: TypeScript project build OOM diagnostic path
 description: When a build stalls and memory grows, check the type-checking process first
 ---

-# TypeScript Project Build OOM Diagnostic Path
+source_event:: [[daily/2026-03-10/build-oom-2026-03-10.md]]
+related_preference:: [[digest/personal/code-style.md]]

-Apply [[digest/personal/code-style.md]] while following this runbook.
+# TypeScript Project Build OOM Diagnostic Path

 ## Symptoms
 The build stalls near the end. CPU usage is low, but memory keeps growing.
@ -320,10 +317,6 @@ The build stalls near the end. CPU usage is low, but memory keeps growing.
 ## Known ineffective paths
 - Deleting `.cache` alone did not resolve the issue on 2026-03-10.
 - Upgrading the terser plugin did not resolve the issue on 2026-03-10.
-
-## Sources
-
-The failed attempts and successful memory adjustment were recorded in [[daily/2026-03-10/build-oom-2026-03-10.md]].
 ```

 Example `digest/personal/code-style.md`:
@ -376,7 +369,7 @@ and upgrading the minification plugin did not help last time.

 - `digest/procedure/` stores both "how to do it" and "which paths failed," letting the agent reuse diagnostic experience.
 - `digest/personal/` stores user preferences so the agent can follow the same engineering style across sessions.
- The source conversation record remains under `session/dialog/`; daily records stay traceable, and digest is only the
+- The original conversation remains under `session/dialog/`; daily records stay traceable, and digest is only the
  long-term distilled result.

 ## Scenario 3: A Personal Second Brain
@ -418,14 +411,13 @@ name: Alice
 description: A friend of the user who often recommends reading material
 ---

+recommended_book:: [[digest/wiki/deep-work.md]]
+source_event:: [[daily/2026-04-20/lunch-with-alice.md]]
+
 # Alice

 ## Reading recommendations
-At lunch on 2026-04-20, Alice recommended [[digest/wiki/deep-work.md]], a book about attention and deep work.
-
-## Sources
-
-The recommendation was recorded in [[daily/2026-04-20/lunch-with-alice.md]].
+At lunch on 2026-04-20, Alice recommended a book about attention and deep work.
 ```

 ### An associative recall
@ -447,7 +439,7 @@ Matches:
 ```text
 digest/personal/alice.md
  outlinks:
-    -> digest/wiki/deep-work.md
+    -> digest/wiki/deep-work.md via predicate=recommended_book
 daily/2026-04-20/lunch-with-alice.md
 ```

--- a/docs/figure/auto-dream-and-proactive.svg
+++ b/docs/figure/auto-dream-and-proactive.svg
@ -1,117 +1,129 @@
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+<svg xmlns="http://www.w3.org/2000/svg" width="1200" height="640" viewBox="0 0 1200 640" role="img" aria-labelledby="title desc">
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+  <desc id="desc">A left-to-right flow from changed daily notes to digest integration, interest topic writing, catalog checkpointing, and proactive reads.</desc>
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+  <rect class="chip" x="660" y="250" width="176" height="44"/>
+  <text class="chip-title" x="748" y="269" text-anchor="middle">BM25 search</text>
+  <text class="chip-text" x="748" y="287" text-anchor="middle">keyword-ranked chunks</text>
+  <rect class="chip" x="660" y="314" width="176" height="44"/>
+  <text class="chip-title" x="748" y="333" text-anchor="middle">optional vector search</text>
+  <text class="chip-text" x="748" y="351" text-anchor="middle">RRF fusion when enabled</text>

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-    <circle class="badge" cx="952" cy="188" r="15"/>
-    <text class="step-num" x="952" y="192" text-anchor="middle">4</text>
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-    <text class="chip-title" x="1026" y="269" text-anchor="middle">top chunks</text>
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-    <text class="chip-text" x="1026" y="351" text-anchor="middle">name, description, via</text>
+  <rect class="panel" x="916" y="150" width="220" height="260"/>
+  <circle class="badge" cx="952" cy="188" r="15"/>
+  <text class="step-num" x="952" y="192" text-anchor="middle">4</text>
+  <text class="step-title" x="980" y="194">Expand context</text>
+  <text class="step-subtitle" x="944" y="224">expand_links</text>
+  <rect class="chip" x="944" y="250" width="164" height="44"/>
+  <text class="chip-title" x="1026" y="269" text-anchor="middle">top chunks</text>
+  <text class="chip-text" x="1026" y="287" text-anchor="middle">path + line range</text>
+  <rect class="chip" x="944" y="314" width="164" height="44"/>
+  <text class="chip-title" x="1026" y="333" text-anchor="middle">outlinks + inlinks</text>
+  <text class="chip-text" x="1026" y="351" text-anchor="middle">name, description, via</text>

-    <path class="arrow" d="M296 280 H348"/>
-    <path class="arrow" d="M580 280 H632"/>
-    <path class="arrow" d="M864 280 H916"/>
+  <path class="arrow" d="M296 280 H348"/>
+  <path class="arrow" d="M580 280 H632"/>
+  <path class="arrow" d="M864 280 H916"/>

-    <rect class="panel" x="142" y="484" width="916" height="76"/>
-    <text class="note" x="190" y="514">Rebuild scope</text>
-    <text class="chip-text" x="190" y="536">reindex adds resource + JSONL.</text>
-    <line class="line" x1="410" y1="500" x2="410" y2="544"/>
-    <text class="chip-title" x="454" y="514">BM25 is enabled by default</text>
-    <text class="chip-text" x="454" y="536">Recall cap: 200; embeddings opt-in.</text>
-    <line class="line" x1="720" y1="500" x2="720" y2="544"/>
-    <text class="chip-title" x="764" y="514">Results stay compact first</text>
-    <text class="chip-text" x="764" y="536">Use read or traverse for deeper expansion.</text>
+  <rect class="panel" x="142" y="484" width="916" height="76"/>
+  <text class="note" x="190" y="514">Default path</text>
+  <text class="chip-text" x="190" y="536">BM25 first, vector optional.</text>
+  <line class="line" x1="410" y1="500" x2="410" y2="544"/>
+  <text class="chip-title" x="454" y="514">BM25 is enabled by default</text>
+  <text class="chip-text" x="454" y="536">embedding_store is empty unless configured.</text>
+  <line class="line" x1="720" y1="500" x2="720" y2="544"/>
+  <text class="chip-title" x="764" y="514">Results stay compact first</text>
+  <text class="chip-text" x="764" y="536">Use read or traverse for deeper expansion.</text>

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-    <text class="note" x="888" y="454" text-anchor="middle">link expansion is contextual, not a full-workspace dump</text>
+  <path class="soft-arrow" d="M1026 410 C1026 456 748 452 748 410"/>
+  <text class="note" x="888" y="454" text-anchor="middle">link expansion is contextual, not a full-workspace dump</text>
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-    <title id="title">ReMe auto memory and auto resource flow</title>
-    <desc id="desc">A concise flow where auto memory and auto resource both create daily cards, which are indexed in the
-        daily memory layer and then used by dream and search.
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-    </defs>
+<svg xmlns="http://www.w3.org/2000/svg" width="1200" height="640" viewBox="0 0 1200 640" role="img" aria-labelledby="title desc">
+  <title id="title">ReMe auto memory and auto resource flow</title>
+  <desc id="desc">A concise flow where auto memory and auto resource both create daily cards, which are indexed in the daily memory layer and then used by dream and search.</desc>
+  <defs>
+    <style>
+      .bg { fill: #fffdf8; }
+      .title { font: 700 30px "Comic Sans MS", "Bradley Hand", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; fill: #1f2430; }
+      .subtitle { font: 14px "Comic Sans MS", "Bradley Hand", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; fill: #556276; }
+      .step-num { font: 700 12px "Comic Sans MS", "Bradley Hand", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; fill: #ffffff; }
+      .step-title { font: 700 18px "Comic Sans MS", "Bradley Hand", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; fill: #1f2430; }
+      .step-subtitle { font: 13px "Comic Sans MS", "Bradley Hand", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; fill: #556276; }
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+      .chip { fill: #f8fbff; stroke: #1f2430; stroke-width: 1.6; rx: 11; ry: 11; stroke-linecap: round; stroke-linejoin: round; stroke-dasharray: 6 5; }
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+      .arrow { stroke: #7f8b9d; stroke-width: 1.45; fill: none; stroke-linecap: round; stroke-linejoin: round; marker-end: url(#arrow); }
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+      .line { stroke: #a3adbd; stroke-width: 1.25; stroke-linecap: round; }
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+    <marker id="arrow" markerWidth="6" markerHeight="6" refX="5" refY="2" orient="auto" markerUnits="strokeWidth">
+      <path d="M0,0 L0,4 L5,2 z" fill="#7f8b9d"/>
+    </marker>
+    <marker id="arrow-soft" markerWidth="6" markerHeight="6" refX="5" refY="2" orient="auto" markerUnits="strokeWidth">
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+  </defs>

-    <rect class="bg" x="0" y="0" width="1200" height="640"/>
-    <text class="title" x="600" y="54" text-anchor="middle">Auto Memory &amp; Auto Resource</text>
-    <text class="subtitle" x="600" y="80" text-anchor="middle">Two ingestion paths create daily cards, then share one daily index and downstream memory flow.</text>
+  <rect class="bg" x="0" y="0" width="1200" height="640"/>
+  <text class="title" x="600" y="54" text-anchor="middle">Auto Memory &amp; Auto Resource</text>
+  <text class="subtitle" x="600" y="80" text-anchor="middle">Two ingestion paths create daily cards, then share one daily index and downstream memory flow.</text>

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-    <circle class="badge" cx="118" cy="170" r="15"/>
-    <text class="step-num" x="118" y="174" text-anchor="middle">1</text>
-    <text class="step-title" x="146" y="176">Inputs</text>
-    <text class="step-subtitle" x="112" y="206">memory + resource</text>
-    <rect class="chip" x="112" y="234" width="244" height="54"/>
-    <text class="chip-title" x="234" y="255" text-anchor="middle">Auto Memory</text>
-    <text class="chip-text" x="234" y="273" text-anchor="middle">conversation -> topic-named card</text>
-    <rect class="chip" x="112" y="312" width="244" height="54"/>
-    <text class="chip-title" x="234" y="333" text-anchor="middle">Auto Resource</text>
-    <text class="chip-text" x="234" y="351" text-anchor="middle">source file -> topic-named card</text>
-    <rect class="chip" x="112" y="388" width="244" height="28"/>
-    <text class="chip-text" x="234" y="407" text-anchor="middle">original sources remain verifiable</text>
+  <rect class="panel" x="84" y="132" width="300" height="300"/>
+  <circle class="badge" cx="118" cy="170" r="15"/>
+  <text class="step-num" x="118" y="174" text-anchor="middle">1</text>
+  <text class="step-title" x="146" y="176">Inputs</text>
+  <text class="step-subtitle" x="112" y="206">memory + resource</text>
+  <rect class="chip" x="112" y="234" width="244" height="54"/>
+  <text class="chip-title" x="234" y="255" text-anchor="middle">Auto Memory</text>
+  <text class="chip-text" x="234" y="273" text-anchor="middle">conversation -> session daily card</text>
+  <rect class="chip" x="112" y="312" width="244" height="54"/>
+  <text class="chip-title" x="234" y="333" text-anchor="middle">Auto Resource</text>
+  <text class="chip-text" x="234" y="351" text-anchor="middle">dated file -> resource daily card</text>
+  <rect class="chip" x="112" y="388" width="244" height="28"/>
+  <text class="chip-text" x="234" y="407" text-anchor="middle">original sources remain verifiable</text>

-    <rect class="panel" x="450" y="132" width="300" height="300"/>
-    <circle class="badge" cx="484" cy="170" r="15"/>
-    <text class="step-num" x="484" y="174" text-anchor="middle">2</text>
-    <text class="step-title" x="512" y="176">Daily Memory Layer</text>
-    <text class="step-subtitle" x="478" y="206">daily/&lt;date&gt;/</text>
-    <rect class="chip" x="478" y="234" width="244" height="54"/>
-    <text class="chip-title" x="600" y="255" text-anchor="middle">daily cards</text>
-    <text class="chip-text" x="600" y="273" text-anchor="middle">facts, context, actions</text>
-    <rect class="chip" x="478" y="312" width="244" height="54"/>
-    <text class="chip-title" x="600" y="333" text-anchor="middle">day index</text>
-    <text class="chip-text" x="600" y="351" text-anchor="middle">daily/&lt;date&gt;.md links cards</text>
-    <rect class="chip" x="478" y="388" width="244" height="28"/>
-    <text class="chip-text" x="600" y="407" text-anchor="middle">one readable daily memory stream</text>
+  <rect class="panel" x="450" y="132" width="300" height="300"/>
+  <circle class="badge" cx="484" cy="170" r="15"/>
+  <text class="step-num" x="484" y="174" text-anchor="middle">2</text>
+  <text class="step-title" x="512" y="176">Daily Memory Layer</text>
+  <text class="step-subtitle" x="478" y="206">daily/&lt;date&gt;/</text>
+  <rect class="chip" x="478" y="234" width="244" height="54"/>
+  <text class="chip-title" x="600" y="255" text-anchor="middle">daily cards</text>
+  <text class="chip-text" x="600" y="273" text-anchor="middle">facts, context, actions</text>
+  <rect class="chip" x="478" y="312" width="244" height="54"/>
+  <text class="chip-title" x="600" y="333" text-anchor="middle">day index</text>
+  <text class="chip-text" x="600" y="351" text-anchor="middle">daily/&lt;date&gt;.md links cards</text>
+  <rect class="chip" x="478" y="388" width="244" height="28"/>
+  <text class="chip-text" x="600" y="407" text-anchor="middle">one readable daily memory stream</text>

-    <rect class="panel" x="816" y="132" width="300" height="300"/>
-    <circle class="badge" cx="850" cy="170" r="15"/>
-    <text class="step-num" x="850" y="174" text-anchor="middle">3</text>
-    <text class="step-title" x="878" y="176">Downstream</text>
-    <text class="step-subtitle" x="844" y="206">dream + search</text>
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-    <text class="chip-title" x="966" y="255" text-anchor="middle">Auto Dream</text>
-    <text class="chip-text" x="966" y="273" text-anchor="middle">daily material -> digest memory</text>
-    <rect class="chip" x="844" y="312" width="244" height="54"/>
-    <text class="chip-title" x="966" y="333" text-anchor="middle">Memory Search</text>
-    <text class="chip-text" x="966" y="351" text-anchor="middle">retrieve daily and digest nodes</text>
-    <rect class="chip" x="844" y="388" width="244" height="28"/>
-    <text class="chip-text" x="966" y="407" text-anchor="middle">daily is the shared ingestion layer</text>
+  <rect class="panel" x="816" y="132" width="300" height="300"/>
+  <circle class="badge" cx="850" cy="170" r="15"/>
+  <text class="step-num" x="850" y="174" text-anchor="middle">3</text>
+  <text class="step-title" x="878" y="176">Downstream</text>
+  <text class="step-subtitle" x="844" y="206">dream + search</text>
+  <rect class="chip" x="844" y="234" width="244" height="54"/>
+  <text class="chip-title" x="966" y="255" text-anchor="middle">Auto Dream</text>
+  <text class="chip-text" x="966" y="273" text-anchor="middle">daily material -> digest memory</text>
+  <rect class="chip" x="844" y="312" width="244" height="54"/>
+  <text class="chip-title" x="966" y="333" text-anchor="middle">Memory Search</text>
+  <text class="chip-text" x="966" y="351" text-anchor="middle">retrieve daily and digest nodes</text>
+  <rect class="chip" x="844" y="388" width="244" height="28"/>
+  <text class="chip-text" x="966" y="407" text-anchor="middle">daily is the shared ingestion layer</text>

-    <path class="arrow" d="M384 282 H450"/>
-    <path class="arrow" d="M750 282 H816"/>
+  <path class="arrow" d="M384 282 H450"/>
+  <path class="arrow" d="M750 282 H816"/>

-    <rect class="panel" x="126" y="502" width="948" height="82"/>
-    <text class="note" x="176" y="532">Conversation source</text>
-    <text class="chip-text" x="176" y="554">session/dialog/&lt;session_id&gt;.jsonl</text>
-    <line class="line" x1="454" y1="518" x2="454" y2="566"/>
-    <text class="chip-title" x="498" y="532">Resource source</text>
-    <text class="chip-text" x="498" y="554">resource/[&lt;date&gt;/]&lt;resource_file&gt;</text>
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-    <text class="chip-title" x="838" y="532">Daily output</text>
-    <text class="chip-text" x="838" y="554">daily/&lt;date&gt;/&lt;name&gt;.md</text>
+  <rect class="panel" x="126" y="502" width="948" height="82"/>
+  <text class="note" x="176" y="532">Conversation source</text>
+  <text class="chip-text" x="176" y="554">session/dialog/&lt;session_id&gt;.jsonl</text>
+  <line class="line" x1="454" y1="518" x2="454" y2="566"/>
+  <text class="chip-title" x="498" y="532">Resource source</text>
+  <text class="chip-text" x="498" y="554">resource/&lt;date&gt;/&lt;resource_file&gt;</text>
+  <line class="line" x1="796" y1="518" x2="796" y2="566"/>
+  <text class="chip-title" x="838" y="532">Daily output</text>
+  <text class="chip-text" x="838" y="554">daily cards plus daily/&lt;date&gt;.md</text>

-    <path class="soft-arrow" d="M966 432 C966 474 600 474 600 432"/>
-    <text class="note" x="782" y="478" text-anchor="middle">downstream flows read the same daily memory layer</text>
+  <path class="soft-arrow" d="M966 432 C966 474 600 474 600 432"/>
+  <text class="note" x="782" y="478" text-anchor="middle">downstream flows read the same daily memory layer</text>
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@ -1,83 +1,97 @@
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-    <title id="title">ReMe design philosophy</title>
-    <desc id="desc">A simple diagram showing ReMe's design philosophy: file-native memory, progressive abstraction, and
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-                markerUnits="strokeWidth">
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+<svg xmlns="http://www.w3.org/2000/svg" width="1200" height="520" viewBox="0 0 1200 520" role="img" aria-labelledby="title desc">
+  <title id="title">ReMe design philosophy</title>
+  <desc id="desc">A simple diagram showing ReMe's design philosophy: file-native memory, progressive abstraction, and explicit relationship graph for agents.</desc>
+  <defs>
+    <style>
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+    <path class="arrow" d="M204 161 H226"/>
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 </svg>
--- a/docs/zh/auto_dream.md
+++ b/docs/zh/auto_dream.md
@ -1,14 +1,13 @@
 # Auto Dream

-`auto_dream` 是 ReMe 的 daily 到 digest 的长期记忆沉淀流程。它默认扫描目标日期及前一天的 daily 输入，只处理相对上次 dream
-发生变化的文件，从整个扫描窗口中抽取少量高价值 memory units，整合进 `digest/`，再生成目标日期可供主动提醒使用的
-`interests.yaml`。
+`auto_dream` 是 ReMe 的 daily 到 digest 的长期记忆沉淀流程。它扫描指定日期的 daily 输入，只处理相对上次 dream
+发生变化的文件，把值得长期保留的内容抽取成 memory units，整合进 `digest/`，再生成当天可供主动提醒使用的 `interests.yaml`。

 <p align="center">
  <img src="../figure/auto-dream-and-proactive.svg" alt="ReMe Auto Dream and Proactive 从 daily 到 digest 再到 proactive 的流程" width="92%">
 </p>

-它消费的 daily 输入通常来自 [Auto Memory](./auto_memory.md) 和 [Auto Resource](./auto_resource.md)。`digest/`、Sources 章节
+它消费的 daily 输入通常来自 [Auto Memory](./auto_memory.md) 和 [Auto Resource](./auto_resource.md)。`digest/`、`derived_from::`
 和 wikilink 的文件语义见 [Memory as File](./memory_as_file.md)；Integrate 阶段的链接策略详见 [Auto Link](./auto_link.md)。
 `interests.yaml` 的读取接口见 [Proactive](./proactive.md)。

@ -26,12 +25,6 @@ auto_dream:
    hint:
      type: string
      default: ""
-    scan_days:
-      type: integer
-      default: 2
-    max_units:
-      type: integer
-      default: 5
    topic_count:
      type: integer
      default: 3
@ -42,8 +35,6 @@ auto_dream:
    - backend: dream_extract_step
      file_catalog: dream
      topic_session_id: interests
-      scan_days: 2
-      max_units: 5
    - backend: dream_integrate_step
    - backend: dream_topics_step
      topic_count: 3
@ -54,36 +45,32 @@ auto_dream:

 参数含义：

-| 参数                   | 作用                                                              |
-|------------------------|-------------------------------------------------------------------|
-| `date`                 | 要处理的日期，格式为 `YYYY-MM-DD`。为空时使用应用时区中的今天。   |
-| `hint`                 | 调用方给抽取和整合阶段的额外指导。                                |
-| `scan_days`            | 以 `date` 结尾的最近日期窗口；默认扫描 2 天，最小为 1。           |
-| `max_units`            | 一次最多抽取多少个可复用 unit；默认 5。                           |
-| `topic_count`          | 最终写入 `interests.yaml` 的 topic 上限，默认 3。                 |
+| 参数                     | 作用                                             |
+|------------------------|------------------------------------------------|
+| `date`                 | 要处理的日期，格式为 `YYYY-MM-DD`。为空时使用应用时区中的今天。         |
+| `hint`                 | 调用方给抽取和整合阶段的额外指导。                              |
+| `topic_count`          | 最终写入 `interests.yaml` 的 topic 上限，默认 3。         |
 | `topic_diversity_days` | 选择 topic 时参考过去多少天的 `interests.yaml` 避免重复，默认 7。 |

 ## 输入和输出

-输入来自以指定日期结尾的最近 `scan_days` 天 daily markdown。例如 `date=2026-06-20`、`scan_days=2` 时会扫描：
+输入来自指定日期的 daily markdown：

 ```text
-daily/2026-06-19.md
-daily/2026-06-19/**/*.md
-daily/2026-06-20.md
-daily/2026-06-20/**/*.md
+daily/<date>.md
+daily/<date>/**/*.md
 ```

-扫描窗口内的 `daily/<date>/interests.yaml` 都不作为抽取输入，避免上一轮主动主题反过来污染下一轮抽取。最终 topic 只写入目标日期。
+`daily/<date>/interests.yaml` 不作为抽取输入，避免上一轮主动主题反过来污染下一轮抽取。

 主要输出有三类：

-| 输出                           | 说明                                              |
-|--------------------------------|---------------------------------------------------|
-| `digest/procedure/*.md`        | 方法、流程、runbook、可执行经验。                 |
-| `digest/personal/*.md`         | 用户、团队、项目相关的偏好、事实、长期上下文。    |
-| `digest/wiki/*.md`             | 通用知识、概念、观察、决策先例。                  |
-| `daily/<date>/interests.yaml`  | 当天值得上层 Agent 主动关注的兴趣主题。           |
+| 输出                                  | 说明                                  |
+|-------------------------------------|-------------------------------------|
+| `digest/procedure/*.md`             | 方法、流程、runbook、可执行经验。                |
+| `digest/personal/*.md`              | 用户、团队、项目相关的偏好、事实、长期上下文。             |
+| `digest/wiki/*.md`                  | 通用知识、概念、观察、决策先例。                    |
+| `daily/<date>/interests.yaml`       | 当天值得上层 Agent 主动关注的兴趣主题。             |
 | `metadata/file_catalog/dream*` | dream 专用 catalog，用于判断 daily 输入是否变化。 |

 ## 四个阶段
@ -92,18 +79,16 @@ daily/2026-06-20/**/*.md

 `dream_extract_step` 做三件事：

-1. 刷新扫描窗口内每天的索引页 `daily/<date>.md`。
-2. 扫描这些日期的索引页和 `daily/<date>/**/*.md`，与 `file_catalog: dream` 中记录的 mtime 对比。
-3. 只把 changed files 一起交给 LLM，全局抽取两类结构化结果：`units` 和 `topics`。
+1. 刷新当天索引页 `daily/<date>.md`。
+2. 扫描 `daily/<date>.md` 和 `daily/<date>/**/*.md`，与 `file_catalog: dream` 中记录的 mtime 对比。
+3. 只把 changed files 交给 LLM，全局抽取两类结构化结果：`units` 和 `topics`。

-`units` 是准备沉淀进 digest 的长期记忆单元，包含 `name`、`bucket`、`summary`、`paths`。一次最多返回 `max_units`
-个，抽取器会优先合并指向同一抽象的跨文件证据，并丢弃短暂提及、逐文件摘要和缺少复用价值的弱候选。`bucket` 只允许
-`procedure`、`personal`、`wiki`；未知值会路由到 `wiki`。
+`units` 是准备沉淀进 digest 的长期记忆单元，包含 `name`、`bucket`、`summary`、`paths`。`bucket` 只允许 `procedure`、
+`personal`、`wiki`；未知值会路由到 `wiki`。

 `topics` 是当天主动兴趣候选，包含 `title`、`reason`、`evidence`、`keywords`、`paths`，后续由 Topics 阶段再筛选。

-如果没有 changed files，Extract 会成功返回空 units；Integrate 随后没有 unit 可处理，Topics 保留目标日期已有的 topics，Finish
-仍会正常汇总 catalog。如果有变化但没有配置 LLM，Extract 会失败，因为抽取依赖 LLM。
+如果没有 changed files，流程会提前成功结束后续抽取工作；如果有变化但没有配置 LLM，Extract 会失败，因为抽取依赖 LLM。

 ### 2. Integrate

@ -116,17 +101,14 @@ node_search, read, frontmatter_read, write, edit, frontmatter_update
 这一阶段承担 `auto_link` 的核心职责：先用 `node_search` 在 digest 节点级召回相似或相关节点，再判断是新建还是更新，最后把来源和相关
 digest 节点写成 wikilink。具体召回、去重和写边规则见 [Auto Link](./auto_link.md)。

-Extract 已经承担“是否值得长期记住”的过滤，因此 Integrate 不提供 `SKIP` 动作：每个进入本阶段的 unit 都应落到且只落到一个
-digest 节点。新增与更新都必须保留来源，并把相关 digest 链接写进有上下文的句子；不能只写裸 Wikilink 或独立的关系字段。
-
 整合动作只有四种：

-| 动作          | 含义                                           |
-|---------------|------------------------------------------------|
-| `CREATE`      | 没有相同抽象，创建新的 digest 节点。           |
-| `CORROBORATE` | 同一记忆再次出现，追加来源或强化表述。         |
+| 动作            | 含义                      |
+|---------------|-------------------------|
+| `CREATE`      | 没有相同抽象，创建新的 digest 节点。  |
+| `CORROBORATE` | 同一记忆再次出现，追加来源或强化表述。     |
 | `REFINE`      | 新材料补充了边界、步骤、前提、适用范围或细节。 |
-| `CORRECT`     | 新材料修正了旧节点的错误、遗漏或冲突。         |
+| `CORRECT`     | 新材料修正了旧节点的错误、遗漏或冲突。     |

 Integrate 成功的 unit 会记录到 `integrate_results`；失败的 unit 会进入 `failed_units`，其来源路径会进入 `failed_paths`。
 Finish 阶段不会 checkpoint 失败路径，保证下次还能重试。
@ -139,7 +121,7 @@ Finish 阶段不会 checkpoint 失败路径，保证下次还能重试。

 ```text
 daily/<date>/interests.yaml
-daily/<过去 topic_diversity_days 天中的每一天>/interests.yaml
+daily/<previous-date>/interests.yaml
 ```

 同一天已有 topics 会被保留，最近 `topic_diversity_days` 天出现过的相似主题会被去重。默认最多写 3 个 topic。配置了 LLM 时会让
@ -167,7 +149,7 @@ topics:
 `dream_finish_step` 负责收尾：

 1. 将成功处理的 changed paths 写入 `file_catalog: dream`。
-2. 将目标日期的 `daily/<date>/interests.yaml` 和扫描窗口内每个已刷新的 day-index 页也写入 catalog。
+2. 将 `daily/<date>/interests.yaml` 和 `daily/<date>.md` 也写入 catalog。
 3. 如果有 upsert 或 delete，持久化 dream catalog。
 4. 返回包含 scanned、changed、integrated、topics、checkpoint 等计数的摘要。

@ -187,12 +169,6 @@ reme auto_dream date=2026-06-20
 reme auto_dream date=2026-06-20 hint="优先沉淀工程决策和长期偏好"
 ```

-覆盖默认扫描窗口和 unit 上限：
-
-```bash
-reme auto_dream date=2026-06-20 scan_days=3 max_units=8
-```
-
 也可以在配置中把同一组 step 放进 `cron` job，例如每天凌晨运行：

 ```yaml
@ -213,8 +189,7 @@ jobs:

 `auto_dream` 只消费 daily 输入，不改写 daily 正文。daily 是事实和现场记录，digest 才是抽象后的长期记忆层。

-`digest` 不是原文复制。正文应保留可复用抽象，Sources 章节用带上下文的完整句子指回来源，例如
-`该决策记录在 [[daily/<date>/decision.md]] 中。`链接写法遵循
+`digest` 不是原文复制。正文应保留可复用抽象，细节通过 `derived_from:: [[daily/<date>/...]]` 指回来源。链接写法遵循
 [Memory as File](./memory_as_file.md) 中的 workspace-relative wikilink 语义。

 `auto_dream` 不凭空生成总览。只有 daily 输入中确实出现、并被抽取为 unit 或 topic 的内容，才会进入 digest 或
--- a/Show more
+++ b/Show more