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‎.github/workflows/agent-memory-client.yml

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name: Agent Memory Client CI
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on:
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push:
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branches: [main]
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tags:
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- 'client/v*.*.*'
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pull_request:
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branches: [main]
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jobs:
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test:
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name: Test (Python ${{ matrix.python-version }})
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install uv
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uses: astral-sh/setup-uv@v3
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- name: Install dependencies
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working-directory: agent-memory-client
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run: uv sync --extra dev
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- name: Lint with Ruff
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working-directory: agent-memory-client
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run: uv run ruff check agent_memory_client
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- name: Check formatting with Ruff formatter
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working-directory: agent-memory-client
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run: uv run ruff format --check agent_memory_client
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- name: Type check with mypy
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working-directory: agent-memory-client
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run: uv run mypy agent_memory_client
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- name: Run tests
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working-directory: agent-memory-client
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run: uv run pytest tests/ --cov=agent_memory_client --cov-report=xml
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publish-testpypi:
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name: Publish to TestPyPI
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needs: test
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if: startsWith(github.ref, 'refs/tags/client/') && contains(github.ref, '-test')
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runs-on: ubuntu-latest
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environment: testpypi
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permissions:
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id-token: write
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contents: read
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.12'
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- name: Install build tools
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run: |
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python -m pip install --upgrade pip
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pip install build
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- name: Build package
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working-directory: agent-memory-client
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run: python -m build
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- name: Publish package to TestPyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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repository-url: https://test.pypi.org/legacy/
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packages-dir: agent-memory-client/dist/
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publish-pypi:
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name: Publish to PyPI
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needs: test
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if: startsWith(github.ref, 'refs/tags/client/') && !contains(github.ref, '-test')
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runs-on: ubuntu-latest
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environment: pypi
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permissions:
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id-token: write
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contents: read
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.12'
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- name: Install build tools
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run: |
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python -m pip install --upgrade pip
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pip install build
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- name: Build package
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working-directory: agent-memory-client
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run: python -m build
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- name: Publish package to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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packages-dir: agent-memory-client/dist/
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# Tag Format Guide:
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# - For TestPyPI (testing): client/v1.0.0-test
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# - For PyPI (production): client/v1.0.0
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#
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# Use the script: python scripts/tag_and_push_client.py --test (for TestPyPI)
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# python scripts/tag_and_push_client.py (for PyPI)
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#
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# Alternative: This project uses trusted publishing, but you can use API Token
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# Authentication (if trusted publishing doesn't work).
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#
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# To do so, uncomment the sections below and add these secrets to your repository:
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# - TEST_PYPI_API_TOKEN (for TestPyPI)
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# - PYPI_API_TOKEN (for PyPI)
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#
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# For TestPyPI job, replace the publish step with:
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# - name: Publish package to TestPyPI
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# uses: pypa/gh-action-pypi-publish@release/v1
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# with:
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# repository-url: https://test.pypi.org/legacy/
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# packages-dir: agent-memory-client/dist/
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# password: ${{ secrets.TEST_PYPI_API_TOKEN }}
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#
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# For PyPI job, replace the publish step with:
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# - name: Publish package to PyPI
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# uses: pypa/gh-action-pypi-publish@release/v1
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# with:
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# packages-dir: agent-memory-client/dist/
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# password: ${{ secrets.PYPI_API_TOKEN }}

‎.github/workflows/python-tests.yml

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pip install uv
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uv sync --all-extras
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- name: Install agent-memory-client
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run: |
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uv pip install -e ./agent-memory-client
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- name: Run tests
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run: |
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uv run pytest --run-api-tests
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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docker:
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needs: test
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runs-on: ubuntu-latest
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if: github.ref == 'refs/heads/main' && github.event_name == 'push'
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Log in to GitHub Container Registry
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uses: docker/login-action@v3
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with:
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registry: ghcr.io
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract version from __init__.py
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id: version
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run: |
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VERSION=$(grep '__version__ =' agent_memory_server/__init__.py | sed 's/__version__ = "\(.*\)"/\1/' || echo "latest")
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echo "version=$VERSION" >> $GITHUB_OUTPUT
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echo "Version: $VERSION"
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- name: Build and push Docker image
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uses: docker/build-push-action@v5
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with:
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context: .
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file: ./Dockerfile
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platforms: linux/amd64,linux/arm64
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push: true
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tags: |
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ghcr.io/${{ github.repository }}:latest
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ghcr.io/${{ github.repository }}:${{ steps.version.outputs.version }}
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cache-from: type=gha
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cache-to: type=gha,mode=max

‎CLAUDE.md

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```bash
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# Development workflow
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uv install # Install dependencies
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uv venv # Create a virtualenv (once)
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source .venv/bin/activate # Activate the virtualenv (start of terminal session)
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uv install --all-extras # Install dependencies
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uv sync --all-extras # Sync latest dependencies
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uv run ruff check # Run linting
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uv run ruff format # Format code
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uv run pytest # Run tests
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uv run pytest tests/ # Run specific test directory
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uv run pytest --run-api-tests # Run all tests
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uv add <dependency> # Add a dependency to pyproject.toml and update lock file
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uv remove <dependency> # Remove a dependency from pyproject.toml and update lock file
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# Server commands
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uv run agent-memory api # Start REST API server (default port 8000)
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IMPORTANT: This project uses `pre-commit`. You should run `pre-commit`
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before committing:
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```bash
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uv run pre-commit install # Install the hooks first
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uv run pre-commit run --all-files
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```
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```python
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# Correct - Use RedisVL queries
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from redisvl.query import VectorQuery, FilterQuery
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query = VectorQuery(vector=embedding, vector_field_name="embedding", return_fields=["text"])
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query = VectorQuery(vector=embedding, vector_field_name="vector", return_fields=["text"])
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# Avoid - Direct redis client searches
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# redis.ft().search(...) # Don't do this

‎Dockerfile

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FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
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WORKDIR /app
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ENV UV_COMPILE_BYTECODE=1
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RUN --mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=uv.lock,target=uv.lock \
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--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
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--mount=type=bind,source=agent-memory-client,target=agent-memory-client \
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uv sync --frozen --no-install-project --no-dev
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ADD . /app
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ENTRYPOINT []
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EXPOSE 8000
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HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
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CMD curl -f http://localhost:8000/v1/health || exit 1
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# Disable auth by default. Can be overridden with environment variable.
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ENV DISABLE_AUTH=true
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# Run the API server
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CMD ["uv", "run", "agent-memory", "api"]
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CMD ["agent-memory", "api", "--host", "0.0.0.0", "--port", "8000"]

‎README.md

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- **Working Memory**
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- Session-scoped storage for messages, structured memories, context, and metadata
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- Automatically summarizes conversations when they exceed the window size
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- Client model-aware token limit management (adapts to the context window of the client's LLM)
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- Automatically summarizes conversations when they exceed a client-configured window size
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- Supports all major OpenAI and Anthropic models
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- Automatic promotion of structured memories to long-term storage
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- Automatic (background) promotion of structured memories to long-term storage
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- **Long-Term Memory**
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- Persistent storage for memories across sessions
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- **Pluggable Vector Store Backends** - Support for multiple vector databases through LangChain VectorStore interface:
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- **Redis** (default) - RedisStack with RediSearch
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- **Chroma** - Open-source vector database
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- **Pinecone** - Managed vector database service
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- **Weaviate** - Open-source vector search engine
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- **Qdrant** - Vector similarity search engine
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- **Milvus** - Cloud-native vector database
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- **PostgreSQL/PGVector** - PostgreSQL with vector extensions
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- **LanceDB** - Embedded vector database
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- **OpenSearch** - Open-source search and analytics suite
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- Semantic search to retrieve memories with advanced filtering system
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- Filter by session, namespace, topics, entities, timestamps, and more
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- Filter by session, user ID, namespace, topics, entities, timestamps, and more
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- Supports both exact match and semantic similarity search
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- Automatic topic modeling for stored memories with BERTopic or configured LLM
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- Automatic Entity Recognition using BERT
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- Automatic Entity Recognition using BERT or configured LLM
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- Memory deduplication and compaction
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- **Production-Grade Memory Isolation**
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- OAuth2/JWT Bearer token authentication
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- Supports RBAC permissions
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- Top-level support for user ID and session ID isolation
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- **Other Features**
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- Namespace support for session and working memory isolation
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- Dedicated SDK offering direct access to API calls _and_ memory operations as tools to pass to your LLM
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- Both a REST interface and MCP server
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- Background task processing for memory indexing and promotion
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- Unified search across working memory and long-term memory
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- Heavy operations run as background tasks
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For detailed information about memory types, their differences, and when to use each, see the [Memory Types Guide](docs/memory-types.md).
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For complete configuration details, see [Configuration Guide](docs/configuration.md).
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For vector store backend options and setup, see [Vector Store Backends](docs/vector-store-backends.md).
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## License
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Apache 2.0 License - see [LICENSE](LICENSE) file for details.
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## Development
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For development setup, testing, and contributing guidelines, see [Development Guide](docs/development.md).

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