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Installation

Use this guide to install ChatSpatial in a local Python environment. If you want a containerized runtime, use the Docker / GHCR guide instead.

Requirements

  • Python 3.11-3.13 (3.12 recommended)
  • 8GB+ RAM (16GB+ for large datasets)
  • macOS, Linux, or Windows
  • Docker only if you choose the container runtime

Choose a Runtime

Runtime Use when Guide
Python environment You want direct control of packages on the host machine Continue below
Docker / GHCR You want the most reproducible runtime or local dependency resolution fails Docker / GHCR

Step 1: Create an Environment

# venv
python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# venv\Scripts\activate   # Windows

# or conda
conda create -n chatspatial python=3.12
conda activate chatspatial

Step 2: Install ChatSpatial

Recommended: use uv for dependency resolution

# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install the MCP server and core analysis stack
uv pip install chatspatial

Why uv? ChatSpatial depends on a large scientific Python stack. Standard pip can fail on deep dependency resolution; uv is more reliable for this environment.

Install options

Option Command Use when
Standard uv pip install chatspatial You want the MCP server, data loading, preprocessing, embeddings, visualization, and core analysis
Method extras uv pip install 'chatspatial[cell-communication,velocity]' You need specific advanced method families
Full uv pip install 'chatspatial[full]' You want the broadest method coverage on a workstation and accept a large install. Requires R ≥ 4.4 on PATH (see below)
Alternative: pip 
pip install --upgrade pip
pip install chatspatial

If you hit resolution-too-deep, switch to uv.

Optional method families

Install only the method families you plan to use:

uv pip install 'chatspatial[cell-communication]'  # LIANA+, CellPhoneDB, FastCCC
uv pip install 'chatspatial[velocity]'            # scVelo
uv pip install 'chatspatial[deep-learning]'       # scVI, scANVI, VeloVI, DestVI backend
uv pip install 'chatspatial[integration]'         # Harmony, BBKNN, Scanorama
uv pip install 'chatspatial[deconvolution]'       # FlashDeconv, Cell2location
uv pip install 'chatspatial[spatial-stats]'       # PySAL/ESDA extensions

ChatSpatial tools fail with targeted installation guidance if you call a method whose optional dependency is not installed.

Step 3: Connect ChatSpatial to an MCP Client

After installation, register the environment's Python executable in your MCP client. The command shape is:

/absolute/path/to/python -m chatspatial server

Use the Configuration Guide for exact client syntax, absolute-path rules, Docker-backed client examples, and the runtime path model.

Step 4: Verify the Installation

python -c "import chatspatial; print(f'ChatSpatial {chatspatial.__version__} ready')"
python -m chatspatial server --help

If both commands work, continue to Quick Start.

Platform Notes

macOS (Intel / x86_64)

Some dependencies in chatspatial[full] do not publish pre-built wheels for Intel Macs:

  • gseapy requires the Rust toolchain to compile from source
  • llvmlite (via numba) requires LLVM to compile from source

Install those prerequisites before the full optional stack:

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source "$HOME/.cargo/env"

# Install LLVM for llvmlite
brew install llvm
export LLVM_CONFIG="$(brew --prefix llvm)/bin/llvm-config"

# Then install ChatSpatial with all optional Python methods
uv pip install 'chatspatial[full]'

Apple Silicon Macs (M1/M2/M3/M4) have pre-built wheels for all dependencies and do not require these steps.

Windows

Not available: SingleR, PETSc

Use instead: Tangram, scANVI, CellAssign for annotation; CellRank works without PETSc.

If Python or MCP dependencies fail to resolve

rm -rf venv
python3.12 -m venv venv
source venv/bin/activate
uv pip install 'chatspatial[full]'

Optional Dependencies

R-based methods

The [full] extra includes rpy2, which requires R to be available on your PATH at install time (it compiles against your R installation). On HPC systems where R is provided via modules, run module load R (or equivalent) before installing. If you only need Python-based methods, the standard install (uv pip install chatspatial) does not require R.

Once R is available, install the R packages used by ChatSpatial:

# Install R 4.4+
Rscript install_r_dependencies.R

STAGATE

git clone https://github.com/QIFEIDKN/STAGATE_pyG.git
cd STAGATE_pyG && python setup.py install

Next Steps