ClimSight

ClimSight is an AI-powered climate decision-support system that combines Large Language Models with multi-source climate data to deliver localized, actionable insights. It serves researchers, policymakers, agricultural planners, urban developers, and anyone who needs to understand how climate change affects real-world decisions.

What makes it different

• Agentic AI pipeline — specialized agents (smart agent, data analysis agent, search agent) orchestrate data retrieval, code execution, and synthesis autonomously
• Multi-source climate data — nextGEMS, ICCP, AWI-CM CMIP6, DestinE, and ERA5 reanalysis via Arraylake
• RAG-augmented responses — retrieves context from IPCC reports and scientific literature
• Interactive map-based interface — click anywhere on the globe and ask a climate question
• Python REPL execution — the agent writes and runs analysis code in a sandboxed Jupyter kernel

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Architecture

ClimSight has two interface modes:

	React UI (new)	Streamlit UI (legacy)
Frontend	React + TypeScript + Tailwind CSS + Vite	Streamlit
Backend	FastAPI + WebSocket (real-time streaming)	Streamlit server
Command	uvicorn + npm run dev	streamlit run

The React UI is the actively developed interface with real-time streaming, a dark/light theme toggle, and a modern component architecture. The Streamlit UI is kept for backwards compatibility.

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Quick Start (React UI)

Prerequisites

• Python 3.11+
• Node.js 18+ and npm
• An OpenAI API key (or other supported LLM provider)

1. Clone & set up the Python environment

git clone https://github.com/CliDyn/climsight.git
cd climsight

# Option A: conda/mamba (recommended)
mamba env create -f environment.yml
conda activate climsight

# Option B: pip + venv
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt
pip install -r requirements-api.txt

2. Download the climate data

python download_data.py

# Optional: DestinE data (~12 GB)
python download_data.py DestinE

3. Configure API keys

Create a .env file in the repo root (or export the variables):

OPENAI_API_KEY="sk-..."

# Optional — enables ERA5 time series retrieval
ARRAYLAKE_API_KEY="your-arraylake-key"

4. Start the FastAPI backend

uvicorn api.main:app --host 0.0.0.0 --port 8000 --reload

The API runs on http://localhost:8000. Health check: GET /health.

5. Start the React frontend

cd frontend
npm install
npm run dev

Open http://localhost:5173 in your browser. The Vite dev server automatically proxies /api and /artifacts requests to the backend.

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Running the Legacy Streamlit UI

If you prefer the original interface:

streamlit run src/climsight/climsight.py

Opens on http://localhost:8501.

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Docker (Stable Release v1.0.0)

Note

The Docker image ships the Streamlit UI (v1.0.0). The React UI is not yet containerized.

export OPENAI_API_KEY="sk-..."
docker pull koldunovn/climsight:stable
docker run -p 8501:8501 -e OPENAI_API_KEY=$OPENAI_API_KEY koldunovn/climsight:stable

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Configuration

ClimSight reads config.yml from the working directory. Key settings:

llm_smart:
  model_type: "openai"
  model_name: "gpt-5.2"         # main reasoning model

use_smart_agent: true            # enable the agentic pipeline
use_era5_data: true              # ERA5 time series via Arraylake
use_powerful_data_analysis: true # agent writes & executes Python code

climate_data_source: "nextGEMS"  # nextGEMS | ICCP | AWI_CM | DestinE

See config.yml for the full reference (climate data sources, RAG settings, system prompts, etc.).

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Project Structure

climsight/
├── api/                    # FastAPI backend
│   ├── main.py             #   app factory, CORS, static files
│   └── routes/             #   REST + WebSocket endpoints
├── frontend/               # React UI (Vite + Tailwind + TypeScript)
│   ├── src/
│   │   ├── App.tsx          #   main app shell
│   │   ├── components/      #   MapPanel, QueryForm, ReportView, SettingsPanel, StatusBar
│   │   └── api/             #   API client
│   ├── package.json
│   └── vite.config.ts
├── src/climsight/          # Core Python engine
│   ├── smart_agent.py      #   agentic orchestration (LangGraph)
│   ├── agent_helpers.py    #   tool definitions & agent utilities
│   ├── session_manager.py  #   per-session state & memory
│   └── tools/              #   ERA5 retrieval, reflection, REPL, search
├── data/                   # Climate datasets (downloaded via download_data.py)
├── rag_db/                 # ChromaDB vector stores for RAG
├── config.yml              # Main configuration
├── environment.yml         # Conda environment spec
├── requirements.txt        # Pip dependencies (core)
├── requirements-api.txt    # Pip dependencies (FastAPI backend)
└── pyproject.toml          # Package metadata

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Batch Processing

The sequential/ directory contains tools for generating, validating, and processing climate questions in bulk. See sequential/README.md.

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Citation

If you use or refer to ClimSight in your work, please cite:

Kuznetsov, I., Jost, A.A., Pantiukhin, D. et al. Transforming climate services with LLMs and multi-source data integration. npj Clim. Action 4, 97 (2025). https://doi.org/10.1038/s44168-025-00300-y

Koldunov, N., Jung, T. Local climate services for all, courtesy of large language models. Commun Earth Environ 5, 13 (2024). https://doi.org/10.1038/s43247-023-01199-1