CASCADE (Cumulative Agentic Skill Creation through Autonomous Development and Evolution) is a multi-agent system for materials science and chemistry research.
The fastest way to get started is using Docker.
- Docker and Docker Compose
- Git
- A Supabase account
- Neo4j database (local installation)
git clone https://github.com/CederGroupHub/CASCADE.git
cd CASCADE- Go to supabase.com and create a new project
- In your project dashboard, go to Project Settings:
- Data API → Copy Project URL →
SUPABASE_URL - API Keys → Click Legacy anon, service_role API keys → Click Reveal on service_role →
SUPABASE_SERVICE_KEY - Click Connect (top center) → Change Method to Session pooler → Copy and replace
[YOUR-PASSWORD]with your password →SUPABASE_DATABASE_URL
- Data API → Copy Project URL →
- Go to SQL Editor and run the schema from
mcp_servers_and_tools/research_server/extracted_code.sql
Note: Supabase UI may change over time. Look for API keys and connection strings in Project Settings. Free-tier Supabase projects may be paused after inactivity - check your project status and resume if needed before running the system.
- Download from neo4j.com/download
- Create a new project and database
- Set a password and start the database
Prerequisite: Java 21 is required
# Download and extract
wget https://dist.neo4j.org/neo4j-community-2025.08.0-unix.tar.gz
tar -xzf neo4j-community-2025.08.0-unix.tar.gz
mv neo4j-community-2025.08.0 ~/neo4j
# Configure to bind to localhost only
echo "server.bolt.listen_address=localhost:7687" >> ~/neo4j/conf/neo4j.conf
echo "server.http.listen_address=localhost:7474" >> ~/neo4j/conf/neo4j.conf
# Start Neo4j (JAVA_HOME path may vary on your system)
# Find yours: find /usr/lib/jvm -name "java-21*" -type d 2>/dev/null | head -1
# Tip: Add "export JAVA_HOME=/usr/lib/jvm/java-21-openjdk-amd64" to ~/.bashrc
~/neo4j/bin/neo4j start
# Set initial password (default password is 'neo4j', then set your new password)
~/neo4j/bin/cypher-shell -u neo4j -p neo4jcp .env.example .env
nano .env # Fill in your API keys and credentials from steps 2-3
# Set your user ID for Docker (required for correct file permissions)
echo "UID=$(id -u)" >> .env
echo "GID=$(id -g)" >> .envPriority: .env file takes precedence. If a variable is not set in .env, it falls back to shell environment variables.
# Make sure Supabase and Neo4j are running first. For Neo4j on Linux:
~/neo4j/bin/neo4j status
# Create the SQLite database file if it doesn't exist (prevents Docker from mounting it as a directory)
touch conversational_system/conversations.db
# Start CASCADE with Docker (open cascade.log to view real-time output)
docker compose up > cascade.log 2>&1 &- CASCADE UI: http://localhost:8501
- MLflow UI: http://localhost:5001 (traces and experiments)
- Neo4j Browser: http://localhost:7474 (optional)
Remote access: If running on a remote server, set up SSH tunnels first:
ssh -f -N -L 8501:localhost:8501 -L 5001:localhost:5001 -L 7474:localhost:7474 username@your_remote_serverThen access the URLs above on your local machine.
CASCADE supports local models via vLLM or other OpenAI-compatible servers.
Add these to your .env file:
OPENAI_BASE_URL=http://localhost:8000/v1
AGENT_MODEL_NAME=your-model-name
REQUIRE_OPENAI_API_KEY=false
USE_ADAPTIVE_AGENTS=trueFor vLLM deployment, SDK modifications, and detailed configuration, see Adaptive Agents Documentation.
# Stop services
docker compose down
# Rebuild after code changes
docker compose up --build > cascade.log 2>&1 &Data is stored on your local filesystem and persists across container restarts:
./conversational_system/conversations.db- Conversation history./conversational_system/saved_code/- Saved code files./conversational_system/mlruns/- MLflow tracking data
Neo4j data is stored in your local Neo4j installation.
To reset all data, manually delete these files/directories.
CASCADE/
├── conversational_system/ # Main conversational agent system
│ ├── frontend/ # Streamlit web interface
│ ├── core/ # Orchestrator and DeepSolver agents
│ ├── deep_solver_with_memory/ # Multi-agent workflow (4-agent architecture)
│ ├── launch.sh # Launch script for local development
│ ├── conversations.db # SQLite database (created after first conversation)
│ ├── ADAPTIVE_AGENTS.md # vLLM and non-OpenAI model configuration
│ └── MLFLOW_TRACING.md # MLflow tracing guide
├── deep_solver_benchmark/ # Benchmark suite and testing
│ ├── deep_solver/ # Main agent implementations
│ ├── deep_solver_free_form/ # Free-form output variant
│ ├── baselines/ # Baseline implementations (including Claude Code)
│ ├── ablation_studies/ # Ablation study variants
│ ├── data_for_demonstration/ # Data files for free-form demonstrations
│ ├── README.md # Benchmark overview and quick start
│ ├── DEVELOPMENT.md # Local development and benchmark guide
│ └── requirements.txt # Python dependencies
├── mcp_servers_and_tools/ # MCP servers and direct tools
│ ├── research_server/ # Code extraction and knowledge graph
│ ├── memory_server/ # Memory consolidation
│ ├── workspace_server/ # Code execution environment
│ └── direct_tools/ # Direct tool implementations (non-MCP)
├── benchmark_tasks_and_results/ # Benchmark questions, answers, and results
│ ├── questions_and_answers/ # Benchmark JSON files (download required)
│ ├── demonstration/ # Free-form demonstration examples
│ └── evaluation/ # Evaluation scripts
├── docker/ # Docker configuration
├── utils/ # Shared utilities
└── .env.example # Environment variable template
Note: Benchmark data files require separate download. See benchmark_tasks_and_results/README.md for instructions.
| Document | Description |
|---|---|
| Development Guide | Local development setup (No Docker) for conversational system and benchmarks |
| Adaptive Agents | Using vLLM and non-OpenAI models |
| Benchmark README | Benchmark structure and variants |