Getting Started with NeuronDB
Introduction
📌 Branch & Version Selection
NeuronDB has three branches with different versions. Choose based on your needs:
| Branch | Version | Status | Use When |
|---|---|---|---|
main | 3.0.0-devel | Latest | New projects, development, latest features (default) |
REL2_STABLE | 2.0.0 | Stable | Production, stable v2.0 features |
REL1_STABLE | 1.0.0 | Stable | Production, maximum stability required |
Note: This documentation reflects version 3.0.0-devel from the main branch. For stable releases, use REL2_STABLE (v2.0.0) or REL1_STABLE (v1.0.0). See GitHub repository for branch details.
NeuronDB is a PostgreSQL AI ecosystem that provides GPU-accelerated vector search, ML inference, hybrid retrieval, and complete agent infrastructure. The ecosystem includes:
- NeuronDB - PostgreSQL extension with vector search, ONNX model inference, and GPU acceleration
- NeuronAgent - REST API and WebSocket agent runtime with long-term memory and tool execution
- NeuronMCP - Model Context Protocol server with 600+ tools for MCP-compatible clients (like Claude Desktop)
- NeuronDesktop - Unified web interface for managing all components
What you can build: Semantic search, RAG pipelines, AI agents with PostgreSQL-backed memory, and MCP integrations - all in one unified ecosystem.
Deployment options: Self-host the core (this repo), use NeuronDB Cloud for managed multi-tenant provisioning, or NeuronDB Hub to build and embed AI agents. See the Ecosystem overview for how they fit together.
Choose Your Path
Pick the installation method that best fits your needs:
| Method | Best For | Time | Difficulty |
|---|---|---|---|
| Simple Start | Beginners, fastest setup | 5 minutes | ⭐ Easy |
| Docker Quick Start | Complete ecosystem, Docker users | 5 minutes | ⭐ Easy |
| Quick Start Guide | Technical users, first queries | 10 minutes | ⭐⭐ Medium |
| Source Build | Production, custom builds, developers | 30+ minutes | ⭐⭐⭐ Advanced |
💡 Note: New here? Use Simple Start for a beginner-friendly guide. For the complete ecosystem with Docker, use Docker Quick Start. Technical users can use Quick Start Guide.
Docker Quick Start
Complete NeuronDB ecosystem running in under 5 minutes with Docker Compose. This method includes all components with GPU support (CUDA, ROCm, Metal) and requires no manual configuration.
Start complete ecosystem
# Clone repository (main branch = 3.0.0-devel)
git clone https://github.com/neurondb-ai/neurondb.git
cd neurondb
# For stable 1.0.0 release, checkout REL1_STABLE branch:
# git checkout REL1_STABLE
# Start all services
docker compose up -d
# Verify services
docker compose psThis starts:
- NeuronDB (PostgreSQL with extension) on port 5433
- NeuronAgent (REST API) on port 8080
- NeuronMCP (MCP server)
- NeuronDesktop (Web UI) on port 3000
Source Build (Advanced)
For production deployments or custom builds, install from source. This requires PostgreSQL 16-18, a C toolchain, and build dependencies.
See the Installation Guide for detailed platform-specific instructions (Ubuntu/Debian, macOS, Rocky Linux/RHEL).
Quick reference (Ubuntu/Debian)
sudo apt-get install -y postgresql-17 postgresql-server-dev-17 build-essential
git clone https://github.com/neurondb-ai/neurondb.git
cd neurondb
# For stable 1.0.0 release, checkout REL1_STABLE branch:
# git checkout REL1_STABLE
cd NeuronDB
make PG_CONFIG=/usr/lib/postgresql/17/bin/pg_config
sudo make install PG_CONFIG=/usr/lib/postgresql/17/bin/pg_configAfter installing the extension, you'll need to separately build and run NeuronAgent, NeuronMCP, and NeuronDesktop. See component documentation for details.
Next Steps
After installation, use these guides:
- SQL Recipe Library - Ready-to-run SQL queries for vector search, hybrid search, and more
- Quick Start Guide - Create your first vector table, generate embeddings, and run semantic search
- NeuronAgent - Build AI agents with REST API and WebSocket
- NeuronMCP - Use 600+ MCP tools with Claude Desktop
- Vector Indexing - Configure HNSW, IVF, and quantization
- RAG Pipelines - Build retrieval augmented generation workflows