Nexus-Router/nexus

Aggregate, govern, and control your AI stack.
## Features
MCP Server Aggregation: Connect multiple MCP servers (STDIO, SSE, HTTP) through a single endpoint
LLM Provider Routing: Unified interface for OpenAI, Anthropic, Google, and AWS Bedrock LLM providers with full tool calling support
Context-Aware Tool Search: Intelligent fuzzy search across all connected tools using natural language queries
Protocol Support: Supports STDIO (subprocess), SSE (Server-Sent Events), and streamable HTTP MCP servers
Flexible Configuration: TOML-based configuration with environment variable substitution
Security: Built-in CORS, CSRF protection, OAuth2, and TLS support
Rate Limiting: Multi-level rate limiting with in-memory or Redis backends
Docker Ready: Available as a container image with minimal configuration needed
## Installation
### Quick Install (Linux/Windows (WSL)/macOS)
bash
curl -fsSL https://nexusrouter.com/install | bash
### Docker
Pull the latest image:
bash
docker pull ghcr.io/grafbase/nexus:latest
Or use the stable version:
bash
docker pull ghcr.io/grafbase/nexus:stable
Or use a specific version:
bash
docker pull ghcr.io/grafbase/nexus:X.Y.Z
### Build from Source
bash
git clone https://github.com/grafbase/nexus
cd nexus
cargo build --release
## Running Nexus
### Using the Binary
bash
nexus
### Using Docker
bash
docker run -p 8000:8000 -v /path/to/config:/etc/nexus.toml ghcr.io/grafbase/nexus:latest
### Docker Compose Example
yaml
services:
nexus:
image: ghcr.io/grafbase/nexus:latest
ports:
"8000:8000"
volumes:
./nexus.toml:/etc/nexus.toml
environment:
GITHUBTOKEN=${GITHUBTOKEN}
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
interval: 30s
timeout: 10s
retries: 3
## Configuration
Create a nexus.toml file to configure Nexus:
toml
# LLM Provider configuration
[llm.providers.openai]
type = "openai"
apikey = "{{ env.OPENAIAPIKEY }}"
forwardtoken = true
# Model configuration (at least one model required per provider)
[llm.providers.openai.models.gpt-4]
[llm.providers.openai.models.gpt-3-5-turbo]
[llm.providers.anthropic]
type = "anthropic"
apikey = "{{ env.ANTHROPICAPIKEY }}"
[llm.providers.anthropic.models.claude-3-5-sonnet-20241022]
# MCP Server configuration
[mcp.servers.github]
url = "https://api.githubcopilot.com/mcp/"
auth.token = "{{ env.GITHUBTOKEN }}"
[mcp.servers.filesystem]
cmd = ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/home/YOURUSERNAME/Desktop"]
[mcp.servers.pythonserver]
cmd = ["python", "-m", "mcpserver"]
env = { PYTHONPATH = "/opt/mcp" }
cwd = "/workspace"
### Configuration Options
#### Server Configuration
server.listenaddress: The address and port Nexus will listen on (default: 127.0.0.1:8000)
server.health.enabled: Enable health endpoint (default: true)
server.health.path: Health check endpoint path (default: /health)
#### LLM Configuration
llm.enabled: Enable LLM functionality (default: true)
llm.protocols.openai.enabled: Enable OpenAI protocol endpoint (default: true)
llm.protocols.openai.path: OpenAI endpoint path (default: /llm/openai)
llm.protocols.anthropic.enabled: Enable Anthropic protocol endpoint (default: false)
llm.protocols.anthropic.path: Anthropic endpoint path (default: /llm/anthropic)