Nexus

🚀 Nexus

Nexus allows you to plug in all your MCP servers, APIs, and LLM providers. It routes everything through a unified endpoint, enabling you to aggregate, govern, and control your AI stack effectively.

🚀 Quick Start

Nexus simplifies the process of integrating multiple MCP servers, APIs, and LLM providers. You can quickly set it up using various methods as described below.

✨ 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 other LLM providers
• 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)

curl -fsSL https://nexusrouter.com/install | bash

Docker

Pull the latest image:

docker pull ghcr.io/grafbase/nexus:latest

Or use the stable version:

docker pull ghcr.io/grafbase/nexus:stable

Or use a specific version:

docker pull ghcr.io/grafbase/nexus:X.Y.Z

Build from Source

git clone https://github.com/grafbase/nexus
cd nexus
cargo build --release

💻 Usage Examples

Running Nexus

Using the Binary

nexus

Using Docker

docker run -p 8000:8000 -v /path/to/config:/etc/nexus.toml ghcr.io/grafbase/nexus:latest

Docker Compose Example

services:
  nexus:
    image: ghcr.io/grafbase/nexus:latest
    ports:
      - "8000:8000"
    volumes:
      - ./nexus.toml:/etc/nexus.toml
    environment:
      - GITHUB_TOKEN=${GITHUB_TOKEN}
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

Configuration

Create a nexus.toml file to configure Nexus:

# LLM Provider configuration
[llm.providers.openai]
type = "openai"
api_key = "{{ env.OPENAI_API_KEY }}"
forward_token = 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"
api_key = "{{ env.ANTHROPIC_API_KEY }}"

[llm.providers.anthropic.models.claude-3-5-sonnet-20241022]

# MCP Server configuration
[mcp.servers.github]
url = "https://api.githubcopilot.com/mcp/"
auth.token = "{{ env.GITHUB_TOKEN }}"

[mcp.servers.filesystem]
cmd = ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/Users/YOUR_USERNAME/Desktop"]

[mcp.servers.python_server]
cmd = ["python", "-m", "mcp_server"]
env = { PYTHONPATH = "/opt/mcp" }
cwd = "/workspace"

Using the LLM API

List Available Models

curl http://localhost:8000/llm/models

Response:

{
  "object": "list",
  "data": [
    {
      "id": "openai_primary/gpt-4-turbo",
      "object": "model",
      "created": 1677651200,
      "owned_by": "openai"
    },
    {
      "id": "claude/claude-3-5-sonnet-20241022",
      "object": "model",
      "created": 1709164800,
      "owned_by": "anthropic"
    },
    {
      "id": "gemini/gemini-1.5-pro",
      "object": "model",
      "created": 1710000000,
      "owned_by": "google"
    }
  ]
}

Chat Completions

Send a chat completion request using the OpenAI-compatible format:

curl -X POST http://localhost:8000/llm/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai_primary/gpt-4-turbo",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello, how are you?"}
    ],
    "temperature": 0.7,
    "max_tokens": 150
  }'

Streaming Responses

curl -X POST http://localhost:8000/llm/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-3-5-sonnet-20241022",
    "messages": [
      {"role": "user", "content": "Write a short poem"}
    ],
    "stream": true,
    "max_tokens": 100
  }'

Using Nexus with LLM SDKs

OpenAI SDK (Python)

from openai import OpenAI

# Point to your Nexus instance
client = OpenAI(
    base_url="http://localhost:8000/llm",
    api_key="your-service-token"  # Use a JWT token if OAuth2 is enabled, or any string if not
)

# Use any configured provider/model
response = client.chat.completions.create(
    model="anthropic/claude-3-5-sonnet-20241022",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

# Streaming works seamlessly
stream = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[
        {"role": "user", "content": "Write a poem"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

OpenAI SDK (Node.js/TypeScript)

import OpenAI from 'openai';

// Configure to use Nexus
const openai = new OpenAI({
  baseURL: 'http://localhost:8000/llm',
  apiKey: 'your-service-token', // Use a JWT token if OAuth2 is enabled, or any string if not
});

// Use any provider through Nexus
const response = await openai.chat.completions.create({
  model: 'google/gemini-1.5-pro',
  messages: [
    { role: 'user', content: 'Explain quantum computing' }
  ],
});

// Streaming with any provider
const stream = await openai.chat.completions.create({
  model: 'anthropic/claude-3-opus-20240229',
  messages: [
    { role: 'user', content: 'Write a story' }
  ],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

LangChain Integration

from langchain_openai import ChatOpenAI

# Use Nexus as the LLM provider
llm = ChatOpenAI(
    base_url="http://localhost:8000/llm",
    api_key="your-service-token",  # Use a JWT token if OAuth2 is enabled
    model="openai/gpt-4-turbo"
)

# Works with any configured provider
claude = ChatOpenAI(
    base_url="http://localhost:8000/llm",
    api_key="your-service-token",  # Use a JWT token if OAuth2 is enabled
    model="anthropic/claude-3-5-sonnet-20241022"
)

cURL with jq for Command Line

# Regular completion (with OAuth2 authentication if enabled)
curl -s http://localhost:8000/llm/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-jwt-token" \
  -d '{
    "model": "openai/gpt-4",
    "messages": [{"role": "user", "content": "Hello"}]
  }' | jq -r '.choices[0].message.content'

# Streaming with SSE parsing
curl -s http://localhost:8000/llm/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-jwt-token" \
  -d '{
    "model": "anthropic/claude-3-5-sonnet-20241022",
    "messages": [{"role": "user", "content": "Write a haiku"}],
    "stream": true
  }' | grep "^data: " | sed 's/^data: //' | jq -r 'select(.choices != null) | .choices[0].delta.content // empty'

📚 Documentation

Configuration Options

Server Configuration

• server.listen_address: 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.path: LLM endpoint path (default: /llm)

For detailed LLM provider configuration, see the LLM Provider Configuration section below.

MCP Configuration

• mcp.enabled: Enable MCP functionality (default: true)
• mcp.path: MCP endpoint path (default: /mcp)
• mcp.enable_structured_content: Control MCP search tool response format (default: true)
  • When true: Uses modern structuredContent field for better performance and type safety
  • When false: Uses legacy content field with Content::json objects for compatibility with older MCP clients

MCP Server Types

1. STDIO Servers: Launch local processes that communicate via standard input/output

   [mcp.servers.my_tool]
   cmd = ["path/to/executable", "--arg1", "--arg2"]
   
   # Optional: Set environment variables
   env = { DEBUG = "1", API_KEY = "{{ env.MY_API_KEY }}" }
   
   # Optional: Set working directory
   cwd = "/path/to/working/directory"
   
   # Optional: Configure stderr handling (default: "null")
   stderr = "inherit"  # Show in console
   # or
   stderr = { file = "/var/log/mcp/server.log" }  # Log to file
   

   Note: STDIO servers must output valid JSON-RPC messages on stdout. The cmd array must have at least one element (the executable).

2. SSE Servers: Connect to Server-Sent Events endpoints

   [mcp.servers.my_sse_server]
   protocol = "sse"
   url = "http://example.com/sse"
   message_url = "http://example.com/messages"  # Optional
   

3. HTTP Servers: Connect to streamable HTTP endpoints

   [mcp.servers.my_http_server]
   protocol = "streamable-http"
   url = "https://api.example.com/mcp"
   

For remote MCP servers, if you omit the protocol Nexus will first try streamable HTTP and then SSE.

Authentication

Add service token authentication to any server:

[mcp.servers.my_server.auth]
token = "your-token-here"
# Or use environment variables
token = "{{ env.MY_API_TOKEN }}"

If you enable OAuth2 authentication to your server, and your downstream servers all use the same authentication server, you can configure Nexus to forward the request access token to the downstream server.

[mcp.servers.my_server.auth]
type = "forward"

OAuth2 Authentication

Configure OAuth2 authentication to protect your Nexus endpoints:

[server.oauth]
url = "https://your-oauth-provider.com/.well-known/jwks.json"
poll_interval = "5m"
expected_issuer = "https://your-oauth-provider.com"
expected_audience = "your-service-audience"

[server.oauth.protected_resource]
resource = "https://your-nexus-instance.com"
authorization_servers = ["https://your-oauth-provider.com"]

OAuth2 configuration options:

• url: JWKs endpoint URL for token validation
• poll_interval: How often to refresh JWKs (optional, default: no polling)
• expected_issuer: Expected iss claim in JWT tokens (optional)
• expected_audience: Expected aud claim in JWT tokens (optional)
• protected_resource.resource: URL of this protected resource
• protected_resource.authorization_servers: List of authorization server URLs

When OAuth2 is enabled, all endpoints except /health and /.well-known/oauth-protected-resource require valid JWT tokens in the Authorization: Bearer <token> header.

Rate Limiting

Nexus supports comprehensive rate limiting to prevent abuse and ensure fair resource usage:

# Global rate limiting configuration
[server.rate_limits]
enabled = true

# Storage backend configuration
[server.rate_limits.storage]
type = "memory"  # or "redis" for distributed rate limiting
# For Redis backend:
# url = "redis://localhost:6379"
# key_prefix = "nexus:rate_limit:"

# Global rate limit (applies to all requests)
[server.rate_limits.global]
limit = 1000
interval = "60s"

# Per-IP rate limit
[server.rate_limits.per_ip]
limit = 100
interval = "60s"

# Per-MCP server rate limits
[mcp.servers.my_server.rate_limits]
limit = 50
interval = "60s"

# Tool-specific rate limits (override server defaults)
[mcp.servers.my_server.rate_limits.tools]
expensive_tool = { limit = 10, interval = "60s" }
cheap_tool = { limit = 100, interval = "60s" }

Rate Limiting Features:

• Multiple levels: Global, per-IP, per-server, and per-tool limits
• Storage backends: In-memory (single instance) or Redis (distributed)
• Flexible intervals: Configure time windows for each limit
• Tool-specific overrides: Set different limits for expensive operations

Redis Backend Configuration:

[server.rate_limits.storage]
type = "redis"
url = "redis://localhost:6379"
key_prefix = "nexus:rate_limit:"
response_timeout = "1s"
connection_timeout = "5s"

# Connection pool settings
[server.rate_limits.storage.pool]
max_size = 16
min_idle = 0
timeout_create = "5s"
timeout_wait = "5s"
timeout_recycle = "300s"

# TLS configuration for Redis (optional)
[server.rate_limits.storage.tls]
enabled = true
ca_cert_path = "/path/to/ca.crt"
client_cert_path = "/path/to/client.crt"  # For mutual TLS
client_key_path = "/path/to/client.key"
# insecure = true  # WARNING: Only for development/testing, skips certificate validation

Note: When configuring tool-specific rate limits, Nexus will warn if you reference tools that don't exist.

LLM Token Rate Limiting

Nexus provides token-based rate limiting for LLM providers to help control costs and prevent abuse. Unlike request-based rate limits, token rate limits count an estimate of actual tokens consumed.

Prerequisites

IMPORTANT: LLM rate limiting requires client identification to be enabled:

[server.client_identification]
enabled = true

# Choose identification methods (at least one required)
client_id.jwt_claim = "sub"                    # Extract ID from JWT 'sub' claim
# or
client_id.http_header = "X-Client-ID"          # Extract ID from HTTP header

# Optional: Limit groups per user (at most one allowed)
group_id.jwt_claim = "groups"                  # JWT claim containing user's group
# or
group_id.http_header = "X-Group-ID"            # Extract ID from HTTP header

# You must provide a list of allowed groups
[server.client_identification.validation]
group_values = ["free", "pro", "max"]

Without client identification, rate limits cannot be enforced and requests will fail with a configuration error.

Configuration Hierarchy

Token rate limits can be configured at four levels, from most to least specific:

1. Model per user + group: Specific model for specific each user in a group
2. Model per user: Specific model for each user
3. Provider per user + group: All models from provider for each user in a group
4. Provider per user: All models from provider for each user

The most specific applicable limit is always used.

Basic Configuration

# Provider-level default rate limit (applies to all models)
[llm.providers.openai.rate_limits.per_user]
input_token_limit = 100000        # 100K input tokens
interval = "1m"                   # Per minute

# Model-specific rate limit (overrides provider default)
[llm.providers.openai.models.gpt-4.rate_limits.per_user]
input_token_limit = 50000         # More restrictive for expensive model
interval = "30s"

Group-Based Rate Limits

Configure different limits for user groups (requires group_id and group_values in client identification):

# Provider-level group limits
[llm.providers.openai.rate_limits.per_user.groups]
free = { input_token_limit = 10000, interval = "60s" }
pro = { input_token_limit = 100000, interval = "60s" }
enterprise = { input_token_limit = 1000000, interval = "60s" }

# Model-specific group limits (override provider groups)
[llm.providers.openai.models.gpt-4.rate_limits.per_user.groups]
free = { input_token_limit = 5000, interval = "60s" }
pro = { input_token_limit = 50000, interval = "60s" }
enterprise = { input_token_limit = 500000, interval = "60s" }

The limits are per user, but you can define different limits if the user is part of a specific group. If the user does not belong to any group, they will be assigned to the per-user limits.

Complete Example

# Client identification (REQUIRED for rate limiting)
[server.client_identification]
enabled = true
client_id.jwt_claim = "sub"
group_id.jwt_claim = "subscription_tier"
[server.client_identification.validation]
group_values = ["free", "pro", "enterprise"]

# OpenAI provider with comprehensive rate limiting
[llm.providers.openai]
type = "openai"
api_key = "{{ env.OPENAI_API_KEY }}"

# Provider-level defaults
[llm.providers.openai.rate_limits.per_user]
input_token_limit = 100000
interval = "60s"

[llm.providers.openai.rate_limits.per_user.groups]
free = { input_token_limit = 10000, interval = "60s" }
pro = { input_token_limit = 100000, interval = "60s" }

# GPT-4 specific limits (more restrictive)
[llm.providers.openai.models.gpt-4]
[llm.providers.openai.models.gpt-4.rate_limits.per_user]
input_token_limit = 50000
interval = "60s"

[llm.providers.openai.models.gpt-4.rate_limits.per_user.groups]
free = { input_token_limit = 5000, interval = "60s" }
pro = { input_token_limit = 50000, interval = "60s" }

# GPT-3.5 uses provider defaults
[llm.providers.openai.models.gpt-3-5-turbo]

How Token Counting Works

1. Input Tokens Only: Rate limiting is based solely on input tokens counted from the request's messages and system prompts
2. No Output Buffering: Output tokens and max_tokens parameter are NOT considered in rate limit calculations
3. Pre-check: Input tokens are checked against rate limits before processing
4. Token Accumulation: Uses a sliding window algorithm to track usage over time

Note: The rate limiting is designed to be predictable and based only on what the client sends, not on variable output sizes.

Rate Limit Response

When rate limited, the server returns a 429 status code. No Retry-After headers are sent to maintain consistency with downstream LLM provider behavior.

Error Responses

When rate limits are exceeded:

{
  "error": {
    "message": "Rate limit exceeded: Token rate limit exceeded. Please try again later.",
    "type": "rate_limit_error",
    "code": 429
  }
}

Important Notes

• Per-User Limits: All limits are per individual user/client ID
• No Shared Pools: Currently, there are no shared/global token pools
• Streaming Support: Token counting works with both regular and streaming responses
• Provider Agnostic: Works consistently across all LLM providers
• Validation: Configuration is validated at startup; invalid group names will cause errors

TLS Configuration

Configure TLS for downstream connections:

[mcp.servers.my_server.tls]
verify_certs = true
accept_invalid_hostnames = false
root_ca_cert_path = "/path/to/ca.pem"
client_cert_path = "/path/to/client.pem"
client_key_path = "/path/to/client.key"

LLM Provider Configuration

Nexus provides a unified interface for multiple LLM providers, allowing you to route chat completions through various services with a consistent API.

Enabling LLM Routing

[llm]
enabled = true  # Enable LLM functionality (default: true)
path = "/llm"   # LLM endpoint path (default: "/llm")

Supported Providers

Nexus currently supports four major LLM providers:

1. OpenAI (including OpenAI-compatible APIs)
2. Anthropic (Claude models)
3. Google (Gemini models)
4. AWS Bedrock (Multiple model families via AWS)

Provider Configuration

Configure one or more LLM providers in your nexus.toml:

OpenAI Provider

[llm.providers.openai]
type = "openai"
api_key = "{{ env.OPENAI_API_KEY }}"
# Optional: Use a custom base URL (for Azure OpenAI, proxies, or compatible APIs)
base_url = "https://api.openai.com/v1"  # Default

# Model Configuration (REQUIRED - at least one model must be configured)
[llm.providers.openai.models.gpt-4]
# Optional: Rename the model for your users
# rename = "smart-model"  # Users will see "openai/smart-model"

[llm.providers.openai.models.gpt-3-5-turbo]
# Models without rename use their original ID

Anthropic Provider

[llm.providers.anthropic]
type = "anthropic"
api_key = "{{ env.ANTHROPIC_API_KEY }}"
# Optional: Use a custom base URL
base_url = "https://api.anthropic.com/v1"  # Default

# Model Configuration (REQUIRED - at least one model must be configured)
[llm.providers.anthropic.models.claude-3-opus-20240229]

[llm.providers.anthropic.models.claude-3-5-sonnet-20241022]

Google Provider

[llm.providers.google]
type = "google"
api_key = "{{ env.GOOGLE_API_KEY }}"
# Optional: Use a custom base URL
base_url = "https://generativelanguage.googleapis.com/v1beta"  # Default

# Model Configuration (REQUIRED - at least one model must be configured)
# Note: Model IDs with dots must be quoted in TOML
[llm.providers.google.models."gemini-1.5-flash"]

[llm.providers.google.models.gemini-pro]

AWS Bedrock Provider

[llm.providers.bedrock]
type = "bedrock"
# Optional: AWS profile to use (defaults to environment settings)
profile = "{{ env.AWS_PROFILE }}"
# Optional: AWS region (defaults to environment or us-east-1)
region = "us-west-2"

# Model Configuration (REQUIRED - at least one model must be configured)
# Bedrock uses model IDs with dots, so they must be quoted
[llm.providers.bedrock.models."anthropic.claude-3-5-sonnet-20241022-v2:0"]

[llm.providers.bedrock.models."anthropic.claude-3-opus-20240229-v1:0"]

[llm.providers.bedrock.models."amazon.nova-micro-v1:0"]

[llm.providers.bedrock.models."meta.llama3-8b-instruct-v1:0"]

[llm.providers.bedrock.models."ai21.jamba-1.5-mini-v1:0"]

# Rename models for simpler access
[llm.providers.bedrock.models.claude-haiku]
rename = "anthropic.claude-3-5-haiku-20241022-v1:0"  # Users will access as "bedrock/claude-haiku"

[llm.providers.bedrock.models.jamba-mini]
rename = "ai21.jamba-1.5-mini-v1:0"  # Users will access as "bedrock/jamba-mini"

AWS Bedrock provides access to multiple foundation models through a single API. Key features:

• Unified Access: Use models from Anthropic, Amazon, Meta, Cohere, and more through one interface
• AWS Integration: Leverages AWS credentials and IAM for authentication
• Regional Availability: Models may vary by AWS region
• Native Streaming: Full streaming support for all compatible models

Authentication: Bedrock uses standard AWS credential chain:

1. Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
2. AWS profile (specified in config or via AWS_PROFILE environment variable)
3. IAM role (when running on EC2/ECS/Lambda)
4. AWS SSO credentials

Supported Model Families:

• AI21 Jamba: Jamba 1.5 Mini and Large models with 256K context window
• Anthropic Claude: All Claude 3 models (Opus, Sonnet, Haiku) and Claude Instant
• Amazon Nova: Nova Micro, Lite, Pro models
• Amazon Titan: Titan Text and Embeddings models
• Meta Llama: Llama 2 and Llama 3 models
• Cohere Command: Command and Command R models
• DeepSeek: DeepSeek R1 reasoning models
• Mistral: Mistral 7B and Mixtral models

Model ID Format: Bedrock model IDs follow the pattern provider.model-name-version:revision, for example:

• ai21.jamba-1.5-mini-v1:0
• anthropic.claude-3-5-sonnet-20241022-v2:0
• amazon.nova-micro-v1:0
• meta.llama3-8b-instruct-v1:0

Model Configuration

Each LLM provider requires explicit model configuration. This ensures that only the models you want to expose are available through Nexus.

Basic Model Configuration

[llm.providers.openai]
type = "openai"
api_key = "{{ env.OPENAI_API_KEY }}"

# Each model you want to expose must be explicitly configured
[llm.providers.openai.models.gpt-4]
[llm.providers.openai.models.gpt-3-5-turbo]

Model Renaming

You can rename models to provide custom identifiers for your users:

[llm.providers.openai.models.gpt-4]
rename = "smart-model"  # Users will access this as "openai/smart-model"

[llm.providers.openai.models.gpt-3-5-turbo]
rename = "fast-model"   # Users will access this as "openai/fast-model"

This is useful for:

• Creating business-friendly model names
• Abstracting away provider-specific model names
• Providing consistent naming across different providers

TOML Syntax for Model IDs

Model IDs that contain dots must be quoted in TOML:

# Correct - dots in model IDs require quotes
[llm.providers.google.models."gemini-1.5-flash"]
[llm.providers.google.models."gemini-1.5-pro"]

# Also correct - no dots, no quotes needed
[llm.providers.google.models.gemini-pro]

Multiple Provider Configuration

You can configure multiple instances of the same provider type with different names:

# Primary OpenAI account
[llm.providers.openai_primary]
type = "openai"
api_key = "{{ env.OPENAI_PRIMARY_KEY }}"

[llm.providers.openai_primary.models.gpt-4]
[llm.providers.openai_primary.models.gpt-3-5-turbo]

# Secondary OpenAI account or Azure OpenAI
[llm.providers.openai_secondary]
type = "openai"
api_key = "{{ env.OPENAI_SECONDARY_KEY }}"
base_url = "https://my-azure-instance.openai.azure.com/v1"

[llm.providers.openai_secondary.models.gpt-4]
rename = "azure-gpt-4"  # Distinguish from primary account

# Anthropic
[llm.providers.claude]
type = "anthropic"
api_key = "{{ env.ANTHROPIC_API_KEY }}"

[llm.providers.claude.models.claude-3-opus-20240229]

# Google Gemini
[llm.providers.gemini]
type = "google"
api_key = "{{ env.GOOGLE_API_KEY }}"

[llm.providers.gemini.models."gemini-1.5-flash"]

Token Forwarding

Nexus supports token forwarding, allowing users to provide their own API keys at request time instead of using the configured keys. This feature is opt-in and disabled by default.

Configuring Token Forwarding

Enable token forwarding for any provider by setting forward_token = true:

[llm.providers.openai]
type = "openai"
api_key = "{{ env.OPENAI_API_KEY }}"  # Fallback key (optional with token forwarding)
forward_token = true  # Enable token forwarding for this provider

[llm.providers.openai.models.gpt-4]
[llm.providers.openai.models.gpt-3-5-turbo]

[llm.providers.anthropic]
type = "anthropic"
# No api_key required when token forwarding is enabled
forward_token = true

[llm.providers.anthropic.models.claude-3-5-sonnet-20241022]

[llm.providers.google]
type = "google"
api_key = "{{ env.GOOGLE_API_KEY }}"
forward_token = false  # Explicitly disabled (default)

[llm.providers.google.models."gemini-1.5-flash"]

Using Token Forwarding

When token forwarding is enabled for a provider, users can pass their own API key using the X-Provider-API-Key header:

# Using your own OpenAI key
curl -X POST http://localhost:8000/llm/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-Provider-API-Key: sk-your-openai-key" \
  -d '{
    "model": "openai/gpt-4",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# Using your own Anthropic key
curl -X POST http://localhost:8000/llm/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-Provider-API-Key: sk-ant-your-anthropic-key" \
  -d '{
    "model": "anthropic/claude-3-opus-20240229",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Token Forwarding Behavior

• When token forwarding is enabled (forward_token = true):

  • User-provided keys (via header) take priority
  • Falls back to configured key if no header is provided
  • Returns 401 error if neither key is available

• When token forwarding is disabled (forward_token = false, default):

  • Always uses the configured API key
  • Ignores the X-Provider-API-Key header
  • Returns 401 error if no configured key exists

Security Considerations

• OAuth2 Integration: When OAuth2 is enabled, users must still authenticate with Nexus even when using token forwarding
• Key Validation: API keys are validated by the provider's API
• No Logging: User-provided keys are never logged
• HTTPS Recommended: Always use HTTPS in production to protect API keys in transit

Provider Limitations

• AWS Bedrock: Token forwarding is not supported for Bedrock providers. Bedrock uses AWS IAM credentials and request signing, which cannot be provided via simple API key headers. You must configure AWS credentials at the provider level (via environment variables, AWS profile, or explicit credentials in configuration).

Provider-Specific Considerations

OpenAI

• Supports all standard OpenAI models (GPT-3.5, GPT-4, etc.)
• Compatible with Azure OpenAI endpoints
• Supports function calling (when available)
• Supports streaming responses with Server-Sent Events (SSE)

Anthropic

• System messages are automatically extracted and placed in the system field
• Messages must alternate between user and assistant roles
• Requires explicit max_tokens parameter (defaults to 4096 if not specified)
• Supports all Claude models (Opus, Sonnet, Haiku)
• Supports streaming responses with Server-Sent Events (SSE)

Google

• Assistant role is automatically mapped to "model" role
• System messages are placed in the systemInstruction field
• Supports Gemini models
• Returns appropriate safety ratings when available
• Supports streaming responses with Server-Sent Events (SSE)

AWS Bedrock

• Automatically detects and routes to appropriate model family (Anthropic, Amazon, Meta, etc.)
• Each model family has its own request/response format, handled transparently
• Uses AWS SDK for authentication and request signing
• Supports all Bedrock features including streaming and model-specific parameters
• Regional endpoint selection based on configuration or AWS defaults
• Model availability varies by AWS region

Error Handling

Nexus provides consistent error responses across all providers:

• 400 Bad Request: Invalid request format or parameters
• 401 Unauthorized: Missing or invalid API key
• 429 Too Many Requests: Rate limit exceeded
• 500 Internal Server Error: Provider API error or network issues

Example error response:

{
  "error": {
    "message": "Invalid model format: expected 'provider/model', got 'invalid-format'",
    "type": "invalid_request_error",
    "code": 400
  }
}

Adding to AI Assistants

Cursor

Add to your Cursor settings:

1. Open Cursor Settings (Cmd+, on macOS)
2. Search for "Model Context Protocol"
3. Enable MCP support
4. Add to the MCP server configuration:

{
  "nexus": {
    "transport": {
      "type": "http",
      "url": "http://localhost:8000/mcp"
    }
  }
}

Make sure Nexus is running on localhost:8000 (or adjust the URL accordingly).

Claude Code

Add to your Claude Code configuration:

1. Open Claude Code and run the command:

   claude mcp add --transport http nexus http://localhost:8000/mcp
   

2. Or add it to your project's .mcp.json file:

   {
     "mcpServers": {
       "nexus": {
         "type": "http",
         "url": "http://localhost:8000/mcp"
       }
     }
   }
   

3. Verify the connection:

   claude mcp list
   

Make sure Nexus is running before starting Claude Code.

🔧 Technical Details

How It Works

MCP Tool Aggregation

Nexus provides two main tools to AI assistants:

1. search: A context-aware tool search that uses fuzzy matching to find relevant tools across all connected MCP servers
2. execute: Executes a specific tool with the provided parameters

When an AI assistant connects to Nexus, it can:

1. Search for tools using natural language queries
2. Discover tool names, descriptions, and required parameters
3. Execute tools from any connected MCP server

All tools from downstream servers are namespaced with their server name (e.g., github__search_code, filesystem__read_file).

LLM Provider Routing

Nexus acts as a unified gateway for multiple LLM providers:

1. Model Discovery: Lists all available models from configured providers with consistent naming
2. Request Routing: Automatically routes requests to the correct provider based on model name
3. Format Translation: Converts between OpenAI's API format and provider-specific formats
4. Response Normalization: Returns consistent response format regardless of provider

Models are namespaced with their provider name (e.g., openai/gpt-4, anthropic/claude-3-opus-20240229).

STDIO Server Integration

STDIO servers are spawned as child processes and communicate via JSON-RPC over standard input/output:

1. Process Management: Nexus automatically manages the lifecycle of STDIO server processes
2. Tool Discovery: Tools from STDIO servers are discovered dynamically and indexed for search
3. Error Handling: If a STDIO process crashes or outputs invalid JSON, appropriate errors are returned
4. Environment Isolation: Each STDIO server runs in its own process with configurable environment

Example Usage

Once configured, AI assistants can interact with Nexus like this:

1. Search for tools:

   User: "I need to search for code on GitHub"
   Assistant: Let me search for GitHub-related tools...
   [Calls search with keywords: ["github", "code", "search"]]
   

2. Execute tools:

   Assistant: I found the `github__search_code` tool. Let me search for your query...
   [Calls execute with name: "github__search_code" and appropriate arguments]
   

Common STDIO Server Examples

Python MCP Server

[mcp.servers.python_tools]
cmd = ["python", "-m", "my_mcp_server"]
env = { PYTHONPATH = "/opt/mcp", PYTHONUNBUFFERED = "1" }
stderr = "inherit"  # See Python output during development

Node.js MCP Server

[mcp.servers.node_tools]
cmd = ["node", "mcp-server.js"]
cwd = "/path/to/project"
env = { NODE_ENV = "production" }

Using npx packages

[mcp.servers.filesystem]
cmd = ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/home/user"]

Troubleshooting STDIO Servers

Server doesn't start

• Check executable path: Ensure the command exists and is executable
• View stderr output: Set stderr = "inherit" temporarily to see error messages
• Verify JSON-RPC output: The server must output valid JSON-RPC on stdout
• Check working directory: Ensure cwd path exists if specified

Tools not appearing

• Wait for initialization: STDIO servers may take a moment to start
• Use search: STDIO tools only appear in search results, not in the base tool list
• Check server logs: Enable stderr logging to see if the server is responding to tool list requests

📄 License

Nexus is licensed under the Mozilla Public License 2.0 (MPL-2.0). See the LICENSE file for details.

Contributing

See CONTRIBUTING.md for guidelines on how to contribute to Nexus.

Support

• Documentation: https://nexusrouter.com/docs
• Issues: https://github.com/grafbase/nexus/issues
• Discord: Grafbase Discord