litellm

n8n community node for LiteLLM - Access 100+ LLMs with unified API

Package Information

Downloads: 61 weekly / 119 monthly

Latest Version: 0.1.1

Author: LiteLLM

Available Nodes

LiteLLM Chat Model

Chat with 100+ LLMs through LiteLLM proxy

Documentation

n8n-nodes-litellm

Official n8n community node for LiteLLM, providing native integration with LiteLLM proxy to access 100+ LLM providers through a unified API.

LiteLLM is an open-source LLM proxy/gateway that provides a unified OpenAI-compatible API for 100+ LLM providers including OpenAI, Anthropic, Google, Azure, AWS Bedrock, and more. It handles load balancing, fallbacks, cost tracking, and rate limiting across all your LLM deployments.

Features

🚀 100+ LLM Providers - Access OpenAI, Anthropic, Google, Azure, AWS Bedrock, and more through a single interface
🔄 Automatic Fallbacks - Configure fallback models for reliability
📊 Built-in Observability - Native support for Langfuse, Datadog, and custom metadata
💰 Cost Tracking - Team-based usage tracking and analytics
🎯 Full OpenAI Compatibility - Standard parameters (temperature, max_tokens, etc.)
🔒 Enterprise Ready - Team management, access control, and rate limiting
⚡ Streaming Support - Real-time response streaming (coming soon)

Installation

Via n8n Community Nodes (Recommended)

Open your n8n instance
Go to Settings → Community Nodes
Search for @berriai/n8n-nodes-litellm
Click Install

Manual Installation

npm install @berriai/n8n-nodes-litellm

Then restart your n8n instance.

Prerequisites

This node requires a running LiteLLM proxy server. If you don't have one set up:

Quick Start with LiteLLM Proxy

Install LiteLLM:
```
pip install litellm[proxy]
```

Create a config file (litellm_config.yaml):

model_list:
  - model_name: gpt-4o-mini
    litellm_params:
      model: gpt-4o-mini
      api_key: os.environ/OPENAI_API_KEY

  - model_name: claude-3-opus
    litellm_params:
      model: claude-3-opus-20240229
      api_key: os.environ/ANTHROPIC_API_KEY

Start the proxy:
```
litellm --config litellm_config.yaml
```

The proxy will start at http://localhost:4000 by default.

📖 Full LiteLLM documentation: https://docs.litellm.ai/docs/proxy/quick_start

Configuration

Credentials Setup

In n8n, create a new LiteLLM API credential
Configure:
- API Key: Your LiteLLM proxy API key (if authentication is enabled)
- Base URL: Your LiteLLM proxy URL (default: http://localhost:4000)

Testing Credentials

The credential includes a built-in test that validates connectivity to your LiteLLM proxy by calling the /v1/models endpoint.

Usage

Basic Chat Completion

The simplest use case - send a message and get a response:

// Node configuration
{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "user",
      "content": "Explain n8n workflows in one sentence"
    }
  ]
}

Multi-Message Conversation

Build conversations with system prompts and message history:

{
  "model": "claude-3-opus",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant specializing in workflow automation."
    },
    {
      "role": "user",
      "content": "How do I connect to a database in n8n?"
    },
    {
      "role": "assistant",
      "content": "You can use database nodes like PostgresDB or MySQL..."
    },
    {
      "role": "user",
      "content": "Can you show me an example?"
    }
  ]
}

Advanced Configuration

Use standard OpenAI parameters for fine-tuned control:

{
  "model": "gpt-4",
  "messages": [...],
  "options": {
    "temperature": 0.7,
    "max_tokens": 2000,
    "top_p": 0.9,
    "frequency_penalty": 0.5,
    "presence_penalty": 0.3,
    "stop": ["END", "###"]
  }
}

LiteLLM-Specific Features

Fallback Models

Automatically retry failed requests with fallback models:

{
  "model": "gpt-4",
  "messages": [...],
  "liteLLMOptions": {
    "fallbacks": "claude-3-opus,gpt-4o-mini"  // Try these if primary fails
  }
}

Team & User Tracking

Track usage by team and user for cost allocation:

{
  "model": "gpt-4",
  "messages": [...],
  "liteLLMOptions": {
    "team_id": "engineering",
    "user": "user@example.com",
    "tags": "production,customer-support"
  }
}

Custom Metadata for Observability

Send metadata to observability platforms (Langfuse, Datadog, etc.):

{
  "model": "gpt-4",
  "messages": [...],
  "liteLLMOptions": {
    "metadata": {
      "workflow_id": "workflow-123",
      "customer_id": "cust-456",
      "environment": "production",
      "session_id": "sess-789"
    }
  }
}

Parameters Reference

Model (Required)

The LLM model to use. Must be configured in your LiteLLM proxy config.

Examples: gpt-4o-mini, claude-3-opus, gemini-pro, llama-3-70b

Messages (Required)

Array of conversation messages. Each message requires:

role: system, user, or assistant
content: The message text

Options (Optional)

Standard OpenAI-compatible parameters:

Parameter	Type	Default	Description
`frequency_penalty`	number	0	Penalty for token frequency (-2.0 to 2.0)
`max_tokens`	number	1000	Maximum tokens to generate
`presence_penalty`	number	0	Penalty for token presence (-2.0 to 2.0)
`stop`	string	-	Comma-separated stop sequences
`stream`	boolean	false	Enable streaming (experimental)
`temperature`	number	0.7	Sampling temperature (0 to 2)
`top_p`	number	1	Nucleus sampling parameter (0 to 1)

LiteLLM Options (Optional)

LiteLLM-specific features:

Parameter	Type	Description
`fallbacks`	string	Comma-separated fallback models
`metadata`	JSON	Custom metadata object for observability
`tags`	string	Comma-separated tags for categorization
`team_id`	string	Team ID for cost tracking
`user`	string	User ID for analytics and rate limiting

Response Format

The node returns the full LiteLLM/OpenAI response:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4o-mini",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "This is the AI response..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 50,
    "total_tokens": 70
  }
}

Tip: Use the Expression {{ $json.choices[0].message.content }} to extract just the response text.

Compatibility

n8n version: 0.220.0 or higher
Node.js: 18.x or higher
LiteLLM: 1.0.0 or higher

Troubleshooting

Connection Refused

Problem: Cannot connect to LiteLLM proxy

Solution:

Verify LiteLLM proxy is running: curl http://localhost:4000/health
Check the Base URL in your credentials matches the proxy address
Ensure firewall allows connections to the proxy port

Authentication Failed

Problem: 401 Unauthorized error

Solution:

If your LiteLLM proxy has authentication enabled, provide the API key in credentials
Check the API key is valid: curl -H "Authorization: Bearer YOUR_KEY" http://localhost:4000/v1/models

Model Not Found

Problem: Model not available error

Solution:

Verify the model is configured in your litellm_config.yaml
Check model name spelling matches your config exactly
Restart LiteLLM proxy after config changes

Rate Limiting

Problem: Too many requests error

Solution:

Configure rate limits in LiteLLM proxy config
Use user parameter to track per-user limits
Implement retry logic in your workflow

Examples

Workflow Automation: Email Response Generator

Trigger (Webhook)
  ↓
LiteLLM Chat Model
  Model: gpt-4o-mini
  System: "Generate professional email responses"
  User: "{{ $json.email_content }}"
  ↓
Send Email

Multi-Provider Reliability

LiteLLM Chat Model
  Model: gpt-4
  Fallbacks: claude-3-opus,gpt-4o-mini
  ↓
(Automatically tries fallbacks if GPT-4 fails)

Cost Tracking by Department

LiteLLM Chat Model
  Model: gpt-4
  Team ID: {{ $json.department }}
  User: {{ $json.user_email }}
  Tags: {{ $json.project_name }}
  ↓
(Track costs in LiteLLM dashboard by team/user)

Resources

LiteLLM Documentation: https://docs.litellm.ai
LiteLLM GitHub: https://github.com/BerriAI/litellm
n8n Documentation: https://docs.n8n.io
LiteLLM Discord: https://discord.com/invite/wuPM9dRgDw

Support

Issues: GitHub Issues
Questions: n8n Community Forum
LiteLLM Support: Discord

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License

Maintainers

LiteLLM Team

Email: krrish@berri.ai
GitHub: @BerriAI

Built with ❤️ by the LiteLLM team

litellmInstall

Package Information

Available Nodes

Documentation

n8n-nodes-litellm

Features

Installation

Via n8n Community Nodes (Recommended)

Manual Installation

Prerequisites

Quick Start with LiteLLM Proxy

Configuration

Credentials Setup

Testing Credentials

Usage

Basic Chat Completion

Multi-Message Conversation

Advanced Configuration

LiteLLM-Specific Features

Fallback Models

Team & User Tracking

Custom Metadata for Observability

Parameters Reference

Model (Required)

Messages (Required)

Options (Optional)

LiteLLM Options (Optional)

Response Format

Compatibility

Troubleshooting

Connection Refused

Authentication Failed

Model Not Found

Rate Limiting

Examples

Workflow Automation: Email Response Generator

Multi-Provider Reliability

Cost Tracking by Department

Resources

Support

Contributing

License

Maintainers

Discussion

litellm