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Installation

Lumen works with Python 3.11+ on Linux, Windows, and Mac.

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Jump to the Quick Start to start chatting with your data.

Bring Your Own LLM

Lumen works with any LLM provider. Choose the approach that fits your needs:

  • ☁️ Cloud providers — OpenAI, Anthropic, Google, Mistral, Azure (easiest to get started)
  • 🖥️ Locally hosted — Ollama, Llama.cpp (free, runs on your machine, no API keys)
  • 🔀 Router/Multi-provider — LiteLLM (unified interface, 100+ models)

Cloud Service Providers

Use hosted LLM APIs from major providers. Fastest to set up, pay-per-use pricing.

pip install 'lumen[ai-openai]'
export OPENAI_API_KEY=sk-...

Get your API key:

  1. Visit platform.openai.com/api-keys
  2. Click "Create new secret key"
  3. Copy the key (starts with sk-)
  4. Set environment variable:
# macOS/Linux
export OPENAI_API_KEY='sk-your-key-here'

# Windows PowerShell
$env:OPENAI_API_KEY='sk-your-key-here'

# Windows CMD
set OPENAI_API_KEY=sk-your-key-here

pip install 'lumen[ai-anthropic]'
export ANTHROPIC_API_KEY=sk-ant-...

Get your API key:

  1. Visit console.anthropic.com/settings/keys
  2. Click "Create Key"
  3. Copy the key (starts with sk-ant-)
  4. Set environment variable:
# macOS/Linux
export ANTHROPIC_API_KEY='sk-ant-your-key-here'

# Windows PowerShell
$env:ANTHROPIC_API_KEY='sk-ant-your-key-here'

# Windows CMD
set ANTHROPIC_API_KEY=sk-ant-your-key-here

pip install 'lumen[ai-google]'
export GEMINI_API_KEY=your-key

Get your API key:

  1. Visit aistudio.google.com/apikey
  2. Click "Create API key"
  3. Choose "Create API key in new project" or select existing project
  4. Copy the key (starts with AIza)
  5. Set environment variable:
# macOS/Linux
export GEMINI_API_KEY='your-key-here'

# Windows PowerShell
$env:GEMINI_API_KEY='your-key-here'

# Windows CMD
set GEMINI_API_KEY=your-key-here

Alternative: You can also use GOOGLE_API_KEY instead of GEMINI_API_KEY.

pip install 'lumen[ai-mistralai]'
export MISTRAL_API_KEY=your-key

Get your API key:

  1. Visit console.mistral.ai/api-keys
  2. Click "Create new key"
  3. Copy the key
  4. Set environment variable:
# macOS/Linux
export MISTRAL_API_KEY='your-key-here'

# Windows PowerShell
$env:MISTRAL_API_KEY='your-key-here'

# Windows CMD
set MISTRAL_API_KEY=your-key-here

pip install 'lumen[ai-openai]'
export AZUREAI_ENDPOINT_KEY=your-key
export AZUREAI_ENDPOINT_URL=https://your-resource.openai.azure.com/

Get your credentials:

  1. Visit portal.azure.com
  2. Navigate to your Azure OpenAI resource
  3. Go to "Keys and Endpoint"
  4. Copy KEY 1 or KEY 2 and your endpoint URL
  5. Set environment variables:
# macOS/Linux
export AZUREAI_ENDPOINT_KEY='your-key-here'
export AZUREAI_ENDPOINT_URL='https://your-resource.openai.azure.com/'

# Windows PowerShell
$env:AZUREAI_ENDPOINT_KEY='your-key-here'
$env:AZUREAI_ENDPOINT_URL='https://your-resource.openai.azure.com/'

# Windows CMD
set AZUREAI_ENDPOINT_KEY=your-key-here
set AZUREAI_ENDPOINT_URL=https://your-resource.openai.azure.com/

pip install 'lumen[ai-mistralai]'
export AZUREAI_ENDPOINT_KEY=your-key
export AZUREAI_ENDPOINT_URL=https://your-resource-endpoint.com/

Get your credentials:

  1. Visit portal.azure.com
  2. Navigate to your Azure AI resource with Mistral deployment
  3. Go to "Keys and Endpoint"
  4. Copy KEY 1 or KEY 2 and your endpoint URL
  5. Set environment variables (same as Azure OpenAI above)

Locally Hosted

Run open-source LLMs on your own machine. No API keys required, full privacy, free to use.

pip install 'lumen[ai-ollama]'

Setup Ollama:

  1. Install Ollama from ollama.com
  2. Start the Ollama service (usually starts automatically)
  3. Pull a model:
ollama pull qwen3:32b
  1. (Optional) Set custom endpoint if not using default:
export OLLAMA_BASE_URL=http://localhost:11434

No additional environment variables needed! Ollama works out of the box.

pip install 'lumen[ai-llama]'

No setup required! The first time you use Llama.cpp, Lumen will automatically download the model you specify. No environment variables needed.

Optional: Set a custom model URL:

lumen-ai serve data.csv --provider llama-cpp \
  --llm-model-url 'https://huggingface.co/unsloth/Qwen2.5-7B-Instruct-GGUF/blob/main/Qwen2.5-7B-Instruct-Q4_K_M.gguf'

pip install 'lumen[ai-openai]'

Anaconda AI Navigator runs models locally on your machine. No API key needed!

Default endpoint: http://localhost:8080/v1

Optional: Set custom endpoint:

export OPENAI_BASE_URL=http://localhost:8080/v1

Router / Multi-Provider

Use a unified interface to access multiple LLM providers and models.

pip install 'lumen[ai-litellm]'

Supports 100+ models across OpenAI, Anthropic, Google, Mistral, and more.

Set environment variables for providers you want to use:

export OPENAI_API_KEY='sk-...'

export ANTHROPIC_API_KEY='sk-ant-...'

export GEMINI_API_KEY='...'

export AZUREAI_ENDPOINT_KEY='...'
export AZUREAI_ENDPOINT_URL='https://...'

pip install aws-sso-util
aws-sso-util login --profile your-profile
# Credentials auto-exported!

aws sso login --profile your-profile
export AWS_PROFILE=your-profile
export AWS_DEFAULT_REGION=us-east-1

export AWS_ACCESS_KEY_ID='...'
export AWS_SECRET_ACCESS_KEY='...'
export AWS_DEFAULT_REGION='us-east-1'

Then use any supported model:

import lumen.ai as lmai

llm = lmai.llm.LiteLLM(
    model_kwargs={
        "default": {"model": "gpt-4.1-mini"},                    # OpenAI
        "edit": {"model": "anthropic/claude-sonnet-4-5"},        # Anthropic
        "sql": {"model": "gemini/gemini-2.5-flash"}             # Google
    }
)
ui = lmai.ExplorerUI(data='data.csv', llm=llm)
ui.servable()

AWS Bedrock is a managed gateway that provides access to foundation models from Anthropic, Meta, Mistral, Amazon, Cohere, and AI21 through a unified API.

pip install 'lumen[ai-anthropic]'  # For AnthropicBedrock
# OR
pip install boto3  # For Bedrock

Authentication:

pip install aws-sso-util
aws-sso-util login --profile your-profile
# Credentials auto-exported!

aws sso login --profile your-profile
export AWS_PROFILE=your-profile
export AWS_DEFAULT_REGION=us-east-1

export AWS_ACCESS_KEY_ID='...'
export AWS_SECRET_ACCESS_KEY='...'
export AWS_DEFAULT_REGION='us-east-1'

Choose your Lumen provider:

Optimized for Claude models using Anthropic's SDK.

import lumen.ai as lmai

llm = lmai.llm.AnthropicBedrock()
ui = lmai.ExplorerUI(data='data.csv', llm=llm)
ui.servable()

Universal access to all Bedrock models using boto3.

import lumen.ai as lmai

llm = lmai.llm.Bedrock(
    model_kwargs={
        "default": {"model": "us.anthropic.claude-sonnet-4-5-20250929-v1:0"},
    }
)
ui = lmai.ExplorerUI(data='data.csv', llm=llm)
ui.servable()

Available models:

  • Anthropic (Claude), Meta (Llama), Mistral, Amazon (Titan), Cohere, AI21
  • Model IDs: us.anthropic.claude-*, meta.llama3-*, mistral.*, amazon.titan-*
  • Full model list

IAM Permissions:

{
  "Effect": "Allow",
  "Action": ["bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream"],
  "Resource": "*"
}

Making Environment Variables Persistent

Set variables permanently so you don't have to export them every session:

Add to ~/.bashrc or ~/.bash_profile:

echo 'export OPENAI_API_KEY="sk-your-key-here"' >> ~/.bashrc
source ~/.bashrc

Add to ~/.zshrc:

echo 'export OPENAI_API_KEY="sk-your-key-here"' >> ~/.zshrc
source ~/.zshrc
  1. Search for "Environment Variables" in Start Menu
  2. Click "Edit the system environment variables"
  3. Click "Environment Variables"
  4. Under "User variables", click "New"
  5. Add variable name (e.g., OPENAI_API_KEY) and value
  6. Click OK
  7. Restart your terminal

Verify Installation

lumen-ai --version

Test your LLM connection:

# Test script
import lumen.ai as lmai

llm = lmai.llm.OpenAI()  # or Anthropic(), Google(), etc.
ui = lmai.ExplorerUI(data='test.csv')
ui.servable()

Next Steps

Ready to start? Head to the Quick Start guide to chat with your first dataset.

Missing Your Favorite LLM?

Missing your favorite LLM? Let us know by submitting a GitHub issue!