Universal Model Support (LiteLLM)

Universal Model Support (LiteLLM)

Lár is built on top of LiteLLM, which means it supports 100+ LLM providers out of the box.

You do NOT need to install separate SDKs (openai, anthropic, google-generativeai). Lár manages the unified interface for you.

1. Quick Start: The .env File

To switch providers, you simply need to:

1. Set the correct Environment Variable for that provider.
2. Use the correct Model String in your LLMNode.

Create a .env file in your project root:

# --- OpenAI ---
OPENAI_API_KEY="sk-..."

# --- Anthropic ---
ANTHROPIC_API_KEY="sk-ant-..."

# --- Google Gemini ---
GEMINI_API_KEY="AIza..."

# --- AWS Bedrock ---
AWS_ACCESS_KEY_ID="AKIA..."
AWS_SECRET_ACCESS_KEY="abc..."
AWS_REGION_NAME="us-east-1"

# --- Azure OpenAI ---
AZURE_API_KEY="..."
AZURE_API_BASE="https://my-resource.openai.azure.com/"
AZURE_API_VERSION="2023-05-15"

2. Supported Model Strings (Cheat Sheet)

Pass these strings to the model_name argument of LLMNode.

3. How to Use Local Models

Lár treats local models as first-class citizens.

Option A: Ollama (Easiest)

1. Install Ollama: ollama.com
2. Pull a Model: ollama pull phi4
3. Run: Make sure the app is running (it listens on port 11434).
4. Code:

node = LLMNode(
    model_name="ollama/phi4", 
    prompt_template="Analyze this: {task}",
    output_key="result"
)

Option B: Llama.cpp / LM Studio / LocalAI

Most local servers offer an "OpenAI Compatible Endpoint". You can point Lár to this endpoint using api_base.

1. Start Server: e.g., python -m llama_cpp.server --model model.gguf --port 8080
2. Code: use openai/custom prefix to force the OpenAI protocol.

node = LLMNode(
    model_name="openai/my-local-model",
    prompt_template="...",
    output_key="res",
    generation_config={
        "api_base": "http://localhost:8080/v1",  # URL of your local server
        "api_key": "sk-local-key"                # Required by some servers (can be fake)
    }
)

4. Advanced Configuration

You can tune model parameters using the generation_config dictionary. This passes arguments directly to the provider's API.

node = LLMNode(
    model_name="gpt-4o",
    prompt_template="...",
    output_key="res",
    generation_config={
        "temperature": 0.2,       # Lower = Deterministic, Higher = Creative
        "max_tokens": 1000,       # Limit the output length
        "top_p": 0.9,             # Nucleus sampling
        "frequency_penalty": 0.0, # Reduce repetition
        "stop": ["User:", "\n"]   # Stop sequences
    }
)

5. Troubleshooting / FAQ

"ProviderNotFoundError: Model not found"

• Check Prefix: Did you forget gemini/ or ollama/?
• Check LiteLLM: Run pip install -U litellm to get the latest model lists.

"AuthenticationError"

• Check .env: Ensure load_dotenv() is called at the TOP of your script.
• Check Key Name: LiteLLM expects specific names (e.g., ANTHROPIC_API_KEY, not CLAUDE_KEY).

"I want to see the raw API request"

Enable verbose logging to debug exactly what is being sent to the LLM.

import os
os.environ["LITELLM_LOG"] = "DEBUG"