LLM Endpoint Configuration

Point your agents at any OpenAI-compatible endpoint with environment variables

Configure where your agents send LLM requests using environment variables.

Quick Start

1. Use OpenAI (default)

   Set your OpenAI API key and create an agent:

   import os
   from praisonaiagents import Agent
   
   os.environ["OPENAI_API_KEY"] = "your-api-key"
   
   agent = Agent(
       name="Research Assistant",
       instructions="You are a helpful research assistant"
   )
   
   result = agent.start("Explain quantum computing in simple terms")
   
2. Use Ollama locally

   Point to your local Ollama instance:

   import os
   from praisonaiagents import Agent
   
   os.environ["OPENAI_BASE_URL"] = "http://localhost:11434/v1"
   os.environ["MODEL_NAME"] = "llama3"
   
   agent = Agent(
       name="Local Assistant",
       instructions="You are a helpful assistant running locally"
   )
   
   result = agent.start("What are the benefits of local LLMs?")
   
3. Use a custom proxy / LiteLLM

   Configure any OpenAI-compatible proxy:

   import os
   from praisonaiagents import Agent
   
   os.environ["OPENAI_BASE_URL"] = "https://your-proxy/v1"
   os.environ["OPENAI_API_KEY"] = "your-proxy-key"
   os.environ["MODEL_NAME"] = "claude-3-sonnet"
   
   agent = Agent(
       name="Proxy Assistant",
       instructions="You are an assistant using a proxy endpoint"
   )
   
   result = agent.start("Compare different AI models")
   

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How It Works

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Environment Variables

Variable	Purpose	Precedence
OPENAI_BASE_URL	LLM endpoint URL (highest priority)	1
OPENAI_API_BASE	LLM endpoint URL (legacy compat)	2
OLLAMA_API_BASE	Ollama endpoint URL	3
MODEL_NAME	Model name (highest priority)	1
OPENAI_MODEL_NAME	Model name (legacy compat)	2
OPENAI_API_KEY	API key (no fallback)	—

Defaults

Setting	Default Value
Model	gpt-4o-mini
Base URL	https://api.openai.com/v1
API Key	None

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Common Patterns

Run against Ollama

bash

export OPENAI_BASE_URL="http://localhost:11434/v1"
export MODEL_NAME="llama3"
python your_agent.py

python

import os
from praisonaiagents import Agent

# Configure Ollama
os.environ["OPENAI_BASE_URL"] = "http://localhost:11434/v1"
os.environ["MODEL_NAME"] = "llama3"

agent = Agent(
    name="Ollama Assistant",
    instructions="You are running on Ollama"
)

result = agent.start("Explain the benefits of local AI")

Run against a corporate OpenAI proxy

bash

export OPENAI_BASE_URL="https://corporate-proxy.company.com/v1"
export OPENAI_API_KEY="your-corporate-key"
export MODEL_NAME="gpt-4"
python your_agent.py

python

import os
from praisonaiagents import Agent

# Configure corporate proxy
os.environ["OPENAI_BASE_URL"] = "https://corporate-proxy.company.com/v1"
os.environ["OPENAI_API_KEY"] = "your-corporate-key"
os.environ["MODEL_NAME"] = "gpt-4"

agent = Agent(
    name="Corporate Assistant",
    instructions="You are using a corporate OpenAI proxy"
)

result = agent.start("Generate a business report")

Use Azure OpenAI / Bedrock via LiteLLM

bash

export OPENAI_BASE_URL="https://your-litellm-proxy/v1"
export OPENAI_API_KEY="your-litellm-key"
export MODEL_NAME="azure/gpt-4"
python your_agent.py

python

import os
from praisonaiagents import Agent

# Configure LiteLLM for Azure
os.environ["OPENAI_BASE_URL"] = "https://your-litellm-proxy/v1"
os.environ["OPENAI_API_KEY"] = "your-litellm-key"
os.environ["MODEL_NAME"] = "azure/gpt-4"

agent = Agent(
    name="Azure Assistant",
    instructions="You are using Azure OpenAI via LiteLLM"
)

result = agent.start("Analyze this data")

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User Interaction Flow

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Best Practices

Set OPENAI_BASE_URL, not OPENAI_API_BASE

OPENAI_BASE_URL is the standard OpenAI SDK environment variable and has the highest precedence. Use this for all new configurations rather than the legacy OPENAI_API_BASE.

Empty string ≠ unset

An empty string value is skipped during resolution, and the next variable in precedence order is tried. To disable a variable, unset it completely rather than setting it to an empty string.

# This skips OPENAI_BASE_URL and tries OPENAI_API_BASE
export OPENAI_BASE_URL=""
export OPENAI_API_BASE="https://proxy.com/v1"

# This uses OPENAI_BASE_URL
unset OPENAI_BASE_URL
export OPENAI_API_BASE="https://proxy.com/v1"

Use .env files for local dev

Create a .env file in your project root for local development:

# .env
OPENAI_BASE_URL=http://localhost:11434/v1
MODEL_NAME=llama3
# OPENAI_API_KEY not needed for Ollama

Load it in your Python code:

from dotenv import load_dotenv
load_dotenv()

from praisonaiagents import Agent
# Environment variables are now loaded

Realtime/WebSocket endpoints

For realtime features, WebSocket URLs are auto-derived from HTTP URLs. The system automatically:

• Converts https:// to wss://
• Strips /v1 suffix to avoid /v1/v1/realtime
• Appends the appropriate realtime path

You only need to set OPENAI_BASE_URL - the realtime endpoint is handled automatically.

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Related

LLM Configuration

Complete LLM configuration options

Models

Supported models and providers