Meta Llama

Property	Details
Description	Meta's Llama API provides access to Meta's family of large language models.
Provider Route on LiteLLM	meta_llama/
Supported Endpoints	/chat/completions, /completions, /responses
API Reference	Llama API Reference ↗

Required Variables

Environment Variables

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

Supported Models

info

All models listed here https://llama.developer.meta.com/docs/models/ are supported. We actively maintain the list of models, token window, etc. here.

Model ID	Input context length	Output context length	Input Modalities	Output Modalities
Llama-4-Scout-17B-16E-Instruct-FP8	128k	4028	Text, Image	Text
Llama-4-Maverick-17B-128E-Instruct-FP8	128k	4028	Text, Image	Text
Llama-3.3-70B-Instruct	128k	4028	Text	Text
Llama-3.3-8B-Instruct	128k	4028	Text	Text

Usage - LiteLLM Python SDK

Non-streaming

Meta Llama Non-streaming Completion

import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# Meta Llama call
response = completion(model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8", messages=messages)

Streaming

Meta Llama Streaming Completion

import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# Meta Llama call with streaming
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

Function Calling

Meta Llama Function Calling

import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

messages = [{"content": "What's the weather like in San Francisco?", "role": "user"}]

# Define the function
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Meta Llama call with function calling
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.tool_calls)

Tool Use

Meta Llama Tool Use

import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

messages = [{"content": "Create a chart showing the population growth of New York City from 2010 to 2020", "role": "user"}]

# Define the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "create_chart",
            "description": "Create a chart with the provided data",
            "parameters": {
                "type": "object",
                "properties": {
                    "chart_type": {
                        "type": "string",
                        "enum": ["bar", "line", "pie", "scatter"],
                        "description": "The type of chart to create"
                    },
                    "title": {
                        "type": "string",
                        "description": "The title of the chart"
                    },
                    "data": {
                        "type": "object",
                        "description": "The data to plot in the chart"
                    }
                },
                "required": ["chart_type", "title", "data"]
            }
        }
    }
]

# Meta Llama call with tool use
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.content)

Usage - LiteLLM Proxy

Add the following to your LiteLLM Proxy configuration file:

config.yaml

model_list:
  - model_name: meta_llama/Llama-3.3-70B-Instruct
    litellm_params:
      model: meta_llama/Llama-3.3-70B-Instruct
      api_key: os.environ/LLAMA_API_KEY

  - model_name: meta_llama/Llama-3.3-8B-Instruct
    litellm_params:
      model: meta_llama/Llama-3.3-8B-Instruct
      api_key: os.environ/LLAMA_API_KEY

Start your LiteLLM Proxy server:

Start LiteLLM Proxy

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

OpenAI SDK

Meta Llama via Proxy - Non-streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Non-streaming response
response = client.chat.completions.create(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[{"role": "user", "content": "Write a short poem about AI."}]
)

print(response.choices[0].message.content)

Meta Llama via Proxy - Streaming

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Streaming response
response = client.chat.completions.create(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

LiteLLM SDK

Meta Llama via Proxy - LiteLLM SDK

import litellm

# Configure LiteLLM to use your proxy
response = litellm.completion(
    model="litellm_proxy/meta_llama/Llama-3.3-70B-Instruct",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key"
)

print(response.choices[0].message.content)

Meta Llama via Proxy - LiteLLM SDK Streaming

import litellm

# Configure LiteLLM to use your proxy with streaming
response = litellm.completion(
    model="litellm_proxy/meta_llama/Llama-3.3-70B-Instruct",
    messages=[{"role": "user", "content": "Write a short poem about AI."}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key",
    stream=True
)

for chunk in response:
    if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

cURL

Meta Llama via Proxy - cURL

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "meta_llama/Llama-3.3-70B-Instruct",
    "messages": [{"role": "user", "content": "Write a short poem about AI."}]
  }'

Meta Llama via Proxy - cURL Streaming

curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-proxy-api-key" \
  -d '{
    "model": "meta_llama/Llama-3.3-70B-Instruct",
    "messages": [{"role": "user", "content": "Write a short poem about AI."}],
    "stream": true
  }'

For more detailed information on using the LiteLLM Proxy, see the LiteLLM Proxy documentation.