Llama API

Name: Strands Agents SDK
Author: Strands Agents

Llama API is a Meta-hosted API service for running Llama models. Meta runs the inference, so you call Llama models over an API without managing your own inference infrastructure.

Installation

Llama API is configured as an optional dependency in Strands Agents. To install, run:

pip install 'strands-agents[llamaapi]'

Usage

After installing llamaapi, you can import and initialize Strands Agents’ Llama API provider as follows:

from strands import Agent
from strands.models.llamaapi import LlamaAPIModel
from strands.vended_tools import notebook

model = LlamaAPIModel(
    client_args={
        "api_key": "<KEY>",
    },
    # **model_config
    model_id="Llama-4-Maverick-17B-128E-Instruct-FP8",
)

agent = Agent(model=model, tools=[notebook])
response = agent('Create a notebook named "ideas" and add three project ideas.')
print(response)

Configuration

Client Configuration

The client_args configure the underlying LlamaAPI client. For a complete list of available arguments, please refer to the LlamaAPI docs.

Model Configuration

The model_config configures the underlying model selected for inference. The supported configurations are:

Parameter	Description	Example	Options
`model_id`	ID of a model to use	`Llama-4-Maverick-17B-128E-Instruct-FP8`	reference
`repetition_penalty`	Controls the likelihood and generating repetitive responses. (minimum: 1, maximum: 2, default: 1)	`1`	reference
`temperature`	Controls randomness of the response by setting a temperature.	`0.7`	reference
`top_p`	Controls diversity of the response by setting a probability threshold when choosing the next token.	`0.9`	reference
`max_completion_tokens`	The maximum number of tokens to generate.	`4096`	reference
`top_k`	Only sample from the top K options for each subsequent token.	`10`	reference

Troubleshooting

Module Not Found

If you encounter the error ModuleNotFoundError: No module named 'llamaapi', this means you haven’t installed the llamaapi dependency in your environment. To fix, run pip install 'strands-agents[llamaapi]'.

Advanced Features

Structured Output

Llama API models support structured output through their tool calling capabilities. When you use Agent.structured_output(), the Strands Harness SDK converts your Pydantic models to tool specifications that Llama models can understand.

from pydantic import BaseModel, Field
from strands import Agent
from strands.models.llamaapi import LlamaAPIModel

class BookAnalysis(BaseModel):
    """Analyze a book's key information."""
    title: str = Field(description="The book's title")
    author: str = Field(description="The book's author")
    genre: str = Field(description="Primary genre or category")
    summary: str = Field(description="Brief summary of the book")
    rating: int = Field(description="Rating from 1-10", ge=1, le=10)

model = LlamaAPIModel(
    client_args={"api_key": "<KEY>"},
    model_id="Llama-4-Maverick-17B-128E-Instruct-FP8",
)

agent = Agent(model=model)

result = agent.structured_output(
    BookAnalysis,
    """
    Analyze this book: "The Hitchhiker's Guide to the Galaxy" by Douglas Adams.
    It's a science fiction comedy about Arthur Dent's adventures through space
    after Earth is destroyed. It's widely considered a classic of humorous sci-fi.
    """
)

print(f"Title: {result.title}")
print(f"Author: {result.author}")
print(f"Genre: {result.genre}")
print(f"Rating: {result.rating}")