Llama API¶

Llama API is a Meta-hosted API service that helps you integrate Llama models into your applications quickly and efficiently.

Llama API provides access to Llama models through a simple API interface, with inference provided by Meta, so you can focus on building AI-powered solutions without managing your own inference infrastructure.

With Llama API, you get access to state-of-the-art AI capabilities through a developer-friendly interface designed for simplicity and performance.

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_tools import calculator

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

agent = Agent(model=model, tools=[calculator])
response = agent("What is 2+2")
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 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}")

References¶

API
LlamaAPI