01Install
Cerebras runs Llama models on wafer-scale chips, delivering the fastest inference available (~2000 tokens/sec).
Install
bashpip install "mtpx[cerebras]"This installs the openai SDK (used for the OpenAI-compatible API). Alternatively, install the native Cerebras SDK:
bashpip install cerebras-cloud-sdk02API Key Setup
API Key Setup
Option 1: .env file (recommended)
- Install dotenv support:
bash pip install python-dotenvOr with the MTP extra:
bash pip install "mtpx[dotenv]"- Create a
.envfile in your project root:
text CEREBRAS_API_KEY=csk-your_key_here- Load it in your code before creating the provider:
python from mtp import Agent
Agent.load_dotenv_if_available() # reads .env fileGet a free API key at cloud.cerebras.ai (no credit card required).
Option 2: System environment variable
bash# Linux/macOS
export CEREBRAS_API_KEY="csk-..."
# Windows PowerShell
$env:CEREBRAS_API_KEY="csk-..."03Quick Start
Quick Start
pythonfrom mtp import Agent
from mtp.providers import Cerebras
Agent.load_dotenv_if_available() # loads CEREBRAS_API_KEY from .env
provider = Cerebras(model="llama-4-scout-17b-16e-instruct")
tools = Agent.ToolRegistry()
agent = Agent(provider=provider, tools=tools)
reply = agent.run_loop("What is 25 * 4 + 10?")
print(reply)04Parameters
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
model | str | "llama-4-scout-17b-16e-instruct" | Cerebras model ID |
api_key | str | None | None | API key (falls back to CEREBRAS_API_KEY env var) |
temperature | float | 0.0 | Sampling temperature |
tool_choice | str | dict | "auto" | Tool selection strategy |
parallel_tool_calls | bool | True | Allow parallel tool calls |
client | Any | None | None | Pre-configured Cerebras client instance |
05Capabilities
Capabilities
| Capability | Value |
|---|---|
| Tool calling | Yes |
| Parallel tool calls | Yes (configurable) |
| Input modalities | text |
| Streaming | Fallback |
| Usage metrics | Rich |
| Reasoning metadata | No |
| Native async | No (uses thread fallback) |
06Recommended Models
Recommended Models
llama-4-scout-17b-16e-instruct— Best tool calling (default)llama-3.3-70b— Strong general purposellama3.1-8b— Fastest
07Full Example
Full Example
pythonfrom mtp import Agent
from mtp.providers import Cerebras
Agent.load_dotenv_if_available()
provider = Cerebras(
model="llama-4-scout-17b-16e-instruct",
temperature=0.0,
parallel_tool_calls=True,
)
tools = Agent.ToolRegistry()
agent = Agent(provider=provider, tools=tools, debug_mode=True)
reply = agent.run_loop(
"Calculate (25 * 4) + 10",
max_rounds=3,
)
print(reply)08Notes
Notes
- Cerebras uses the native
cerebras-cloud-sdkwhen available, falls back to the OpenAI client pointed at Cerebras endpoint. - Text-only input (no image/audio/video support).
- The
parallel_tool_callsparameter is gracefully handled if the SDK version doesn't support it.
09Source
Source
src/mtp/providers/cerebras_provider.py