strands-mlx 0.2.4

Use MLX in Strands Agents

strands-mlx

Running Strands Agents locally on Apple Silicon - inference, fine-tuning, vision in Python

MLX provider for Strands Agents with LoRA training pipelines.

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Requirements: Python ≤3.13, macOS/Linux

# Text models
pip install strands-mlx

# With vision/audio/video
pip install "strands-mlx[vision]"

# Environment setup
uv venv --python 3.13 && source .venv/bin/activate

# Install dependencies
uv pip install strands-agents strands-mlx strands-agents-tools

---

Quick Start

agent.py

from strands import Agent
from strands_mlx import MLXModel
from strands_tools import calculator

model = MLXModel(model_id="mlx-community/Qwen3-1.7B-4bit")
agent = Agent(model=model, tools=[calculator])

agent("What is 29 * 42?")

# Run with uv
uv run agent.py

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Architecture

graph LR
    A[Agent Conversations] -->|MLXSessionManager| B[Training Data JSONL]
    B -->|dataset_splitter| C[train/valid/test]
    C -->|mlx_trainer| D[LoRA Adapter]
    D -->|MLXModel| E[Domain Expert Agent]
    E -.->|Continuous Learning| A
    
    style A fill:#e1f5ff
    style E fill:#d4edda
    style D fill:#fff3cd

The complete training cycle: Agents collect their own training data → fine-tune themselves → become domain experts → continue learning.

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Train Your Own Model

4 steps: Collect → Split → Train → Use

1. Collect Training Data

from strands import Agent
from strands_tools import calculator
from strands_mlx import MLXSessionManager, dataset_splitter, mlx_trainer

session = MLXSessionManager(session_id="my_training", storage_dir="./dataset")
agent = Agent(model=model, tools=[calculator, dataset_splitter, mlx_trainer], session_manager=session)

# Have conversations - auto-saved to JSONL
agent("Teach me about quantum computing")
agent("Calculate 15 * 7")

# Saved to: ./dataset/my_training.jsonl

2. Split Dataset

agent.tool.dataset_splitter(
    input_path="./dataset/my_training.jsonl"
)
# Creates train.jsonl, valid.jsonl, test.jsonl (80/10/10 split)

3. Train with LoRA

agent.tool.mlx_trainer(
    action="train",
    config={
        "model": "mlx-community/Qwen3-1.7B-4bit",
        "data": "./dataset/my_training",
        "adapter_path": "./adapter",
        "iters": 200,
        "learning_rate": 1e-5,
        "batch_size": 1
    }
)

4. Use Trained Model

from strands import Agent
from strands_mlx import MLXModel

trained = MLXModel("mlx-community/Qwen3-1.7B-4bit", adapter_path="./adapter")
agent = Agent(model=trained)

agent("Explain quantum computing")  # Uses trained knowledge!

---

Vision Models

from strands_mlx import MLXVisionModel

model = MLXVisionModel(model_id="mlx-community/Qwen2-VL-2B-Instruct-4bit")
agent = Agent(model=model)

agent("Describe: <image>photo.jpg</image>")
agent("Transcribe: <audio>speech.wav</audio>")
agent("What happens: <video>clip.mp4</video>")

---

Training Tools

Tool	Purpose
mlx_trainer	Background LoRA training
dataset_splitter	Split JSONL → train/valid/test
validate_training_data	Check format & token counts
mlx_invoke	Runtime model switching
mlx_vision_invoke	Vision as a tool

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Advanced Training

YAML config file:

model: mlx-community/Qwen3-1.7B-4bit
data: ./training_data
iters: 1000
learning_rate: 1e-5
lora_parameters:
  rank: 8
  scale: 16.0
lr_schedule:
  name: cosine_decay
  warmup: 100
optimizer: adamw

Use config:

agent.tool.mlx_trainer(action="train", config="./lora_config.yaml")

---

Popular Models

Text:

• mlx-community/Qwen3-1.7B-4bit (recommended)
• mlx-community/Qwen3-4B-4bit
• mlx-community/Llama-3.2-1B-4bit
• mlx-community/gemma-2-2b-it-4bit

Vision:

• mlx-community/Qwen2-VL-2B-Instruct-4bit (recommended)
• mlx-community/Qwen2-Audio-7B-Instruct (audio)
• mlx-community/llava-v1.6-mistral-7b-4bit

Community models at mlx-community

---

Troubleshooting

Out of memory:

config = {
    "grad_checkpoint": True,
    "batch_size": 1,
    "max_seq_length": 1024
}

Model degraded:

config = {
    "iters": 200,  # Lower for small datasets
    "learning_rate": 1e-5  # Conservative
}

---

Resources

• Strands Agents
• MLX
• mlx-community models

---

Citation

@software{strands_mlx2025,
  author = {Cagatay Cali},
  title = {strands-mlx: MLX Model Provider for Strands Agents},
  year = {2025},
  url = {https://github.com/cagataycali/strands-mlx}
}

Apache 2 License | Built with MLX, MLX-LM, and Strands Agents