MLauto

A faithful replication of the AutoGluon-Assistant architecture, implemented in LangGraph.

MLauto automates solving ML tasks end-to-end: it understands the data, selects the right library, retrieves relevant tutorials, generates and executes code inside Docker, and uses MCTS to intelligently search the solution space — backtracking out of dead ends.

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Architecture

Phase 1 — Perception
  scan_data → find_description_files → generate_task_description → select_tools
      → [Semantic Memory] retrieve_tutorials → [Episodic Memory] rerank_tutorials

Phase 2 — Iterative Coding (MCTS loop)
  select_node → expand_node → retrieve_node_tutorials → rerank_node_tutorials
      → generate_python_code → generate_bash_script → execute_and_evaluate
      → backpropagate → (repeat or done)

Modules

Module	Maps to	Role
perception_agent/	DataPerceptionAgent etc.	Understand data & select tools
semantic_memory/	RetrieverAgent	FAISS + BGE tutorial search
episodic_memory/	RerankerAgent	LLM-based tutorial selection
iterativecoding_agent/	CodingAgent + NodeManager	MCTS code generation loop
shared/	Core infrastructure	State, LLM, Node, NodeManager, TutorialIndexer

Quick Start

1. Build the Docker Image

MLauto executes all generated code inside an isolated Docker container. You must build the base executor image first:

# Build the docker image (make sure the Docker daemon is running)
docker build -t mlauto-executor:latest .

2. Install Dependencies & Setup

pip install -r requirements.txt

# Set your OpenAI API key
export OPENAI_API_KEY=sk-...

3. Start the Standalone MCP Servers

The Semantic Memory and Episodic Memory modules run as fully standalone, standard-compliant MCP servers communicating over HTTPS (Server-Sent Events). You must start both servers first (make sure to run these from inside the MLauto directory so Python can resolve the packages):

# In Terminal 1: Start Semantic Memory MCP Server (Port 8010)
cd MLauto
uvicorn semantic_memory.mcp_server:app --port 8010

# In Terminal 2: Start Episodic Memory MCP Server (Port 8011)
cd MLauto
uvicorn episodic_memory.mcp_server:app --port 8011

4. Run the Pipeline

Once both servers are running, you can launch the end-to-end MLauto agent pipeline in a separate terminal:

python run.py /home/administrator/dreamlab/data1 \
    -u "Solve the regression problem using the provided data. Output the final submission in a CSV file containing predictions on the test set." \
    -o ./my_results1.3 \
    -v 4 \
    -n 10    

Arguments Explained:

• /path/to/your/dataset: (Required) The absolute or relative path to your input data folder. This is a positional argument, so it requires no flag.
• -u / --user-input: (Required) The specific instructions or task description for the ML agent.
• -v / --verbosity: (Optional) Sets the terminal logging level from 0 to 4. The default is 2 (INFO). We recommend 3 (DETAIL) for tracking the MCTS tree progress, and 4 (DEBUG) for viewing raw LLM prompts.
• -o / --output: (Optional) The directory where generated code, logs, and state snapshots will be saved. If omitted, it auto-generates a unique folder in ./runs.
• -n / --max-iterations: (Optional) Overrides the maximum MCTS tree search iterations specified in config.yaml.
• -c / --config: (Optional) Path to a custom YAML configuration file.

Config

Edit config.yaml to control:

• LLM model and temperature
• MCTS parameters (iterations, exploration constant, failure penalty)
• Tutorial retrieval (top-k, condensed vs full, max length)
• Docker execution settings