AI TaskVector

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Beta version! Use it at your own risk.

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Core concept: AI agents creating and training AI agents for users' needs.

A low-code programming framework and GUI visual editor for AI assistants to help users solve business and engineering tasks by composing specific workflows from units/pipilines, and configure/perform training via GUI and chat conversation—minimizing hand-written code.

• Language agnostic graph: the Graph is capable of carring units written in any language.
• Native runtime: Python-based graph execution.
• External runtimes (workflow conversion compatibility): Node-RED, Pyflow, ComFy, n8n, etc. You can drop in an external workflow as is, modify and export back. Use the external runtime "roundtrip" feature for RL training.
• Offline local models (no external API is required)

Assistants (co-pilots)

• Workflow Designer to create/modify workflows, generate custom units (if allowed), make integrations.
• RL Coach to train/fine-tune models.

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Quick start

1. Install (from repo root)

python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
pip install -r requirements.txt

2. Open the Constructor GUI (Flet)

Desktop app: workflow graph (canvas), training config, run/test, and AI chat (Workflow Designer / RL Coach).

pip install -r gui/flet/requirements.txt
python -m gui.flet.main

• Workflow: Load or paste a process graph (Node-RED/PyFlow/n8n/YAML); edit on canvas; run workflow, report, grep, GitHub from chat.
• Training: Load/edit training config (goal, rewards, callbacks); run training or test a saved model.
• Chat: Talk to Workflow Designer (graph edits) or RL Coach (training config); edits are applied to graph or config.

3. Train from the command line (optional)

python runtime/train.py --config config/examples/training_config.yaml

Use --process-config for a custom process graph; use --checkpoint to resume. All behavior is driven by the config files the assistants (or you) produce.

4. Test a trained model

python scripts/test_model.py ./models/temperature-control-agent/best/best_model

For a visual tank demo and manual sliders (thermodynamic example):

python -m environments.custom.thermodynamics.water_tank_simulator --config config/examples/training_config.yaml --model ./models/temperature-control-agent/best/best_model

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Docker

You can run the app (and optionally the Ollama LLM server) in Docker. The image includes the full stack: main app, RAG, Flet GUI, and units (e.g. web_search). Works with classic Docker (e.g. 2022) and newer BuildKit. If you hit No space left on device during build, free disk space or set TMPDIR or PIP_CACHE_DIR to a directory on a larger drive before running docker build.

Build and run with Docker Compose (app + Ollama)

From the repo root:

docker compose build
docker compose up

Then open the Flet GUI in your browser at http://localhost:8550. The app is configured to use the Ollama service automatically via OLLAMA_HOST.

Pull a model in Ollama (one-time):

docker compose exec ollama ollama pull llama3.2

Models are stored in a persistent volume (ollama_data).

Build and run the app image only

docker build -t ai-taskvector .
docker run --rm -p 8550:8550 -e FLET_WEB=1 -e FLET_SERVER_PORT=8550 ai-taskvector

Open http://localhost:8550. If Ollama runs on your host, point the app at it with:

docker run --rm -p 8550:8550 -e OLLAMA_HOST=http://host.docker.internal:11434 ai-taskvector flet run gui/flet/main.py --web -p 8550

Environment variables

Variable	Description
OLLAMA_HOST	Ollama server URL (default: http://127.0.0.1:11434). In Compose, set to http://ollama:11434.
OLLAMA_MODEL	Default model name (e.g. llama3.2) when not set in GUI settings.
OLLAMA_API_KEY	Optional; for Ollama Cloud.

Files

• Dockerfile — Full install (main + RAG + Flet GUI + units); default command runs the Flet GUI.
• docker-compose.yml — App + Ollama service; Flet runs in web mode on port 8550.

Apply assistant edits from the CLI:

# Process Assistant: apply graph edit (add/remove/connect units)
python -m assistants apply_graph --graph config/examples/temperature_process.yaml --edit edit.json [--out path]

# Training Assistant: apply config edit (goal, rewards, hyperparameters)
python -m assistants apply_config --config config/examples/training_config.yaml --edit edit.json [--out path]

License

MIT — use and modify for your projects.