Flyte & Union.ai Tutorials

Tutorials and examples for building AI agents, ML pipelines, and data workflows with Flyte 2.

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Featured

Example	Description
Code Mode — NYC Taxi analyst	Claude writes one program, the Monty sandbox runs it, and its loops fan out into durable parallel tasks over real NYC taxi data
LangGraph Research Pipeline	Research agent pipeline — LangGraph orchestrates planning and quality gates, Flyte fans out parallel researcher tasks
LangGraph ReAct Agent	Build a ReAct agent with LangGraph + OpenAI on Flyte
Stable Diffusion	Generate images from text prompts with SDXL Turbo on GPU
DuckDB ETL	Extract and transform data with DuckDB SQL
Fraud Detection with Feast	Real-time fraud scoring with Feast feature store + XGBoost

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Get Started

Tutorial	What you'll learn
Flyte Basics	Flyte 2 fundamentals — tasks, pipelines, error handling, TaskEnvironment, ReusePolicy, map()
LangGraph ReAct Agent	Build a ReAct agent with tools in a single file
Stable Diffusion	GPU inference with Flyte reports
Image Classifier	Fine-tune ResNet18 on HuggingFace dataset with PyTorch
DuckDB ETL	Data pipeline with DuckDB SQL and Flyte reports
Snowflake ETL	ETL pipeline with the Snowflake connector
Flyte Local Dev	Local dev features — TUI, caching, reports, tracing, serving (no cluster needed)
Fraud Detection with Feast	Fraud scoring pipeline with Feast feature store, XGBoost, and real-time serving

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Agents

Tutorial	Description
Code Mode — NYC Taxi analyst	The agent writes a program instead of calling tools one at a time. It runs in the Monty sandbox, and a loop in the generated code becomes a fan-out of durable, parallel query tasks over 3M+ real taxi trips
LangGraph Research Pipeline	Research agent pipeline — LangGraph orchestrates planning and quality gates, Flyte fans out parallel researcher tasks via Tavily web search
LangGraph ReAct Agent	Single-file ReAct agent with LangGraph
Planner Multi-Agent System	Scalable planner multi-agent system
ReAct Multi-Agent System	Adaptive ReAct multi-agent system
Debate Multi-Agent System	Multi-agent debate pattern
Manager Multi-Agent System	Manager-worker agent delegation
Sequential Multi-Agent System	Sequential agent pipeline
Autoresearch	Autoresearch-style self-healing agent on Flyte

MCP

Tutorial	Description
MCP Recipe Assistant	Build and deploy a recipe assistant MCP server on Union

LLM Fine-Tuning

Tutorial	Description
LoRA / QLoRA / Full	Fine-tune an LLM on text-to-SQL with LoRA, QLoRA, or full fine-tuning — live training reports, FastAPI serving, Gradio UI
GRPO — Code Generation	Teach a model to write Python with GRPO — reward = sandboxed test execution, MBPP dataset, live reward/pass-rate charts
GRPO	GRPO fine-tuning on math/reasoning tasks
DPO	Direct Preference Optimization for alignment
PPO	Proximal Policy Optimization for RLHF

ML / AI

Tutorial	Description
Fraud Detection with Feast	Fraud scoring pipeline with Feast feature store, XGBoost, and real-time serving
Stable Diffusion	Image generation with SDXL Turbo
Image Classifier	Fine-tune ResNet18 on Beans dataset
DETR Object Detection	Fine-tune DETR for object detection with live mAP charts

Biotech / Life Sciences

Tutorial	Description
Genomic Variant Effect Prediction	Score DNA mutations with HuggingFace Carbon genomic foundation model — zero-shot pathogenicity prediction on BRCA2, TP53, KRAS, and more
DNA Sequence Generation & Analysis	Generate DNA with Carbon and compare to real genes — GC content, codon usage, ORFs, dinucleotide frequencies
Gene Comparison Across Species	Compare homologous genes across 6 species with Carbon scoring, phylogenetic trees, and ESMFold 3D structure comparison
Protein Sequence Analysis	Analyze protein properties, compute sequence similarity, run ESM-2 embeddings, and predict 3D structures with ESMFold
Drug Molecule Screening	Virtual drug screening — compute physicochemical properties, apply Lipinski's Rule of Five, rank candidates by drug-likeness
Cell Microscopy Classification	Fine-tune a Vision Transformer (ViT) to classify blood cell types from microscopy images

Data

Tutorial	Description
DuckDB ETL	SQL-based data pipeline with DuckDB
Snowflake ETL	ETL with Snowflake connector
Lance Streaming for Vision	Convert a swarm of tiny per-sample image files (real CPPE-5 detection data) into one Lance dataset, benchmark per-file vs Lance streaming against real object storage, then train / evaluate / explore a Faster R-CNN streamed straight from S3 on a T4

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Setup

# Clone the repository
git clone https://github.com/unionai/workshops
cd workshops

# Navigate to any tutorial
cd tutorials/langgraph_agent_research

# Create virtual environment and install dependencies
uv venv .venv --python 3.11
source .venv/bin/activate
uv pip install -r requirements.txt

Flyte Cluster (for remote runs)

flyte create config \
    --endpoint <your-endpoint> \
    --auth-type headless \
    --builder remote \
    --domain development \
    --project flytesnacks

Don't have a cluster? Request access at flyte.org.

Run examples

Remote:

uv run flyte run workflow.py research_pipeline --query "Compare quantum computing approaches"

Local:

uv run flyte run --local workflow.py research_pipeline --query "Compare quantum computing approaches"

Local with TUI:

uv run flyte run --local --tui workflow.py research_pipeline --query "Compare quantum computing approaches"

Start TUI dashboard:

uv run flyte start tui