Teradata Agent Recipes

A collection of easy-to-run AI agent examples built on Teradata. Each recipe is a self-contained Python application that exposes a standard HTTP endpoint, allowing a single React UI to interact with any of them.

All 7 recipes can be discovered, set up, and launched from a centralized launcher at the project root—no need to cd into individual directories.

Environment Setup

Before running agents, you need to configure your credentials and endpoints.

Step 1: Create your .env file

cp .env.example .env

Step 2: Edit .env with your credentials

Open .env and populate the required variables:

Variable	Required For	Description	Example
`TD_HOST`	All recipes	Teradata host (ClearScape or on-premise)	`myinstance-xxx.env.clearscape.teradata.com`
`TD_USER`	All recipes	Teradata username	`demo_user`
`TD_PASSWORD`	All recipes	Teradata password	`YourPassword123`
`LLM_API_KEY`	All recipes	API key for your LLM provider	`sk-...` (OpenAI) or `claude-...` (Anthropic)
`LLM_PROVIDER`	Optional	LLM provider: openai, anthropic, bedrock, litellm (default: openai)	`openai`
`LITELLM_URL`	If using litellm provider	Chat completions endpoint of liteLLM proxy server	`http://localhost:4000/v1/chat/completions`
`TD_MCP_URL`	enterprise-templates/governed	URL to Teradata MCP server	`https://mcp-server.example.com:8443`
`TD_UES_URI`	enterprise-templates/vector-search, open-analytics	Teradata Unified Execution Server URI	`https://ues.example.com`
`TD_PAT`	enterprise-templates/vector-search, open-analytics	Personal Access Token for UES	`your-pat-token`
`TD_PEM_PATH`	enterprise-templates/vector-search, open-analytics	Path to PEM certificate file	`/path/to/cert.pem`

Security Note: Never commit the .env file to version control. It's listed in .gitignore to prevent accidental credential leaks.

Recipe Families

Family	Tier	Recipes	Ports	What you need
basic-agents	1	data-analyst, data-analyst-multiturn, data-analyst-dbt, data-analyst-mcp	8001-8004	Teradata host, username, password, LLM API key
enterprise-templates	2+	governed, vector-search, open-analytics	8005-8007	Tier 1 + MCP server (governed) / UES URI (vector, analytics)

Quick Start (Recommended)

Prerequisites: Complete the Environment Setup section above by creating and populating your .env file.

# From project root: setup all recipes + UI
python launcher.py all --setup

# Or use shell wrapper (auto-adds UI)
./start-agents.sh all         # Mac/Linux
.\start-agents.ps1 all        # Windows

Status: Currently, the basic recipe is fully documented and available. Other recipes are coming soon with full documentation.

Then:

UI: http://localhost:5173
Recipe endpoints: http://localhost:8001-8007

See LAUNCHER.md for all launcher options.

Recipes at a Glance

Learning & Foundations (basic-agents)

Recipe	Port	Complexity	Status	What it teaches
`data-analyst`	8001	beginner	✅ Available	Build your first NL-to-SQL agent
`data-analyst-multiturn`	8002	beginner	🔜 Coming soon	Add conversation memory
`data-analyst-dbt`	8003	intermediate	🔜 Coming soon	Integrate with dbt metadata
`data-analyst-mcp`	8004	intermediate	🔜 Coming soon	Use MCP for dynamic tool discovery

Enterprise Patterns (enterprise-templates)

Template	Port	Complexity	Status	What it shows
`governed`	8005	intermediate	🔜 Coming soon	Data governance & row-level security
`vector-search`	8006	advanced	🔜 Coming soon	Semantic search at scale
`open-analytics`	8007	advanced	🔜 Coming soon	Server-side computation via OAF
git

Agent Behavior

Each recipe demonstrates different AI agent patterns. See the behavior descriptions in individual recipe documentation:

data-analyst — Single-turn NL-to-SQL with dynamic schema introspection. Works with teddy_retailers dataset.
data-analyst-multiturn — Coming soon
data-analyst-dbt — Coming soon
data-analyst-mcp — Coming soon
governed — Coming soon
vector-search — Coming soon
open-analytics — Coming soon

Project Structure

teradata-agent-recipes/
├── launcher.py              # Central launcher (auto-discovers all recipes)
├── start-agents.sh / .ps1   # Shell wrappers (convenience, auto-adds UI)
├── LAUNCHER.md              # Complete launcher documentation
├── .env.example             # Environment template
├── .gitignore               # Git ignore rules
│
├── shared/                  # Shared utilities for all recipes
│   ├── config.py            # Environment & configuration loading
│   ├── connection.py        # Teradata connection factory
│   ├── server_factory.py    # FastAPI app creation (metadata, routing, LLM setup)
│   ├── recipe_loader.py     # recipe.yaml parser & validator
│   └── llm_providers.py     # Unified LLM provider adapters
│
├── recipes/
│   ├── basic_agents/        # Learning & foundational recipes
│   │   ├── data_analyst/
│   │   │   ├── recipe.yaml          # Metadata (name, port, family, tier, providers)
│   │   │   ├── server.py            # FastAPI app entry point
│   │   │   ├── agent.py             # Agent logic & tool definitions
│   │   │   ├── pyproject.toml       # Python dependencies (uv format)
│   │   │   ├── uv.lock              # Dependency lock file (reproducible installs)
│   │   │   ├── README.md            # Recipe documentation
│   │   │   └── setup.sql            # Sample data SQL
│   │   ├── data_analyst_multiturn/  # Multi-turn variant with conversation history
│   │   ├── data_analyst_dbt/        # DBT catalog integration variant
│   │   └── data_analyst_mcp/        # MCP server integration variant
│   │
│   └── enterprise_templates/        # Production-ready patterns
│       ├── governed/                # Data governance & row-level security
│       ├── vector-search/           # Semantic search at scale
│       └── open-analytics/          # Server-side Python computation
│
├── ui/                      # React application (Vite + Tailwind)
│   ├── src/
│   │   ├── components/
│   │   ├── hooks/
│   │   └── modes/
│   ├── package.json
│   ├── vite.config.js
│   └── tailwind.config.js
│
└── index.yaml               # Machine-readable recipe catalog

Architecture Highlights

1. Centralized Launcher

Auto-discovers all recipes by scanning for recipe.yaml + server.py pairs
Supports interactive menu, CLI args, and setup operations
Manages process lifecycle: startup, monitoring, graceful shutdown
See LAUNCHER.md for full documentation

2. Recipe Metadata as Single Source of Truth

Each recipe's recipe.yaml defines:

name: Display name
family: basic-agents or enterprise-templates
tier: Dependency level (1 for basic, 2+ for enterprise)
port: Default port number
display_mode: UI mode (chat, visual, etc.)
llm_providers: Supported providers (openai, anthropic, bedrock)

Example:

name: Basic SQL agent
family: basic-agents
tier: 1
port: 8001
display_mode: chat
llm_providers: [openai, anthropic, bedrock]

3. Shared Server Factory Pattern

server_factory.py creates FastAPI apps uniformly:

Loads metadata from recipe.yaml
Routes agent logic from recipe's agent.py
Injects configuration (databases, LLM providers, etc.)
Exposes /info (metadata) and /invoke (agent execution) endpoints

4. Unified LLM Provider Support

Each recipe family implements multi-provider support:

Basic Agents (recipes/basic_agents/llm_providers.py):

OpenAI: gpt-4o
Anthropic: claude-opus-4-5
AWS Bedrock: anthropic.claude-opus-4-5-v1:0
LiteLLM: HTTP proxy for any provider (see LITELLM_URL below)

How to use:

Set LLM_PROVIDER in .env to your choice (default: openai)
If using liteLLM, also set LITELLM_URL to your proxy endpoint
All recipes use the same provider interface—no code changes needed

Switch providers without code changes:

export LLM_PROVIDER=anthropic
python launcher.py data-analyst

LiteLLM Setup: LiteLLM is an open-source proxy that unifies access to 100+ LLM providers. To use it:

# Install: pip install litellm
# Start proxy pointing to your chosen provider:
litellm --model gpt-4o  # routes to OpenAI
litellm --model claude-opus-4-5  # routes to Anthropic
litellm --model bedrock/anthropic.claude-opus-4-5-v1:0  # routes to AWS Bedrock

# Then in .env:
LLM_PROVIDER=litellm
LITELLM_URL=http://localhost:4000/v1/chat/completions
LLM_API_KEY=your-provider-api-key

5. Per-Recipe Configuration

Each recipe owns its configuration:

basic-agents: SQL agents with schema introspection or MCP tool discovery
enterprise-templates: Governance (RLS), semantic search, or advanced analytics

No global TD_DATABASES env var.

6. Reproducible Installs with uv

Per-recipe pyproject.toml (Python dependencies)
Locked via per-recipe uv.lock (reproducible)
Fast parallel installs via uv sync
No virtual environment conflicts

Using the Launcher

All launcher options work through both launcher.py and shell wrappers (with auto-UI inclusion).

Setup (Install Dependencies)

# Setup all recipes
python launcher.py setup

# Setup specific recipes
python launcher.py setup data-analyst data-analyst-multiturn

# Setup across families
python launcher.py setup data-analyst governed open-analytics

# Setup UI only
python launcher.py setup ui

Launch (Run Services)

# Interactive menu
python launcher.py

# Named recipes (shell wrapper auto-adds UI)
./start-agents.sh data-analyst data-analyst-multiturn
.\start-agents.ps1 data-analyst data-analyst-multiturn

# All recipes + UI
python launcher.py all

# Specific recipes: recipes only (shell adds UI)
python launcher.py data-analyst data-analyst-mcp

# UI only
python launcher.py ui

Combined Setup + Launch

# One command: setup all, then launch
python launcher.py all --setup

# Setup then launch specific recipes
python launcher.py data-analyst --setup

See LAUNCHER.md for complete documentation and advanced usage.

Environment Configuration

Create a .env file at project root (use .env.example as template):

# Teradata connection (required for all recipes)
TD_HOST=your-teradata-host
TD_USER=your-username
TD_PASSWORD=your-password

# LLM provider (default: openai)
# Options: openai, anthropic, bedrock, litellm
LLM_PROVIDER=openai

# LLM API keys (choose based on LLM_PROVIDER)
OPENAI_API_KEY=your-openai-key
ANTHROPIC_API_KEY=your-anthropic-key
AWS_ACCESS_KEY_ID=your-aws-key
AWS_SECRET_ACCESS_KEY=your-aws-secret

# MCP server (required for sql recipe in basic-agents & governed in enterprise-templates)
TERADATA_MCP_SERVER_URL=http://localhost:5000

# Advanced features (required for vector-search and open-analytics in enterprise-templates)
UES_URI=https://your-ues-host
UES_PAT=your-personal-access-token
UES_PEM_FILE=/path/to/pem/file

Each recipe sources this .env automatically via shared/config.py.

Port Conventions

Port	Recipe	Family
8001	basic	basic-agents
8002	data-analyst-multiturn	basic-agents
8003	data-analyst-dbt	basic-agents
8004	sql	basic-agents
8005	governed	enterprise-templates
8006	vector-search	enterprise-templates
8007	open-analytics	enterprise-templates
5173	ui	N/A

Typical Workflows

Workflow 1: Try All Recipes

python launcher.py all --setup
# Visit http://localhost:5173
# Select recipes from dropdown

Workflow 2: Develop One Recipe

python launcher.py setup data-analyst
# Edit recipes/basic_agents/data_analyst/agent.py
python launcher.py data-analyst

Workflow 3: Test LLM Provider Switch

export LLM_PROVIDER=anthropic
python launcher.py data-analyst
curl -X POST http://localhost:8001/invoke \
  -H "Content-Type: application/json" \
  -d '{"query": "your question", "llm_provider": "anthropic"}'

Workflow 4: Setup Specific Family

# Setup only basic agents
python launcher.py setup data-analyst data-analyst-dbt data-analyst-multiturn

# Launch them
./start-agents.sh data-analyst data-analyst-dbt data-analyst-multiturn

Next Steps

Set up environment: Copy .env.example to .env, add credentials
Setup recipes: python launcher.py all --setup
Launch: python launcher.py all or ./start-agents.sh all
Access UI: http://localhost:5173
Deploy: Each recipe is a standalone FastAPI app—deploy to any server

See LAUNCHER.md for complete launcher documentation.

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Repository files navigation

Teradata Agent Recipes

Environment Setup

Recipe Families

Quick Start (Recommended)

Recipes at a Glance

Learning & Foundations (basic-agents)

Enterprise Patterns (enterprise-templates)

Agent Behavior

Project Structure

Architecture Highlights

1. Centralized Launcher

2. Recipe Metadata as Single Source of Truth

3. Shared Server Factory Pattern

4. Unified LLM Provider Support

5. Per-Recipe Configuration

6. Reproducible Installs with uv

Using the Launcher

Setup (Install Dependencies)

Launch (Run Services)

Combined Setup + Launch

Environment Configuration

Port Conventions

Typical Workflows

Workflow 1: Try All Recipes

Workflow 2: Develop One Recipe

Workflow 3: Test LLM Provider Switch

Workflow 4: Setup Specific Family

Next Steps

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Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
recipes		recipes
shared		shared
ui		ui
.env.example		.env.example
.gitignore		.gitignore
LAUNCHER.md		LAUNCHER.md
README.md		README.md
index.yaml		index.yaml
launcher.py		launcher.py
start-agents.ps1		start-agents.ps1
start-agents.sh		start-agents.sh

Folders and files

Latest commit

History

Repository files navigation

Teradata Agent Recipes

Environment Setup

Recipe Families

Quick Start (Recommended)

Recipes at a Glance

Learning & Foundations (basic-agents)

Enterprise Patterns (enterprise-templates)

Agent Behavior

Project Structure

Architecture Highlights

1. Centralized Launcher

2. Recipe Metadata as Single Source of Truth

3. Shared Server Factory Pattern

4. Unified LLM Provider Support

5. Per-Recipe Configuration

6. Reproducible Installs with uv

Using the Launcher

Setup (Install Dependencies)

Launch (Run Services)

Combined Setup + Launch

Environment Configuration

Port Conventions

Typical Workflows

Workflow 1: Try All Recipes

Workflow 2: Develop One Recipe

Workflow 3: Test LLM Provider Switch

Workflow 4: Setup Specific Family

Next Steps

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