Describe your agent. Mage builds it.

Tell Mage what you need in plain English. It designs the data model, generates everything, and deploys a production AI agent to Databricks Apps. Or use the visual setup panel for full control. One pip install. Zero boilerplate.

Install from PyPI View on GitHub
$ pip install brickforge && $ brickforge
Beta
BrickForge Setup App
Meet Mage

Your AI agent builds AI agents.

Mage is a conversational assistant that lives inside BrickForge. Describe what you want. Mage designs the data model, generates tables and functions, writes the system prompt, and deploys. Two modes for two kinds of builders.

Magic Mode

Just describe it.

For non-technical users. Say what your agent should do. Mage handles everything silently -- data, logic, prompts, deploy. You get a live URL.

"I need an agent for my pet hotel. Bookings, room availability, check-in/check-out."
Setting up your data home... Teaching your agent new tricks... Launching into orbit.
Your agent is live at https://your-workspace.cloud.databricks.com/apps/pet-hotel
Author Mode

Full visibility. Your call.

For technical users. Mage shows the full spec -- tables, columns, functions, params. You review, adjust, approve. Every step explained, every choice yours.

Here's the data model I'd recommend: 4 tables (rooms, bookings, guests, services), 6 UC functions...
"Add a loyalty_points column to guests. And a check_loyalty function."
Updated. Ready to generate?
How It Works

Two paths. Same result. Zero boilerplate.

Let Mage handle it conversationally, or use the visual setup panel for step-by-step control. Both produce the same production-grade agent.

1

Connect

  • Authenticate to any Databricks workspace via one-click OAuth
  • Token stored in your OS keychain, never on disk
  • Pick a SQL warehouse and Unity Catalog schema
2

Generate

  • Describe your domain in plain English
  • AI generates table schemas, synthetic data, SQL functions, stored procedures
  • System prompts and knowledge base tailored to your use case
3

Wire

  • Pick a Foundation Model endpoint
  • Connect Genie, KA, Vector Search, APIs, MCP, A2A agents
  • Toggle features like memory and charts
4

Deploy

  • One click. Bundles code + config + chat UI, deploys as a Databricks App
  • Auto-grants every permission the app needs: tables, functions, warehouse, Genie, serving endpoints, Lakebase
  • No manual SP configuration, no chasing permission errors. You get a live URL
5

Iterate

  • Save projects, export as .forge bundles, share with colleagues
  • Push to GitHub. Switch between workspaces
  • Clean up resources when done
Setup Panel

18 blocks. Every resource covered.

Each block follows the same pattern: choose an approach, configure, execute, done. No terminal needed.

1 Workspace 2 SQL Warehouse 3 Unity Catalog 4 Data Tables 5 Functions 6 Model Endpoint 7 Agent Prompt 8 Genie Space 9 Agent Bricks 10 Vector Search 11 MCP Servers 12 REST APIs 13 A2A Agents 14 Features 15 Lakebase 16 MLflow 17 Deploy 18 GitHub
Capabilities

Everything an agent needs. Built in.

BrickForge wires together the full Databricks platform into a single deployable agent. Here's what your agent ships with.

AI Data Generation

  • Describe a domain, get relational table schemas
  • Synthetic CSV data, UC functions, stored procedures
  • 5-layer self-healing SQL pipeline

Genie NL-to-SQL

  • Agent asks Genie questions in natural language
  • Genie writes the SQL, queries your tables, returns results
  • No prompt engineering for SQL generation

Knowledge Assistants

  • Upload PDFs, DOCX, HTML, markdown
  • Grounded answers with source citations
  • Vector Search as complementary retrieval path

External APIs & MCP

  • Connect any REST API or MCP server as a tool
  • UC-governed connections or direct HTTP with auth
  • Each becomes a callable agent tool

One-Click Deploy + Auto Grants

  • Bundles agent code + chat UI + config, deploys via Databricks SDK
  • Detects app service principal, grants SELECT on tables, EXECUTE on functions, CAN_USE on warehouse, CAN_RUN on Genie, CAN_QUERY on endpoints
  • Zero manual permission work. No more chasing 403s after deploy

Per-User Memory

  • Long-term memory backed by Lakebase
  • Remembers preferences, facts, context across sessions
  • Save, recall, delete - scoped per user
Terminal
$ pip install brickforge
Successfully installed brickforge-0.1.33

$ brickforge
BrickForge v0.1.33
[+] Server running at http://localhost:9000
[+] Setup App ready - open in browser
Agent Tools

What your deployed agent can do

Every tool is auto-discovered, dynamically loaded, and feature-gated. No hardcoding.

Tool How it works Discovery
UC Functions Auto-discovered from your schema. Parameterized SQL queries as callable tools. Auto
Stored Procedures CALL proc(params) for data mutations. Update records, trigger workflows. Auto
Genie MCP Natural-language questions translated to SQL via Databricks Genie. Config
Knowledge Assistant RAG over uploaded documents. Grounded answers with citations. Toggle
Vector Search Semantic document retrieval via Databricks Vector Search index. Config
External APIs REST calls via UC connections or direct HTTP. GET, POST, PUT, DELETE. Config
MCP Servers Any MCP-compatible tool server. Weather, Slack, custom services. Config
A2A Agents Delegate to remote agents via Google A2A protocol (JSON-RPC). Config
Charts Generate bar, line, area, and pie charts inline in chat. Toggle
Memory Per-user long-term memory. Save, recall, delete across sessions. Toggle
Custom Tools Drop a Python file with @tool functions into tools/. Auto-loaded. Auto
Architecture

What gets deployed

Two services in one Databricks App. The agent + the chat UI.

Databricks App (your workspace) | +- MLflow AgentServer [port 8000] | |-- LangGraph agent (LangChain tools + Foundation Model) | |-- /invocations endpoint | +-- Tools: Genie, KA, Vector Search, UC Functions, | APIs, MCP, A2A, Memory, Charts | +- Chat UI [port 3000] |-- React frontend (Vercel AI SDK) |-- Express backend (auth, chat proxy, SSE) +-- Live data panel, structured response blocks, action buttons, inline charts Config: Single config.json shipped in bundle Agent reads at boot, flattens to env vars No app.yaml env vars needed
Portable Agents

Build once. Deploy anywhere. Own your code.

Every agent you build is fully portable. Export it, version it, share it, deploy it to any workspace, or take over the code entirely.

.forge Bundles

Your entire agent in one file. Config, data schemas, SQL functions, stored procedures, system prompt, knowledge base - all packaged as an open, portable .forge.zip bundle.

Workflows this enables:
  • Design an agent locally, export the bundle, deploy to a test workspace
  • Share the bundle with a customer, partner, or another team
  • Import into a production workspace - different host, different schema, same agent
  • Store bundles as versioned snapshots - roll back anytime
  • Hand off a fully working agent without sharing credentials or access

GitHub Export

Push your entire agent to a private GitHub repo in one click. All generated code is yours - versioned, readable, ready to fork.

What you get:
  • Full agent source: LangGraph runtime, tool definitions, SQL functions, prompts
  • Generated data layer: table schemas, seed CSVs, stored procedures
  • Config as code: config.json with all wiring (tokens stripped)
  • Vibe code on top - use Claude Code, Cursor, or any AI IDE to customize
  • Your agent becomes a real software project, not a locked platform artifact
Use Cases

Adapt to any domain

BrickForge ships with a flight-operations reference agent, but the data gen wizard lets you build for any industry.

Airport Operations

Check-in Performance Advisor

  • Monitor zone metrics, detect bottlenecks, redeploy staff
  • Live dashboard refreshes as the agent acts
  • Ships as the reference implementation
Financial Services

Portfolio Risk Monitor

  • Query positions, run compliance checks
  • Generate allocation reports
  • Reads from UC tables, answers from regulatory docs via KA
Healthcare

Clinical Trial Assistant

  • Track enrollment metrics, adverse events, site performance
  • Query structured trial data
  • Retrieve protocol documents in one conversation
Retail & Supply Chain

Inventory Ops Agent

  • Monitor stock levels, forecast demand
  • Trigger replenishment procedures
  • Connect POS data, warehouse systems, supplier APIs
Energy & IoT

Grid Monitoring Agent

  • Ingest sensor data, detect anomalies
  • Dispatch maintenance
  • Combine real-time Genie queries with equipment manuals
Your Domain

Describe it. Build it.

  • Type a domain description
  • BrickForge generates tables, functions, prompts, knowledge base
  • Review, tweak, deploy. Any domain, any use case.
Evaluation

Automate your agent's evaluation

Ship with confidence. BrickForge includes a built-in MLflow evaluation pipeline so you can measure agent quality before every deploy.

LLM Judge Scoring

  • Custom Claude-based LLM judge evaluates every response
  • Scores on correctness, relevance, groundedness, and completeness
  • Baseline vs with-guideline comparison in a single run

MLflow Tracking

  • Results logged to MLflow experiments - compare runs side by side
  • Track score distributions, failure modes, regressions
  • Ships with curated test datasets for the reference domain

Iterate on Quality

  • Tweak prompts, tools, or knowledge base - re-run eval to measure impact
  • Catch regressions before they reach production
  • Build your own test datasets from real conversations

One command. Tell Mage. Live agent.

Run brickforge, describe what you need, and Mage deploys a production AI agent to Databricks in minutes.

Get Started Star on GitHub