# Fireworks Tech Graph — Generate Technical Diagrams from Natural Language

> An AI-powered tool that converts natural language descriptions into production-quality SVG and PNG technical diagrams supporting 7 visual styles, UML notation, and AI agent workflow patterns.

## Install

Save in your project root:

# Fireworks Tech Graph — Generate Technical Diagrams from Natural Language

## Quick Use
```bash
pip install fireworks-tech-graph

# Generate a system architecture diagram
fireworks-graph "microservice architecture with API gateway, auth service, user service, and PostgreSQL database" --style modern --output arch.svg

# Generate a UML sequence diagram
fireworks-graph "user login flow: browser sends credentials to auth service, auth validates with DB, returns JWT" --type sequence --output login.svg
```

## Introduction
Fireworks Tech Graph transforms plain English descriptions into polished technical diagrams. Instead of wrestling with diagramming tools or learning DOT syntax, developers describe what they want and receive publication-ready SVG or PNG output suitable for documentation, presentations, and architecture reviews.

## What Fireworks Tech Graph Does
- Converts natural language into SVG and PNG technical diagrams
- Supports 7 visual styles from minimal wireframe to polished corporate
- Generates UML diagrams including sequence, class, and activity types
- Renders AI and agent workflow patterns with specialized node types
- Produces deterministic output suitable for version control

## Architecture Overview
The tool uses a Python CLI that parses natural language input, maps it to an intermediate graph representation, and then renders it through a layout engine. The LLM interprets the description and produces structured JSON describing nodes, edges, and groupings. A rendering pipeline applies the selected visual style, handles automatic layout with collision avoidance, and exports the final SVG or rasterized PNG.

## Self-Hosting & Configuration
- Install via pip with Python 3.10+
- Requires an LLM API key (OpenAI, Anthropic, or compatible local endpoint)
- Configure default style, output format, and model in `~/.fireworks-graph/config.yaml`
- Supports batch mode for generating multiple diagrams from a manifest file
- No external services required beyond the LLM provider

## Key Features
- Seven distinct visual styles from technical to presentation-ready
- First-class UML support with correct notation and layout rules
- Agent workflow patterns with specialized nodes for tools, models, and memory
- Deterministic rendering for consistent output across runs
- CLI and Python API for integration into documentation pipelines

## Comparison with Similar Tools
- **Mermaid** — text DSL for diagrams; Fireworks uses natural language, no syntax to learn
- **Excalidraw** — manual drawing tool; Fireworks auto-generates from descriptions
- **PlantUML** — structured UML markup; Fireworks accepts plain English input
- **D2** — declarative diagram language; Fireworks eliminates the need for a custom DSL

## FAQ
**Q: Can I customize the visual styles?**
A: Yes, styles are defined as JSON theme files. You can modify colors, fonts, spacing, and node shapes to match your brand.

**Q: Does it handle large diagrams well?**
A: The layout engine handles diagrams with up to 50-60 nodes comfortably. For very large architectures, breaking into sub-diagrams is recommended.

**Q: Can I use it without a cloud LLM?**
A: Yes, point it at any OpenAI-compatible local endpoint such as Ollama or vLLM for fully offline operation.

**Q: Is the SVG output accessible?**
A: Generated SVGs include title and description metadata. Adding full ARIA labels is on the roadmap.

## Sources
- https://github.com/yizhiyanhua-ai/fireworks-tech-graph

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Source: https://tokrepo.com/en/workflows/asset-87682447
Author: AI Open Source