spec4 0.1.3

AI-assisted software project planning — from idea to executable coding phases

Spec4 AI

AI-assisted software project planning — from idea to executable coding phases.

Spec4 is a Dash app (using Dash Mantine Components) that guides you through three stages of project planning using a pipeline of specialised LLM agents. Start with a rough idea and finish with a set of structured, ordered development phases ready to hand to an AI coding agent like Claude Code.

Quick Demo Video

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Requirements

• Python 3.12+
• uv package manager
• An API key for at least one supported LLM provider
• (Optional) A Tavily API key for web search

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Installation

Option 1 — Install from PyPI (recommended for most users):

uv tool install spec4 --refresh
spec4

Option 2 — Run from source (for contributors and developers):

git clone https://github.com/robertcrowe/spec4
cd spec4
make spec4

make spec4 runs uv sync (creates a .venv and installs all dependencies) then launches the app. All packages stay inside .venv — nothing is installed into your global Python.

Subsequent runs: make run reuses the existing .venv.

The app will be available at http://localhost:8050 in both cases.

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Features

• Four-stage planning pipeline — Reviewer (optional) → Brainstormer → StackAdvisor → Phaser
• Any LLM provider — works with OpenAI, Anthropic, Google Gemini, Cohere, and Mistral via LiteLLM
• Web search grounding — all agents can search the web via Tavily to find canonical documentation
• Saved credentials — optionally remember your provider, model, and API keys in the browser (localStorage via dcc.Store — never sent to or stored on the server)
• Incremental output — each agent produces a downloadable JSON or ZIP file you can reuse in a later session
• Jump-in anywhere — start at StackAdvisor or Phaser by uploading previously saved output
• Project persistence — artifacts saved to a .spec4/ folder inside your chosen project directory

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Agents

🔍 Reviewer (optional)

Analyzes an existing project directory to understand its architecture, technology stack, and coding style. Results inform Brainstormer and StackAdvisor when working on brownfield projects. Produces code_review.json.

🧠 Brainstormer

Develops a clear project vision through focused, one-at-a-time questions. Identifies technical standards via web search and embeds canonical documentation links in the output. Produces vision.json.

⚙️ StackAdvisor

Recommends languages, frameworks, hosting, and infrastructure based on the vision. Compares options, explains trade-offs, and uses web search to ground every recommendation. Produces stack.json.

📋 Phaser

Decomposes the vision and stack into an ordered sequence of development phases:

• Phase 1 is always a steel thread — a minimal end-to-end path that validates the core architecture
• Each phase builds on the previous one
• Stack spec fidelity — confirms before adding any dependency not in the stack spec
• Verification criteria — every phase includes the exact command needed to confirm it succeeded

Produces phases.zip with one JSON file per phase.

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Usage

1. Select a project directory — new or existing; artifacts are saved to .spec4/ inside it.
2. Connect — select a provider, enter your API key, and choose a model. Optionally add a Tavily key for web search.
3. Choose a starting point — pick an agent to begin with.
4. Plan — chat with each agent. When an agent completes, download the result and continue to the next agent.

Picking up where you left off

Each session auto-saves to .spec4/ inside your project directory. On a future visit, select the same directory and previously completed artifacts will be loaded automatically. You can also upload JSON files manually on the agent-select screen.

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Project structure

src/spec4/
├── app.py                  # Dash entry point — app wiring, root layout, page render
├── app_constants.py        # Shared constants (theme, routes, fonts)
├── session.py              # Session defaults, agent runner, artifact persistence
├── layouts.py              # All page layout functions
├── callbacks.py            # All Dash server-side callbacks
├── providers.py            # Provider/model registry, live model fetching
├── tavily_mcp.py           # Tavily web search integration
├── project_manager.py      # .spec4/ artifact persistence
├── a2a_bus.py              # In-memory A2A task bus
└── agents/
    ├── reviewer.py         # Code review agent
    ├── brainstormer.py     # Vision development agent
    ├── stack_advisor.py    # Technology stack recommendation agent
    └── phaser.py           # Incremental phase planning agent
tests/                      # pytest test suite
Makefile                    # Common commands

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Development

make spec4       # First-time setup: create .venv, install deps, and launch
make install     # Create .venv and install all dependencies (uv sync)
make run         # Start the app (http://localhost:8050)
make dev         # Start with debug/hot-reload enabled
make test        # Run tests
make lint        # Lint check with ruff
make serve       # Production server via gunicorn (requires: uv add gunicorn)

# Add a dependency (always use uv so it stays in .venv)
uv add <package>
uv add --dev <package>

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Supported providers

Provider	Models fetched from
OpenAI	api.openai.com/v1/models
Anthropic	api.anthropic.com/v1/models
Google Gemini	generativelanguage.googleapis.com
Cohere	api.cohere.com/v2/models
Mistral	api.mistral.ai/v1/models

Models are fetched live from each provider's API when you connect, with a hardcoded fallback list if the API is unavailable.

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License

Apache 2.0 — see LICENSE for details.