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358baece761fd05bff1057e51dff3a06a1939aa7
- Add package.json with dependencies and scripts for development and build - Create PostCSS configuration for Tailwind CSS and autoprefixer - Implement main App component with chat functionality and message handling - Create API module for sending messages to backend - Add global CSS file with Tailwind directives and custom scrollbar styles - Set up entry point for React application in main.tsx - Define TypeScript environment types for Vite - Configure Tailwind CSS with content paths - Set up TypeScript configuration with strict settings - Configure Vite with React plugin and API proxy for development
Agent Alpha 🤖
An agentic AI application built with pydantic-ai, featuring a FastAPI backend and a React + Vite frontend.
Project Structure
agent_alpha/
├── agent_a.py # Standalone agent runner (CLI)
├── pyproject.toml # Python deps & metadata
├── backend/
│ ├── app.py # FastAPI application factory
│ ├── main.py # Uvicorn entry point
│ ├── core/
│ │ └── agent.py # Agent lifecycle & inference
│ └── routes/
│ ├── chat.py # POST /api/chat
│ └── health.py # GET /api/health
├── frontend/
│ ├── index.html
│ ├── package.json
│ ├── vite.config.ts
│ ├── tsconfig.json
│ ├── tailwind.config.js
│ └── src/
│ ├── main.tsx # React entry
│ ├── App.tsx # Chat UI
│ ├── api.ts # API client
│ └── index.css # Tailwind base
└── skills/ # Agent skill definitions
Quick Start
1. Python environment
uv sync # Create .venv & install deps
2. Backend (FastAPI)
uv run python -m backend.main
# → http://localhost:8000
# → API docs at http://localhost:8000/docs
3. Frontend (React + Vite)
cd frontend
npm install
npm run dev
# → http://localhost:5173
The Vite dev server proxies /api/* requests to the FastAPI backend.
API Endpoints
| Method | Path | Description |
|---|---|---|
| GET | /api/health |
Health check |
| POST | /api/chat |
Send a message to the agent |
POST /api/chat
{ "message": "What are your skills?", "session_id": null }
{ "reply": "I can help you with...", "session_id": null }
Key Capabilities
- Code execution via sandboxed
CodeMode - Web search (DuckDuckGo / provider-adaptive)
- MCP tool integration (Hacker News, etc.)
- Context management (sliding window + LLM compaction)
- Memory persistence (
./MEMORY.md) - Sub-agents (e.g.,
researcher) - Task tracking with subtask support
- Safety shields: cost caps, input guards, tool approval, secret redaction, stuck-loop detection
Skills
Skills are loaded from the ./skills/ directory. See each skill's SKILL.md for details.
analyzing-financial-statements/applying-brand-guidelines/creating-financial-models/
Languages
Python
77.4%
TypeScript
20.9%
Makefile
0.8%
Dockerfile
0.7%