AgenticBuild 🤖

Autonomous Full-Stack AI Engineer

AgenticBuild is an AI-native platform that transforms natural language into production-ready web applications. Unlike traditional "one-shot" generators, it uses Agentic Loops powered by LangGraph to architect, implement, and self-correct code until it works.

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✨ Key Features

🧠 Intelligent Orchestration

AgenticBuild uses a stateful Directed Acyclic Graph (DAG) powered by LangGraph to manage the lifecycle of every request.

graph TD
    %% Entry Point
    START((START)) --> Mode{Mode Check}
    
    %% Main Branches
    Mode -->|"Chat Only"| Chat[Chat Node]
    Mode -->|"Build Project"| Analyzer[Analyzer Node]
    
    %% Feasibility Gate
    Analyzer --> Feasible{Is it<br/>Feasible?}
    Feasible -->|No| Chat
    Feasible -->|Yes| Namer[Namer Node]
    
    %% Main Pipeline
    subgraph "Autonomous Pipeline"
        Namer --> Architect[Architect Node]
        Architect --> Coder[Coder Node]
        Coder --> Validator[Validator Node]
    end
    
    %% Self-Healing Logic
    Validator --> Result{Result?}
    
    %% Routing Paths
    Result -->|Success| Writer[Writer Node]
    Result -->|"Bug Found (Retry < 5)"| Coder
    Result -->|"Max Retries Exceeded"| Chat
    
    %% Endings
    Writer --> END((END))
    Chat --> END
    
    %% Visual Styles
    style START fill:#0f172a,stroke:#38bdf8,color:#fff
    style END fill:#0f172a,stroke:#38bdf8,color:#fff
    
    classDef expertNode fill:#1e293b,stroke:#38bdf8,stroke-width:2px,color:#fff
    class Analyzer,Chat,Namer,Architect,Coder,Validator,Writer expertNode
    
    classDef decision fill:#0f172a,stroke:#38bdf8,stroke-dasharray: 5 5,color:#fff
    class Mode,Feasible,Result decision

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• Self-Healing Code Engine: Implements a recursive "Test-and-Repair" loop. If the generated code has bugs or formatting issues, the agent detects, analyzes, and fixes them autonomously across multiple retries.
• LangGraph State Management: Uses stateful directed acyclic graphs (DAGs) to manage complex multi-step reasoning, ensuring high architectural consistency.
• Smart Fallback Mechanism: Automatically pivots from complex multi-file architectures to robust single-file applications if technical constraints (like token limits) are hit.
• Feasibility Analysis: Pre-screens every request to ensure it stays within system capabilities, providing 💡 Suggested Alternatives for non-feasible tasks.

🛠️ Advanced Project Management

• Incremental Updates: Unlike one-shot generators, AgenticBuild understands your existing codebase. You can request updates, add features, or refactor existing projects through conversational dialogue.
• Multi-Session Workstreams: Create and manage multiple concurrent project builds. Switch between different feature branches without losing progress.
• Dynamic Model Switching: Hot-swap between different "AI brains" (OpenAI 120B, Llama 4 Scout, Llama 3.3, etc.) at runtime to balance speed, cost, and reasoning power.
• Native ZIP Exports: Download your completed projects as ready-to-deploy archives directly from the sidebar.

🛡️ Enterprise-Ready Core

• Multi-Tenant Security: Built-in JWT authentication ensures that sessions, projects, and history are strictly isolated between users.
• Context Pruning: High-efficiency history management and character capping allow the agent to handle large projects without hitting server request size limits.
• Glassmorphism UI: A high-end, modern dashboard with blur effects, responsive layouts, and real-time build status tracking.

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🚀 Quick Start

1. Setup Environment

# Install uv (modern package manager)
pip install uv

# Sync dependencies
uv sync

2. Configure Credentials

Create a .env file from .env.example:

LLM_PROVIDER=groq
GROQ_API_KEY=your_key_here
GROQ_MODEL_NAME=openai/gpt-oss-120b

3. Launch AgenticBuild

uv run python init_and_run.py

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🛠️ Development Methodology

AgenticBuild was developed adopting the new era of AI-First Engineering, leveraging autonomous agent orchestration to build an autonomous agent platform.

• Gemini CLI & Superpowers plugin: This project was architected and stabilized using the Gemini CLI (leveraging the Gemini 3.1 Pro model), utilizing the Superpowers plugin for deep codebase research and tool integration.
• Plan & Action Workflow: The development followed a strict Research -> Strategy (Plan Mode) -> Execution (Action Mode) cycle.
• The Modern Era: This project serves as a testament to the transition from manual coding to Agentic Orchestration—a workflow that demands higher-level system design skills and a focus on responsible, secure AI usage.

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🏗️ Technical Stack

• Backend: Python 3.14, FastAPI, SQLModel (SQLite), JWT
• AI Engine: LangGraph, LangChain, Groq (OpenAI 120B / Llama 4 / Llama 3.3)
• Frontend: Streamlit, LocalStorage API, GSAP, Tailwind CSS
• Capabilities: High-fidelity Single-Page Apps (Three.js/GSAP) and Standalone Scripts

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🧹 Individual Execution

If you prefer to run the services separately:

• Backend: uv run uvicorn backend.app.main:app --reload
• Frontend: uv run streamlit run frontend/app.py

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🚧 Roadmap & Security (TODO)

• UI Stabilization: Tighten up visual consistency and refine Glassmorphism components.
• Filesystem Sandboxing: Implement containerized sandboxing for the agent's file-writing capabilities.
• Adversarial Robustness: Implement multi-layered scaffolding to detect and block complex prompt injection attacks.
• Security Auditing: Integrate an automated auditor node to scan generated code for vulnerabilities.

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🖼️ Visual Gallery

Click on any image to view in high resolution.