AgentBuilder 🤖

UI-first AI Agent Platform - Build, run, and observe AI agents with ease.

🌟 Overview

AgentBuilder is a modern, high-performance platform for creating and orchestrating AI agents. It provides a sleek UI to configure agents, define multi-agent workflows, and monitor execution with detailed logs and grounding metrics.

Built on the powerful agenticaiframework, AgentBuilder bridges the gap between complex agentic logic and user-friendly interfaces.

✨ Key Features

🤖 Visual Agent Builder: Configure roles, goals, and capabilities without writing code.
🔄 Workflow Orchestration: Design multi-agent teams (Sequential, Supervisor, Peer coordination).
📚 Advanced RAG (Knowledge Base):
- Google Gemini Integration: Uses text-embedding-004 for high-fidelity retrieval.
- Pinecone Vector Database: Scalable, high-performance vector search.
- Strict Grounding: Built-in instructions to prevent hallucinations and cite sources.
🎮 Immersive Playground:
- Prompt Studio: Sleek, glassmorphism-inspired interface for agent testing.
- Grounded Results: View "Knowledge Atoms" and confidence scores for every RAG response.
📊 Observability: Real-time execution logs, step-by-step tool monitoring, and token/cost estimation.
🛠️ Extensible Tool Registry: Built-in tools for Web Search, Document Parsing, and more.

🚀 Quick Start

Backend (Python)

Install Dependencies:

cd backend
python -m venv venv
source venv/bin/activate  # .\venv\Scripts\activate on Windows
pip install -r requirements.txt

Configure Environment: Create a .env file in the backend/ directory:

GEMINI_API_KEY=your_gemini_key
PINECONE_API_KEY=your_pinecone_key
PINECONE_INDEX_NAME=rag-index
MONGO_URI=mongodb://localhost:27017
MONGO_DB_NAME=agentbuilder

Run Server:
```
uvicorn main:app --reload
```

Frontend (Next.js)

Install Dependencies:
```
cd frontend
npm install
```
Run Development Server:
```
npm run dev
```

🏗️ Architecture

AgentBuilder uses a modern decoupled architecture:

Core: agenticaiframework for agent/workflow execution.
API: FastAPI providing a robust REST layer.
Frontend: Next.js 14 with Tailwind CSS and Framer Motion for a premium DX.
Vector Store: Pinecone for scalable RAG implementation.
Database: MongoDB for agent configurations and execution history.

📖 Documentation

Detailed documentation is available in the /docs directory:

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Chebaleomkar/AgentBuilder

README