autogpt-agents

Files (3)

• SKILL.md
• advanced-usage.md · references/
• troubleshooting.md · references/

SKILL.md (first lines)

# AutoGPT - Autonomous AI Agent Platform

Comprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.

## When to use AutoGPT

**Use AutoGPT when:**
- Building autonomous agents that run continuously
- Creating visual workflow-based AI agents
- Deploying agents with external triggers (webhooks, schedules)
- Building complex multi-step automation pipelines
- Need a no-code/low-code agent builder

**Key features:**
- **Visual Agent Builder**: Drag-and-drop node-based workflow editor
- **Continuous Execution**: Agents run persistently with triggers
- **Marketplace**: Pre-built agents and blocks to share/reuse
- **Block System**: Modular components for LLM, tools, integrations
- **Forge Toolkit**: Developer tools for custom agent creation
- **Benchmark System**: Standardized agent performance testing

**Use alternatives instead:**
- **LangChain/LlamaIndex**: If you need more control over agent logic
- **CrewAI**: For role-based multi-agent collaboration
- **OpenAI Assistants**: For simple hosted agent deployments
- **Semantic Kernel**: For Microsoft ecosystem integration

## Quick start

### Installation (Docker)

```bash
# Clone repository
git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT/autogpt_platform

# Copy environment file
cp .env.example .env

# Start backend services
docker compose up -d --build

# Start frontend (in separate terminal)
cd frontend
cp .env.example .env
npm install
npm run dev
```

### Access the platform

- **Frontend UI**: http://localhost:3000
- **Backend API**: http://localhost:8006/api
- **WebSocket**: ws://localhost:8001/ws

## Architecture overview

AutoGPT has two main systems:

### AutoGPT Platform (Production)
- Visual agent builder with React frontend
- FastAPI backend with execution engine
- PostgreSQL + Redis + RabbitMQ infrastructure

### AutoGPT Classic (Development)
- **Forge**: Agent development toolkit
- **Benchmark**: Performance testing framework
- **CLI**: Command-line interface for development

## Core concepts

### Graphs and nodes

Agents are represented as **graphs** containing **nodes** connected by **links**:

```
Graph (Agent)
  ├── Node (Input)
  │   └── Block (AgentInputBlock)
  ├── Node (Process)
  │   └── Block (LLMBlock)
  ├── Node (Decision)
  │   └── Block (SmartDecisionMaker)
  └── Node (Output)
      └── Block (AgentOutputBlock)
```

### Blocks

Blocks are reusable functional components:

| Block Type | Purpose |
|------------|---------|
| `INPUT` | Agent entry points |
| `OUTPUT` | Agent outputs |
| `AI` | LLM calls, text generation |
| `WEBHOOK` | External triggers |
| `STANDARD` | General operations |
| `AGENT` | Nested agent execution |

### Execution flow

```
User/Trigger → Graph Execution → Node Execution → Block.execute()
     ↓              ↓                 ↓
  Inputs      Queue System      Output Yields
```

## Building agents

### Using the visual builder

1. **Open Agent Builder** at http://localhost:3000
2. **Add blocks** from the BlocksControl panel
3. **Connect nodes** by dragging between handles
4. **Configure inputs** in each node
5. **Run agent** using PrimaryActionBar

### Available blocks

From Orchestra-Research/AI-Research-SKILLs, MIT.

More from Orchestra-Research/AI-Research-SKILLs