autogpt-agents

# 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

**AI Blocks:**
- `AITextGeneratorBlock` - Generate text with LLMs
- `AIConversationBlock` - Multi-turn conversations
- `SmartDecisionMakerBlock` - Conditional logic

**Integration Blocks:**
- GitHub, Google, Discord, Notion connectors
- Webhook triggers and handlers
- HTTP request blocks

**Control Blocks:**
- Input/Output blocks
- Branching and decision nodes
- Loop and iteration blocks

## Agent execution

### Trigger types

**Manual execution:**
```http
POST /api/v1/graphs/{graph_id}/execute
Content-Type: application/json

{
  "inputs": {
    "input_name": "value"
  }
}
```

**Webhook trigger:**
```http
POST /api/v1/webhooks/{webhook_id}
Content-Type: application/json

{
  "data": "webhook payload"
}
```

**Scheduled execution:**
```json
{
  "schedule": "0 */2 * * *",
  "graph_id": "graph-uuid",
  "inputs": {}
}
```

### Monitoring execution

**WebSocket updates:**
```javascript
const ws = new WebSocket('ws://localhost:8001/ws');

ws.onmessage = (event) => {
  const update = JSON.parse(event.data);
  console.log(`Node ${update.node_id}: ${update.status}`);
};
```

**REST API polling:**
```http
GET /api/v1/executions/{execution_id}
```

## Using Forge (Development)

### Create custom agent

```bash
# Setup forge environment
cd classic
./run setup

# Create new agent from template
./run forge create my-agent

# Start agent server
./run forge start my-agent
```

### Agent structure

```
my-agent/
├── agent.py          # Main agent logic
├── abilities/        # Custom abilities
│   ├── __init__.py
│   └── custom.py
├── prompts/          # Prompt templates
└── config.yaml       # Agent configuration
```

### Implement custom ability

```python
from forge import Ability, ability

@ability(
    name="custom_search",
    description="Search for information",
    parameters={
        "query": {"type": "string", "description": "Search query"}
    }
)
def custom_search(query: str) -> str:
    """Custom search ability."""
    # Implement search logic
    result = perform_search(query)
    return result
```

## Benchmarking agents

### Run benchmarks

```bash
# Run all benchmarks
./run benchmark

# Run specific category
./run benchmark --category coding

# Run with specific agent
./run benchmark --agent my-agent
```

### Benchmark categories

- **Coding**: Code generation and debugging
- **Retrieval**: Information finding
- **Web**: Web browsing and interaction
- **Writing**: Text generation tasks

### VCR cassettes

Benchmarks use recorded HTTP responses for reproducibility:

```bash
# Record new cassettes
./run benchmark --record

# Run with existing cassettes
./run benchmark --playback
```

## Integrations

### Adding credentials

1. Navigate to Profile > Integrations
2. Select provider (OpenAI, GitHub, Google, etc.)
3. Enter API keys o