---
name: autogpt-agents
description: Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
license: MIT
metadata:
  version: 1.0.0
  category: llm-applications
  maintainer: Kalaris Labs
  tags: Agents, AutoGPT, Autonomous Agents, Workflow Automation, Visual Builder, AI Platform
  dependencies: autogpt-platform>=0.4.0
---

# 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 or authorize OAuth
4. Credentials are encrypted and stored securely

### Using credentials in blocks

Blocks automatically access user credentials:

```python
class MyLLMBlock(Block):
    def execute(self, inputs):
        # Credentials are injected by the system
        credentials = self.get_credentials("openai")
        client = OpenAI(api_key=credentials.api_key)
        # ...
```

### Supported providers

| Provider | Auth Type | Use Cases |
|----------|-----------|-----------|
| OpenAI | API Key | LLM, embeddings |
| Anthropic | API Key | Claude models |
| GitHub | OAuth | Code, repos |
| Google | OAuth | Drive, Gmail, Calendar |
| Discord | Bot Token | Messaging |
| Notion | OAuth | Documents |

## Deployment

### Docker production setup

```yaml
# docker-compose.prod.yml
services:
  rest_server:
    image: autogpt/platform-backend
    environment:
      - DATABASE_URL=postgresql://...
      - REDIS_URL=redis://redis:6379
    ports:
      - "8006:8006"

  executor:
    image: autogpt/platform-backend
    command: poetry run executor

  frontend:
    image: autogpt/platform-frontend
    ports:
      - "3000:3000"
```

### Environment variables

| Variable | Purpose |
|----------|---------|
| `DATABASE_URL` | PostgreSQL connection |
| `REDIS_URL` | Redis connection |
| `RABBITMQ_URL` | RabbitMQ connection |
| `ENCRYPTION_KEY` | Credential encryption |
| `SUPABASE_URL` | Authentication |

### Generate encryption key

```bash
cd autogpt_platform/backend
poetry run cli gen-encrypt-key
```

## Best practices

1. **Start simple**: Begin with 3-5 node agents
2. **Test incrementally**: Run and test after each change
3. **Use webhooks**: External triggers for event-driven agents
4. **Monitor costs**: Track LLM API usage via credits system
5. **Version agents**: Save working versions before changes
6. **Benchmark**: Use agbenchmark to validate agent quality

## Common issues

**Services not starting:**
```bash
# Check container status
docker compose ps

# View logs
docker compose logs rest_server

# Restart services
docker compose restart
```

**Database connection issues:**
```bash
# Run migrations
cd backend
poetry run prisma migrate deploy
```

**Agent execution stuck:**
```bash
# Check RabbitMQ queue
# Visit http://localhost:15672 (guest/guest)

# Clear stuck executions
docker compose restart executor
```

## References

- **[Advanced Usage](references/advanced-usage.md)** - Custom blocks, deployment, scaling
- **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging

## Resources

- **Documentation**: https://docs.agpt.co
- **Repository**: https://github.com/Significant-Gravitas/AutoGPT
- **Discord**: https://discord.gg/autogpt
- **License**: MIT (Classic) / Polyform Shield (Platform)

## Agent operating procedure

1. **Check the environment.** Confirm the framework version, model provider, API keys and rate limits.
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Test a single call or chain with a known input and inspect raw outputs.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Evaluate on a small labeled set; check structured outputs against their schema; log prompts and responses.
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Outputs do not match the expected schema | Add validation and retries, tighten the schema, or simplify the prompt. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Never send private or sensitive data to external APIs without the user's consent.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

## Related skills

- `langchain`: Framework for building LLM-powered applications with agents, chains, and RAG.
- `crewai-multi-agent`: Multi-agent orchestration framework for autonomous AI collaboration.
- `evolving-ai-agents`: Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms.