autogpt-agents

SKILL.md

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)

# 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

Open Agent Builder at http://localhost:3000

Add blocks from the BlocksControl panel

Connect nodes by dragging between handles

Configure inputs in each node

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:

POST /api/v1/graphs/{graph_id}/execute
Content-Type: application/json

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

Webhook trigger:

POST /api/v1/webhooks/{webhook_id}
Content-Type: application/json

{
  "data": "webhook payload"
}

Scheduled execution:

{
  "schedule": "0 */2 * * *",
  "graph_id": "graph-uuid",
  "inputs": {}
}

Monitoring execution

WebSocket updates:

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:

GET /api/v1/executions/{execution_id}

Using Forge (Development)

Create custom agent

# 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

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

# 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:

# Record new cassettes
./run benchmark --record

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

Integrations

Adding credentials

Navigate to Profile > Integrations

Select provider (OpenAI, GitHub, Google, etc.)

Enter API keys or authorize OAuth

Credentials are encrypted and stored securely

Using credentials in blocks

Blocks automatically access user credentials:

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

# 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

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

Best practices

Start simple: Begin with 3-5 node agents

Test incrementally: Run and test after each change

Use webhooks: External triggers for event-driven agents

Monitor costs: Track LLM API usage via credits system

Version agents: Save working versions before changes

Benchmark: Use agbenchmark to validate agent quality

Common issues

Services not starting:

# Check container status
docker compose ps

# View logs
docker compose logs rest_server

# Restart services
docker compose restart

Database connection issues:

# Run migrations
cd backend
poetry run prisma migrate deploy

Agent execution stuck:

# Check RabbitMQ queue
# Visit http://localhost:15672 (guest/guest)

# Clear stuck executions
docker compose restart executor

References

Advanced Usage - Custom blocks, deployment, scaling

Troubleshooting - 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)