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.
npxskills add zechenzhangagi/ai-research-skills--skill autogpt-agentsLoading…
Comprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.
Use AutoGPT when:
Key features:
Use alternatives instead:
# 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
AutoGPT has two main systems:
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 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 |
User/Trigger → Graph Execution → Node Execution → Block.execute()
↓ ↓ ↓
Inputs Queue System Output Yields
AI Blocks:
AITextGeneratorBlock - Generate text with LLMsAIConversationBlock - Multi-turn conversationsSmartDecisionMakerBlock - Conditional logicIntegration Blocks:
Control Blocks:
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": {}
}
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}
# 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
my-agent/
├── agent.py # Main agent logic
├── abilities/ # Custom abilities
│ ├── __init__.py
│ └── custom.py
├── prompts/ # Prompt templates
└── config.yaml # Agent configuration
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
# Run all benchmarks
./run benchmark
# Run specific category
./run benchmark --category coding
# Run with specific agent
./run benchmark --agent my-agent
Benchmarks use recorded HTTP responses for reproducibility:
# Record new cassettes
./run benchmark --record
# Run with existing cassettes
./run benchmark --playback
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)
# ...
| Provider | Auth Type | Use Cases |
|---|---|---|
| OpenAI | API Key | LLM, embeddings |
| Anthropic | API Key | Claude models |
| GitHub | OAuth | Code, repos |
| OAuth | Drive, Gmail, Calendar | |
| Discord | Bot Token | Messaging |
| Notion | OAuth | Documents |
# 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"
| Variable | Purpose |
|---|---|
DATABASE_URL | PostgreSQL connection |
REDIS_URL | Redis connection |
RABBITMQ_URL | RabbitMQ connection |
ENCRYPTION_KEY | Credential encryption |
SUPABASE_URL | Authentication |
cd autogpt_platform/backend
poetry run cli gen-encrypt-key
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
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).