Autogpt Agents Skill for Claude

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)