autogpt-agents

仓库: davila7/claude-code-templates
安装量: 275
排名: #3254

安装

npx skills add https://github.com/davila7/claude-code-templates --skill 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)

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)

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