autogpt-agents

Q: How do I install autogpt-agents?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill autogpt-agents in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/autogpt-agents/ (personal) or .claude/skills/autogpt-agents/ (project).

by orchestra-researchProductivity & Automation

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.

Installs: 949
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Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill autogpt-agents

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads autogpt-agents automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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)

FAQ

What does the autogpt-agents skill do?

How do I install autogpt-agents?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill autogpt-agents in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/autogpt-agents/ (personal) or .claude/skills/autogpt-agents/ (project).

Which AI agents can use autogpt-agents?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is autogpt-agents safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains autogpt-agents?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 949 recorded installs.