Autogpt Agents

What it does

When your agent uses it

• Creating visual workflow agents
• Deploying persistent autonomous agents
• Building complex multi-step AI automation systems

Example prompts

• “/autogpt-agents”

Requirements

• Python 3
• Node.js
• Docker
• A credential in ENCRYPTION_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Open Agent Builder at http://localhost:3000
2. 2Add blocks from the BlocksControl panel
3. 3Connect nodes by dragging between handles
4. 4Configure inputs in each node
5. 5Run agent using PrimaryActionBar

What it can do on your machine

Read from SKILL.md and the folder at commit 773a529. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • docker
  • npm
  • poetry
  • git

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  Also links to:

  • docs.agpt.co
  • discord.gg

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • ENCRYPTION_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Autogpt Agents loads about 2.3k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 558 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~56

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:48

  cp .env.example .env

• NoteMentions a .env fileSKILL.md:55

  cp .env.example .env

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Orchestra-Research/AI-Research-SKILLs at commit 773a529, republished under its MIT licence (© Orchestra-Research). 558 words, ~2,300 tokens.

name

autogpt-agents

description

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.

version

1.0.0

author

Orchestra Research

license

MIT

tags

Agents, AutoGPT, Autonomous Agents, Workflow Automation, Visual Builder, AI Platform

dependencies

autogpt-platform>=0.4.0

# 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

Graph (Agent)
  ├── Node (Input)
  │   └── Block (AgentInputBlock)
  ├── Node (Process)
  │   └── Block (LLMBlock)
  ├── Node (Decision)
  │   └── Block (SmartDecisionMaker)
  └── Node (Output)
      └── Block (AgentOutputBlock)

User/Trigger → Graph Execution → Node Execution → Block.execute()
     ↓              ↓                 ↓
  Inputs      Queue System      Output Yields

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

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

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

{
  "data": "webhook payload"
}

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

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}`);
};

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

# Record new cassettes
./run benchmark --record

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

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)
        # ...

# 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"

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

# Check container status
docker compose ps

# View logs
docker compose logs rest_server

# Restart services
docker compose restart

# Run migrations
cd backend
poetry run prisma migrate deploy

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

# Clear stuck executions
docker compose restart executor

© Orchestra-Research, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in 14-agents/autogpt of Orchestra-Research/AI-Research-SKILLs.

• SKILL.md
• references/advanced-usage.md
• references/troubleshooting.md

Open the folder on GitHubat commit 773a529

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Orchestra-Research/AI-Research-SKILLs, which our catalogue first saw on October 7, 2026.

• davila7/claude-code-templatescli-tool/components/skills/ai-research/agents-autogpt
• OpenLAIR/dr-clawskills/agents/autogpt