AutoGPT Autonomous Agent

Deploy AutoGPT on Clore.ai — run the original autonomous AI agent platform with web browsing, code execution, and long-horizon task automation.

Overview

AutoGPT is the pioneering open-source autonomous AI agent platform, with 175K+ GitHub stars — one of the most starred repositories on GitHub. Originally a Python CLI tool that went viral in 2023, AutoGPT has evolved into a full-featured platform with a web frontend, visual workflow builder, multi-agent orchestration, and a self-improving agent benchmark suite.

The current AutoGPT Platform consists of:

• Frontend — Next.js visual agent builder (port 3000)

• Backend / API — FastAPI service handling agent execution (port 8000)

• Agent executor — Python workers running autonomous task loops

• Postgres — persistent storage for agent state and runs

• Redis — job queue and pub/sub

• Minio — S3-compatible object storage for agent artifacts

On Clore.ai, AutoGPT runs entirely on CPU (it delegates LLM calls to cloud APIs), making it affordable at $0.05–0.20/hr. You can optionally integrate local models via its OpenAI-compatible provider support.

Key capabilities:

• 🤖 Autonomous agents — agents decompose tasks into sub-goals and execute them iteratively

• 🌐 Web browsing — agents can search the web, scrape pages, and synthesize information

• 💻 Code execution — sandboxed Python execution environment for coding agents

• 📁 File operations — read, write, and manage files as part of task execution

• 🔗 Multi-agent — spawn specialized sub-agents and orchestrate them hierarchically

• 🧠 Long-term memory — vector-backed memory persisted across sessions

• 📈 Agent benchmarking — built-in AgentBenchmark suite for evaluating agent performance

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Requirements

AutoGPT's compute requirements depend on whether you use cloud LLM APIs (default) or local models. The platform itself is lightweight.

Configuration	GPU	VRAM	System RAM	Disk	Clore.ai Price
Minimal (cloud APIs)	None / CPU	—	4 GB	20 GB	~$0.05/hr (CPU)
Standard	None / CPU	—	8 GB	40 GB	~$0.08/hr
Recommended	None / CPU	—	16 GB	60 GB	~$0.12/hr
+ Local LLM (Ollama)	RTX 3090	24 GB	16 GB	80 GB	$0.07–0.21/hr
+ Large Local LLM	A100 40 GB	40 GB	32 GB	100 GB	bare metal

Note: AutoGPT uses API-based LLMs by default (OpenAI GPT-4, Anthropic Claude, etc.). A GPU is only useful if you configure a local model endpoint via Ollama or another OpenAI-compatible server.

API Keys Required

You'll need at least one of:

• OpenAI API Key (GPT-4o recommended for best agent performance)

• Anthropic API Key (Claude 3.5 Sonnet is excellent for agents)

• Google AI Key (Gemini models supported)

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Quick Start

1. Rent a Clore.ai server

Log into clore.ai and launch a server with:

• 2+ CPU cores, 8 GB RAM minimum

• Exposed ports 8000 (backend API) and 3000 (frontend)

• SSH access enabled

• 20+ GB disk space

2. Connect to the server

ssh root@<clore-server-ip> -p <ssh-port>

# Update packages
apt-get update && apt-get upgrade -y

# Verify Docker and Compose
docker --version
docker compose version   # Must be v2.x

3. Clone and configure AutoGPT

# Clone the repository
git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT/autogpt_platform

# Copy environment template
cp .env.example .env

# Edit the environment file
nano .env

4. Set required environment variables

# Minimum required in .env:

# ── LLM Provider (pick at least one) ────────────────────────────────────────
OPENAI_API_KEY=sk-...
# ANTHROPIC_API_KEY=sk-ant-...

# ── Secret keys (CHANGE THESE) ──────────────────────────────────────────────
APP_SECRET_KEY=$(openssl rand -hex 32)
JWT_SECRET_KEY=$(openssl rand -hex 32)

# ── Database ─────────────────────────────────────────────────────────────────
DATABASE_URL=postgresql://postgres:password@db:5432/autogpt

# ── Backend URL (set to your Clore server IP) ────────────────────────────────
NEXT_PUBLIC_AGPT_SERVER_URL=http://<clore-server-ip>:8000/api

5. Build and launch

# Build images and start all services (first run takes 5-10 minutes)
docker compose up -d --build

# Watch build and startup progress
docker compose logs -f

# After startup, verify all containers are running
docker compose ps

6. Verify services are healthy

# Check each service
docker compose ps

# Expected running services:
# autogpt_platform-db-1        Up (healthy)
# autogpt_platform-redis-1     Up
# autogpt_platform-minio-1     Up
# autogpt_platform-backend-1   Up
# autogpt_platform-frontend-1  Up
# autogpt_platform-executor-1  Up

# Test backend API
curl http://localhost:8000/api/health
# Expected: {"status": "ok"}

7. Access AutoGPT

Open your browser:

• Frontend: http://<clore-server-ip>:3000

• Backend API: http://<clore-server-ip>:8000

• API Docs (Swagger): http://<clore-server-ip>:8000/docs

Create an account on the frontend, configure your LLM provider in Settings, and start building agents.

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Configuration

Full .env reference

# ── Application ──────────────────────────────────────────────────────────────
ENVIRONMENT=production
APP_SECRET_KEY=<generate-with-openssl-rand-hex-32>
JWT_SECRET_KEY=<generate-with-openssl-rand-hex-32>
ALLOWED_ORIGINS=http://<clore-server-ip>:3000

# ── LLM Providers ────────────────────────────────────────────────────────────
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GROQ_API_KEY=gsk_...

# ── OpenAI-compatible (for local models via Ollama/vLLM) ────────────────────
OPENAI_API_BASE=http://localhost:11434/v1   # Ollama's OpenAI-compatible endpoint
OPENAI_API_KEY=ollama                        # Dummy key for Ollama

# ── Database ─────────────────────────────────────────────────────────────────
DATABASE_URL=postgresql://postgres:password@db:5432/autogpt
POSTGRES_USER=postgres
POSTGRES_PASSWORD=password    # Change in production!
POSTGRES_DB=autogpt

# ── Redis (job queue) ────────────────────────────────────────────────────────
REDIS_URL=redis://redis:6379/0

# ── Minio (object storage) ──────────────────────────────────────────────────
MINIO_ROOT_USER=minioadmin
MINIO_ROOT_PASSWORD=minioadmin   # Change in production!
MINIO_URL=http://minio:9000

# ── Frontend ─────────────────────────────────────────────────────────────────
NEXT_PUBLIC_AGPT_SERVER_URL=http://<clore-server-ip>:8000/api
NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
NEXTAUTH_URL=http://<clore-server-ip>:3000

Customizing agent capabilities

# Enable web browsing capability
WEB_BROWSER_ENABLED=true
SELENIUM_CHROME_DRIVER_URL=http://selenium:4444/wd/hub

# Add Selenium to docker-compose for web browsing:
# services:
#   selenium:
#     image: selenium/standalone-chrome:latest
#     shm_size: 2gb
#     ports:
#       - "4444:4444"

# File system access (agent workspace)
WORKSPACE_PATH=/workspace
RESTRICT_TO_WORKSPACE=true

Managing agent executor scaling

# Scale executor workers for parallel agent runs
docker compose up -d --scale executor=4

# Monitor executor logs
docker compose logs -f executor

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GPU Acceleration

AutoGPT delegates all LLM inference to external providers by default. To use local GPU-accelerated models:

Connect to Ollama on the same server

# Install Ollama on the Clore server
curl -fsSL https://ollama.com/install.sh | sh

# Pull a capable model (Llama 3 70B needs A100, 8B works on RTX 3090)
ollama pull llama3:8b
# For best agent performance:
ollama pull llama3.1:70b   # Requires A100 40GB+

# Make Ollama accessible to Docker containers
OLLAMA_HOST=0.0.0.0 ollama serve &

# Test the OpenAI-compatible endpoint
curl http://localhost:11434/v1/models

In .env, point AutoGPT at Ollama:

OPENAI_API_BASE=http://host.docker.internal:11434/v1
OPENAI_API_KEY=ollama
OPENAI_DEFAULT_MODEL=llama3.1:70b

Performance note: Autonomous agents make many sequential LLM calls. Local models on RTX 3090 (~30 tok/s) work, but A100 80GB enables faster iteration. See GPU Comparison.

Local model recommendations for agents

Model	Agent Quality	VRAM	Clore GPU
Llama 3 8B	Fair	8 GB	RTX 3080
Llama 3.1 8B Instruct	Good	8 GB	RTX 3080
Llama 3.1 70B	Excellent	40 GB	A100 40GB
Mixtral 8x7B	Good	24 GB	RTX 3090
Qwen 2.5 72B	Excellent	40 GB	A100 40GB

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Tips & Best Practices

Cost management on Clore.ai

# AutoGPT's biggest cost is often LLM API calls, not compute
# Set token budget limits in agent configuration:
# MAX_TOKENS_PER_RUN=100000
# MAX_COST_PER_RUN=1.00   # USD

# Backup agent state before stopping the Clore instance
docker exec autogpt_platform-db-1 \
  pg_dump -U postgres autogpt | gzip > autogpt-backup-$(date +%Y%m%d).sql.gz

# Copy to local machine
scp -P <port> root@<ip>:autogpt-backup-*.sql.gz ./

Updating AutoGPT

cd AutoGPT

# Pull latest changes
git pull origin master

cd autogpt_platform

# Rebuild with new code
docker compose down
docker compose up -d --build

# Run DB migrations if needed
docker compose exec backend alembic upgrade head

Monitoring agent runs

# Watch backend logs for agent activity
docker compose logs -f backend executor

# Monitor system resources during agent runs
htop
# or
docker stats

# View agent run history via API
curl http://localhost:8000/api/v1/runs | python3 -m json.tool

Security hardening

# NEVER expose ports 8000/3000 directly to the internet without auth
# Use Nginx or Caddy as a reverse proxy with HTTPS:

# Caddyfile:
# autogpt.yourdomain.com {
#     reverse_proxy localhost:3000
# }
# api.autogpt.yourdomain.com {
#     reverse_proxy localhost:8000
# }

# Restrict agent file system access
RESTRICT_TO_WORKSPACE=true
WORKSPACE_PATH=/agent-workspace

# Disable user registration after initial setup
ALLOW_SIGNUP=false

Optimizing build times

# First build is slow (~10 min); subsequent builds use cache
# Use BuildKit for faster builds:
DOCKER_BUILDKIT=1 docker compose up -d --build

# Pre-pull base images to speed up builds
docker pull python:3.11-slim
docker pull node:20-alpine

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Troubleshooting

Build fails with out-of-memory error

# Docker build needs sufficient memory (4GB+)
# Add swap space if needed:
fallocate -l 8G /swapfile
chmod 600 /swapfile
mkswap /swapfile
swapon /swapfile

# Make permanent
echo '/swapfile none swap sw 0 0' >> /etc/fstab

Backend returns 500 / "Database not ready"

# Check DB health
docker compose ps db
docker compose logs db

# Run DB migrations manually
docker compose exec backend alembic upgrade head

# If migrations fail, check DATABASE_URL in .env
docker compose exec backend printenv DATABASE_URL

Frontend shows "Failed to connect to backend"

# Verify NEXT_PUBLIC_AGPT_SERVER_URL is set correctly
grep NEXT_PUBLIC_AGPT_SERVER_URL .env

# Must be the public IP, not localhost (browser makes this request)
NEXT_PUBLIC_AGPT_SERVER_URL=http://<clore-server-ip>:8000/api

# Rebuild frontend after changing this env var
docker compose up -d --build frontend

Agent executor crashes / gets OOM killed

# Check memory usage
docker stats autogpt_platform-executor-1

# Limit executor memory and add restart policy
# In docker-compose.yaml:
#   executor:
#     mem_limit: 2g
#     restart: unless-stopped

# Or reduce concurrent agent runs
MAX_CONCURRENT_RUNS=2

Redis connection refused

# Check Redis is running
docker compose ps redis
docker compose logs redis

# Test connectivity from backend
docker compose exec backend redis-cli -h redis ping
# Expected: PONG

# If Redis requires auth, set:
REDIS_URL=redis://:password@redis:6379/0

Agent stuck in loop

# AutoGPT agents can sometimes enter infinite loops
# Set a maximum cycle limit:
MAX_AGENT_CYCLES=50

# Or interrupt a running agent via the API:
curl -X POST http://localhost:8000/api/v1/runs/<run-id>/stop

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Further Reading

• AutoGPT Official Documentation

• AutoGPT GitHub Repository

• AutoGPT Platform Setup Guide

• Running Ollama on Clore.ai

• Running vLLM on Clore.ai

• Clore.ai GPU Comparison

• AutoGPT Discord Community