#2
B
Rank #205
AutoGPT
Build, deploy, and run autonomous AI agents — one of the original projects that popularized agentic AI workflows, now a full platform with a marketplace and visual builder.
60.0
/ 100
⚙ Full Stack
Python backend (agent execution engine) + Next.js frontend, Postgres for agent/run state, Redis for task queuing. Requires an LLM provider API key (OpenAI/Anthropic) to actually run agents.
📈 Scaling Analysis
Stateless frontend and backend API layers scale horizontally behind Postgres/Redis. The real constraint is LLM API cost and rate limits, not the platform's own architecture — running many concurrent agents means many concurrent LLM calls, which is a cost/quota problem more than a scaling one.
🚀 Running on Nexlayer
Several pods: backend, frontend, postgres.pod, redis.pod. LLM API keys are set server-side as env vars. Agent execution can be resource-intensive depending on workflow complexity — size CPU/memory limits accordingly.