full-stack-ai-agent-template vs generative-ai-project-template
Two of the top ai api backends, side by side: score, setup, license, activity and what each review found.
full-stack-ai-agent-template
Project generator for FastAPI and Next.js apps with agents and RAG
generative-ai-project-template
uv workspace with FastAPI, NiceGUI, LiteLLM and Promptfoo evals
| What we compare | full-stack-ai-agent-template | generative-ai-project-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 52, popular | 13, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 96, healthy | 60, fair |
| Easy to run | 0, hard | 50, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 1.9k | 118 |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
full-stack-ai-agent-template
CLI (pip install fastapi-fullstack) scaffolding a FastAPI backend and Next.js frontend via a wizard or presets, with Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph or DeepAgents, RAG on Milvus, Qdrant, pgvector or Chroma, WebSocket chat, JWT, OAuth, admin, teams, Stripe billing, Celery, Docker and K8s. make bootstrap starts Postgres, migrations and a seeded admin; projects can pull template upgrades later.
Who it is for: Python teams scaffolding a full-stack agent product
Strengths
- Wizard and presets pick framework, vector store, auth and billing per project
- Template upgrades merge into generated projects on a branch
- Chat UI renders tool calls, plans, subagents, charts and Python execution
- 100% coverage badge and CI on the generator
Weaknesses
- Generator, not a repo to fork; output size depends on options chosen
- Seeds admin@example.com with a fixed password; rotate before exposing
- Optional services (Milvus, Redis, Celery) raise the local footprint
- Windows needs GNU Make or WSL2
- Python, Pydantic AI, Pydantic Deep Agents, LangChain, LangGraph
- Needs PostgreSQL, Redis (optional), Milvus, Qdrant, pgvector or ChromaDB (RAG), Stripe (billing), LLM provider API key
generative-ai-project-template
Python 3.12 uv workspace with a FastAPI backend (port 8000) and NiceGUI frontend (port 8080) for chat, information extraction and RAG over documents, with models served locally by Ollama or through any LiteLLM provider. Ships Makefiles for install, run, test, Docker (CPU and CUDA compose), pre-commit with ruff and detect-secrets, pytest, Promptfoo and Ragas evals, GitHub Actions, Renovate and an mkdocs site.
Who it is for: Python teams starting an LLM app with evals built in
Strengths
- LiteLLM naming lets you switch between Ollama and cloud models by env
- Promptfoo and Ragas evaluation wired into the template
- CPU and CUDA docker compose variants
- CI tests the app against local Ollama models
Weaknesses
- NiceGUI frontend is unusual for product UIs
- No auth, database or persistence described
- Ubuntu 22.04 or macOS only per prerequisites
- CUDA path installs PyTorch; heavier install
- Python, LiteLLM (any provider), Ollama
- Needs Ollama (local models) or an LLM provider key via LiteLLM
- GitHub template
- Docker
- env example file
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