fastapi-langgraph-agent-production-ready-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.
fastapi-langgraph-agent-production-ready-template
FastAPI service for a LangGraph agent with auth, memory and tracing
generative-ai-project-template
uv workspace with FastAPI, NiceGUI, LiteLLM and Promptfoo evals
| What we compare | fastapi-langgraph-agent-production-ready-template | generative-ai-project-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 63, popular | 13, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 46, patchy | 60, fair |
| Easy to run | 17, hard | 50, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 2.7k | 118 |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Sep 2026 | Sep 2026 |
| Last release | None published | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
fastapi-langgraph-agent-production-ready-template
FastAPI backend with a stateful LangGraph agent (Postgres checkpointing, tool calling, human-in-the-loop), mem0 long-term memory on pgvector, JWT auth and sessions, slowapi rate limiting, Alembic migrations, optional Valkey/Redis cache, Langfuse tracing, Prometheus and Grafana, and evals. make docker-up starts the API on port 8000 with PostgreSQL. OpenAI only via ChatOpenAI; any OpenAI-compatible base URL works.
Who it is for: Python teams productionizing a single LangGraph agent
Strengths
- JWT sessions, rate limiting and structured per-request logging included
- mem0 long-term memory runs in-process on pgvector; no mem0 cloud
- Circular model fallback with retries and a total timeout budget
- Langfuse, Prometheus and Grafana wired; Langfuse can be disabled
Weaknesses
- OpenAI (or OpenAI-compatible) only; multi-provider is an open issue
- README leads with a sponsor pitch for Atlas Cloud
- Needs the pgvector extension and an OpenAI key for memory
- Not a GitHub template; clone and strip
- Python, OpenAI via langchain_openai.ChatOpenAI (any OpenAI-compatible base URL)
- Needs PostgreSQL with pgvector, OpenAI API key, Valkey/Redis (optional), Langfuse (optional)
- Docker
- env example file
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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