fastapi-langgraph-agent-production-ready-template vs full-stack-ai-agent-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
full-stack-ai-agent-template
Project generator for FastAPI and Next.js apps with agents and RAG
| What we compare | fastapi-langgraph-agent-production-ready-template | full-stack-ai-agent-template |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 63, popular | 52, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 46, patchy | 96, healthy |
| Easy to run | 17, hard | 0, hard |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 2.7k | 1.9k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Sep 2026 | Oct 2026 |
| Last release | None published | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Not needed | Not needed |
| 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
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
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