ArchestackBest of Vibe Coding: List on GitHub ↗1 star (opens in a new tab)

agent-landing-zone vs fastapi-langgraph-agent-production-ready-template

Two of the top projects in Backend and cloud templates, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.

22nd of 14 in Backend and cloud templates

agent-landing-zone

Azure infrastructure templates for deploying enterprise agent apps on Microsoft Foundry

62 out of 100 GitHub ↗ (opens in a new tab)
#44th of 14 in Backend and cloud templates

fastapi-langgraph-agent-production-ready-template

FastAPI service for a LangGraph agent with auth, memory and tracing

55 out of 100 GitHub ↗ (opens in a new tab)

Verdict

agent-landing-zone ranks higher, 62 to 55. Of the 5 score parts we measure for both, agent-landing-zone leads on 2, fastapi-langgraph-agent-production-ready-template on 2 and 1 is even.

  • Adoptionfastapi-langgraph-agent-production-ready-template leads by 13 points, 51 to 38.
  • FreshnessEven at 100.
  • Maintenanceagent-landing-zone leads by 41 points, 98 to 57: a wide gap.
  • Easy to runagent-landing-zone leads by 16 points, 33 to 17: a wide gap.
  • Agent-readyfastapi-langgraph-agent-production-ready-template leads by 40 points, 70 to 30: a wide gap.

Pick agent-landing-zone if you are

Azure teams deploying network-isolated enterprise agent apps with IaC

  • Infrastructure-only mode via `azd provision`, with apps deployed later using `azd deploy`
  • Component versions pinned in manifest.json for reproducible releases

Pick fastapi-langgraph-agent-production-ready-template if you are

Python teams productionizing a single LangGraph agent

  • JWT sessions, rate limiting and structured per-request logging included
  • mem0 long-term memory runs in-process on pgvector; no mem0 cloud
agent-landing-zone vs fastapi-langgraph-agent-production-ready-template: score parts and facts
What we compareagent-landing-zonefastapi-langgraph-agent-production-ready-template
Score parts, out of 100
Adoption38, known51, popular
Freshness100, active100, active
Maintenance98, healthy57, fair
Easy to run33, some setup17, hard
Agent-ready30, minimal70, partly
Facts from GitHub and the README
Stars1.2k2.7k
LicenseMIT (permissive)MIT (permissive)
Last commitOct 2026Oct 2026
Last releaseOct 2026None published
LanguagePythonPython
DockerNoYes
GPUNot statedNot needed
arm64 or Apple SiliconNot statedNot stated

agent-landing-zone

Agent Landing Zone is an azd and Bicep template that provisions a Zero-Trust Azure environment for agent applications on Microsoft Foundry. `azd up` deploys the infrastructure plus UI, orchestrator and ingestion apps, each from its own Azure repository. `azd provision` deploys the infrastructure alone. It was previously named GPT-RAG.

Who it is for: Azure teams deploying network-isolated enterprise agent apps with IaC

Strengths

  • Infrastructure-only mode via `azd provision`, with apps deployed later using `azd deploy`
  • Component versions pinned in manifest.json for reproducible releases
  • Custom apps can replace the defaults through app-definition.json
  • MIT license; docs cover network-isolated deployment

Weaknesses

  • Tied to Azure and Microsoft Foundry; no other cloud is mentioned
  • Default UI, orchestrator and ingestion code live in three separate repositories
  • README gives no cost, quota or resource sizing figures
  • Prerequisites and configuration are only in external docs, not the README
  • Python
  • Needs Azure, Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure Developer CLI
  • GitHub template
Documentation ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)

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
Repository on GitHub ↗ (opens in a new tab)

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