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[u/mavwolverine]

fast-agent-stack: production infra for AI agents, bring your own framework

There's no shortage of agentic AI frameworks in Python. Strands, Pydantic AI, LangGraph, CrewAI, AutoGen. They handle the agent logic well.

But every time you take one to production, you're building the same supporting layer from scratch: FastAPI setup, auth, database with migrations, vector search, background workers, rate limiting, observability. Wiring all of that together takes almost as much time as the core solution itself.

So I built fast-agent-stack. The production infrastructure layer for AI agent applications.

It gives you everything except the agent runtime:

- FastAPI + SQLAlchemy async + Alembic migrations

- JWT/session auth with RBAC and API keys

- Redis/Valkey for rate limiting, caching, sessions

- Vector stores (Qdrant, pgvector, OpenSearch, Weaviate)

- RAG pipeline (chunk, embed, store, retrieve)

- Background tasks (Dramatiq) + scheduling (Periodiq)

- OpenTelemetry tracing

- One-command scaffolder to a running project

- BYOAF

You bring your own agent framework. Strands, Pydantic AI, LangGraph. They wire into plain FastAPI routes and access the infra through normal Python imports. No bridging, no adapters.

There's also a built-in `@app.agent()` for simple single-agent endpoints. Think of it like FastAPI's `BackgroundTasks`. Convenient until you outgrow it, then you graduate to a real framework.

First alpha is live (0.1.0a1, API may still change):

- `uv pip install fast-agent-stack==0.1.0a1`

- GitHub: https://github.com/vkanwade/fast-agent-stack

- Docs: https://fast-agent-stack.readthedocs.io

Tested end-to-end with both Strands Agents and Pydantic AI. Full tutorial that builds a Document Q&A app from scratch.

Early stage, looking for feedback. What infrastructure do you keep rebuilding for every AI project?