AIArsenal/Stacks/Build a Production RAG Bot This Weekend
STACK RECIPE

Build a Production RAG Bot This Weekend

Retrieval-augmented chat over your docs, deployed and shareable, with observability baked in.

FOR
Engineers building internal knowledge bots or customer support AI
BUDGET
$0-20/mo
DIFFICULTY
Intermediate
TIME TO SHIP
2 days
TOOLS
5 tools
THE STACK

5 tools that work together

01 · AGENT FRAMEWORK
AgnoMIT; plug-and-play storage

Agno benchmarks 529× faster than LangGraph and has cleaner storage primitives — less boilerplate, more velocity.

TRY →
  • ▸Developers building performance-critical AI agents requiring minimal memory.
  • ▸Engineers scaling complex, production-grade agent systems efficiently.
OR SWAP IN
02 · PERSISTENT MEMORY LAYER
Mem0Apache 2.0; 21K stars

Mem0 gives hierarchical memory (user/session/agent) with 26% better retrieval accuracy than full-context.

TRY →
  • ▸Devs building AI agents or chatbots requiring robust, hierarchical memory.
  • ▸Teams needing self-hosted AI memory for data privacy and full control.
03 · VECTOR DATABASE
AWS SageMaker Studio LabT4 GPU; 4hr sessions; no card

ChromaDB runs embedded for dev + has a free cloud tier for production — simplest path to a working RAG.

TRY →
  • ▸Students & learners needing free GPU access for ML projects without billing.
  • ▸ML enthusiasts prototyping models on a T4 GPU with persistent JupyterLab.
OR SWAP IN
04 · LLM API
Google Gemini APIFlash: 250 req/day; Pro: 100 req/day; no credit card

Gemini Flash 2.5 is cheap-to-free and handles RAG answer generation with 1M-token context.

TRY →
  • ▸Prototypers who need a generous free LLM API
  • ▸Anyone who wants 1M-token context on the free tier
OR SWAP IN
05 · LLM OBSERVABILITY
LiteLLMFully free; proxy server

Langfuse's self-hosted OSS + free cloud tier traces every retrieval and generation step — critical for debugging RAG.

TRY →
  • ▸Developers building multi-LLM apps needing flexible provider switching & fallbacks.
  • ▸Teams prioritizing data privacy with self-hosted LLM routing infrastructure.
OR SWAP IN
QUICK START

How to ship it

  1. 1Install Agno + Mem0 + ChromaDB client (all pip installs)
  2. 2Chunk your docs (markdown, PDFs) and embed into Chroma with sentence-transformers
  3. 3Wire an Agno agent with Chroma retrieval tool + Gemini Flash as the LLM
  4. 4Add Mem0 for conversational memory across sessions
  5. 5Deploy to Cloud Run/Vercel and pipe all traces through Langfuse
⚠ WATCH OUT

Chroma Cloud free tier has row limits — large doc corpuses need the $50/mo tier or self-hosting. Embedding API costs scale with corpus size; use a local sentence-transformers model to keep it free.

Want this stack customized for you?

Ask AIArsenal to tailor it to your use case — budget, scale, privacy needs. It knows every tool in this stack.

ASK AIARSENAL →
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