Choosing Your RAG Stack: A Plain-English Guide to LangChain, LlamaIndex, LangGraph & More

Friday, 2 a.m. — The demo works until a 30‑MB PDF hits the pipeline and your Franken‑RAG stack melts. You patched LangChain with LlamaIndex and taped on LangGraph for state‑management, but now debugging feels like tracing spaghetti in the dark. Let’s untangle that mess, one framework at a time.

1  RAG in Plain English 

Retrieval‑Augmented Generation (RAG) is a two‑step tango: fetch the right knowledge → ask the LLM to answer only from that knowledge. Non‑tech metaphor: You hand your smart intern the exact folder of docs before she writes the report. Boom—fewer hallucinations, fresher answers.

ELI‑5: RAG is Google‑search plus ChatGPT in one breath—first you search, then you chat using only the search results.

2  Why All These Frameworks?

Each library grew to solve a specific pain‑point, then expanded:

Think of them as ingredients, not mutually exclusive winners.

3  Framework Deep‑Dives—for Non‑Tech & Tech

3.1  LangChain

• Non‑tech TL;DR  — LangChain is a command centre that sequences LLM tasks: “search docs → summarise → translate → email.”
• Tech In 60 s  — Provides Chains, Tools, and an Agent abstraction. Powerful callback system but state is ephemeral unless you bolt on memory or LangGraph.
• Exact fit — Hack‑week POCs, chatbots that call 3‑4 APIs, teams that value ecosystem over strict guarantees.

Personas

• Startup founder demoing in 24 h.
• Customer‑success team plugging in FAQ search with zero infra.

3.2  LlamaIndex

• Non‑tech TL;DR  — Think of LlamaIndex as the industrial scanner that chops, chunks, and files away your docs so the LLM can cite them later.
• Tech In 60 s  — Ships loaders (PDF, Notion, GSheets), smart chunkers (SentenceWindow, Hierarchical), auto‑embeds to any vector DB. Slightly opinionated retrieval flow.
• Exact fit — Content‑heavy apps—policy manuals, legal archives, courseware—where ingest quality is king.

Personas

• Compliance officer needing citations.
• Ed‑tech PM uploading lecture slides each night.

3.3  LangGraph

• Non‑tech TL;DR  — LangGraph is the air‑traffic controller—it remembers which plane (task) is where and reroutes when storms hit.
• Tech In 60 s  — DAG engine wrapping LangChain nodes; supports branching, retries, streaming, human‑approval edges. Feels like Airflow Lite for LLMs.
• Exact fit — Long‑running or multi‑step workflows: eg. document triage → extraction → redaction → filing.

Personas

• Ops engineer automating KYC pipeline.
• Product team building multi‑turn, human‑in‑loop agents.

3.4  Friends & Utilities (One‑liners)

• Guardrails.ai — Validate JSON / reject PII before it leaks.
• PromptLayer — Git for prompts + tracing.
• Autogen — Orchestrate conversation between multiple agents.

4  Mini‑Benchmark — Latency × Cost Heat‑Map

Before we dive into architecture diagrams and decision trees, it helps to see real data—even if it’s just a lightweight test—so you can calibrate hype against hard numbers.

What we measured

• 10 000 documents (1 KB chunks)
• Single-region (us-east-1) inference using Mistral-7B
• Three end-to-end stacks:LangChain ＋ PineconeLlamaIndex ＋ pgvectorLangGraph ＋ Qdrant
• LangChain ＋ Pinecone
• LlamaIndex ＋ pgvector
• LangGraph ＋ Qdrant

For each stack we recorded p95 latency (the time 95 % of queries return under) and the cloud cost per 1 000 requests (compute ＋ storage ＋ egress). The colour-coded heat-map highlights quick-and-cheap cells in green and slow-or-pricey cells in red.

Test setup — 10 K docs, 1 KB chunks, Mistral-7B, us-east-1. Green = faster / lower cost; red = slower / higher cost.

Take‑home: pgvector beats managed Pinecone on cost once volume rises; LangGraph adds negligible overhead to latency. LangChain + Pinecone is the quickest to prototype, but at 0.92 USD/1 K calls it costs ~2× LlamaIndex + pgvector. If your SaaS expects 5 M requests/month, that’s an extra $2 000 in OpEx for ~65 ms faster p95. Worth it? Depends on your latency SLO.

📉 Failure-Mode Autopsy — 

“The Case of the Vanishing Recall”

Context. A fintech chatbot launched with LlamaIndex + pgvector and boasted 86 % answer-recall on day 1. Two weeks later customer queries were timing out or returning “Sorry, I don’t know.” Offline tests showed recall had cratered to 42 %.

Root cause. Daily data ingest grew the vector store from 6 M to 14 M embeddings. IVFFlat lists/probes were never retuned, so new vectors hid behind sub-optimal centroids → nearest-neighbour search missed relevant chunks.

Fix. Rebuilt index nightly with HNSW; promoted “hot” embeddings to an SSD tier; bumped probes from 10 → 30. Recall snapped back to 83 %. Prevention checklist.

• Track recall@5 in CI on a labelled set.
• Auto-rebuild index once growth ≥ 20 %.
• Log query-latency histogram; alert if p95 ↑ 25 %.

Take-home: RAG degradation is often index drift, not model drift—monitor the vector layer like you monitor the LLM.

🔒 Security & Compliance Quick-Check

*Guardrails.ai isn’t a RAG framework but bolts on trust & validation—pair it with whichever stack you choose.

Interpretation:

• Use Guardrails.ai alongside any stack to satisfy SOC 2 “evidence” and GDPR “data-minimisation” clauses.
• LangGraph shines for audit trails—every node event can stream to SIEM.
• If row-level ACLs matter (multitenant SaaS), combine your framework with pgvector inside Postgres so SQL handles the security, not the LLM.

5  Architecture Blueprints

5.1  Quick POC

5.2  Production RAG

5.3  Agentic Workflow

6  Decision Matrix—Which Combo When?

7  Two‑Week RAG Sprint (DIY)

1. Day 1–3 — Build POC with LangChain + Pinecone. Baseline metrics.
2. Day 4–7 — Swap in LlamaIndex loaders + pgvector. Snapshot cost.
3. Day 8–10 — Introduce LangGraph flow & Guardrails. Add tests.
4. Day 11–14 — Shadow traffic, measure recall. Green‑light prod.

8  Where 8tomic Labs Fits

Need a whiteboard‑to‑prod partner? Our RAG Stack Blueprint maps your data, latency SLOs, and budget to the right combo—no spaghetti. Book a 30‑minute call and reclaim your weekends.

Book your 30-minute call ↗

Written by Arpan Mukherjee

Founder & CEO @ 8tomic Labs