Paid service · async · no calls
Pick the right agent stack before you burn weeks on the wrong one.
A paid, async, vendor-neutral architecture review. Submit your use case, constraints, and current shortlist — get back a written recommendation for the stack I'd actually build, grounded in the same independent 247-tool index behind this site.
Why this exists
AI-agent advice is noisy. Framework docs are often sales funnels, vendor blogs quietly route you toward their own platform, and most “best agent stack” posts flatten everything into the same few names without asking what you're actually building. You don't need hype — you need a sober answer to one question:
“Given my use case, data, budget, team, and constraints — what should I build with?”
What you get
Recommended architecture pattern
The shape of the system for your use case — and why that shape, not a trendier one.
Component-by-component stack
Every layer (ingestion, retrieval, orchestration, eval, hosting…) with 2–3 vetted options, trade-offs, and clear “skip if” notes.
Implementation checklist
The concrete steps to get from prototype to a first usable version — in order.
Watch-outs
The specific failure modes for your case: retrieval quality, cost traps, eval gaps, lock-in.
One async revision
Reply once if I misread a constraint and I’ll correct it. Delivered as a private web link + PDF.
How it works
- 1
Submit your use case
Fill the form below — task, users, data, constraints, budget, and anything you’ve ruled out.
- 2
Scope confirmation by email
I confirm it’s a fit. If it’s too broad, or really needs build help, I’ll say so — before you pay a cent.
- 3
Pay via a secure link
A one-time payment through a merchant-of-record (Gumroad / LemonSqueezy). No card details touch this site.
- 4
Review delivered in 4 business days
You get the written review by email — a private web link and a PDF.
Why independent matters
Agent Search Engine is a sponsor-blind directory of AI agents, MCP servers, frameworks, and agent infrastructure. Rankings are never sold. If the right answer is self-hosting, I'll say that. If it's a commercial platform, I'll say that. If the right answer is “don't use agents here,” I'll say that too. The point isn't to validate a favorite vendor — it's to help you avoid an expensive wrong turn.
It's written by the person who builds and maintains that index — so you're getting a read from someone who tracks the whole landscape as it moves each week, not a one-off take.
A real example
What a review actually looks like
Example use case: a RAG knowledge assistant for a small B2B SaaS support team— answering internal questions from help docs, product docs, and selected past tickets, with citations; starting narrow, avoiding enterprise spend, maintainable by a small team. Here's the shape of the recommendation.
Recommended pattern
Start with a boring, inspectable RAG pipeline before any autonomous behavior: ingest from stable sources → normalize to clean markdown → chunk with metadata → embed → retrieve with filters → (optionally) rerank → answer with citations, logging every question, retrieved chunk, and answer. A 30–50 question eval set from real tickets exposes bad chunking and hallucinations before launch. Don't start with a multi-agent system— the first version is a grounded answer engine with escalation, not a “support agent” that invents policy.
The stack, layer by layer
| Layer | Good options | Why | Skip / avoid if |
|---|---|---|---|
| Ingestion + cleaning | Direct export · Firecrawl · Scrapling | Prefer a direct CMS / help-desk / repo export; Firecrawl to turn web docs into clean markdown; Scrapling only as a fallback for brittle pages (mind each site's terms). | Scraping as your primary source when a clean export exists — raw HTML noise quietly wrecks retrieval. |
| Chunking + pipeline | Haystack · RAGFlow · Dify | Code-first control (Haystack) vs. a packaged RAG platform (RAGFlow / Dify). | Don’t trust default chunk settings until you’ve tested them on your actual docs. |
| Vector store | Platform-native (Dify / RAGFlow) · pgvector / Qdrant | Use the platform’s native knowledge base first; a separate store buys control. | Don’t stand up a separate vector DB on day one without a real scale or multi-tenancy reason. |
| Retrieval + rerank | Haystack · RAGFlow | Hybrid retrieval + metadata filters + reranking — where quality is won. | Avoid “top-k vector search only” on messy support docs — it retrieves similar-but-wrong chunks. |
| Orchestration | LangGraph · Pydantic AI · LangChain | Explicit states (retrieve → answer → escalate); typed boundaries; broad integrations. | Skip multi-agent (CrewAI / AutoGen) for v1 — it adds moving parts before retrieval is proven. |
| Memory | Simple chat history · mem0 | For support-doc Q&A, short-term chat history is usually enough; reach for mem0 only if you truly need user-specific memory across sessions. | Don’t add long-term memory before you know what should persist — bad memory is another wrong-answer source. |
| Eval + observability | Langfuse · Phoenix · Ragas · DeepEval | Log query, retrieved chunks, answer, citations, latency, cost — then run a fixed eval set. | Don’t launch on vibes. RAG regressions are subtle: a prettier answer can be less grounded. |
| Hosting | Self-hosted Dify / RAGFlow + a small DB | For low-spend, self-host one platform plus a small database; keep re-index scripts boring. | Avoid Kubernetes unless you already operate it — the ops load will outweigh the value for v1. |
What to watch out for
- Retrieval quality dominates model quality — the wrong chunk just makes a better model write a more convincing wrong answer. Build a 30–50 question eval set first.
- Chunking needs product context — generic splitting separates a pricing rule from its exception. Preserve headings, product area, version, date, and source URL as metadata.
- Stale docs are worse than no docs — old policies that stay searchable fail loudly. Add freshness metadata and re-index on a schedule.
- Cost blowups come from invisible loops — reranking, long context, and multi-step flows multiply spend. Cap retrieved chunks, answer length, retries, and tool calls before users touch it.
Questions
How fast do I get it?
Within 4 business days after payment and a complete intake form.
Do you do calls?
No — async only. Form, email, written report. That keeps the price sane and the recommendation focused.
Is it truly vendor-neutral?
Yes. Agent Search Engine is sponsor-blind: rankings are never sold and paid placements never influence recommendations. No vendor kickbacks steer the answer.
What if I disagree with it?
That’s fine — the review explains the reasoning and trade-offs so you can weigh it yourself. You also get one async revision if I misunderstood a stated constraint.
Do you implement the stack for me?
No. This is architecture and tool selection, not build work. You get a practical implementation checklist; you or your team build it.
What do you need from me?
Your use case, target users, data sources, privacy constraints, budget, preferred language/frameworks, hosting constraints, current shortlist, and anything you’ve already ruled out.
Is my information kept private?
Yes. Your intake and review are never published, reused as a case study, or shared with vendors. Please don’t submit secrets, API keys, or regulated data.
Request your Stack Review
Tell me what you're building. I'll confirm the scope fits and send a payment link — nothing to pay until then. Prefer email? hello@agentsearchengine.app