CrawlBot AI vs. Rasa Open Source
Rasa is a powerful open source framework for NLU and dialog management. CrawlBot is built for grounded website answers with citations, freshness controls, and hardened embeds. Here is how they differ and how to combine them.
Comparison
| Dimension | CrawlBot AI | Rasa Open Source |
|---|---|---|
| Grounding | Hybrid RAG with refusal policy and citations | Intents, entities, stories, and policies |
| Freshness | Sitemap-first crawl, IndexNow, incremental recrawl | You build crawling and knowledge sync |
| Analytics | Per-embed impressions, opens, chats, messages, fallback reasons | Custom telemetry you implement |
| Security | SRI, strict widget CSP, origin checks, SSO, formal threat model | You configure headers, auth, and infra |
| Hosting | Managed SaaS | Self host or buy Rasa Enterprise |
| Multi-tenant | Agency friendly styling and quotas per tenant | Build your own multi-tenant isolation |
When CrawlBot fits best
- You want fast, cited answers on marketing, docs, or pricing pages without building NLU pipelines.
- Agencies manage multiple brands and need isolated styling, quotas, and analytics.
- Security teams require strict CSP and origin validation for embeds.
- Ops teams want retrieval transparency to reduce hallucinations quickly.
When to lean on Rasa
- Complex transactional flows and channel orchestration require custom policies.
- You have engineering capacity to own model training, hosting, and observability.
- On-prem or strict data residency requirements demand self hosting.
Pairing both
- Deploy CrawlBot on public pages for grounded Q&A with citations.
- Keep Rasa for transactional flows and custom channels.
- Route specific intents from CrawlBot to Rasa when flows are better suited.
- Feed CrawlBot fallback reasons into Rasa backlog to decide which intents to formalize.
Grounded answers and custom NLU serve different needs. Pairing CrawlBot with Rasa gives visitors clarity while complex flows stay under your control.