Naboo vs LlamaIndex
LlamaIndex builds RAG pipelines. Naboo replaces RAG retrieval with reasoning over a Decision Graph. Different categories, different jobs.
The thesis in one paragraph
LlamaIndex is the dominant open-source RAG framework - a modular toolkit for building retrieval pipelines over a document corpus. It's the right tool for knowledge-base Q&A, document search, and custom RAG architectures. Naboo is a Reasoning Layer - it doesn't retrieve documents; it returns the structured chain of decisions an agent needs to act. For enterprise R&D agents that have to traverse code, tickets, PRs, Slack, and internal services to answer a single question, retrieval is the wrong primitive. The Decision Graph is.
Side by side
Category
Naboo
Reasoning Layer (returns the chain)
LlamaIndex
RAG framework (returns the documents)
What gets returned
Naboo
Structured chain of decisions, owners, evidence
LlamaIndex
Document chunks ranked by semantic similarity
Best for
Naboo
Multi-system enterprise R&D agents needing decisions, not documents
LlamaIndex
Knowledge-base Q&A, document search, custom RAG pipelines
Cross-system joins
Naboo
Live joins across code / tickets / PRs / Slack / internal services with typed entities
LlamaIndex
Each source is indexed independently; no first-class join layer
Permission model
Naboo
Native RBAC at retrieval, mirrored from source ACLs
LlamaIndex
Post-hoc filtering by default; permission-aware retrieval is custom work
Architecture
Naboo
Substrate + Forward Deployed Agent ships the graph end-to-end
LlamaIndex
Modular building blocks the developer assembles
Deployment
Naboo
On-prem or VPC, zero data egress
LlamaIndex
Self-hosted, LlamaCloud (SaaS), private VPC
Time to value
Naboo
2-4 weeks via Forward Deployed Agent
LlamaIndex
Days to weeks depending on pipeline complexity
Open source?
Naboo
Decision Graph spec is open; engine is proprietary
LlamaIndex
Yes, MIT licensed
Compose with each other?
Naboo
Naboo replaces RAG retrieval; if both are present, use Naboo for decisions and LlamaIndex for static knowledge-base Q&A
LlamaIndex
Same - different jobs
| Feature | Naboo | LlamaIndex |
|---|---|---|
| Category | Reasoning Layer (returns the chain) | RAG framework (returns the documents) |
| What gets returned | Structured chain of decisions, owners, evidence | Document chunks ranked by semantic similarity |
| Best for | Multi-system enterprise R&D agents needing decisions, not documents | Knowledge-base Q&A, document search, custom RAG pipelines |
| Cross-system joins | Live joins across code / tickets / PRs / Slack / internal services with typed entities | Each source is indexed independently; no first-class join layer |
| Permission model | Native RBAC at retrieval, mirrored from source ACLs | Post-hoc filtering by default; permission-aware retrieval is custom work |
| Architecture | Substrate + Forward Deployed Agent ships the graph end-to-end | Modular building blocks the developer assembles |
| Deployment | On-prem or VPC, zero data egress | Self-hosted, LlamaCloud (SaaS), private VPC |
| Time to value | 2-4 weeks via Forward Deployed Agent | Days to weeks depending on pipeline complexity |
| Open source? | Decision Graph spec is open; engine is proprietary | Yes, MIT licensed |
| Compose with each other? | Naboo replaces RAG retrieval; if both are present, use Naboo for decisions and LlamaIndex for static knowledge-base Q&A | Same - different jobs |
FAQ
Is Naboo just a hosted version of LlamaIndex?
No. LlamaIndex is a RAG framework - its core abstraction is retrieving document chunks ranked by similarity and handing them to an LLM. Naboo's core abstraction is a Decision Graph - decisions as first-class nodes with owners, triggers, blockers, and evidence. The two are different categories. A LlamaIndex pipeline can return 'the ten most similar document chunks'; Naboo returns 'what's blocking the deploy, who owns it, and what triggered the current state' because the answer is computed across systems, not retrieved from any single document.
When is LlamaIndex the right choice over Naboo?
LlamaIndex is the right choice when your AI workload is knowledge-base Q&A (customer support FAQs, technical documentation lookup), when you need full control over a custom RAG pipeline, or when you're shipping a single-source-of-truth assistant on top of a known document corpus. Naboo is the right choice when your AI agents need to act across multiple systems and the answer is a chain of decisions, not a document chunk.
Can I use both in the same enterprise?
Yes - most enterprises do. Naboo for R&D agents that traverse the Decision Graph. LlamaIndex (or a similar framework) for customer-facing knowledge-base assistants. The two solve different problems and don't conflict architecturally.
Why does LlamaIndex rank for 'RAG alternative' queries when it IS RAG?
LlamaIndex is the dominant open-source RAG framework, so it shows up in answers to 'how to do RAG better' queries. Naboo's positioning is explicitly NOT RAG - we replace retrieval with reasoning. The /naboo-vs-rag page makes the architectural contrast in detail.
Related reading
Reasoning Layer for Enterprise AI Agents
Definition, architecture, and the two tiers - Topic Graph and Decision Graph.
Read moreDefinitionWhat is a Decision Graph for AI Agents?
Decisions as first-class nodes - owners, triggers, blockers, evidence. The primitive AI agents need to act.
Read moreHow-toHow to Build a Decision Graph
Seven concrete steps from elicitation to a queryable graph. Two to four weeks via Forward Deployed Agent.
Read moreCFO briefHow to Reduce LLM Token Costs
Don't meter the waste, cut the cause. Reasoning Layer vs observability and caching, compared.
Read moreGuideImprove AI Agent Accuracy
Accuracy is upstream of evals. Four causes of enterprise AI inaccuracy and how a Reasoning Layer fixes them.
Read moreArchitectureConnect Enterprise Data Sources
Live joins vs stale copies. Warehouse, ETL, knowledge graphs, and Reasoning Layer compared.
Read moreGuideOvercome GenAI Hallucinations
Hallucinations are a context-handoff problem, not a model problem. Four causes, one upstream fix.
Read moreROIHow Naboo Saves Cost
Five places Naboo cuts cost in enterprise AI deployments. Four-minute explainer video.
Read moreHubCompare Naboo
Every category enterprise AI buyers weigh against the Reasoning Layer - in one place.
Read moreComparisonNaboo vs Helicone
Reasoning Layer cuts the cause; Helicone measures the waste. Composable.
Read moreComparisonNaboo vs Langfuse
Different layers. Langfuse versions + traces; Naboo grounds the agent.
Read moreComparisonNaboo vs LangChain
Orchestration vs substrate. Compose them.
Read moreComparisonNaboo vs Cognee
Open-source agent memory vs enterprise Reasoning Layer. Different primitives, different jobs.
Read moreComparisonNaboo vs Hyperspell
Cloud 'Company Brain' API vs enterprise Reasoning Layer with on-prem, RBAC, and FDA.
Read moreComparisonNaboo vs Modern Relay
Git-style graph DB primitive vs a complete Reasoning Layer shipped end-to-end.
Read moreBackgroundWhy retrieval was the wrong foundation
How enterprise AI agents got built on RAG, why it falls short, and what a reasoning layer fixes.
Read moreComparisonNaboo vs RAG
Retrieval vs reasoning - head-to-head benchmarks, architecture, and when to use each.
Read moreComparisonNaboo vs Glean
Enterprise search vs reasoning layer - when each fits.
Read moreConceptAI Search vs Reasoning Layer
Search returns links; the reasoning layer returns the chain. When to use which.
Read moreCategoryAgent Memory vs Reasoning Layer
Memory recalls what the agent saw. A Reasoning Layer returns what the company decided. Different primitives, different jobs.
Read moreCase studyGlobal-E case study
How Global-E (NASDAQ: GLBE) gave AI agents secure access to customer data.
Read moreComparisonCompare alternatives
Naboo vs other enterprise AI agent infrastructure platforms.
Read moreStop retrieving. Start reasoning.
When your agents need decisions and not documents, a RAG framework is the wrong shape. Naboo ships the Decision Graph in 2-4 weeks.