Uvi vs LlamaIndex
LlamaIndex builds RAG pipelines. Uvi 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. Uvi 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
- Uvi
- Reasoning Layer (returns the chain)
- LlamaIndex
- RAG framework (returns the documents)
What gets returned
- Uvi
- Structured chain of decisions, owners, evidence
- LlamaIndex
- Document chunks ranked by semantic similarity
Best for
- Uvi
- Multi-system enterprise R&D agents needing decisions, not documents
- LlamaIndex
- Knowledge-base Q&A, document search, custom RAG pipelines
Cross-system joins
- Uvi
- 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
- Uvi
- Native RBAC at retrieval, mirrored from source ACLs
- LlamaIndex
- Post-hoc filtering by default; permission-aware retrieval is custom work
Architecture
- Uvi
- Substrate + Forward Deployed Agent ships the graph end-to-end
- LlamaIndex
- Modular building blocks the developer assembles
Deployment
- Uvi
- On-prem or VPC, zero data egress
- LlamaIndex
- Self-hosted, LlamaCloud (SaaS), private VPC
Time to value
- Uvi
- 2-4 weeks via Forward Deployed Agent
- LlamaIndex
- Days to weeks depending on pipeline complexity
Open source?
- Uvi
- Decision Graph spec is open; engine is proprietary
- LlamaIndex
- Yes, MIT licensed
Compose with each other?
- Uvi
- Uvi replaces RAG retrieval; if both are present, use Uvi for decisions and LlamaIndex for static knowledge-base Q&A
- LlamaIndex
- Same - different jobs
| Uvi | 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? | Uvi replaces RAG retrieval; if both are present, use Uvi for decisions and LlamaIndex for static knowledge-base Q&A | Same - different jobs |
FAQ
Is Uvi 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. Uvi'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'; Uvi 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 Uvi?
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. Uvi 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. Uvi 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. Uvi's positioning is explicitly NOT RAG - we replace retrieval with reasoning. The /uvi-vs-rag page makes the architectural contrast in detail.
Stop retrieving. Start reasoning.
When your agents need decisions and not documents, a RAG framework is the wrong shape. Uvi ships the Decision Graph in 2-4 weeks.