Faultmap vs LlamaIndex

LlamaIndex builds the retrieval layer and the agent workflows over your data. Faultmap maps where those workflows will break before the first document is indexed.

Side by side

Faultmapbefore the build

LlamaIndexafter the build

When it runs

Before the build, in the design phase

At the build — you ingest data, build indexes, and wire up agent workflows before running

What it needs

Your goal, personas, data, and tools

Data sources, LLM credentials, index configuration, and agent workflow definitions

What it produces

A map of where it breaks, plus the first test suite

A running retrieval-augmented agent that answers questions and takes actions over your private data

The question it answers

Where will this agent break, before I build it?

How do I build an agent that works over my private documents and data?

We do not replace LlamaIndex. Faultmap runs one step earlier. Keep using LlamaIndex after the build.

See where YOUR agent breaks

Faultmap maps the likely breaks from your goal and data before you build. First cohort: agents that use SQL as the data source. First 20 tests free. Full validation dataset $10 one time, per agent.

Reserve your beta spot

What LlamaIndex is

LlamaIndex in one sentence. Where Faultmap fits with it.

An open-source Python framework for building agents over data. Provides data connectors for PDFs, APIs, SQL, and more; vector indexes and query engines for retrieval; and Workflow orchestration for agents that combine retrieval with tool use and multi-step reasoning.

RAG and agent framework

Where Faultmap fits before it

Run Faultmap in design to catch the breaks before you build.
Build the agent against the test suite Faultmap hands you.
Use LlamaIndex after launch for rag and agent framework.

Map the breaks before LlamaIndex sees them.

Preview 20 agent-specific validation tests free. No card, no code.

Preview 20 tests