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vsFaultmap 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 buildLlamaIndexafter 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.
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 frameworkWhere 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.
Run a free Faultmap on your goal and your data. No card, no code.