Eval directory
Evals for LlamaIndex
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for LlamaIndex AI products.
About LlamaIndex
LlamaIndex is a data framework for building RAG and agent applications over private data — documents/nodes, indexes (VectorStoreIndex), retrievers and query engines, the IngestionPipeline, plus LlamaParse and LlamaCloud for managed document parsing and retrieval.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
0/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for LlamaIndex
8 packs ready to run.
Agents And Workflows
Evaluates LlamaIndex's Agents & Workflows across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Documents Nodes And Ingestion
Evaluates LlamaIndex's Documents, Nodes & Ingestion across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Embeddings And Vector Stores
Evaluates LlamaIndex's Embeddings & Vector Stores across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Indexes
Evaluates LlamaIndex's Indexes across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Llamaparse And Llamacloud
Evaluates LlamaIndex's LlamaParse / LlamaCloud across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Observability Settings And Safety
Evaluates LlamaIndex's Observability, Settings & Safety across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Retrievers And Query Engines
Evaluates LlamaIndex's Retrievers & Query Engines across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Structured Outputs And Extraction
Evaluates LlamaIndex's Structured Outputs & Extraction across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's RAG / Data Framework eval coverage.
Why eval LlamaIndex AI
LlamaIndex's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for LlamaIndex measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against LlamaIndex's public product surface and runnable in Corsac with your own data.