Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developers building document workflows, RAG systems, and agentic knowledge tools who need more than a raw vector database or one-off parser.
Where this stands now
run-llama/llama_index ranks #16 of 3231 tracked Radar items by composite score (8.9 against a section median of 4.9). The section currently carries 2115 Bronze, 645 Gold, 471 Silver. RepoRadar has retained observations for this record since 2026-06-19 (113 days in the current window). Signal extremes versus the section: momentum at the 85th percentile; novelty at the 72th percentile.
Who should use it
Who should skip it
Move on from run-llama/llama_index if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
run-llama/llama_index is tracked by RepoRadar as a framework in the Radar section. First seen 2026-06-19; the source record was last checked on 2026-06-19. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for run-llama/llama_index are workflow potential (10.0) and practical usefulness (9.0), while setup ease (6.4) trails — that balance shapes where it fits best. This page summarizes the public evidence on the linked source page and states where additional review is still needed.
How this item is evaluated
The run-llama/llama_index record combines a 8.9/10 composite score with separate popularity (84.0), risk (conditional), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.
Questions worth asking before you adopt this
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
LlamaIndex workflows often process private documents and may call managed parsing or extraction services, so audit exactly which files leave your environment before using cloud components.