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Build LLM apps on top of your data with LlamaIndex

LlamaIndex helps developers ingest, index, retrieve, and use external data in LLM applications, making it a strong fit for RAG and knowledge-driven AI products.

31
Hotness score
78
Reliability score
50,956
Stars
over 3 years
Age
0
Published reviews
0
Questions

Scores

Hotness and reliability

The two headline scores, measured daily, with 30, 90, and 180 day views.

Hotness score

Jul 19, 2026

Current31

Previous34

Weekly average

100500
Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

180 observed daily rows. Missing days are not fabricated.

Hotness formula

40% Hot today + 40% Hot this week + 20% Breakout.

  • 40% Hot today: 27
  • 40% Hot this week: 43
  • 20% Breakout: 1
  • Stars gained: 1d: 14
  • Stars gained: 7d: 137
  • Stars gained: 14d: 270
  • Stars gained: 30d: 50956
  • Stars gained: 90d: 50956

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

  • Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
  • Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
  • Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
  • Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
  • Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
  • Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))
DateStarsStars 1dStars 7dStars 14dStars 30dStars 90dSame-day multiplierWeekly multiplierFortnight multiplierHot todayHot weekBreakoutStored score

Reliability score

Jul 19, 2026

Current78

Previous78

Weekly average

100500
Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

180 observed daily rows. Missing days are not fabricated.

Reliability formula

30% Continuity + 30% Closure + 20% Shipping + 10% Liveness + 10% Support burden.

  • 30% Continuity: 82
  • 30% Closure: 81
  • 20% Shipping: 48
  • 10% Liveness: 100
  • 10% Support burden: 78

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

  • Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
  • Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
  • Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
  • Support burden = 100 − 2 × open issues per 1,000 stars
  • Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

DateIssues openedIssues closedPRs openedPRs mergedReleasesStarsOpen issuesPushed days agoContinuityClosureShippingLivenessSupport burdenLicenseObserved daysMissing daysNeeds healingStored score

Reliability breakdown

What drives the reliability score

Component scores measured daily from GitHub activity (2026-07-18).

Continuity30% of headline82

Activity on 2 of 90 tracked days in the last 90 and 1 of 30 in the last 30.

Activity pulse
Closure30% of headline81

Merged 0 of 0 PRs opened and closed 0 of 0 issues opened over the last 90 tracked days — PR flow carries 55% of this component, issue flow 45%.

PRs merged (90d)PRs opened (90d)Issues closed (90d)Issues opened (90d)
Shipping20% of headline48

9 releases in the last 180 days, 2 in the last 90 and 1 in the last 30 — a steady cadence scores highest.

Releases
Liveness10% of headline100

Last push 4 days ago — freshness decays as pushes age (roughly halves every 83 days without a push).

Repository
Support burden10% of headline78

Open-issue load isn't tracked for this repo yet.

Open issuesStargazers
Adoption confidence100

Modeled — how confidently teams are adopting this repo. Stargazers

Maintenance quality82

Modeled from PR/issue responsiveness and upkeep signals. Activity pulse

Risk score16

Modeled — lower is better; adoption and continuity risk. Repository

Stays active (30d)89%

Modeled chance the repo stays active over 30 days. Activity pulse

Stays active (90d)86%

Modeled chance the repo stays active over 90 days. Activity pulse

Release rhythm (180d)67

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)13%

Modeled — lower is better; concentration of commits in few maintainers. Contributors graph

Topics

Explore related topics

Jump into the topic listings this repository belongs to.

Llamaindex

Measured history

Project metrics

Weekly GitHub totals with 30, 90, and 180 day views.

Issues opened

Mar 8, 2026

Current17

Previous13

Weekly total

1790
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

Issues closed

Mar 8, 2026

Current28

Previous22

Weekly total

28140
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests opened

Mar 8, 2026

Current47

Previous54

Weekly total

54270
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests closed

Mar 8, 2026

Current45

Previous46

Weekly total

48240
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

46 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests merged

Mar 8, 2026

Current27

Previous25

Weekly total

38190
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

46 observed daily rows. Missing days are not fabricated.

DateValue

Issue close ratio (daily)

Mar 8, 2026

Current0.00

Previous1.00

Daily ratio — a gap means nothing was opened that day

How this works: each day's value is issues closed that day ÷ issues opened that day. Days when nothing was opened render as gaps — never a rolling average or a made-up zero. Values above 1 mean the project closed more issues than arrived that day.

6.003.000.00
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

36 observed daily rows. Missing days are not fabricated.

Per-day formula: issues closed that day ÷ issues opened that day; blank when nothing was opened (a gap, not a zero).

DateIssues openedIssues closedStored score

Pull request close ratio (daily)

Mar 7, 2026

Current0.00

Previous0.88

Daily ratio — a gap means nothing was opened that day

How this works: each day's value is pull requests closed that day ÷ pull requests opened that day. Days when nothing was opened render as gaps — never a rolling average or a made-up zero. Values above 1 mean the project closed more pull requests than arrived that day.

4.502.250.00
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

45 observed daily rows. Missing days are not fabricated.

Per-day formula: pull requests closed that day ÷ pull requests opened that day; blank when nothing was opened (a gap, not a zero).

DatePRs openedPRs closedStored score

Releases

Jul 19, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub release published_at events bucketed by UTC day; drafts are excluded.

180 observed daily rows. Missing days are not fabricated.

DateValue

About Llama_index

LlamaIndex is a popular framework for teams building LLM applications that need structured access to documents, APIs, and private knowledge sources. It is designed around data ingestion, indexing, retrieval, and query workflows, which makes it especially relevant for retrieval-augmented generation. Developers often evaluate it when they need a faster path from raw data to a usable LLM application without hand-rolling every connector and retrieval layer. Its ecosystem and abstractions make it a practical choice for prototyping knowledge assistants, internal search, and domain-specific chat experiences.

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