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Build Semantic Search and RAG Workflows

txtai gives developers an open source way to combine embeddings, retrieval, and LLM orchestration in one platform.

16
Hotness score
59
Reliability score
12,738
Stars
almost 6 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

Current16

Previous17

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: 22
  • 40% Hot this week: 28
  • 20% Breakout: 10
  • Stars gained: 1d: 4
  • Stars gained: 7d: 18
  • Stars gained: 14d: 32
  • Stars gained: 30d: 66
  • Stars gained: 90d: 12738

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

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

Reliability score

Jul 19, 2026

Current59

Previous59

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: 36
  • 30% Closure: 55
  • 20% Shipping: 53
  • 10% Liveness: 100
  • 10% Support burden: 99

Per-day formula: 0.30 Γ— Continuity + 0.30 Γ— Closure + 0.20 Γ— Shipping + 0.10 Γ— Liveness + 0.10 Γ— 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 Γ— (20% Γ— release ratio 30d + 30% Γ— release ratio 90d + 50% Γ— release ratio 180d); ratios are releases Γ· 2, 6, and 12, capped at 1
  • Support burden = 100 βˆ’ 2 Γ— open issues per 1,000 stars
  • Liveness = 100 Γ— exp(βˆ’pushed days ago Γ· 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 headline36

Activity on 4 of 90 tracked days in the last 90 and 1 of 30 in the last 30.

Activity pulse
Closure30% of headline55

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

PRs merged (90d)PRs opened (90d)Issues closed (90d)Issues opened (90d)
Shipping20% of headline53

8 releases in the last 180 days, 4 in the last 90 and 1 in the last 30 β€” a steady cadence scores highest.

Releases
Liveness10% of headline100

Last push 6 days ago β€” freshness decays as pushes age (roughly halves every 83 days without a push).

Repository
Support burden10% of headline99

7 open issues against 12,738 stars β€” about 1 open issues per 1,000 stars. Lighter backlogs score higher.

Open issuesStargazers
Adoption confidence88

Modeled β€” how confidently teams are adopting this repo. Stargazers

Maintenance quality63

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

Risk score18

Modeled β€” lower is better; adoption and continuity risk. Repository

Stays active (30d)51%

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

Stays active (90d)62%

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

Release rhythm (180d)68

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)61%

Modeled β€” lower is better; concentration of commits in few maintainers. Contributors graph

Topics

Explore related topics

Jump into the topic listings this repository belongs to.

EmbeddingsNlpLarge Language ModelsTransformersSentence Embeddings

Measured history

Project metrics

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

Issues opened

Mar 8, 2026

Current0

Previous1

Weekly total

950
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

Current0

Previous1

Weekly total

950
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

Current0

Previous0

Weekly total

1260
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

Current0

Previous0

Weekly total

1370
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

Current0

Previous0

Weekly total

320
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)

Feb 24, 2026

Current1.00

Previous1.00

Daily ratio β€” a gap means nothing was opened that day

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

3.001.500.00
Full metrics details

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

12 observed daily rows. Missing days are not fabricated.

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

DateIssues openedIssues closedStored score

Pull request close ratio (daily)

Feb 20, 2026

Current1.00

Previous0.00

Daily ratio β€” a gap means nothing was opened that day

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

1.100.550.00
Full metrics details

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

4 observed daily rows. Missing days are not fabricated.

Per-day formula: pull requests closed that day Γ· 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 Txtai

txtai is positioned as an all-in-one embeddings database for teams building semantic search and retrieval-driven AI systems. It brings together indexing, similarity search, and LLM workflow orchestration, which makes it relevant for applications that need both retrieval and generation in one stack. For builders working on RAG pipelines or search-heavy AI products, txtai offers a practical open source foundation with a broad scope.

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