Adept has raised more funding at $415M; Unstructured leads in job demand with 2 mentions. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Adept: 412 GitHub stars, $415M funded vs
Unstructured: 190.9K npm + PyPI downloads/mo (+3% MoM), 14.3K GitHub stars, 2 active contributors, 3.4K repos importing, $105M funded
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
Unstructured leads across 5 of 7 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
HN Mentions (30d)
Momentum (0-100)
For production adoption: Unstructured is depended on by 271 packages. Unstructured has 190.9K package downloads per month. Unstructured appears in 3.4K GitHub repositories.
For growth trajectory: Unstructured has 14.3K GitHub stars versus 412 for Adept. Adept received 2 Hacker News mentions in the last 30 days compared to 1 for Unstructured.
For longevity/risk: Adept has raised $415M in total disclosed funding, while Unstructured has raised $105M. Adept's most recent round was Series B, while Unstructured's most recent round was Series A. Adept operates in AI Agents while Unstructured focuses on AI Infrastructure.
Unstructured leads on developer adoption with 190.9K monthly package downloads. Adept counters with stronger Hacker News discussion at 2 mentions (30d). For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.
← Scroll to compare →
| Metric | Updated Mar 21 | Updated Mar 21 |
|---|---|---|
| Website | adept.ai → | unstructured.io → |
| Description | AI agent company building general-purpose AI assistants. | ETL for unstructured data preprocessing |
| Data Confidence Source: AI-BuzzUpdates: DailyNote: Composite score (0-100) based on field completeness, metric freshness, and identifier coverage. Higher = more reliable data.Methodology → | Excellent (84%) No packages tracked | Excellent (83%) |
| Signal Coverage Source: AI-BuzzUpdates: DailyNote: Number of non-null adoption metrics tracked. Companies with more signals have more reliable composite scores.Methodology → | 2/7 metrics | 6/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | $415M✓ | $105M |
| Last Funding | Series B Mar 2023 | Series A Jan 2024 |
| Developer Adoption | ||
| Momentum | 37Moderate | 39Moderate✓ |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | - | 0 |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | - | 190.9K |
| PyPI Trend (30d) | - | |
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | Not tracked | 190.9K+3%✓ |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 0 | 2✓ |
| Contributors Trend | - | -1 contributors |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | - | 271 |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | - | 3.4K |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | 6.3% of HN mentions | 6.5% of HN mentions |
| Status | Acquired | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 2✓ | 1 |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 0 | 1✓ |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 1 | 2✓ |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 412 | 14.3K✓ |
| Founded | 2022 | 2022 |
| Primary Category | ||
| Data Last Updated | ||
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
Licensed CC BY 4.0. Free to cite with attribution.
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Which AI companies are developers actually adopting? We track npm and PyPI downloads for hundreds of companies. Get the biggest shifts delivered weekly.
Compare metric trends across companies over time
Comparing PyPI Downloads over 30 days: Unstructured (190.9K).
Source: PyPI (BigQuery) | Methodology | CC BY 4.0
| Date | Adept | Unstructured |
|---|---|---|
| Feb 27 | - | 90.7K |
| Mar 6 | - | 95.5K |
| Mar 13 | - | 76.3K |
| Mar 20 | - | 212.5K |
| Mar 21 | - | 190.9K |
