Weights & Biases leads in npm + PyPI downloads at 3x; Weights & Biases has raised more funding at $250M; Weights & Biases leads in active contributors with 9 (30d); CrewAI leads in job demand with 1 mentions. Updated daily with live metrics.
Data sources: npm, PyPI, GitHub, Crunchbase, Hacker News, Reddit, job boards. Methodology →
CrewAI: 218.6K npm + PyPI downloads/mo (+5% MoM), 46.7K GitHub stars, 3 active contributors, 3 dependents, $18M funded vs
Weights & Biases: 743.3K npm + PyPI downloads/mo (+5% MoM), 10.9K GitHub stars, 9 active contributors, 9.3K repos importing, $250M funded
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
Weights & Biases leads across 4 of 6 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
Momentum (0-100)
For production adoption: Weights & Biases is depended on by 1.9K packages versus 548 for CrewAI. Weights & Biases leads in package downloads with 743.3K per month compared to 218.6K for CrewAI. Weights & Biases appears in 9.3K GitHub repositories.
For growth trajectory: CrewAI has 46.7K GitHub stars versus 10.9K for Weights & Biases.
For longevity/risk: Weights & Biases has raised $250M in total disclosed funding, while CrewAI has raised $18M. CrewAI's most recent round was Seed, while Weights & Biases's most recent round was Strategic Investment. Weights & Biases attracted 9 active contributors in the last 30 days compared to 3 for CrewAI. CrewAI operates in AI Agents while Weights & Biases focuses on AI Infrastructure.
Weights & Biases leads on developer adoption with 743.3K monthly package downloads. CrewAI counters with 46.7K GitHub stars. 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 | crewai.com → | wandb.ai → |
| Description | Framework for building AI agent teams. Orchestrates multiple AI agents. | ML experiment tracking and model management platform. |
| 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 (95%) | Excellent (88%) |
| Signal Coverage Source: AI-BuzzUpdates: DailyNote: Number of non-null adoption metrics tracked. Companies with more signals have more reliable composite scores.Methodology → | 5/7 metrics | 5/7 metrics |
| Total Disclosed Funding Source: Public records / manual researchUpdates: WeeklyMethodology → | $18M | $250M✓ |
| Last Funding | Seed Jan 2024 | Strategic Investment Nov 2023 |
| Developer Adoption | ||
| Momentum | 39Moderate✓ | 32Moderate |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 216✓ | 0 |
| npm Trend (30d) | - | |
| PyPI Registry Downloads (30d) Source: PyPI (Google BigQuery)Updates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 218.3K | 743.3K✓ |
| PyPI Trend (30d) | ||
| Total Downloads - npm + PyPI (30d) Source: npm + PyPI registriesUpdates: DailyNote: Sum of npm and PyPI; excludes other package managersMethodology → | 218.6K+5% | 743.3K+5%✓ |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 3 | 9✓ |
| Contributors Trend | -1 contributors | +80% |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | 3 | - |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 545 | 1.9K✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 0 | 9.3K✓ |
| HN Discussion Share Source: Hacker News (Algolia API)Updates: DailyNote: Share of HN mentions within the company's primary categoryMethodology → | 21.2% of HN mentions | 0.1% of HN mentions |
| Status | Private | Private |
| HN Mentions (30d) Source: Hacker News (Algolia API)Updates: DailyMethodology → | 0✓ | 0 |
| HN Mentions Trend (30d) | ||
| Reddit Mentions (30d) Source: Reddit (search API)Updates: DailyNote: Counts posts/comments mentioning the company by nameMethodology →Exclusive | 1 | 2✓ |
| Job Mentions (30d) Source: Job board aggregationUpdates: DailyNote: Counts job listings mentioning the company's technologyMethodology →Exclusive | 1✓ | 1 |
| GitHub Stars Source: GitHub APIUpdates: DailyNote: Stars are bookmarks — a popularity signal, not a usage indicatorMethodology → | 46.7K✓ | 10.9K |
| Founded | 2024 | 2018 |
| 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.
Which AI companies are developers actually adopting? We track npm and PyPI downloads for hundreds of companies. Get the biggest shifts delivered weekly.
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Comparing npm Downloads over 30 days: CrewAI (216).
Source: npm registry | Methodology | CC BY 4.0
| Date | CrewAI | Weights & Biases |
|---|---|---|
| Feb 27 | 52 | - |
| Mar 6 | 34 | - |
| Mar 13 | 40 | - |
| Mar 20 | 0 | - |
| Mar 21 | 216 | - |
