LangChain leads in npm + PyPI downloads at 50x; LangChain has raised more funding at $35M; LangChain leads in active contributors with 9 (30d); LangChain leads in npm dependents with 1.6K; LangChain leads in job demand with 2 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
LangChain: 11.0M npm + PyPI downloads/mo (-11% MoM), 130.5K GitHub stars, 9 active contributors, 1.6K dependents, 21.6K repos importing, $35M funded
Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources. Methodology →
LangChain leads across 5 of 6 key metrics
Downloads (30d)
Disclosed Funding
GitHub Stars
Package Dependents
Repos Importing (code adoption)
Momentum (0-100)
For production adoption: LangChain is depended on by 6.1K packages versus 548 for CrewAI. LangChain leads in package downloads with 11.0M per month compared to 218.6K for CrewAI (LangChain -11% MoM). LangChain appears in 21.6K GitHub repositories.
For growth trajectory: LangChain has 130.5K GitHub stars versus 46.7K for CrewAI.
For longevity/risk: LangChain has raised $35M in total disclosed funding, while CrewAI has raised $18M. CrewAI's most recent round was Seed, while LangChain's most recent round was Series A. LangChain attracted 9 active contributors in the last 30 days compared to 3 for CrewAI. CrewAI operates in AI Agents while LangChain focuses on Foundation Models.
LangChain leads on developer adoption with 11.0M monthly package downloads. CrewAI has 218.6K monthly downloads and is growing its developer footprint. 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 → | langchain.com → |
| Description | Framework for building AI agent teams. Orchestrates multiple AI agents. | Framework for building LLM-powered applications. Chains, agents, RAG. |
| 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 (94%) |
| 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 | $35M✓ |
| Last Funding | Seed Jan 2024 | Series A Feb 2024 |
| Developer Adoption | ||
| Momentum | 39Moderate✓ | 38Moderate |
| npm Registry Downloads (30d) Source: npm registryUpdates: DailyNote: Includes all package installations including CI/CD pipelines and mirrorsMethodology → | 216 | 949.7K✓ (2 packages) |
| 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 | 10.1M✓ (2 packages) |
| 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% | 11.0M-11%✓ (2 packages) |
| Active Contributors/Day Source: GitHub APIUpdates: DailyNote: Tracks designated public repos per company, not all company GitHub activityMethodology → | 3 | 9✓ |
| Contributors Trend | -1 contributors | +7 contributors |
| npm Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct and dev dependentsMethodology → | 3 | 1.6K✓ |
| PyPI Dependents Source: Libraries.io APIUpdates: WeeklyNote: Counts direct reverse dependencies from PyPIMethodology → | 545 | 4.5K✓ |
| Code Adoption (repos) Source: ecosyste.ms Packages APIUpdates: DailyNote: Approximate count; excludes forksMethodology →Exclusive | 0 | 21.6K✓ |
| 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 | 1.9% 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✓ | 0 |
| 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 → | 46.7K | 130.5K✓ |
| Founded | 2024 | 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 npm Downloads over 30 days: LangChain (949.7K), CrewAI (216).
Source: npm registry | Methodology | CC BY 4.0
| Date | CrewAI | LangChain |
|---|---|---|
| Feb 27 | 52 | 431.7K |
| Mar 6 | 34 | 497.2K |
| Mar 13 | 40 | 458.9K |
| Mar 20 | 0 | 0 |
| Mar 21 | 216 | 949.7K |
