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Langfuse logoLlamaIndex logo

Langfuse vs LlamaIndex

Langfuse leads in package downloads at 2x; Langfuse has raised more funding at $5M; Langfuse leads in active contributors with 6 (30d); LlamaIndex leads in npm dependents with 111; LlamaIndex leads in code adoption with 1.5K repos importing; Langfuse leads in job demand with 1 mentions. Updated daily with live metrics.

Quick Comparison

Langfuse: 18.4M downloads/mo (+20%), 23.3K GitHub stars, 6 active contributors, 72 dependents, 4 repos importing, $5M funded vs

LlamaIndex: 10.6M downloads/mo (+26%), 47.8K GitHub stars, 1 active contributors, 111 dependents, 1.5K repos importing, $85K funded

Source: AI-Buzz Developer Adoption Index (DAI). Updated daily from 12 data sources.

LangfuseLlamaIndex
18.4M
10.6M

Downloads (30d)

$5M
$85K

Disclosed Funding

23.3K
47.8K

GitHub Stars

389
757

Package Dependents

4
1.5K

Repos Importing (code adoption)

43
47

Momentum (0-100)

Key Differences

For production adoption: LlamaIndex is depended on by 757 packages versus 389 for Langfuse. Langfuse leads in package downloads with 18.4M per month compared to 10.6M for LlamaIndex (Langfuse +20% MoM, LlamaIndex +26% MoM). LlamaIndex appears in 1.5K GitHub repositories compared to 4 for Langfuse.

For growth trajectory: LlamaIndex has 47.8K GitHub stars versus 23.3K for Langfuse.

For longevity/risk: Langfuse has raised $5M in total disclosed funding, while LlamaIndex has raised $85K. Langfuse's most recent round was Seed, while LlamaIndex's most recent round was Seed. Langfuse attracted 6 active contributors in the last 30 days compared to 1 for LlamaIndex. Langfuse operates in AI Infrastructure while LlamaIndex focuses on Foundation Models.

Bottom Line

Langfuse leads on developer adoption with 18.4M monthly package downloads. LlamaIndex counters with 47.8K GitHub stars, reflecting strong developer awareness. For a deeper look, visit each company's full profile for trend charts, funding rounds, and community sentiment data.

Scroll to compare

MetricLangfuse logoLangfuse
Updated Mar 18
LlamaIndex logoLlamaIndex
Updated Mar 18
Websitelangfuse.com llamaindex.ai
DescriptionOpen-source LLM observability platform. Monitor and debug AI applications.Framework for building LLM applications with RAG. Data framework for AI.
Total Disclosed Funding
$5M
$85K
Last Funding
Seed
May 2024
Seed
Jun 2023
Developer Adoption
Momentum
43Moderate
47Moderate
npm Registry Downloads (30d)
3.2M
442.3K
npm Trend (30d)
+12%
+34%
PyPI Registry Downloads (30d)
15.2M
10.2M
PyPI Trend (30d)
+22%
+26%
Total Downloads (30d)
18.4M+20%
10.6M+26%
Active Contributors/Day
6
1
Contributors Trend+50%-50%
npm Dependents
72
111
PyPI Dependents
317
646
Code Adoption (repos) Exclusive
4
1.5K
HN Discussion Share 2.3% of HN mentions0.2% of HN mentions
StatusPrivatePrivate
HN Mentions (30d)
0
0
Reddit Mentions (30d) Exclusive
0
2
Job Mentions (30d) Exclusive
1
1
GitHub Stars
23.3K
47.8K
Founded20232022
Primary Category
Data Last Updated

Frequently Asked Questions

Which has more developer adoption, Langfuse or LlamaIndex?
Langfuse has 18.4M package downloads per month, making it the more widely adopted tool among developers.Langfuse profile
Which has more real-world code adoption, Langfuse or LlamaIndex?
LlamaIndex appears in 1.5K GitHub repositories that import its packages, compared to 4 for Langfuse. This metric is exclusive to AI-Buzz.How we measure code adoption
Which is mentioned more in job listings, Langfuse or LlamaIndex?
Langfuse appeared in 1 job listings in the last 30 days, compared to 1 for LlamaIndex. Job mention data is tracked exclusively by AI-Buzz.How we track job mentions
Which is growing faster, Langfuse or LlamaIndex?
LlamaIndex has a momentum score of 47/100, indicating moderate growth trajectory. Momentum is calculated from download trends, GitHub activity, and community signals.Developer Adoption Index
Which company has more funding, Langfuse or LlamaIndex?
Langfuse has raised $5M in total disclosed funding.AI funding data

Download comparison data

Source: AI-Buzz Developer Adoption Index. Free to use with attribution.

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