What's a good company AI adoption rate?
langchain.dev is at 91.0%. This is 47.3pp above the community median (43.7%)..
91.0%
↑47.3pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
langchain.dev Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the langchain.dev engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The langchain.dev engineering team reports 91.0% AI adoption, 1.51× productivity lift, and 66.9% code quality across recent work.
These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.
What this report measures
We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.
How to interpret these metrics
Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.
AI Adoption Rate
HIGH
91.0%
AI assistance is present in 91.0% of recent commits for langchain.dev.
AI Productivity Lift
HIGH
1.51×
AI-enabled workflows deliver an estimated 51% lift in throughput.
AI Code Quality
LOW
66.9%
Review insights show 66.9% overall code health on AI-supported changes.
How is the langchain.dev team performing with AI?
The langchain.dev engineering team reports 91.0% AI adoption, translating into 1.51× productivity lift while sustaining 66.9% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.
Manager Questions Answered
Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.
Does AI actually make developers faster?
langchain.dev operates at 1.51×. This is 0.37× above the community median (1.13×)..
1.51×
↑0.37× above1.13× Community Median
Double down on automation around QA and release prep to compound the gains already in flight.
How does AI affect code quality?
langchain.dev holds AI-assisted quality at 66.9%. This is 43.6pp above the community median (23.3%)..
66.9%
Roughly in line23.3% Community Median
Invest in AI-specific test checklists and shadow reviews to keep quality slightly ahead of peers.
How evenly is AI use distributed across our team?
56.1% of AI commits come from the most active contributors.
56.1%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
langchain.dev combines strong adoption, lift, and quality control—making the ROI story executive-ready.
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Link these metrics to deployment frequency and incident cost to convert engineering wins into business KPIs.
See how your full organization compares
Unlock personalized insights across all your repositories, teams, and contributors.
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How Your Company Ranks
See how top engineering organizations compare across AI adoption, productivity lift, and code quality.
AI Adoption
% of commits with AI assistance
?
Companies in this quartile:
ID
idesie.com
(2904.2%)
IN
inngest.com
(1429.6%)
FM
fmease.dev
(87.5%)
DI
dimagi.com
(87.5%)
Top 25% of teams adopt AI in 65-75% of their commits.
Productivity Lift
Cycle-time improvement vs baseline
?
Companies in this quartile:
IN
inngest.com
(4.82×)
U.
u.nus.edu
(2.87×)
AC
acad.pucrs.br
(1.12×)
MC
mcornholio.ru
(1.12×)
Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.
Code Quality
Post-merge defect rate
?
Companies in this quartile:
IN
inngest.com
(701.7%)
ID
idesie.com
(649.2%)
GZ
gzgz.dev
(20.0%)
GW
gwu.edu
(20.0%)
Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.
Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).
Top contributors
Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.
Contributor
Commits
AI Usage
Productivity Lift
Code Quality
PS
Palash Shah
Commits47
AI Usage98.0%
Productivity Lift2.00x
Code Quality96.0%
IN
infra
Commits217
AI Usage88.7%
Productivity Lift1.94x
Code Quality23.4%
VB
Vadym Barda
Commits394
AI Usage93.8%
Productivity Lift1.87x
Code Quality95.2%
LH
Lauren Hirata Singh
Commits217
AI Usage93.2%
Productivity Lift1.74x
Code Quality73.6%
LM
Lance Martin
Commits43
AI Usage82.3%
Productivity Lift1.74x
Code Quality60.4%
Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.
Cross-Organization Network
Shared Repositories
15Activity
1,321 Commits197
171
193
216
112
169
142
121
0
0
0
0
LessMore
Your Network
34 Peopleangus-langchain
Memberads2280
Memberlc-arjun
Memberbvs-langchain
Memberbrody@langchain.dev
Membercasparb
Memberlc-chad
Memberphvash
Memberdenis-at-langchain
MemberWhy these metrics matter for engineering managers
Faster delivery
1.4x lift → predictable roadmaps
Safer velocity
93% quality → lower rollback risk
Equitable gains
AI less dependency on heroes
Governance
Depth monitoring audit-ready
