Q4 2025 · United Kingdom
2 edtech startups based in United Kingdom ranked by GitHub engineering acceleration. Filtered from our broader EdTech sector rankings.
| # | Company | Stage | Geo | Commits (14d) | Change | Contributors | Contrib. Growth | New Repos | Signal |
|---|---|---|---|---|---|---|---|---|---|
| 1 | codeforlife-education Giving everyone the ability to shape technology's future View signal profile → | Pre-seed | UK | 1 | +0% | 4 | +0% | 1 | Framework migration |
| 2 | oaknational Creating an openly licensed, fully resourced and AI-enabled curriculum for teachers and pupils as a not for profit and i View signal profile → | Series A/B | UK | 189 | -39% | 46 | +0% | 1 | Framework migration |
Sorted by commit velocity change (14-day window, descending). Data last updated Q4 2025. Geography from GitHub org profiles.
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In Q4 2025, codeforlife-education leads edtech startups in United Kingdom with 1 commits over a 14-day window (+0% change) and 4 active contributors. Across all 2 tracked United Kingdom-based startups in this sector, the average 14-day commit velocity is 95 commits. The dominant signal pattern is "Framework migration", which typically indicates significant technical migration, which often precedes a pivot or platform upgrade.
United Kingdom accounts for 2 of the edtech startups in our tracking dataset for Q4 2025. This geographic view filters the broader sector rankings to help investors focused on United Kingdom-based deal flow identify engineering acceleration patterns within their target geography. Regional concentrations often reflect local regulatory environments, talent pools, and investor networks that shape startup trajectories differently from global averages.
We derive startup geography primarily from the GitHub organization profile location field, supplemented by a manually curated enrichment database of known startup headquarters. This means startups without a public GitHub location may appear in our global sector rankings but not in geographic filters. The geographic classification uses broad regions (United Kingdom, etc.) rather than city-level granularity to provide meaningful sample sizes for comparison.