AI writes code. That is expected now. But planning, security, compliance, and deployments? Those gaps remain. I have run contributor programs for years. I have never seen a community respond to technology like this.
That is why we opened GitLab Duo Agent Platform and invited developers worldwide to build AI agents that help teams ship secure software faster. Not chatbots that answer questions, but agents that jump into workflows, respond to events, and act on your behalf. The GitLab AI Hackathon ran from February 9 to March 25, 2026, on Devpost, the hackathon platform. Google Cloud and Anthropic joined as co-sponsors.
When my team planned this hackathon with Google Cloud and Anthropic, I asked the judges to score four things: technical work, design, potential impact, and idea quality. We hoped for strong turnout. What we got surprised all of us. Nineteen judges spent 18 days reviewing every entry. Google Cloud and Anthropic provided judges, prizes, and cloud access. The community built hundreds of agents and flows because they wanted to solve these problems.
Nearly 7,000 developers showed up. They built 600+ agents and flows in weeks. The prizes across all categories totaled $65,000 from GitLab, Google Cloud, and Anthropic.
If you have ever watched a senior engineer leave and take half the team's knowledge with them, you know why the winning project hit so hard.
Read on to find out what the community built.
Grand Prize: LORE
LORE, the Living Organizational Record Engine, uses eight agents with a router that sends each question to the right agent, logic to prevent circular loops in the knowledge graph, a visual dashboard, and carbon tracking. The command-line tool ships with 43 tests (yes, 43 tests in a hackathon project).
LORE solves a real problem: the knowledge that lives in engineers' heads and walks out the door when they leave. In my experience, a hackathon project with 43 tests is rare. That many tests in a hackathon project tells you something about the team behind it.
Judge April Guo (Anthropic) wrote: "This feels like a product, not a hackathon project."
Google Cloud winners
Gitdefender won the Google Cloud Grand Prize. It works inside code review workflows, finding and fixing security issues. It spots the bug, writes the fix, and opens the code review. No developer needs to step in.
Aegis won the Google Cloud Runner Up. It gives AI-powered explanations for every decision it makes, deployed to Google Cloud and ready for production use.
Anthropic winners
GraphDev won the Anthropic Grand Prize. It maps code links and shows how systems change over time. Judge Aboobacker MK (GitLab) noted it was "in sync with our work on GitLab knowledge graph." Judge Ayush Billore (GitLab) wrote: "Loved the demo and UX, super useful for understanding how the system evolved and what gets impacted by changes." You can see the full impact of a change before you make it.
DocSync won the Anthropic Runner Up. It uses three agents: Detector, Writer, and Reviewer. If DocSync is confident in the fix, it opens a code review. If not, it creates an issue for a human to check.
Category winners
Most Technically Impressive
Database migrations break things. Time-Traveler creates a safe copy of your production setup, runs the migration against that copy, and reports the result. It runs five agents connected by a bridge, with real Google Cloud deployment, real PostgreSQL migrations, and real data.
Most Impactful
RedAgent checks AI-generated security reports, closing the trust gap between AI findings and developer action. If your team uses AI for security scanning, you know this problem. I have seen teams dismiss AI findings because they could not verify them. RedAgent gives teams a way to check AI output before it reaches developers.
Easiest to Use
Launch Control delivers polished UX and solid infrastructure, and scored well on sustainability too.
The sustainability signal
Five projects won prizes or bonuses for environmental impact. Software delivery has a carbon cost as CI/CD pipelines, but now LLMs also run compute at scale. We created the Green Agent category to challenge developers to measure and reduce that footprint. Stacy Cline and Kim Buncle from GitLab's sustainability team helped judge the Green Agent category.
Green Agent prize
GreenPipe scans CI/CD pipelines for environmental impact and produces carbon footprint reports. Judges Kim Buncle and Rajesh Agadi (Google) both backed the project.
Sustainable Design bonus
Sustainable Design bonuses were awarded to the projects with exceptional sustainability practices in their design, from model optimization techniques to energy-efficient architecture choices.
- BugFlow turned one bug report into 10 fixes in 20 minutes.
- DELTA Cyber Reasoning is automated fuzz testing for security.
- CarbonLint applied code analysis to energy use.
- TFGuardian features a carbon footprint analyzer, among other agents.
Congratulations on all the Sustainable Design bonus winners!
Judge Jens-Joris Decorte (TechWolf) cited the result: Costs dropped from $556 to $18 per month, a 96% carbon cut (that is a $538 monthly saving with a sustainability label on it).
Honorable mentions and the long tail
Six projects received honorable mentions:
- SecurityMonkey injects known vulnerabilities into a test branch and scores how well your security scanners catch them.
- stregent monitors CI/CD pipelines and lets developers investigate and merge fixes from WhatsApp without opening a laptop.
- Compliance Sentinel scores every merge request for compliance risk and blocks the merge if critical violations are detected.
- Carbon Tracker calculates the carbon footprint of each CI/CD pipeline job and posts optimization tips on the merge request.
- RepoWarden is the first Living Specification Engine, an AI system that captures why code was written, not just what it does.
- MR Compliance Auditor collects evidence across merge requests, maps it to SOC 2 controls, and streams compliance scores to a live dashboard.
My favorite quote from the judging came from Luca Chun Lun Lit (Anthropic), who described stregent's mobile-first approach: "Being able to essentially code from your phone is a next level in the engineering experience."
Explore the 600+ entries in the project gallery.
What comes next
Every agent in this hackathon worked within a single project. They still delivered impressive results. Some participants ran a local knowledge graph alongside their agents to surface code relationships and dependencies within the repo. LORE captures project history. Gitdefender finds vulnerabilities. Pairing agents with richer local context is already helping contributors build sharper tools. The next hackathon will build on what contributors are already doing with richer context. Sign up on contributors.gitlab.com to be the first to know when details drop.
Get started
A special thanks to Lee Tickett (GitLab) and Mattias Michaux (GitLab) for orchestrating the orchestrators and innovators behind this hackathon!
Thank you to every developer who submitted. Nearly 7,000 of you showed what GitLab Duo Agent Platform can do when a community decides to build. I am proud of what you built here, and I cannot wait to see what you build next.
Build your own agent on GitLab Duo Agent Platform. Browse community-built agents in the AI Catalog. You orchestrate. AI accelerates.
Facts Only
GitLab, Google Cloud, and Anthropic co-sponsored the GitLab AI Hackathon from February 9 to March 25, 2026.
The hackathon focused on building AI agents that integrate into workflows, not just chatbots.
Nearly 7,000 developers participated, creating over 600 agents and flows.
Prizes totaled $65,000, awarded by GitLab, Google Cloud, and Anthropic.
Judges evaluated entries on technical work, design, potential impact, and idea quality.
LORE won the Grand Prize, featuring eight agents and 43 tests to retain organizational knowledge.
Gitdefender won the Google Cloud Grand Prize for automating security fixes in code reviews.
GraphDev won the Anthropic Grand Prize for mapping code dependencies and system evolution.
Five projects received sustainability awards, including GreenPipe for carbon footprint reporting.
The hackathon emphasized AI agents solving real-world problems like security, compliance, and knowledge retention.
GitLab Duo Agent Platform was used to build and deploy the agents.
The next hackathon will focus on richer context for AI agents.
Executive Summary
Full Take
The GitLab AI Hackathon represents a significant moment in the evolution of AI-assisted software development, where the focus shifts from passive tools to active agents embedded in workflows. The sheer scale of participation—nearly 7,000 developers building over 600 projects—suggests a broad recognition of AI's potential to address persistent challenges like knowledge retention (LORE), security automation (Gitdefender), and compliance (RedAgent). The emphasis on sustainability, with projects like GreenPipe and CarbonLint, reflects a growing awareness of the environmental costs of AI and CI/CD pipelines, a rare but necessary consideration in tech innovation.
However, the narrative also raises questions about the long-term implications of AI agents in software development. While the hackathon showcases impressive technical achievements, the reliance on AI for critical tasks like security fixes (Gitdefender) and compliance audits (Compliance Sentinel) could introduce new risks. For instance, if AI-generated fixes are automatically merged without human oversight, who bears responsibility for errors? The article celebrates the speed and efficiency of these agents, but it doesn’t explore the potential for over-reliance or the ethical dilemmas of delegating decision-making to AI.
The sustainability focus is commendable, but it also highlights a tension: AI itself is resource-intensive, and while projects like GreenPipe aim to mitigate its footprint, the broader question remains whether AI-driven development is inherently sustainable. The hackathon’s success suggests a future where AI agents are ubiquitous in software workflows, but it also underscores the need for guardrails—human oversight, transparency, and accountability—to ensure these tools serve developers rather than replace them.
Patterns detected: none
Bridge questions:
How might the integration of AI agents into workflows change the role of human developers in the long term?
What safeguards are needed to ensure AI-generated fixes and compliance checks don’t introduce new vulnerabilities?
Is the push for sustainability in AI development sufficient to offset its inherent resource demands, or is a more fundamental rethinking needed?
Sentinel — Human
This analysis exhibits strong human signals, presenting specific, contextualized data and a personal narrative typical of investigative or feature journalism rather than generalized synthetic content.