Developer Tools Startups funded by Y Combinator (YC) 2026

GitLab is the first single application for the entire DevOps lifecycle. Only GitLab enables Concurrent DevOps, unlocking organizations from the constraints of today’s toolchain. GitLab provides unmatched visibility, radical new levels of efficiency and comprehensive governance to significantly compress the time between planning a change and monitoring its effect. This makes the software lifecycle 200% faster, radically improving the speed of business. GitLab and Concurrent DevOps collapses cycle times by driving higher efficiency across all stages of the software development lifecycle. For the first time, Product, Development, QA, Security, and Operations teams can work concurrently in a single application. There’s no need to integrate and synchronize tools, or waste time waiting for handoffs. Everyone contributes to a single conversation, instead of managing multiple threads across disparate tools. And only GitLab gives teams complete visibility across the lifecycle with a single, trusted source of data to simplify troubleshooting and drive accountability. All activity is governed by consistent controls, making security and compliance first-class citizens instead of an afterthought. Built on Open Source, GitLab leverages the community contributions of thousands of developers and millions of users to continuously deliver new DevOps innovations. More than 100,000 organizations from startups to global enterprise organizations, including Ticketmaster, Jaguar Land Rover, NASDAQ, Dish Network and Comcast trust GitLab to deliver great software at new speeds.

Our mission is to make every search interaction meaningful & rewarding, through developer-friendly and enterprise-grade APIs. Algolia is the leading Search & Discovery API for websites and mobile apps. We help the most innovative companies across e-commerce, media and SaaS industries create powerful, relevant and scalable discovery experiences for their users. Unlike other solutions, our hosted platform reduces the complexities of building and scaling a fast, relevant digital experience and helps teams accelerate development time. More than 9,000 companies like Under Armour, Twitch, Periscope, Medium and Stripe rely on Algolia to manage 50+ billion search queries a month. Founded in 2012, we're backed by $184M in funding from Accel Partners, Alven Capital, Point Nine Capital and Storm Ventures. The team is headquartered in San Francisco with offices in Paris, London, New York, and Atlanta. To learn more, visit www.algolia.com. --- We’re looking for talented, passionate people to build the world’s best search technology. As an ownership-driven company, we seek team members who thrive under autonomy and diversity. We're committed to building an inclusive and diverse workplace. We care about each other and the world around us, and embrace talented people regardless of their race, age, ancestry, religion, sex, gender identity, sexual orientation, marital status, color, veteran status, disability and socioeconomic background

Since graduating from YC, Amplitude (W12) has made it to IPO and beyond by helping cutting edge startups become category leaders. We’ve pioneered the digital analytics category to become the go-to solution for AI pioneers (Cruise, Midjourney), apps that touch millions of consumers (Doordash, Coinbase), and the best in B2B (Atlassian, Rippling, Canva). With product analytics, experiment, CDP, session replay, and more, Amplitude’s digital analytics platform shows you what your users love, where they’re getting stuck, and what keeps them coming back. You want to build a generational business, and we have the capabilities and pricing plans to guide you every step of the way. Check out our special deal for YC companies and get started on your analytics journey.

Agent-first development environment. Emdash enables engineering teams to develop software faster, by running multiple coding agents in parallel. We're an open-source and provider-agnostic desktop application.

We’re building the infrastructure for a future AI co-scientist, starting with computational research domains. Our thesis is that capable AI scientists require two things built in tandem: (1) a human-centric product suite that generates high-quality process data, and (2) the research infrastructure to turn that data into increasingly autonomous systems. We’re creating the product and training infrastructure needed for research agents that can execute the full scientific loop. Synthetic Sciences Web is our platform where scientists delegate complex research tasks to swarms of AI co-scientists across reading → hypotheses → methods → experimentation → results → drafting, even while they sleep. In parallel, we develop RL environments and process-based training data for LLMs, starting with agentic coding environments for ML research workflows.

Infrastructure for voice-native commerce. Add secure, conversational checkout to AI agents with a single API.

Your agent processed a refund without looking up the order ID, costing you thousands. You only found out three hours later from a support ticket. Evals, output scoring, and observability can reduce the likelihood of mistakes like these occurring - but there's no solution that inspects and prevents an action as it’s about to execute. Salus does that. We’ve built an API that wraps around your agent and checks its actions at run time, blocking incorrect ones and providing immediate feedback to guide retries. Kevin and Vedant were roommates at Stanford, where they both studied computer science.

Lucent automatically watches your user sessions and alerts you when users hit bugs or UX issues. You're recording thousands of sessions but nobody's watching them. Lucent does - catching silent issues breaking your product that you'd otherwise never know about.

Sentrial is the observability platform for AI agents in production. When your agent loops indefinitely, hallucinates, or frustrates users, Sentrial detects it in real-time, diagnoses the root cause, and recommends specific fixes. We provide the monitoring, evaluation, and debugging infrastructure that traditional tools weren't built for, so teams can ship agents with confidence and iterate based on real production behavior, not just evals/benchmarks.

VOYGR helps AI apps and agents understand and act in the real world with comprehensive place data. Unlike static mapping APIs, VOYGR combines accurate place intelligence with fresh web context – news, articles, and events. Real-world interactions rely on maps: 10M+ apps/websites, up to 40% of search queries, and 20% of LLM prompts need local context. Yet a restaurant recommendation can fail because the place is closed, menus aren’t searchable, or delivery goes to the wrong entrance. AI pushes expectations further – answering semantic prompts like “specialty coffee shops in SF with Wi-Fi and YC founders", and taking actions like making reservations or placing orders. We’re experienced builders in maps, search, and ML to solve this. Vlad knows this space inside and out – he worked on the Google Maps APIs GTM, plus firsthand customer experience from building in ridesharing and travel. Yarik has spent over a decade leading ML/search teams at Apple, Google, and Meta. Several customers already run VOYGR to continuously validate places data accuracy at scale, and we outperform on accuracy and coverage. Maps were built for humans browsing pins; agents need continuously updated place ground truth they can act on – from copilots and in-car assistants to delivery robots and AR. As agents become the interface to commerce and services, VOYGR aims to be the default ground-truth layer.

Mendral is an AI DevOps Engineer that observes, diagnoses, acts, and learns continuously. It autonomously handles CI failures, flaky tests, slow builds, broken releases, and code reviews, improving with every build and run. Over time, Mendral evolves into a self-improving delivery system that manages performance, quality, security, and compliance, all without manual intervention.

Compresr provides an API that compresses LLM context without losing what matters. It’s a drop-in for agents and RAG that cuts token costs and improves accuracy.

Jinba lets enterprise teams vibe-code AI workflows instead of drag and drop. Describe what you need in plain language, and your whole company can start using it immediately. No engineers required. Enterprise-grade permissions, audit logging, and on-prem deployment built in. We serve 40,000 enterprise users at major financial institutions. Build something your colleagues want.

Sparkles is like Lovable for existing projects, so even Sharon from Marketing can change the button styling on the front page without going back and forth with the dev team. Someone technical connects their GitHub to Sparkles, sets up the repository and environment variables, and then anyone on the team with a @yourcompany.com email can log in on Sparkles and create pull requests without needing to know what Git is.

Chamber puts your AI infrastructure on autopilot. Our agentic AI enabled platform orchestrates, governs, and optimizes your AI infrastructure so teams can run ~50% more workloads on the same GPUs without manual intervention. It operates like an autonomous infrastructure team: continuously monitoring GPU clusters, forecasting demand, detecting unhealthy nodes, and reallocating resources in real time to where they create the most impact. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.

We’re building AI agents that do on-call support for data-heavy industries like financial services and fintech, healthcare, and insurance. On-call engineers in these industries need to inspect data to debug production issues. We monitor both data and infrastructure for issues and use AI agents to debug and suggest fixes in minutes. Data is especially sensitive in regulated industries, so we offer on-prem deployments and hardware-backed secure environments that let agents safely use production data as context while debugging. Mitch and Shipra founded Corelayer after building data infrastructure together at Goldman Sachs, where they spent many late nights and weekends debugging systems that processed 100s of billions of rows a day.

Edge AI is inevitable, but shipping it is painful: every device class behaves differently, runtimes vary, models are huge, and performance collapses under memory/power constraints. RunAnywhere turns that into an enterprise-ready workflow: one SDK to run models on-device, plus a control plane to manage models, enforce policies, and measure outcomes across thousands of devices.

We’d like to automate monitoring. To advance that mission, we’re building Sonarly. Software has two phases: build time and run time. Coding agents are getting great at build time, but production is still a mess of charts, logs, and noisy alerts. Sonarly triages production alerts, deduping - prioritizing, and attaching the missing context - so teams can trust what pages them and fix the right issue fast. As you deploy new code, Sonarly keeps alerts and investigations up to date automatically.

A control panel for your AI coding agents. Run Claude Codes in parallel locally or in remote sandboxes, automate Linear tasks, PR reviews for teams that ship fast

JSX Tool is a browser extension that hooks into your React project, so you can easily locate any React component and file with just a click. This gives developers the ability to articulate visual requests to LLMs that without JSX Tool, are either vague or cumbersome to communicate in English and code snippets. On top of that JSX Tool is a full featured IDE tailored specifically for React Development, so users never have to leave the context of their browser when developing React code.

Captain delivers the most accurate general-purpose retrieval engine for unstructured data. Connect file stores and effortlessly retrieve knowledge with much higher accuracy than RAG (Avg: 78% → 95% + citations).

Jarmin.ai is your 24/7 ML eng employee that you hire. Just talk to Jarmin like any other employee and Jarmin handles your AI/ML work from there. Completely hand over full initiatives or individual tasks. Jarmin’s founding team is from Meta SuperIntelligence Labs (Staff Engineers), Apple (Machine Learning Engineer), AWS (scaling infra), Lockheed-Martin (Research), and JPMorganChase (SWE) with experience building AI/ML and automating complex work of Researchers, Data Scientists, ML Engineers with AI.

Inspector helps front-end engineers ship faster with AI by automating the most frustrating part of the job — gathering context between the browser and the codebase. Inspector is an AI IDE for front-end that connects your browser and codebase. Inspector lets you select elements visually and prompt changes, giving coding agents access to the same runtime context developers have: visuals, elements, and logs. With Inspector, you’re editing right in the front-end, not buried under tabs and screenshots.

Scott AI is the agentic workspace where teams align on spec before any codegen runs. Teams already use multiple coding agents to build. Different coding agents will continue to evolve and have diverse strengths. An engineer will entrust a long-running agent to add dark-mode end-to-end while they pair carefully with an IDE-based agent to land a critical DB migration. These execution agents cannot be the decision layer because they are biased to their own tool, they are not neutral. Scott AI is the neutral, agent agnostic decision layer for the age of multi agent building. Teams will use Scott as their decision workspace to finally ditch google docs and notion as their primary spec alignment layer.

Lemma enables AI agents to continuously improve from user feedback and production outcomes. Instead of relying on manual prompt tuning and reactive debugging, Lemma detects when an agent’s performance drifts, pinpoints the exact step causing failures, generates optimized prompt candidates, and automatically delivers improvements through our API or by opening a pull request in your codebase. Built by ex-AI engineers who felt this pain firsthand, Lemma turns static agents into self-improving systems, helping teams ship reliable AI products faster with compounding performance gains over time. Deploy 1x. Learn forever.

Rovr streamlines engineering operations by automating intake, triage, routing, and cross-team coordination. It replaces internal forms, manual Slack triage, and scattered docs with an AI front desk that understands requests, assigns owners, updates context, and posts status changes directly into Slack. Rovr’s built-in ops CRM gives teams a single system of record for eng requests — from tooling access to platform incidents to dev-infra workflows — reducing operational drag across the entire org.

Parse lets developers build APIs for any website. Unlike traditional scraping tools that rely on slow, expensive headless browsers, Parse reverse-engineers websites' underlying network requests and generates API endpoints automatically. This makes Parse 10-100x faster and cheaper than browser automation,.

Moss is building the real-time semantic search runtime for conversational and multimodal AI. Our system enables voice agents, copilots, and chat interfaces to retrieve, reason, and respond in sub-10 ms, delivering the responsiveness that makes AI interactions feel truly natural. If you’ve ever built a conversational or voice AI product, you’ve felt the lag. That moment when an agent pauses and the illusion of intelligence breaks. The bottleneck is almost always retrieval. Each query hops across networks and databases, adding delay and cost. Moss eliminates that gap by keeping retrieval close to where the agent runs. Moss runs natively across browsers, mobile devices, and servers with an optimized vector index built in Rust and WebAssembly. It enables teams to build AI products that feel instant, contextual, and adaptive. The experiences that sustain engagement and unlock new kinds of interaction. For our customers, this translates into tangible business value: stronger user retention, higher conversion, and entirely new product categories made possible by real-time understanding. Moss is already powering production pilots across voice AI and developer platforms, achieving sub-10 ms retrieval and 70–90% token savings compared to traditional pipelines.

free codegen from brainrot workflows in your IDE.

Hyperspell (YC F25) gives AI agents memory. It connects to tools like Slack, Gmail, Notion, and Drive so agents can recall, reason, and remember across company knowledge. Developers use Hyperspell’s API to turn disconnected agents into AI coworkers that understand their work. Conor and Manu founded Hyperspell after building their own workplace agent and seeing how limited AI is without memory. They have over 15 years of experience in machine learning and have scaled API-based products to $30M ARR.

Compyle is a less autonomous coding agent that asks before it acts. We're like Cursor or Claude Code - but you won't need to rewrite your codebase every time you use it for more than a small task. We built Compyle because we were frustrated with the direction other coding agents are headed. They're all racing to be more autonomous, running for hours without human input. This doesn't work for real production code. When agents run autonomously, they make countless small decisions that pile up in your codebase. Engineers lose ownership - they don't know what was implemented, can't effectively debug or maintain it, and struggle to build on top of it. The code becomes a black box, and they're forced to throw it away. Compyle keeps engineers in the driver's seat.

Fastshot is a no-code mobile development platform. Our mission is to "Democratize mobile app creation with AI" Anyone can develop a mobile app from idea to App Stores within minutes just by chatting with AI.

Understand user patterns and find silent failures in your AI agents. The Context Company helps teams analyze AI agent conversations to surface user behavior patterns, performance trends, and moments where agents struggle - so teams know what’s working, what’s not, and what to improve next. We turn raw conversations into structured signals: topic clustering, user feedback analysis, custom pattern tracking, and alerts. All alongside traditional observability like traces, tool calls, latency, and costs. Get started in less than 10 lines of code.

Sourcebot is an open source code understanding platform for massive codebases. We’re used by thousands of engineers in some of the largest companies in the world, including NVIDIA, Red Hat, Wikimedia, and Arista Networks. Understanding code, not writing it, is the primary bottleneck for large engineering teams. For developers, this means onboarding onto complex codebases faster. For AI agents, it means getting the necessary code context to minimize hallucinations and maximize cohesion within the wider codebase. Sourcebot solves this by giving developers and AI agents the ability to regex search across millions of lines of code instantly, as well as ask questions across thousands of repos using any flagship reasoning model. Being open source and on-prem, we can get deployed in enterprises within minutes before other tools even get approval. We met a decade ago at McGill University and have been building together full time for over 2 years. We’ve personally felt the pain of understanding large codebases while working at Microsoft, EA, Ubisoft, Google, and Meta.

A platform where users can automate web workflows using natural language prompts. We handle deterministic playback of browser workflows and fall back to LLMs where necessary, ensuring reliability.

S2 is a serverless datastore for real-time, streaming data. The more we worked on data systems, the more we felt like there was a missing building block. The seamless experience of object storage simply did not exist for durable streams – so we set out to fix that! S2 reimagines streams as an unlimited and access-controlled web resource. Like if Kafka and S3 had a baby. - Building agents? Use granular streams to persist context in real time, make workflows auditable, and coordinate between agents. - Publishing fast-moving data? Streams support high fanout and can be accessed directly over the internet, so they are a great fit for broadcasting real-time feeds. - Sandboxing? Emit to per-instance streams, and read history or live events just as easily. - Local-first and multiplayer experiences? Propagate updates reliably and efficiently. Stop dealing with clusters, and start streaming.

At Deeptrace, we’re building AI agents that investigate and resolve production alerts end-to-end. Deeptrace cuts through the noise, pinpoints root causes instantly, and gives engineering teams back what they need most: the freedom to build instead of firefighting. In production, we're already seeing a reduction in time spent triaging, investigating, and resolving alerts with some of the fastest growing companies in the world.

Specific lets users build and deploy backends without code. They simply connect their preferred coding agent and we build, deploy and scale a backend for any project automatically.

Dedalus Labs is building Vercel for AI Agents. We host MCP servers on our cloud and handle the boring stuff like autoscaling and load balancing so users can go from idea to production in a single click. Our OpenAI-compatible SDK lets users orchestrate complex agentic workflows through one clean API. Route between any LLM provider, connect any MCP tools from our hosted marketplace, and ship in minutes.

Our mission is to build self-improving AI agents. We're here to close the last mile reliability gap. Through calibrated llm judges and automatic evaluations, we help companies optimize their agents 10x faster than doing manual experimentation.

For our first product, we built Rao: an AI coding assistant into RStudio — an IDE used by up to 5 million data scientists and statisticians who use the R programming language to analyze data. Rao could read, write, and edit code while understanding the user's context (codebase and environmental variables).

Fluidize accelerates R&D for scientists and engineers by using AI to build and run simulations and experiments. Our platform speeds up development pipelines by automating setup, execution, validation, and scaling, allowing engineers to focus on their science.

Omnara brings Claude Code and Codex to your phone. Start a coding session on your desktop, then seamlessly hand off to mobile and keep building whether you're commuting, traveling, or away from your desk. Code in local or remote environments with full context preservation across devices. Plus, use voice to code hands-free by having natural conversations with Omnara to build, debug, and ship from anywhere.

Kernel is crazy fast, open source infra for AI agents to access the internet 🧠🕸️ - Built on unikernels for crazy fast cold starts and low resource usage - Sub-150ms cold starts on managed headful browsers - Built-in stealth mode with residential proxies and automatic CAPTCHA solving - Full observability: session recording, live view, and full logs - Persistent sessions that maintain cookies and auth across invocations - Native support for all browser and computer use frameworks - SOC 2 compliant and HIPAA-ready Trusted by Cash App, Rye, and 1000+ others. Backed by Accel & Y Combinator (S25).

The world's best AI video generator that turns your documents and/or prompts into whiteboard explainer videos. In addition, we have a similar product that generates podcast-style audio explainers from your documents/prompts. API access available for both video and conversational audio. Product of Red Tree Inc. Please reach out at founders@golpoai.com

VibeFlow generates full-stack web apps from prompts with n8n-style visual workflows for the backend. Users of tools like Lovable or v0 can easily iterate over frontends because they can see them, while backends remain closed black boxes that are hard to evolve through prompting. VibeFlow opens up backend logic, making it visual and editable, with powerful integrations built in.

Manufact provides open-source dev tools and infrastructure for MCP to help dev teams quickly build and deploy MCP Servers for AI agents & MCP Apps for ChatGPT and Claude. We developed the mcp-use SDK, which has over 5M downloads and 9,000 GitHub stars, and is used by 20% of the US 500. Create MCP servers/apps in minutes, connect your GitHub repo, and make them immediately available to AI agents and ChatGPT.

You built the demo in a weekend. Now you need auth, a prod-grade database, real-time events, AI observability, cron jobs, email, monitoring… and you needed them yesterday. We spent a decade at our previous startup, building a platform while scaling to millions of users and customers like Meta & Netflix. Now we put that into an open-source framework, so others wouldn’t have to do the same from scratch. Modelence is a batteries-included framework that abstracts away boilerplate so you can just focus on building your product logic and nothing else - it’s like Next.js + Vercel + Supabase on a single platform, built for full-stack TypeScript. Shipping production apps is much harder than building a prototype that runs locally and 95% of all production apps share the same building blocks. We did a massive amount of work on our previous platforms that was not specific to our product, and we’ve created Modelence to abstract that away: - Authentication - Database setup - Hosting / CDN - AI integration (LLM observability, prompts) - Real-time events - Cron jobs - Email service - Monitoring - Analytics - Admin dashboards Andrej Karpathy recently posted about this same exact problem: https://x.com/karpathy/status/1905051558783418370 and explained it further in his YC AI Startup School talk: https://youtu.be/LCEmiRjPEtQ?feature=shared&t=1940