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Overview
Kilo Code builds AI-powered social content tools that help teams craft multichannel product stories. Its platform generates tailored content ideas and assets for multiple channels—such as posts, captions, and visuals—so you can quickly create cohesive narratives across platforms. You input product details and messaging goals, and the tool outputs ready-to-use content for various social networks. The product differentiates itself by focusing on end-to-end multichannel storytelling automation rather than single-channel content creation, aiming to streamline the entire storytelling workflow. The goal is to help teams efficiently communicate product stories at scale by generating and organizing content across channels, with a Private Preview available for early access.
About Kilo Code
Industries
Enterprise Software
AI & Machine Learning
Company Size
1-10
Company Stage
N/A
Total Funding
N/A
Headquarters
Londonderry, New Hampshire
Founded
2022
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Simplify's Take
What believers are saying
- July 23, 2026 Ling 3.0 Flash launch gives budget-conscious teams cheaper agentic coding.
- FriendliAI integration delivered up to 7x faster inference and 72% lower costs.
- Kilo for Slack, launched January 2026, expands usage beyond engineering into company-wide workflows.
What critics are saying
- Anaconda integration can slow product velocity and blur Kilo Code's independent identity.
- Free LLM credits attracted account farms; attackers exploited signups until device fingerprinting blocked them.
- Model-routing commoditization compresses margins as users compare frontier, open-weight, and free alternatives.
What makes Kilo Code unique
- Kilo Code supports 500-plus models, reducing lock-in to OpenAI or Anthropic.
- Anaconda acquired Kilo Code on July 15, 2026, validating its open-source model-agnostic stack.
- Kilo Code reaches 3 million developers across VS Code, JetBrains, web, and CLI.
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AI coding agents are blowing through budgets - Replit, Kilo Code, and Symbotic explain how they're managing it. 3 hours ago At Kilo Code, engineers are reading or writing code themselves only about 1% of the time now, according to co-founder Emilie Schario - the rest is agents. That shift is forcing new questions onto dev teams: which systems are safe to hand over, who cleans up when models goof up, how to support multi-model architectures, and whether skyrocketing token bills mean real progress or just burned IT budget. As far as tech leads from Replit, Kilo Code, and Symbotic are concerned, it's a natural - and welcome - evolution as agentic AI becomes embedded into more and more enterprise workflows. "Unless something's really broken or debugging, 99% of the time engineers are not reading or writing code anymore," Emilie Schario, co-founder of Kilo Code, said at VB Transform 2026. AI good at greenfield, not so great at brownfield. For Jared Go, distinguished engineer for AI and cloud at warehouse automation company Symbotic, the current moment is about directing the focus of AI. "These are my criteria," he said. "Let's look at it from the lens of security, elegance, clean, concise code, water tightness." That way, AI does most of the heavy lifting, and human code review isn't as critical. Human involvement becomes necessary further down the line, Go noted, because agents don't make strong product decisions. "Greenfield [building brand new codebases] is so easy for agents. Brownfield [writing, updating, or maintaining existing code] we all know is where the actual challenge lies." Replit takes a bit of a different tack: While the company has "gone very agentic," they've been more conservative with AI coding, explained Amol Jain, head of product engineering. An agent reviews each pull request (PR) and assigns it a risk score; low-risk PRs are self-merged by their author, while others go to human reviewers who read the code and give feedback. "The idea was human on the loop, not human in the loop," Jain said. Replit's internal tool is essentially self-driving for software engineers; devs give a task to agents, which do end to end planning, implementation, and testing. "It's a fleet of agents that run in their own cloud virtual machines (VMs) with access controls behind token proxies so they're secure," Jain said. He shared one example where an engineer couldn't repro or solve a "very gnarly bug" deep in its systems. It was sent to an AI manager agent, which told it to go to sleep. The manager agent then spun up a bunch of underlying agents that found the issue; it subsequently spun up a bunch more agents that found the fix. Six hours later, AI had a PR ready for the bug that had puzzled human engineers. Multi-model is the future. AI providers are also evolving beyond the lock-in model, as customers increasingly demand multi-model choice. Kilo Code, for its part, supports 500-plus models in its gateway. "Your software that you're using to do agentic engineering should be decoupled from the model that you're using to do it," Schario said. For instance, Schario said companies often use expensive frontier-tier models to architect a project, then switch to a less expensive open-weight model for the rest of the work. It's also important to respect model provider limitations, such as when they need to work in closed or isolated environments or providers in their specific regions. "It's factoring in what's important to you, what limitations you've set, what data retention policies you've established, what keys you've brought in, what commits you might have... into that routing decision," Schario said. Replit, similarly, tends to have a better sense of the cost versus capability spectrum than its customers, Jain contended. "We are essentially making the decisions on users' behalf of what model to use when, in what capacity, to minimize cost and maximize capability." To tokenmaxx or not to tokenmaxx. Of course, an important consideration as AI adoption increases is runaway costs, which has led to some enterprises tracking and capping AI use through tokenmaxxing. Concerns come from both sides, Schario said: internally and from customers. From the latter, she's hearing, "I accidentally spent my whole AI budget for the year... so what do I do now?" In response, Schario said Kilo Code points customers to the same workflow: use expensive models for planning, then open-weight models for affordability. Further, sharing skills, strong guidance, and Model Context Protocol (MCP) will empower models. "Realizing where you can really uplevel your team to help them get the most out of the models they're using is going to make a big difference," Schario said. Internally, meanwhile, Schario noted one particular engineer that has a "heavy foot" and is constantly at the top of the usage board. "I regularly have to nudge, 'What are you doing there?'" she said. It's easy to look at a $600 bill for daily work and react, "Wow, that's so much," but looking at the amount of work completed can sometimes justify the cost. "Cost per pull request is the metric that I'm paying attention to right now," Schario said. "It feels like the closest proximity for how I can measure value." Ultimately, AI changes how enterprises are thinking about ROI because spend is not the problem. "The spend with no return on that spend is the problem." Symbotic, for its part, has set per-month cost tiers for its employees. The company built a tool that gives managers visibility into PRs and usage trends. They can then move users up or down a tier as they see fit, Go explained. "Having a cap and seeing how many people went up in cap this month makes a big difference when you're trying to corral these costs and make things efficient," Go said. When Cursor - which Symbotic uses heavily - ended a legacy discount that had grandfathered the company into a flat per-request rate even for frontier models, and moved everyone to full pricing, it forced a company-wide reckoning on efficiency, Go said. "People were saying, 'You should try this model... This works better for this C# code, this whatever,'" he said. But the cost problem is increasingly moving out of IT; Replit, for one, broadened agents beyond engineering, and eventually found that a user on the support side had "blown through an insane amount of money," Jain said. When they looked under the hood, they figured out it was because they were running an automation on GPT 5.5 Pro Max. "At least till that point, the ROI was rather clear," Jain said. "We could see engineering productivity 3X, so no one had questioned it yet." Visibility that isn't "anti-productive," model routing, and sensible defaults are critical, he emphasized. "Most tasks do not need the frontier."
Announcing Ling 3.0 Flash: free on Kilo for a limited time. Welcome to the flash model wars for intelligent efficiency. Jul 23, 2026 Kilo is excited to announce that Ling 3.0 Flash, the latest breakthrough from inclusionAI (Ant Group), is now available on Kilo. To celebrate the rollout, developers can access and build with Ling 3.0 Flash 100% free for a limited time. Whether you are running multi-turn coding agents or building daily agentic workflows on tight token budgets, Ling 3.0 Flash brings high capabilities at lightning speed. Give it a spin today in the Kilo CLI and IDE extensions and help it rise up on the Kilo Leaderboard like previous Ring and Ling models have done! The rise of highly efficient open-weight models. The open-weight landscape has shifted dramatically. The industry is no longer solely chasing sheer parameter bloat or the most advanced reasoning on the planet. Instead, the focus has moved to architectural efficiency. I first highlighted this trend with a focus on Gemini and StepFun flash models back in May (a year ago in AI time...), and this new Ling model continues the trend with a focus on autonomy and efficiency. Early testing has found the model remarkably capable of following a path of thought and taking action. Sparse Mixture-of-Experts (MoE) architectures and hybrid attention stacks have proven that you do not need to fire 70B+ dense parameters on every token to achieve top-tier performance. Hot on the heels of the latest model from Google DeepMind, Gemini 3.6 Flash, Ling 3.0 Flash represents the pinnacle of this new wave: * Massive Capacity, Minimal Footprint: Houses 124B total parameters while activating only ~5.1B parameters per token. * Ultra-Low Latency: Optimized for high-throughput, token-efficient inference, drastically cutting down execution time and compute costs. * Pretty Big Context: Features a native 256K context window (extendable up to 1M), perfect for long-horizon task stability and deep repository reasoning. This rise of hyper-efficient open-weight models means developers can finally run production-grade agentic tasks without sacrificing speed or burning through API budgets. Standing on the shoulders of Ling & Ring. In the world of inclusionAI, Ling models serve as fast, general-purpose base models designed for high-throughput production, while Ring models are specialized, deep-reasoning "thinking" variant built on top of Ling for more complex workflows. But this new model seems to combine both families. Ling 3.0 Flash does not exist in isolation; it fuses the best innovations from inclusionAI's established model families: * The Ling Series (e.g., Ling-2.6-1T, Ling-2.6-Flash): Known for non-thinking flagship throughput, rapid execution, and massive linear attention stacks built for ultra-fast context processing. * The Ring Series (e.g., Ring-2.6-1T): Ant Group's dedicated reasoning line, engineered specifically for deep step-by-step logic and complex problem-solving. Ling 3.0 Flash bridges these two worlds. By combining the raw speed and low active-parameter overhead of the Flash line with a hybrid Reasoning mode inspired by the Ring series, Ling 3.0 Flash dynamically scales its thinking effort depending on task difficulty - delivering logic precision when needed without wasting tokens on simpler prompts. New Ways to Control your AI Coding Spend. Cost-efficiency is not just about using lighter models; it is also about having the right infrastructure controls in place. As its team wrote recently in More Ways to Control AI Coding Spend, Kilo has rolled out several new platform features to help you manage your API budgets effectively. The new Ling model is free for a limited time, but pricing is expected to be extremely affordable - and the model is designed to be used in tandem with heavyweights from frontier labs too. By taking advantage of custom usage limits, prompt caching, and intelligent model routing, you can confidently deploy autonomous agents and coding assistants at scale. Pairing these granular spend-control features with a free-to-use, ultra-efficient model like Ling 3.0 Flash ensures you maximize your development runway without unexpected bills. It's a model can take you further by following its own logic across turns. Try Ling 3.0 Flash today. Take advantage of the limited-time FREE availability and test Ling 3.0 Flash on your toughest agentic workloads today, whether you're coding for fun or driving a major startup. * Start building: Access the model wherever you use Kilo Code * Compare benchmarks: See where it ranks on the live Kilo AI Leaderboard And don't forget to check out the latest open-weight releases on the Kilo Open-Weight Model Tracker. InclusionAI hasn't yet released the weights for this new release, but they are expected to release them at some point as they typically do on Hugging Face. Launched a year ago Kilo is the all-in-one agentic engineering platform for software developers. #1 on Open Source Product of the Month on Product Hunt. 3M+ Kilo Coders. 30T+ tokens processed. Follow Kilo: kilo.ai/social
Anaconda buys Kilo, the open source coding agent that answers to no single model maker. Summary: This is a summary of an article originally published by The New Stack. Read the full original article here Anaconda, a significant player in the data science and machine learning arena, has recently expanded its capabilities by acquiring Kilo, an open-source project focused on managing cloud-native machine learning workloads. This acquisition marks a pivotal step for Anaconda as it integrates Kilo's innovative tools to enhance its existing offerings and better serve the needs of DevOps teams working with data-driven applications. The incorporation of Kilo aims to streamline the deployment and management of machine learning models within cloud environments, addressing one of the major challenges faced by DevOps practitioners. By leveraging Kilo's capabilities, Anaconda empowers teams to automate machine learning workflows, ensuring that models can be easily updated and scaled in accordance with evolving business requirements. Moreover, this acquisition illustrates a growing trend within the tech industry where specialized open-source tools are becoming integral components in the DevOps ecosystem. As organizations strive for efficiency and agility, solutions that facilitate seamless integration of machine learning processes into existing CI/CD pipelines are increasingly essential. The synergy between Anaconda and Kilo promises to drive these advancements, ultimately fostering a more robust and responsive infrastructure for data science endeavors.
From preview to production: Tencent's Hy3 model has arrived. Free in Kilo for a limited time. Jul 06, 2026 Remember Hy3 Preview - the model that completely shook up its workflows during its trial phase? As one of its most popular free preview models for coding, it quickly became the go-to assistant for devs looking to build smarter, faster, and more efficiently. Kilo is thrilled to announce that the much-anticipated General Availability (GA) release of Hy3, totally free for a limited time everywhere you use Kilo. That means you get elite, production-grade AI assistance right in your VS Code sidebar, cloud agents, its CLI, you name it! All completely free of charge with high limits thanks to compute from Novita. Why Hy3 is your new coding superpower. Developed by the team at Tencent, the GA release of Hy3 isn't just a minor update; it's a massive leap forward in reasoning, agentic workflows, and production-grade stability. Moving beyond the preview stage, the team scaled up Reinforcement Learning (RL) training and fine-tuned post-training data quality. In blind tests with real-world expert workflows, Hy3 consistently outperformed similar-sized models and rivaled flagship open-source models with 2x to 5x the parameters. By utilizing Kilo's VS Code extension, you can seamlessly bring these massive upgrades straight into your active workspace. Key real-world improvements in the GA release include: * Production-Grade Tool Calling: Massive upgrades to tool-call success rates and error recovery, drastically minimizing invalid calls or infinite loops while using agentic features in your editor. * Plummeting Hallucinations: Thanks to rigorous data cleaning and specific training constraints, the internal hallucination rate has dropped from 12.5% to just 5.4% - meaning more accurate code syntax and fewer phantom bugs. * Long-Context Intent Tracking: Upgraded multi-turn dialogue capabilities ensure your complex coding intents don't drift or decay over long, multi-step refactoring sessions in your codebase. In my view, Tencent's expertise in video game development across their massive portfolio has helped improve this model's capabilities around coding in general. It's highly efficient (following the trend from both closed and open labs lately) and strong at both front-end and back-end development tasks. Under the hood: the specs. Hy3 continues the trend of recent open-weight releases focused on both efficiency and capabilities for daily agentic work. Here are the specs: * Architecture: Mixture-of-Experts (MoE) * Total Parameters: 295B * Activated Parameters: 21B * Context Length: 256K * Number of Experts: 192 experts, top-8 activated It's great to see the 256K context length for Kilo users with large codebases and complex projects, and with 295B total parameters (while keeping inference lightning-fast by activating 21B parameters per token), Hy3 is perfectly engineered to parse entire files and tackle massive documentation without breaking a sweat - making it the ultimate companion for your VS Code environment. Kilo'll get the stats live in PinchBench and KiloBench soon as well. Watch the Kilo Leaderboard for updates. Start building for free today. It's time to move your daily dev projects out of the preview era and into full, robust production without ever leaving your editor. Just select Tencent Hy3 from the model dropdown wherever you use Kilo. Happy coding! Launched a year ago Kilo is the all-in-one agentic engineering platform for software developers. #1 on Open Source Product of the Month on Product Hunt. 3M+ Kilo Coders. 30T+ tokens processed. Follow Kilo: kilo.ai/social
How Kilo Code and FriendliAI bring open source AI coding agents to production with NVIDIA Nemotron. * Kilo and FriendliAI are partnering to make production-grade AI coding agents faster, more accurate, and significantly more cost-efficient with NVIDIA Nemotron open models. * Configure Nemotron 3 Ultra in Kilo Code using FriendliAI as your inference provider, directly inside your IDE. * Using FriendliAI, Kilo achieved up to 7x faster inference compared to several other providers and cut costs by up to 72% on complex agent workloads by routing to NVIDIA Nemotron 3 Ultra. Introduction. Kilo and FriendliAI are partnering to help engineers build AI agents at scale with NVIDIA Nemotron open models. Production agents thrive when built using specialized models, both closed and open, and its priority is to make developing high-performance agents accessible, performant, and cost-effective. NVIDIA is a leader in open models that enable developers, enterprises and nations to build AI applications and agents that they can trust, control, and customize. NVIDIA open Nemotron family combines strong reasoning performance with efficient deployment, providing open weights, training data, and recipes for building specialized AI agents. The Nemotron family offers reasoning models in 3 sizes: Nano, Super, and Ultra for different deployment needs. Nano provides cost efficiency with high accuracy specialized sub-agents, Super delivers highest efficiency with leading accuracy for reasoning and tool calling for multi-agent applications, and Ultra is designed for applications demanding the highest reasoning accuracy for complex agentic tasks. Nemotron 3 Ultra, newest member of the Nemotron family, is a 550B-parameter Mixture-of-Experts model with 55B active parameters, built for frontier reasoning and orchestration in agentic systems. Kilo is an all-in-one agentic engineering platform for software developers. Their open-source coding harness, Kilo Code, is a flexible AI coding assistant centered on model freedom - giving developers the ability to plug in their existing model providers alongside open-weight models. You can experience top open models on Kilo Code, like Nemotron 3 Ultra, directly in your terminal or VS Code. FriendliAI sits directly within Kilo's orchestrator and maximizes efficiency. Its inference platform is optimized for real-time coding workloads, giving Kilo Code the speed and efficiency it needs to scale. Through continuous batching, also known as iteration batching, and memory optimization, FriendliAI enables Kilo Code to feed massive multi-file codebases into NVIDIA Nemotron models without degrading performance, running out of memory, or spiking costs. Together, Kilo, FriendliAI, and NVIDIA provide an open stack for building production AI coding agents - from intelligent orchestration and optimized inference to frontier reasoning. In this blog, FriendliAI Inc. will walk through configuring Kilo Code with FriendliAI and Nemotron 3 Ultra in your existing workflow. Configure NVIDIA Nemotron 3 Ultra in Kilo Code with FriendliAI. Kilo Code is built around a simple idea: developers should be free to choose the models and infrastructure that best fit their workflows. Rather than locking teams into a single AI provider, Kilo Code enables developers to connect their preferred inference platforms and open models directly into their coding environment. By combining Kilo Code, NVIDIA Nemotron 3 Ultra, and FriendliAI, developers gain access to a frontier reasoning model optimized for complex software engineering tasks while maintaining the performance, scalability, and cost efficiency required for production workloads. Connecting FriendliAI to Kilo Code. Getting started requires only a few configuration steps: * Deploy Nemotron 3 Ultra on FriendliAI Begin by deploying Nemotron 3 Ultra through the Friendli Suite. Once deployed, you'll receive an Endpoint ID and API credentials that can be used by external applications. 2. Configure FriendliAI as a Custom Provider in Kilo Code Within Kilo Code, navigate to the Provider settings and select a Custom Provider configuration. Enter your FriendliAI endpoint URL, Endpoint ID, and API key to connect your deployment. 3. Start Building with Nemotron 3 Ultra After configuration is complete, Nemotron 3 Ultra becomes available directly within Kilo Code's model selector. Developers can immediately begin using the model for code generation, repository analysis, debugging, tool use, long-context reasoning, and multi-step agentic workflows - all without leaving their IDE. With the integration complete, Kilo Code can route requests directly to your FriendliAI deployment, providing access to Nemotron 3 Ultra inside your existing development workflow. Why FriendliAI for Nemotron 3. Large reasoning models are most valuable when they can process substantial context, reason across multiple files, and respond quickly enough to keep developers in flow. Running these workloads efficiently requires more than simply hosting a model. Production inference depends on optimized scheduling, batching, memory management, and efficient GPU utilization. FriendliAI's inference platform is optimized for high-throughput, low-latency agentic workloads. This enables coding agents powered by NVIDIA Nemotron 3 Ultra to work with large codebases and long-context prompts while maintaining responsiveness and controlling infrastructure costs. How Kilo uses FriendliAI to optimize cost and performance. Over the past year, Kilo Code has tested several different inference providers hosting a range of both open and proprietary models. According to Kilo's internal evaluations using GLM-5 usage, they were especially impressed by FriendliAI, which consistently delivered up to 7x faster inference than several other providers while significantly reducing error rates. FriendliAI is now a core component of the Kilo stack, enabling high-performance access to the latest open models. To further reduce friction, Kilo developed an auto-routing feature that automatically selects the optimal model for specific tasks like planning, coding, or data analysis. This routing is organized into four distinct modes: * Auto: Frontier, which offers maximum capability with the best available models when cost is not an issue. * Auto: Balanced, which offers strong performance at a lower cost. * Auto: Efficient, which offers the lowest cost per task, with capability matched to difficulty. * Auto: Free, which features the best available free models. These modes leverage models from labs like OpenAI and Anthropic alongside open models from MiniMax, Z.ai, Alibaba Qwen, and NVIDIA. The centerpiece of this collaboration is NVIDIA Nemotron 3 Ultra, an open model built for long-running agents. Nemotron 3 Ultra, recently led the open-weight category of PinchBench leaderboard, a benchmark that evaluates models' performance on real-world agentic tasks in OpenClaw. By intelligently routing complex reasoning tasks to Nemotron 3 Ultra, Kilo reports cost reductions of up to 72% on complex agentic coding tasks while maintaining frontier-level performance. As agentic engineering continues to evolve to support an even wider range of tasks, Kilo is excited to continue working with both FriendliAI and NVIDIA to optimize cost and performance hand-in-hand. This strategic partnership integrates Kilo Code's sophisticated agent orchestration layer with FriendliAI's optimized inference platform and NVIDIA's remarkably flexible Nemotron open models. There's no longer a reason to overpay for dependable AI. Build the stack that works for you. Production AI coding agents work best when each layer of the stack handles what it's built for. Kilo Code orchestrates across models intelligently, FriendliAI delivers production-grade inference performance that keeps developers productive. NVIDIA Nemotron 3 Ultra open model provides advanced reasoning that you can customize, fine-tune, and deploy for specialized AI agents. Together, they're already powering real results - Kilo users are running 7x faster on FriendliAI and cutting costs by up to 72% on complex agentic tasks by routing to Nemotron 3 Ultra. Open, optimized AI development is no longer a trade-off between performance and price. Together, the three layers give developers everything needed to ship production-grade AI coding agents without compromising on performance or cost, while leaving more room for model freedom. Explore NVIDIA Nemotron 3 Ultra, bring frontier open models directly into your IDE with Kilo Code, and get started with FriendliAI to deploy and serve your favorite models with optimized inference.
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About Kilo Code
Industries
Enterprise Software
AI & Machine Learning
Company Size
1-10
Company Stage
N/A
Total Funding
N/A
Headquarters
Londonderry, New Hampshire
Founded
2022
There are no jobs for Kilo Code right now.
Find jobs on Simplify and start your career today