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Published on 05.02.2026
TLDR: Kilo's new Review mode brings AI-powered code analysis directly into your IDE before you commit or push, offering security and performance checks alongside their existing GitHub-integrated Code Reviewer for a two-stage review workflow.
Summary:
Code reviews traditionally happen after you've already committed and pushed your work—a moment when making significant changes feels costly and cumbersome. Kilo is disrupting this workflow by introducing Review mode, a new specialized agent mode that works directly in your editor on code that exists only on your machine. This represents a fundamental shift in how developers can think about quality assurance: catching issues while you're still in the flow of development, not after you've already shipped the code upstream.
The timing matters. An hour into implementing a feature, you can run Review mode to get feedback while pivoting in a different direction is still easy and natural. Instead of your teammates focusing on null handling and forgotten error cases during code review, they can concentrate on architectural decisions and design patterns. Review mode essentially becomes your pre-flight checklist—catching obvious issues before they become GitHub comments and team discussions.
What makes this particularly powerful for engineering teams is the breadth of what Review mode analyzes. Beyond style violations, it catches security vulnerabilities, performance issues, logic errors, missing error handling, test coverage gaps, and maintainability concerns. The feedback comes structured with explanations rather than as raw warnings, and because you're still in your IDE, acting on suggestions is immediate. For architects and teams building systems with strict quality standards, this means every single piece of code gets looked at twice before merging—once locally and once in GitHub—without adding manual review steps. The workflow becomes continuous and frictionless rather than a gate you wait at.
The flexibility in model selection ensures you can optimize for your use case. Running a quick refactor? Use a faster, cheaper model. Working on security-sensitive code? Bring in heavier reasoning models. For a limited time, MiniMax M2.1 and GLM 4.7 offer free reviews, making this accessible to anyone curious about trying it.
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Tradeoffs:
Link: Introducing Local Code Reviews: One Click from Your IDE
TLDR: Developers with ChatGPT Plus or Pro subscriptions can now authenticate into Kilo's IDE extensions using OAuth and access OpenAI's top coding models without additional API charges or separate billing.
Summary:
One of the friction points in the AI coding ecosystem has always been subscription fragmentation. You might have ChatGPT Plus for personal exploration, Cursor for editing, and a separate Claude subscription for other tools—each with its own billing, its own credit system, and its own authentication dance. Kilo is addressing this by letting you leverage an existing ChatGPT subscription directly within the IDE.
The setup is remarkably clean. Instead of managing API keys and dealing with pay-as-you-go billing, you click "Sign in to OpenAI Codex," authenticate in your browser using OAuth, and you're done. Your ChatGPT subscription becomes the access mechanism. Usage counts against your existing ChatGPT subscription limits rather than incurring separate API charges. This changes the economics for developers already paying for ChatGPT—suddenly, agentic workflows in Kilo become "free" in the sense that they're included in a subscription they already have.
The model lineup available through this integration reflects OpenAI's current coding-focused releases: GPT-5.2-Codex for agentic workflows and multi-file refactors, GPT-5.2 for general-purpose reasoning, GPT-5-Mini for fast iterations, and reasoning models like o3 for complex planning and debugging scenarios. For teams and architects, this means you can orchestrate different models across different modes—using o3 in Architect mode for system planning, then GPT-5.2-Codex for actual code generation, then o3 again for complex debugging. You're optimizing the model to the task rather than being locked into a single offering.
The limitation is explicit: this covers only the Codex catalog models and the cloud features like Cloud Agents and Kilo Deploy require separate handling. But for the core development workflow—code generation, refactoring, debugging, file editing, and terminal execution—you're covered by your existing subscription. For architects building systems where cost optimization matters, understanding that you can blend different LLM providers across different tools becomes increasingly important.
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Link: Use your ChatGPT subscription inside Kilo
TLDR: Kilo is launching a year-long competitive arena called Kilo League featuring weekly challenges, quarterly majors, and a $50,000 grand prize to help developers master agentic engineering from architecture to deployment.
Summary:
The Kilo League represents a philosophical shift in how we evaluate AI engineering skills. Traditional hackathons measure how fast you can type. The Kilo League measures how well you can orchestrate agents. Instead of asking "can you solve this problem?" it asks "can you direct AI agents to solve this problem while you focus on the decisions that matter?"
The structure spans the entire year with at least two challenges per month. These aren't abstract coding problems—they're engineering scenarios. You might be asked to architect a database schema using only natural language, or deploy a serverless application using a multi-agent swarm. You're practicing the full AI engineering lifecycle: LLM-Driven Architecture, Agentic Coding and Debugging, Deployment and Optimization, and Multi-Agent Workflows.
Progress is tracked through your Kilo Account, building what they call an "Agentic Profile." The weekly Speedruns feed into quarterly Majors, which determine who qualifies for the end-of-year Championship bracket where the grand prize awaits. Early winners get immediate rewards—the first challenge offers $500 in Kilo Credits—and top earners get exclusive access to frontier models and inference providers.
For engineers and architects, this represents something genuinely new. It's not about writing the most elegant syntax or solving the classic algorithm challenge. It's about understanding agent capabilities, knowing when to parallelize work across multiple agents versus coordinating a single agent, managing context windows strategically, and building systems that leverage AI's strengths rather than fighting its limitations. The competitive pressure accelerates learning in a way that traditional tutorials cannot.
The technical enabler behind some of this is Cloud Agents with webhook support. You can configure webhooks that trigger agents programmatically via HTTP requests, turning Kilo into an event-driven automation platform. This opens possibilities like CI/CD pipelines that spin up agents on pull request events, Discord bots that kick off coding tasks, or cron jobs that run refactoring scripts at 3 AM. The League challenges encourage you to get creative with this infrastructure.
Key takeaways:
Link: Announcing the Kilo League
TLDR: Kilo shipped Review mode for local code analysis, added ChatGPT Plus/Pro subscription support, streamlined onboarding with zero-config defaults, and integrated upstream improvements including native tool calling and webhook triggers for Cloud Agents.
Summary:
The past week represents a cascade of shipping activity across Kilo's entire platform. The headline is Review mode hitting #1 Product of the Day on Product Hunt, but the shipping didn't stop there. This roundup captures the breadth of what's being built: refinements to agent behavior configuration, improvements to how code changes are visualized, and infrastructure changes that hint at where the platform is heading.
On the core IDE extension side, the Agent Manager now lets you select which mode—Code, Architect, Debug, Ask, Orchestrator—you want when starting sessions, and you can switch modes mid-session without losing context. That flexibility matters because different tasks have different cognitive patterns. You might start in Ask mode to understand a codebase, switch to Architect mode to plan refactoring, then move to Code mode for implementation. The mode selector appears in the session header so it's always accessible.
Task headers now display GitHub-style diff stats showing lines added and removed in real time. This small visual affordance provides immediate visibility into the scope of changes you're making, which matters when you're assessing whether you're overcomplicating something or missing edge cases. For teams reviewing pull requests or architects auditing the breadth of changes, this becomes genuinely useful information at a glance.
The configuration architecture got centralized. Mode, MCP, Rules, and Workflows configuration all moved into the Agent Behavior area, and a new Skills tab lets you view and manage installed skills. You get notifications when skills are added or removed from your project or global configuration. For architects managing large teams where consistency and visibility into what's installed matters, this centralization means you're not hunting through five different menus to understand your setup.
On the onboarding front, new users now start immediately with a default Kilo Code Gateway profile using a free model. No configuration required. You install and you're coding. This removes friction that was likely blocking exploration from developers who wanted to try Kilo but weren't ready to commit to setting up API keys.
The upstream integration from Roo Code includes native tool calling improvements across providers and other stability enhancements. Cloud Agents now support webhook triggers, which turns Kilo into an event-driven automation platform. Configure a webhook with an Agent Environment Profile and prompt template, and external systems can kick off Cloud Agent sessions via HTTP requests. Use placeholders like {{body}}, {{bodyJson}}, and {{headers}} to dynamically reference incoming payloads. This infrastructure is what powers the Kilo League challenges and enables creative CI/CD and automation scenarios.
App Builder added two starter templates: a Resume/CV website and a Startup Landing Page. Picking a template and personalizing from there removes the "blank canvas" cognitive load that stops some people from starting.
Key takeaways:
Link: Kilo Code Weekly Product Roundup | Feb 2, 2025
TLDR: When Kilo made Kimi K2.5 free for a week, developers used it 3x more than forecasted, especially for Architect mode, revealing that open-source models genuinely converge with enterprise offerings in capability—but the cost structure remains complicated.
Summary:
The experiment was simple: make Kimi K2.5 completely free in Kilo Code for seven days and observe what developers build. The results revealed something profound about where AI development is heading and where it's stuck.
The usage numbers told the first story. Within hours, Kilo Coders were running 50+ billion tokens per day on OpenRouter, a 3x surge over forecasted adoption. This wasn't curiosity-driven experimentation where people click once and move on. Developers integrated K2.5 into real workflows, stress-testing it through complex coding challenges and architectural scenarios. The message was unmistakable: when developers get access to genuinely powerful new models, especially free ones, they'll run with them.
Even more telling was Kimi K2.5's rapid ascent in Architect mode. Within days it became one of the top-performing models for architectural planning and system design. Typically climbing to the top takes weeks regardless of marketing. Developers praised its reasoning through large codebases, suggesting refactoring strategies, and maintaining context across complex projects. The performance was real enough to overcome inertia.
This points to a broader convergence: open-source models are genuinely closing the capability gap with enterprise-grade offerings. Moonshot AI is pushing boundaries in visual understanding and reasoning, and the pace of improvement is accelerating. This shift has profound implications. Teams no longer need enterprise contracts to access production-grade reasoning. The barrier between "free and open" and "premium and proprietary" continues narrowing.
But here's where the narrative gets complicated. Kimi K2.5 features automatic context caching promising to reduce input costs by 75%—from $0.60 per million tokens to $0.10 for cached tokens. In theory, this makes K2.5 incredibly cost-effective for applications that reuse context extensively. In practice, the model's behavior undermines those savings.
According to Artificial Analysis benchmarks, Kimi K2 Thinking consumed 140 million tokens to complete their evaluation—2.5x more than DeepSeek-V3.2 and double GPT-5-Codex. The verbosity isn't a bug in caching; it's the model's fundamental reasoning style. K2.5 generates extensive reasoning tokens and in agent mode can execute up to 1,500 tool calls per task. When output tokens cost $3.00 per million versus $0.50-$0.60 for input (or $0.10 cached), the arithmetic turns brutal. One Hacker News commenter noted K2.5 is "10x the price per output token" on some providers. It's still cheaper than Claude Opus for API usage, but if you're on a ChatGPT or Claude subscription, the equation becomes less compelling.
This reveals a pattern across reasoning models: better thinking means more tokens, no matter how "efficient" marketing claims. The question becomes whether quality justifies cost for your specific use case. For context, MiniMax M2.1 showed better cost-benefit ratios for many workflows despite being less capable in some areas—which is why Kilo made it the default for CLI 1.0 and kept it free.
The future might involve spending $100K per developer annually on AI tools. Understanding these cost dynamics becomes increasingly critical.
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Link: What We Learned from a Week of Free Kimi K2.5
TLDR: Kilo CLI 1.0 launches as a feature-complete command-line interface providing access to 500+ AI models, built on the open-source OpenCode foundation with zero lock-in and full bring-your-own-keys support.
Summary:
The command line has returned as a primary interface for AI-driven development. Kilo CLI 1.0 represents a philosophical commitment to this vision: a single tool that replaces the juggling of multiple AI assistants, each with different APIs, different pricing models, and different capabilities.
From one terminal, you access 500+ models. You can generate entire projects from descriptions, refactor existing codebases, automate repetitive tasks, or debug complex issues—all without context-switching to different tools. The setup removes friction. Three steps: install globally, sign in or add your API keys, then run kilo in any directory. You're coding with AI assistance immediately.
What differentiates Kilo CLI from thin wrappers around existing tools is the depth of integration with the broader Kilo platform while maintaining the speed and extensibility that made the original OpenCode foundation compelling. Kilo is contributing improvements and bug fixes upstream, signaling a genuine commitment to open source rather than strip-mining a project for a commercial layer.
The architecture supports session sync across devices. Start a task on your mobile device, continue in VS Code, finish in a JetBrains IDE—your session history, active agents, and variables follow you. For architects and engineering leaders, this means work is never stranded in a single environment. Context persists.
The openness is real. Kilo is MIT-licensed and open source. You can inspect exactly what's happening, customize behavior for your team, and integrate it into proprietary systems without vendor lock-in. There's no hidden context compression, no automatic model switching, no silently truncated input. You see context window sizes for each request and full prompts, giving you complete visibility and control.
For teams building systems where deployment to production matters, Kilo CLI serves as the bridge. Move from commit to live in one motion with AI code reviews and deployments without leaving the terminal. The workflow becomes write, review, deploy—all driven by agents, all managed from one place.
Key takeaways:
Link: Kilo - Kilo CLI | The Complete CLI for Agentic Engineering
TLDR: Kilo is an all-in-one agentic engineering platform offering unified development across IDE extensions, CLI, and web interfaces with transparent pricing on 500+ models and advanced features like Orchestrator Mode, Memory Bank, and multi-device session sync.
Summary:
Kilo frames itself not as another code editor or AI assistant, but as a platform for agentic engineering—a fundamentally different way of thinking about software development where AI agents handle execution while engineers focus on direction and decisions.
The multi-interface consistency matters. You start a task on your mobile device, migrate to VS Code, finish in JetBrains, then deploy via CLI—without losing context. Your session history, active agents, and variables follow you across every interface. For distributed teams and engineers who move between machines, this persistence becomes essential infrastructure rather than convenience feature.
The Orchestrator Mode represents next-level thinking about productivity. Instead of writing code sequentially, you can parallelize: use Orchestrator Mode to plan, Code Review to build, and Architect mode to audit simultaneously. The mental model shifts from "write, wait for review, iterate" to "direct agents to handle multiple concerns in parallel." For architects and teams, this multiplication of effective output matters when you're trying to accelerate delivery without scaling headcount.
Memory Bank addresses a persistent pain point: context fragmentation. Stop repeating architectural decisions to new team members. Capture decisions into Memory Bank and let onboarding accelerate automatically. This becomes increasingly valuable as teams grow and consistency becomes harder to maintain through synchronous discussion.
Tab Autocomplete with intelligent suggestions aims to let you ship code at thought speed. Eliminate boilerplate so you're not manufacturing syntactic noise between ideas. For engineering leaders, this translates to developers spending more time thinking about design and less time typing familiar patterns.
The pricing model is refreshingly transparent. More than 60 providers. More than 500 models. You pay the exact list price from Anthropic, OpenAI, and Google—no commission, no hidden fees. Kilo makes money on Teams and Enterprise plans where value compounds through standardization and management. You can use stealth models for free, fallback when you hit quotas, run models locally via Ollama or LM Studio, or bring your own API keys for any provider. There's explicit acknowledgment that no single model fits all scenarios, and the platform helps you navigate that complexity.
For enterprises, the AI Management Dashboard tracks adoption rates, measures new hire speed, and lets you prove return on investment. You see exactly where credits are going, which models are being used, and whether agentic engineering is actually accelerating your delivery.
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