{"slug": "introducing-jetbrains-context-repository-intelligence-for-coding-agents", "title": "Introducing JetBrains Context: Repository Intelligence for Coding Agents", "summary": "JetBrains launched JetBrains Context, a repository intelligence layer for coding agents, now available in early access at no additional cost with JetBrains AI subscriptions. The tool integrates with Claude Code, Codex CLI, and Junie CLI, and works from JetBrains IDEs, Air, VS Code, and other supported editors to help agents efficiently access relevant repository knowledge instead of repeatedly searching and reading files.", "body_md": "## JetBrains AI\n\nSupercharge your tools with AI-powered features inside many JetBrains products\n\n[Agentic AI](/ai/category/agentic-ai/)\n\n[AI](/ai/category/ai/)\n\n[News](/ai/category/news/)\n\n[Releases](/ai/category/releases/)\n\n# Introducing JetBrains Context: Repository Intelligence for Coding Agents\n\nToday, we’re launching JetBrains Context, a new repository intelligence layer that helps coding agents work more efficiently and produce higher-quality results on complex codebases. As part of the [JetBrains AI for Teams and Organizations rollout](https://blog.jetbrains.com/blog/2026/07/07/jetbrains-ai-for-teams-and-organizations-from-fragmented-ai-usage-to-coordinated-software-development/), JetBrains Context is now available in early access at no additional cost with your JetBrains AI subscription. It integrates with Claude Code, Codex CLI, and Junie CLI, and can be used from JetBrains IDEs, Air, VS Code, and other supported editors.\n\nJetBrains has always focused on making developers more efficient, even when working with the most complex codebases. Historically, that meant providing intelligent features like autocomplete, code analysis, and navigation. Today, we’re extending that same productivity to AI agents by giving them the repository intelligence they need to write, validate, and review code effectively.\n\nIn enterprise-scale codebases, context is essential. It’s the difference between mediocre results that require painstaking review and rework and efficient agentic coding that understands your codebase, APIs, dependencies, implementation patterns, and engineering conventions.\n\nJust like developers, agents don’t need much hand-holding on small-scale proof-of-concept projects, but they need extra context: the unwritten institutional knowledge and insight that allows them to work effectively with large codebases.\n\n## The new reality for agents\n\nOur investment in context echoes a growing need among developers to upskill their agents. Whereas developers used to be satisfied with just about any AI-driven output, now the “honeymoon phase” is ending and things are getting more complicated. The expectations placed on developers are increasing, and the scope of AI usage is growing as developers strive to meet the new requirements. The demand for AI ROI translates to shortened timelines for teams and emerging AI quotas. This means we can no longer spend limitless tokens to brute-force problems with top model crunching. The demands of reality are quickly catching up to the agents’ newfound superpowers.\n\nWhat was once a mere inconvenience is slowly turning into a bottleneck. Most problem-solving and feature development tasks require agents to perform extensive code exploration to understand the current state or get a reference for the planned changes. In practice this means running searches, spinning up exploration agents, and reading files – activities that often eat up time and tokens. Even with today’s most capable models, limited context and repository visibility can prevent agents from finding the right code, locating good implementation examples, or identifying existing patterns worth reusing. The result is simple: The less time agents spend exploring repositories, the more time they can spend on the task at hand. That’s exactly the problem JetBrains Context was designed to address.\n\n## What JetBrains Context is\n\nJetBrains Context is a repository intelligence layer for coding agents such as Claude Agent, OpenAI Codex, and JetBrains Junie. It incrementally builds a semantic index of your repositories and provides semantic retrieval, helping agents access relevant repository knowledge instead of repeatedly searching and reading files. Instead of relying solely on keyword searches and repeated file exploration, it can ask any question directly or look up related terms or concepts. To make that work, we rely on two main components: a backend that incrementally indexes the repo and semantic search tools that allow agents to query that data.\n\nOne important capability we’re excited to be rolling out is **multi-repo search**. Instead of being limited to the current repository, agents can discover relevant code across your organization’s codebase, including repositories that aren’t checked out locally. This helps them validate APIs and dependencies, understand the impact of changes, or locate reusable code in remote projects. The net effect is maintaining a higher bar of code quality, avoiding unnecessary work, and increasing architecture conformity across the codebase.\n\n## The proof is in the pudding (or, should we say, the testing)\n\nWe validated JetBrains Context on 205 open-source SWE-bench tasks, 175 production-monorepo tasks, and 1,953 code-localization tasks. Across these benchmarks, JetBrains Context reduced agent turns by up to **68%**, latency by up to **59%**, and execution cost by up to **48%**.\n\nMeasuring AI systems consistently isn’t easy. Modern coding agents are inherently probabilistic, so we designed our evaluation around established industry benchmarks and production-scale repositories. Through evaluations, we managed to improve this technology to the point where we can confidently say it makes a huge difference in time, cost, and code quality for large codebases.\n\n## JetBrains Context early access is already included with your subscription!\n\nFollow the instructions on our [landing page](https://www.jetbrains.com/agentic-software-development/context/?utm_source=blog&utm_medium=social&utm_campaign=JetBrains-Context) to get started:\n\n- Install the CLI with a simple\n`GET`\n\nrequest. - Authenticate using the CLI\n`jbcontext login`\n\ncommand. Your credentials will work as-is. A JetBrains AI license is needed, but no quota will be consumed by JetBrains Context. - Navigate to your project folder and set up JetBrains Context for your preferred coding agent using the\n`jbcontext setup-agent`\n\ncommand. You might also do it globally in the user scope. - JetBrains Context will pre-index your code automatically by agent hooks, or you can do it explicitly by calling the\n`jbcontext index`\n\ncommand. Your source code is not stored on JetBrains Context servers.\n\nFrom now on, as you go about your normal coding, you’ll see the agent is equipped with new repository intelligence capabilities that make it much more productive.\n\nAfter trying JetBrains Context out, if you’re unsure about whether it’s helping you or not, you can check out our new built-in analytics! Just hit `jbcontext analyze`\n\nto see cost and time savings based on your real time data.\n\nWe’d love to hear what you think. Share your feedback, ask questions, or tell us about your experience in the comments below. You can also use the `jbcontext send-feedback `\n\ncommand directly from the CLI.\n\n#### Subscribe to JetBrains AI Blog updates", "url": "https://wpnews.pro/news/introducing-jetbrains-context-repository-intelligence-for-coding-agents", "canonical_source": "https://blog.jetbrains.com/ai/2026/07/introducing-jetbrains-context-repository-intelligence-for-coding-agents/", "published_at": "2026-07-21 13:40:22+00:00", "updated_at": "2026-07-21 15:42:11.838477+00:00", "lang": "en", "topics": ["developer-tools", "ai-tools", "ai-agents", "artificial-intelligence"], "entities": ["JetBrains", "JetBrains Context", "Claude Code", "Codex CLI", "Junie CLI", "JetBrains IDEs", "VS Code"], "alternates": {"html": "https://wpnews.pro/news/introducing-jetbrains-context-repository-intelligence-for-coding-agents", "markdown": "https://wpnews.pro/news/introducing-jetbrains-context-repository-intelligence-for-coding-agents.md", "text": "https://wpnews.pro/news/introducing-jetbrains-context-repository-intelligence-for-coding-agents.txt", "jsonld": "https://wpnews.pro/news/introducing-jetbrains-context-repository-intelligence-for-coding-agents.jsonld"}}