Bloop – Answer questions about your code with an LLM agent
Hi HN! We launched bloop 10 weeks ago (https://news.ycombinator.com/item?id=35236275) and received a huge amount of feedback (both positive + constructive). We've undertaken a rewrite of the core search framework, which now acts as an LLM agent, significantly improving the number of queries that can be successfully answered. There's a bunch of hype surrounding LLM agents, but we're positive this is one of the first implementations of an agent that can deliver immediate value for engineers working on existing projects, especially larger ones. We'll do a full write up of how the…
In plain words
Bloop is a developer assistant that uses an LLM agent to answer questions about codebases by searching local and remote repositories with natural language, regex, and filters. It is designed for engineers working on existing projects, particularly larger ones, who need to quickly understand and navigate code. The tool's core search framework operates as an agent that interprets complex queries and retrieves relevant code sections.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! We launched bloop 10 weeks ago (https://news.ycombinator.com/item?id=35236275) and received a huge amount of feedback (both positive + constructive). We've undertaken a rewrite of the core search framework, which now acts as an LLM agent, significantly improving the number of queries that can be successfully answered. There's a bunch of hype surrounding LLM agents, but we're positive this is one of the first implementations of an agent that can deliver immediate value for engineers working on existing projects, especially larger ones. We'll do a full write up of how the agent works and the tools it can use soon, but we wanted to share our progress, now that we've got a stable release. bloop is a developer assistant that uses GPT-4 to answer questions about your codebase. The agent searches both your local and remote repositories with natural language, regex and filtered queries. Some of the ways engineers use bloop to improve their efficiency when working on large codebases: - Summarise how large files work and how multiple files work together - Understand how to use open source libraries when documentation is lacking - Identify the origin of errors - Ask questions about English-language codebases in other languages - Reduce code duplication by checking for existing functionality - Write new code, taking into account existing codebase context (eg: "write a dockerfile for this project") bloop runs as a free desktop app on Mac, Windows and Linux: https://github.com/bloopAI/bloop/releases/latest. On desktop, your code is indexed with a MiniLM embedding model and stored locally, meaning at index time your codebase stays private. 'Private' here means that no code is shared with us or OpenAI at index time, and when a search is made only relevant code snippets are shared to generate the response. (This is more or less the same data usage as Copilot). We also have a paid cloud offering for teams ($45 per user per month). Members of the same organisation can search a shared index hosted by us and will get access to enterprise only features down the line (currently there's no feature gap between desktop and cloud).
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