Show HN: Give your AI agent a brain that understands your codebase
Bitloops is an alpha-stage tool that creates a local, typed, and queryable model of a codebase to improve AI agent efficiency and collaboration. It reduces redundant repository crawling by enabling agents to query structured data through DevQL and maintain shared context across sessions. The system supports multiple AI coding agents and provides features like checkpoint history, reviewable workflows, and local data storage by default.
- ▪Bitloops builds a local, queryable model of codebases to improve AI agent understanding and reduce redundant file crawling.
- ▪The tool supports AI agents like Claude Code, Codex, Cursor, Gemini, Copilot, and OpenCode by providing a shared, maintained codebase model.
- ▪Bitloops captures AI-assisted work in sessions and checkpoints linked to commits, prompts, and file changes for reviewability.
- ▪Users can query codebase state using DevQL and GraphQL through a local dashboard or integrations.
- ▪Data is stored locally by default, with optional remote stores and telemetry controlled by configuration and user consent.
Opening excerpt (first ~120 words) tap to expand
Stop giving your AI agents the same repo tour. Bitloops builds and maintains a local, typed, queryable model of your codebase so AI agents, developers, and reviewers can work from shared system state instead of rediscovering the repository from raw text. Website · Docs · Quickstart · DevQL · Discussions What Bitloops Gives You AI coding agents are powerful, but most of them still start every task by crawling the repository again: read files, grep for symbols, infer architecture, guess which tests matter, inspect old docs, and compress all of that into a prompt. Bitloops gives them a maintained operating picture instead. You need Bitloops gives you Better agent context A local, queryable model of files, artefacts, symbols, dependencies, tests, checkpoints, and history.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.