Cursor indexes your codebase. It still doesn't know why it's shaped that way.
Your .cursorrules file is doing its best, but it only knows what you remembered to write. Bitloops automates context engineering for Cursor — capturing every architectural decision so Cursor's context is always accurate, not aspirational.
curl -sSL https://bitloops.com/install.sh | bashWhat is Cursor?
Cursor is an AI-first code editor built on VS Code by the team at Anysphere. It combines the familiar editing experience of VS Code with deeply integrated AI features — inline edits, a multi-file agent mode, and codebase-aware chat. Cursor indexes your entire project into a semantic vector store, enabling AI-assisted development with more context than a typical IDE extension can provide. Its agent mode can autonomously plan, implement, and test multi-step tasks, while its tab-completion and inline diff features make day-to-day AI-assisted coding feel natural. Developers use .cursorrules files and @mentions to provide project context, but this manual context management is what Bitloops automates.
AI-native editor
Built from the ground up with AI at its core — not bolted on. Every feature, from tab-completion to multi-file edits, is designed around AI-assisted development workflows.
Codebase-aware chat
Chat with AI that has semantically indexed your entire project using vector search — ask questions, get explanations, and plan changes with full codebase context via @codebase.
Cursor Agent mode
Cursor's agent autonomously implements multi-step tasks — planning the approach, writing code, running terminal commands, and iterating on errors across multiple files.
Inline edits and completions
Intelligent tab-completions and inline diffs let you accept, reject, or refine AI suggestions in real time — keeping you in flow without context switching.
You're already doing this. Just manually.
Cursor power users know the drill: you maintain a .cursorrules file to get consistent suggestions, and often an AGENTS.md for context that works across your team's other AI tools. But both require ongoing upkeep, grow unwieldy over time, and still can't capture the reasoning behind why your codebase is structured the way it is. This manual context engineering breaks down as projects and teams scale.
What you're maintaining today
The problems with this approach
.cursorrulesCursor's workspace rules file — manually written coding conventions, architectural patterns, and constraints that guide Cursor's AI suggestions
Your rules outgrow your file
.cursorrules gets long, inconsistent, and eventually ignored. The more your project evolves, the faster it drifts from reality.
AGENTS.mdA shared context file readable by Cursor, Claude Code, and other AI coding tools — useful when your team uses multiple agents
Rules without reasoning
Cursor knows what patterns to follow but not why they were chosen — so suggestions break down the moment a task requires architectural judgement.
@file, @folder, @codebaseManually adding context via Cursor's @ mention system every chat — referencing individual files, folders, or triggering full codebase semantic search each session
Context locked to one tool
Your .cursorrules doesn't help a colleague who uses Claude Code or Copilot. Context is siloed per tool and per developer, fragmenting your team's knowledge.
What you're maintaining today
.cursorrulesCursor's workspace rules file — manually written coding conventions, architectural patterns, and constraints that guide Cursor's AI suggestions
AGENTS.mdA shared context file readable by Cursor, Claude Code, and other AI coding tools — useful when your team uses multiple agents
@file, @folder, @codebaseManually adding context via Cursor's @ mention system every chat — referencing individual files, folders, or triggering full codebase semantic search each session
The problems with this approach
Your rules outgrow your file
.cursorrules gets long, inconsistent, and eventually ignored. The more your project evolves, the faster it drifts from reality.
Rules without reasoning
Cursor knows what patterns to follow but not why they were chosen — so suggestions break down the moment a task requires architectural judgement.
Context locked to one tool
Your .cursorrules doesn't help a colleague who uses Claude Code or Copilot. Context is siloed per tool and per developer, fragmenting your team's knowledge.
Why Cursor users need Bitloops
Cursor indexes your codebase for semantic search, but indexing shows what code exists — not why it's structured that way. Bitloops adds the reasoning layer: architectural decisions, design constraints, and decision history that transform AI suggestions from code-aware to architecture-aware.
Replaces your .cursorrules and AGENTS.md
Stop manually maintaining rules files that grow stale and unwieldy. Bitloops builds and updates your project's context layer automatically from real conversations and commits.
Session continuity across your team
Cursor sessions are ephemeral — Bitloops ensures context and decisions persist across every session, every developer, and every branch of your project.
Constraint-aware code generation
Define your project's architectural rules and design patterns once. Bitloops enforces them across every AI-generated suggestion, preventing architectural drift before it starts.
Full audit trail for AI-generated code
Trace every AI-generated code change back to the Cursor conversation and reasoning that produced it — essential for code review, onboarding, and compliance.
Set up in 60 seconds
Install the Bitloops CLI
One command to install Bitloops on macOS, Linux, or Windows. Works with Homebrew, curl, and Cargo.
curl -sSL https://bitloops.com/install.sh | bashInitialize your repository
Run bitloops init in your project to set up the context engineering layer. Bitloops detects your project structure and AI tools automatically.
bitloops initUse Cursor as usual
Bitloops runs locally in the background — capturing reasoning, linking decisions to git commits, and building your project's semantic context graph. Your Cursor workflow stays unchanged.
Everything you get with Bitloops + Cursor
Automatic decision capture
Every Cursor conversation — chat, agent mode, inline edits — is recorded and linked to the resulting code changes, building a complete AI development history.
Context injection beyond indexing
Bitloops feeds architectural context, past decisions, and project constraints into every Cursor session — going deeper than Cursor's built-in codebase indexing.
Semantic codebase model
Builds a structured graph of your codebase — module boundaries, dependency relationships, architectural layers — that complements Cursor's vector-based indexing.
Commit-level AI attribution
Every git commit knows which Cursor conversation produced it. Reviewers see the full reasoning chain, making code review faster and more informed.
Architectural constraint enforcement
Define your project's architectural rules and design patterns once. Bitloops enforces them across all AI-generated code — whether from Cursor, Claude Code, or any other tool.
Privacy-first and open source
Bitloops is fully open source and runs locally. Your code, conversations, and architectural context never leave your machine — no third-party data sharing.
Also works with
Bitloops integrates with all major AI coding agents.
Get Started with Bitloops.
Apply what you learn in these hubs to real AI-assisted delivery workflows with shared context, traceable reasoning, and architecture-aware engineering practices.
curl -sSL https://bitloops.com/install.sh | bash
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