Works with:
Give your AI agents high-signal context in milliseconds.
Bitloops continuously models your codebase and development history so agents can retrieve architecture, decisions, and intent instantly — instead of crawling your repositories.
curl -sSL https://bitloops.com/install.sh | bashGit captures what changed.
Nothing captures the why.
What Git Captures
Commit
Diffs
Records what changed — not why.
What AI Coding Loses
Architectural decisions — “Why this design pattern”
Design constraints — “Performance, security, scalability”
Trade-offs made — “Speed vs. maintainability”
Previous discussions — “Rejected ideas and feedback”
The result
- Code drifts from architectural intent
- Developers repeat context in every prompt
- Wasted tokens rebuilding past decisions
- Extra tooling for review and documentation
Today, AI coding tools operate in isolation, forcing agents to rebuild context repeatedly. This wastes resources, creates inconsistencies, and loses the critical “why” behind every decision.
Works with your agents
AI discussion & reasoning
Semantic analysis
AST analysis
Constraint validation
Codebase
Git
Bitloops doesn't replace Git
Every agent. Every session. Always in context.
Bitloops builds a semantic model of your codebase and captures every AI interaction — so context accumulates instead of disappearing.
Built for teams building real software with AI
Capture development reasoning, connect it to Git history, and guide AI with structured context and constraints.
Capture AI conversations
Teams working across multiple AI coding tools
Bitloops captures prompts, reasoning, and discussions across agents like Claude Code, Cursor, Codex, and Gemini so development sessions don't start from zero.
Link reasoning to commits
Teams that need traceability for AI-generated code
Every AI session is linked to the Git commits it produced, turning development reasoning into part of your repository history.
Inject structured context
Codebases with architectural context and standards
Bitloops injects structured repository context into every session so agents understand your architecture, patterns, and constraints.
Enforce constraints
Teams enforcing engineering rules on AI-generated code
Architectural constraints can be applied automatically so generated code respects your domain boundaries and design rules.
Designed for how AI development actually works
The architecture behind Bitloops: local infrastructure, development attribution, structured context, and enforceable engineering rules.
Your code never leaves your environment
Core properties
- Runs locally as a CLI
- Works fully offline
- Data stored directly in your repository
Outcome
Infrastructure you control — not a cloud proxy.
One install. Zero disruption.
Bitloops runs locally while you keep using your existing AI tools. Install once. Your workflow stays the same.
Install
curl -sSL https://bitloops.com/install.sh | bashConnect
bitloops initAuto-detects supported AI assistants and connects them.
Work
Keep using your existing agents. While you work, Bitloops:
- Captures AI conversations
- Links reasoning to commits
- Injects structured repository context
- Records workflow metadata
Apache 2.0. No lock-in.
Open infrastructure you can inspect, run, and extend on your own terms.
View the source on GitHubInspectable
Read the code and understand exactly how Bitloops works.
Runnable
Run Bitloops locally in your own environment.
Extensible
Add integrations, policies, and workflows around your team's needs.
Vendor-neutral
Adopt Bitloops without locking your development workflow to a single provider.
Quick comparison
A practical view of where Bitloops fits compared to standalone assistants and cloud platforms.
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
Get Started
GitHub
Discord