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Works with:

Claude CodeCursorGeminiOpenCodeGitHub Copilot (Coming Soon)Codex (Coming Soon)

Git captures what changed.Bitloops captures why.

The open-source context layer for AI-native development. Captures the full developer–AI conversation on every commit and builds a structured semantic model of your codebase that you and your agents can query.

curl -sSL https://bitloops.com/install.sh | bash
Open Source
Local-first
Agent-agnostic

AI Coding Is Powerful. But It's Chaotic.

Modern teams are adopting AI coding assistants fast, but the workflow controls are still missing.

Modern teams are using

Claude Code
Gemini
Cursor
OpenCode
Agent frameworks

But today

  • No unified conversation history
  • No audit trail between prompts and commits
  • No structured context management
  • No enforcement of architectural constraints
  • Sensitive code flows through opaque systems

You do not control the workflow.

You do not control the guarantees.

You do not control the long-term impact.

Bitloops Is the Infrastructure Layer for AI Coding

Bitloops runs locally as a Rust CLI and gives teams a reliable workflow foundation.

01

Tracks AI assistant conversations

02

Links them to git commits

03

Injects targeted, structured context

04

Enforces constraints on generated code (Coming Soon)

All without sending your code to our servers.

Core Pillars

The platform architecture is built around privacy, attribution, context intelligence, and enforceable engineering constraints.

Pillar 1

Your Code Never Leaves Your Environment

Local-first foundation

  • Runs locally
  • Works offline
  • Stores data in your repository
  • Soon: configurable self-hosted DB
  • No code or commit history sent to Bitloops
  • Optional high-level telemetry (opt-in only)

Enterprise-ready posture

  • No compliance headaches
  • No vendor data exposure
  • No training on your code

Bitloops is infrastructure, not a cloud proxy.

Pillar 2

Every AI Conversation Connected to Real Commits

Tracks conversations across

  • Claude Code
  • Gemini
  • Cursor
  • OpenCode
  • Agent frameworks

Associates sessions with

  • Branch
  • Commit
  • Diff
  • Author

Dashboard visibility

  • AI session timeline
  • Prompt -> response -> commit mapping
  • Per-agent contribution breakdown
  • AI contribution vs human edits
  • Repository-level activity insights

Outcome

  • Auditable
  • Measurable
  • Reviewable

Version control for AI interactions.

Pillar 3

Stop Manually Managing Context

Developers today

  • Attach files manually
  • Maintain context docs
  • Re-explain architecture to every agent
  • Copy-paste between tools

Bitloops does this instead

  • Extracts relevant project context
  • Injects only targeted information
  • Maintains shared organizational knowledge
  • Reduces prompt overhead

Context as infrastructure, not tribal knowledge.

Pillar 4
Coming Soon

AI Output That Respects Your Architecture

Future capabilities

  • Enforce architectural rules
  • Prevent anti-patterns
  • Enforce domain boundaries
  • Validate design invariants
  • Ensure codebase-wide consistency

Hybrid enforcement approach

  • Deterministic rules
  • Static analysis
  • LLM-based reasoning
  • Policy enforcement

This is not AI linting. It is organizational constraint enforcement for AI-generated code.

Local Dashboard. Zero Cloud Dependency.

`bitloops dashboard` launches a local web server for direct observability.

bitloops dashboard

  • AI session history
  • Agent comparison
  • Commit mapping
  • Context usage
  • Contribution metrics
  • Constraint violations (Coming Soon)

Full observability. No SaaS required.

How It Works

What Bitloops does locally while you keep using your existing AI tools.

Step 1

Install

Choose your preferred install method.

curl

curl -sSL https://bitloops.com/install.sh | bash

brew

brew install bitloops/tap/bitloops

cargo

cargo install bitloops
Step 2

Enable and connect agents

bitloops enable auto-detects supported assistants.

Step 3

Work as usual

Keep using your AI tools and existing developer workflow.

Step 4

Bitloops tracks and structures workflow metadata

Git hooks, local metadata, structured storage, and an agent-agnostic abstraction layer run in the background.

  • Git hooks
  • Local metadata
  • Structured storage
  • Agent-agnostic abstraction layer

Apache 2.0. No Lock-In.

Open codebase you can inspect, extend, and run yourself.

Open foundation

  • Fully open
  • Extensible
  • Pluggable
  • Vendor-neutral

Built for

  • Platform teams
  • DevEx teams
  • OSS-oriented engineers

Quick Comparison

A practical view of where Bitloops fits compared to standalone assistants and cloud platforms.

FeatureAI AgentsCloud AI PlatformsBitloops
Local-first
Git-linked sessions
Cross-agent tracking
Deterministic constraint enforcement
Vendor lock-inHighHighNone

Who Bitloops Is For

Best fit for teams already relying on AI coding tools in real projects.

For

  • Engineering teams using AI heavily
  • CTOs concerned about governance
  • Platform engineering teams
  • Security-conscious organizations
  • Teams scaling AI usage

Roadmap

Upcoming work based on user feedback and the current engineering roadmap.

Constraint enforcement engine
Self-hosted DB option
Policy-as-code
Team-level dashboards
CI/CD integration
Enterprise features

Bring Structure to AI Coding.

curl -sSL https://bitloops.com/install.sh | bash
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Git for AI Coding.
The default infrastructure for AI-native software development.

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