Works with:
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 | bashGit captures what changed. Nothing captures the why.
Your tools work in isolation, rebuilding context from scratch each time. That, wastes tokens, introduces divergence, and leaves the reasoning behind every change, lost.
Multi-agent fragmentation
- Each session starts from zero
- Agents build siloed models
- No shared ground truth
Inefficiency and cost
- Repeated context reconstruction wastes tokens
- Architectural constraints get re-explained
- Decisions aren't preserved across tools
One shared memory supports every agent you already use.
Bitloops connects Cursor, Claude Code, Gemini CLI, and others to the same repository-scoped knowledge store — so sessions stop resetting and tools stop diverging.
Bitloops
Shared memory layer
Codebase
Git (diffs)
Bitloops doesn't replace Git.
All agents read from the same store.
Every commit writes back a record.
No data leaves the machine.
Works with any tool-call-capable agent.
Install once. Keep your setup.
- Same tools, shared memory. No tool changes. Bitloops plugs in on what you already use.
- Sessions stop starting from zero. Every agent picks up where the last session ended.
- Tools stop diverging. Claude Code and Cursor draw from the same shared history.
- Context loads deliberately. Structured, ranked, and pruned — not a dump of everything.
- Every commit closes the loop. The full session is written back as a record on every commit.
- Lower token waste, less rework. The right context, not the maximum context.
Bitloops Is the Infrastructure Layer for AI Coding
Bitloops runs locally as a Rust CLI and gives teams a reliable workflow foundation.
Tracks AI assistant conversations
Links them to git commits
Injects targeted, structured context
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.
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.
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.
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.
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.
Install
Choose your preferred install method.
curl
curl -sSL https://bitloops.com/install.sh | bashbrew
brew install bitloops/tap/bitloopscargo
cargo install bitloopsEnable and connect agents
bitloops enable auto-detects supported assistants.
Work as usual
Keep using your AI tools and existing developer workflow.
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.
- Fully open
- Extensible
- Pluggable
- Vendor-neutral
- Platform teams
- DevEx teams
- OSS-oriented engineers
Quick Comparison
A practical view of where Bitloops fits compared to standalone assistants and cloud platforms.
| Feature | AI Agents | Cloud AI Platforms | Bitloops |
|---|---|---|---|
| Local-first | ❌ | ❌ | ✅ |
| Git-linked sessions | ❌ | ❌ | ✅ |
| Cross-agent tracking | ❌ | ❌ | ✅ |
| Deterministic constraint enforcement | ❌ | ❌ | ✅ |
| Vendor lock-in | High | High | None |
Who Bitloops Is For
Best fit for teams already relying on AI coding tools in real projects.
- 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.
Get Started with Bitloops.
curl -sSL https://bitloops.com/install.sh | bash