Features
Captures AI prompts, reasoning, and discussions
Links AI sessions to Git commits for traceability
Injects structured repository context (architecture, patterns, constraints)
Enforces architectural constraints on AI-generated code
Local-first: runs offline, data stored in repository
Works with Claude Code, Cursor, Codex, Gemini, Copilot, OpenCode
Semantic model built continuously via AST analysis
Commit-aware context retrieval to reduce token consumption
Faster onboarding for new team members
Open-source CLI tool (Apache 2.0)
Repository-scoped context
Supports constraint enforcement (coming soon)
Agent-agnostic: works with multiple agents simultaneously
Auto-detects and connects AI assistants via 'bitloops init'
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
