Deterministic repository context packing for AI coding agents: selects, compresses, and budgets only the files a task needs. Measured 83% fewer input tokens at the same task coverage, fully local, no LLM in the loop.
Deterministic, local-first repository context for coding agents. Maps an issue, prompt, or git diff to ranked files to read first, likely test commands, and review-risk notes—no API key required.
Local-first deterministic project memory for AI coding agents, with context packs, decisions, gates, risks, scoped claims and explicit checkpoints in project-owned files.
A local-private context-handoff tool for AI agents, preserving decisions, evidence, and verbatim code in a SQLite ledger and providing zone indicators to prevent context rot across sessions.
Provides persistent memory and semantic file discovery for AI coding agents, enabling them to remember changes and find relevant files across sessions.
Persistent, self-curating memory for coding agents. It enables local, zero-cost context recall through MCP tools with hybrid retrieval and autonomous consolidation.
