Codebase Context gives AI agents understanding of your codebase through semantic code search, team conventions, patterns, and memory, so they use fewer tokens, spend less time, and produce better, more familiar output.

Turn your team's AI coding sessions, GitHub code, and docs into one shared, searchable memory — a self-hosted git truth store you query right from your editor over MCP.

Project memory for coding agents: team-shareable engineering experience assets with Milvus semantic retrieval.

Watercooler: your team's shared reasoning layer for agentic coding. Preserve the "why" around your code.

Give every AI agent persistent memory of your team's knowledge. No vector DB, no RAG — just Git + BM25 + 114 tokens per session.

Open-source team memory layer for AI coding agents — markdown files in git, user→team→org hierarchy, cross-vendor MCP server. Apache-2.0.

The memory layer for your team. Mneme auto-extracts decisions, questions, and action items from AI chats — and pings you when they go stale

跨 AI 助手项目上下文管理 Skill — seamless project handoffs between Claude Code, Cursor, Codex, CherryStudio & other AI agents. Structured Markdown context with 3-layer progressive disclosure, issue tracking & decision governance. Zero dependencies, fully offline, privacy-first.

AI-native operating system for persistent team memory, roadmaps, brand, and product ops