ai-devkit vs Graft
ai-devkit: AI DevKit is a local-first operating layer for teams running several coding agents. One .ai-devkit.json reconciles skills, MCP and setup across Claude Code, Codex CLI, Gemini CLI, opencode, Pi, Cursor, Copilot and others; agent console is a live TUI for running sessions; agent send routes prompts, logs or stdin to a running agent; and a local SQLite memory is searched over MCP when needed. A dev-lifecycle skill guides requirements, design, planning, implementation, testing and review.; Graft: Graft is an open-source context layer designed to accelerate and optimize coding agents like Claude Code, Cursor, and Gemini by providing deep contextual understanding of large codebases. It builds a human-readable code graph and linked markdown nodes, enabling agents to operate faster, cheaper, and with higher correctness by avoiding repeated code exploration.
TL;DR
Keeping one setup across Claude Code, Codex and Cursor in a team repo
- You want to generate a readable, interconnected Markdown knowledge graph of your repository so coding agents (Claude Code, Cursor) can grep and explore without expensive re-indexing.
- You want to reduce context token waste by giving agents precise map-like documentation of modules and call paths.
- You need an enterprise graph database with semantic vector embeddings and Cypher queries; code-graph-rag with Neo4j fits that scale.
- You only have a small, single-file script or a trivial repository.
Side-by-Side Comparison
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