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AkashGoenka/coldstart

by Various

Self-maintaining and persistent codebase memory for AI coding agents — a deterministic AST index plus agent-written notes that flag themselves stale when the code changes. Works wi

MCP

AkashGoenka/coldstart

Added 3 Sept 2026

#ai-agents #ai-coding #ai-memory-system #ast #claude-code #cli #code-intelligence #code-navigation

Overview

Coldstart provides persistent codebase memory for AI coding agents. It maintains a deterministic AST index and agent-written notes that automatically flag themselves stale when code changes. It integrates with Claude Code, Cursor, and Codex via MCP and CLI.

Best for

Best for
Developers using AI coding agents who need persistent codebase context.

Use cases

  • Persist agent notes across sessions
  • Automatically flag stale notes when code changes
  • Integrate with Claude Code, Cursor, or Codex via MCP

How to use

Install

npm install -g @cstart/coldstart

Tested with

Claude Desktop, Claude Code, Cursor

Notes

Coldstart provides persistent codebase memory for AI coding agents. It maintains a deterministic AST index and agent-written notes that automatically flag themselves stale when code changes. It integrates with Claude Code, Cursor, and Codex via MCP and CLI.

9 stars on GitHub. Last updated 2026-09-01. Licensed MIT.

Use cases

  • Persist agent notes across sessions
  • Automatically flag stale notes when code changes
  • Integrate with Claude Code, Cursor, or Codex via MCP

Pros

  • Deterministic AST index provides reliable code structure
  • Self-maintaining notes reduce manual upkeep
  • Works with multiple popular AI coding agents

Cons

  • Low adoption (9 stars) suggests limited community validation
  • Requires setup of MCP and CLI for each tool
  • Agent-written notes may still be incomplete or biased

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Deterministic AST index provides reliable code structure
  • Self-maintaining notes reduce manual upkeep
  • Works with multiple popular AI coding agents

Cons

  • Low adoption (9 stars) suggests limited community validation
  • Requires setup of MCP and CLI for each tool
  • Agent-written notes may still be incomplete or biased
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