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Is It Right for Me?

ctx

Good Fit

ctx shines when context matters more than code.

If any of these sound like your project, it's worth trying:

  • Multi-session AI work: You use AI across many sessions on the same codebase, and re-explaining is slowing you down.
  • Architectural decisions that matter: Your project has non-obvious choices (database, auth strategy, API design) that the AI keeps second-guessing.
  • "Why" matters as much as "what": you need the AI to understand rationale, not just current code
  • Team handoffs: Multiple people (or multiple AI tools) work on the same project and need shared context.
  • AI-assisted development across tools: Uou switch between Claude Code, Cursor, Copilot, or other tools and want context to follow the project, not the tool.
  • Long-lived projects: Anything you'll work on for weeks or months, where accumulated knowledge has compounding value.

May Not Be the Right Fit

ctx adds overhead that isn't worth it for every project. Be honest about when to skip it:

  • One-off scripts: If the project is a single file you'll finish today, there's nothing to remember.
  • RAG-only workflows: If retrieval from an external knowledge base already gives the agent everything it needs for each session, adding ctx may be unnecessary. RAG retrieves information; ctx defines the project's working memory: They are complementary.
  • No AI involvement: ctx is designed for human-AI workflows; without an AI consumer, the files are just documentation.
  • Enterprise-managed context platforms: If your organization provides centralized context services, ctx may duplicate that layer.

For a deeper technical comparison with RAG, prompt management tools, and agent frameworks, see ctx and Similar Tools.

Project Size Guide

Solo Developer, Single Repo

This is ctx's sweet spot.

You get the most value here: one person, one project, decisions, and learnings accumulating over time. Setup takes 5 minutes and the .context/ directory directory stays small, and every session gets faster.

Small Team, One or Two Repos

Works well.

Context files commit to git, so the whole team shares the same decisions and conventions. Each person's AI starts with the team's decisions already loaded. Merge conflicts on .context/ files are rare and easy to resolve (they are just Markdown).

Multiple Repos or Larger Teams

ctx operates per repository.

Each repo has its own .context/ directory with its own decisions, tasks, and learnings. This matches the way code, ownership, and history already work in git.

There is no built-in cross-repo context layer.

For organizations that need centralized, organization-wide knowledge, ctx complements a platform solution by providing durable, project-local working memory for AI sessions.

5-Minute Trial

Zero commitment. Try it, and delete .context/ if it's not for you.

Using Claude Code?

Install the ctx plugin from the Marketplace for Claude-native hooks, skills, and automatic context loading:

  1. Type /plugin and press Enter
  2. Select Marketplaces → Add Marketplace
  3. Enter ActiveMemory/ctx
  4. Back in /plugin, select Install and choose ctx

You'll still need the ctx binary for the CLI: See Getting Started for install options.

# 1. Initialize
cd your-project
ctx init

# 2. Add one real decision from your project
ctx add decision "Your actual architectural choice" \
  --context "What prompted this decision" \
  --rationale "Why you chose this approach" \
  --consequences "What changes as a result"

# 3. Check what the AI will see
ctx status

# 4. Start an AI session and ask: "Do you remember?"

If the AI cites your decision back to you, it's working.

Want to remove it later? One command:

rm -rf .context/

No dependencies to uninstall. No configuration to revert. Just files.

Ready to try it out?