CLAUDE.md is a static file you maintain — the agent reads it but never writes back. ctlsurf is a shared notebook the agent updates as it works. Next session, it picks up where it left off.
The Workflow: In our interactive demo, you'll see how ctlsurf transforms AI coding workflows:
Example: An agent implementing "user authentication" documents: "Added JWT-based auth" (summary), "Used existing User model" (assumption), and "Skipped refresh token implementation" (simplified). No more mystery about what your AI actually did.
You write a CLAUDE.md with your architecture decisions, coding standards, context. The agent reads it. Good. But then it finishes a task and... nothing comes back. What did it decide? What did it skip? What should the next session know?
CLAUDE.md is read-only. The agent consumes it but never writes back. So every session starts from scratch. You re-explain. You re-discover. You lose momentum.
You need a notebook the agent reads AND writes to.
| CLAUDE.md | ctlsurf | |
|---|---|---|
| Direction | You → Agent | You ↔ Agent |
| Who writes | You, manually | Both, via MCP |
| Survives sessions | Your instructions do | Everything does |
| Agent accountability | None | What it built, decided, skipped |
| Task handoff | Copy-paste into chat | Agent checks for tasks on start |
The agent reads it. The agent writes to it. You can see everything.
Architecture decisions, coding standards, past attempts — all in the notebook. The agent reads it at session start. No more re-explaining.
Highlight it, annotate it, turn it into an instruction. The agent picks it up immediately. You're steering, not starting over.
ctlsurf connects via MCP — the open protocol for AI tools. Any MCP-compatible agent can read and write to your notebook.
What was built, what was tried, what was skipped and why. The notebook is human-readable. No git-diffing. No guessing.
When an agent completes a task, it documents what it built, what it assumed, and what it quietly dropped.
No more guessing what the AI did. Every completed task includes:
The "simplified or skipped" field is the most important - it catches when agents give up on parts of tasks without telling you.
When you spot something the agent simplified or skipped that shouldn't have been, you can reopen the task with feedback:
This creates an accountability loop - agents can't silently cut corners because you'll see exactly what they skipped and can push back.
Three steps to persistent AI context
Add ctlsurf to your MCP config. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible tool.
On session start, the agent reads your architecture decisions, past work, and pending tasks. No re-explaining.
As it works, the agent documents what it built, what it decided, and what it skipped. Next session picks up where this one left off.
MCP is an open standard created by Anthropic that allows AI assistants to connect to external tools and data sources. ctlsurf is built as an MCP server, meaning any MCP-compatible AI coding assistant can connect to it seamlessly.
Setup is simple: Add a few lines to your MCP configuration file, and your AI agent gains access to 50+ ctlsurf tools for managing pages, tasks, skills, and documentation.
No code changes required. Your existing AI coding workflow stays the same - ctlsurf just gives your agent a persistent memory and knowledge base to work with.
Works with your existing tools
From solo developers to engineering teams
Maintain shared context across sprints, agents, and tools. Everyone stays aligned on decisions and progress.
Understand why features shipped a certain way, with a traceable history of decisions and trade-offs.
Coordinate long-running tasks with evolving state instead of isolated prompts. Context persists across sessions.
Define workflows with guardrails that guide AI agents through complex tasks consistently.
Skills are structured workflow templates that guide AI agents through complex, multi-step tasks. Think of them as playbooks or runbooks that ensure consistency and quality across your team's AI-assisted work.
Each skill contains:
Example Use Cases: API debugging workflows, code review checklists, deployment procedures, security audit processes, feature implementation patterns.
Systematic approach to debugging
Browse and fork skills from the community marketplace. Find battle-tested workflows for common development tasks and customize them for your team's needs.
Start free, upgrade when you need more
For individual developers
For power users and teams
Introductory price
Not another CLAUDE.md — that's read-only. Not a memory layer like claude-mem — that's invisible. Not an observability dashboard like claude-devtools — that's passive. ctlsurf is a notebook both you and the agent write to.
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