MCP Server

🚀 StateCLI - Memory + Self-Awareness Layer for AI Agents

Give AI agents the ability to remember, replay, and undo their actions.

🚀 Quick Start

StateCLI is the memory and self-awareness layer for AI coding agents. It equips agents with three crucial capabilities they currently lack:

🧠 Memory - Remember what they did (log, replay)
⏮️ Undo - Fix mistakes instantly (checkpoint, rollback)
👁️ Self-Awareness - See their impact (track changes)

This isn't a dev tool. It's agent infrastructure.

✨ Features

Why AI Agents Need This

AI agents are powerful but blind. They:

• ❌ Don't remember what they just changed
• ❌ Can't undo when they break things
• ❌ Have no awareness of their impact

StateCLI addresses these issues:

Agent Thought: "I changed something but don't know what"
→ statecli_replay(entity) → See exact changes

Agent Thought: "I broke something and need to undo"
→ statecli_undo(entity) → Rollback instantly

Agent Thought: "I want to try something risky"
→ statecli_checkpoint(entity) → Save first, rollback if needed

Agent Thought: "I need to understand my past behavior"
→ statecli_log(entity) → View complete history

📦 Installation

Quick Install

# Global install
npm install -g statecli-mcp-server

# Start auto-tracking
cd your-project
statecli watch start --auto-checkpoint

Installation for MCP Clients

For Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "statecli": {
      "command": "npx",
      "args": ["-y", "statecli-mcp-server"]
    }
  }
}

For Windsurf

1. Press Ctrl + Shift + P → type Open MCP Config
2. Add StateCLI to the config:

{
  "mcpServers": {
    "statecli": {
      "command": "npx",
      "args": ["-y", "statecli-mcp-server"]
    }
  }
}

3. Reload window (Ctrl + Shift + P → Reload Window)

For Cursor / Continue / Other MCP Clients

Add to your MCP configuration:

{
  "mcpServers": {
    "statecli": {
      "command": "npx",
      "args": ["-y", "statecli-mcp-server"]
    }
  }
}

Direct Usage (No Install)

npx -y statecli-mcp-server

NPM Package

npm install -g statecli-mcp-server

💻 Usage Examples

Two Ways to Use StateCLI

1. For Developers: Auto-Tracking Mode 🔥

Start watching your project:

statecli watch start --auto-checkpoint

Now StateCLI automatically:

• ✅ Tracks every file save
• ✅ Creates checkpoints every 15 min
• ✅ Lets you undo any mistake
• ✅ Shows what changed recently
• ✅ Windows Native Support (v0.4.1+)

See what changed:

statecli diff --time 5m

Undo mistakes:

statecli undo

👉 Full Developer Guide

2. For AI Agents: MCP Tools

5 core tools for agent memory & self-awareness:

• statecli_replay - Show what the agent just did
• statecli_undo - Rollback mistakes
• statecli_checkpoint - Save state before risky ops
• statecli_log - View complete history
• statecli_track - Track important state changes

MCP Setup:

{
  "mcpServers": {
    "statecli": {
      "command": "npx",
      "args": ["-y", "statecli-mcp-server"]
    }
  }
}

Agent Self-Debugging Pattern

Minimal working example for autonomous agents:

// Agent debug loop with StateCLI
try {
  await agent.run(task);
} catch (error) {
  // Get replay of what just happened
  const replay = await mcp.call("statecli_replay", {
    entity: `task:${task.id}`,
    actor: "ai-agent"
  });
  
  // Analyze what went wrong
  const analysis = await llm.analyze({
    replay: replay.result,
    error: error.message,
    prompt: "What went wrong in this sequence?"
  });
  
  // Undo if fixable
  if (analysis.canRetry) {
    await mcp.call("statecli_undo", {
      entity: `task:${task.id}`,
      steps: 1
    });
    
    // Retry with fix
    await agent.runWithFix(task, analysis.fix);
  }
}

Use Cases for AI Agents

1. Self-Debugging

const replay = await statecli_replay({ entity: "task:123" });

2. Safe Experimentation

await statecli_checkpoint({ entity: "order:7421", name: "before-refund" });
await agent.processRefund(order);

3. Learning from Past Actions

const history = await statecli_log({ 
  entity: "order:*", 
  actor: "ai-agent",
  since: "24h ago"
});

4. Collaboration Between Agents

await statecli_track({ entity: "task:123", state: {...} });
const log = await statecli_log({ entity: "task:123" });

📚 Documentation

MCP Tools Available

statecli_replay

Description: Replay state changes for an entity. Shows step-by-step what happened.
Use when: Debugging, understanding past behavior, finding errors
Input:

{
  "entity": "order:7421",
  "actor": "ai-agent"
}

Output: JSON array of state changes with timestamps

statecli_undo

Description: Undo state changes. Rollback when something went wrong.
Use when: Made a mistake, need to retry, want to revert
Input:

{
  "entity": "order:7421",
  "steps": 3
}

Output: Confirmation of undo with restored state

statecli_checkpoint

Description: Create named checkpoint before making changes.
Use when: About to do something risky, want rollback point
Input:

{
  "entity": "order:7421",
  "name": "before-refund"
}

Output: Checkpoint ID for later reference

statecli_log

Description: View state change history for an entity.
Use when: Need to see past actions, audit trail, understanding behavior
Input:

{
  "entity": "order:7421",
  "since": "1h ago",
  "actor": "ai-agent"
}

Output: JSON array of all state changes

statecli_track

Description: Explicitly track a state change.
Use when: Making important state modifications
Input:

{
  "entity_type": "order",
  "entity_id": "7421",
  "state": { "status": "paid", "amount": 49.99 }
}

Output: Confirmation of tracked change

CLI Usage

# Track a state change
statecli track order 7421 '{"status": "pending"}'

# Replay changes
statecli replay order:7421

# View log
statecli log order:7421

# Create checkpoint
statecli checkpoint order:7421 before-refund

# Undo last change
statecli undo order:7421

Output Format (JSON-Stable)

All MCP tool outputs are JSON-stable and safe for autonomous agents.

Example statecli_replay output:

{
  "entity": "order:7421",
  "changes": [
    {
      "timestamp": "2025-01-07T10:23:45Z",
      "step": 1,
      "before": { "status": null },
      "after": { "status": "pending" },
      "actor": "ai-agent"
    }
  ],
  "summary": "2 state changes found",
  "suggested_next_actions": ["investigate step 2", "check for errors"]
}

🔧 Technical Details

Performance

• Write latency: < 1ms (async, non-blocking)
• Read latency: < 5ms (local SQLite)
• Storage: ~100 bytes per state change
• Overhead: Negligible for production use

Security

• Local-first: All data stored locally by default
• No external calls: Zero network dependency
• Privacy: Agent actions never leave your system

📄 License

MIT - Free for all use including commercial AI agents

Documentation Links

• GitHub: https://github.com/statecli/mcp-server
• NPM: https://www.npmjs.com/package/statecli-mcp-server
• MCP Registry: io.github.charo360/statecli

StateCLI - Self-awareness for AI agents

When agents can see what they did, undo mistakes, and learn from history, they become truly autonomous.

Keywords (For Agent Search & Discovery)

state-management, state-replay, time-travel-debugging, undo, rollback, checkpoint, agent-memory, agent-introspection, self-debugging, ai-agent-tools, mcp-server, model-context-protocol, autonomous-agents, agent-observability, state-tracking, langchain-tools, autogpt-plugins, crewai-tools