Cursor 2.0: Multi-Agent Coding Setup

Link to section: Background: Why Cursor 2.0 Matters NowBackground: Why Cursor 2.0 Matters Now

Cursor 2.0 shipped in October 2025 as a significant departure from its earlier point releases. The headline feature is parallel multi-agent execution, which lets you run up to eight AI agents simultaneously on the same coding task. Each agent operates in an isolated sandbox using git worktrees or remote machines, meaning they can't collide or corrupt your main branch.

This addresses a real friction point I've hit repeatedly over the past year with single-agent tools: you prompt once, you get one path forward. If the agent makes a questionable architectural choice early on, you either accept it or start over. With Cursor 2.0, you can spin up three agents in parallel with different architectural strategies, then inspect their results side by side and cherry-pick the best implementation.

The release also introduced Composer, Cursor's first proprietary model built for agentic workflows. Cursor claims it's roughly 4x faster than similarly capable models at completing coding tasks, with most turns finishing under 30 seconds. For codebases, that latency difference compounds quickly when you're running multi-turn workflows.

Link to section: Key Changes in Cursor 2.0Key Changes in Cursor 2.0

• Multi-agent parallel execution up to eight agents per prompt
• Composer model, a proprietary agent optimized for low-latency code generation
• Aggregated diff viewer to see all agent changes in one place
• Embedded browser with DOM tools for debugging UI workflows
• Git worktree and remote machine support for isolation
• Model selection per phase: use a reasoning model for planning, switch to Composer for fast execution

Link to section: Installation and Environment SetupInstallation and Environment Setup

Start by upgrading to Cursor 2.0. If you're on 1.x, the auto-update should trigger within a few hours. To force it, open Cursor, click Help (top menu), then Check for Updates.

cursor --version

If you're on Linux or prefer manual installation, grab the latest release:

brew upgrade cursor
 
wget https://download.cursor.sh/linux/x64/latest
sudo dpkg -i cursor-2.0-linux-x64.deb
 
which cursor
cursor --version

After upgrading, Cursor will prompt you to sign in or create an account if you haven't already. The free tier includes limited multi-agent runs. Pro tier ($20/month) includes unlimited parallel agents and higher daily limits on Composer usage.

Link to section: Configuring Your First Multi-Agent TaskConfiguring Your First Multi-Agent Task

Open Cursor and create a new project or navigate to an existing repository. I'll use a simple example: building a user authentication flow from scratch.

In the main editor, look for the "Agents" tab on the left sidebar. Click it. You should see an empty agent queue with a plus icon. Click the plus to create a new agent group.

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A dialog appears asking for your task description. Type something specific and actionable:

Build a secure user authentication system with:
- JWT token generation and validation
- Password hashing with bcrypt
- Rate-limited login endpoint (5 attempts per 15 minutes)
- Logout and token refresh flow
- Unit tests for all auth functions
- Error messages without info leakage

This is not a casual prompt. Be explicit about requirements, constraints, and edge cases. The more structured your description, the better each agent's output.

Now click "Add Agents." Cursor shows you a dropdown to select how many agents to spin up. For this task, I'll select three:

Agent 1: Architecture-focused (plan the structure first)
Agent 2: Performance-focused (optimize for speed and memory)
Agent 3: Security-focused (prioritize edge cases and hardening)

Each agent runs in its own git worktree. Behind the scenes, Cursor executes:

git worktree add /tmp/cursor-agent-1 HEAD
git worktree add /tmp/cursor-agent-2 HEAD
git worktree add /tmp/cursor-agent-3 HEAD

Each worktree gets a complete copy of your repo at the current commit. No conflicts between agents.

Link to section: Choosing Your Model per AgentChoosing Your Model per Agent

Before running the agents, you can assign a model to each one. Click on Agent 1 and select the model dropdown. You'll see options:

• Composer (default, fastest)
• GPT-5 with reasoning (slower but more thorough planning)
• Claude Sonnet 4.5 (strong all-arounder)

For this auth task, I set:

• Agent 1: GPT-5 with reasoning (spend extra time on architecture)
• Agent 2: Composer (generate code quickly)
• Agent 3: Composer with safety rules (add security constraints inline)

Click "Run All Agents" to start. Cursor opens a live progress panel showing each agent's execution. You'll see:

[Agent 1] Analyzing requirements... (Step 1/8)
[Agent 2] Generating auth module... (Step 1/12)
[Agent 3] Implementing token validation... (Step 1/10)

Agent latency varies. With Composer, expect 20 to 40 seconds per turn. GPT-5 reasoning mode takes 90 to 180 seconds depending on complexity. In my test run, all three finished within 4 minutes for the auth task.

Link to section: Reviewing Aggregated DiffsReviewing Aggregated Diffs

Once agents complete, Cursor's killer feature kicks in: the aggregated diff viewer. Instead of reviewing three separate branches, you see one unified interface showing:

• Common changes (all agents agree)
• Conflicts (agents disagreed on implementation)
• Divergences (unique paths each agent took)

Click the "Diffs" tab. You'll see a split view:

Left side lists all changed files across all agents. Right side shows the diff with color coding:

• Green: all agents agree
• Yellow: two or more agents agree, one differs
• Red: all agents took different approaches

For the auth example, all agents agreed on basic JWT flow. But they diverged on rate-limiting strategy:

Agent 1 proposed Redis-backed rate limiting (scalable, needs infra). Agent 2 proposed in-memory queue with LRU eviction (simpler, stateless). Agent 3 proposed a hybrid: memory cache with optional Redis fallback.

I preferred Agent 3's hybrid. Click the radio button next to that agent's version and Cursor stages it for merge.

Link to section: Merging and Cleaning Up WorktreesMerging and Cleaning Up Worktrees

After selecting which agent's implementation to use for each file, click "Merge Selected." Cursor runs:

git merge agent-3-auth-flow --no-edit
git merge agent-1-architecture-docs --no-edit
git merge agent-2-performance-notes --no-edit
git worktree remove /tmp/cursor-agent-1
git worktree remove /tmp/cursor-agent-2
git worktree remove /tmp/cursor-agent-3

Your main branch now contains the merged code. All worktrees are cleaned up. One key thing: Cursor creates a single commit for the merged result, not three separate commits. You get a clean history.

Link to section: Comparing Cursor 2.0 Against AlternativesComparing Cursor 2.0 Against Alternatives

How does this stack up against GitHub Copilot and Windsurf, the other major agentic editors?

For large refactors or multi-file architectural changes, Cursor 2.0's parallel execution cuts iteration time roughly in half versus single-agent tools. The tradeoff: you pay for compute. Running three agents in parallel is three times the API cost, though Cursor bundles monthly usage with the Pro tier.

Link to section: Real-World Workflow: Building a Feature End-to-EndReal-World Workflow: Building a Feature End-to-End

Let me walk through a concrete scenario I hit last week: adding a real-time notification system to an existing Next.js app.

Step 1: Open Cursor and navigate to my project root.

cd ~/projects/my-saas-app
cursor .

Step 2: Open the Agents panel and describe the task:

Add real-time notifications using Server-Sent Events (SSE):
- New notifications endpoint at /api/notifications/stream
- Notify all subscribed clients when events occur
- Store notification history in PostgreSQL (attachments, read status)
- Frontend WebSocket client with automatic reconnect and exponential backoff
- Mark notifications as read with optimistic UI updates
- Unread notification badge on navbar, persists across sessions
- Test coverage for SSE connection, disconnect, and reconnect scenarios
Constraints: Must work with next-auth, no third-party notification service

Step 3: Spin up two agents this time (one for backend, one for frontend).

Backend Agent: Backend architecture, database schema, API route Frontend Agent: Client-side hooks, UI components, reconnection logic

Step 4: While agents run, I skim the initial generated code in Cursor's preview panel. After 3 minutes, both agents finish. I see:

Backend agent created:

// /app/api/notifications/stream/route.ts
export async function GET(request: NextRequest) {
  const session = await getServerSession(authOptions);
  if (!session) return new Response('Unauthorized', { status: 401 });
 
  const encoder = new TextEncoder();
  const responseStream = new ReadableStream({
    async start(controller) {
      // SSE setup
      controller.enqueue(encoder.encode('data: connected\n\n'));
      const eventSource = db.notifications.watch({ userId: session.user.id });
      eventSource.on('change', (doc) => {
        controller.enqueue(encoder.encode(`data: ${JSON.stringify(doc)}\n\n`));
      });
    },
  });
  return new Response(responseStream, {
    headers: {
      'Content-Type': 'text/event-stream',
      'Cache-Control': 'no-cache',
    },
  });
}

Frontend agent created a custom hook:

// lib/useNotifications.ts
export function useNotifications() {
  const [notifications, setNotifications] = useState([]);
  const [isConnected, setIsConnected] = useState(false);
  const retryCount = useRef(0);
  const maxRetries = 5;
 
  useEffect(() => {
    const connect = () => {
      const eventSource = new EventSource('/api/notifications/stream');
      
      eventSource.onmessage = (event) => {
        if (event.data === 'connected') {
          setIsConnected(true);
          retryCount.current = 0;
        } else {
          setNotifications(prev => [...prev, JSON.parse(event.data)]);
        }
      };
 
      eventSource.onerror = () => {
        eventSource.close();
        setIsConnected(false);
        
        if (retryCount.current < maxRetries) {
          const delay = Math.pow(2, retryCount.current) * 1000;
          retryCount.current += 1;
          setTimeout(connect, delay);
        }
      };
    };
 
    connect();
  }, []);
 
  return { notifications, isConnected };
}

Step 5: Review the aggregated diff. Both agents imported different SSE libraries. Backend used native Node ReadableStream. Frontend used EventSource (web standard). Both are solid choices. No merge conflicts.

Step 6: Merge and commit.

git add -A
git commit -m "feat: add real-time notifications with SSE"

Step 7: Test locally. Start the dev server:

npm run dev

I open DevTools, navigate to the Notifications stream endpoint, and see SSE frames flowing in real-time as I trigger test notifications. The frontend reconnect logic kicked in when I paused the server for 10 seconds, then resumed cleanly.

Total time from prompt to working feature: 8 minutes. With a single agent, I'd estimate 25 to 35 minutes of back-and-forth refinement.

Link to section: Configuration for Monorepos and WorkspacesConfiguration for Monorepos and Workspaces

If your project uses a monorepo (pnpm workspaces, yarn workspaces, or Nx), tell Cursor upfront. Click Settings (gear icon in left sidebar) and toggle "Monorepo Mode." Cursor then:

• Indexes all workspace packages
• Respects workspace-level tsconfig.json and build configs
• Avoids cross-workspace import violations
• Runs agents with workspace scope awareness

For an Nx monorepo:

// .cursorrc (optional config file)
{
  "monorepo": "nx",
  "indexedPaths": ["apps/api", "apps/web", "libs"],
  "excludedPaths": ["node_modules", "dist", ".next"]
}

Cursor will pick this up and scope agent operations accordingly. I tested this on a Nx workspace with 12 libraries and 3 apps. Agents correctly avoided importing from sibling apps without going through the public API entry points.

Link to section: Practical Limitations and GotchasPractical Limitations and Gotchas

I've run multi-agent tasks daily for two weeks. Here's what bit me:

1. Agent hallucination on existing APIs: Agents sometimes generate code calling functions that don't exist. Solution: include a focused context file (/api/helpers.md) that lists every exported function and its signature.

2. Merge conflicts on package.json: If both agents add dependencies, you'll have a conflict. Cursor auto-merges common additions but flags divergences. Manually review package.json after merge.

3. Rate limiting: If you fire up 8 agents simultaneously, you hit OpenAI/Anthropic rate limits fast. Cursor queues overflow and they wait. Check your Account Settings to see current usage.

4. Worktree storage: Each worktree copies your entire repo. On a 500 MB monorepo with 8 agents, you temporarily use 4 GB of disk. Not a blocker, but worth knowing.

Link to section: When to Use Single Agent vs Multi-AgentWhen to Use Single Agent vs Multi-Agent

Single agent is fine for:

• Small bug fixes or isolated features
• Copy-pasting a pattern from existing code
• One-liners or simple scaffolding

Multi-agent shines for:

• Architecture decisions with multiple valid paths
• Large refactors where exploration saves time
• Security-critical code (run agents with divergent threat models, then merge the most robust one)
• Unknown problem domains where you want multiple solution angles

Link to section: Outlook and Next StepsOutlook and Next Steps

Cursor 2.0's multi-agent setup is stable. The Composer model is fast enough for daily use. My main wish: better conflict detection. Sometimes agents silently make incompatible assumptions about shared state or API contracts. A pre-merge lint pass would catch more of these.

For now, treat multi-agent mode as a powerful iteration tool, not a replacement for code review. Always read diffs before merging, especially in security-sensitive code.

If you're on the fence about upgrading from Copilot or other single-agent editors, the parallel execution alone justifies the switch for large codebases or complex features. Even the free tier gives you one or two multi-agent runs per day, which is enough to test the model.

cursor --version
 
cursor login
 
 

One last thing: Cursor syncs your worktrees to a local cache for faster reconnection on your next parallel task. Your .git/worktrees directory grows over time. Clean it up monthly:

git worktree prune

That's it. You're ready to parallelize your coding workflows.