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Copy-paste examples for real-world AI agent use cases. All code is tested and production-ready.

Cursor Clone

Build a code editor with AI assistance that searches and edits autonomously.
What it does: Agent searches codebase → makes edits → verifies → repeats until done. No manual intervention needed.

PR Review Bot

Automated code review with full codebase context. Catches security issues, performance problems, and suggests improvements.

Self-Healing Agent

Autonomous bug fixing: agent finds the issue, patches code, and verifies the fix with browser tests.
How it works: Agent searches codebase for bug location → makes the fix → tests in browser → reports success/failure with video proof.

CI/CD E2E Testing

Natural language E2E tests that run on every PR. Get video recordings of failures automatically.
Cost: ~$0.10 per test suite run. Videos auto-delete after 7 days. Contact support for higher concurrency limits.
When tests fail, the video is embedded directly in the PR comment:
GitHub renders this as a playable video directly in the PR. No need to click links.

Test Debugging

When tests fail, get instant video replay with console errors and network logs.
Video embeds in GitHub: GitHub renders <video> tags natively, so your team sees failures instantly in issues/PRs.

Agentic GitHub App

Autonomous GitHub bot that resolves issues, creates PRs, and explains changes.
  1. Create GitHub App with repo access
  2. Deploy this code to Vercel/Railway/Fly
  3. Set webhook URL to your deployment
  4. Add MORPH_API_KEY, ANTHROPIC_API_KEY, GITHUB_TOKEN to env
  5. Bot automatically handles new issues
What to add:
  • Human approval workflow before merging
  • Tests run on PR before merge
  • Fallback to request help if agent is stuck

Code Migration

Migrate entire codebases between frameworks with consistent patterns.
Use cases: React → Vue, JavaScript → TypeScript, Class components → Hooks, REST → GraphQL

Smart Model Routing

Send each request to the right model for its difficulty — Opus for hard prompts, Haiku for easy ones — using the router’s classify endpoint. You keep the mapping; the router just reads the prompt.
What it does: One classify call reads the prompt’s difficulty, then you route to the model you picked for that tier — cheap models for simple work, your strongest model only when it’s needed.

More Examples

Documentation Bot

Auto-generate docs from code

Security Auditor

Scan for vulnerabilities

Refactoring Agent

Large-scale code cleanup

Test Generator

Generate unit & E2E tests

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API Reference

Complete API docs

Dashboard

Get API keys & monitor usage

Discord Community

Ask questions & share builds

Support

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