What is Morph Fast Apply?

TL;DR

Morph is just one simple tool you add to your AI agent. That's it.

Your agent calls edit_file("my_file.js", "add error handling", "// ... existing code ...") and Morph merges the changes into your actual file at 10,500+ tokens/second.

No complex setup. No new workflows. Just add the tool and your agent gets fast, accurate code editing.

The Problem: AI coding assistants are great at suggesting changes, but terrible at applying them to existing code.

The Simple Solution: Add one edit_file tool to your agent. Morph handles the complex merging behind the scenes.

How It Works (3 Simple Steps)

1. You: "Add error handling to this function"
2. Claude/GPT-4o: Calls edit_file() with the changes
3. Morph: Merges the changes and returns the complete updated file

That's it. Your agent now has reliable code editing.

The Actual Implementation

Adding Morph to your agent is literally just adding one tool definition:

Then make one API call to merge it. That's literally it. Five minutes to integrate.

Why This Matters

Search & Replace requires a separate tool call for each chunk being edited. Multiple edits = multiple round trips.

Morph Fast Apply handles all edits to a file in a single call. The model describes what it wants to change, and FastApply merges everything at once. Fewer tool calls, faster completion.

What is Morph?

Morph provides the missing pieces for building AI coding agents:

1. Morph Fast Apply - Merge AI-suggested changes with your existing code (10,500+ tokens/second)
2. Morph Embed - Find relevant files using semantic and syntactic search
3. Morph Rerank - Pack your context window with only the most relevant code

Think of it as the infrastructure layer that makes AI coding tools actually work in production.

Why Traditional Software Fails

You might wonder: "Why not just use diff tools or write matching algorithms?"

The answer lies in Andrej Karpathy's famous "Software 2.0" insight: some problems are better solved by training neural networks than writing explicit rules.

Code merging is a perfect example where context matters more than syntax:

The Semantic Challenge

AI Suggestion:

Your Original Code:

The Problem: Traditional diff tools see items vs item_list and fail. A specialized model understands they're the same thing semantically.

Large File Challenges

• Frontier models struggle with line numbers in 1000+ line files
• Context windows get overwhelmed with big codebases
• Partial function updates break traditional diff tools
• Neural networks can learn to focus on relevant sections

Subtle Dependencies

• Import statements need careful handling
• Type definitions might need updates
• Test files often need corresponding changes
• Models can learn these relationships from examples

How Morph Fast Apply Works

1. Semantic Understanding

Instead of text matching, Morph understands code meaning:

• Matches calculate_total(items) with calculate_total(item_list)
• Preserves your coding style and formatting
• Handles renamed variables intelligently

2. Speculative Edits

We use a variant of speculative decoding:

• Uses your original code as a strong prior
• Processes unchanged code in parallel
• Streams changes as they're processed

3. Trained on Real AI Outputs

Morph is specifically trained on how frontier models actually suggest changes:

• Handles // ... existing code ... comments
• Deals with missing imports
• Understands lazy update patterns

The Speed Advantage

Morph Fast Apply: 10,500+ tokens/second throughput, 1-3 second latency

The key advantage is that FastApply handles all edits in one call. Search & replace needs separate tool calls for each chunk, which adds up.

This enables:

• Full file edits in a single API call
• Fewer tool call round trips
• ~35% faster end-to-end task completion

Real-World Example

Your Original Code:

AI Suggestion:

Morph Output:

Clean, accurate, preserves your style.

The Technical Magic

Inference Optimizations

• Smart KV caching on the inference side
• Speculative decoding with original code as prior
• Custom attention heads trained specifically for code editing

Efficient Token Usage

• Minimizes expensive frontier model calls
• Uses specialized models for implementation details
• Enables cost-effective code updates at scale

What's Next

This approach opens up exciting possibilities:

• Frontier models for high-level planning and reasoning
• Specialized models for implementation and merging
• Recursive application of changes at different abstraction levels

We're also working on:

• File routing (automatically determining which files need updates)
• Longer context models for entire codebases
• Self-hosting options for enterprise security

Getting Started

Ready to try Morph? Here's what to do:

1. Sign up for an API key - Get started in minutes
2. Try the Playground - Test it with your own code
3. Read the API docs - Integrate into your workflow

For Enterprise: Contact us at info@morphllm.com for dedicated instances and larger models.

The future of AI coding isn't about replacing developers—it's about giving them superpowers. Morph Fast Apply is the missing piece that makes AI coding assistants actually useful in production.