Skip to main content

Get your API key

Sign up and start building in 30 seconds

Fast Apply

Code merging at 10,500 tok/s, 98% accuracy

WarpGrep

RL-trained code search subagent, #1 on SWE-Bench Pro

MCP Integration

Drop into Claude Code, Cursor, Codex

SDK

Build custom agent workflows

​
What is Morph?

Coding agents are good at reasoning about code. They’re bad at the mechanical work surrounding that reasoning: searching large codebases without polluting their context, merging edits into files without breaking things, verifying that changes actually work. These aren’t intelligence problems. They’re infrastructure problems. Morph solves them with specialized models and task-specific inference engines. Fast Apply takes original code and an edit snippet and merges them at 10,500 tokens/sec with 98% accuracy. It’s a 7B model trained specifically on code merging, served on custom CUDA kernels. Your agent describes the changes; Fast Apply produces the merged file in milliseconds. WarpGrep is an RL-trained search subagent. Instead of grepping sequentially (10-20 serial tool calls, each one adding noise to your context window), WarpGrep runs in its own isolated context, issues 8 parallel tool calls per turn, finds the right code in 3.8 steps, and returns only the precise file/line-range spans your model needs. Paired with Opus, Codex, or MiniMax, it reaches #1 on SWE-Bench Pro while making the system 15.6% cheaper and 28% faster. Both are drop-in tools. OpenAI-compatible API. Claude writes the edit, Fast Apply merges it. Claude needs context, WarpGrep finds it. Start Here → Quickstart Guide

​
Why Subagents

The default approach to improving coding agents is making the main model bigger and smarter. Longer context windows. Better reasoning. More parameters. The research points in a different direction. Agents spend 60%+ of their time searching, not coding. The search results they accumulate degrade their performance as context grows. Anthropic’s own multi-agent system outperformed single-agent Opus by 90%, not because the subagents were smarter, but because the lead agent’s context stayed clean. The fix isn’t a smarter single model. It’s delegating mechanical tasks to specialized models that run in isolated contexts, do the dirty work, and return only the signal. That’s what Morph builds.

​
How It Works

For file edits:
  1. Your agent outputs a lazy edit snippet (just the changes, using // ... existing code ... markers)
  2. Call Morph’s Fast Apply API to merge it
  3. Write the result to your filesystem
For code search:
  1. Your agent needs to find the authentication middleware
  2. Call WarpGrep with a natural language query
  3. Get back ranked file/line-range spans with precise context, no noise
No infrastructure changes required. Works with any model, any framework.

​
Models

Fast Apply

Code merging at 10,500 tok/s, 98% accuracy. Custom CUDA kernels, speculative decoding

WarpGrep

RL-trained parallel search. 8 tool calls/turn, 4 turns, sub-6s completion

Embeddings

Code-specific embeddings trained on millions of commits

Reranking

Rerank search results for dense, relevant context packing

​
Speed and Conversion

We’ve worked with nearly all of the top coding agent platforms. Within user cohorts that don’t hit errors, conversion rates roughly double when speeds double. There’s a floor below which users leave and a ceiling above which faster doesn’t help. Between those bounds, the relationship is nearly linear. Cognition’s “Semi-Async Valley of Death” captures this well: work either needs to happen in a few seconds (preserving flow state) or run autonomously for hours. The middle zone, where the user is waiting but can’t do anything else, destroys productivity. The probability of breaking flow increases roughly 10% per second. Fast Apply operates at 10,500 tok/s because that keeps file edits under 1-3 seconds. At that speed, the edit step disappears from the user’s perception.
Morph Fast ApplyClaude SonnetGPT-4o
Speed10,500 tok/s~80 tok/s~100 tok/s
Accuracy98%95%92%
Cost$0.80-1.20/M tok$15/M tok$10/M tok

​
Next Steps

If you’re improving an existing agent:

Fast Apply Quickstart

Replace full-file rewrites with sub-second merging

WarpGrep Guide

Add isolated code search that protects your model’s context
If you’re building from scratch:

Morph SDK

Full toolkit: Fast Apply, WarpGrep, context management

MCP Integration

Plug into Claude Code, Cursor, or Codex in minutes
Try it directly:

API Playground

Test Fast Apply and WarpGrep with live examples

​
Enterprise

Dedicated instances, self-hosted deployments, and zero data retention. 99.9% uptime SLA, SOC2, SSO.

Talk to Sales

Custom deployments and volume pricing