
Benchmarked on a 9,000 token file, ~4 seconds
Founded by Tejas Bhakta
There’s no good way to apply edits that models want to make into files. Outputting the full file again is slow and expensive. Diff/Patch edits are brittle and a poor product experience.
In a production setting, AI agents need to update thousands of files. What about when you have a 50k token docx to update? Or when you need to be world-class at retrieving relevant info from a 500+ file repo?
Morph is the foundational infrastructure for AI Coding Agents that work and feel amazing - not a quick demo.
Tired: Chunked RAG and having Claude re-output full files
Wired: Syntax-aware embeddings, reranking, and Fast Apply models = the perfect product experience
Cursor and Windsurf roughly do this:
They provide:
Morph Apply: Fast Apply model: Merge updates from GPT-4o, Claude, and others into your files in under 2s (1600 toks/second)
Morph Embeddings: Syntax-aware embeddings, built for code
Morph Reranking: Rerank functions/classes or file snippets to stuff your context with only the relevant context - every time.
Morph SDK: Intelligently watch for file changes + smarter embeddings.
Morph Fast Apply dropped errors by 8x vs. patch-based edits in their internal IDE and worked on their largest files - Staff Eng @ Fortune-50