Introducing WarpGrep, a fast context subagent that improves coding agent performance. WarpGrep speeds up coding tasks 40% and reduces context rot by 70% on long horizon tasks by treating context retrieval as its own RL trained system. Inspired by Cognition’s SWE-Grep - we’re opening access to Claude Code, Codex, OpenCode or any coding agent via MCP (or through our SDK)
WarpGrep is one of those upgrades that feels obvious in hindsight: coding agents aren’t slow because they “reason slowly,” they’re slow because they waste most of their budget stumbling around the repo. Fix search and suddenly the whole agent stack wakes up.
What Morph shipped here isn’t another semantic search wrapper. It’s a purpose-built, inference-optimized subagent that treats context retrieval as a first-class RL problem. Parallel grep calls, strict turn budgets, heavy prefill, and an engine trained to keep recall high without drowning models in junk. The result is simple: agents stop hallucinating irrelevant files, stay inside the task boundary, and get 40 percent faster in real workflows.
If you’re building anything agentic, this is the layer that quietly decides whether your product feels instant or unusable. SWE-Grep proved the concept, WarpGrep makes it accessible across Claude Code, Codex, OpenCode, and anything MCP-compatible.
"It's a bit unintuitive why you would need an apply model - read more about it on our blog: https://morphllm.com/blog" - this isn't clickable in your website.
This will be a big part of killing the hickup feeling when we're on a dev roll. Well done!
4500+ tokens/sec is fast! I've been looking for something that handles the 'merge' step better than the standard cursor-style diffs.
Question for you: Does Morph maintain a context of the full project structure when applying these edits, or is it strictly file-level? I’m tackling some similar context-window challenges with my own tool (SquarePact) for legal docs, so I'm always interested in how others handle large-context merges.
Good luck with the launch today!
About WarpGrep on Product Hunt
“Remove context rot + improve code search ”
WarpGrep launched on Product Hunt on December 9th, 2025 and earned 118 upvotes and 7 comments, placing #13 on the daily leaderboard. Introducing WarpGrep, a fast context subagent that improves coding agent performance. WarpGrep speeds up coding tasks 40% and reduces context rot by 70% on long horizon tasks by treating context retrieval as its own RL trained system. Inspired by Cognition’s SWE-Grep - we’re opening access to Claude Code, Codex, OpenCode or any coding agent via MCP (or through our SDK)
WarpGrep was featured in Text Editors (16.8k followers) and Artificial Intelligence (469k followers) on Product Hunt. Together, these topics include over 95.3k products, making this a competitive space to launch in.
Who hunted WarpGrep?
WarpGrep was hunted by Tejas Bhakta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how WarpGrep stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Tejas Bhakta