The intelligence, search, and execution infrastructure for AI coding agents.
Powered by the OpenRewrite Lossless Semantic Tree (LST), Moderne enables agents to search code at semantic depth, retrieve precomputed architectural context, and execute deterministic large-scale changes across thousands of repositories — with less token consumption, faster results, and governed, verifiable outcomes.
Most agent token waste isn't a model problem — it's a tooling problem. Agents search without type context, rebuild architectural knowledge every session, and execute migrations line by line when a single recipe call would do it in seconds. Moderne fixes the layer underneath: structured search, precomputed context, and deterministic execution that run on CPU instead of burning LLM budget.
This isn't AI vs. Moderne. It's AI with Moderne vs. AI without the right tools.
Capability
AI alone
AI agent + Moderne
Four capabilities that compound across every interaction.
Not one improvement — a stack that changes the economics of agent-driven engineering.
Most coding agents fall back on ripgrep or vector search, then burn tokens reading files to confirm what they found. Trigrep's trigram index is built from the LST — it includes type and symbol data — so the initial search is the final answer. No follow-up reads. Sub-second results across your entire organization.
The real savings: not the search, but the reads it replaces.
<1 sec
Results across the org
0
Follow-up file reads
Agents waste their most expensive tokens at the start of every session, re-generating architectural understanding from scratch. Prethink runs CPU-only static analysis on a regular cadence, so structured context is ready before the agent even begins.
Don't blame the agent. Blame the context.
5 sec
Architecture context retrieval
vs. ~2 min
Agent alone + 60K tokens
For large migrations, agents without tools burn millions of tokens re-learning what a recipe already knows — making naïve scripting attempts, hitting edge cases, starting over. With Moderne recipes, one tool call replaces the entire loop.
"Even without the tools, the model recognizes that it wants them." — Jonathan Schneider, Founder & CEO
30K
Tokens, Java 8→25
vs. 61M
Without tools
~3 min
vs. 45+ min
Tool gaps compound over time. By mining agent chat transcripts, Moderne identifies where agents fall back on expensive patterns, builds new recipes from those patterns, and lowers token consumption on the next run. Each engagement makes the next one more efficient.
A virtuous cycle of product-led cost reduction.
