The ultimatecontext layerfor AI agents.
A completely local knowledge graph for your AI agents, coupled with a memory layer that knows when the code it remembers has changed.
Compatible with
Claude Code
Cursor
Windsurf
Cline
Continue
Codex
OpenCode
Zed
Antigravity
GitHub CopilotAn agent can hold a fixed amount at once. Without a graph underneath it, that budget fills with whole files, and only three of them hold the answer. Koragraph sends the resolved subgraph instead, so the same answer costs a fraction of the window.
Three repositories are parsed into three graphs, then the calls that leave one service for another are resolved between them into a single graph. Asking about one function returns an answer that has already crossed a repository boundary.
ONE MAP OF EVERYTHING YOU HAVE BUILT.
Koragraph parses every repository you point it at into a single graph, then keeps it current as the code moves. Not an index of text that looked similar to your question, but a resolved structure, with an edge only where there is really an edge.
- 01Every service, file, endpoint and table
- 02The calls between them, resolved against the syntax tree rather than guessed by name
- 03The links that cross a repository boundary, which is where a single-repo tool goes quiet
Research
Our research work.
What we sell, we test first. The design, the task suite and the grading go out with the result, whichever way the result went.
01
94% of Frontier Retrieval Quality from a 494M-Parameter Model
A 494M-parameter model fine-tuned for 2.43 T4-hours, about $0.85 of commodity GPU time, writes the method descriptions a code knowledge graph is searched on at 93.8% of the frontier teacher’s retrieval quality, and beats a hosted model Koragraph itself was paying for.
93.8%of frontier teacher retrieval quality, hit@8
02
Can Graph-Based Context Engineering Substitute for Model Scale?
A cheap model given graph-derived context solved 9 of 11 repository-level tasks at $0.027 per solved task. A frontier agentic system searching the same repositories with grep solved 9 as well, at $2.82.
2 → 9cheap tier, tasks solved, grep → graph
Korablog.
Notes on what we are seeing in agent research, from the team building Koragraph.
Two Millennium Prize Results in One Month, and a Credit Fight Over Who Actually Found the Idea
Claude needed sixty agents to push a Riemann hypothesis bound to 67.2 percent. OpenAI needed ten thousand to prove Navier-Stokes blow-up, and now disputes credit with the two humans who found the route first.
18 of 19 Frontier Models Score Lower on Agentic Work Than on Reasoning, and Graph Theory Already Named the Bottleneck
18 of 19 frontier models score lower on agentic work than on reasoning. Graph learning named this squeeze years ago: over-squashing, and effective resistance finds the bottleneck.
GPT-6 Astra Scored 62.7% and 99.9% on the Same Benchmark. The Difference Was Memory
GPT-6 Astra scored 37 points higher on ARC-AGI-3 with the same weights. New rate-distortion research explains why memory, not intelligence, made the gap.