AI Context Engine for Coding Agents

Powered by proprietary PRAT algorithm, Xanther gives your coding agents deep codebase understanding — delivering precise architectural context on every tool call via MCP.

Your agents are smart enough. They just need better context.

On mini-swe-agent: Sonnet 4.0 + XCE went from 66% → 73.4% — an older-gen model beating raw Sonnet 4.6 and reaching Opus-level with cascade hybrid at 76.8%

0%

SWE-bench Verified

$0

/instance

Cost per resolved instance

0x

cheaper than frontier

vs Claude 4.5 Opus

Proprietary Algorith

Powered by PRAT

The Persistent Recursive Abstract Tree algorithm transforms coding agents into ones that never forget — recursively linking every code element to the right architectural context.

SWE-bench Verified — The Race

Sonnet 4.0
66%
Sonnet 4.5
72%
Sonnet 4.6
72%
Sonnet 4.0 + XCE
XCE BOOST
73.4%
Opus 4.5
76.8%
MiniMax M2.5
75.8%
MiniMax M2.5 + XCE
XCE BOOST
78.2%

With XCE, Sonnet 4.0 overtakes 4.5 & 4.6 — MiniMax M2.5 beats Opus at 16x lower cost

See it in action

Watch how XCE supercharges a coding agent in real time.

Demo video coming soon

Two engines. One smarter agent.

Context Engine gives your agent expertise. Session Engine gives it memory.

Context Engine

Deep codebase understanding — your agent gets precise architectural context on every tool call, so it always knows where it is and what matters.

Session Engine

Persistent memory and cross-session state management — your agent remembers decisions, preferences, and context across conversations.

Real results on SWE-bench Verified

All on mini-swe-agent: Sonnet 4.0 + XCE 66% → 73.4% — older-gen beating raw Sonnet 4.6, reaching Opus-level at 76.8% with cascade hybrid

MiniMax M2.5 + XCE: 78.2% on SWE-bench Verified — beating Claude Opus at 76.8%, at 16x lower cost

0%

SWE-bench Verified

MiniMax M2.5 + XCE

$0

Per Instance

16x cheaper than frontier

0%

Sonnet 4.0 + XCE

Up from 66% → 76.8% cascade hybrid

Ready to supercharge your coding agents?

Give your agents the context they need to solve real-world issues.