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Xanther
Context & memory for AI coding agents, over MCP
Xanther provides two open-source engines for AI coding agents. XCE indexes your codebase into a queryable knowledge graph; XME gives agents persistent, cross-session memory. Both run locally and work over MCP with Claude Code, Cursor, Kiro, Codex, and more.
Hi Product Hunt
Coding agents are smart, but they start every session from zero — re-reading files, forgetting decisions, and burning tokens re-deriving structure. Xanther fixes that with two open-source engines:
XCE — Context Engine
Indexes your repo into a multi-layer knowledge graph: AST structure (tree-sitter) → LLM summaries → detailed docs → per-module architecture. Your agent queries it over MCP instead of grepping. It also does impact analysis (what breaks if I change this) and traceability (code ↔ architecture).
XME — Memory Engine
Persistent, cross-session memory: episodic transcripts, an extracted fact graph (decisions, attempts, preferences) with dedup, and a live working-context layer. Your agent stops re-suggesting approaches you already tried and reverted.
Both are free and open source (MIT / Apache 2.0), run locally (Neo4j in Docker; XME also has a zero-Docker SQLite mode), and work with any MCP-compatible tool — Claude Code, Cursor, Kiro, Codex, and more.
On mini-swe-agent + SWE-bench Verified, adding XCE moved Sonnet 4.0 from 66% → 73.4%, and MiniMax M2.5 + XCE hit 78.2% at ~$0.22/instance (full methodology in the repo).
https://github.com/Xanther-Ai/xa...https://github.com/Xanther-Ai/xa...
It's early and open — I'd love your feedback on the graph schema, the memory/dedup approach, and the MCP tool surface. Come say hi in Discord: https://discord.com/invite/p27qt...
Thanks for checking it out!
About Xanther on Product Hunt
“Context & memory for AI coding agents, over MCP”
Xanther was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #14 on the daily leaderboard. Xanther provides two open-source engines for AI coding agents. XCE indexes your codebase into a queryable knowledge graph; XME gives agents persistent, cross-session memory. Both run locally and work over MCP with Claude Code, Cursor, Kiro, Codex, and more.
On the analytics side, Xanther competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Xanther performed against the three products that launched closest to it on the same day.
Who hunted Xanther?
Xanther was hunted by Raj Pratim Bhattacharya. 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.
For a complete overview of Xanther including community comment highlights and product details, visit the product overview.
Raj Pratim Bhattacharya