qortex

Knowledge that learns

Your AI assistant forgets everything between conversations. qortex adds a knowledge graph that learns from every interaction. One command to install. Zero config.

Install

Claude Code

claude mcp add qortex -- uvx qortex mcp-serve

Cursor / Windsurf

uvx qortex mcp-serve  # add as stdio MCP server in settings

Any MCP client

pip install qortex[all] && qortex mcp-serve

What happens next

Once installed, your assistant automatically:

1. Searches the knowledge graph before answering architecture questions
2. Retrieves relevant concepts, relationships, and rules (not just similar text)
3. Learns from your feedback: accepted results get boosted, rejected ones get suppressed
4. Persists everything to SQLite so knowledge survives restarts

No config files. No API keys for the knowledge layer. Just start asking questions.

The difference

	Vanilla RAG	qortex
Retrieval	Cosine similarity (what's textually similar)	Graph-enhanced (what's structurally relevant)
Context	Flat chunks	Concepts + typed edges + rules
Learning	Static	Adapts from every accept/reject signal
Cross-references	None	Traverses REQUIRES, REFINES, USES edges

Prove it

Call qortex_compare to see the difference on your own data:

{
  "summary": "Graph-enhanced retrieval found 2 item(s) that cosine missed, surfaced 1 rule(s), replaced 1 distractor(s).",
  "diff": {
    "graph_found_that_cosine_missed": [
      {"rank": 3, "id": "security:JWTValidation", "score": 0.72}
    ],
    "cosine_found_that_graph_dropped": [
      {"rank": 4, "id": "security:PasswordHashing", "score": 0.68}
    ],
    "rank_changes": [
      {"id": "security:AuthMiddleware", "vec_rank": 3, "graph_rank": 1, "delta": 2}
    ]
  }
}

Graph retrieval promotes structurally connected concepts (AuthMiddleware depends on JWTValidation) and demotes textually similar but unrelated results.

How it works

• Auto-ingest: Feed it docs, specs, or code. LLM extraction builds concepts, typed edges, and rules automatically.
• Graph retrieval: Queries combine vector similarity with structural graph traversal. Related concepts get promoted even if they don't share keywords.
• Adaptive learning: Every qortex_feedback call updates retrieval weights. The system gets smarter the more you use it.
• Persistent by default: SQLite stores the knowledge graph, vector index, and learning state across restarts.

For framework authors

agno KnowledgeProtocol

from qortex.adapters.agno import QortexKnowledgeSource

knowledge = QortexKnowledgeSource(domains=["security"])
agent = Agent(knowledge=knowledge)

LangChain VectorStore

from langchain_qortex import QortexVectorStore

vs = QortexVectorStore.from_texts(texts, embedding, domain="security")
retriever = vs.as_retriever()

See langchain-qortex for the standalone package.

Mastra MastraVector

import { QortexVector } from "@peleke.s/mastra-qortex";

const qortex = new QortexVector({ id: "qortex" });
await qortex.createIndex({ indexName: "docs", dimension: 384 });
const results = await qortex.query({ indexName: "docs", queryVector: q, topK: 10 });

See @peleke.s/mastra-qortex for the standalone package.

Framework	Package	Language	Interface
agno	Built-in adapter	Python	KnowledgeProtocol
LangChain	langchain-qortex	Python	VectorStore ABC
Mastra	@peleke.s/mastra-qortex	TypeScript	MastraVector abstract class
Any MCP client	Built-in MCP server	Any	MCP tools (JSON-RPC)

Install extras

Capability	Install	What you get
Core + MCP tools	pip install qortex	Knowledge graph, MCP server, vector-level tools. Consumers provide embeddings.
Text-level search	pip install qortex[vec]	qortex embeds text with sentence-transformers. Adds ~2GB for PyTorch + model weights.
Persistent vectors	pip install qortex[vec-sqlite]	SQLite-backed vector index. Without this, vectors are in-memory only.
Production graph	pip install qortex[memgraph]	Memgraph backend for production-scale graph operations.
Everything	pip install qortex[all]	All of the above.

License

MIT