Context Store & Knowledge Graph for AI Agents
The Context Store backbone for the AI Development Lifecycle (AI-DLC). Eliminate context drift, resolve blast-radius in 1 hop, and power GitHub Copilot, Claude & Antigravity with a hybrid Graph + Vector RAG memory.
npx @cortex-context/cli init
Maintaining knowledge for AI agents should not depend on fragile manual prompts. Cortex Context follows an 80% Deterministic / 20% Cognitive architecture to ensure high-fidelity context at zero maintenance cost.
AST parsers analyze code structure on every commit. Backstage service catalogs, ADR manifests, and git diffs are deterministically ingested without AI hallucination.
AI agents focus exclusively on high-value cognitive tasks: synthesizing architectural intent, speculative draft exploration, and domain term clarification.
Instead of having LLMs read 10 files sequentially to deduce callers and dependencies, Cortex queries the AST graph in 1 lightweight request.
Deterministic parsing creates [:CALLS], [:IMPORTS], [:EXTENDS], and [:DEFINES] relationships directly in Neo4j.
Test refactors and feature branches in an isolated shadow state. Querying with your branch parameter returns a merged view of canonical main + speculative draft overrides.
Every piece of product, architecture, and code knowledge maps into an interconnected multi-dimensional graph.
A TypeScript CLI that wires your workspace to the knowledge graph in three phases: local Docker stack, MCP server configuration, and branch-aware post-commit git hooks.
domain_id filtering across semantic vector and graph search.
catalog-info.yaml files to build unified service and system catalogs.
Plug Cortex into your developer toolchain and AI coding assistants.
Start querying your codebase knowledge in minutes.
Stop pasting repetitive context into chat. Build a live, queryable Context Store your AI agents can rely on.