Hicortex

Memory that shows up before your agent asks. One memory across every agent, every project, every machine — they stop assuming and start knowing.

• One brain, every harness — Claude Code, Hermes, OpenClaw, Pi, and any MCP-compatible agent share the same memory.
• Pushed, not pulled — a compact recall index is injected on every prompt, so the decisions, corrections, and context an agent needs are already in front of it. No re-explaining, no copy-paste, nothing to maintain. Zero LLM calls per turn — no API cost or rate-limit hit from recall.
• Consolidates overnight — each night it reads the day's sessions, distills what matters, and turns it into lessons, links, and a knowledge graph.
• Local-first — raw sessions never leave the machine; only distilled memory is stored.

Install

npx @gamaze/hicortex init

Auto-detects your environment, configures one LLM (Ollama, the Claude CLI, or an API key), installs a local daemon (launchd on macOS, systemd on Linux), and registers MCP tools with Claude Code.

For multi-machine setups, point thin clients at a shared server — no local DB or LLM on the clients:

npx @gamaze/hicortex init --server https://your-server.example.com

Pi connects via pi-mcp-adapter; Hermes and OpenClaw via their plugins. See the install docs.

How it works

CAPTURE (nightly)        CONSOLIDATE (nightly)             RECALL (every prompt)
sessions → denoise       score · reflect · link            a compact index of
→ POST /distill          decay · dedup · supersede         relevant memories is
                         (one model, all phases)           pushed into the prompt
                                                           → full text lazy-loaded

Memories strengthen when agents use them, fade when they don't, and link to related ones automatically. Retrieval is hybrid BM25 + vector search — zero-LLM at query time.

Features

• Per-prompt recall push — relevant memory lands in context every turn; the agent fetches full content with hicortex_get only when it needs it.
• Memory analytics at /dashboard — growth, recall adoption, and a nightly digest of what was learned.
• Knowledge graph at /viz — memories clustered by domain, connected by relationship edges.
• Domains & tags — multi-tag classification with a configurable vocabulary; your categories drift with your data.
• Lessons from reflection — nightly reflection extracts general, reusable lessons, not just episode logs.
• Dedup & supersession — near-duplicates merged; stale decisions and corrections superseded, not re-surfaced.
• Standing context layer — hand-edited "who you are / how to work" Markdown, injected every session, never decayed.

MCP

Nine MCP tools — hicortex_search, hicortex_get, hicortex_recent, hicortex_ingest, hicortex_lessons, hicortex_index, hicortex_graph, hicortex_update, hicortex_delete — plus a /learn skill to save explicit learnings. Full reference →

Stack

TypeScript · Node.js 20+ · SQLite + sqlite-vec + FTS5 (semantic + full-text in one DB) · ONNX embeddings (bge-small-en, CPU) · MCP over HTTP/SSE · one configurable LLM (Ollama, Claude CLI, or any OpenAI-compatible endpoint).

Development

git clone https://github.com/gamaze-labs/hicortex.git
cd hicortex/packages/hicortex
npm install && npm run build && npm test

Contributions welcome — see CONTRIBUTING.md.

Links

• Website: hicortex.gamaze.com
• Docs: hicortex.gamaze.com/docs
• Changelog: CHANGELOG.md
• npm: @gamaze/hicortex
• Issues: gamaze-labs/hicortex/issues
• Security: SECURITY.md

License

Personal and noncommercial use is free under the PolyForm Noncommercial License 1.0.0. Commercial use requires a per-seat license — see hicortex.gamaze.com.