Installs a Hermes plugin that gives Hermes agents the same shared memory: per-prompt recall push, identity and lessons context, and Hicortex's nine memory tools.
Can be configured as the local LLM backend that runs Hicortex's nightly consolidation — scoring, reflection, linking, decay, dedup, and supersession of captured sessions — keeping raw sessions on the machine.
Supports any OpenAI-compatible API endpoint as the configurable LLM used for offline memory consolidation and reflection over captured sessions.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Hicortex show me lessons I've learned about deploying to production"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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, OpenCode, 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 Learnings, links, and a knowledge graph.
Local-first — raw sessions never leave the machine; only distilled memory is stored.
npx @gamaze/hicortex initAuto-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.cominit auto-detects the other harnesses and installs their clients: a Pi extension (~/.pi/agent/extensions/hicortex.ts — pushed recall, identity + lessons, the nine tools; or copy pi-extension/hicortex/index.ts there manually), an OpenCode plugin (~/.config/opencode/plugins/hicortex.ts — the same trio; or copy opencode-plugin/hicortex/index.ts there manually), the Hermes plugin, and the OpenClaw plugin. pi-mcp-adapter remains a generic MCP escape hatch for any harness (verified against the SSE endpoint) — Pi no longer needs it. See the install docs.
Related MCP server: Cortex
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-loadedMemories 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.
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.
Learnings from reflection — nightly reflection extracts general, reusable Learnings, not just Experience 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.
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 →
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).
git clone https://github.com/gamaze-labs/hicortex.git
cd hicortexAGENTS.md at the repository root defines the machine-checkable verification contract. "Done" means the full command chain exits with code 0. The contract mirrors what CI runs. Contributors — human or agent — run it before claiming work complete.
Contributions welcome — see CONTRIBUTING.md.
Website: hicortex.gamaze.com
Docs: hicortex.gamaze.com/docs
Changelog: CHANGELOG.md
npm: @gamaze/hicortex
Issues: gamaze-labs/hicortex/issues
Security: SECURITY.md
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.
This server cannot be deployed
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Shared memory for all your AI agents, your whole team and every MCP client — save, search, recall.
Your memory, everywhere AI goes. Build knowledge once, access it via MCP anywhere.
