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MCP server · Documentation

Hicortex

Human-like memory for self-improving AI agents. Automatic capturing, nightly reflection, and cross-agent learning. Works with Claude Code and OpenClaw.

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About Hicortex

Hicortex is an MCP server in the Documentation category: human-like memory for self-improving AI agents. Automatic capturing, nightly reflection, and cross-agent learning. Works with Claude Code and OpenClaw. It has been installed 0 times through Conduid.

Install

Claude Code
claude mcp add hicortex -- npx -y @gamaze/hicortex
npx
npx -y @gamaze/hicortex

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

Hicortex

npm Node

Self-improving long-term memory for AI agents. Capture sessions, distill lessons overnight, inject them on the next run. Works with Claude Code, Pi, OpenClaw, and any MCP-compatible agent.

Named after the hippocampus (fast encoding) and neocortex (slow consolidation) — the two brain systems that turn fleeting experiences into lasting knowledge.

Install

npx @gamaze/hicortex init

That's it. Auto-detects your environment, picks an LLM (Ollama / Claude CLI / API key), installs a local daemon (launchd on macOS, systemd on Linux), and registers MCP tools with Claude Code.

For multi-machine setups, point clients at a shared server:

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

Pi agents

Pi agents connect via pi-mcp-adapter. Add to ~/.pi/agent/mcp.json:

{
  "mcpServers": {
    "hicortex": {
      "url": "http://localhost:8787/sse",
      "auth": "bearer",
      "bearerTokenEnv": "HICORTEX_TOKEN",
      "lifecycle": "keep-alive"
    }
  }
}

The nightly pipeline auto-detects Pi sessions at ~/.pi/agent/sessions/ alongside CC sessions. Set lessonTarget in ~/.hicortex/config.json to inject lessons into your agent's learning file (e.g., .pi/EXPERIENCE.md) instead of the default ~/.claude/CLAUDE.md.

Full docs: hicortex.gamaze.com/docs

What it does

INGEST (nightly)             CONSOLIDATE (nightly)        RETRIEVE (instant)
┌──────────────────┐        ┌──────────────────────┐      ┌─────────────────────┐
│ Session transcripts        │ 1. Score importance  │      │ BM25 + vector search│
│ → LLM distillation         │    (local LLM)       │      │ → RRF fusion        │
│ → Local embedding          │ 2. Reflect & learn   │      │ → Graph traversal   │
│ → Store                    │    (cloud LLM)       │      │ → Composite scoring │
└──────────────────┘        │ 3. Auto-link by      │      │ → Strengthen on     │
                             │    vector similarity │      │    access           │
                             │ 4. Decay + prune     │      └─────────────────────┘
                             └──────────────────────┘
                                       ↓
                             Lessons (memory_type="lesson")
                                       ↓
                             Injected into CLAUDE.md / agent context

Memories decay slower the more important and frequently used they are, strengthen on retrieval, and are linked automatically to related memories. Retrieval is zero-LLM: BM25 full-text + vector search fused with Reciprocal Rank Fusion, scored by similarity (40%) + strength (30%) + connections (20%) + recency (10%).

MCP tools

Eight MCP tools your agent can call:

Tool Purpose
hicortex_search Semantic search across all stored memories
hicortex_context Recent decisions + project state for the current session
hicortex_ingest Store a memory directly
hicortex_lessons Actionable lessons from nightly reflection
hicortex_index Knowledge domain index — what topics are stored
hicortex_graph Graph traversal: neighbors, hubs, shortest paths
hicortex_update Fix incorrect memories (re-embeds on content change)
hicortex_delete Remove memories with cascade cleanup

Plus skills: /learn to save explicit learnings.

Stack

  • TypeScript, Node.js 18+
  • better-sqlite3 + sqlite-vec + FTS5 (semantic + full-text search in one DB)
  • @huggingface/transformers (bge-small-en-v1.5 ONNX, runs on CPU)
  • MCP protocol over HTTP/SSE (Claude Code, Pi, OpenClaw, any MCP client)
  • Multi-provider LLM — Ollama, Claude CLI, OpenAI, Anthropic, Google, OpenRouter, or any OpenAI-compatible endpoint
  • Auto-detects Ollama models, Claude CLI, API keys during setup

Architecture: Server + Client

  Client A                   Server                    Client B
  ┌──────────┐              ┌──────────────┐          ┌──────────┐
  │CC sessions│              │   Shared DB   │          │CC sessions│
  │    ↓      │  POST        │              │  POST    │    ↓      │
  │ Distill   │──/ingest───→│  Embed+Store  │←/ingest──│ Distill   │
  │ (local)   │              │      ↓       │          │ (local)   │
  │           │  MCP         │ Consolidate  │   MCP    │           │
  │    CC    ←│──(search)───│ (score,link, │──(search)→│   CC     │
  │           │              │  reflect)    │          │           │
  └──────────┘              └──────────────┘          └──────────┘

Server mode — local DB + MCP server + nightly consolidation. Client mode — distill locally for privacy, POST memories to a shared server.

Open source + commercial Pro

Hicortex is MIT-licensed and free forever. The npm package is the complete client: capture, distillation, retrieval, MCP tools, multi-client architecture.

Commercial Pro features (lesson selection engine, validation, cross-agent learning, prescriptive distillation, smart context assembly) are sold separately by Gamaze. Pro is server-side intelligence — no separate npm package, no client-side license keys to bypass. You point your client at a Pro server and the same code calls Pro endpoints if available.

This is the open-core model:

  • OSS (this repo): the memory client. Anyone can self-host, fork, modify, ship in their own product.
  • Pro (commercial): the intelligence layer. Funds OSS development, runs as a SaaS or licensed self-host.

See hicortex.gamaze.com for pricing and Pro features.

Project layout

packages/hicortex/    The npm package (@gamaze/hicortex)
  src/                       TypeScript source
    cli.ts                   CLI entry: server, init, nightly, status, uninstall
    init.ts                  Interactive setup wizard
    mcp-server.ts            HTTP/SSE MCP server (persistent daemon)
    nightly.ts               Nightly pipeline: distill + consolidate + inject
    consolidate.ts           Importance scoring, reflection, linking, decay
    distiller.ts             Transcript → LLM → memories
    storage.ts, db.ts        SQLite + sqlite-vec + FTS5
    retrieval.ts             BM25 + vector search with RRF fusion
    embedder.ts              Local ONNX embeddings
    llm.ts                   Multi-provider LLM client
    features.ts              Centralized feature gating
    claude-md.ts             CLAUDE.md lesson injection
    prompts.ts               LLM prompt templates
    license.ts               License validation
    transcript-reader.ts     Claude Code .jsonl reader
    index.ts                 OpenClaw plugin entry
  skills/                    Bundled OpenClaw skills (/learn, etc.)
  openclaw.plugin.json       OpenClaw plugin manifest

Development

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

See CONTRIBUTING.md for the contribution guide.

License

MIT — see LICENSE.

Links

README mirrored from the source repository 4 months ago. The original is authoritative.

Questions

About Hicortex

How do I install Hicortex?

Run claude mcp add hicortex -- npx -y @gamaze/hicortex, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Hicortex safe to use with an AI agent?

Its trust score is 37 out of 100 (low). Conduid hasn't run static security checks on this repository yet, so review the source yourself before granting it credentials. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Hicortex still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.