Hicortex — AI Fleet Memory MCP server

Hicortex — AI Fleet Memory

Shared memory for AI agents — it corrects itself overnight, and what one agent learns, the whole fleet knows. 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 — in every supported coding agent, 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.

Install

npx @gamaze/hicortex init
Claude Desktop: one "yes" during 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

init auto-detects the other harnesses and installs their clients: a Pi extension (~/.pi/agent/extensions/hicortex.ts — pushed recall, identity + lessons, the ten 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.

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.

The first nightly run captures the last 7 days of sessions by default (not your entire history) — run hicortex nightly --recapture-window once to import more.

Features

Shortened. The full README is on GitHub.