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.

Related MCP server: Lumena MCP Server

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 <days> once to import more.

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.

• Learnings from reflection — nightly reflection extracts general, reusable Learnings, not just Experience logs.

• Self-correcting store — every night, stale facts are rewritten in place with dated provenance; near-duplicates resolve into one (verbatim copies kept free, merges recoverable); superseded decisions are demoted, never re-surfaced. No zombie memory.

• Unprompted by design — coding agents get recall injected via hooks; instruction-capable clients (Claude Desktop, Cursor-class) get standing instructions, so memory is used without being asked. Plain MCP clients keep full search.

• Self-calibrating — recall, decay and merge boundaries report their own statistics; tuning is measured, never guessed.

• 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

AGENTS.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.

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.