lunaris

Sub-25 ms recall over millions of bi-temporal facts, with provable atomicity and a graph that's opt-in.

A production-grade agent-memory engine in Rust, with first-class Python and TypeScript SDKs and a zero-Rust MCP server for coding agents. Raw observations in; structured, bi-temporal facts out. Backed by Moon, the high-performance Redis-compatible substrate — and, as of 0.7.0, only Moon. The Postgres and SQLite backends were removed; see 0.6 → 0.7 if you are on one.

Documentation: the full guide lives in the Lunaris Book or (mdbook serve docs/book locally . live). First time here? docs/POSITIONING.md is the one-page pitch + honest "use a different tool when…" criteria. How it works: docs/ARCHITECTURE.md — the layered design and the Moon advantage map, every claim proof-anchored.

1. Give your AI agent memory (MCP)

The MCP server gives any MCP-capable agent eleven memory tools — seven durable-memory tools (memory.ingest, memory.recall, memory.forget, memory.list_scopes, memory.record_decision, memory.record_edit, memory.status) plus four working-memory scratchpad tools (memory.scratchpad_write, memory.scratchpad_read, memory.scratchpad_grep, memory.scratchpad_consolidate). Both install paths download a prebuilt native binary on first run — no Rust toolchain required (linux-x64/arm64, darwin-x64/arm64, win32-x64).

Claude Code — one command, either runner: