FluctlightDB

The memory engine for AI agents — not a vector database with an agent SDK bolted on.

· GitHub

Mission

Goal: become the default database for agent memory — the way SQLite became the default embedded DB for apps.

Long-term agent memory is a third data model (alongside relational facts and vector similarity). FluctlightDB defines engine-level experience() / activate() semantics — episodes, cue-driven recall, provenance, consolidation — not app glue on top of Chroma or Mem0.

Who it's for — agents that learn and retain across sessions (from chat, tools, files, and APIs), recall under paraphrase, prefer verified evidence over casual conversation at recall time, and — in monorepos — share a project brain across Cursor, Claude Code, and Codex (fluctlight-project init). No hosted SaaS required.

What “learning” means — not fine-tuning. The agent writes episodes (experience()), recalls under new cues (activate()), and consolidates over time (sleep() / checkpoint()). See the full README and Manifesto.

Typical fits: coding agents, ops bots, research assistants, NPCs.

Install

pip install "fluctlightdb[native]"

from fluctlightdb import connect

brain = connect("/tmp/my-agent-brain")
brain.experience("User prefers dark mode", context="settings", salience=0.8)
print(brain.activate("dark mode"))  # offline lexical cue (paraphrase needs semantic_vector)
brain.checkpoint()

HTTP-only (no Rust extension): pip install fluctlightdb

Benchmarks (June 2026)

Frozen JSON: benchmarks/results/paper-2026-07-09.json

LoCoMo evidence recall ≠ Mem0 LLM-as-judge QA — different metrics; compare only when labeled.

Docs

• Getting started
• Multi-agent monorepos — fluctlight-project init, MCP, hooks, handoffs
• Platform compatibility — Windows, macOS, Linux
• Full README & reproduction
• Paper (LaTeX)