I've been working on a Mac-native agent framework for about a year. One of the hardest problems: making the agent actually remember context across sessions in a way that's useful, not just "here's your last 10 messages."

What I ended up with is a knowledge graph — entities (people, projects, tools, decisions) with typed relations, stored locally, updated automatically as you interact.

When you say "draft the Q3 report like last time," it knows:

• What "last time" looks like (the template, the tone, who received it)
• Who the stakeholders are
• Which files are relevant

The rough architecture

• Agent loop runs locally on macOS (Swift daemon + Go backend)
• LLM-agnostic: Claude, GPT-4o, Gemini, Ollama all work
• Memory tier 1: SQLite graph with embedding-based fuzzy retrieval
• Memory tier 2: Standard MEMORY.md files per project
• Memory tier 3: Nightly-trained personal memory model, updated while you sleep
• Tools: file ops, browser (Playwright), calendar, terminal, screen
• IM bridge: agent can push updates to Slack/LINE/Feishu while you're away

The part that surprised me most

The memory graph changed how I use the agent more than any LLM upgrade did.

When an agent has persistent structured context, you stop re-explaining everything and start delegating actual work. The shift from "AI as search box" to "AI as coworker" isn't about the model — it's about memory architecture.

What broke along the way

Auto-approval for filesystem ops — lost a config file on day 3. Everything destructive now requires a human confirm step.

Trying to be a general assistant from day one — it works way better when you give it a specific workflow to own first. I started with "write and send my weekly status report."

Ignoring the LLM choice — for agentic tasks with tool chains, Claude Sonnet/Opus vs GPT-4o makes a meaningful difference in error recovery rate.

The project

I turned this into Kocoro (open-sourced the core runtime: github.com/Kocoro-lab/Kocoro). It's a Mac-native AI agent — local-first, memory graph built in, IM notifications, LLM-agnostic.

Just opened a closed beta — drop a comment if you want an invite.

What are others using for persistent agent memory on local setups?