r/LovingOpenSourceAI

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Hi r/OpenSourceAI,

I built metaBrain, an open-source local document memory for AI agents and developer workflows.

The project came from a problem I kept having while using agents for coding: useful context ends up scattered everywhere. Some of it is in markdown planning files, some in JSON state, some in scratch notes, some in task logs, and some in project-specific docs. Moving from one project to another gets tedious, and bootstrapping a new idea often means rebuilding the same context again.

metaBrain is my attempt to give that context one local, searchable place to live.

Documents are addressed with filesystem-like paths, for example:

/notes/today

/projects/foo/release-plan

/agents/openclaw/task-state

Under the hood they are stored in a local LevelDB-backed database with tags, metadata, lexical search, version history, zstd compression, JSONL export, and patch-based updates.

The CLI currently supports:

- put/get/list documents

- search by text, tags, and metadata

- patch documents with unified diffs

- inspect version history

- export the store as JSONL

- prune old versions

One goal was to make the tool discoverable by agents themselves. In my own workflow, I can point OpenClaw at the repo, ask it to discover the available commands, and then have it use `mb` directly as part of its task. That gives both me and the agent a shared local memory store instead of a pile of files spread across the workspace.

The repo is here:

https://github.com/OpenCow42/metaBrain

It is released under the permissive BSD 3-Clause license.

Install on macOS:

brew tap OpenCow42/tap && brew install mb

Example:

mb init

mb put /notes/today "Remember this project context." --tag planning --meta source=agent

mb search "project context"

mb get /notes/today

It is early. Right now it is a Swift package with a CLI and embeddable core library. I’m also planning a native Mac app to browse and inspect the database visually.

I’d be especially interested in feedback from open-source AI/tooling people:

- Is this useful as a local-first alternative to scattered agent memory files?

- Would an MCP server be the most useful next integration?

- What would make this easier or safer for agents to use autonomously?

Few years back it mostly felt like companies open sourced stuff for community goodwill or just dev marketing, but now it genuinely feels like one of the strongest ways to build trust and distribution.

You can kinda see it everywhere now. Hugging Face with open models and AI infra stuff, Bifrost in the LLM gateway space, Supabase with backend infra, Unsloth around LLM fine tuning, and a lot of newer AI/devtool startups too.

Feels like companies are realizing that once developers trust your product, other tech companies slowly start trusting it too. And open source speeds that up a lot because people can actually inspect things themselves instead of just reading polished landing pages.

Especially in AI where people are getting more skeptical of black box products, this feels even more relevant now.

Honestly feels like open source is slowly turning into an actual competitive advantage instead of just a philosophy/community thing. Part of why I recently started building my own open source org too.