brainOS-HQ

Brain OS

Community brainOS-HQ
Updated

Persistent operational memory for AI agents. Decisions, priorities, and project state that survive across sessions.

Brain OS

brainos-hq.com

Your AI remembers conversations. It still forgets project state.

Brain OS gives agents operational state: decisions, plans, blockers, and priorities that survive across sessions.

What is this?

AI agents are powerful inside a session, but long-running work has more state than any one chat: what you decided, what's blocked, what's active, and what should not be reopened. Brain OS gives agents operational state, not conversation logs:

  • Entities — track projects, deals, initiatives with status, momentum, blockers, and next moves
  • Decisions — log what was decided, why, what alternatives were rejected, and when to revisit
  • Patterns — detect recurring blockers, stale work, avoidance signals, and theme convergence
  • Focus — prioritize what to work on based on urgency, momentum, leverage, and staleness
  • Semantic recall — search memory by meaning, not just ID

Brain OS is an MCP server that works with any MCP-compatible client: Claude Code, Cursor, Zed, GitHub Copilot, OpenAI Codex, Windsurf, or any agent that speaks the protocol.

What it looks like in use

Before the agent acts, it can check whether a proposed move conflicts with an existing decision:

> decision_check({ proposal: "switch to Postgres for the new service" })

{
  "verdict": "conflict",
  "conflicting_decision": {
    "id": "dec_2026_03_14_db_choice",
    "decision": "Use SQLite for all local-first projects",
    "reason": "Lower ops burden, no infra to run, fits single-user scope",
    "rejected_alternatives": ["Postgres", "DuckDB"],
    "logged_at": "2026-03-14"
  },
  "guidance": "Re-litigating a settled choice. Surface the prior reasoning to the user before proceeding."
}

That's the wedge: structured state with enforcement, so agents stop re-opening questions you already answered.

Quick start

# In your project
npx brain-os init

This does three things:

  1. Creates a .brain/ directory with your entity, decision, and pattern stores.
  2. Installs slash commands into .claude/commands/ so you can run /brain, /brain:focus, /brain:decide, etc. directly in Claude Code. Bare aliases (/focus, /decide, etc.) install alongside for brevity.
  3. Drops agent-instructions pointer files so any MCP-compatible client behaves consistently: AGENTS.md (canonical, cross-tool) plus thin pointer files for Claude Code (CLAUDE.md), GitHub Copilot (.github/copilot-instructions.md), Cursor (.cursor/rules/brain-os.mdc), Zed (.zed/rules.md), and Windsurf (.windsurfrules).

Flags:

  • npx brain-os init --minimal — install only AGENTS.md + CLAUDE.md, skip the other client pointers (clean-repo mode)
  • npx brain-os init --no-commands — skip slash commands (MCP server only)
  • npx brain-os init --no-agent-instructions — skip all agent-instructions pointer files

Connect to Claude Code

claude mcp add brain-os -- npx brain-os serve

Connect to Cursor / other MCP clients

Add to your MCP config:

{
  "brain-os": {
    "command": "npx",
    "args": ["-y", "brain-os", "serve"]
  }
}

Configure semantic search (optional)

The semantic_recall tool needs an embeddings provider. Everything else (entity_update, decision_log, plan_*, etc.) works without one.

Pick a provider by adding BRAIN_EMBEDDINGS to your MCP server env:

{
  "brain-os": {
    "command": "npx",
    "args": ["-y", "brain-os", "serve"],
    "env": {
      "BRAIN_EMBEDDINGS": "local"
    }
  }
}
Mode What it does Setup
local Downloads a ~100MB on-device model (Xenova/all-MiniLM-L6-v2). Runs on your CPU. No data leaves your machine. Just set BRAIN_EMBEDDINGS=local. First call to semantic_recall triggers the model download (~30s on good wifi).
openai Uses text-embedding-3-small via the OpenAI API. Faster than local. Costs ~$0.02 per million tokens. Set both BRAIN_EMBEDDINGS=openai and OPENAI_API_KEY=sk-...

If BRAIN_EMBEDDINGS is unset, semantic_recall returns a clear error with this config snippet. No silent downloads, no surprise API calls.

Tools

Tool Description
entity_read Read operational state of one or all tracked entities
entity_update Update entity state — status, momentum, blockers, next moves
decision_log Log a strategic decision with reasoning and alternatives
decision_check Check a proposed action against active decisions — returns clear/caution/conflict
decision_refresh Refresh an existing decision: bump review_date, append evidence, change status. Metadata only — does not mutate decision content.
focus_get Get prioritized recommendations on what to work on
pattern_detect Analyze patterns across all entities
memory_check Audit memory quality — flags stale data, contradictions, noise
memory_commit End-of-session commit — save all state changes
semantic_recall Search memory by meaning using natural language
audit_log Read the full mutation history — what changed, when, by whom
plan_set Set an ordered plan for an entity — step 1 becomes active next_move
plan_advance Complete or skip a step (requires evidence/reason) — auto-promotes next
plan_add Add steps to an existing plan
plan_read View plan progress and current step

Slash commands

brain-os init installs slash commands into .claude/commands/ so the agent has a clear vocabulary for working with operational state. Each command installs in two forms: /brain:* (canonical, documented form) and a bare alias (/decide, /focus, etc.) for power-user brevity. /brain is the namespace root and installs once.

It also installs:

  • BRAIN_OS_PROTOCOL.md at .claude/brain-os/PROTOCOL.md (project) and ~/.claude/brain-os/PROTOCOL.md (user). The protocol governs tool routing: when an agent runs a Brain OS slash command, it reads the protocol first, then calls entity_read/plan_read/focus_get/etc. as primary. Pulse files become fallback only.
  • brain-os-mode subagent at .claude/agents/brain-os-mode.md. When the main agent delegates Brain OS work to a subagent (e.g. Claude Code's Task tool), it picks up under the same protocol — no risk of subagents falling back to generic file search.
  • Optional routing-guard hook at templates/hooks/brain-os-routing-guard.py. Opt-in PreToolUse hook that warns if pulse files are read while a .brain/ workspace exists. Install instructions are printed by brain-os init.
Command Alias What it does
/brain Project scanner: overview of all entities, freshness, decisions, alerts
/brain:focus /focus "What should I work on today, and why?" with evidence
/brain:decide /decide Capture a strategic decision (with conflict check before logging)
/brain:strategy /strategy Strategic thinking partner: think a decision through before building
/brain:wrap /wrap Session wrap: update entity state, capture decisions, detect momentum shifts
/brain:patterns /patterns Detect patterns across entities: recurring blockers, avoidance, themes
/brain:retro /retro Weekly or monthly retrospective: what shipped, what stalled, what's hidden
/brain:graph /graph Show how entities connect, leverage opportunities, shared decisions

Idempotent install

Re-running init is safe and repair-aware: existing Brain OS commands are preserved, and any missing form is installed. If a command path is taken by another tool, that path is skipped and reported — your file is never overwritten. You can install Brain OS into a project with existing /decide or /focus commands and the namespaced /brain:* forms will still land.

How it works

Brain OS stores everything as local JSON files in a .brain/ directory:

.brain/
  entities/     — one file per tracked entity
  decisions/    — decision log
  patterns/     — detected patterns
  config.json   — workspace settings

No cloud. No database. No account. Your data stays on your machine.

Why no UI?

The interface is the agent. Brain OS is read and written through MCP tool calls — /brain, /focus, /decide, decision_check, etc. — surfaced inline by whichever client you use (Claude Code, Cursor, etc.). There's no separate dashboard to keep open, no second tab to context-switch into, no UI state that can drift from the underlying files.

This is a design choice, not a missing feature. Brain OS state lives at the same level as your code; the agent is already there, already in the conversation, already the right surface to ask "what's the priority right now?" Adding a human dashboard would split attention between two interfaces for the same data.

If you want a visual at-a-glance view, .brain/ is plain JSON — render it however you want. The public MCP server stays agent-native by design.

Teams & sync

Brain OS is single-user by design today. But because .brain/ is just local JSON files, teams can share a brain through any synced filesystem — no product changes needed:

Approach Pros Cons
Git — commit .brain/ to the repo Diff/merge tools, version history, intentional sync points Manual git pull; merge conflicts on simultaneous edits
Dropbox / Drive shared folder Real-time-ish, no manual steps Concurrent writes can create conflict files; embeddings.json rewrites often
NFS / SMB / S3 mount Truly real-time Requires infrastructure setup

This works without any built-in sync because every Brain OS tool call reads fresh from disk — there's no in-memory cache to invalidate. Whatever your filesystem syncs, the next tool call sees. Same applies cross-tool: log a decision from Claude Code on Monday, open Cursor on Tuesday — same brain, both agents.

Native encrypted team sync with proper merge semantics is on the roadmap. The local-first foundation today is what makes that federation additive, not a retrofit.

Auto-loaded status

When an MCP client connects, Brain OS exposes a brain://status resource with an operational overview — active entities, alerts, top priority, and recent decisions. The agent starts every session with context, not amnesia.

Testing

Brain OS ships a smoke test suite at tests/smoke.mjs, wired to npm test and run on every push by .github/workflows/audit.yml. Run locally:

npm test

Current coverage (regression + happy-path):

  • decision_log — type-collision no-supersede, explicit supersedes works, cross-entity supersession rejected
  • decision_check — keyword-only flag stays caution without embeddings (no false STOPs), asymmetric semantic comparison (rejected vs chosen facet)
  • decision_refresh — clears dangling superseded_by when status transitions away from superseded
  • plan_advance — no over-promotion when an active step already exists
  • entity_update — apply diff and record changes, missing-entity error, mode_reason required when parking
  • semantic_recall — throws EmbeddingsNotConfiguredError (not generic Error) when BRAIN_EMBEDDINGS is unset

Known gaps (no direct coverage yet): focus_get scoring, pattern_detect heuristics, entity_* happy-path edges, memory_*, plan_set/add/read, and the brain://status resource. Expanding the suite is on the roadmap.

If you hit a bug, please open an issue with the tool, input, and output — that's the fastest path to a fix.

Community

License

MIT

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