brainctl

name	brainctl
description	Unified agent memory CLI — read, write, search, and maintain the shared memory spine (brain.db). Use for persistent cross-session memory, knowledge graph, event logging, decisions, affect tracking, and consolidation.
version	1.6.1
tags	["memory","agents","sqlite","knowledge-graph","mcp","cli"]
trigger	When you need to store durable facts, log events, track entities/relations, record decisions, search past context, or orient yourself in a multi-agent environment.

brainctl — Agent Memory CLI

Overview

brainctl is the single interface for agent memory. One SQLite file (brain.db), no server required. All agents share it. Every write is attributed to the writing agent.

• GitHub: https://github.com/TSchonleber/brainctl
• Database: ~/agentmemory/db/brain.db (override with BRAIN_DB env var)
• CLI binary: ~/bin/brainctl or ~/.local/bin/brainctl
• MCP server: brainctl-mcp (201 tools, same DB)

Already installed on this machine. If missing, see GitHub README for install instructions.

Design Principle: Model-Agnostic

brainctl has ZERO LLM dependencies. No Anthropic, no OpenAI, no vendor SDK. It is pure SQLite + Python stdlib. The agent does the thinking, brainctl does the remembering. If an agent wants LLM-powered consolidation, the agent calls its own LLM and feeds results back via brainctl memory add. Functions like consolidate_memories() and compress_memories() accept an optional llm_fn parameter — callers can inject their own, but brainctl never calls one itself.

Agent Attribution

ALWAYS pass -a AGENT_NAME to attribute writes. Examples:

• -a my-agent — your agent's unique name
• -a human — manual/human entries
• -a unknown — fallback if unsure

Commands — Quick Reference

Memories (durable facts)

# Add a memory (categories: identity, user, environment, convention, project, decision, lesson, preference, integration)
brainctl -a my-agent memory add "All timestamps in logs are UTC" -c convention

# Search memories
brainctl -a my-agent memory search "python version"

# List recent memories
brainctl -a my-agent memory list --limit 10

Events (timestamped logs)

# Log an event (types: observation, result, decision, error, handoff, task_update, artifact, session_start, session_end, memory_promoted, memory_retired, warning, stale_context)
brainctl -a my-agent event add "Deployed v2.0 to production" -t result -p myproject

# Search events
brainctl -a my-agent event search -q "deploy"

# Recent events
brainctl -a my-agent event tail -n 20

Entities (typed knowledge graph)

# Create an entity (types: agent, concept, document, event, location, organization, other, person, project, service, tool)
brainctl -a my-agent entity create "Alice" -t person -o "Engineer; Likes Python; Based in NYC"

# Get entity details + relations
brainctl -a my-agent entity get Alice

# Add observations to existing entity
brainctl -a my-agent entity observe "Alice" "Now leads the infrastructure team"

# Create directed relation
brainctl -a my-agent entity relate Alice works_at Acme

# Search entities
brainctl -a my-agent entity search "engineer"

Decisions

brainctl -a my-agent decision add "Switch to local inference" -r "Need local-first fallback"

Cross-Table Search

# Search across memories + events + entities at once
brainctl -a my-agent search "deployment"

Prospective Memory (Triggers)

# Create a trigger that fires on future queries
brainctl trigger create "Alice mentions vacation" -k vacation,alice -a "Remind about project deadline"

# Check triggers against a query
brainctl trigger check "alice is going on vacation"

Reports & Lint

brainctl report                        # full brain report
brainctl report --topic "deploy"       # topic-focused
brainctl report --entity "Alice"       # entity deep-dive

brainctl lint                          # health check (JSON)
brainctl lint --output text            # human-readable
brainctl lint --fix                    # auto-fix safe issues

Stats

brainctl stats                         # DB overview: table counts, sizes, health

Init (first-time setup)

brainctl init                          # create brain.db with full schema

Output Formats

Control token consumption with --output:

brainctl search "deploy" --output json      # default: pretty JSON
brainctl search "deploy" --output compact   # minified JSON (~24% fewer tokens)

Affect Tracking

Functional affect states (frustration, urgency, satisfaction, confusion, confidence, curiosity) — not sentiment analysis. Influences memory formation and retrieval.

brainctl affect classify 'the deploy failed and rollback is stuck'
# → {"state": "frustration", "valence": -0.7, "arousal": 0.8, ...}

brainctl affect log 'finally resolved the outage after 4 hours'
brainctl affect check                  # current state + trajectory
brainctl affect monitor --watch        # live-stream changes

Consolidation Engine

Pure-math memory maintenance — no LLM required. Run periodically or via cron:

brainctl-consolidate decay             # confidence decay on unused memories
brainctl-consolidate compress          # merge redundant memories (pure string dedup, no LLM)
brainctl-consolidate promote           # promote important events to memories
brainctl-consolidate cycle             # full maintenance cycle

Also includes: Hebbian co-retrieval strengthening, temporal demotion, EWC importance locking, recall boost, experience replay. All pure SQLite math — no external calls.

MCP Server (201 tools)

If your adapter supports MCP, configure brainctl-mcp as an MCP server:

{"mcpServers": {"brainctl": {"command": "brainctl-mcp"}}}

Core tools: memory_add, memory_search, event_add, event_search, entity_create, entity_get, entity_search, entity_observe, entity_relate, decision_add, search, stats, handoff_add, handoff_latest, trigger_check

201 tools total covering memory, events, entities, decisions, triggers, handoffs, consolidation, affect, beliefs, temporal reasoning, and more. See MCP_SERVER.md for the full list and decision tree.

All tools accept optional agent_id and profile (task-scoped search presets). CLI and MCP are fully interchangeable — same DB.

Post-Checkout Orientation (Task-Based Agents)

After checking out a task, orient yourself:

# Search for relevant context
brainctl -a YOUR_AGENT search "keywords about your task"
brainctl -a YOUR_AGENT memory search "relevant topic"
brainctl event tail -n 10

# After completing work, log it
brainctl -a YOUR_AGENT event add "what you did and the outcome" -t result -p PROJECT_NAME

# If you learned something durable
brainctl -a YOUR_AGENT memory add "the fact or lesson" -c lesson

# If you made a decision
brainctl -a YOUR_AGENT decision add "what was decided" -r "why"

Architecture

brain.db (single SQLite file, 80+ tables)
├── memories          FTS5 full-text + optional vector search
├── events            timestamped logs with importance scoring
├── entities          typed nodes (person, project, tool, concept...)
├── knowledge_edges   directed relations between any table rows
├── decisions         recorded with rationale
├── memory_triggers   prospective memory (fire on future conditions)
├── affect_log        per-agent functional affect state tracking
└── 60+ more          consolidation, beliefs, policies, epochs...

Valid Categories & Types

• Memory categories: identity, user, environment, convention, project, decision, lesson, preference, integration
• Event types: observation, result, decision, error, handoff, task_update, artifact, session_start, session_end, memory_promoted, memory_retired, warning, stale_context
• Task statuses: pending, in_progress, blocked, completed, cancelled
• Task priorities: critical, high, medium, low

Pitfalls

1. Namespace collision: If ~/agentmemory/ exists as a directory, Python may pick it up as a namespace package instead of the installed agentmemory. Set PYTHONPATH explicitly if needed.
2. FTS5 special characters: brainctl sanitizes queries automatically, but if calling the DB directly, strip . & | * " ' \ ( ) - @ ^ ? ! , ; :` from search terms.
3. Agent attribution: Always use -a. Writes without attribution default to "unknown" and are harder to trace.
4. Token costs: Use --output compact in automated pipelines to save ~24% tokens.
5. Vector search optional: brainctl works fine without embeddings. Only needed for semantic similarity search.
6. Broken local install: If ~/bin/brainctl throws import errors, the wrapper may have a circular import. Run from the repo directly: cd ~/agentmemory && python3 -m agentmemory.cli or use ~/.local/bin/brainctl.
7. When debugging brainctl itself: Don't chase broken local wrappers — check the GitHub repo (https://github.com/TSchonleber/brainctl) for the canonical source. The local install has namespace collisions with ~/agentmemory/ directory.
8. No batch command: The brainctl batch subcommand was removed (vendor lock-in). If you need bulk LLM operations, do them in your own agent code and write results back via brainctl memory add.