Autonomous Claude Code Orchestration

Build. Learn. Evolve.
Without stopping.

Monomind wraps Claude Code with persistent memory, multi-agent swarms, a code knowledge graph, and self-optimizing neural training โ€” all wired into one CLI.

GitHub
0Agent Types
0CLI Commands
0Hook Events
0Packages
Why Monomind?
๐Ÿง 
Persistent Memory
Four-tier memory palace (L0โ€“L3) with BM25 search, HNSW vector indexing, and AgentDB backends. Context survives across sessions.
๐Ÿ
Swarm Topologies
Hierarchical, mesh, adaptive, and hybrid multi-agent swarms with Raft, Byzantine, and CRDT consensus. 15-agent domain hierarchy.
๐Ÿ”—
Knowledge Graph
Monograph builds a full dependency graph of your codebase โ€” 23 MCP tools for impact analysis, path finding, and community detection.
โšก
Self-Optimizing
SONA neural architecture with LoRA fine-tuning and EWC++ memory preservation. Learns from every hook event without catastrophic forgetting.
๐Ÿ”
Autonomous Loops
--tillend runs a command until nothing is left to do. /mastermind:autodev --tillend researches, builds, reviews, and loops while you sleep.
๐ŸŽฃ
Hook System
22 hook events, 10+ background workers. Pre/post-edit, pre/post-task, session, learning, and agent-team hooks with zero-overhead dispatch.
Quick Install
# Install globally npm install -g monomind # Wire into Claude Code claude mcp add monomind npx monomind@latest mcp start # Initialize a project npx monomind init npx monomind doctor --fix
1. Installation
# Option A: global install npm install -g monomind # Option B: npx (always latest) npx monomind@latest --version
2. MCP Setup

The MCP server requires the explicit mcp start subcommand. Auto-detect is disabled by default (set MONOMIND_MCP_AUTODETECT=1 to restore legacy behavior).

claude mcp add monomind npx monomind@latest mcp start # Verify tools are registered claude mcp list
3. Project Initialization
cd your-project npx monomind init # Interactive wizard: topology, memory backend, hooks npx monomind daemon start # Starts background workers (security, health, learning...) npx monomind doctor --fix # Checks: Node 20+, git, config, daemon, DB, MCP, disk
4. First Session
claude โ€” live session
5. Autonomous Loop
# Research โ†’ Build โ†’ Review until nothing left /mastermind:autodev --tillend # 9 improvements, then stop /mastermind:autodev 9 --tillend # Review loop until clean /mastermind:review --tillend --auto
Core Commands Quick Reference
CommandWhat it does
npx monomind initInitialize project with wizard
npx monomind doctorHealth diagnostics; auto-fix with --fix
npx monomind daemon startStart background worker daemon
npx monomind memory storeStore a memory with optional namespace
npx monomind memory searchBM25 + semantic hybrid search
npx monomind swarm initInitialize a multi-agent swarm
npx monomind hooks listShow all registered hooks
/mastermind:autodevAutonomous build loop inside Claude Code
System Diagram
@monomind/cli 41 commands Entry point @monomind/hooks 17 hook types ยท 12 workers Intelligence pipeline @monomind/memory AgentDB + HNSW Memory Palace L0โ€“L3 @monomind/swarm 6 topologies 5 consensus algorithms @monomind/neural SONA + LoRA + EWC++ Self-optimizing @monomind/security CVE remediation Input validation Monograph 23 MCP tools ยท Code knowledge graph @monomind/guidance Governance control plane
Package Inventory
PackagePathPurpose
@monomind/clipackages/@monomind/cli/CLI entry point โ€” 41 commands
@monomind/hookspackages/@monomind/hooks/17 hooks + 12 background workers
@monomind/memorypackages/@monomind/memory/AgentDB + HNSW vector search
@monomind/securitypackages/@monomind/security/Input validation, CVE remediation
@monomind/guidancepackages/@monomind/guidance/Governance control plane
@monomind/swarmpackages/@monomind/swarm/Multi-agent swarm coordination
@monomind/neuralpackages/@monomind/neural/SONA + LoRA + EWC++ training
monographpackages/monograph/Code knowledge graph, 23 MCP tools
monomind/ (root)Umbrella package published to npm
3-Tier Model Routing
TierHandlerLatencyCostUse Cases
1Agent Booster (WASM)<1ms$0Simple transforms โ€” skip LLM entirely
2Claude Haiku~500ms$0.0002Simple tasks, complexity <30%
3Claude Sonnet/Opus2โ€“5s$0.003โ€“0.015Architecture, security, complex reasoning
Memory Palace โ€” Four Tiers
Memory Palace visualization Memory Palace tier diagram
L0
In-flight
Current session context. Stored in drawers.jsonl. Each drawer has a key, value, tags array, timestamp, and score. Ephemeral โ€” not persisted across sessions unless promoted.
L1
Working
Cross-session working memory. Closets store aggregated entries from multiple drawers. Closet boost adds +0.5 per matching search term. BM25 parameters: K1=1.5, B=0.75.
L2
Long-term
Persisted embeddings. AgentDB with HNSW indexing. Hybrid backend combines SQLite for exact lookups with AgentDB for semantic search. 150xโ€“12,500x speedup over brute-force.
L3
Shared
Cross-agent namespace. PartitionedHNSW shards by agent domain. Federation allows swarm agents to read each other's L3 without owning writes. Knowledge graph edges land here.
Storage Backends
BackendUse CaseNotes
SQLiteBackendSmall projects, single-agentDefault. No external deps.
AgentDBBackendVector semantic searchPure-TypeScript HNSW index.
HybridBackendProduction (recommended)SQLite + AgentDB per ADR-009. Auto-routes by query type.
PartitionedHNSWMulti-agent L3Domain-sharded; agent-scoped writes.
DiskAnnBackendVery large datasetsDisk-resident ANN index.
Monograph โ€” Code Knowledge Graph

Monograph builds a SQLite-backed graph of your codebase โ€” nodes are files/classes/functions, edges are imports/calls/dependencies. Use it before touching 3+ files.

ToolWhen to use
monograph_suggestStart every task โ€” returns relevant files ranked by task description
monograph_queryBM25 keyword search; returns file + line number
monograph_impactBefore changing anything โ€” full blast radius analysis
monograph_context360ยฐ view of a file: importers, imports, siblings
monograph_god_nodesHigh-centrality files (external/test auto-filtered)
monograph_shortest_pathHow two modules connect
monograph_renameDry-run multi-file rename with graph + text occurrences
monograph_detect_changesMap git diff to affected graph nodes
monograph_communityFiles forming a cohesive module cluster
monograph_cypherAd-hoc graph queries (Cypher syntax)
Learning Pipeline
Step 1
RETRIEVE
HNSW ANN search across L2/L3 tiers. Returns top-K neighbors by cosine similarity in sub-millisecond time.
Step 2
JUDGE
Relevance verdicts computed by RuVector. High/low/neutral signals flow to the distillation step.
Step 3
DISTILL
LoRA fine-tune on high-signal trajectories. Rank 1โ€“16 adapter applied to base model without full retraining.
Step 4
CONSOLIDATE
EWC++ (lambda 1500โ€“2500) prevents catastrophic forgetting while incorporating new patterns. Elastic weight consolidation preserves prior task performance.
Hook Events
EventCategoryDescription
pre-editcoreContext and agent suggestions before editing a file
post-editcoreRecord edit outcome for neural pattern learning
pre-commandcoreIntercept command before execution
post-commandcoreRecord command result
pre-taskcoreRegister task start; get AGENT_BOOSTER_AVAILABLE or TASK_MODEL_RECOMMENDATION
post-taskcoreRecord completion; train patterns
session-startsessionRestore cross-session context
session-endsessionPersist state; flush working memory
session-restoresessionReplay session state into context
notifysessionPush notification to user or external system
routeintelligence3-tier model routing decision
explainintelligenceExplain decision rationale
pretrainintelligencePre-training signal ingestion
build-agentsintelligenceAuto-spawn agents for a task
transferintelligenceTransfer learning across tasks
teammate-idleteamsAuto-assign task to idle teammate
task-completedteamsTrain patterns; notify team lead
Background Workers
WorkerPriorityFunction
securitycriticalContinuous security scanning and CVE alerting
healthcriticalSystem health monitoring, daemon watchdog
swarmcriticalSwarm coordinator heartbeat and agent lifecycle
performancenormalToken cost tracking, latency profiling
patternsnormalPattern extraction from hook trajectories
learningnormalSONA LoRA fine-tune on high-signal runs
gitnormalCommit analysis, branch diff summaries
dddlowDomain-driven design pattern detection
adrlowArchitecture Decision Record harvesting
cachebgContext preloading and cache warming
Hook Configuration
// .claude/settings.json { "hooks": { "pre-task": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "npx monomind hooks pre-task --description \"$TASK\"" }]}], "post-edit": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "npx monomind hooks post-edit --file \"$FILE\"" }]}] } }
Swarm topology diagram
Topologies
Hierarchical
Queen โ†’ domain leads โ†’ workers. Best for complex feature development. Default.
Mesh
Peer-to-peer, all agents equal. Best for distributed analysis with no single authority.
Hierarchical-Mesh
Queen manages peer clusters. Recommended for large swarms.
Adaptive
Reconfigures topology dynamically. High overhead โ€” use for long-running sessions.
Centralized
Single coordinator, fan-out workers. Fastest for homogeneous parallel tasks.
Star
Hub-and-spoke. Testing swarms โ€” one orchestrator, N independent runners.
Consensus Algorithms
AlgorithmFault ToleranceBest For
Raftf < n/2Default. Strong consistency, single leader. Production code changes.
Byzantinef < n/3Untrusted agent environments, security-critical decisions.
GossipEventualLarge swarms where propagation latency is acceptable.
CRDTPartition-tolerantConcurrent state merges without coordination (offline agents).
QuorumMajorityRead-heavy workloads with strict majority agreement.
15-Agent Domain Hierarchy
DomainAgents
Queen / OrchestrationQueenCoordinator, AttentionCoordinator
ArchitectureArchitect, SystemDesigner
EngineeringCoder, BackendDev, FrontendDev
QualityTester, Reviewer, SecurityAuditor
Memory / IntelligenceMemorySpecialist, NeuralTrainer
OperationsPerfEngineer, MonitorAgent
ResearchResearchAgent
Quick Launch
# Hierarchical swarm, 8 agents, specialized strategy npx monomind swarm init \ --topology hierarchical \ --max-agents 8 \ --strategy specialized # Hive-mind with Byzantine consensus npx monomind hive-mind init \ --topology hierarchical-mesh \ --consensus byzantine # Interactive topology picker in Claude Code /mastermind
Neural learning visualization
SONA Operating Modes
ModeLoRA RankBatchLatency TargetMemory
real-time1โ€“21<50ms<512MB
balanced4โ€“84<500ms<2GB
research1616uncapped<8GB
edge11<10ms<256MB
batch8โ€“1632offline<16GB
LoRA + EWC++
LoRA Fine-Tuning
Low-Rank Adaptation injects trainable rank decomposition matrices into attention layers. Rank 1โ€“16. No full model retraining โ€” only adapter weights update.
EWC++ Memory
Elastic Weight Consolidation with lambda 1500โ€“2500. Constrains gradient updates on weights important to prior tasks. Prevents catastrophic forgetting across training rounds.
RL Algorithms
AlgorithmUse Case
PPODefault RL trainer. Proximal Policy Optimization โ€” stable, on-policy.
DQNDeep Q-Network โ€” discrete action spaces, tool selection.
A2CAdvantage Actor-Critic โ€” faster convergence for parallel environments.
DecisionTransformerSequence-based RL โ€” models trajectories as token sequences.
QLearningClassic tabular Q โ€” lightweight for small state spaces.
SARSAOn-policy TD โ€” conservative, lower variance than Q-Learning.
CuriosityModuleIntrinsic motivation โ€” exploration bonus for novel states.
CLI
# Train with Flash Attention (2.49xโ€“7.47x speedup) npx monomind neural train --flash # Mixture-of-Experts training npx monomind neural train --moe # Optimize existing model npx monomind neural optimize --method quantize npx monomind neural optimize --method compact
init
Project init wizard โ€” topology, memory, hooks
init --preset minimal
Initialize with a named preset configuration
agent spawn
Spawn a named agent by type
agent list
List active agents and their status
agent status <id>
Detailed agent info and metrics
agent stop <id>
Gracefully terminate an agent
agent metrics
Performance metrics by agent type
swarm init
Initialize a multi-agent swarm
swarm status
Health report: agents, tasks, memory
swarm spawn
Add an agent to a running swarm
swarm monitor
Live swarm telemetry stream
swarm stop
Gracefully shutdown all swarm agents
memory store
Store a memory entry with optional namespace
memory search
BM25 + semantic hybrid search
memory list
List memories by namespace or tag
memory delete <key>
Remove a memory entry
memory export
Export memories to JSON
memory import
Import memories from JSON
memory stats
Entry counts, backend info, index health
mcp start
Start MCP server (required for claude mcp add)
mcp list
List all registered MCP tools
mcp status
Server health and connection info
task create
Create a task with title, description, priority
task list
Show tasks filtered by status or board
task update <id>
Update task status, priority, assignee
task delete <id>
Remove a task permanently
hooks pre-task
Register task start; get routing recommendation
hooks post-task
Record completion; trigger learning
hooks pre-edit
Context suggestions before file edit
hooks post-edit
Record edit outcome for patterns
hooks session-start
Restore cross-session context
hooks session-end
Persist state; flush working memory
hooks route
3-tier model routing decision
hooks worker list
List active background workers
hive-mind init
Initialize hive with topology + consensus
hive-mind spawn
Spawn workers; --claude launches Claude as Queen
hive-mind status
Active workers, metrics, consensus state
daemon start
Start the background worker daemon
daemon stop
Gracefully stop all daemon processes
daemon status
PID, uptime, worker health report
neural train --flash
Train with Flash Attention (2.49xโ€“7.47x speedup)
neural optimize
Optimize model: quantize, analyze, compact
neural status
Training state, LoRA rank, EWC lambda
security scan
Run CVE and vulnerability scan
security validate
Input validation and sanitization check
performance profile
Record latency and token cost profiles
performance report
Time-series performance metrics
doctor
System diagnostics โ€” all components
doctor --fix
Auto-fix: Node version, DB init, daemon start
monograph build
Build the code knowledge graph
monograph search
BM25, semantic, hybrid search across graph
monograph stats
Node/edge counts, top concepts by importance
monograph watch
Watch mode: incremental rebuild on file changes
browse open <url>
Open URL in CDP browser client
browse snapshot -i
Get interactive elements (93% less context)
browse click @e1
Click element by snapshot ref
/browse
UI testing via monomind browse CDP client
/mastermind
Swarm topology picker โ€” all 11 modes, one recommendation
/sparc
Execute SPARC methodology workflows
/tokens
Token usage dashboard: today/week/30days/month
/ts
Toggle statusline between full and compact mode
/mastermind:autodev
Autonomous researchโ†’buildโ†’review loop
/mastermind:build
Build a specific feature from a brief
/mastermind:review
Iterative code review until clean
/mastermind:architect
System architecture and design
/mastermind:research
Deep research with structured output
/mastermind:release
Manage a software release
/mastermind:idea
Idea generation and evaluation
/mastermind:ops
Operations planning and tracking
/mastermind:finance
Financial modeling and analysis
/mastermind:marketing
Marketing strategy and content
/mastermind:brain
Inspect and manage mastermind brain context
/mastermind:techport
Technical portfolio โ€” assess project state
/mastermind:master
High-level project master control
/monomind:do
Execute tasks from monotask board
/monomind:createtask
Decompose prompt into tasks on monotask board
/monomind:improve
Deep component improvement pipeline
/monomind:review
Multi-agent iterative review with auto-fix
/monomind:repeat
Universal loop wrapper โ€” repeat any command
/monomind:understand
Semantic enrichment for monograph
/monomind:adr
Draft Architecture Decision Record
/monomind:budget
Budget status: today, month, limits, autotune
/monomind:graph-status
Single-line graph stats: nodes, edges, freshness
/monomind:loops
List active --tillend/--repeat loops
/monomind:swarm
Swarm coordination reference
/monomind:help
Master quick-reference: all skills and commands
/swarm:development
Hierarchical: architectโ†’coderโ†’tester flow
/swarm:analysis
Mesh + adaptive for codebase analysis
/swarm:testing
Star + parallel for all test types
/swarm:optimization
Profiling, bottleneck detection, optimization
/swarm:swarm-modes
Overview of all swarm topologies
/swarm:swarm-strategies
Decision matrix for strategy selection
/sparc:architect
System architecture and design
/sparc:code
Auto-coder implementation mode
/sparc:tdd
Test-driven development
/sparc:debugger
Debug mode โ€” trace + fix
/sparc:reviewer
Code review mode
/sparc:security-review
Security audit and hardening
/sparc:documenter
Documentation generation
/github:pr-manager
Full PR lifecycle with swarm coordination
/github:issue-tracker
Issue creation and progress tracking
/github:release-manager
Release coordination and CI/CD
/monodesign craft
Shape + build a UI feature end-to-end
/monodesign shape
Plan UX/UI before writing code
/monodesign audit
Technical quality: a11y, perf, responsive
/monodesign critique
UX design review with heuristic scoring
/monodesign polish
Final quality pass before shipping
/monomotion
GSAP animation skill โ€” all motion work
/monograph:monograph-build
Build knowledge graph from codebase
/monograph:monograph-search
BM25, semantic, hybrid graph search
/monograph:monograph-watch
Incremental rebuild on file changes
/hooks:setup
Configure hooks in settings.json
/hooks:overview
How hooks wire into Claude Code
/memory:README
Full memory CLI reference: all 14 subcommands
autodev โ€” The Autonomous Build Loop
Phase 1
Research
Parallel: git log, file scan, package.json, README, TODO/FIXME grep, monograph god nodes, memory search for already-built items. Returns ranked list of 3โ€“5 improvement candidates.
Phase 2
Selection
Picks best candidate by feasibility ร— blast-radius ร— focus alignment. Stores selection to AgentDB memory. Focus flags: --focus security, --focus dx, --focus performance.
Phase 3
Build
Invokes mastermind:build with concrete spec, acceptance criteria, and blast radius guard. Spawns architect โ†’ coder โ†’ tester โ†’ reviewer agent chain.
Phase 4
Review Loop
Invokes mastermind:review inline. Runs Code Reviewer + Security Engineer + Reality Checker in parallel. Repeats up to 5 iterations until zero findings.
Phase 5
Log
Records completion to memory. If count > 1, continues to next improvement. If --tillend, schedules next session via ScheduleWakeup.
--tillend Loop Mechanics
Session 1: /mastermind:autodev --tillend โ†’ runs โ†’ schedules ScheduleWakeup(delay=60s) Session 2: wakeup fires โ†’ checks staleness guard โ†’ if --rep N < state.currentRep: SKIP (stale wakeup) โ†’ else: run โ†’ schedules next wakeup Session N: empty round (zero findings AND zero actions) โ†’ emits loop:complete โ†’ removes .monomind/loops/{id}.json โ†’ STOP # Stop a loop manually touch .monomind/loops/{loop-id}.stop
Universal Flags
FlagDefaultPurpose
--tillendoffRepeat until empty round (zero findings, zero actions)
--repeat <N>offRepeat exactly N times
--wait <seconds>60Minimum seconds between repeat runs
--maxruns <N>50Safety cap for --tillend
--autooffNo confirmation prompts
--confirmoffAsk before each action
--focus <area>โ€”Bias toward: security, dx, performance
All Mastermind Commands
CommandPurpose
/mastermind:autodevAutonomous research โ†’ build โ†’ review loop
/mastermind:buildBuild a specific feature from a brief
/mastermind:reviewIterative code review; auto-fixes findings
/mastermind:architectSystem architecture design and review
/mastermind:researchDeep research with structured output
/mastermind:techportTechnical portfolio โ€” assess project state
/mastermind:ideaIdea generation and evaluation
/mastermind:opsOperations planning and tracking
/mastermind:financeFinancial modeling and analysis
/mastermind:marketingMarketing strategy and content
/mastermind:salesSales strategy and pipeline
/mastermind:contentContent creation and strategy
/mastermind:createorgCreate and configure an organization structure
/mastermind:runorgRun organization-level workflows
/mastermind:releaseManage a software release
/mastermind:masterHigh-level project master control
/mastermind:brainInspect and manage brain context
/mastermind:goalsGoal tracking and progress
Brain Protocol
// Brain Load (session start) 1. Search AgentDB namespace "mastermind:{domain}" 2. Three tiers: raw records (last 7 days) โ†’ weekly summaries โ†’ long-term principles 3. Inject combined BRAIN CONTEXT block into skill // Brain Write (session end) 1. Store key decisions + findings to AgentDB 2. Update weekly summaries and principles
Overview

The dashboard is a single-page application with a 196px fixed sidebar and a scrollable main area. URL parameters support deep-linking: ?proj=<path> switches project context and &sess=<id> opens a specific session.

Topbar ElementDescription
Live dotPulses green when an active Claude Code session is detected
Today cost badgeCumulative USD spend for the current calendar day
Session cost tickerRunning cost of the currently focused session, updates in real time
Activity chipCurrent agent type and task description from the most recent hook event
โฌก MastermindOpens the Mastermind overlay (violet hexagon button)
โš‘ BudgetOpens a modal for setting daily and monthly spend limits (USD). Fields: Daily limit, Monthly limit. Once saved, the alerts rail shows a warning when actual spend approaches the limit.
โŒ˜K SearchOpens the command palette for cross-view navigation
? HelpShows keyboard shortcuts reference
โ†บ RefreshForce-reloads current view data

Status strip (shown below topbar when data is present):

ChipMeaning
HNSW ON / OFFWhether the HNSW vector index is loaded and searchable
PATTERNS NNumber of learned routing patterns in AgentDB
CHUNKS NNumber of indexed text chunks available for semantic search
SWARM topologyActive swarm topology name (hierarchical, mesh, adaptiveโ€ฆ)
LAST ROUTEAgent name that the routing hook most recently selected

The sidebar footer shows your GitHub username and the absolute path of the current working directory.

Now โ€” Live Feed

The Now view is a three-column layout: feed pane (left), optional detail panel (slides in from the right edge of the feed), and metrics pane (252px, fixed right).

Feed ControlKeyBehavior
Play / PauseSpaceToggle live-tail; pausing freezes the list without disconnecting
Clearโ€”Removes all entries from the visible feed buffer
Filter/Opens inline search โ€” filters entries by text or tool name
Jump to liveGScrolls to the newest entry and resumes live-tail
Ambient modeAHides all chrome; only the feed remains
Replayโ€”Re-plays the current session from the beginning at configurable speed
FocusFExpands the feed to full width, hiding metrics pane
Exportโ€”Downloads visible feed entries as JSONL

Feed entry types:

TypeAppearance
Tool eventsColored category icon (file, bash, web, memory, agentโ€ฆ), tool name, condensed args
User messagesItalic text with a person icon and timestamp
Error entriesRed left border, error message, optional stack trace expand

Additional feed UI elements:

  • Session context bar โ€” pinned at top of feed: session ID, model, start time, current cost
  • Recap card โ€” collapsible summary of the previous session's key decisions
  • Replay bar โ€” scrubber, speed selector (0.5ร—, 1ร—, 2ร—, 4ร—), play/pause when in replay mode
  • Timeline bar โ€” horizontal ruler showing tool-call density over session time
  • Time-range filter โ€” drag handles on the timeline to zoom into a time window
  • Weekly card โ€” dismissible summary card (stored in localStorage) showing 7-day stats
  • Daily digest card โ€” dismissible AI-generated digest of today's activity
  • Feed minimap โ€” 8px-wide column of colored pips on the right scroll edge for quick navigation

Group rows โ€” when 3 or more consecutive events share the same category, they are collapsed into a single row showing the count and a โ–ธ expand affordance. Clicking the group row expands all entries in place.

Detail panel: slides in 280px when an entry is clicked. Shows full event metadata, raw JSON payload, and a Copy button.

Metrics pane groups (252px, right):

GroupWhat it shows
TodayToday API cost, call count, and a 7-day activity sparkline
Active LoopsRunning loop names with live countdown timers
Recent SessionsLast 5 sessions with cost and duration chips
Tool UsageBar chart of most-called tools in the current session
Burn RateThree rolling gauge rows โ€” 5 min, 15 min, 60 min โ€” each showing tool-call rate in calls/minute. Color: green < 4 calls/min, amber 4โ€“10, red โ‰ฅ 10
Session LanesParallel swimlanes showing concurrent agent activity
Cache EfficiencyPer-session cache hit rate rows (cached tokens / total input tokens), with the rolling average shown in the group header.
Token VelocityTokens/minute sparkline for the current session
30-Day Cost TrendBar chart of daily spend over the last 30 days
Activity HeatmapHour-of-day ร— day-of-week grid colored by event density
Projects

Projects are discovered from Monomind's config registry. The view renders an auto-fill card grid (min 240px) with a filter input at the top.

Card ElementDescription
Active badgeGreen "ACTIVE" pill โ€” shown when the project's directory is the current working directory
Health score circle0โ€“100 score: green โ‰ฅ 70, amber 40โ€“69, red < 40
Name & pathProject display name and filesystem path
Sessions countTotal recorded sessions for this project
Memories countNumber of memory files scoped to this project
Last activeRelative time since the last session event

Clicking a card switches the global project context (updates the ?proj= URL param) and navigates to the Sessions view filtered to that project.

Sessions

Sessions are created automatically when you use Claude Code in any project. Start a session by opening Claude Code in your project directory:

# Sessions appear automatically โ€” just use Claude Code # The dashboard discovers sessions from ~/.claude/projects/<project>/

Sessions are grouped by recency: Today, Yesterday, This week, Older โ€” each group header is collapsible. A 12-week clickable calendar heatmap appears above the session list. Each cell represents one day; opacity scales with session count. Clicking a cell filters the session list to that day. The heatmap header shows a week-over-week delta indicator (e.g. โ†‘12%, โ†“5%, โ†’ flat).

Each session row includes:

  • Prompt text (truncated) with expand on hover
  • Timestamp (relative) and compacted badge (when the session was auto-compacted)
  • Summary excerpt โ€” 2-line clamp of the AI-generated session summary
  • Meta line: duration ยท N messages ยท $cost
  • Anomaly badges: costly (cost > 2ร— average) and errors (tool errors detected)
  • Auto-tags: topic tags inferred from session content (e.g. refactor, bug-fix)
  • File chips: up to 5 file paths touched in the session
  • Context saturation bar: horizontal progress showing % of context window consumed (message count vs. limit)
  • Notes โ€” each row has a โœŽ notes toggle (shown in blue when a note exists). Click to expand an inline textarea for free-form notes stored in localStorage, keyed by session ID.
  • Error drawer โ€” the error anomaly badge (N err) is clickable. Clicking it toggles an inline drawer below the row listing all tool error messages from that session.
  • Custom tags โ€” a '+ tag' affordance on each row adds manually typed labels (stored in localStorage). Custom tags appear as blue-toned pills alongside auto-tags and are included in the sessions filter.
  • Context window pressure gauge โ€” a separate 3px bar based on actual input token count vs the 200k context ceiling. Color: green (normal), amber (warn), red (critical). Shows a 'N% ctx' label. Distinct from the message-count saturation bar.

Toolbar toggle buttons:

ButtonEffect
StarredFilter to bookmarked sessions only
LeaderboardSort by cost descending โ€” shows most expensive sessions first
ModelsShow model name chip on each row
ErrorsFilter to sessions that contain tool errors
ToolsShow tool-call summary chips on each row
ProjectsShow project path chip on each row
CSVExport current filtered session list as CSV
PatternsShow learned routing pattern badges
CompareEnable multi-select mode for session diff (select exactly 2)
TimelineSwitch to a Gantt-style timeline visualization
DonutShow a cost-by-model donut chart above the list
ReportOpens the Report Card modal with a structured plain-text summary of session activity for the current period. Includes a โŽ˜ Copy button โ€” content is copied to clipboard, not downloaded.

Period filter: Day / Week / Month / All โ€” scopes the session list and heatmap to the selected window. Bulk actions become available when multiple rows are checked: Export, Bookmark, Clear.

Session diff: selecting exactly 2 sessions with Compare mode opens a side-by-side panel showing cost delta, duration delta, tool overlap, and unique tool calls per session.

Loops

Loops are created manually from the dashboard (+New Loop button) or via slash commands:

# Create a loop from Claude Code /mastermind:autodev --tillend # autonomous loop until nothing left /mastermind:review --tillend --auto # review loop until clean /monomind:repeat --every 1h /mastermind:autodev # custom repeat schedule

A loop is a scheduled automation: a prompt that re-runs at a fixed interval until a max-rep limit is reached or it is manually stopped.

Loop Card ElementDescription
NameHuman-readable loop label
Interval chipRepeat cadence (e.g. 1h, 30m, daily)
Countdown timerLive seconds-ticking countdown to the next run
Progress barcurrentRep / maxReps fill bar
Status badgerunning, paused, or completed
Stop buttonImmediately cancels the loop and records final state

Expand a loop card to see: full prompt text, interval, started-at timestamp, last-run timestamp, run count, and a sparkline of per-run duration history.

Create Loop form fields:

FieldRequiredDefault
PromptYesโ€”
NameNoAuto-generated from prompt
IntervalNo1h
Max RepsNoUnlimited
Tokens

Token data is collected automatically from every Claude Code session. No setup required โ€” costs appear as soon as API calls are made.

The Tokens view provides cost and usage analytics with period-based filtering.

๐Ÿ’ต
Today Cost
Cumulative USD spend for the current calendar day across all sessions
๐Ÿ“ž
Today Calls
Total API calls made today
๐Ÿ“…
Month Cost
Spend for the current calendar month
๐Ÿ”ข
Total Tokens
Cumulative input + output tokens across all recorded sessions

Period tabs โ€” Today / Week / 30 Days / Month โ€” each fetches /api/token-usage?period=X and re-renders the chart and table.

Animated bar chart: 400ms ease-out-cubic entrance animation. Bar colors follow threshold logic:

  • Green โ€” cost < average ร— 0.5 (low spend day)
  • Amber โ€” cost in normal range
  • Red โ€” cost โ‰ฅ average ร— 1.5 (high spend day)

The current day's bar is full opacity; all other bars render at 50% opacity. Below the chart, a session breakdown table shows: Session, Calls, Tokens, Cost per row.

Memory

The Memory view has two distinct data sources: Memories are explicit .md files with YAML frontmatter stored in the .monomind/memories/ directory. Chunks are indexed text segments extracted from project source files and documentation for semantic search.

Memory TypeColorPurpose
userIndigoPersonal preferences, identity, working style
feedbackAmberAgent routing decisions and confidence scores
projectTealProject-scoped facts, patterns, decisions
referenceVioletReference documentation, API facts
handoffPinkCross-session state for next session pickup

Memories are created in three ways:

# 1. Auto-memory hook (runs during Claude Code sessions) # Files written to: ~/.claude/projects/<project>/memory/ # 2. From the dashboard # Click "+ New Memory" โ†’ choose type โ†’ fill frontmatter # 3. Via Claude Code memory commands monomind memory store --key "preference" --value "..." --namespace user

The Memory view has 7 sub-tabs:

TabDescription
MemoriesSplit-pane list + detail editor. Full CRUD: create, edit (modal with type/title/content fields), delete. New Memory button offers 4 type templates: user (personal preferences/identity), feedback (routing decisions), project (project-scoped facts), reference (documentation/API facts). Each pre-populates a minimal YAML front matter stub.
RoutingRouting feedback log โ€” each entry shows agent chosen, confidence %, task description, and timestamp. YAML front matter stub format for new memories:
---
name: my preference
description: Short description of this memory
type: user
---

Content body here...
UsagePeriod tabs + stat grid (cost, calls, tokens, cache efficiency) + 5 bar charts: Models, Activity by hour, Tools, MCP Servers, Projects
ADRsArchitecture Decision Records as filterable cards. Status badges: Accepted, Proposed, Deprecated. Category filters: implementation, guidance
SwarmSwarm run list + canvas topology visualization โ€” static snapshot drawing, drawn once when a run is selected. Hierarchical: queen node (labeled Q, teal, larger) at top, worker nodes spread below, straight edges connecting them. Mesh/other: nodes in a circle with all-to-all edges. Event log for selected run. Clean Data button purges stale run data
ChunksStats bar (chunk count, skill namespaces, total namespaces). Filter by namespace or keyword. Chunk cards show source file, excerpt, and namespace. Edit/Delete per card. Build Docs button triggers indexing with progress polling
Agent GraphSummary stat cards, session list, agent spawn bar chart (agents per session), tool usage bar chart (calls per tool across all sessions)

Routing tab: Routing data is collected automatically whenever Claude Code routes a task to an agent. No setup required. Use /hooks:overview to understand the routing hook.

ADRs tab: ADRs are created via the slash command or discovered automatically from existing ADR files:

/monomind:adr # draft an ADR from accumulated decision markers in the codebase

Swarm tab: Swarm history is created when you run agent organizations:

/mastermind:createorg # create an org with roles and topology /mastermind:runorg # start the org โ€” agents run and swarm data is recorded

Chunks tab: Chunks are built by indexing your project's documentation and skill files:

# From the dashboard: click "โŠ• Build Docs" in the Chunks tab # From Claude Code: /monomind:understand # semantic enrichment โ€” indexes all .md/.txt files monomind daemon start # background daemon also indexes incrementally

Agent Graph tab: The Agent Graph is populated automatically by sessions that spawn sub-agents. Spawn agents by running complex tasks via /mastermind:build, /mastermind:autodev, or any orchestration command.

Orgs

Create and manage agent organizations:

/mastermind:createorg # define a new org: roles, hierarchy, goal, topology /mastermind:runorg # start a saved org # Or use the "โฌก Mastermind" button โ†’ Create Org tab for a form-based workflow

Organizations are multi-agent structures with roles, goals, routines, and approval workflows. Each org card shows: a running dot (live indicator), name, goal description, topology/role chips, and an avatar stack of active agents.

Each org has 29 tabs:

TabWhat it shows
ChartOrg hierarchy as an animated SVG โ€” nodes for each role, animated edges for reporting lines
RolesRole cards listing responsibilities, required skills, and reporting structure
ActivityChronological event log of all agent actions within the org
HealthMetrics grid: task completion rate, error rate, average response time, agent uptime
HeartbeatsPer-agent heartbeat list showing last ping time and status (alive/stale/dead)
TasksFull task list with status, assignee, priority, and due date
CostsCost breakdown by agent, role, and time period with totals
MembersHuman and agent member list with role assignments
GoalsHierarchical goal tree with progress bars for each objective and sub-objective
BoardKanban board with columns: Open, In Progress, Blocked, Done, Cancelled
LiveReal-time agent activity feed; auto-refreshes every 5 seconds. Shows currently running agents and their current task
ApprovalsApprove/reject queue for actions that require human sign-off
SecretsSecret names and scopes โ€” values are always masked (never shown in plaintext)
SettingsOrg configuration form that generates the equivalent CLI command for reproducibility
RoutinesCron table of scheduled automations: name, schedule expression, last run, next run, status
My IssuesIssues assigned to the current user or agent
BudgetsFill bars showing spend vs. budget limit per agent and in aggregate
PluginsInstalled plugin status cards: name, version, enabled toggle, health indicator
Charts14-day activity heatmap + per-agent bar charts for calls and cost
ProjectsCard grid of projects associated with this org
SkillsRole ร— skill matrix โ€” cells show proficiency level (none/basic/expert)
WorkspacesPer-agent workspace rows: branch, working directory path, running services
InvitesPending invitations with invite token, role, and expiry timestamp
AgentsFull agent table: name, type, status, model, last active, total calls, total cost
EnvironmentsAgent environment configs: driver (node/python/docker), host, status
AccessMembers grouped by role tier with their granted permissions listed
IssuesFull issue table (up to 100 rows): title, status, priority, assignee, labels, created date
Join QueuePending join requests showing requester (human ๐Ÿ‘ค or agent ๐Ÿค– emoji) and approval status
ThreadsDiscussion threads: subject, author, message count, linked issue
Monograph

The knowledge graph must be built before Monograph tabs show data:

# Build the graph (analyzes imports, calls, dependencies) /monograph:monograph-build # full build monomind monograph build --code-only # code only, skip docs # From the dashboard: Graph tab โ†’ click "โ†บ REBUILD" # Enable watch mode to auto-rebuild on file changes: WATCH toggle in Graph tab

The Monograph view provides a full knowledge-graph interface for your codebase. It has 7 tabs:

TabDescription
OverviewStat cards (total nodes, total edges, average degree, type counts), top 6 nodes by degree, and a type-distribution breakdown
GraphInteractive force-directed graph in an iframe. WATCH toggle enables file-system watching so the graph auto-rebuilds on file changes. REBUILD button forces a full rebuild immediately
AnalyzeTwo groups of analyses: 9 client-side (Stats, PageRank, Dead Code, Components, Topological Sort, Hubs, Cycles, Surprises, Communities) and 20 server-side MCP analyses (Health Score, Risk Profile, Hotspots, Maintainability, Complexity, CRAP Score, Instability, Coupling Balance, Circular Dependencies, Unlinked References, Dead Exports, Reachability, Boundary Check, Bridges, Cohesion, Largest Files, Languages, Churn Hotspots, Detect Changes, Authors)
Query6 query forms: Search nodes (BM25 keyword), Impact analysis (upstream/downstream dependents), Context (360ยฐ view of a file), Shortest path (between two nodes), Cypher query (raw graph query), Ask the graph (natural language)
ExportDownload the graph in: JSON, SVG, GraphML, or Cypher format
ReportRendered GRAPH_REPORT with summary stats, top nodes by degree, and type distribution
WikiSearchable node browser โ€” type filter pills, mode toggle (All / Docs / Code), full-text search. Clicking a node opens a detail panel with Impact, Context, and Explain actions

The Wiki tab shows code nodes immediately after a graph build. To also see documentation nodes (from .md/.mdx/.txt files), click BUILD DOCS inside the Wiki tab or run /monomind:understand.

Global Feed

A unified activity stream across all registered projects, accessible from the sidebar "โŠ• Global Feed" item.

The Global Feed populates automatically as you use Claude Code across multiple projects. No setup required โ€” add more projects via monomind init in each project directory.

Each event entry displays a project tag chip alongside the event data, so you can see which project generated each activity. The feed is assembled by fetching /api/projects and then merging session events from all project paths.

Unlike the Now view (which is scoped to one project), the Global Feed gives a workspace-wide perspective โ€” useful for multi-project workflows and general activity monitoring.

Mastermind Overlay

The Mastermind overlay opens by clicking the violet โฌก Mastermind button in the topbar. It has 6 tabs:

TabDescription
OrgsList of all orgs โ€” click to switch the dashboard's active org context
SkillsSearchable catalog of all 35+ slash-command skills. Click any skill to copy the /skill-name command to clipboard
LoopsRunning loops with live countdown timers and Stop buttons โ€” mirrors the Loops view in a compact panel. Create loops with /mastermind:autodev --tillend or the +New Loop button in the Loops view.
Create OrgForm that generates and copies the equivalent /mastermind:createorg CLI command when submitted
MetricsSummary cards for current cost, API calls, routing confidence distribution, and active swarm topology
GraphNode and edge counts for the Monograph index, list of top god nodes, and an Open Monograph button
Command Palette โŒ˜K

Open with โŒ˜K or the Search button in the topbar. Results are grouped:

GroupContents
SessionsRecent sessions โ€” select to open that session in the Now view
MemoryMemory entries โ€” select to open the memory detail editor
ProjectsRegistered projects โ€” select to switch project context
OrgsOrganizations โ€” select to switch to that org's view
SkillsSlash-command skills โ€” select to copy the command
ActionsQuick actions: Open Monograph, Refresh, Toggle Compact, Open Loops
TabsAppears when an org is selected โ€” lists all 29 org tabs for direct navigation

Type a > prefix in the palette input to switch to cross-session full-text search mode โ€” queries all session content, not just titles.

Keyboard Shortcuts
KeyActionContext
โŒ˜KOpen command paletteGlobal
?Show keyboard shortcuts helpGlobal
RRefresh current viewGlobal
EscClose open panels or modalsGlobal
JNavigate down in listNow, Sessions
KNavigate up in listNow, Sessions
โ†ตOpen detail panel / jump to sessionNow, Sessions
/Open feed searchNow
GJump to live session (newest entry)Now
AToggle ambient modeNow
FToggle focus mode (full-width feed)Now
SpacePlay / pause live tailNow
API Reference

All endpoints are served by the local dashboard server at localhost:4242. SSE endpoints stream newline-delimited JSON.

Navigation / Init

MethodEndpointDescription
GET/api/git-userGit username and email
GET/api/projectsAll registered projects
GET/api/statusSystem health: HNSW, memory

Sessions

MethodEndpointDescription
GET/api/session-journalSession metadata list
GET/api/sessionEvents for one session file
GET (SSE)/api/events-streamLive tool event stream
GET/api/search-sessionsFull-text session search
GET/api/session-errorsSession error list
GET/api/tool-errorsTool error list per session
GET/api/tool-rankingTool call frequency
GET/api/project-costsPer-project cost breakdown

Memory

MethodEndpointDescription
GET/api/sectionNamed data sections (e.g. tokens)
GET/api/memory/statsAgentDB stats
GET/api/memory-files.md file list
PUT / DELETE/api/memory-fileCreate or delete memory file
GET/api/token-usageToken usage, accepts period param
GET/api/routing-feedbackRouting feedback log
GET/api/adrsArchitecture Decision Records

Loops

MethodEndpointDescription
GET/api/loopsActive loops
POST/api/loops/createCreate loop
GET/api/loops/stopStop a loop

Swarm

MethodEndpointDescription
GET/api/swarm-historySwarm run list
GET/api/swarm-eventsEvents for a run
DELETE/api/swarm-cleanPurge swarm data

Knowledge (Chunks)

MethodEndpointDescription
GET/api/section?name=knowledgeIndexed chunks
POST / DELETE/api/knowledge-chunkChunk CRUD
POST/api/monograph-build-docsTrigger docs indexing
GET/api/monograph-build-docs-statusIndexing progress

Graph (Monograph)

MethodEndpointDescription
POST/api/monograph-buildRebuild graph
POST/api/monograph-watch-toggleToggle file watch
GET/api/monograph-watch-statusWatch state
GET/api/monograph-graphGraph nodes/edges
GET/api/monograph-reportGRAPH_REPORT
GET/api/monograph-queryBM25 node search
GET/api/monograph-pathShortest path between two nodes
GET/api/monograph-explainNode explanation
GET/api/monograph-wiki-searchWiki full-text search
GET/api/graphAgent graph data
POST/api/mcp/callMCP tool proxy

Orgs

MethodEndpointDescription
GET/api/orgsOrg list
GET/api/org/:nameOrg detail
POST/api/orgs/:name/stopStop org
GET/api/org/:name/activityOrg activity log
GET/api/org/:name/healthOrg health metrics
GET/api/org/:name/agentsOrg agents
GET/api/org/:name/budgetsOrg budgets
GET/api/org/:name/membersOrg members
GET/api/org/:name/issuesOrg issues
GET / POST/api/org/:name/approvalsApproval queue
GET/api/org/:name/secretsSecret names and scopes
GET/api/org/:name/routinesScheduled routines
GET/api/org/:name/my-issuesIssues assigned to current user
GET/api/org/:name/pluginsInstalled plugins
GET/api/org/:name/projectsAssociated projects
GET/api/org/:name/workspacesAgent workspaces
GET/api/org/:name/invitesPending invitations
GET/api/org/:name/environmentsAgent environment configs
GET/api/org/:name/join-requestsPending join requests
GET/api/org/:name/threadsDiscussion threads
Autonomous Organizations

Assemble an AI team.
Let it run itself.

Design a multi-agent organization in one command. Define roles, hierarchy, and communication topology. Start it as a persistent daemon that coordinates agents, manages tasks, and delivers output while you focus elsewhere.

/mastermind:createorg content-team # design the org
/mastermind:runorg --org content-team # start it
Persistent daemon Shared task board Memory-backed Auto-topology
Two commands

From goal to running organization

createorg designs and persists the structure. runorg loads it and starts the boss agent, which coordinates all roles from a shared task board.

01
/mastermind:createorg
Design the org

Describe a goal. Monomind derives roles, maps agent types, builds the communication topology, and saves a config to .monomind/orgs/.

02
Org config saved
Roles and topology

JSON definition with role IDs, agent type slugs, communication edges (command, report, feedback, handoff), governance policy, and a monotask board.

03
/mastermind:runorg
Start the daemon

Spawns the boss agent in the background. Boss coordinates all roles, claims tasks from the board, checkpoints every 30 minutes, and loops until stopped.

/mastermind:createorg

Design your organization

One prompt. Monomind infers roles, maps each to a specialized agent type, derives the optimal communication topology, and saves a persistent JSON config ready to run.

mastermind:createorg
/ mastermind:createorg content-team   "Build and publish 3 blog posts per week"   Deriving roles from goal...   โ•” ORG: content-team โ•‘ TOPOLOGY: star (5 roles)   boss โ†’ coordinator writer โ†’ Content Creator reviewer โ†’ reviewer marketer โ†’ Growth Hacker seo โ†’ SEO Specialist   Type "go" to save, or describe changes. โ†’ go   โœ“ Saved .monomind/orgs/content-team.json   โ†’ Run: /mastermind:runorg --org content-team  
๐ŸŽฏ
Goal-derived roles
Describe what the org should achieve. Monomind infers which roles are needed and maps each to the right specialist agent type from 60+ available.
๐Ÿ”€
Auto topology
1-3 roles get mesh (direct), 4-6 get star, 7+ get hierarchical with middle managers. Communication edges are derived automatically.
๐Ÿ’พ
Persistent config
The org definition is saved to JSON with board IDs, column IDs, governance policy, token budget, and memory namespace. Ready to run any time.
๐Ÿ›ก๏ธ
Governance modes
auto: agents act freely. board: sensitive actions require approval. strict: every external action gates on human review.
/mastermind:runorg

Start the autonomous loop

The boss agent spawns in the background. It coordinates all roles, assigns tasks from a shared board, collects results into memory, and loops on a checkpoint interval. It runs until you stop it.

Active agents
CEO / Boss
coordinator ยท reports_to: none
running
Content Writer
Content Creator ยท reports_to: boss
running
SEO Specialist
SEO Specialist ยท reports_to: boss
spawning
Content Reviewer
reviewer ยท reports_to: boss
waiting
Growth Marketer
Growth Hacker ยท reports_to: boss
waiting
Stop running org:
curl -X POST http://localhost:4242/api/orgs/content-team/stop
Task board
Todo
Doing
Done
Write: HNSW vs brute-force
role: writer
Keyword research: MCP tooling
role: seo
Draft: Claude Code vs Cursor
role: writer ยท claimed
SEO audit: recent posts
role: seo ยท spawning
Monomind v1 walkthrough
role: writer
Edit and approve: intro post
role: reviewer
Social distribution, week 1
role: marketer
โ— checkpoint every 30 min โ— memory: org:content-team
Communication topology

Three structures, auto-selected

createorg picks the topology from team size. Every topology uses the same edge types: command, report, feedback, handoff.

topology: mesh
Mesh
All agents communicate directly. Best for small, tight collaborations where everyone coordinates with everyone else.
Best for 1โ€“3 roles. Quick iteration, no bottleneck coordinator.
topology: star
Star
Boss at center. All roles report directly. Optimal for 4โ€“6 agents, with central coordination and no middle management overhead.
Best for 4โ€“6 roles. Direct reporting, fast feedback loops.
topology: hierarchical
Hierarchical
Boss at top, middle manager(s) in between, executors at leaf level. Scales to large orgs with many concurrent roles.
Best for 7+ roles. Scales coordination without overloading the boss.
Use cases

Sample teams you can launch. The sky is the limit.

Every org is just a JSON definition and two commands. Here are five starting points. Copy the createorg prompt and run.

โœ๏ธ
Content Marketing
Weekly blog posts, SEO-optimized, reviewed and distributed autonomously
โš™๏ธ
Software Dev
Architect, coders, tester building features end-to-end
๐Ÿ”ฌ
Research Lab
Researchers gather, analysts synthesize, writers produce reports
๐Ÿ“ฃ
Sales Outreach
Research prospects, write sequences, track pipeline and follow up
๐Ÿ›ก๏ธ
DevOps and Security
SRE lead, security auditor, perf engineer monitoring and remediating
Team composition
Run it