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Monomind - Enterprise AI agent orchestration platform. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration

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Readme

Monomind — AI Agent Orchestration

Monomind Logo

Monomind

The orchestration layer that turns Claude Code into an autonomous engineering team.

npm version downloads stars license node

Quickstart  •  What It Does  •  Features  •  Agent Catalog  •  Swarms  •  Commands  •  Memory


Why Monomind?

You already use Claude Code. Monomind makes it 10x more powerful.

Instead of one AI assistant handling everything, Monomind coordinates 230+ specialized agents — architects, security auditors, performance engineers, frontend developers, database optimizers — each with domain expertise, working in parallel swarms that review each other's work.

The difference:

  • Without Monomind: You prompt Claude, it does its best across every domain.
  • With Monomind: Claude spawns the right specialist for each subtask, coordinates them in fault-tolerant swarms, remembers everything across sessions, and learns from every interaction.

One command. Entire engineering workflows. Zero babysitting.


Quickstart

# Install globally
npm install -g monomind

# Initialize in any project
cd your-project
monomind init

# Add MCP server to Claude Code
claude mcp add monomind npx monomind mcp start

That's it. Monomind is now active in your Claude Code sessions.


Monomind Control — Live Dashboard

Monomind Control Dashboard

Real-time visibility into every project, session, agent, memory, route decision, and token spend — all in one terminal-native dashboard.


What Monomind Does

From Prompt to Production

Monomind turns high-level instructions into coordinated multi-agent execution:

You: "Add webhook delivery with retries and dead-letter queue"

Monomind:
  1. Routes to Software Architect → designs the system
  2. Spawns backend-dev → implements webhook dispatcher
  3. Spawns backend-dev → implements retry logic with exponential backoff
  4. Spawns Database Optimizer → designs dead-letter queue schema
  5. Spawns tester → writes integration tests
  6. Spawns Code Reviewer → reviews all changes
  7. Commits, reports, moves to next task

Autonomous Task Pipelines

# Turn a spec into executable tasks, then run them
/monomind:createtask docs/specs/webhook-system.md

# Or let it generate ideas, evaluate, and execute
/monomind:idea add real-time collaboration to the editor

# Pick up tasks and execute them autonomously
/monomind:do

Features

230+ Specialized Agents

Not generic "code assistants" — domain experts with targeted system prompts, each optimized for a specific class of work.

Category Count Examples
Engineering 23 Backend Architect, Frontend Developer, Database Optimizer, Embedded Firmware Engineer, SRE
Marketing 27 SEO Specialist, TikTok Strategist, Content Creator, Growth Hacker, LinkedIn Content Creator
Specialized 27 Legal Compliance, Finance Tracker, Salesforce Architect, Document Generator, MCP Builder
Game Dev 20 Unity Architect, Unreal Systems Engineer, Godot Scripter, Roblox Systems Scripter
Sales 8 Deal Strategist, Sales Engineer, Pipeline Analyst, Outbound Strategist
Design 8 UI Designer, UX Researcher, Brand Guardian, Visual Storyteller
Paid Media 7 PPC Strategist, Ad Creative Strategist, Programmatic Buyer, Tracking Specialist
Support 6 Support Responder, Analytics Reporter, Study Abroad Advisor, Trend Researcher
Product 5 Product Manager, Sprint Prioritizer, UX Researcher, Experiment Tracker
Academic 5 Anthropologist, Historian, Psychologist, Geographer, Narratologist
And more... 94+ Consensus, Swarm Coordination, Neural, SPARC, Architecture, DevOps, Testing

Two-Stage LLM Routing

Monomind doesn't guess which agent to use — it asks an LLM.

Stage 1: "This task is about SEO optimization" → marketing domain
Stage 2: "Best fit in marketing: SEO Specialist" → spawns SEO Specialist

Runs in under 2 seconds via Haiku. Falls back to keyword scoring if the API is unavailable.

Swarm Orchestration

Coordinate multiple agents working on the same problem:

Topology Best For
Hierarchical Feature development — coordinator delegates to specialists
Mesh Research — all agents share findings peer-to-peer
Hierarchical-Mesh Complex projects — structured delegation with cross-talk
Adaptive Unknown complexity — topology evolves based on task

Consensus protocols: Raft (leader-based), Byzantine (fault-tolerant), Gossip (eventually consistent), CRDT (conflict-free), Quorum (majority vote).

Swarm Topology

# Let Monomind pick the best topology
/mastermind

# Or configure manually
monomind swarm init --topology hierarchical --agents 8 --strategy specialized

Self-Learning Memory

Every interaction makes Monomind smarter:

  • AgentDB — Persistent vector memory with HNSW indexing (150x-12,500x faster search)
  • Knowledge Graph — Full dependency mapping of your codebase via Graphify
  • Session Continuity — Pick up exactly where you left off across sessions
  • Neural Patterns — SONA learning adapts routing and agent behavior over time
  • Memory Palace — Visual dashboard for exploring stored knowledge

Memory Palace — Browse memories, sessions, knowledge, and swarms

17 Hooks + 12 Background Workers

Monomind hooks into every phase of your Claude Code workflow:

Hook What It Does
pre-task Routes to the best agent before execution starts
post-task Learns from outcomes, updates neural patterns
pre-edit Validates changes against project conventions
post-edit Indexes new code into the knowledge graph
session-start Restores context, preloads relevant memory
session-end Persists learnings, updates metrics

Background workers handle: optimization, consolidation, prediction, auditing, documentation, refactoring, benchmarking, and test gap analysis — all running autonomously.


Agent Catalog

Development

Agent Specialty
coder General implementation with TDD
backend-dev APIs, databases, server-side logic
Frontend Developer React, Vue, Angular, CSS systems
mobile-dev React Native, iOS, Android
Rapid Prototyper Fast MVPs and proof-of-concepts
Solidity Smart Contract Engineer EVM, DeFi, gas optimization
WeChat Mini Program Developer WXML/WXSS, WeChat ecosystem
Embedded Firmware Engineer ESP32, ARM Cortex-M, FreeRTOS
visionOS Spatial Engineer SwiftUI volumetric, Liquid Glass

Architecture & Quality

Agent Specialty
Software Architect System design, DDD, architectural patterns
Code Reviewer Correctness, security, performance review
Security Engineer Threat modeling, vulnerability assessment
Database Optimizer Schema design, query tuning, indexing
SRE SLOs, error budgets, chaos engineering

Marketing & Growth

Agent Specialty
SEO Specialist Technical SEO, content optimization
TikTok Strategist Viral content, algorithm optimization
LinkedIn Content Creator Thought leadership, professional content
Growth Hacker Viral loops, conversion funnels
Content Creator Multi-platform editorial calendars

Game Development

Agent Specialty
Unity Architect ScriptableObjects, modular systems
Unreal Systems Engineer C++/Blueprint, Nanite, Lumen
Godot Gameplay Scripter GDScript 2.0, signal architecture
Roblox Systems Scripter Luau, client-server, DataStore

See all 230 agents →


Swarm Orchestration

How Swarms Work

Swarm Inspector — topology graph, agent roles, and communication logs

/mastermind "implement authentication system with OAuth2, JWT, and role-based access"

Monomind recommends: Hierarchical swarm, 6 agents, Raft consensus

  Queen Coordinator
  ├── Software Architect    → designs auth architecture
  ├── backend-dev           → implements OAuth2 flow
  ├── backend-dev           → implements JWT + RBAC
  ├── Security Engineer     → audits for vulnerabilities
  ├── tester                → writes auth test suite
  └── Code Reviewer         → reviews everything before merge

Anti-Drift Protection

Swarms don't just run — they stay on track:

  • Raft consensus — Leader maintains authoritative state, prevents conflicting changes
  • Frequent checkpointspost-task hooks validate progress after every step
  • Shared memory namespace — All agents in a swarm see the same context
  • Review cycles — Code reviewer validates before any task is marked done

Commands

Slash Commands (Inside Claude Code)

Command What It Does
/monomind:createtask <spec> Ingests a prompt, file, or folder → generates full implementation plan → creates self-contained tasks on monotask
/monomind:idea <prompt> Research swarm generates ideas → PM evaluates → architect decomposes into tasks
/monomind:do Picks up tasks, executes with assigned agents, reviews, fixes bugs, loops
/mastermind Analyzes your task and recommends the optimal swarm topology
/specialagent <task> Two-stage LLM routing to find the perfect specialist agent

CLI Commands

monomind agent spawn --type coder       # Spawn a specific agent
monomind agent list                      # List running agents
monomind swarm init                      # Initialize a swarm
monomind memory search "auth patterns"   # Search vector memory
monomind hooks route --task "fix bug"    # Route to best agent
monomind doctor --fix                    # Diagnose and fix issues
monomind daemon start                    # Start background workers

41 CLI commands across: agent management, swarm coordination, memory, sessions, hooks, neural training, security, performance profiling, and more.

Session Inspector

Every session is recorded and browsable — tool calls, agent spawns, memory operations, and full conversation replay:

Session Inspector — full conversation replay with tool breakdown


Memory & Intelligence

Knowledge Graph (Monograph)

Monomind builds a full dependency graph of your codebase — automatically queried before every task:

# What files are relevant to my task?
monograph_suggest "add webhook retry logic"
# → returns ranked list of files with relevance scores

# What depends on UserService?
monograph_query "UserService dependencies"
# → returns file paths + line numbers

# Find the most connected files in the codebase
monograph_god_nodes
# → returns high-centrality internal files (external/test filtered out)

All monograph tools are called automatically by hooks and slash commands — you don't need to invoke them manually.

Vector Memory (AgentDB + HNSW)

Every insight, pattern, and decision is stored in searchable vector memory:

  • 150x-12,500x faster than brute-force search via HNSW indexing
  • Hybrid backend — SQLite for structured data, AgentDB for semantic search
  • Cross-session persistence — context survives restarts

Neural Learning (SONA)

Self-Optimizing Neural Adaptation learns from every task:

  • Pattern recognition improves agent routing over time
  • Trajectory tracking identifies what works and what doesn't
  • Automatic model adaptation with <0.05ms overhead

Architecture

┌─────────────────────────────────────────────────────────────┐
│                         Monomind                            │
├──────────────┬──────────────┬──────────────┬───────────────┤
│   230+ Agents │  Swarm Engine │  Memory Layer │  Intelligence │
│              │              │              │               │
│  Specialized │  Hierarchical │  AgentDB     │  SONA Neural  │
│  agent defs  │  Mesh/Raft   │  HNSW Vector │  Pattern      │
│  + routing   │  consensus   │  Knowledge   │  Learning     │
│              │              │  Graph       │               │
├──────────────┴──────────────┴──────────────┴───────────────┤
│                     17 Hooks + 12 Workers                   │
├─────────────────────────────────────────────────────────────┤
│              MCP Server (stdio/http/websocket)              │
├─────────────────────────────────────────────────────────────┤
│                    Claude Code Runtime                      │
└─────────────────────────────────────────────────────────────┘

Key Packages

Package Purpose
@monomind/cli 41 commands, agent definitions, slash commands, hooks, MCP server
@monomind/memory AgentDB with HNSW vector search
@monomind/hooks 17 lifecycle hooks + 12 background workers
@monomind/security Input validation, CVE remediation
@monomind/guidance Governance control plane

Performance

Metric Result
Agent routing <2s (LLM) / <5ms (keyword fallback)
Vector search 150x-12,500x faster (HNSW)
SONA learning <0.05ms per adaptation
Session restore <500ms cold start
Memory reduction 50-75% vs baseline

Who Uses Monomind?

Monomind is built for teams and individuals who use Claude Code for serious engineering work:

  • Solo developers who want the power of a full engineering team
  • Startups shipping features faster with autonomous agent pipelines
  • Enterprise teams coordinating complex multi-module changes
  • Game studios using specialized Unity/Unreal/Godot agents
  • Marketing teams running content operations with domain-specific agents
  • Security teams automating audit and compliance workflows

Contributing

git clone https://github.com/nokhodian/monomind.git
cd monomind
pnpm install
monomind doctor --fix

See CONTRIBUTING.md for guidelines.


License

MIT License — See LICENSE for details.


Stop prompting. Start orchestrating.

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