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

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).

# 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

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

/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 checkpoints — post-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:

Memory & Intelligence

Knowledge Graph (Graphify)

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

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

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

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

All graphify 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.