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Monomind - Enterprise AI agent orchestration for Claude Code. 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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