MyConvergio

You are one person building what used to require fifty. MyConvergio is the team you don't have.

Your AI agent says "done." It isn't. There are bugs, no tests, and it pushed to main. Sound familiar?

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The problem

When you work alone with AI, three things break:

1. No one reviews AI output. Agents say "done" when they're not. Broken code reaches main because there's no second pair of eyes.
2. No one plans the architecture. AI generates code fast — but without a tech architect, security reviewer, or DevOps engineer, the result is fragile and insecure.
3. No one manages the business. You're the CEO, CFO, PM, designer, and marketer — all at once. Strategic decisions get no structured analysis.

The pattern that works is Planner → Worker → Judge. Not a solo agent hoping for the best.

This isn't opinion — the data is in:

Finding	Source
AI-assisted code produces 1.7x more logical bugs than human code	CodeRabbit 2026
90% AI adoption → +9% bugs, +91% review time, +154% PR size	Cortex Engineering Benchmark 2026
Cognitive complexity rises 39% in agent-assisted repos	Faros AI 2026
Change failure rate +30%, incidents per PR +23.5%	TFIR 2026
Equal-status multi-agent coordination failed at scale	Codebridge 2026

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What MyConvergio does

MyConvergio gives you a complete, trusted team of 85 AI specialists — from code architects to security auditors to CFOs — orchestrated through a quality pipeline that prevents AI from lying about being done.

It installs as a layer on top of Claude Code, GitHub Copilot CLI, Gemini, and OpenCode. You don't change your editor or workflow.

Working alone with AI	With MyConvergio
Agent says "done" and you trust it	Thor validator checks 9 quality gates independently
No one reviews your architecture	Baccio (architect) + Rex (reviewer) + Luca (security)
Financial decisions are gut feelings	Amy (CFO) + Fiona (market analyst) + Domik (McKinsey)
Two agents edit the same file	File locking blocks the second agent — zero silent overwrites
CI dumps 2000 lines into context	Digest scripts compress to 50-line JSON — 10x less tokens
Agents burn $50/day on wasted tokens	Isolated subagents + token tracking save 50-70% per task
"How many tasks are done?" — no idea	SQLite plan DB + real-time Control Room dashboard
One machine, one agent at a time	Mesh: distribute across every machine you own
Locked into one AI provider	Route each task to the best model across providers

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Who is this for

The innovator who builds alone but refuses to ship garbage.

• Solo founders who need an architect, reviewer, security expert, CFO, and PM — without hiring anyone
• Solopreneurs building real products for real people, not demo apps that impress nobody
• Indie developers who want enterprise-grade quality gates on AI-generated code
• Small teams (2-5) who need to punch above their weight with AI agents
• Anyone who has merged AI-generated code and found bugs in production

You want AI that ships reliable, secure, production-ready software — with independent verification that the work is actually done. This is your team.

How does it compare?

These tools serve different categories (orchestration layer vs coding agent vs framework vs SaaS). Comparison focuses on capabilities relevant to solo builders shipping production software.

Capability	MyConvergio	CrewAI	Devin	Cursor/Windsurf	LangGraph
Independent validation	Thor (9 gates + DB trigger)	Custom	Plan review	None	Custom
Business intelligence	15 agents (CFO→VC→McKinsey)	None	None	None	None
Parallel orchestration	Wave-based + file locking	Hierarchical	Parallel VMs	Limited	Graph-based
Git isolation	Worktrees + merge automation	None	N/A	None	None
Provider agnostic	Claude+Copilot+Gemini+OC	Any LLM	Cognition only	Single LLM	Any LLM
Cost tracking	Per-model, per-task, per-plan	None	Opaque (ACUs)	None	None
Multi-machine (mesh)	SSH/Tailscale routing	No	Cloud only	No	No
Enforcement hooks	31 automatic	Custom	None	None	Custom
Token optimization	Digest+isolation (50-70% cut)	None	N/A	None	None
Open source	Yes (CC BY-NC-SA)	Yes (MIT)	No ($500/mo team)	No	Yes (MIT)
Target user	Solo builder	Dev teams	Engineering orgs	Individual devs	ML engineers

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The Control Room

Real-time visibility into plans, agents, mesh peers, costs, and execution — from your browser.

Active missions with per-task execution flow (Execute → Submit → Thor → Done), mesh network topology, live task pipeline, and an integrated terminal — SSH into mesh peers, run commands, and manage plans directly from the browser.

Cost-per-model breakdown, token burn over time, plan execution history. Every dollar spent on AI is tracked and attributed to the task that consumed it.

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How it works

Core pipeline

flowchart LR
    A["/prompt"] --> B["/plan"]
    B --> C["/execute"]
    C --> D{"Thor\n9 Gates"}
    D -->|fail| C
    D -->|pass| E["Auto Merge"]
    E --> F["main ✓"]

    C --> R{Router}
    R --> P1["Claude"]
    R --> P2["Copilot"]
    R --> P3["Gemini"]
    R --> P4["OpenCode"]

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/prompt extracts structured requirements. /plan decomposes into waves of parallel tasks with file-level dependency tracking. /execute runs isolated agents per task with TDD, file locking, and worktree isolation. Thor validates each task against 9 gates before allowing merge. Auto Merge rebases, runs CI, resolves review comments, squash merges, and cleans up.

Thor: the agent that says no

Generation without verification is a net negative. Thor is the independent validator that rejects incomplete work.

flowchart LR
    subgraph "Per-Task (G1-G4, G8-G9)"
        G1["1. Scope"] --> G2["2. Quality"] --> G3["3. Standards"]
        G3 --> G4["4. Repo"] --> G8["8. TDD"] --> G9["9. ADR"]
    end
    subgraph "Per-Wave (G5-G7, Build)"
        G5["5. Docs"] --> G6["6. Git"] --> G7["7. Perf"] --> GB["Build"]
    end
    G9 --> G5
    GB --> R["Release Ready"]

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Thor runs as a separate agent with fresh context — zero assumptions from the executor. Tasks move from submitted → done only through Thor. A SQLite trigger enforces this — even raw SQL cannot bypass it.

Wave merge strategy

flowchart LR
    subgraph "Theme: Auth"
        W1["W1 batch"] --> W2["W2 sync"]
    end
    subgraph "Theme: UI"
        W3["W3 batch"] --> W4["W4 sync"]
    end
    W2 --> PR1["PR #1"]
    W4 --> PR2["PR #2"]
    PR1 --> M["main"]
    PR2 --> M

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Tasks group into waves by theme. Each wave gets its own git worktree. Merge is fully automated: rebase → push → CI → review comment resolution → squash merge → cleanup.

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Beyond code: your full AI team

Most AI coding tools give you a code generator. MyConvergio gives you an entire organization.

flowchart TB
    YOU["You\n(Founder)"] --> ALI["Ali\nChief of Staff"]
    ALI --> TECH["Technical"]
    ALI --> BIZ["Business"]
    ALI --> OPS["Operations"]

    TECH --> BA["Baccio\nArchitect"]
    TECH --> DA["Dario\nDebugger"]
    TECH --> RX["Rex\nReviewer"]
    TECH --> LU["Luca\nSecurity"]

    BIZ --> AM["Amy\nCFO"]
    BIZ --> FI["Fiona\nMarkets"]
    BIZ --> DM["Domik\nMcKinsey"]

    OPS --> MA["Marcello\nProduct"]
    OPS --> SO["Sofia\nMarketing"]
    OPS --> SA["Sara\nUX Design"]

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Domain	Agents	What they do
Orchestration & QA	27	Plan, execute, validate, merge. Thor, strategic-planner, wave merge
Technical Development	13	Architecture, debugging, DevOps, performance, code review, data science
Business Intelligence	15	CFO analysis, market research, VC evaluation, McKinsey frameworks
Operations & PM	15	Product management, marketing, sales, HR, customer success
Compliance & Legal	5	Security audit, legal review, HIPAA, government affairs, AI ethics
Design & UX	4	UX design, creative direction, design thinking, accessibility
Release Management	6	Release lifecycle, ecosystem sync, hardening checks

These agents work together through structured orchestration — not isolated chatbots:

• Ali (Chief of Staff) coordinates cross-domain requests — ask one agent, get a synthesized answer from all relevant specialists
• Amy (CFO) builds financial models with cultural market adjustment — global ROI analysis, not just spreadsheets
• Fiona (Market Analyst) provides live-verified market intelligence — never hallucinated, always sourced
• Domik (McKinsey) applies quantitative scoring across 6 dimensions for investment decisions
• Research Report Generator produces institution-grade equity research — LaTeX output, data integrity guaranteed
• Behice (Cultural Coach) navigates US, UK, Middle East, Nordic, and Asia-Pacific business dynamics

See the Agent Portfolio for sample outputs and detailed capabilities.

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Mesh networking: every machine you own becomes a worker

That old MacBook gathering dust? It's now a build worker. A $5/month Linux VPS? A parallel executor. Your desktop at home? Heavy compute while your laptop stays mobile. Zero extra cost.

flowchart LR
    CO["Your Laptop\n(Coordinator)"] --> |"privacy"| OL["Old MacBook\nOllama"]
    CO --> |"code"| CP["Desktop\nClaude"]
    CO --> |"bulk"| VM["$5 VPS\nCopilot"]
    OL --> TH["Thor"]
    CP --> TH
    VM --> TH
    TH --> M["main"]

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The coordinator scores peers by cost, load, and privacy constraints, then routes tasks to the best available machine:

• Privacy-sensitive code stays on your local Ollama node — never touches the cloud
• Compute-heavy tasks go to your most powerful machine
• Bulk work goes to the cheapest peer (free Copilot on a VPS beats paid API calls)
• Multiple projects run in parallel across different machines, all feeding one dashboard

All peers sync via SSH/Tailscale. Config, repos, credentials, and the plan DB stay aligned across machines with one command: mesh-sync-all.sh. Live migration moves a running plan to another peer mid-execution.

Dashboard Delegation

Delegate plans directly from the Control Room — click the 🚀 icon on any active mission:

1. Select target node — see OS, CPU load, active tasks, online status
2. Auto preflight — 6 streaming checks run and self-heal:
   • SSH reachability, heartbeat (auto-restarts if stale), config sync (auto-syncs if diverged), Claude CLI, disk space
3. One-click delegate — full sync (Phase 0) + migration (Phase 1-5) streamed live to a modal
4. tmux session — plan runs in plan-{ID} on target; terminal icons auto-attach

Node Power Management

Button	When	What it does
⚡ Wake	Node offline	Sends Wake-on-LAN magic packet (needs mac_address in peers.conf)
🔄 Reboot	Node frozen	SSH sudo reboot with post-reboot polling

Auto-Sync Protocol

No manual sync needed — everything propagates automatically:

Event	Action
Plan completes	Results pushed to all online peers
Node boots / reconnects	Heartbeat daemon pulls latest from coordinator
Every ~5 minutes	Heartbeat loop checks for updates
Before delegation	Full sync (config + DB + repos) to target

Quick Start: Mesh Setup

# 1. Install MyConvergio on each machine
curl -fsSL https://raw.githubusercontent.com/Roberdan/MyConvergio/master/install.sh | bash

# 2. Configure peers (edit with your real hosts)
cp config/peers.conf.example ~/.claude/config/peers.conf
# Set: ssh_alias, user, os, tailscale_ip, capabilities, role, mac_address

# 3. Bootstrap remote peer
scripts/mesh/bootstrap-peer.sh my-linux

# 4. Push credentials
scripts/mesh/mesh-auth-sync.sh push --peer my-linux

# 5. Start heartbeat daemon (auto-syncs on start)
scripts/mesh/mesh-heartbeat.sh start

# 6. Launch Control Room
python3 scripts/dashboard_web/server.py --port 8420
# Open http://localhost:8420

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Enforcement layer

31 hooks that run automatically on every tool call — no discipline required.

Hook	Trigger	What it does
worktree-guard	git ops	Blocks commits on main when worktrees exist
enforce-plan-db-safe	task done	Forces Thor validation before marking done
enforce-plan-edit	file edits	Blocks direct edits outside task-executor
secret-scanner	pre-commit	Detects API keys, tokens, credentials
enforce-line-limit	post-edit	Rejects files over 250 lines
session-file-lock	file edits	Prevents parallel agents overwriting each other
prefer-ci-summary	bash commands	Forces digest scripts over raw CI output

Hooks work on both Claude Code and Copilot CLI. Zero config after install.

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Model routing

Use the right model for each job. No provider lock-in. Models are user-configurable.

Task	Primary	Default model	Fallback
Requirements	Claude	Opus	Gemini Pro
Planning	Claude	Opus	Gemini Pro
Code generation	Copilot	Codex	Claude Sonnet
Validation	Claude	Sonnet	Copilot
Bulk fixes	Copilot	GPT-mini	Claude Haiku
Research	Gemini	Pro	Claude Sonnet

Frontier models for reasoning, fast models for execution. The plan-and-execute pattern significantly reduces costs vs using frontier models for everything.

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Token optimization

AI tokens are money. Every wasted token is a wasted dollar. MyConvergio is obsessively optimized to minimize token consumption:

Technique	Saving	How
Isolated subagents	50-70%	Each task-executor gets fresh context (~30K tokens vs 100K inherited)
Digest scripts	10x	CI/build/test output compressed to compact JSON before entering context
Compact instruction format	30-40%	Tables over prose, commands over descriptions in all agent/rule files
Token tracking per task	Visibility	Every token attributed to plan → wave → task → model in SQLite
Copilot-first delegation	$0	Trivial tasks routed to free Copilot; Claude reserved for reasoning
Auto context compression	Continuous	Long conversations auto-compressed with state preserved in memory

31 hooks enforce this automatically. prefer-ci-summary blocks raw npm build output (2000+ lines) and forces digest scripts (~50 lines). enforce-line-limit rejects files over 250 lines — because agents lose context in long files.

Result: A 14-task plan that would burn $80+ in raw Opus tokens costs ~$15 with MyConvergio's optimization stack.

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Quick start

Platforms: macOS and Linux natively. Windows via WSL 2 (Ubuntu recommended).

One-line install

curl -sSL https://raw.githubusercontent.com/Roberdan/MyConvergio/master/install.sh | bash

Clone and make

git clone https://github.com/Roberdan/MyConvergio.git && cd MyConvergio
make install

Modular install

make install-tier TIER=minimal   # 9 core agents (~50KB)
make install-tier TIER=standard  # 20 agents (~200KB)
make install                     # all 85 agents (~600KB)

After install

Pick a settings template based on your hardware:

cp ~/.myconvergio/.claude/settings-templates/high-spec.json ~/.claude/settings.json  # 32GB+ RAM
cp ~/.myconvergio/.claude/settings-templates/mid-spec.json  ~/.claude/settings.json  # 16GB RAM
cp ~/.myconvergio/.claude/settings-templates/low-spec.json  ~/.claude/settings.json  # 8GB RAM

Without this step, hooks won't run. This is the difference between "AI with guardrails" and "AI hoping for the best."

What happens next

Open your terminal with Claude Code or Copilot CLI and type:

/prompt I want to build a REST API for user authentication with JWT

MyConvergio extracts requirements, asks clarifying questions, generates a structured plan with parallel tasks, executes each task in isolation with TDD, validates through Thor's 9 quality gates, and auto-merges to main. You approve the plan — the system does the rest.

Dashboards

MyConvergio includes two dashboards for monitoring plans, agents, and mesh nodes:

Dashboard	What	How to run
Control Room (web)	Full browser UI with plan drill-down, mesh topology, integrated terminals, cost analytics	python3 ~/.claude/scripts/dashboard_web/server.py then open http://localhost:8420
pianits (terminal)	Lightweight TUI for quick checks inside tmux/SSH sessions — auto-refresh, drill-down, quit with q	~/.claude/scripts/pianits

Recommended aliases

Add these to your shell profile for quick access:

macOS / Linux (~/.zshrc or ~/.bashrc)

# Convergio dashboards
alias piani='open http://localhost:8420'           # macOS: opens browser
# alias piani='xdg-open http://localhost:8420'     # Linux: opens browser
alias pianits='~/.claude/scripts/pianits'

Windows (PowerShell profile)

# Convergio dashboards
function piani { Start-Process "http://localhost:8420" }
Set-Alias pianits "$env:USERPROFILE\.claude\scripts\pianits"

pianits interactive keys: q quit · r refresh · <number> + Enter = drill-down · b back · auto-refreshes every 10s.

To run the Control Room server on startup, add to your shell profile:

# Start Control Room in background (if not already running)
pgrep -f "dashboard_web/server.py" >/dev/null || python3 ~/.claude/scripts/dashboard_web/server.py &>/dev/null &

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Documentation

Guide	Description
Getting Started	Install, first plan, first execution
Core Concepts	Plans, waves, Thor, file locking
Workflow Guide	End-to-end delivery flow
Infrastructure	SQLite schema, scripts, hooks
Agent Portfolio	Full catalog of all 85 agents
ADRs	Architecture Decision Records

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License

CC BY-NC-SA 4.0 — Free for individuals and non-commercial use. This license protects against commercial resale while keeping MyConvergio free for solo builders, students, and open-source projects. Commercial licensing available on request.

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MyConvergio 10.1.0 | 3 Mar 2026

You don't need to hire a team. You need a team that can't lie to you. Thor makes sure they don't.

If this resonates, star the repo — it helps others find it.